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"contourf"], "tokens": 176, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_58_errorbar.return.gca_errorbar_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2742, "end_line": 2754, "span_ids": ["csd", "errorbar"], "tokens": 229, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_59_eventplot.return.gca_eventplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2757, "end_line": 2767, "span_ids": ["eventplot", "errorbar"], "tokens": 148, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_60_grid.return.gca_grid_visible_visibl": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2770, "end_line": 2801, "span_ids": ["eventplot", "fill", "grid", "fill_betweenx", "fill_between"], "tokens": 369, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_64_hexbin.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2804, "end_line": 2820, "span_ids": ["grid", "hexbin"], "tokens": 237, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_65_hist.return.gca_hist_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2823, "end_line": 2835, "span_ids": ["hist", "hexbin"], "tokens": 183, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_66_stairs.return.gca_stairs_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2838, "end_line": 2846, "span_ids": ["hist", "stairs"], "tokens": 105, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_67_hist2d.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2849, "end_line": 2859, "span_ids": ["hist2d", "stairs"], "tokens": 144, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_68_hlines.return.gca_hlines_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2862, "end_line": 2870, "span_ids": ["hlines", "hist2d"], "tokens": 110, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_69_imshow.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2873, "end_line": 2888, "span_ids": ["imshow", "hlines"], "tokens": 191, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_70_loglog.return.gca_loglog_args_kwa": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2891, "end_line": 2906, "span_ids": ["imshow", "legend", "locator_params", "loglog"], "tokens": 169, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_73_magnitude_spectrum.return.gca_magnitude_spectrum_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2909, "end_line": 2917, "span_ids": ["magnitude_spectrum", "loglog"], "tokens": 123, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_74_minorticks_on.return.gca_minorticks_on_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2920, "end_line": 2935, "span_ids": ["magnitude_spectrum", "minorticks_off", "minorticks_on", "margins"], "tokens": 168, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_77_pcolor.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2938, "end_line": 2948, "span_ids": ["pcolor", "minorticks_on"], "tokens": 132, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_78_pcolormesh.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2951, "end_line": 2962, "span_ids": ["pcolor", "pcolormesh"], "tokens": 152, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_79_phase_spectrum.return.gca_phase_spectrum_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2965, "end_line": 2972, "span_ids": ["phase_spectrum", "pcolormesh"], "tokens": 116, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_80_pie.return.gca_pie_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2975, "end_line": 2990, "span_ids": ["phase_spectrum", "pie"], "tokens": 227, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_81_plot_date.return.gca_plot_date_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2993, "end_line": 3008, "span_ids": ["pie", "plot_date", "plot"], "tokens": 204, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_83_psd.return.gca_psd_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3011, "end_line": 3021, "span_ids": ["psd", "plot_date"], "tokens": 172, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_84_quiverkey.return.gca_quiverkey_Q_X_Y_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3024, "end_line": 3036, "span_ids": ["quiverkey", "quiver", "psd"], "tokens": 156, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_86_scatter.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3039, "end_line": 3051, "span_ids": ["quiverkey", "scatter"], "tokens": 177, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_87_semilogy.return.gca_semilogy_args_k": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3054, "end_line": 3063, "span_ids": ["semilogx", "semilogy", "scatter"], "tokens": 111, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_89_specgram.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3066, "end_line": 3080, "span_ids": ["specgram", "semilogy"], "tokens": 221, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_90_stackplot.return.gca_stackplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3083, "end_line": 3102, "span_ids": ["specgram", "stackplot", "spy"], "tokens": 226, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_92_step.return.gca_step_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3105, "end_line": 3124, "span_ids": ["step", "stackplot", "stem"], "tokens": 223, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_94_streamplot.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3127, "end_line": 3143, "span_ids": ["step", "streamplot"], "tokens": 215, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_95_table.return.gca_table_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3146, "end_line": 3159, "span_ids": ["streamplot", "table"], "tokens": 192, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_96_tricontourf.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3162, "end_line": 3198, "span_ids": ["tick_params", "ticklabel_format", "table", "tricontourf", "text", "tricontour"], "tokens": 401, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_101_triplot.return.gca_triplot_args_kw": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3201, "end_line": 3216, "span_ids": ["tricontourf", "triplot", "tripcolor"], "tokens": 184, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_103_violinplot.return.gca_violinplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3219, "end_line": 3230, "span_ids": ["violinplot", "triplot"], "tokens": 166, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_104_vlines.return.gca_vlines_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3233, "end_line": 3241, "span_ids": ["vlines", "violinplot"], "tokens": 110, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_105_xcorr.return.gca_xcorr_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3244, "end_line": 3252, "span_ids": ["vlines", "xcorr"], "tokens": 129, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_106_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3255, "end_line": 3319, "span_ids": ["inferno", "hot", "hsv", "summer", "winter", "plasma", "copper", "xlabel", "xscale", "title", "impl:20", "jet", "autumn", "cool", "yscale", "sci", "pink", "bone", 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"None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_fignum_exists_disconnect.return.gcf_canvas_mpl_disconne": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 868, "end_line": 909, "span_ids": ["get_current_fig_manager", "connect", "fignum_exists", "get_fignums", "get_figlabels", "disconnect"], "tokens": 279, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_subplot_tool_subplot_tool.if_hasattr_tb_configure.else_.raise_ValueError_subplot": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1645, "end_line": 1662, "span_ids": ["subplot_tool"], "tokens": 125, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py__Plotting_Info__get_plot_commands.return.sorted_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2021, "end_line": 2044, "span_ids": ["plotting", "get_plot_commands", "thetagrids"], "tokens": 189, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py__setup_pyplot_info_docstrings__setup_pyplot_info_docstrings.for_cm_name_in_colormaps_.if_cm_name_in_globals_.globals()[cm_name].__doc__.f_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2047, "end_line": 2095, "span_ids": ["_setup_pyplot_info_docstrings"], "tokens": 415, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_polar_polar.return.ax_plot_args_kwargs_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2221, "end_line": 2240, "span_ids": ["polar"], "tokens": 141, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py__If_rcParams_backend_fa_if_rcParams_backend_fal.dict___setitem___rcParams": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2212, "end_line": 2219, "span_ids": ["polar", "impl:16"], "tokens": 115, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_22_None_32": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2217, "end_line": 2292, "span_ids": ["figtext", "gci", "tight_layout", "ginput", "subplots_adjust", "angle_spectrum", "waitforbuttonpress", "suptitle", "gca", "figimage", "acorr"], "tokens": 780, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_annotate_None_42": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2293, "end_line": 2361, "span_ids": ["bar", "axis", "axline", "annotate", "arrow", "autoscale", "axhline", "axhspan", "axvline", "axvspan"], "tokens": 776, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_barbs_bar_label.return.gca_bar_label_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2361, "end_line": 2385, "span_ids": ["barbs", "bar_label", "barh"], "tokens": 252, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_45_boxplot.return.gca_boxplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2435, "end_line": 2457, "span_ids": ["bar_label", "boxplot"], "tokens": 341, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_46_clabel.return.gca_clabel_CS_levels_l": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2460, "end_line": 2471, "span_ids": ["broken_barh", "clabel", "boxplot"], "tokens": 146, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_48_cohere.return.gca_cohere_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2474, "end_line": 2484, "span_ids": ["cohere", "clabel"], "tokens": 182, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_49_contourf.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2487, "end_line": 2504, "span_ids": ["cohere", "contourf", "contour"], "tokens": 196, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_51_csd.return.gca_csd_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2507, "end_line": 2517, "span_ids": ["csd", "contourf"], "tokens": 176, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_52_errorbar.return.gca_errorbar_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2520, "end_line": 2532, "span_ids": ["csd", "errorbar"], "tokens": 229, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_53_eventplot.return.gca_eventplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2535, "end_line": 2545, "span_ids": ["eventplot", "errorbar"], "tokens": 148, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_54_grid.return.gca_grid_visible_visibl": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2548, "end_line": 2579, "span_ids": ["eventplot", "fill", "grid", "fill_betweenx", "fill_between"], "tokens": 369, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_58_hexbin.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2582, "end_line": 2598, "span_ids": ["grid", "hexbin"], "tokens": 237, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_59_hist.return.gca_hist_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2601, "end_line": 2613, "span_ids": ["hist", "hexbin"], "tokens": 183, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_60_stairs.return.gca_stairs_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2616, "end_line": 2624, "span_ids": ["hist", "stairs"], "tokens": 105, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_61_hist2d.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2627, "end_line": 2637, "span_ids": ["hist2d", "stairs"], "tokens": 144, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_62_hlines.return.gca_hlines_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2640, "end_line": 2648, "span_ids": ["hlines", "hist2d"], "tokens": 110, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_63_imshow.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2651, "end_line": 2667, "span_ids": ["imshow", "hlines"], "tokens": 203, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_64_loglog.return.gca_loglog_args_kwa": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2670, "end_line": 2685, "span_ids": ["imshow", "legend", "locator_params", "loglog"], "tokens": 169, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_67_magnitude_spectrum.return.gca_magnitude_spectrum_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2688, "end_line": 2696, "span_ids": ["magnitude_spectrum", "loglog"], "tokens": 123, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_68_minorticks_on.return.gca_minorticks_on_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2699, "end_line": 2714, "span_ids": ["magnitude_spectrum", "minorticks_off", "minorticks_on", "margins"], "tokens": 168, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_71_pcolor.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2717, "end_line": 2727, "span_ids": ["pcolor", "minorticks_on"], "tokens": 132, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_72_pcolormesh.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2730, "end_line": 2741, "span_ids": ["pcolor", "pcolormesh"], "tokens": 152, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_73_phase_spectrum.return.gca_phase_spectrum_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2744, "end_line": 2751, "span_ids": ["phase_spectrum", "pcolormesh"], "tokens": 116, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_74_pie.return.gca_pie_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2754, "end_line": 2769, "span_ids": ["phase_spectrum", "pie"], "tokens": 227, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_75_plot_date.return.gca_plot_date_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2772, "end_line": 2787, "span_ids": ["pie", "plot_date", "plot"], "tokens": 204, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_77_psd.return.gca_psd_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2790, "end_line": 2800, "span_ids": ["psd", "plot_date"], "tokens": 172, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_78_quiverkey.return.gca_quiverkey_Q_X_Y_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2803, "end_line": 2816, "span_ids": ["quiverkey", "quiver", "psd"], "tokens": 156, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_80_scatter.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2819, "end_line": 2831, "span_ids": ["quiverkey", "scatter"], "tokens": 177, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_81_semilogy.return.gca_semilogy_args_k": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2834, "end_line": 2843, "span_ids": ["semilogx", "semilogy", "scatter"], "tokens": 111, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_83_specgram.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2846, "end_line": 2860, "span_ids": ["specgram", "semilogy"], "tokens": 221, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_84_stackplot.return.gca_stackplot_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2863, "end_line": 2882, "span_ids": ["specgram", "stackplot", "spy"], "tokens": 226, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_86_step.return.gca_step_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2872, "end_line": 2892, "span_ids": ["step", "stackplot", "stem"], "tokens": 230, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_88_streamplot.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2911, "end_line": 2929, "span_ids": ["step", "streamplot"], 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"/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_70_minorticks_on.return.gca_minorticks_on_": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2652, "end_line": 2667, "span_ids": ["magnitude_spectrum", "minorticks_off", "minorticks_on", "margins"], "tokens": 168, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_73_pcolor.return.__ret": {"file_path": "lib/matplotlib/pyplot.py", "file_name": "pyplot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2670, "end_line": 2680, "span_ids": ["pcolor", "minorticks_on"], "tokens": 132, "_node_type": "TextNode", "document_id": "None", "doc_id": "None", "ref_doc_id": "None"}, "/tmp/repos/swe-bench_matplotlib__matplotlib/lib/matplotlib/pyplot.py_None_74_pcolormesh.return.__ret": 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{"/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/__init__.py_itertools_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/__init__.py_itertools_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 49, "span_ids": ["impl", "requires_dask", "randn", "parameterized", "randint", "imports"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\n\nimport numpy as np\n\n_counter = itertools.count()\n\n\ndef parameterized(names, params):\n def decorator(func):\n func.param_names = names\n func.params = params\n return func\n\n return decorator\n\n\ndef requires_dask():\n try:\n import dask # noqa: F401\n except ImportError:\n raise NotImplementedError()\n\n\ndef randn(shape, frac_nan=None, chunks=None, seed=0):\n rng = np.random.RandomState(seed)\n if chunks is None:\n x = rng.standard_normal(shape)\n else:\n import dask.array as da\n\n rng = da.random.RandomState(seed)\n x = rng.standard_normal(shape, chunks=chunks)\n\n if frac_nan is not None:\n inds = rng.choice(range(x.size), int(x.size * frac_nan))\n x.flat[inds] = np.nan\n\n return x\n\n\ndef randint(low, high=None, size=None, frac_minus=None, seed=0):\n rng = np.random.RandomState(seed)\n x = rng.randint(low, high, size)\n if frac_minus is not None:\n inds = rng.choice(range(x.size), int(x.size * frac_minus))\n x.flat[inds] = -1\n\n return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/combine.py_np_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/combine.py_np_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 39, "span_ids": ["Combine.time_combine_manual", "Combine.setup", "Combine", "imports", "Combine.time_auto_combine"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nimport xarray as xr\n\n\nclass Combine:\n \"\"\"Benchmark concatenating and merging large datasets\"\"\"\n\n def setup(self):\n \"\"\"Create 4 datasets with two different variables\"\"\"\n\n t_size, x_size, y_size = 100, 900, 800\n t = np.arange(t_size)\n data = np.random.randn(t_size, x_size, y_size)\n\n self.dsA0 = xr.Dataset(\n {\"A\": xr.DataArray(data, coords={\"T\": t}, dims=(\"T\", \"X\", \"Y\"))}\n )\n self.dsA1 = xr.Dataset(\n {\"A\": xr.DataArray(data, coords={\"T\": t + t_size}, dims=(\"T\", \"X\", \"Y\"))}\n )\n self.dsB0 = xr.Dataset(\n {\"B\": xr.DataArray(data, coords={\"T\": t}, dims=(\"T\", \"X\", \"Y\"))}\n )\n self.dsB1 = xr.Dataset(\n {\"B\": xr.DataArray(data, coords={\"T\": t + t_size}, dims=(\"T\", \"X\", \"Y\"))}\n )\n\n def time_combine_manual(self):\n datasets = [[self.dsA0, self.dsA1], [self.dsB0, self.dsB1]]\n\n xr.combine_manual(datasets, concat_dim=[None, \"t\"])\n\n def time_auto_combine(self):\n \"\"\"Also has to load and arrange t coordinate\"\"\"\n datasets = [self.dsA0, self.dsA1, self.dsB0, self.dsB1]\n\n xr.combine_auto(datasets)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataarray_missing.py_pd_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataarray_missing.py_pd_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataarray_missing.py", "file_name": "dataarray_missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 75, "span_ids": ["impl", "make_bench_data", "time_interpolate_na", "impl:8", "impl:2", "time_bfill", "impl:12", "docstring", "time_ffill", "imports:4", "imports", "impl:4"], "tokens": 531}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import pandas as pd\n\nimport xarray as xr\n\nfrom . import randn, requires_dask\n\ntry:\n import dask # noqa: F401\nexcept ImportError:\n pass\n\n\ndef make_bench_data(shape, frac_nan, chunks):\n vals = randn(shape, frac_nan)\n coords = {\"time\": pd.date_range(\"2000-01-01\", freq=\"D\", periods=shape[0])}\n da = xr.DataArray(vals, dims=(\"time\", \"x\", \"y\"), coords=coords)\n\n if chunks is not None:\n da = da.chunk(chunks)\n\n return da\n\n\ndef time_interpolate_na(shape, chunks, method, limit):\n if chunks is not None:\n requires_dask()\n da = make_bench_data(shape, 0.1, chunks=chunks)\n actual = da.interpolate_na(dim=\"time\", method=\"linear\", limit=limit)\n\n if chunks is not None:\n actual = actual.compute()\n\n\ntime_interpolate_na.param_names = [\"shape\", \"chunks\", \"method\", \"limit\"]\ntime_interpolate_na.params = (\n [(3650, 200, 400), (100, 25, 25)],\n [None, {\"x\": 25, \"y\": 25}],\n [\"linear\", \"spline\", \"quadratic\", \"cubic\"],\n [None, 3],\n)\n\n\ndef time_ffill(shape, chunks, limit):\n\n da = make_bench_data(shape, 0.1, chunks=chunks)\n actual = da.ffill(dim=\"time\", limit=limit)\n\n if chunks is not None:\n actual = actual.compute()\n\n\ntime_ffill.param_names = [\"shape\", \"chunks\", \"limit\"]\ntime_ffill.params = (\n [(3650, 200, 400), (100, 25, 25)],\n [None, {\"x\": 25, \"y\": 25}],\n [None, 3],\n)\n\n\ndef time_bfill(shape, chunks, limit):\n\n da = make_bench_data(shape, 0.1, chunks=chunks)\n actual = da.bfill(dim=\"time\", limit=limit)\n\n if chunks is not None:\n actual = actual.compute()\n\n\ntime_bfill.param_names = [\"shape\", \"chunks\", \"limit\"]\ntime_bfill.params = (\n [(3650, 200, 400), (100, 25, 25)],\n [None, {\"x\": 25, \"y\": 25}],\n [None, 3],\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_os_IOSingleNetCDF.make_ds.self.vinds._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_os_IOSingleNetCDF.make_ds.self.vinds._", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 93, "span_ids": ["impl", "imports:6", "impl:2", "IOSingleNetCDF.make_ds", "imports", "IOSingleNetCDF"], "tokens": 715}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\n\nimport numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom . import randint, randn, requires_dask\n\ntry:\n import dask\n import dask.multiprocessing\nexcept ImportError:\n pass\n\n\nos.environ[\"HDF5_USE_FILE_LOCKING\"] = \"FALSE\"\n\n\nclass IOSingleNetCDF:\n \"\"\"\n A few examples that benchmark reading/writing a single netCDF file with\n xarray\n \"\"\"\n\n timeout = 300.0\n repeat = 1\n number = 5\n\n def make_ds(self):\n\n # single Dataset\n self.ds = xr.Dataset()\n self.nt = 1000\n self.nx = 90\n self.ny = 45\n\n self.block_chunks = {\n \"time\": self.nt / 4,\n \"lon\": self.nx / 3,\n \"lat\": self.ny / 3,\n }\n\n self.time_chunks = {\"time\": int(self.nt / 36)}\n\n times = pd.date_range(\"1970-01-01\", periods=self.nt, freq=\"D\")\n lons = xr.DataArray(\n np.linspace(0, 360, self.nx),\n dims=(\"lon\",),\n attrs={\"units\": \"degrees east\", \"long_name\": \"longitude\"},\n )\n lats = xr.DataArray(\n np.linspace(-90, 90, self.ny),\n dims=(\"lat\",),\n attrs={\"units\": \"degrees north\", \"long_name\": \"latitude\"},\n )\n self.ds[\"foo\"] = xr.DataArray(\n randn((self.nt, self.nx, self.ny), frac_nan=0.2),\n coords={\"lon\": lons, \"lat\": lats, \"time\": times},\n dims=(\"time\", \"lon\", \"lat\"),\n name=\"foo\",\n encoding=None,\n attrs={\"units\": \"foo units\", \"description\": \"a description\"},\n )\n self.ds[\"bar\"] = xr.DataArray(\n randn((self.nt, self.nx, self.ny), frac_nan=0.2),\n coords={\"lon\": lons, \"lat\": lats, \"time\": times},\n dims=(\"time\", \"lon\", \"lat\"),\n name=\"bar\",\n encoding=None,\n attrs={\"units\": \"bar units\", \"description\": \"a description\"},\n )\n self.ds[\"baz\"] = xr.DataArray(\n randn((self.nx, self.ny), frac_nan=0.2).astype(np.float32),\n coords={\"lon\": lons, \"lat\": lats},\n dims=(\"lon\", \"lat\"),\n name=\"baz\",\n encoding=None,\n attrs={\"units\": \"baz units\", \"description\": \"a description\"},\n )\n\n self.ds.attrs = {\"history\": \"created for xarray benchmarking\"}\n\n self.oinds = {\n \"time\": randint(0, self.nt, 120),\n \"lon\": randint(0, self.nx, 20),\n \"lat\": randint(0, self.ny, 10),\n }\n self.vinds = {\n \"time\": xr.DataArray(randint(0, self.nt, 120), dims=\"x\"),\n \"lon\": xr.DataArray(randint(0, self.nx, 120), dims=\"x\"),\n \"lat\": slice(3, 20),\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOWriteSingleNetCDF3_IOReadSingleNetCDF4.time_vectorized_indexing.ds_1.ds_isel_self_vinds_loa": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOWriteSingleNetCDF3_IOReadSingleNetCDF4.time_vectorized_indexing.ds_1.ds_isel_self_vinds_loa", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 126, "span_ids": ["IOReadSingleNetCDF4", "IOReadSingleNetCDF4.time_vectorized_indexing", "IOWriteSingleNetCDF3.setup", "IOWriteSingleNetCDF3.time_write_dataset_netcdf4", "IOReadSingleNetCDF4.setup", "IOReadSingleNetCDF4.time_load_dataset_netcdf4", "IOWriteSingleNetCDF3.time_write_dataset_scipy", "IOWriteSingleNetCDF3", "IOReadSingleNetCDF4.time_orthogonal_indexing"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOWriteSingleNetCDF3(IOSingleNetCDF):\n def setup(self):\n self.format = \"NETCDF3_64BIT\"\n self.make_ds()\n\n def time_write_dataset_netcdf4(self):\n self.ds.to_netcdf(\"test_netcdf4_write.nc\", engine=\"netcdf4\", format=self.format)\n\n def time_write_dataset_scipy(self):\n self.ds.to_netcdf(\"test_scipy_write.nc\", engine=\"scipy\", format=self.format)\n\n\nclass IOReadSingleNetCDF4(IOSingleNetCDF):\n def setup(self):\n\n self.make_ds()\n\n self.filepath = \"test_single_file.nc4.nc\"\n self.format = \"NETCDF4\"\n self.ds.to_netcdf(self.filepath, format=self.format)\n\n def time_load_dataset_netcdf4(self):\n xr.open_dataset(self.filepath, engine=\"netcdf4\").load()\n\n def time_orthogonal_indexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"netcdf4\")\n ds = ds.isel(**self.oinds).load()\n\n def time_vectorized_indexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"netcdf4\")\n ds = ds.isel(**self.vinds).load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF3_IOReadSingleNetCDF3.time_vectorized_indexing.ds_1.ds_isel_self_vinds_loa": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF3_IOReadSingleNetCDF3.time_vectorized_indexing.ds_1.ds_isel_self_vinds_loa", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 129, "end_line": 147, "span_ids": ["IOReadSingleNetCDF3", "IOReadSingleNetCDF3.time_load_dataset_scipy", "IOReadSingleNetCDF3.time_vectorized_indexing", "IOReadSingleNetCDF3.time_orthogonal_indexing", "IOReadSingleNetCDF3.setup"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadSingleNetCDF3(IOReadSingleNetCDF4):\n def setup(self):\n\n self.make_ds()\n\n self.filepath = \"test_single_file.nc3.nc\"\n self.format = \"NETCDF3_64BIT\"\n self.ds.to_netcdf(self.filepath, format=self.format)\n\n def time_load_dataset_scipy(self):\n xr.open_dataset(self.filepath, engine=\"scipy\").load()\n\n def time_orthogonal_indexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"scipy\")\n ds = ds.isel(**self.oinds).load()\n\n def time_vectorized_indexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"scipy\")\n ds = ds.isel(**self.vinds).load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF4Dask_IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks_multiprocessing.with_dask_config_set_sche.xr_open_dataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF4Dask_IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks_multiprocessing.with_dask_config_set_sche.xr_open_dataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 150, "end_line": 187, "span_ids": ["IOReadSingleNetCDF4Dask", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks_multiprocessing", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks_multiprocessing", "IOReadSingleNetCDF4Dask.setup", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks_oindexing", "IOReadSingleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks_vindexing"], "tokens": 349}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadSingleNetCDF4Dask(IOSingleNetCDF):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n\n self.filepath = \"test_single_file.nc4.nc\"\n self.format = \"NETCDF4\"\n self.ds.to_netcdf(self.filepath, format=self.format)\n\n def time_load_dataset_netcdf4_with_block_chunks(self):\n xr.open_dataset(\n self.filepath, engine=\"netcdf4\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_netcdf4_with_block_chunks_oindexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"netcdf4\", chunks=self.block_chunks)\n ds = ds.isel(**self.oinds).load()\n\n def time_load_dataset_netcdf4_with_block_chunks_vindexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"netcdf4\", chunks=self.block_chunks)\n ds = ds.isel(**self.vinds).load()\n\n def time_load_dataset_netcdf4_with_block_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_dataset(\n self.filepath, engine=\"netcdf4\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_netcdf4_with_time_chunks(self):\n xr.open_dataset(self.filepath, engine=\"netcdf4\", chunks=self.time_chunks).load()\n\n def time_load_dataset_netcdf4_with_time_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_dataset(\n self.filepath, engine=\"netcdf4\", chunks=self.time_chunks\n ).load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF3Dask_IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_time_chunks.with_dask_config_set_sche.xr_open_dataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadSingleNetCDF3Dask_IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_time_chunks.with_dask_config_set_sche.xr_open_dataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 219, "span_ids": ["IOReadSingleNetCDF3Dask.setup", "IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_block_chunks", "IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_block_chunks_vindexing", "IOReadSingleNetCDF3Dask", "IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_block_chunks_oindexing", "IOReadSingleNetCDF3Dask.time_load_dataset_scipy_with_time_chunks"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadSingleNetCDF3Dask(IOReadSingleNetCDF4Dask):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n\n self.filepath = \"test_single_file.nc3.nc\"\n self.format = \"NETCDF3_64BIT\"\n self.ds.to_netcdf(self.filepath, format=self.format)\n\n def time_load_dataset_scipy_with_block_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_dataset(\n self.filepath, engine=\"scipy\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_scipy_with_block_chunks_oindexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"scipy\", chunks=self.block_chunks)\n ds = ds.isel(**self.oinds).load()\n\n def time_load_dataset_scipy_with_block_chunks_vindexing(self):\n ds = xr.open_dataset(self.filepath, engine=\"scipy\", chunks=self.block_chunks)\n ds = ds.isel(**self.vinds).load()\n\n def time_load_dataset_scipy_with_time_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_dataset(\n self.filepath, engine=\"scipy\", chunks=self.time_chunks\n ).load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOMultipleNetCDF_IOMultipleNetCDF.make_ds.for_i_times_in_enumerate.self_filenames_list_appen": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOMultipleNetCDF_IOMultipleNetCDF.make_ds.for_i_times_in_enumerate.self_filenames_list_appen", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 222, "end_line": 296, "span_ids": ["IOMultipleNetCDF", "IOMultipleNetCDF.make_ds"], "tokens": 613}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOMultipleNetCDF:\n \"\"\"\n A few examples that benchmark reading/writing multiple netCDF files with\n xarray\n \"\"\"\n\n timeout = 300.0\n repeat = 1\n number = 5\n\n def make_ds(self, nfiles=10):\n\n # multiple Dataset\n self.ds = xr.Dataset()\n self.nt = 1000\n self.nx = 90\n self.ny = 45\n self.nfiles = nfiles\n\n self.block_chunks = {\n \"time\": self.nt / 4,\n \"lon\": self.nx / 3,\n \"lat\": self.ny / 3,\n }\n\n self.time_chunks = {\"time\": int(self.nt / 36)}\n\n self.time_vars = np.split(\n pd.date_range(\"1970-01-01\", periods=self.nt, freq=\"D\"), self.nfiles\n )\n\n self.ds_list = []\n self.filenames_list = []\n for i, times in enumerate(self.time_vars):\n ds = xr.Dataset()\n nt = len(times)\n lons = xr.DataArray(\n np.linspace(0, 360, self.nx),\n dims=(\"lon\",),\n attrs={\"units\": \"degrees east\", \"long_name\": \"longitude\"},\n )\n lats = xr.DataArray(\n np.linspace(-90, 90, self.ny),\n dims=(\"lat\",),\n attrs={\"units\": \"degrees north\", \"long_name\": \"latitude\"},\n )\n ds[\"foo\"] = xr.DataArray(\n randn((nt, self.nx, self.ny), frac_nan=0.2),\n coords={\"lon\": lons, \"lat\": lats, \"time\": times},\n dims=(\"time\", \"lon\", \"lat\"),\n name=\"foo\",\n encoding=None,\n attrs={\"units\": \"foo units\", \"description\": \"a description\"},\n )\n ds[\"bar\"] = xr.DataArray(\n randn((nt, self.nx, self.ny), frac_nan=0.2),\n coords={\"lon\": lons, \"lat\": lats, \"time\": times},\n dims=(\"time\", \"lon\", \"lat\"),\n name=\"bar\",\n encoding=None,\n attrs={\"units\": \"bar units\", \"description\": \"a description\"},\n )\n ds[\"baz\"] = xr.DataArray(\n randn((self.nx, self.ny), frac_nan=0.2).astype(np.float32),\n coords={\"lon\": lons, \"lat\": lats},\n dims=(\"lon\", \"lat\"),\n name=\"baz\",\n encoding=None,\n attrs={\"units\": \"baz units\", \"description\": \"a description\"},\n )\n\n ds.attrs = {\"history\": \"created for xarray benchmarking\"}\n\n self.ds_list.append(ds)\n self.filenames_list.append(\"test_netcdf_%i.nc\" % i)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOWriteMultipleNetCDF3_IOWriteMultipleNetCDF3.time_write_dataset_scipy.xr_save_mfdataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOWriteMultipleNetCDF3_IOWriteMultipleNetCDF3.time_write_dataset_scipy.xr_save_mfdataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 299, "end_line": 312, "span_ids": ["IOWriteMultipleNetCDF3.time_write_dataset_netcdf4", "IOWriteMultipleNetCDF3.setup", "IOWriteMultipleNetCDF3.time_write_dataset_scipy", "IOWriteMultipleNetCDF3"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOWriteMultipleNetCDF3(IOMultipleNetCDF):\n def setup(self):\n self.make_ds()\n self.format = \"NETCDF3_64BIT\"\n\n def time_write_dataset_netcdf4(self):\n xr.save_mfdataset(\n self.ds_list, self.filenames_list, engine=\"netcdf4\", format=self.format\n )\n\n def time_write_dataset_scipy(self):\n xr.save_mfdataset(\n self.ds_list, self.filenames_list, engine=\"scipy\", format=self.format\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF4_IOReadMultipleNetCDF4.time_open_dataset_netcdf4.xr_open_mfdataset_self_fi": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF4_IOReadMultipleNetCDF4.time_open_dataset_netcdf4.xr_open_mfdataset_self_fi", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 315, "end_line": 328, "span_ids": ["IOReadMultipleNetCDF4.time_load_dataset_netcdf4", "IOReadMultipleNetCDF4", "IOReadMultipleNetCDF4.time_open_dataset_netcdf4", "IOReadMultipleNetCDF4.setup"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadMultipleNetCDF4(IOMultipleNetCDF):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n self.format = \"NETCDF4\"\n xr.save_mfdataset(self.ds_list, self.filenames_list, format=self.format)\n\n def time_load_dataset_netcdf4(self):\n xr.open_mfdataset(self.filenames_list, engine=\"netcdf4\").load()\n\n def time_open_dataset_netcdf4(self):\n xr.open_mfdataset(self.filenames_list, engine=\"netcdf4\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF3_IOReadMultipleNetCDF3.time_open_dataset_scipy.xr_open_mfdataset_self_fi": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF3_IOReadMultipleNetCDF3.time_open_dataset_scipy.xr_open_mfdataset_self_fi", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 331, "end_line": 344, "span_ids": ["IOReadMultipleNetCDF3", "IOReadMultipleNetCDF3.setup", "IOReadMultipleNetCDF3.time_load_dataset_scipy", "IOReadMultipleNetCDF3.time_open_dataset_scipy"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadMultipleNetCDF3(IOReadMultipleNetCDF4):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n self.format = \"NETCDF3_64BIT\"\n xr.save_mfdataset(self.ds_list, self.filenames_list, format=self.format)\n\n def time_load_dataset_scipy(self):\n xr.open_mfdataset(self.filenames_list, engine=\"scipy\").load()\n\n def time_open_dataset_scipy(self):\n xr.open_mfdataset(self.filenames_list, engine=\"scipy\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF4Dask_IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_time_chunks_multiprocessing.with_dask_config_set_sche.xr_open_mfdataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF4Dask_IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_time_chunks_multiprocessing.with_dask_config_set_sche.xr_open_mfdataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 347, "end_line": 398, "span_ids": ["IOReadMultipleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks_multiprocessing", "IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_block_chunks", "IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_time_chunks_multiprocessing", "IOReadMultipleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks", "IOReadMultipleNetCDF4Dask.time_load_dataset_netcdf4_with_time_chunks", "IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_time_chunks", "IOReadMultipleNetCDF4Dask.time_load_dataset_netcdf4_with_block_chunks_multiprocessing", "IOReadMultipleNetCDF4Dask.time_open_dataset_netcdf4_with_block_chunks_multiprocessing", "IOReadMultipleNetCDF4Dask", "IOReadMultipleNetCDF4Dask.setup"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadMultipleNetCDF4Dask(IOMultipleNetCDF):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n self.format = \"NETCDF4\"\n xr.save_mfdataset(self.ds_list, self.filenames_list, format=self.format)\n\n def time_load_dataset_netcdf4_with_block_chunks(self):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_netcdf4_with_block_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_netcdf4_with_time_chunks(self):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.time_chunks\n ).load()\n\n def time_load_dataset_netcdf4_with_time_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.time_chunks\n ).load()\n\n def time_open_dataset_netcdf4_with_block_chunks(self):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.block_chunks\n )\n\n def time_open_dataset_netcdf4_with_block_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.block_chunks\n )\n\n def time_open_dataset_netcdf4_with_time_chunks(self):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.time_chunks\n )\n\n def time_open_dataset_netcdf4_with_time_chunks_multiprocessing(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"netcdf4\", chunks=self.time_chunks\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF3Dask_IOReadMultipleNetCDF3Dask.time_open_dataset_scipy_with_time_chunks.with_dask_config_set_sche.xr_open_mfdataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_IOReadMultipleNetCDF3Dask_IOReadMultipleNetCDF3Dask.time_open_dataset_scipy_with_time_chunks.with_dask_config_set_sche.xr_open_mfdataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 401, "end_line": 432, "span_ids": ["IOReadMultipleNetCDF3Dask", "IOReadMultipleNetCDF3Dask.time_open_dataset_scipy_with_time_chunks", "IOReadMultipleNetCDF3Dask.setup", "IOReadMultipleNetCDF3Dask.time_load_dataset_scipy_with_block_chunks", "IOReadMultipleNetCDF3Dask.time_load_dataset_scipy_with_time_chunks", "IOReadMultipleNetCDF3Dask.time_open_dataset_scipy_with_block_chunks"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IOReadMultipleNetCDF3Dask(IOReadMultipleNetCDF4Dask):\n def setup(self):\n\n requires_dask()\n\n self.make_ds()\n self.format = \"NETCDF3_64BIT\"\n xr.save_mfdataset(self.ds_list, self.filenames_list, format=self.format)\n\n def time_load_dataset_scipy_with_block_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"scipy\", chunks=self.block_chunks\n ).load()\n\n def time_load_dataset_scipy_with_time_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"scipy\", chunks=self.time_chunks\n ).load()\n\n def time_open_dataset_scipy_with_block_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"scipy\", chunks=self.block_chunks\n )\n\n def time_open_dataset_scipy_with_time_chunks(self):\n with dask.config.set(scheduler=\"multiprocessing\"):\n xr.open_mfdataset(\n self.filenames_list, engine=\"scipy\", chunks=self.time_chunks\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_create_delayed_write_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/dataset_io.py_create_delayed_write_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/dataset_io.py", "file_name": "dataset_io.py", "file_type": "text/x-python", "category": "implementation", "start_line": 435, "end_line": 470, "span_ids": ["IOWriteNetCDFDask.time_write", "create_delayed_write", "IOWriteNetCDFDask", "IOWriteNetCDFDask.setup", "IOWriteNetCDFDaskDistributed.time_write", "IOWriteNetCDFDaskDistributed", "IOWriteNetCDFDaskDistributed.setup", "IOWriteNetCDFDaskDistributed.cleanup"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_delayed_write():\n import dask.array as da\n\n vals = da.random.random(300, chunks=(1,))\n ds = xr.Dataset({\"vals\": ([\"a\"], vals)})\n return ds.to_netcdf(\"file.nc\", engine=\"netcdf4\", compute=False)\n\n\nclass IOWriteNetCDFDask:\n timeout = 60\n repeat = 1\n number = 5\n\n def setup(self):\n requires_dask()\n self.write = create_delayed_write()\n\n def time_write(self):\n self.write.compute()\n\n\nclass IOWriteNetCDFDaskDistributed:\n def setup(self):\n try:\n import distributed\n except ImportError:\n raise NotImplementedError()\n self.client = distributed.Client()\n self.write = create_delayed_write()\n\n def cleanup(self):\n self.client.shutdown()\n\n def time_write(self):\n self.write.compute()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_np_vectorized_assignment_values._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_np_vectorized_assignment_values._", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 57, "span_ids": ["impl:17", "impl", "imports"], "tokens": 730}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom . import randint, randn, requires_dask\n\nnx = 3000\nny = 2000\nnt = 1000\n\nbasic_indexes = {\n \"1slice\": {\"x\": slice(0, 3)},\n \"1slice-1scalar\": {\"x\": 0, \"y\": slice(None, None, 3)},\n \"2slicess-1scalar\": {\"x\": slice(3, -3, 3), \"y\": 1, \"t\": slice(None, -3, 3)},\n}\n\nbasic_assignment_values = {\n \"1slice\": xr.DataArray(randn((3, ny), frac_nan=0.1), dims=[\"x\", \"y\"]),\n \"1slice-1scalar\": xr.DataArray(randn(int(ny / 3) + 1, frac_nan=0.1), dims=[\"y\"]),\n \"2slicess-1scalar\": xr.DataArray(\n randn(int((nx - 6) / 3), frac_nan=0.1), dims=[\"x\"]\n ),\n}\n\nouter_indexes = {\n \"1d\": {\"x\": randint(0, nx, 400)},\n \"2d\": {\"x\": randint(0, nx, 500), \"y\": randint(0, ny, 400)},\n \"2d-1scalar\": {\"x\": randint(0, nx, 100), \"y\": 1, \"t\": randint(0, nt, 400)},\n}\n\nouter_assignment_values = {\n \"1d\": xr.DataArray(randn((400, ny), frac_nan=0.1), dims=[\"x\", \"y\"]),\n \"2d\": xr.DataArray(randn((500, 400), frac_nan=0.1), dims=[\"x\", \"y\"]),\n \"2d-1scalar\": xr.DataArray(randn(100, frac_nan=0.1), dims=[\"x\"]),\n}\n\nvectorized_indexes = {\n \"1-1d\": {\"x\": xr.DataArray(randint(0, nx, 400), dims=\"a\")},\n \"2-1d\": {\n \"x\": xr.DataArray(randint(0, nx, 400), dims=\"a\"),\n \"y\": xr.DataArray(randint(0, ny, 400), dims=\"a\"),\n },\n \"3-2d\": {\n \"x\": xr.DataArray(randint(0, nx, 400).reshape(4, 100), dims=[\"a\", \"b\"]),\n \"y\": xr.DataArray(randint(0, ny, 400).reshape(4, 100), dims=[\"a\", \"b\"]),\n \"t\": xr.DataArray(randint(0, nt, 400).reshape(4, 100), dims=[\"a\", \"b\"]),\n },\n}\n\nvectorized_assignment_values = {\n \"1-1d\": xr.DataArray(randn((400, 2000)), dims=[\"a\", \"y\"], coords={\"a\": randn(400)}),\n \"2-1d\": xr.DataArray(randn(400), dims=[\"a\"], coords={\"a\": randn(400)}),\n \"3-2d\": xr.DataArray(\n randn((4, 100)), dims=[\"a\", \"b\"], coords={\"a\": randn(4), \"b\": randn(100)}\n ),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Base_Base.setup.self.ds.xr_Dataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Base_Base.setup.self.ds.xr_Dataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 74, "span_ids": ["Base", "Base.setup"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Base:\n def setup(self, key):\n self.ds = xr.Dataset(\n {\n \"var1\": ((\"x\", \"y\"), randn((nx, ny), frac_nan=0.1)),\n \"var2\": ((\"x\", \"t\"), randn((nx, nt))),\n \"var3\": ((\"t\",), randn(nt)),\n },\n coords={\n \"x\": np.arange(nx),\n \"y\": np.linspace(0, 1, ny),\n \"t\": pd.date_range(\"1970-01-01\", periods=nt, freq=\"D\"),\n \"x_coords\": (\"x\", np.linspace(1.1, 2.1, nx)),\n },\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Indexing_Indexing.time_indexing_vectorized.params._list_vectorized_indexes_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Indexing_Indexing.time_indexing_vectorized.params._list_vectorized_indexes_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 94, "span_ids": ["Indexing", "Indexing.time_indexing_outer", "Indexing:10", "Indexing:6", "Indexing:2", "Indexing.time_indexing_vectorized", "Indexing.time_indexing_basic"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Indexing(Base):\n def time_indexing_basic(self, key):\n self.ds.isel(**basic_indexes[key]).load()\n\n time_indexing_basic.param_names = [\"key\"]\n time_indexing_basic.params = [list(basic_indexes.keys())]\n\n def time_indexing_outer(self, key):\n self.ds.isel(**outer_indexes[key]).load()\n\n time_indexing_outer.param_names = [\"key\"]\n time_indexing_outer.params = [list(outer_indexes.keys())]\n\n def time_indexing_vectorized(self, key):\n self.ds.isel(**vectorized_indexes[key]).load()\n\n time_indexing_vectorized.param_names = [\"key\"]\n time_indexing_vectorized.params = [list(vectorized_indexes.keys())]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Assignment_Assignment.time_assignment_vectorized.params._list_vectorized_indexes_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_Assignment_Assignment.time_assignment_vectorized.params._list_vectorized_indexes_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 97, "end_line": 120, "span_ids": ["Assignment.time_assignment_vectorized", "Assignment:6", "Assignment.time_assignment_basic", "Assignment.time_assignment_outer", "Assignment:10", "Assignment:2", "Assignment"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Assignment(Base):\n def time_assignment_basic(self, key):\n ind = basic_indexes[key]\n val = basic_assignment_values[key]\n self.ds[\"var1\"][ind.get(\"x\", slice(None)), ind.get(\"y\", slice(None))] = val\n\n time_assignment_basic.param_names = [\"key\"]\n time_assignment_basic.params = [list(basic_indexes.keys())]\n\n def time_assignment_outer(self, key):\n ind = outer_indexes[key]\n val = outer_assignment_values[key]\n self.ds[\"var1\"][ind.get(\"x\", slice(None)), ind.get(\"y\", slice(None))] = val\n\n time_assignment_outer.param_names = [\"key\"]\n time_assignment_outer.params = [list(outer_indexes.keys())]\n\n def time_assignment_vectorized(self, key):\n ind = vectorized_indexes[key]\n val = vectorized_assignment_values[key]\n self.ds[\"var1\"][ind.get(\"x\", slice(None)), ind.get(\"y\", slice(None))] = val\n\n time_assignment_vectorized.param_names = [\"key\"]\n time_assignment_vectorized.params = [list(vectorized_indexes.keys())]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_IndexingDask_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/indexing.py_IndexingDask_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 141, "span_ids": ["IndexingDask", "BooleanIndexing.setup", "IndexingDask.setup", "BooleanIndexing.time_indexing", "BooleanIndexing"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexingDask(Indexing):\n def setup(self, key):\n requires_dask()\n super().setup(key)\n self.ds = self.ds.chunk({\"x\": 100, \"y\": 50, \"t\": 50})\n\n\nclass BooleanIndexing:\n # https://github.com/pydata/xarray/issues/2227\n def setup(self):\n self.ds = xr.Dataset(\n {\"a\": (\"time\", np.arange(10_000_000))},\n coords={\"time\": np.arange(10_000_000)},\n )\n self.time_filter = self.ds.time > 50_000\n\n def time_indexing(self):\n self.ds.isel(time=self.time_filter)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_np_new_y_long.np_linspace_0_1_0_9_100": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_np_new_y_long.np_linspace_0_1_0_9_100", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/interp.py", "file_name": "interp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 22, "span_ids": ["impl", "imports"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom . import parameterized, randn, requires_dask\n\nnx = 3000\nlong_nx = 30000000\nny = 2000\nnt = 1000\nwindow = 20\n\nrandn_xy = randn((nx, ny), frac_nan=0.1)\nrandn_xt = randn((nx, nt))\nrandn_t = randn((nt,))\nrandn_long = randn((long_nx,), frac_nan=0.1)\n\n\nnew_x_short = np.linspace(0.3 * nx, 0.7 * nx, 100)\nnew_x_long = np.linspace(0.3 * nx, 0.7 * nx, 1000)\nnew_y_long = np.linspace(0.1, 0.9, 1000)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_Interpolation_Interpolation.setup.self.ds.xr_Dataset_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_Interpolation_Interpolation.setup.self.ds.xr_Dataset_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/interp.py", "file_name": "interp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 25, "end_line": 39, "span_ids": ["Interpolation", "Interpolation.setup"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Interpolation:\n def setup(self, *args, **kwargs):\n self.ds = xr.Dataset(\n {\n \"var1\": ((\"x\", \"y\"), randn_xy),\n \"var2\": ((\"x\", \"t\"), randn_xt),\n \"var3\": ((\"t\",), randn_t),\n },\n coords={\n \"x\": np.arange(nx),\n \"y\": np.linspace(0, 1, ny),\n \"t\": pd.date_range(\"1970-01-01\", periods=nt, freq=\"D\"),\n \"x_coords\": (\"x\", np.linspace(1.1, 2.1, nx)),\n },\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_Interpolation.time_interpolation_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/interp.py_Interpolation.time_interpolation_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/interp.py", "file_name": "interp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 41, "end_line": 56, "span_ids": ["Interpolation.time_interpolation", "InterpolationDask.setup", "InterpolationDask", "Interpolation.time_interpolation_2d"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Interpolation:\n\n @parameterized([\"method\", \"is_short\"], ([\"linear\", \"cubic\"], [True, False]))\n def time_interpolation(self, method, is_short):\n new_x = new_x_short if is_short else new_x_long\n self.ds.interp(x=new_x, method=method).load()\n\n @parameterized([\"method\"], ([\"linear\", \"nearest\"]))\n def time_interpolation_2d(self, method):\n self.ds.interp(x=new_x_long, y=new_y_long, method=method).load()\n\n\nclass InterpolationDask(Interpolation):\n def setup(self, *args, **kwargs):\n requires_dask()\n super().setup(**kwargs)\n self.ds = self.ds.chunk({\"t\": 50})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/reindexing.py_np_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/reindexing.py_np_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/reindexing.py", "file_name": "reindexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 49, "span_ids": ["Reindex.time_2d_fine_some_missing", "Reindex", "Reindex.time_1d_fine_some_missing", "Reindex.time_2d_coarse", "ReindexDask", "imports", "ReindexDask.setup", "Reindex.time_2d_fine_all_found", "Reindex.time_1d_fine_all_found", "Reindex.time_1d_coarse", "Reindex.setup"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nimport xarray as xr\n\nfrom . import requires_dask\n\n\nclass Reindex:\n def setup(self):\n data = np.random.RandomState(0).randn(1000, 100, 100)\n self.ds = xr.Dataset(\n {\"temperature\": ((\"time\", \"x\", \"y\"), data)},\n coords={\"time\": np.arange(1000), \"x\": np.arange(100), \"y\": np.arange(100)},\n )\n\n def time_1d_coarse(self):\n self.ds.reindex(time=np.arange(0, 1000, 5)).load()\n\n def time_1d_fine_all_found(self):\n self.ds.reindex(time=np.arange(0, 1000, 0.5), method=\"nearest\").load()\n\n def time_1d_fine_some_missing(self):\n self.ds.reindex(\n time=np.arange(0, 1000, 0.5), method=\"nearest\", tolerance=0.1\n ).load()\n\n def time_2d_coarse(self):\n self.ds.reindex(x=np.arange(0, 100, 2), y=np.arange(0, 100, 2)).load()\n\n def time_2d_fine_all_found(self):\n self.ds.reindex(\n x=np.arange(0, 100, 0.5), y=np.arange(0, 100, 0.5), method=\"nearest\"\n ).load()\n\n def time_2d_fine_some_missing(self):\n self.ds.reindex(\n x=np.arange(0, 100, 0.5),\n y=np.arange(0, 100, 0.5),\n method=\"nearest\",\n tolerance=0.1,\n ).load()\n\n\nclass ReindexDask(Reindex):\n def setup(self):\n requires_dask()\n super().setup()\n self.ds = self.ds.chunk({\"time\": 100})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_np_randn_long.randn_long_nx_frac_na": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_np_randn_long.randn_long_nx_frac_na", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 17, "span_ids": ["impl", "imports"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom . import parameterized, randn, requires_dask\n\nnx = 3000\nlong_nx = 30000000\nny = 2000\nnt = 1000\nwindow = 20\n\nrandn_xy = randn((nx, ny), frac_nan=0.1)\nrandn_xt = randn((nx, nt))\nrandn_t = randn((nt,))\nrandn_long = randn((long_nx,), frac_nan=0.1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_Rolling_Rolling.setup.self.da_long.xr_DataArray_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_Rolling_Rolling.setup.self.da_long.xr_DataArray_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 20, "end_line": 37, "span_ids": ["Rolling.setup", "Rolling"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rolling:\n def setup(self, *args, **kwargs):\n self.ds = xr.Dataset(\n {\n \"var1\": ((\"x\", \"y\"), randn_xy),\n \"var2\": ((\"x\", \"t\"), randn_xt),\n \"var3\": ((\"t\",), randn_t),\n },\n coords={\n \"x\": np.arange(nx),\n \"y\": np.linspace(0, 1, ny),\n \"t\": pd.date_range(\"1970-01-01\", periods=nt, freq=\"D\"),\n \"x_coords\": (\"x\", np.linspace(1.1, 2.1, nx)),\n },\n )\n self.da_long = xr.DataArray(\n randn_long, dims=\"x\", coords={\"x\": np.arange(long_nx) * 0.1}\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_Rolling.time_rolling_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/rolling.py_Rolling.time_rolling_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 39, "end_line": 70, "span_ids": ["Rolling.time_rolling_np", "Rolling.time_rolling_construct", "RollingDask.setup", "Rolling.time_rolling", "RollingDask", "Rolling.time_rolling_long"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rolling:\n\n @parameterized([\"func\", \"center\"], ([\"mean\", \"count\"], [True, False]))\n def time_rolling(self, func, center):\n getattr(self.ds.rolling(x=window, center=center), func)().load()\n\n @parameterized([\"func\", \"pandas\"], ([\"mean\", \"count\"], [True, False]))\n def time_rolling_long(self, func, pandas):\n if pandas:\n se = self.da_long.to_series()\n getattr(se.rolling(window=window), func)()\n else:\n getattr(self.da_long.rolling(x=window), func)().load()\n\n @parameterized([\"window_\", \"min_periods\"], ([20, 40], [5, None]))\n def time_rolling_np(self, window_, min_periods):\n self.ds.rolling(x=window_, center=False, min_periods=min_periods).reduce(\n getattr(np, \"nanmean\")\n ).load()\n\n @parameterized([\"center\", \"stride\"], ([True, False], [1, 200]))\n def time_rolling_construct(self, center, stride):\n self.ds.rolling(x=window, center=center).construct(\n \"window_dim\", stride=stride\n ).mean(dim=\"window_dim\").load()\n\n\nclass RollingDask(Rolling):\n def setup(self, *args, **kwargs):\n requires_dask()\n super().setup(**kwargs)\n self.ds = self.ds.chunk({\"x\": 100, \"y\": 50, \"t\": 50})\n self.da_long = self.da_long.chunk({\"x\": 10000})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/unstacking.py_np_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/asv_bench/benchmarks/unstacking.py_np_", "embedding": null, "metadata": {"file_path": "asv_bench/benchmarks/unstacking.py", "file_name": "unstacking.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 25, "span_ids": ["Unstacking", "Unstacking.time_unstack_slow", "UnstackingDask", "UnstackingDask.setup", "Unstacking.setup", "imports", "Unstacking.time_unstack_fast"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nimport xarray as xr\n\nfrom . import requires_dask\n\n\nclass Unstacking:\n def setup(self):\n data = np.random.RandomState(0).randn(1, 1000, 500)\n self.ds = xr.DataArray(data).stack(flat_dim=[\"dim_1\", \"dim_2\"])\n\n def time_unstack_fast(self):\n self.ds.unstack(\"flat_dim\")\n\n def time_unstack_slow(self):\n self.ds[:, ::-1].unstack(\"flat_dim\")\n\n\nclass UnstackingDask(Unstacking):\n def setup(self, *args, **kwargs):\n requires_dask()\n super().setup(**kwargs)\n self.ds = self.ds.chunk({\"flat_dim\": 50})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py__Fetch_from_conda_datab_error.print_ERROR_msg_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py__Fetch_from_conda_datab_error.print_ERROR_msg_", "embedding": null, "metadata": {"file_path": "ci/min_deps_check.py", "file_name": "min_deps_check.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 34, "span_ids": ["error", "impl", "docstring", "imports"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Fetch from conda database all available versions of the xarray dependencies and their\npublication date. Compare it against requirements/py36-min-all-deps.yml to verify the\npolicy on obsolete dependencies is being followed. Print a pretty report :)\n\"\"\"\nimport subprocess\nimport sys\nfrom concurrent.futures import ThreadPoolExecutor\nfrom datetime import datetime, timedelta\nfrom typing import Dict, Iterator, Tuple\n\nimport yaml\n\nIGNORE_DEPS = {\n \"black\",\n \"coveralls\",\n \"flake8\",\n \"hypothesis\",\n \"mypy\",\n \"pip\",\n \"pytest\",\n \"pytest-cov\",\n \"pytest-env\",\n}\n\nPOLICY_MONTHS = {\"python\": 42, \"numpy\": 24, \"pandas\": 12, \"scipy\": 12}\nPOLICY_MONTHS_DEFAULT = 6\n\nhas_errors = False\n\n\ndef error(msg: str) -> None:\n global has_errors\n has_errors = True\n print(\"ERROR:\", msg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_parse_requirements_parse_requirements.for_row_in_contents_depe.None_1.except_ValueError_.error_failed_to_parse_ve": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_parse_requirements_parse_requirements.for_row_in_contents_depe.None_1.except_ValueError_.error_failed_to_parse_ve", "embedding": null, "metadata": {"file_path": "ci/min_deps_check.py", "file_name": "min_deps_check.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 63, "span_ids": ["parse_requirements"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def parse_requirements(fname) -> Iterator[Tuple[str, int, int]]:\n \"\"\"Load requirements/py36-min-all-deps.yml\n\n Yield (package name, major version, minor version)\n \"\"\"\n global has_errors\n\n with open(fname) as fh:\n contents = yaml.safe_load(fh)\n for row in contents[\"dependencies\"]:\n if isinstance(row, dict) and list(row) == [\"pip\"]:\n continue\n pkg, eq, version = row.partition(\"=\")\n if pkg.rstrip(\"<>\") in IGNORE_DEPS:\n continue\n if pkg.endswith(\"<\") or pkg.endswith(\">\") or eq != \"=\":\n error(\"package should be pinned with exact version: \" + row)\n continue\n try:\n major, minor = version.split(\".\")\n except ValueError:\n error(\"expected major.minor (without patch): \" + row)\n continue\n try:\n yield pkg, int(major), int(minor)\n except ValueError:\n error(\"failed to parse version: \" + row)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_query_conda_query_conda.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_query_conda_query_conda.return.out", "embedding": null, "metadata": {"file_path": "ci/min_deps_check.py", "file_name": "min_deps_check.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 108, "span_ids": ["query_conda"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def query_conda(pkg: str) -> Dict[Tuple[int, int], datetime]:\n \"\"\"Query the conda repository for a specific package\n\n Return map of {(major version, minor version): publication date}\n \"\"\"\n stdout = subprocess.check_output(\n [\"conda\", \"search\", pkg, \"--info\", \"-c\", \"defaults\", \"-c\", \"conda-forge\"]\n )\n out = {} # type: Dict[Tuple[int, int], datetime]\n major = None\n minor = None\n\n for row in stdout.decode(\"utf-8\").splitlines():\n label, _, value = row.partition(\":\")\n label = label.strip()\n if label == \"file name\":\n value = value.strip()[len(pkg) :]\n major, minor = value.split(\"-\")[1].split(\".\")[:2]\n major = int(major)\n minor = int(minor)\n if label == \"timestamp\":\n assert major is not None\n assert minor is not None\n ts = datetime.strptime(value.split()[0].strip(), \"%Y-%m-%d\")\n\n if (major, minor) in out:\n out[major, minor] = min(out[major, minor], ts)\n else:\n out[major, minor] = ts\n\n # Hardcoded fix to work around incorrect dates in conda\n if pkg == \"python\":\n out.update(\n {\n (2, 7): datetime(2010, 6, 3),\n (3, 5): datetime(2015, 9, 13),\n (3, 6): datetime(2016, 12, 23),\n (3, 7): datetime(2018, 6, 27),\n (3, 8): datetime(2019, 10, 14),\n }\n )\n\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_process_pkg_process_pkg.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_process_pkg_process_pkg.return._", "embedding": null, "metadata": {"file_path": "ci/min_deps_check.py", "file_name": "min_deps_check.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 165, "span_ids": ["process_pkg"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def process_pkg(\n pkg: str, req_major: int, req_minor: int\n) -> Tuple[str, int, int, str, int, int, str, str]:\n \"\"\"Compare package version from requirements file to available versions in conda.\n Return row to build pandas dataframe:\n\n - package name\n - major version in requirements file\n - minor version in requirements file\n - publication date of version in requirements file (YYYY-MM-DD)\n - major version suggested by policy\n - minor version suggested by policy\n - publication date of version suggested by policy (YYYY-MM-DD)\n - status (\"<\", \"=\", \"> (!)\")\n \"\"\"\n print(\"Analyzing %s...\" % pkg)\n versions = query_conda(pkg)\n\n try:\n req_published = versions[req_major, req_minor]\n except KeyError:\n error(\"not found in conda: \" + pkg)\n return pkg, req_major, req_minor, \"-\", 0, 0, \"-\", \"(!)\"\n\n policy_months = POLICY_MONTHS.get(pkg, POLICY_MONTHS_DEFAULT)\n policy_published = datetime.now() - timedelta(days=policy_months * 30)\n\n policy_major = req_major\n policy_minor = req_minor\n policy_published_actual = req_published\n for (major, minor), published in reversed(sorted(versions.items())):\n if published < policy_published:\n break\n policy_major = major\n policy_minor = minor\n policy_published_actual = published\n\n if (req_major, req_minor) < (policy_major, policy_minor):\n status = \"<\"\n elif (req_major, req_minor) > (policy_major, policy_minor):\n status = \"> (!)\"\n error(\"Package is too new: \" + pkg)\n else:\n status = \"=\"\n\n return (\n pkg,\n req_major,\n req_minor,\n req_published.strftime(\"%Y-%m-%d\"),\n policy_major,\n policy_minor,\n policy_published_actual.strftime(\"%Y-%m-%d\"),\n status,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_main_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/ci/min_deps_check.py_main_", "embedding": null, "metadata": {"file_path": "ci/min_deps_check.py", "file_name": "min_deps_check.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 188, "span_ids": ["main", "impl:9"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def main() -> None:\n fname = sys.argv[1]\n with ThreadPoolExecutor(8) as ex:\n futures = [\n ex.submit(process_pkg, pkg, major, minor)\n for pkg, major, minor in parse_requirements(fname)\n ]\n rows = [f.result() for f in futures]\n\n print(\"Package Required Policy Status\")\n print(\"------------- ----------------- ----------------- ------\")\n fmt = \"{:13} {:>1d}.{:<2d} ({:10}) {:>1d}.{:<2d} ({:10}) {}\"\n for row in rows:\n print(fmt.format(*row))\n\n assert not has_errors\n\n\nif __name__ == \"__main__\":\n main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/conftest.py__Configuration_for_pyte_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/conftest.py__Configuration_for_pyte_", "embedding": null, "metadata": {"file_path": "conftest.py", "file_name": "conftest.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 24, "span_ids": ["pytest_runtest_setup", "docstring", "imports", "pytest_addoption"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Configuration for pytest.\"\"\"\n\nimport pytest\n\n\ndef pytest_addoption(parser):\n \"\"\"Add command-line flags for pytest.\"\"\"\n parser.addoption(\"--run-flaky\", action=\"store_true\", help=\"runs flaky tests\")\n parser.addoption(\n \"--run-network-tests\",\n action=\"store_true\",\n help=\"runs tests requiring a network connection\",\n )\n\n\ndef pytest_runtest_setup(item):\n # based on https://stackoverflow.com/questions/47559524\n if \"flaky\" in item.keywords and not item.config.getoption(\"--run-flaky\"):\n pytest.skip(\"set --run-flaky option to run flaky tests\")\n if \"network\" in item.keywords and not item.config.getoption(\"--run-network-tests\"):\n pytest.skip(\n \"set --run-network-tests to run test requiring an \" \"internet connection\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py__coding_utf_8___The_full_version_inclu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py__coding_utf_8___The_full_version_inclu", "embedding": null, "metadata": {"file_path": "doc/conf.py", "file_name": "conf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 125, "span_ids": ["impl", "impl:18", "imports:9", "docstring", "impl:15", "imports", "imports:7", "imports:8", "impl:13"], "tokens": 794}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n#\n# xarray documentation build configuration file, created by\n# sphinx-quickstart on Thu Feb 6 18:57:54 2014.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n\nimport datetime\nimport os\nimport subprocess\nimport sys\nfrom contextlib import suppress\n\nimport xarray\n\nallowed_failures = set()\n\nprint(\"python exec:\", sys.executable)\nprint(\"sys.path:\", sys.path)\n\nif \"conda\" in sys.executable:\n print(\"conda environment:\")\n subprocess.run([\"conda\", \"list\"])\nelse:\n print(\"pip environment:\")\n subprocess.run([\"pip\", \"list\"])\n\nprint(\"xarray: %s, %s\" % (xarray.__version__, xarray.__file__))\n\nwith suppress(ImportError):\n import matplotlib\n\n matplotlib.use(\"Agg\")\n\ntry:\n import rasterio\nexcept ImportError:\n allowed_failures.update(\n [\"gallery/plot_rasterio_rgb.py\", \"gallery/plot_rasterio.py\"]\n )\n\ntry:\n import cartopy\nexcept ImportError:\n allowed_failures.update(\n [\n \"gallery/plot_cartopy_facetgrid.py\",\n \"gallery/plot_rasterio_rgb.py\",\n \"gallery/plot_rasterio.py\",\n ]\n )\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.extlinks\",\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.napoleon\",\n \"numpydoc\",\n \"IPython.sphinxext.ipython_directive\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n \"sphinx_gallery.gen_gallery\",\n]\n\nextlinks = {\n \"issue\": (\"https://github.com/pydata/xarray/issues/%s\", \"GH\"),\n \"pull\": (\"https://github.com/pydata/xarray/pull/%s\", \"PR\"),\n}\n\nsphinx_gallery_conf = {\n \"examples_dirs\": \"gallery\",\n \"gallery_dirs\": \"auto_gallery\",\n \"backreferences_dir\": False,\n \"expected_failing_examples\": list(allowed_failures),\n}\n\nautosummary_generate = True\nautodoc_typehints = \"none\"\n\nnapoleon_use_param = True\nnapoleon_use_rtype = True\n\nnumpydoc_class_members_toctree = True\nnumpydoc_show_class_members = False\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix of source filenames.\nsource_suffix = \".rst\"\n\n# The encoding of source files.\n# source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"xarray\"\ncopyright = \"2014-%s, xarray Developers\" % datetime.datetime.now().year\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = xarray.__version__.split(\"+\")[0]\n# The full version, including alpha/beta/rc tags.", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py_release__Options_for_LaTeX_ou": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py_release__Options_for_LaTeX_ou", "embedding": null, "metadata": {"file_path": "doc/conf.py", "file_name": "conf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 261, "span_ids": ["impl:74", "impl:50", "impl:64"], "tokens": 972}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "release = xarray.__version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n# language = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n# today = ''\n# Else, today_fmt is used as the format for a strftime call.\ntoday_fmt = \"%Y-%m-%d\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = [\"_build\"]\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n# default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n# add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n# add_module_names = True\npygments_style = \"sphinx\"\n\n# A list of ignored prefixes for module index sorting.\n# modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n# keep_warnings = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\"logo_only\": True}\n\n# Add any paths that contain custom themes here, relative to this directory.\n# html_theme_path = []\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n# html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\n# html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = \"_static/dataset-diagram-logo.png\"\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = \"_static/favicon.ico\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# Sometimes the savefig directory doesn't exist and needs to be created\n# https://github.com/ipython/ipython/issues/8733\n# becomes obsolete when we can pin ipython>=5.2; see ci/requirements/doc.yml\nipython_savefig_dir = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"_build\", \"html\", \"_static\"\n)\nif not os.path.exists(ipython_savefig_dir):\n os.makedirs(ipython_savefig_dir)\n\n# Add any extra paths that contain custom files (such as robots.txt or\n# .htaccess) here, relative to this directory. These files are copied\n# directly to the root of the documentation.\n# html_extra_path = []\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\nhtml_last_updated_fmt = today_fmt\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\n# html_use_smartypants = True\n\n# Custom sidebar templates, maps document names to template names.\n# html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n# html_additional_pages = {}\n\n# If false, no module index is generated.\n# html_domain_indices = True\n\n# If false, no index is generated.\n# html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n# html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n# html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n# html_show_sphinx = True\nhtmlhelp_basename = \"xarraydoc\"\n\n\n# -- Options for LaTeX output ---------------------------------------------", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py_latex_elements_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/conf.py_latex_elements_", "embedding": null, "metadata": {"file_path": "doc/conf.py", "file_name": "conf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 263, "end_line": 349, "span_ids": ["impl:84", "impl:74"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "latex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n # 'preamble': '',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\nlatex_documents = [\n (\"index\", \"xarray.tex\", \"xarray Documentation\", \"xarray Developers\", \"manual\")\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\n# latex_logo = None\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n# latex_use_parts = False\n\n# If true, show page references after internal links.\n# latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n# latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n# latex_appendices = []\n\n# If false, no module index is generated.\n# latex_domain_indices = True\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\nman_pages = [(\"index\", \"xarray\", \"xarray Documentation\", [\"xarray Developers\"], 1)]\n\n# If true, show URL addresses after external links.\n# man_show_urls = False\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\ntexinfo_documents = [\n (\n \"index\",\n \"xarray\",\n \"xarray Documentation\",\n \"xarray Developers\",\n \"xarray\",\n \"N-D labeled arrays and datasets in Python.\",\n \"Miscellaneous\",\n )\n]\n\n# Documents to append as an appendix to all manuals.\n# texinfo_appendices = []\n\n# If false, no module index is generated.\n# texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n# texinfo_show_urls = 'footnote'\n\n# If true, do not generate a @detailmenu in the \"Top\" node's menu.\n# texinfo_no_detailmenu = False\n\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3/\", None),\n \"pandas\": (\"https://pandas.pydata.org/pandas-docs/stable/\", None),\n \"iris\": (\"http://scitools.org.uk/iris/docs/latest/\", None),\n \"numpy\": (\"https://docs.scipy.org/doc/numpy/\", None),\n \"numba\": (\"https://numba.pydata.org/numba-doc/latest/\", None),\n \"matplotlib\": (\"https://matplotlib.org/\", None),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/examples/_code/accessor_example.py_xr_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/examples/_code/accessor_example.py_xr_", "embedding": null, "metadata": {"file_path": "doc/examples/_code/accessor_example.py", "file_name": "accessor_example.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 24, "span_ids": ["GeoAccessor.center", "GeoAccessor", "GeoAccessor.plot", "imports"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import xarray as xr\n\n\n@xr.register_dataset_accessor(\"geo\")\nclass GeoAccessor:\n def __init__(self, xarray_obj):\n self._obj = xarray_obj\n self._center = None\n\n @property\n def center(self):\n \"\"\"Return the geographic center point of this dataset.\"\"\"\n if self._center is None:\n # we can use a cache on our accessor objects, because accessors\n # themselves are cached on instances that access them.\n lon = self._obj.latitude\n lat = self._obj.longitude\n self._center = (float(lon.mean()), float(lat.mean()))\n return self._center\n\n def plot(self):\n \"\"\"Plot data on a map.\"\"\"\n return \"plotting!\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/examples/_code/weather_data_setup.py_np_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/examples/_code/weather_data_setup.py_np_", "embedding": null, "metadata": {"file_path": "doc/examples/_code/weather_data_setup.py", "file_name": "weather_data_setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 23, "span_ids": ["impl", "imports"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nimport xarray as xr\n\nnp.random.seed(123)\n\ntimes = pd.date_range(\"2000-01-01\", \"2001-12-31\", name=\"time\")\nannual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 365.25 - 0.28))\n\nbase = 10 + 15 * annual_cycle.reshape(-1, 1)\ntmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)\ntmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3)\n\nds = xr.Dataset(\n {\n \"tmin\": ((\"time\", \"location\"), tmin_values),\n \"tmax\": ((\"time\", \"location\"), tmax_values),\n },\n {\"time\": times, \"location\": [\"IA\", \"IN\", \"IL\"]},\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_cartopy_facetgrid.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_cartopy_facetgrid.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_cartopy_facetgrid.py", "file_name": "plot_cartopy_facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 47, "span_ids": ["impl", "docstring", "imports"], "tokens": 335}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n==================================\nMultiple plots and map projections\n==================================\n\nControl the map projection parameters on multiple axes\n\nThis example illustrates how to plot multiple maps and control their extent\nand aspect ratio.\n\nFor more details see `this discussion`_ on github.\n\n.. _this discussion: https://github.com/pydata/xarray/issues/1397#issuecomment-299190567\n\"\"\"\n\n\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\nimport xarray as xr\n\n# Load the data\nds = xr.tutorial.load_dataset(\"air_temperature\")\nair = ds.air.isel(time=[0, 724]) - 273.15\n\n# This is the map projection we want to plot *onto*\nmap_proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=45)\n\np = air.plot(\n transform=ccrs.PlateCarree(), # the data's projection\n col=\"time\",\n col_wrap=1, # multiplot settings\n aspect=ds.dims[\"lon\"] / ds.dims[\"lat\"], # for a sensible figsize\n subplot_kws={\"projection\": map_proj}, # the plot's projection\n)\n\n# We have to set the map's options on all four axes\nfor ax in p.axes.flat:\n ax.coastlines()\n ax.set_extent([-160, -30, 5, 75])\n # Without this aspect attributes the maps will look chaotic and the\n # \"extent\" attribute above will be ignored\n ax.set_aspect(\"equal\")\n\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_colorbar_center.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_colorbar_center.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_colorbar_center.py", "file_name": "plot_colorbar_center.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 45, "span_ids": ["impl", "docstring", "imports"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n==================\nCentered colormaps\n==================\n\nxarray's automatic colormaps choice\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\n\nimport xarray as xr\n\n# Load the data\nds = xr.tutorial.load_dataset(\"air_temperature\")\nair = ds.air.isel(time=0)\n\nf, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))\n\n# The first plot (in kelvins) chooses \"viridis\" and uses the data's min/max\nair.plot(ax=ax1, cbar_kwargs={\"label\": \"K\"})\nax1.set_title(\"Kelvins: default\")\nax2.set_xlabel(\"\")\n\n# The second plot (in celsius) now chooses \"BuRd\" and centers min/max around 0\nairc = air - 273.15\nairc.plot(ax=ax2, cbar_kwargs={\"label\": \"\u00b0C\"})\nax2.set_title(\"Celsius: default\")\nax2.set_xlabel(\"\")\nax2.set_ylabel(\"\")\n\n# The center doesn't have to be 0\nair.plot(ax=ax3, center=273.15, cbar_kwargs={\"label\": \"K\"})\nax3.set_title(\"Kelvins: center=273.15\")\n\n# Or it can be ignored\nairc.plot(ax=ax4, center=False, cbar_kwargs={\"label\": \"\u00b0C\"})\nax4.set_title(\"Celsius: center=False\")\nax4.set_ylabel(\"\")\n\n# Mke it nice\nplt.tight_layout()\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_control_colorbar.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_control_colorbar.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_control_colorbar.py", "file_name": "plot_control_colorbar.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 34, "span_ids": ["impl", "docstring", "imports"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n===========================\nControl the plot's colorbar\n===========================\n\nUse ``cbar_kwargs`` keyword to specify the number of ticks.\nThe ``spacing`` kwarg can be used to draw proportional ticks.\n\"\"\"\nimport matplotlib.pyplot as plt\n\nimport xarray as xr\n\n# Load the data\nair_temp = xr.tutorial.load_dataset(\"air_temperature\")\nair2d = air_temp.air.isel(time=500)\n\n# Prepare the figure\nf, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))\n\n# Irregular levels to illustrate the use of a proportional colorbar\nlevels = [245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 310, 340]\n\n# Plot data\nair2d.plot(ax=ax1, levels=levels)\nair2d.plot(ax=ax2, levels=levels, cbar_kwargs={\"ticks\": levels})\nair2d.plot(\n ax=ax3, levels=levels, cbar_kwargs={\"ticks\": levels, \"spacing\": \"proportional\"}\n)\n\n# Show plots\nplt.tight_layout()\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_lines_from_2d.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_lines_from_2d.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_lines_from_2d.py", "file_name": "plot_lines_from_2d.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 40, "span_ids": ["impl", "docstring", "imports"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n==================================\nMultiple lines from a 2d DataArray\n==================================\n\n\nUse :py:func:`xarray.plot.line` on a 2d DataArray to plot selections as\nmultiple lines.\n\nSee :ref:`plotting.multiplelines` for more details.\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\n\nimport xarray as xr\n\n# Load the data\nds = xr.tutorial.load_dataset(\"air_temperature\")\nair = ds.air - 273.15 # to celsius\n\n# Prepare the figure\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharey=True)\n\n# Selected latitude indices\nisel_lats = [10, 15, 20]\n\n# Temperature vs longitude plot - illustrates the \"hue\" kwarg\nair.isel(time=0, lat=isel_lats).plot.line(ax=ax1, hue=\"lat\")\nax1.set_ylabel(\"\u00b0C\")\n\n# Temperature vs time plot - illustrates the \"x\" and \"add_legend\" kwargs\nair.isel(lon=30, lat=isel_lats).plot.line(ax=ax2, x=\"time\", add_legend=False)\nax2.set_ylabel(\"\")\n\n# Show\nplt.tight_layout()\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_rasterio.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_rasterio.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_rasterio.py", "file_name": "plot_rasterio.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 56, "span_ids": ["impl", "docstring", "imports"], "tokens": 410}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n.. _recipes.rasterio:\n\n=================================\nParsing rasterio's geocoordinates\n=================================\n\n\nConverting a projection's cartesian coordinates into 2D longitudes and\nlatitudes.\n\nThese new coordinates might be handy for plotting and indexing, but it should\nbe kept in mind that a grid which is regular in projection coordinates will\nlikely be irregular in lon/lat. It is often recommended to work in the data's\noriginal map projection (see :ref:`recipes.rasterio_rgb`).\n\"\"\"\n\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom rasterio.warp import transform\n\nimport xarray as xr\n\n# Read the data\nurl = \"https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif\"\nda = xr.open_rasterio(url)\n\n# Compute the lon/lat coordinates with rasterio.warp.transform\nny, nx = len(da[\"y\"]), len(da[\"x\"])\nx, y = np.meshgrid(da[\"x\"], da[\"y\"])\n\n# Rasterio works with 1D arrays\nlon, lat = transform(da.crs, {\"init\": \"EPSG:4326\"}, x.flatten(), y.flatten())\nlon = np.asarray(lon).reshape((ny, nx))\nlat = np.asarray(lat).reshape((ny, nx))\nda.coords[\"lon\"] = ((\"y\", \"x\"), lon)\nda.coords[\"lat\"] = ((\"y\", \"x\"), lat)\n\n# Compute a greyscale out of the rgb image\ngreyscale = da.mean(dim=\"band\")\n\n# Plot on a map\nax = plt.subplot(projection=ccrs.PlateCarree())\ngreyscale.plot(\n ax=ax,\n x=\"lon\",\n y=\"lat\",\n transform=ccrs.PlateCarree(),\n cmap=\"Greys_r\",\n add_colorbar=False,\n)\nax.coastlines(\"10m\", color=\"r\")\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_rasterio_rgb.py__coding_utf_8__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/doc/gallery/plot_rasterio_rgb.py__coding_utf_8__", "embedding": null, "metadata": {"file_path": "doc/gallery/plot_rasterio_rgb.py", "file_name": "plot_rasterio_rgb.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 34, "span_ids": ["impl", "docstring", "imports"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -*- coding: utf-8 -*-\n\"\"\"\n.. _recipes.rasterio_rgb:\n\n============================\nimshow() and map projections\n============================\n\nUsing rasterio's projection information for more accurate plots.\n\nThis example extends :ref:`recipes.rasterio` and plots the image in the\noriginal map projection instead of relying on pcolormesh and a map\ntransformation.\n\"\"\"\n\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\nimport xarray as xr\n\n# Read the data\nurl = \"https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif\"\nda = xr.open_rasterio(url)\n\n# The data is in UTM projection. We have to set it manually until\n# https://github.com/SciTools/cartopy/issues/813 is implemented\ncrs = ccrs.UTM(\"18N\")\n\n# Plot on a map\nax = plt.subplot(projection=crs)\nda.plot.imshow(ax=ax, rgb=\"band\", transform=crs)\nax.coastlines(\"10m\", color=\"r\")\nplt.show()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/properties/test_encode_decode.py___test_CFMask_coder_roundtrip.xr_testing_assert_identic": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/properties/test_encode_decode.py___test_CFMask_coder_roundtrip.xr_testing_assert_identic", "embedding": null, "metadata": {"file_path": "properties/test_encode_decode.py", "file_name": "test_encode_decode.py", "file_type": "text/x-python", "category": "test", "start_line": 1, "end_line": 41, "span_ids": ["impl", "impl:2", "docstring", "test_CFMask_coder_roundtrip", "docstring:2", "imports", "imports:2"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"\nProperty-based tests for encoding/decoding methods.\n\nThese ones pass, just as you'd hope!\n\n\"\"\"\nimport pytest # isort:skip\n\npytest.importorskip(\"hypothesis\")\n\nimport hypothesis.extra.numpy as npst\nimport hypothesis.strategies as st\nfrom hypothesis import given, settings\n\nimport xarray as xr\n\n# Run for a while - arrays are a bigger search space than usual\nsettings.register_profile(\"ci\", deadline=None)\nsettings.load_profile(\"ci\")\n\n\nan_array = npst.arrays(\n dtype=st.one_of(\n npst.unsigned_integer_dtypes(), npst.integer_dtypes(), npst.floating_dtypes()\n ),\n shape=npst.array_shapes(max_side=3), # max_side specified for performance\n)\n\n\n@pytest.mark.slow\n@given(st.data(), an_array)\ndef test_CFMask_coder_roundtrip(data, arr):\n names = data.draw(\n st.lists(st.text(), min_size=arr.ndim, max_size=arr.ndim, unique=True).map(\n tuple\n )\n )\n original = xr.Variable(names, arr)\n coder = xr.coding.variables.CFMaskCoder()\n roundtripped = coder.decode(coder.encode(original))\n xr.testing.assert_identical(original, roundtripped)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/properties/test_encode_decode.py_test_CFScaleOffset_coder_roundtrip_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/properties/test_encode_decode.py_test_CFScaleOffset_coder_roundtrip_", "embedding": null, "metadata": {"file_path": "properties/test_encode_decode.py", "file_name": "test_encode_decode.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 56, "span_ids": ["test_CFScaleOffset_coder_roundtrip"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.mark.slow\n@given(st.data(), an_array)\ndef test_CFScaleOffset_coder_roundtrip(data, arr):\n names = data.draw(\n st.lists(st.text(), min_size=arr.ndim, max_size=arr.ndim, unique=True).map(\n tuple\n )\n )\n original = xr.Variable(names, arr)\n coder = xr.coding.variables.CFScaleOffsetCoder()\n roundtripped = coder.decode(coder.encode(original))\n xr.testing.assert_identical(original, roundtripped)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/setup.py__usr_bin_env_python_DESCRIPTION._N_D_labeled_arrays_and_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/setup.py__usr_bin_env_python_DESCRIPTION._N_D_labeled_arrays_and_d", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 30, "span_ids": ["impl", "docstring", "imports"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "#!/usr/bin/env python\nimport sys\n\nimport versioneer\nfrom setuptools import find_packages, setup\n\nDISTNAME = \"xarray\"\nLICENSE = \"Apache\"\nAUTHOR = \"xarray Developers\"\nAUTHOR_EMAIL = \"xarray@googlegroups.com\"\nURL = \"https://github.com/pydata/xarray\"\nCLASSIFIERS = [\n \"Development Status :: 5 - Production/Stable\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n \"Intended Audience :: Science/Research\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Topic :: Scientific/Engineering\",\n]\n\nPYTHON_REQUIRES = \">=3.6\"\nINSTALL_REQUIRES = [\"numpy >= 1.14\", \"pandas >= 0.24\"]\nneeds_pytest = {\"pytest\", \"test\", \"ptr\"}.intersection(sys.argv)\nSETUP_REQUIRES = [\"pytest-runner >= 4.2\"] if needs_pytest else []\nTESTS_REQUIRE = [\"pytest >= 2.7.1\"]\n\nDESCRIPTION = \"N-D labeled arrays and datasets in Python\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/setup.py_LONG_DESCRIPTION_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/setup.py_LONG_DESCRIPTION_", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 109, "span_ids": ["impl", "impl:27"], "tokens": 732}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "LONG_DESCRIPTION = \"\"\"\n**xarray** (formerly **xray**) is an open source project and Python package\nthat makes working with labelled multi-dimensional arrays simple,\nefficient, and fun!\n\nXarray introduces labels in the form of dimensions, coordinates and\nattributes on top of raw NumPy_-like arrays, which allows for a more\nintuitive, more concise, and less error-prone developer experience.\nThe package includes a large and growing library of domain-agnostic functions\nfor advanced analytics and visualization with these data structures.\n\nXarray was inspired by and borrows heavily from pandas_, the popular data\nanalysis package focused on labelled tabular data.\nIt is particularly tailored to working with netCDF_ files, which were the\nsource of xarray's data model, and integrates tightly with dask_ for parallel\ncomputing.\n\n.. _NumPy: https://www.numpy.org\n.. _pandas: https://pandas.pydata.org\n.. _dask: https://dask.org\n.. _netCDF: https://www.unidata.ucar.edu/software/netcdf\n\nWhy xarray?\n-----------\n\nMulti-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called\n\"tensors\") are an essential part of computational science.\nThey are encountered in a wide range of fields, including physics, astronomy,\ngeoscience, bioinformatics, engineering, finance, and deep learning.\nIn Python, NumPy_ provides the fundamental data structure and API for\nworking with raw ND arrays.\nHowever, real-world datasets are usually more than just raw numbers;\nthey have labels which encode information about how the array values map\nto locations in space, time, etc.\n\nXarray doesn't just keep track of labels on arrays -- it uses them to provide a\npowerful and concise interface. For example:\n\n- Apply operations over dimensions by name: ``x.sum('time')``.\n- Select values by label instead of integer location:\n ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``.\n- Mathematical operations (e.g., ``x - y``) vectorize across multiple\n dimensions (array broadcasting) based on dimension names, not shape.\n- Flexible split-apply-combine operations with groupby:\n ``x.groupby('time.dayofyear').mean()``.\n- Database like alignment based on coordinate labels that smoothly\n handles missing values: ``x, y = xr.align(x, y, join='outer')``.\n- Keep track of arbitrary metadata in the form of a Python dictionary:\n ``x.attrs``.\n\nLearn more\n----------\n\n- Documentation: http://xarray.pydata.org\n- Issue tracker: http://github.com/pydata/xarray/issues\n- Source code: http://github.com/pydata/xarray\n- SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk\n\"\"\"\n\n\nsetup(\n name=DISTNAME,\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n license=LICENSE,\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n classifiers=CLASSIFIERS,\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n python_requires=PYTHON_REQUIRES,\n install_requires=INSTALL_REQUIRES,\n setup_requires=SETUP_REQUIRES,\n tests_require=TESTS_REQUIRE,\n url=URL,\n packages=find_packages(),\n package_data={\"xarray\": [\"py.typed\", \"tests/data/*\"]},\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py__flake8_noqa_get_root.return.root": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py__flake8_noqa_get_root.return.root", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 338, "span_ids": ["impl", "impl:2", "docstring", "get_root", "imports", "imports:7", "imports:8", "VersioneerConfig"], "tokens": 526}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# flake8: noqa\n\n# Version: 0.18\n\n\nimport errno\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\n\ntry:\n import configparser\nexcept ImportError:\n import ConfigParser as configparser\n\n\nclass VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_root():\n \"\"\"Get the project root directory.\n\n We require that all commands are run from the project root, i.e. the\n directory that contains setup.py, setup.cfg, and versioneer.py .\n \"\"\"\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\n \"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\"\n )\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n me = os.path.realpath(os.path.abspath(__file__))\n me_dir = os.path.normcase(os.path.splitext(me)[0])\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n if me_dir != vsr_dir:\n print(\n \"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(me), versioneer_py)\n )\n except NameError:\n pass\n return root", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_config_from_root_get_config_from_root.return.cfg": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_config_from_root_get_config_from_root.return.cfg", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 341, "end_line": 368, "span_ids": ["get_config_from_root"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_config_from_root(root):\n \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n # This might raise EnvironmentError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n setup_cfg = os.path.join(root, \"setup.cfg\")\n parser = configparser.SafeConfigParser()\n with open(setup_cfg, \"r\") as f:\n parser.readfp(f)\n VCS = parser.get(\"versioneer\", \"VCS\") # mandatory\n\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None\n\n cfg = VersioneerConfig()\n cfg.VCS = VCS\n cfg.style = get(parser, \"style\") or \"\"\n cfg.versionfile_source = get(parser, \"versionfile_source\")\n cfg.versionfile_build = get(parser, \"versionfile_build\")\n cfg.tag_prefix = get(parser, \"tag_prefix\")\n if cfg.tag_prefix in (\"''\", '\"\"'):\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n cfg.verbose = get(parser, \"verbose\")\n return cfg", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_NotThisMethod_register_vcs_handler.return.decorate": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_NotThisMethod_register_vcs_handler.return.decorate", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 371, "end_line": 390, "span_ids": ["register_vcs_handler", "impl:3", "NotThisMethod", "impl:4"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\n# these dictionaries contain VCS-specific tools\nLONG_VERSION_PY = {}\nHANDLERS = {}\n\n\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Decorator to mark a method as the handler for a particular VCS.\"\"\"\n\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n\n return decorate", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_run_command_run_command.return.stdout_p_returncode": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_run_command_run_command.return.stdout_p_returncode", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 393, "end_line": 429, "span_ids": ["run_command"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen(\n [c] + args,\n cwd=cwd,\n env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr else None),\n )\n break\n except OSError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_get_keywords_git_get_keywords.return.keywords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_get_keywords_git_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 957, "end_line": 983, "span_ids": ["git_get_keywords"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n f.close()\n except OSError:\n pass\n return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_versions_from_keywords_git_versions_from_keywords.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_versions_from_keywords_git_versions_from_keywords.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 986, "end_line": 1045, "span_ids": ["git_versions_from_keywords"], "tokens": 721}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n date = keywords.get(\"date\")\n if date is not None:\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r\"\\d\", r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix) :]\n if verbose:\n print(\"picking %s\" % r)\n return {\n \"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False,\n \"error\": None,\n \"date\": date,\n }\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\n \"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False,\n \"error\": \"no suitable tags\",\n \"date\": None,\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1048, "end_line": 1146, "span_ids": ["git_pieces_from_vcs"], "tokens": 892}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = run_command(\n GITS,\n [\n \"describe\",\n \"--tags\",\n \"--dirty\",\n \"--always\",\n \"--long\",\n \"--match\",\n \"%s*\" % tag_prefix,\n ],\n cwd=root,\n )\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[: git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r\"^(.+)-(\\d+)-g([0-9a-f]+)$\", git_describe)\n if not mo:\n # unparseable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = \"unable to parse git-describe output: '%s'\" % describe_out\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%s' doesn't start with prefix '%s'\"\n print(fmt % (full_tag, tag_prefix))\n pieces[\"error\"] = \"tag '%s' doesn't start with prefix '%s'\" % (\n full_tag,\n tag_prefix,\n )\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix) :]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n count_out, rc = run_command(GITS, [\"rev-list\", \"HEAD\", \"--count\"], cwd=root)\n pieces[\"distance\"] = int(count_out) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = run_command(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[\n 0\n ].strip()\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_do_vcs_install_do_vcs_install.run_command_GITS_add_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_do_vcs_install_do_vcs_install.run_command_GITS_add_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1149, "end_line": 1184, "span_ids": ["do_vcs_install"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def do_vcs_install(manifest_in, versionfile_source, ipy):\n \"\"\"Git-specific installation logic for Versioneer.\n\n For Git, this means creating/changing .gitattributes to mark _version.py\n for export-subst keyword substitution.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n files = [manifest_in, versionfile_source]\n if ipy:\n files.append(ipy)\n try:\n me = __file__\n if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n me = os.path.splitext(me)[0] + \".py\"\n versioneer_file = os.path.relpath(me)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n f = open(\".gitattributes\", \"r\")\n for line in f.readlines():\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n f.close()\n except OSError:\n pass\n if not present:\n f = open(\".gitattributes\", \"a+\")\n f.write(\"%s export-subst\\n\" % versionfile_source)\n f.close()\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1187, "end_line": 1215, "span_ids": ["versions_from_parentdir"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for i in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\n \"version\": dirname[len(parentdir_prefix) :],\n \"full-revisionid\": None,\n \"dirty\": False,\n \"error\": None,\n \"date\": None,\n }\n else:\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\n \"Tried directories %s but none started with prefix %s\"\n % (str(rootdirs), parentdir_prefix)\n )\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_SHORT_VERSION_PY_versions_from_file.return.json_loads_mo_group_1_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_SHORT_VERSION_PY_versions_from_file.return.json_loads_mo_group_1_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1218, "end_line": 1252, "span_ids": ["versions_from_file", "impl:8"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SHORT_VERSION_PY = \"\"\"\n# This file was generated by 'versioneer.py' (0.18) from\n# revision-control system data, or from the parent directory name of an\n# unpacked source archive. Distribution tarballs contain a pre-generated copy\n# of this file.\n\nimport json\n\nversion_json = '''\n%s\n''' # END VERSION_JSON\n\n\ndef get_versions():\n return json.loads(version_json)\n\"\"\"\n\n\ndef versions_from_file(filename):\n \"\"\"Try to determine the version from _version.py if present.\"\"\"\n try:\n with open(filename) as f:\n contents = f.read()\n except OSError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(\n r\"version_json = '''\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n mo = re.search(\n r\"version_json = '''\\r\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_write_to_version_file_plus_or_dot.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_write_to_version_file_plus_or_dot.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1255, "end_line": 1269, "span_ids": ["write_to_version_file", "plus_or_dot"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_to_version_file(filename, versions):\n \"\"\"Write the given version number to the given _version.py file.\"\"\"\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set %s to '%s'\" % (filename, versions[\"version\"]))\n\n\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_render_pep440_pre.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_render_pep440_pre.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1272, "end_line": 1309, "span_ids": ["render_pep440_pre", "render_pep440"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.post.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_post_render_pep440_post.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_post_render_pep440_post.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1312, "end_line": 1336, "span_ids": ["render_pep440_post"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_old_render_pep440_old.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_pep440_old_render_pep440_old.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1339, "end_line": 1358, "span_ids": ["render_pep440_old"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Eexceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_git_describe_render_git_describe.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_git_describe_render_git_describe.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1361, "end_line": 1378, "span_ids": ["render_git_describe"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_git_describe_long_render_git_describe_long.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_git_describe_long_render_git_describe_long.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1381, "end_line": 1398, "span_ids": ["render_git_describe_long"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_VersioneerBadRootError._The_project_root_direc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_render_VersioneerBadRootError._The_project_root_direc", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1401, "end_line": 1440, "span_ids": ["render", "VersioneerBadRootError"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\n \"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None,\n }\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\n \"version\": rendered,\n \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"],\n \"error\": None,\n \"date\": pieces.get(\"date\"),\n }\n\n\nclass VersioneerBadRootError(Exception):\n \"\"\"The project root directory is unknown or missing key files.\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_versions_get_versions.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_versions_get_versions.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1443, "end_line": 1521, "span_ids": ["get_versions"], "tokens": 623}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_versions(verbose=False):\n \"\"\"Get the project version from whatever source is available.\n\n Returns dict with two keys: 'version' and 'full'.\n \"\"\"\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert (\n cfg.versionfile_source is not None\n ), \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file %s %s\" % (versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n ver = render(pieces, cfg.style)\n if verbose:\n print(\"got version from VCS %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n if verbose:\n print(\"got version from parentdir %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n if verbose:\n print(\"unable to compute version\")\n\n return {\n \"version\": \"0+unknown\",\n \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to compute version\",\n \"date\": None,\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_version_get_cmdclass.from_distutils_core_impor": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_version_get_cmdclass.from_distutils_core_impor", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1524, "end_line": 1549, "span_ids": ["get_cmdclass", "get_version"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_version():\n \"\"\"Get the short version string for this project.\"\"\"\n return get_versions()[\"version\"]\n\n\ndef get_cmdclass():\n \"\"\"Get the custom setuptools/distutils subclasses used by Versioneer.\"\"\"\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/warner/python-versioneer/issues/52\n\n cmds = {}\n\n # we add \"version\" to both distutils and setuptools\n from distutils.core import Command\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmd_version_get_cmdclass.cmd_version.run.if_vers_error_.print_error_s_vers": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmd_version_get_cmdclass.cmd_version.run.if_vers_error_.print_error_s_vers", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1551, "end_line": 1569, "span_ids": ["get_cmdclass.cmd_version:2", "get_cmdclass.cmd_version"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass():\n # ... other code\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmds_version_cmd_ver_get_cmdclass.if_cx_Freeze_in_sys_mod.del_cmds_build_py_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmds_version_cmd_ver_get_cmdclass.if_cx_Freeze_in_sys_mod.del_cmds_build_py_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1571, "end_line": 1644, "span_ids": ["get_cmdclass.cmd_version:2", "get_cmdclass.if_cx_Freeze_in_sys_mod.cmd_build_exe", "get_cmdclass.if_cx_Freeze_in_sys_mod.cmd_build_exe.run", "get_cmdclass.cmd_build_py.run", "get_cmdclass.cmd_build_py"], "tokens": 676}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass():\n # ... other code\n\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in both distutils and setuptools\n #\n # most invocation pathways end up running build_py:\n # distutils/build -> build_py\n # distutils/install -> distutils/build ->..\n # setuptools/bdist_wheel -> distutils/install ->..\n # setuptools/bdist_egg -> distutils/install_lib -> build_py\n # setuptools/install -> bdist_egg ->..\n # setuptools/develop -> ?\n # pip install:\n # copies source tree to a tempdir before running egg_info/etc\n # if .git isn't copied too, 'git describe' will fail\n # then does setup.py bdist_wheel, or sometimes setup.py install\n # setup.py egg_info -> ?\n\n # we override different \"build_py\" commands for both environments\n if \"setuptools\" in sys.modules:\n from setuptools.command.build_py import build_py as _build_py\n else:\n from distutils.command.build_py import build_py as _build_py\n\n class cmd_build_py(_build_py):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n\n cmds[\"build_py\"] = cmd_build_py\n\n if \"cx_Freeze\" in sys.modules: # cx_freeze enabled?\n from cx_Freeze.dist import build_exe as _build_exe\n\n # nczeczulin reports that py2exe won't like the pep440-style string\n # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.\n # setup(console=[{\n # \"version\": versioneer.get_version().split(\"+\", 1)[0], # FILEVERSION\n # \"product_version\": versioneer.get_version(),\n # ...\n\n class cmd_build_exe(_build_exe):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n target_versionfile = cfg.versionfile_source\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n\n _build_exe.run(self)\n os.unlink(target_versionfile)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n cmds[\"build_exe\"] = cmd_build_exe\n del cmds[\"build_py\"]\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.if_py2exe_in_sys_module_get_cmdclass.None_4.else_.from_distutils_command_sd": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.if_py2exe_in_sys_module_get_cmdclass.None_4.else_.from_distutils_command_sd", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1646, "end_line": 1682, "span_ids": ["get_cmdclass.if_py2exe_in_sys_module.cmd_py2exe", "get_cmdclass.if_py2exe_in_sys_module.cmd_py2exe.run", "get_cmdclass.if_cx_Freeze_in_sys_mod.cmd_build_exe.run"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass():\n # ... other code\n\n if \"py2exe\" in sys.modules: # py2exe enabled?\n try:\n from py2exe.distutils_buildexe import py2exe as _py2exe # py3\n except ImportError:\n from py2exe.build_exe import py2exe as _py2exe # py2\n\n class cmd_py2exe(_py2exe):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n target_versionfile = cfg.versionfile_source\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n\n _py2exe.run(self)\n os.unlink(target_versionfile)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n cmds[\"py2exe\"] = cmd_py2exe\n\n # we override different \"sdist\" commands for both environments\n if \"setuptools\" in sys.modules:\n from setuptools.command.sdist import sdist as _sdist\n else:\n from distutils.command.sdist import sdist as _sdist\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmd_sdist_get_cmdclass.return.cmds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_get_cmdclass.cmd_sdist_get_cmdclass.return.cmds", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1684, "end_line": 1708, "span_ids": ["get_cmdclass.cmd_sdist", "get_cmdclass.cmd_sdist.run"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass():\n # ... other code\n\n class cmd_sdist(_sdist):\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)\n\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(\n target_versionfile, self._versioneer_generated_versions\n )\n\n cmds[\"sdist\"] = cmd_sdist\n\n return cmds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_CONFIG_ERROR_INIT_PY_SNIPPET._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_CONFIG_ERROR_INIT_PY_SNIPPET._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1711, "end_line": 1752, "span_ids": ["impl:10"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "CONFIG_ERROR = \"\"\"\nsetup.cfg is missing the necessary Versioneer configuration. You need\na section like:\n\n [versioneer]\n VCS = git\n style = pep440\n versionfile_source = src/myproject/_version.py\n versionfile_build = myproject/_version.py\n tag_prefix =\n parentdir_prefix = myproject-\n\nYou will also need to edit your setup.py to use the results:\n\n import versioneer\n setup(version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(), ...)\n\nPlease read the docstring in ./versioneer.py for configuration instructions,\nedit setup.cfg, and re-run the installer or 'python versioneer.py setup'.\n\"\"\"\n\nSAMPLE_CONFIG = \"\"\"\n# See the docstring in versioneer.py for instructions. Note that you must\n# re-run 'versioneer.py setup' after changing this section, and commit the\n# resulting files.\n\n[versioneer]\n#VCS = git\n#style = pep440\n#versionfile_source =\n#versionfile_build =\n#tag_prefix =\n#parentdir_prefix =\n\n\"\"\"\n\nINIT_PY_SNIPPET = \"\"\"\nfrom ._version import get_versions\n__version__ = get_versions()['version']\ndel get_versions\n\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_do_setup_do_setup.return.0": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_do_setup_do_setup.return.0", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1755, "end_line": 1837, "span_ids": ["do_setup"], "tokens": 765}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def do_setup():\n \"\"\"Main VCS-independent setup function for installing Versioneer.\"\"\"\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (OSError, configparser.NoSectionError, configparser.NoOptionError) as e:\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\", file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except OSError:\n old = \"\"\n if INIT_PY_SNIPPET not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(INIT_PY_SNIPPET)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make sure both the top-level \"versioneer.py\" and versionfile_source\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n # they'll be copied into source distributions. Pip won't be able to\n # install the package without this.\n manifest_in = os.path.join(root, \"MANIFEST.in\")\n simple_includes = set()\n try:\n with open(manifest_in, \"r\") as f:\n for line in f:\n if line.startswith(\"include \"):\n for include in line.split()[1:]:\n simple_includes.add(include)\n except OSError:\n pass\n # That doesn't cover everything MANIFEST.in can do\n # (http://docs.python.org/2/distutils/sourcedist.html#commands), so\n # it might give some false negatives. Appending redundant 'include'\n # lines is safe, though.\n if \"versioneer.py\" not in simple_includes:\n print(\" appending 'versioneer.py' to MANIFEST.in\")\n with open(manifest_in, \"a\") as f:\n f.write(\"include versioneer.py\\n\")\n else:\n print(\" 'versioneer.py' already in MANIFEST.in\")\n if cfg.versionfile_source not in simple_includes:\n print(\n \" appending versionfile_source ('%s') to MANIFEST.in\"\n % cfg.versionfile_source\n )\n with open(manifest_in, \"a\") as f:\n f.write(\"include %s\\n\" % cfg.versionfile_source)\n else:\n print(\" versionfile_source already in MANIFEST.in\")\n\n # Make VCS-specific changes. For git, this means creating/changing\n # .gitattributes to mark _version.py for export-subst keyword\n # substitution.\n do_vcs_install(manifest_in, cfg.versionfile_source, ipy)\n return 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_scan_setup_py_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/versioneer.py_scan_setup_py_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1840, "end_line": 1884, "span_ids": ["scan_setup_py", "impl:16"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def scan_setup_py():\n \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors\n\n\nif __name__ == \"__main__\":\n cmd = sys.argv[1]\n if cmd == \"setup\":\n errors = do_setup()\n errors += scan_setup_py()\n if errors:\n sys.exit(1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/__init__.py__isort_skip_file__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/__init__.py__isort_skip_file__", "embedding": null, "metadata": {"file_path": "xarray/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 45, "span_ids": ["imports:2", "impl", "docstring", "imports"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\" isort:skip_file \"\"\"\n# flake8: noqa\n\nfrom ._version import get_versions\n\n__version__ = get_versions()[\"version\"]\ndel get_versions\n\nfrom .core.alignment import align, broadcast\nfrom .core.common import full_like, zeros_like, ones_like\nfrom .core.concat import concat\nfrom .core.combine import combine_by_coords, combine_nested, auto_combine\nfrom .core.computation import apply_ufunc, dot, where\nfrom .core.extensions import register_dataarray_accessor, register_dataset_accessor\nfrom .core.variable import as_variable, Variable, IndexVariable, Coordinate\nfrom .core.dataset import Dataset\nfrom .core.dataarray import DataArray\nfrom .core.merge import merge, MergeError\nfrom .core.options import set_options\nfrom .core.parallel import map_blocks\n\nfrom .backends.api import (\n open_dataset,\n open_dataarray,\n open_mfdataset,\n save_mfdataset,\n load_dataset,\n load_dataarray,\n)\nfrom .backends.rasterio_ import open_rasterio\nfrom .backends.zarr import open_zarr\n\nfrom .conventions import decode_cf, SerializationWarning\n\nfrom .coding.cftime_offsets import cftime_range\nfrom .coding.cftimeindex import CFTimeIndex\n\nfrom .util.print_versions import show_versions\n\nfrom . import tutorial\nfrom . import ufuncs\nfrom . import testing\n\nfrom .core.common import ALL_DIMS", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py__This_file_helps_to_comp_sys": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py__This_file_helps_to_comp_sys", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 16, "span_ids": ["docstring", "imports"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# This file helps to compute a version number in source trees obtained from\n# git-archive tarball (such as those provided by githubs download-from-tag\n# feature). Distribution tarballs (built by setup.py sdist) and build\n# directories (produced by setup.py build) will contain a much shorter file\n# that just contains the computed version number.\n\n# This file is released into the public domain. Generated by\n# versioneer-0.18 (https://github.com/warner/python-versioneer)\n\n\"\"\"Git implementation of _version.py.\"\"\"\n\nimport errno\nimport os\nimport re\nimport subprocess\nimport sys", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_get_keywords_get_keywords.return.keywords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_get_keywords_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 19, "end_line": 29, "span_ids": ["get_keywords"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_keywords():\n \"\"\"Get the keywords needed to look up the version information.\"\"\"\n # these strings will be replaced by git during git-archive.\n # setup.py/versioneer.py will grep for the variable names, so they must\n # each be defined on a line of their own. _version.py will just call\n # get_keywords().\n git_refnames = \"$Format:%d$\"\n git_full = \"$Format:%H$\"\n git_date = \"$Format:%ci$\"\n keywords = {\"refnames\": git_refnames, \"full\": git_full, \"date\": git_date}\n return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_VersioneerConfig_register_vcs_handler.return.decorate": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_VersioneerConfig_register_vcs_handler.return.decorate", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 68, "span_ids": ["impl", "impl:2", "register_vcs_handler", "get_config", "VersioneerConfig", "NotThisMethod"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_config():\n \"\"\"Create, populate and return the VersioneerConfig() object.\"\"\"\n # these strings are filled in when 'setup.py versioneer' creates\n # _version.py\n cfg = VersioneerConfig()\n cfg.VCS = \"git\"\n cfg.style = \"pep440\"\n cfg.tag_prefix = \"v\"\n cfg.parentdir_prefix = \"xarray-\"\n cfg.versionfile_source = \"xarray/_version.py\"\n cfg.verbose = False\n return cfg\n\n\nclass NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\nLONG_VERSION_PY = {}\nHANDLERS = {}\n\n\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Decorator to mark a method as the handler for a particular VCS.\"\"\"\n\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n\n return decorate", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_run_command_run_command.return.stdout_p_returncode": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_run_command_run_command.return.stdout_p_returncode", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 107, "span_ids": ["run_command"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen(\n [c] + args,\n cwd=cwd,\n env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr else None),\n )\n break\n except OSError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 138, "span_ids": ["versions_from_parentdir"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for i in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\n \"version\": dirname[len(parentdir_prefix) :],\n \"full-revisionid\": None,\n \"dirty\": False,\n \"error\": None,\n \"date\": None,\n }\n else:\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\n \"Tried directories %s but none started with prefix %s\"\n % (str(rootdirs), parentdir_prefix)\n )\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_get_keywords_git_get_keywords.return.keywords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_get_keywords_git_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 167, "span_ids": ["git_get_keywords"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n f.close()\n except OSError:\n pass\n return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_versions_from_keywords_git_versions_from_keywords.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_versions_from_keywords_git_versions_from_keywords.return._", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 170, "end_line": 229, "span_ids": ["git_versions_from_keywords"], "tokens": 721}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n date = keywords.get(\"date\")\n if date is not None:\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r\"\\d\", r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix) :]\n if verbose:\n print(\"picking %s\" % r)\n return {\n \"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False,\n \"error\": None,\n \"date\": date,\n }\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\n \"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False,\n \"error\": \"no suitable tags\",\n \"date\": None,\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 330, "span_ids": ["git_pieces_from_vcs"], "tokens": 892}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = run_command(\n GITS,\n [\n \"describe\",\n \"--tags\",\n \"--dirty\",\n \"--always\",\n \"--long\",\n \"--match\",\n \"%s*\" % tag_prefix,\n ],\n cwd=root,\n )\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[: git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r\"^(.+)-(\\d+)-g([0-9a-f]+)$\", git_describe)\n if not mo:\n # unparseable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = \"unable to parse git-describe output: '%s'\" % describe_out\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%s' doesn't start with prefix '%s'\"\n print(fmt % (full_tag, tag_prefix))\n pieces[\"error\"] = \"tag '%s' doesn't start with prefix '%s'\" % (\n full_tag,\n tag_prefix,\n )\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix) :]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n count_out, rc = run_command(GITS, [\"rev-list\", \"HEAD\", \"--count\"], cwd=root)\n pieces[\"distance\"] = int(count_out) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = run_command(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[\n 0\n ].strip()\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_plus_or_dot_render_pep440.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_plus_or_dot_render_pep440.return.rendered", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 333, "end_line": 361, "span_ids": ["render_pep440", "plus_or_dot"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"\n\n\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_pep440_pre_render_pep440_post.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_pep440_pre_render_pep440_post.return.rendered", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 364, "end_line": 404, "span_ids": ["render_pep440_pre", "render_pep440_post"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_pre(pieces):\n \"\"\"TAG[.post.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered\n\n\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_pep440_old_render_pep440_old.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_pep440_old_render_pep440_old.return.rendered", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 407, "end_line": 426, "span_ids": ["render_pep440_old"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Eexceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_git_describe_render_git_describe.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_git_describe_render_git_describe.return.rendered", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 429, "end_line": 446, "span_ids": ["render_git_describe"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_git_describe_long_render_git_describe_long.return.rendered": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_git_describe_long_render_git_describe_long.return.rendered", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 449, "end_line": 466, "span_ids": ["render_git_describe_long"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_render.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_render_render.return._", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 469, "end_line": 504, "span_ids": ["render"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\n \"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None,\n }\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\n \"version\": rendered,\n \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"],\n \"error\": None,\n \"date\": pieces.get(\"date\"),\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_get_versions_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/_version.py_get_versions_", "embedding": null, "metadata": {"file_path": "xarray/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 507, "end_line": 557, "span_ids": ["get_versions"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_versions():\n \"\"\"Get version information or return default if unable to do so.\"\"\"\n # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have\n # __file__, we can work backwards from there to the root. Some\n # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which\n # case we can only use expanded keywords.\n\n cfg = get_config()\n verbose = cfg.verbose\n\n try:\n return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)\n except NotThisMethod:\n pass\n\n try:\n root = os.path.realpath(__file__)\n # versionfile_source is the relative path from the top of the source\n # tree (where the .git directory might live) to this file. Invert\n # this to find the root from __file__.\n for i in cfg.versionfile_source.split(\"/\"):\n root = os.path.dirname(root)\n except NameError:\n return {\n \"version\": \"0+unknown\",\n \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to find root of source tree\",\n \"date\": None,\n }\n\n try:\n pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n return render(pieces, cfg.style)\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n except NotThisMethod:\n pass\n\n return {\n \"version\": \"0+unknown\",\n \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to compute version\",\n \"date\": None,\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/__init__.py__Backend_objects_for_sa_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/__init__.py__Backend_objects_for_sa_", "embedding": null, "metadata": {"file_path": "xarray/backends/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 33, "span_ids": ["impl", "docstring", "imports"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Backend objects for saving and loading data\n\nDataStores provide a uniform interface for saving and loading data in different\nformats. They should not be used directly, but rather through Dataset objects.\n\"\"\"\nfrom .cfgrib_ import CfGribDataStore\nfrom .common import AbstractDataStore\nfrom .file_manager import CachingFileManager, DummyFileManager, FileManager\nfrom .h5netcdf_ import H5NetCDFStore\nfrom .memory import InMemoryDataStore\nfrom .netCDF4_ import NetCDF4DataStore\nfrom .pseudonetcdf_ import PseudoNetCDFDataStore\nfrom .pydap_ import PydapDataStore\nfrom .pynio_ import NioDataStore\nfrom .scipy_ import ScipyDataStore\nfrom .zarr import ZarrStore\n\n__all__ = [\n \"AbstractDataStore\",\n \"FileManager\",\n \"CachingFileManager\",\n \"CfGribDataStore\",\n \"DummyFileManager\",\n \"InMemoryDataStore\",\n \"NetCDF4DataStore\",\n \"PydapDataStore\",\n \"NioDataStore\",\n \"ScipyDataStore\",\n \"H5NetCDFStore\",\n \"ZarrStore\",\n \"PseudoNetCDFDataStore\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_os.path__get_default_engine_remote_uri.return.engine": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_os.path__get_default_engine_remote_uri.return.engine", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 58, "span_ids": ["impl", "impl:3", "imports", "_get_default_engine_remote_uri", "imports:16"], "tokens": 309}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os.path\nimport warnings\nfrom glob import glob\nfrom io import BytesIO\nfrom numbers import Number\nfrom pathlib import Path\nfrom textwrap import dedent\nfrom typing import (\n TYPE_CHECKING,\n Callable,\n Dict,\n Hashable,\n Iterable,\n Mapping,\n Tuple,\n Union,\n)\n\nimport numpy as np\n\nfrom .. import DataArray, Dataset, auto_combine, backends, coding, conventions\nfrom ..core import indexing\nfrom ..core.combine import (\n _infer_concat_order_from_positions,\n _nested_combine,\n combine_by_coords,\n)\nfrom ..core.utils import close_on_error, is_grib_path, is_remote_uri\nfrom .common import AbstractDataStore, ArrayWriter\nfrom .locks import _get_scheduler\n\nif TYPE_CHECKING:\n try:\n from dask.delayed import Delayed\n except ImportError:\n Delayed = None\n\n\nDATAARRAY_NAME = \"__xarray_dataarray_name__\"\nDATAARRAY_VARIABLE = \"__xarray_dataarray_variable__\"\n\n\ndef _get_default_engine_remote_uri():\n try:\n import netCDF4 # noqa: F401\n\n engine = \"netcdf4\"\n except ImportError: # pragma: no cover\n try:\n import pydap # noqa: F401\n\n engine = \"pydap\"\n except ImportError:\n raise ValueError(\n \"netCDF4 or pydap is required for accessing \"\n \"remote datasets via OPeNDAP\"\n )\n return engine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine_grib__get_default_engine_grib.if_msgs_.else_.raise_ValueError_PyNIO_o": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine_grib__get_default_engine_grib.if_msgs_.else_.raise_ValueError_PyNIO_o", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 61, "end_line": 78, "span_ids": ["_get_default_engine_grib"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_default_engine_grib():\n msgs = []\n try:\n import Nio # noqa: F401\n\n msgs += [\"set engine='pynio' to access GRIB files with PyNIO\"]\n except ImportError: # pragma: no cover\n pass\n try:\n import cfgrib # noqa: F401\n\n msgs += [\"set engine='cfgrib' to access GRIB files with cfgrib\"]\n except ImportError: # pragma: no cover\n pass\n if msgs:\n raise ValueError(\" or\\n\".join(msgs))\n else:\n raise ValueError(\"PyNIO or cfgrib is required for accessing \" \"GRIB files\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine_gz__get_default_engine_netcdf.return.engine": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine_gz__get_default_engine_netcdf.return.engine", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 106, "span_ids": ["_get_default_engine_netcdf", "_get_default_engine_gz"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_default_engine_gz():\n try:\n import scipy # noqa: F401\n\n engine = \"scipy\"\n except ImportError: # pragma: no cover\n raise ValueError(\"scipy is required for accessing .gz files\")\n return engine\n\n\ndef _get_default_engine_netcdf():\n try:\n import netCDF4 # noqa: F401\n\n engine = \"netcdf4\"\n except ImportError: # pragma: no cover\n try:\n import scipy.io.netcdf # noqa: F401\n\n engine = \"scipy\"\n except ImportError:\n raise ValueError(\n \"cannot read or write netCDF files without \"\n \"netCDF4-python or scipy installed\"\n )\n return engine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_engine_from_magic_number__get_engine_from_magic_number.return.engine": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_engine_from_magic_number__get_engine_from_magic_number.return.engine", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 109, "end_line": 139, "span_ids": ["_get_engine_from_magic_number"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_engine_from_magic_number(filename_or_obj):\n # check byte header to determine file type\n if isinstance(filename_or_obj, bytes):\n magic_number = filename_or_obj[:8]\n else:\n if filename_or_obj.tell() != 0:\n raise ValueError(\n \"file-like object read/write pointer not at zero \"\n \"please close and reopen, or use a context \"\n \"manager\"\n )\n magic_number = filename_or_obj.read(8)\n filename_or_obj.seek(0)\n\n if magic_number.startswith(b\"CDF\"):\n engine = \"scipy\"\n elif magic_number.startswith(b\"\\211HDF\\r\\n\\032\\n\"):\n engine = \"h5netcdf\"\n if isinstance(filename_or_obj, bytes):\n raise ValueError(\n \"can't open netCDF4/HDF5 as bytes \"\n \"try passing a path or file-like object\"\n )\n else:\n if isinstance(filename_or_obj, bytes) and len(filename_or_obj) > 80:\n filename_or_obj = filename_or_obj[:80] + b\"...\"\n raise ValueError(\n \"{} is not a valid netCDF file \"\n \"did you mean to pass a string for a path instead?\".format(filename_or_obj)\n )\n return engine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine__validate_dataset_names.for_k_in_dataset_variable.check_name_k_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__get_default_engine__validate_dataset_names.for_k_in_dataset_variable.check_name_k_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 142, "end_line": 179, "span_ids": ["_get_default_engine", "_normalize_path", "_validate_dataset_names"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_default_engine(path, allow_remote=False):\n if allow_remote and is_remote_uri(path):\n engine = _get_default_engine_remote_uri()\n elif is_grib_path(path):\n engine = _get_default_engine_grib()\n elif path.endswith(\".gz\"):\n engine = _get_default_engine_gz()\n else:\n engine = _get_default_engine_netcdf()\n return engine\n\n\ndef _normalize_path(path):\n if is_remote_uri(path):\n return path\n else:\n return os.path.abspath(os.path.expanduser(path))\n\n\ndef _validate_dataset_names(dataset):\n \"\"\"DataArray.name and Dataset keys must be a string or None\"\"\"\n\n def check_name(name):\n if isinstance(name, str):\n if not name:\n raise ValueError(\n \"Invalid name for DataArray or Dataset key: \"\n \"string must be length 1 or greater for \"\n \"serialization to netCDF files\"\n )\n elif name is not None:\n raise TypeError(\n \"DataArray.name or Dataset key must be either a \"\n \"string or None for serialization to netCDF files\"\n )\n\n for k in dataset.variables:\n check_name(k)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_attrs__validate_attrs.for_variable_in_dataset_v.for_k_v_in_variable_attr.check_attr_k_v_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_attrs__validate_attrs.for_variable_in_dataset_v.for_k_v_in_variable_attr.check_attr_k_v_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 182, "end_line": 216, "span_ids": ["_validate_attrs"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _validate_attrs(dataset):\n \"\"\"`attrs` must have a string key and a value which is either: a number,\n a string, an ndarray or a list/tuple of numbers/strings.\n \"\"\"\n\n def check_attr(name, value):\n if isinstance(name, str):\n if not name:\n raise ValueError(\n \"Invalid name for attr: string must be \"\n \"length 1 or greater for serialization to \"\n \"netCDF files\"\n )\n else:\n raise TypeError(\n \"Invalid name for attr: {} must be a string for \"\n \"serialization to netCDF files\".format(name)\n )\n\n if not isinstance(value, (str, Number, np.ndarray, np.number, list, tuple)):\n raise TypeError(\n \"Invalid value for attr: {} must be a number, \"\n \"a string, an ndarray or a list/tuple of \"\n \"numbers/strings for serialization to netCDF \"\n \"files\".format(value)\n )\n\n # Check attrs on the dataset itself\n for k, v in dataset.attrs.items():\n check_attr(k, v)\n\n # Check attrs on each variable within the dataset\n for variable in dataset.variables.values():\n for k, v in variable.attrs.items():\n check_attr(k, v)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__protect_dataset_variables_inplace_load_dataset.with_open_dataset_filenam.return.ds_load_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__protect_dataset_variables_inplace_load_dataset.with_open_dataset_filenam.return.ds_load_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 219, "end_line": 258, "span_ids": ["load_dataset", "_finalize_store", "_protect_dataset_variables_inplace"], "tokens": 290}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _protect_dataset_variables_inplace(dataset, cache):\n for name, variable in dataset.variables.items():\n if name not in variable.dims:\n # no need to protect IndexVariable objects\n data = indexing.CopyOnWriteArray(variable._data)\n if cache:\n data = indexing.MemoryCachedArray(data)\n variable.data = data\n\n\ndef _finalize_store(write, store):\n \"\"\" Finalize this store by explicitly syncing and closing\"\"\"\n del write # ensure writing is done first\n store.close()\n\n\ndef load_dataset(filename_or_obj, **kwargs):\n \"\"\"Open, load into memory, and close a Dataset from a file or file-like\n object.\n\n This is a thin wrapper around :py:meth:`~xarray.open_dataset`. It differs\n from `open_dataset` in that it loads the Dataset into memory, closes the\n file, and returns the Dataset. In contrast, `open_dataset` keeps the file\n handle open and lazy loads its contents. All parameters are passed directly\n to `open_dataset`. See that documentation for further details.\n\n Returns\n -------\n dataset : Dataset\n The newly created Dataset.\n\n See Also\n --------\n open_dataset\n \"\"\"\n if \"cache\" in kwargs:\n raise TypeError(\"cache has no effect in this context\")\n\n with open_dataset(filename_or_obj, **kwargs) as ds:\n return ds.load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_load_dataarray_load_dataarray.with_open_dataarray_filen.return.da_load_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_load_dataarray_load_dataarray.with_open_dataarray_filen.return.da_load_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 261, "end_line": 284, "span_ids": ["load_dataarray"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_dataarray(filename_or_obj, **kwargs):\n \"\"\"Open, load into memory, and close a DataArray from a file or file-like\n object containing a single data variable.\n\n This is a thin wrapper around :py:meth:`~xarray.open_dataarray`. It differs\n from `open_dataarray` in that it loads the Dataset into memory, closes the\n file, and returns the Dataset. In contrast, `open_dataarray` keeps the file\n handle open and lazy loads its contents. All parameters are passed directly\n to `open_dataarray`. See that documentation for further details.\n\n Returns\n -------\n datarray : DataArray\n The newly created DataArray.\n\n See Also\n --------\n open_dataarray\n \"\"\"\n if \"cache\" in kwargs:\n raise TypeError(\"cache has no effect in this context\")\n\n with open_dataarray(filename_or_obj, **kwargs) as da:\n return da.load()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset_open_dataset._Open_and_decode_a_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset_open_dataset._Open_and_decode_a_data", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 287, "end_line": 399, "span_ids": ["open_dataset"], "tokens": 1198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataset(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n \"\"\"Open and decode a dataset from a file or file-like object.\n\n Parameters\n ----------\n filename_or_obj : str, Path, file or xarray.backends.*DataStore\n Strings and Path objects are interpreted as a path to a netCDF file\n or an OpenDAP URL and opened with python-netCDF4, unless the filename\n ends with .gz, in which case the file is gunzipped and opened with\n scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like\n objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).\n group : str, optional\n Path to the netCDF4 group in the given file to open (only works for\n netCDF4 files).\n decode_cf : bool, optional\n Whether to decode these variables, assuming they were saved according\n to CF conventions.\n mask_and_scale : bool, optional\n If True, replace array values equal to `_FillValue` with NA and scale\n values according to the formula `original_values * scale_factor +\n add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are\n taken from variable attributes (if they exist). If the `_FillValue` or\n `missing_value` attribute contains multiple values a warning will be\n issued and all array values matching one of the multiple values will\n be replaced by NA. mask_and_scale defaults to True except for the\n pseudonetcdf backend.\n decode_times : bool, optional\n If True, decode times encoded in the standard NetCDF datetime format\n into datetime objects. Otherwise, leave them encoded as numbers.\n autoclose : bool, optional\n If True, automatically close files to avoid OS Error of too many files\n being open. However, this option doesn't work with streams, e.g.,\n BytesIO.\n concat_characters : bool, optional\n If True, concatenate along the last dimension of character arrays to\n form string arrays. Dimensions will only be concatenated over (and\n removed) if they have no corresponding variable and if they are only\n used as the last dimension of character arrays.\n decode_coords : bool, optional\n If True, decode the 'coordinates' attribute to identify coordinates in\n the resulting dataset.\n engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib', \\\n 'pseudonetcdf'}, optional\n Engine to use when reading files. If not provided, the default engine\n is chosen based on available dependencies, with a preference for\n 'netcdf4'.\n chunks : int or dict, optional\n If chunks is provided, it used to load the new dataset into dask\n arrays. ``chunks={}`` loads the dataset with dask using a single\n chunk for all arrays.\n lock : False or duck threading.Lock, optional\n Resource lock to use when reading data from disk. Only relevant when\n using dask or another form of parallelism. By default, appropriate\n locks are chosen to safely read and write files with the currently\n active dask scheduler.\n cache : bool, optional\n If True, cache data loaded from the underlying datastore in memory as\n NumPy arrays when accessed to avoid reading from the underlying data-\n store multiple times. Defaults to True unless you specify the `chunks`\n argument to use dask, in which case it defaults to False. Does not\n change the behavior of coordinates corresponding to dimensions, which\n always load their data from disk into a ``pandas.Index``.\n drop_variables: string or iterable, optional\n A variable or list of variables to exclude from being parsed from the\n dataset. This may be useful to drop variables with problems or\n inconsistent values.\n backend_kwargs: dictionary, optional\n A dictionary of keyword arguments to pass on to the backend. This\n may be useful when backend options would improve performance or\n allow user control of dataset processing.\n use_cftime: bool, optional\n Only relevant if encoded dates come from a standard calendar\n (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not\n specified). If None (default), attempt to decode times to\n ``np.datetime64[ns]`` objects; if this is not possible, decode times to\n ``cftime.datetime`` objects. If True, always decode times to\n ``cftime.datetime`` objects, regardless of whether or not they can be\n represented using ``np.datetime64[ns]`` objects. If False, always\n decode times to ``np.datetime64[ns]`` objects; if this is not possible\n raise an error.\n\n Returns\n -------\n dataset : Dataset\n The newly created dataset.\n\n Notes\n -----\n ``open_dataset`` opens the file with read-only access. When you modify\n values of a Dataset, even one linked to files on disk, only the in-memory\n copy you are manipulating in xarray is modified: the original file on disk\n is never touched.\n\n See Also\n --------\n open_mfdataset\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.engines_open_dataset.if_backend_kwargs_is_None.backend_kwargs._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.engines_open_dataset.if_backend_kwargs_is_None.backend_kwargs._", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 400, "end_line": 440, "span_ids": ["open_dataset"], "tokens": 345}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataset(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n engines = [\n None,\n \"netcdf4\",\n \"scipy\",\n \"pydap\",\n \"h5netcdf\",\n \"pynio\",\n \"cfgrib\",\n \"pseudonetcdf\",\n ]\n if engine not in engines:\n raise ValueError(\n \"unrecognized engine for open_dataset: {}\\n\"\n \"must be one of: {}\".format(engine, engines)\n )\n\n if autoclose is not None:\n warnings.warn(\n \"The autoclose argument is no longer used by \"\n \"xarray.open_dataset() and is now ignored; it will be removed in \"\n \"a future version of xarray. If necessary, you can control the \"\n \"maximum number of simultaneous open files with \"\n \"xarray.set_options(file_cache_maxsize=...).\",\n FutureWarning,\n stacklevel=2,\n )\n\n if mask_and_scale is None:\n mask_and_scale = not engine == \"pseudonetcdf\"\n\n if not decode_cf:\n mask_and_scale = False\n decode_times = False\n concat_characters = False\n decode_coords = False\n\n if cache is None:\n cache = chunks is None\n\n if backend_kwargs is None:\n backend_kwargs = {}\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.maybe_decode_store_open_dataset.maybe_decode_store.return.ds2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.maybe_decode_store_open_dataset.maybe_decode_store.return.ds2", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 442, "end_line": 484, "span_ids": ["open_dataset"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataset(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n # ... other code\n\n def maybe_decode_store(store, lock=False):\n ds = conventions.decode_cf(\n store,\n mask_and_scale=mask_and_scale,\n decode_times=decode_times,\n concat_characters=concat_characters,\n decode_coords=decode_coords,\n drop_variables=drop_variables,\n use_cftime=use_cftime,\n )\n\n _protect_dataset_variables_inplace(ds, cache)\n\n if chunks is not None:\n from dask.base import tokenize\n\n # if passed an actual file path, augment the token with\n # the file modification time\n if isinstance(filename_or_obj, str) and not is_remote_uri(filename_or_obj):\n mtime = os.path.getmtime(filename_or_obj)\n else:\n mtime = None\n token = tokenize(\n filename_or_obj,\n mtime,\n group,\n decode_cf,\n mask_and_scale,\n decode_times,\n concat_characters,\n decode_coords,\n engine,\n chunks,\n drop_variables,\n use_cftime,\n )\n name_prefix = \"open_dataset-%s\" % token\n ds2 = ds.chunk(chunks, name_prefix=name_prefix, token=token)\n ds2._file_obj = ds._file_obj\n else:\n ds2 = ds\n\n return ds2\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.if_isinstance_filename_or_open_dataset.return.ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataset.if_isinstance_filename_or_open_dataset.return.ds", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 486, "end_line": 542, "span_ids": ["open_dataset"], "tokens": 594}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataset(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n # ... other code\n\n if isinstance(filename_or_obj, Path):\n filename_or_obj = str(filename_or_obj)\n\n if isinstance(filename_or_obj, AbstractDataStore):\n store = filename_or_obj\n\n elif isinstance(filename_or_obj, str):\n filename_or_obj = _normalize_path(filename_or_obj)\n\n if engine is None:\n engine = _get_default_engine(filename_or_obj, allow_remote=True)\n if engine == \"netcdf4\":\n store = backends.NetCDF4DataStore.open(\n filename_or_obj, group=group, lock=lock, **backend_kwargs\n )\n elif engine == \"scipy\":\n store = backends.ScipyDataStore(filename_or_obj, **backend_kwargs)\n elif engine == \"pydap\":\n store = backends.PydapDataStore.open(filename_or_obj, **backend_kwargs)\n elif engine == \"h5netcdf\":\n store = backends.H5NetCDFStore(\n filename_or_obj, group=group, lock=lock, **backend_kwargs\n )\n elif engine == \"pynio\":\n store = backends.NioDataStore(filename_or_obj, lock=lock, **backend_kwargs)\n elif engine == \"pseudonetcdf\":\n store = backends.PseudoNetCDFDataStore.open(\n filename_or_obj, lock=lock, **backend_kwargs\n )\n elif engine == \"cfgrib\":\n store = backends.CfGribDataStore(\n filename_or_obj, lock=lock, **backend_kwargs\n )\n\n else:\n if engine not in [None, \"scipy\", \"h5netcdf\"]:\n raise ValueError(\n \"can only read bytes or file-like objects \"\n \"with engine='scipy' or 'h5netcdf'\"\n )\n engine = _get_engine_from_magic_number(filename_or_obj)\n if engine == \"scipy\":\n store = backends.ScipyDataStore(filename_or_obj, **backend_kwargs)\n elif engine == \"h5netcdf\":\n store = backends.H5NetCDFStore(\n filename_or_obj, group=group, lock=lock, **backend_kwargs\n )\n\n with close_on_error(store):\n ds = maybe_decode_store(store)\n\n # Ensure source filename always stored in dataset object (GH issue #2550)\n if \"source\" not in ds.encoding:\n if isinstance(filename_or_obj, str):\n ds.encoding[\"source\"] = filename_or_obj\n\n return ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataarray_open_dataarray._Open_an_DataArray_from": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataarray_open_dataarray._Open_an_DataArray_from", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 545, "end_line": 653, "span_ids": ["open_dataarray"], "tokens": 1147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataarray(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n \"\"\"Open an DataArray from a file or file-like object containing a single\n data variable.\n\n This is designed to read netCDF files with only one data variable. If\n multiple variables are present then a ValueError is raised.\n\n Parameters\n ----------\n filename_or_obj : str, Path, file or xarray.backends.*DataStore\n Strings and Paths are interpreted as a path to a netCDF file or an\n OpenDAP URL and opened with python-netCDF4, unless the filename ends\n with .gz, in which case the file is gunzipped and opened with\n scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like\n objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).\n group : str, optional\n Path to the netCDF4 group in the given file to open (only works for\n netCDF4 files).\n decode_cf : bool, optional\n Whether to decode these variables, assuming they were saved according\n to CF conventions.\n mask_and_scale : bool, optional\n If True, replace array values equal to `_FillValue` with NA and scale\n values according to the formula `original_values * scale_factor +\n add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are\n taken from variable attributes (if they exist). If the `_FillValue` or\n `missing_value` attribute contains multiple values a warning will be\n issued and all array values matching one of the multiple values will\n be replaced by NA. mask_and_scale defaults to True except for the\n pseudonetcdf backend.\n decode_times : bool, optional\n If True, decode times encoded in the standard NetCDF datetime format\n into datetime objects. Otherwise, leave them encoded as numbers.\n concat_characters : bool, optional\n If True, concatenate along the last dimension of character arrays to\n form string arrays. Dimensions will only be concatenated over (and\n removed) if they have no corresponding variable and if they are only\n used as the last dimension of character arrays.\n decode_coords : bool, optional\n If True, decode the 'coordinates' attribute to identify coordinates in\n the resulting dataset.\n engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib'}, \\\n optional\n Engine to use when reading files. If not provided, the default engine\n is chosen based on available dependencies, with a preference for\n 'netcdf4'.\n chunks : int or dict, optional\n If chunks is provided, it used to load the new dataset into dask\n arrays.\n lock : False or duck threading.Lock, optional\n Resource lock to use when reading data from disk. Only relevant when\n using dask or another form of parallelism. By default, appropriate\n locks are chosen to safely read and write files with the currently\n active dask scheduler.\n cache : bool, optional\n If True, cache data loaded from the underlying datastore in memory as\n NumPy arrays when accessed to avoid reading from the underlying data-\n store multiple times. Defaults to True unless you specify the `chunks`\n argument to use dask, in which case it defaults to False. Does not\n change the behavior of coordinates corresponding to dimensions, which\n always load their data from disk into a ``pandas.Index``.\n drop_variables: string or iterable, optional\n A variable or list of variables to exclude from being parsed from the\n dataset. This may be useful to drop variables with problems or\n inconsistent values.\n backend_kwargs: dictionary, optional\n A dictionary of keyword arguments to pass on to the backend. This\n may be useful when backend options would improve performance or\n allow user control of dataset processing.\n use_cftime: bool, optional\n Only relevant if encoded dates come from a standard calendar\n (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not\n specified). If None (default), attempt to decode times to\n ``np.datetime64[ns]`` objects; if this is not possible, decode times to\n ``cftime.datetime`` objects. If True, always decode times to\n ``cftime.datetime`` objects, regardless of whether or not they can be\n represented using ``np.datetime64[ns]`` objects. If False, always\n decode times to ``np.datetime64[ns]`` objects; if this is not possible\n raise an error.\n\n Notes\n -----\n This is designed to be fully compatible with `DataArray.to_netcdf`. Saving\n using `DataArray.to_netcdf` and then loading with this function will\n produce an identical result.\n\n All parameters are passed directly to `xarray.open_dataset`. See that\n documentation for further details.\n\n See also\n --------\n open_dataset\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataarray.dataset_open_dataarray.return.data_array": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_dataarray.dataset_open_dataarray.return.data_array", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 655, "end_line": 693, "span_ids": ["open_dataarray"], "tokens": 334}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_dataarray(\n filename_or_obj,\n group=None,\n decode_cf=True,\n mask_and_scale=None,\n decode_times=True,\n autoclose=None,\n concat_characters=True,\n decode_coords=True,\n engine=None,\n chunks=None,\n lock=None,\n cache=None,\n drop_variables=None,\n backend_kwargs=None,\n use_cftime=None,\n):\n\n dataset = open_dataset(\n filename_or_obj,\n group=group,\n decode_cf=decode_cf,\n mask_and_scale=mask_and_scale,\n decode_times=decode_times,\n autoclose=autoclose,\n concat_characters=concat_characters,\n decode_coords=decode_coords,\n engine=engine,\n chunks=chunks,\n lock=lock,\n cache=cache,\n drop_variables=drop_variables,\n backend_kwargs=backend_kwargs,\n use_cftime=use_cftime,\n )\n\n if len(dataset.data_vars) != 1:\n raise ValueError(\n \"Given file dataset contains more than one data \"\n \"variable. Please read with xarray.open_dataset and \"\n \"then select the variable you want.\"\n )\n else:\n data_array, = dataset.data_vars.values()\n\n data_array._file_obj = dataset._file_obj\n\n # Reset names if they were changed during saving\n # to ensure that we can 'roundtrip' perfectly\n if DATAARRAY_NAME in dataset.attrs:\n data_array.name = dataset.attrs[DATAARRAY_NAME]\n del dataset.attrs[DATAARRAY_NAME]\n\n if data_array.name == DATAARRAY_VARIABLE:\n data_array.name = None\n\n return data_array", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__MultiFileCloser_open_mfdataset": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__MultiFileCloser_open_mfdataset", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 696, "end_line": 963, "span_ids": ["open_mfdataset", "_MultiFileCloser.close", "_MultiFileCloser"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _MultiFileCloser:\n __slots__ = (\"file_objs\",)\n\n def __init__(self, file_objs):\n self.file_objs = file_objs\n\n def close(self):\n for f in self.file_objs:\n f.close()\n\n\ndef open_mfdataset(\n paths,\n chunks=None,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n preprocess=None,\n engine=None,\n lock=None,\n data_vars=\"all\",\n coords=\"different\",\n combine=\"_old_auto\",\n autoclose=None,\n parallel=False,\n join=\"outer\",\n **kwargs\n):\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset._Open_multiple_files_as_open_mfdataset._Open_multiple_files_as": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset._Open_multiple_files_as_open_mfdataset._Open_multiple_files_as", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 723, "end_line": 854, "span_ids": ["open_mfdataset"], "tokens": 1627}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_mfdataset(\n paths,\n chunks=None,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n preprocess=None,\n engine=None,\n lock=None,\n data_vars=\"all\",\n coords=\"different\",\n combine=\"_old_auto\",\n autoclose=None,\n parallel=False,\n join=\"outer\",\n **kwargs\n):\n \"\"\"Open multiple files as a single dataset.\n\n If combine='by_coords' then the function ``combine_by_coords`` is used to combine\n the datasets into one before returning the result, and if combine='nested' then\n ``combine_nested`` is used. The filepaths must be structured according to which\n combining function is used, the details of which are given in the documentation for\n ``combine_by_coords`` and ``combine_nested``. By default the old (now deprecated)\n ``auto_combine`` will be used, please specify either ``combine='by_coords'`` or\n ``combine='nested'`` in future. Requires dask to be installed. See documentation for\n details on dask [1]. Attributes from the first dataset file are used for the\n combined dataset.\n\n Parameters\n ----------\n paths : str or sequence\n Either a string glob in the form \"path/to/my/files/*.nc\" or an explicit list of\n files to open. Paths can be given as strings or as pathlib Paths. If\n concatenation along more than one dimension is desired, then ``paths`` must be a\n nested list-of-lists (see ``manual_combine`` for details). (A string glob will\n be expanded to a 1-dimensional list.)\n chunks : int or dict, optional\n Dictionary with keys given by dimension names and values given by chunk sizes.\n In general, these should divide the dimensions of each dataset. If int, chunk\n each dimension by ``chunks``. By default, chunks will be chosen to load entire\n input files into memory at once. This has a major impact on performance: please\n see the full documentation for more details [2].\n concat_dim : str, or list of str, DataArray, Index or None, optional\n Dimensions to concatenate files along. You only need to provide this argument\n if any of the dimensions along which you want to concatenate is not a dimension\n in the original datasets, e.g., if you want to stack a collection of 2D arrays\n along a third dimension. Set ``concat_dim=[..., None, ...]`` explicitly to\n disable concatenation along a particular dimension.\n combine : {'by_coords', 'nested'}, optional\n Whether ``xarray.combine_by_coords`` or ``xarray.combine_nested`` is used to\n combine all the data. If this argument is not provided, `xarray.auto_combine` is\n used, but in the future this behavior will switch to use\n `xarray.combine_by_coords` by default.\n compat : {'identical', 'equals', 'broadcast_equals',\n 'no_conflicts', 'override'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts when merging:\n * 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n * 'equals': all values and dimensions must be the same.\n * 'identical': all values, dimensions and attributes must be the\n same.\n * 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n * 'override': skip comparing and pick variable from first dataset\n preprocess : callable, optional\n If provided, call this function on each dataset prior to concatenation.\n You can find the file-name from which each dataset was loaded in\n ``ds.encoding['source']``.\n engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib'}, \\\n optional\n Engine to use when reading files. If not provided, the default engine\n is chosen based on available dependencies, with a preference for\n 'netcdf4'.\n lock : False or duck threading.Lock, optional\n Resource lock to use when reading data from disk. Only relevant when\n using dask or another form of parallelism. By default, appropriate\n locks are chosen to safely read and write files with the currently\n active dask scheduler.\n data_vars : {'minimal', 'different', 'all' or list of str}, optional\n These data variables will be concatenated together:\n * 'minimal': Only data variables in which the dimension already\n appears are included.\n * 'different': Data variables which are not equal (ignoring\n attributes) across all datasets are also concatenated (as well as\n all for which dimension already appears). Beware: this option may\n load the data payload of data variables into memory if they are not\n already loaded.\n * 'all': All data variables will be concatenated.\n * list of str: The listed data variables will be concatenated, in\n addition to the 'minimal' data variables.\n coords : {'minimal', 'different', 'all' or list of str}, optional\n These coordinate variables will be concatenated together:\n * 'minimal': Only coordinates in which the dimension already appears\n are included.\n * 'different': Coordinates which are not equal (ignoring attributes)\n across all datasets are also concatenated (as well as all for which\n dimension already appears). Beware: this option may load the data\n payload of coordinate variables into memory if they are not already\n loaded.\n * 'all': All coordinate variables will be concatenated, except\n those corresponding to other dimensions.\n * list of str: The listed coordinate variables will be concatenated,\n in addition the 'minimal' coordinates.\n parallel : bool, optional\n If True, the open and preprocess steps of this function will be\n performed in parallel using ``dask.delayed``. Default is False.\n join : {'outer', 'inner', 'left', 'right', 'exact, 'override'}, optional\n String indicating how to combine differing indexes\n (excluding concat_dim) in objects\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n **kwargs : optional\n Additional arguments passed on to :py:func:`xarray.open_dataset`.\n\n Returns\n -------\n xarray.Dataset\n\n Notes\n -----\n ``open_mfdataset`` opens files with read-only access. When you modify values\n of a Dataset, even one linked to files on disk, only the in-memory copy you\n are manipulating in xarray is modified: the original file on disk is never\n touched.\n\n See Also\n --------\n combine_by_coords\n combine_nested\n auto_combine\n open_dataset\n\n References\n ----------\n\n .. [1] http://xarray.pydata.org/en/stable/dask.html\n .. [2] http://xarray.pydata.org/en/stable/dask.html#chunking-and-performance\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset.if_isinstance_paths_str__open_mfdataset._Combine_all_datasets_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset.if_isinstance_paths_str__open_mfdataset._Combine_all_datasets_c", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 855, "end_line": 908, "span_ids": ["open_mfdataset"], "tokens": 556}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_mfdataset(\n paths,\n chunks=None,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n preprocess=None,\n engine=None,\n lock=None,\n data_vars=\"all\",\n coords=\"different\",\n combine=\"_old_auto\",\n autoclose=None,\n parallel=False,\n join=\"outer\",\n **kwargs\n):\n if isinstance(paths, str):\n if is_remote_uri(paths):\n raise ValueError(\n \"cannot do wild-card matching for paths that are remote URLs: \"\n \"{!r}. Instead, supply paths as an explicit list of strings.\".format(\n paths\n )\n )\n paths = sorted(glob(paths))\n else:\n paths = [str(p) if isinstance(p, Path) else p for p in paths]\n\n if not paths:\n raise OSError(\"no files to open\")\n\n # If combine='by_coords' then this is unnecessary, but quick.\n # If combine='nested' then this creates a flat list which is easier to\n # iterate over, while saving the originally-supplied structure as \"ids\"\n if combine == \"nested\":\n if str(concat_dim) == \"_not_supplied\":\n raise ValueError(\"Must supply concat_dim when using \" \"combine='nested'\")\n else:\n if isinstance(concat_dim, (str, DataArray)) or concat_dim is None:\n concat_dim = [concat_dim]\n combined_ids_paths = _infer_concat_order_from_positions(paths)\n ids, paths = (list(combined_ids_paths.keys()), list(combined_ids_paths.values()))\n\n open_kwargs = dict(\n engine=engine, chunks=chunks or {}, lock=lock, autoclose=autoclose, **kwargs\n )\n\n if parallel:\n import dask\n\n # wrap the open_dataset, getattr, and preprocess with delayed\n open_ = dask.delayed(open_dataset)\n getattr_ = dask.delayed(getattr)\n if preprocess is not None:\n preprocess = dask.delayed(preprocess)\n else:\n open_ = open_dataset\n getattr_ = getattr\n\n datasets = [open_(p, **open_kwargs) for p in paths]\n file_objs = [getattr_(ds, \"_file_obj\") for ds in datasets]\n if preprocess is not None:\n datasets = [preprocess(ds) for ds in datasets]\n\n if parallel:\n # calling compute here will return the datasets/file_objs lists,\n # the underlying datasets will still be stored as dask arrays\n datasets, file_objs = dask.compute(datasets, file_objs)\n\n # Combine all datasets, closing them in case of a ValueError\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset.try__WRITEABLE_STORES._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_open_mfdataset.try__WRITEABLE_STORES._", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 909, "end_line": 970, "span_ids": ["open_mfdataset", "impl:10"], "tokens": 527}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_mfdataset(\n paths,\n chunks=None,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n preprocess=None,\n engine=None,\n lock=None,\n data_vars=\"all\",\n coords=\"different\",\n combine=\"_old_auto\",\n autoclose=None,\n parallel=False,\n join=\"outer\",\n **kwargs\n):\n # ... other code\n try:\n if combine == \"_old_auto\":\n # Use the old auto_combine for now\n # Remove this after deprecation cycle from #2616 is complete\n basic_msg = dedent(\n \"\"\"\\\n In xarray version 0.15 the default behaviour of `open_mfdataset`\n will change. To retain the existing behavior, pass\n combine='nested'. To use future default behavior, pass\n combine='by_coords'. See\n http://xarray.pydata.org/en/stable/combining.html#combining-multi\n \"\"\"\n )\n warnings.warn(basic_msg, FutureWarning, stacklevel=2)\n\n combined = auto_combine(\n datasets,\n concat_dim=concat_dim,\n compat=compat,\n data_vars=data_vars,\n coords=coords,\n join=join,\n from_openmfds=True,\n )\n elif combine == \"nested\":\n # Combined nested list by successive concat and merge operations\n # along each dimension, using structure given by \"ids\"\n combined = _nested_combine(\n datasets,\n concat_dims=concat_dim,\n compat=compat,\n data_vars=data_vars,\n coords=coords,\n ids=ids,\n join=join,\n )\n elif combine == \"by_coords\":\n # Redo ordering from coordinates, ignoring how they were ordered\n # previously\n combined = combine_by_coords(\n datasets, compat=compat, data_vars=data_vars, coords=coords, join=join\n )\n else:\n raise ValueError(\n \"{} is an invalid option for the keyword argument\"\n \" ``combine``\".format(combine)\n )\n except ValueError:\n for ds in datasets:\n ds.close()\n raise\n\n combined._file_obj = _MultiFileCloser(file_objs)\n combined.attrs = datasets[0].attrs\n return combined\n\n\nWRITEABLE_STORES: Dict[str, Callable] = {\n \"netcdf4\": backends.NetCDF4DataStore.open,\n \"scipy\": backends.ScipyDataStore,\n \"h5netcdf\": backends.H5NetCDFStore,\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_netcdf_to_netcdf._to_avoid_this_mess_of_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_netcdf_to_netcdf._to_avoid_this_mess_of_c", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 973, "end_line": 1066, "span_ids": ["to_netcdf"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_netcdf(\n dataset: Dataset,\n path_or_file=None,\n mode: str = \"w\",\n format: str = None,\n group: str = None,\n engine: str = None,\n encoding: Mapping = None,\n unlimited_dims: Iterable[Hashable] = None,\n compute: bool = True,\n multifile: bool = False,\n invalid_netcdf: bool = False,\n) -> Union[Tuple[ArrayWriter, AbstractDataStore], bytes, \"Delayed\", None]:\n \"\"\"This function creates an appropriate datastore for writing a dataset to\n disk as a netCDF file\n\n See `Dataset.to_netcdf` for full API docs.\n\n The ``multifile`` argument is only for the private use of save_mfdataset.\n \"\"\"\n if isinstance(path_or_file, Path):\n path_or_file = str(path_or_file)\n\n if encoding is None:\n encoding = {}\n\n if path_or_file is None:\n if engine is None:\n engine = \"scipy\"\n elif engine != \"scipy\":\n raise ValueError(\n \"invalid engine for creating bytes with \"\n \"to_netcdf: %r. Only the default engine \"\n \"or engine='scipy' is supported\" % engine\n )\n if not compute:\n raise NotImplementedError(\n \"to_netcdf() with compute=False is not yet implemented when \"\n \"returning bytes\"\n )\n elif isinstance(path_or_file, str):\n if engine is None:\n engine = _get_default_engine(path_or_file)\n path_or_file = _normalize_path(path_or_file)\n else: # file-like object\n engine = \"scipy\"\n\n # validate Dataset keys, DataArray names, and attr keys/values\n _validate_dataset_names(dataset)\n _validate_attrs(dataset)\n\n try:\n store_open = WRITEABLE_STORES[engine]\n except KeyError:\n raise ValueError(\"unrecognized engine for to_netcdf: %r\" % engine)\n\n if format is not None:\n format = format.upper()\n\n # handle scheduler specific logic\n scheduler = _get_scheduler()\n have_chunks = any(v.chunks for v in dataset.variables.values())\n\n autoclose = have_chunks and scheduler in [\"distributed\", \"multiprocessing\"]\n if autoclose and engine == \"scipy\":\n raise NotImplementedError(\n \"Writing netCDF files with the %s backend \"\n \"is not currently supported with dask's %s \"\n \"scheduler\" % (engine, scheduler)\n )\n\n target = path_or_file if path_or_file is not None else BytesIO()\n kwargs = dict(autoclose=True) if autoclose else {}\n if invalid_netcdf:\n if engine == \"h5netcdf\":\n kwargs[\"invalid_netcdf\"] = invalid_netcdf\n else:\n raise ValueError(\n \"unrecognized option 'invalid_netcdf' for engine %s\" % engine\n )\n store = store_open(target, mode, format, group, **kwargs)\n\n if unlimited_dims is None:\n unlimited_dims = dataset.encoding.get(\"unlimited_dims\", None)\n if unlimited_dims is not None:\n if isinstance(unlimited_dims, str) or not isinstance(unlimited_dims, Iterable):\n unlimited_dims = [unlimited_dims]\n else:\n unlimited_dims = list(unlimited_dims)\n\n writer = ArrayWriter()\n\n # TODO: figure out how to refactor this logic (here and in save_mfdataset)\n # to avoid this mess of conditionals\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_netcdf.None_1_to_netcdf.return.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_netcdf.None_1_to_netcdf.return.None", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1067, "end_line": 1092, "span_ids": ["to_netcdf"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_netcdf(\n dataset: Dataset,\n path_or_file=None,\n mode: str = \"w\",\n format: str = None,\n group: str = None,\n engine: str = None,\n encoding: Mapping = None,\n unlimited_dims: Iterable[Hashable] = None,\n compute: bool = True,\n multifile: bool = False,\n invalid_netcdf: bool = False,\n) -> Union[Tuple[ArrayWriter, AbstractDataStore], bytes, \"Delayed\", None]:\n # ... other code\n try:\n # TODO: allow this work (setting up the file for writing array data)\n # to be parallelized with dask\n dump_to_store(\n dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims\n )\n if autoclose:\n store.close()\n\n if multifile:\n return writer, store\n\n writes = writer.sync(compute=compute)\n\n if path_or_file is None:\n store.sync()\n return target.getvalue()\n finally:\n if not multifile and compute:\n store.close()\n\n if not compute:\n import dask\n\n return dask.delayed(_finalize_store)(writes, store)\n return None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_dump_to_store_dump_to_store.store_store_variables_at": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_dump_to_store_dump_to_store.store_store_variables_at", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1095, "end_line": 1117, "span_ids": ["dump_to_store"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dump_to_store(\n dataset, store, writer=None, encoder=None, encoding=None, unlimited_dims=None\n):\n \"\"\"Store dataset contents to a backends.*DataStore object.\"\"\"\n if writer is None:\n writer = ArrayWriter()\n\n if encoding is None:\n encoding = {}\n\n variables, attrs = conventions.encode_dataset_coordinates(dataset)\n\n check_encoding = set()\n for k, enc in encoding.items():\n # no need to shallow copy the variable again; that already happened\n # in encode_dataset_coordinates\n variables[k].encoding = enc\n check_encoding.add(k)\n\n if encoder:\n variables, attrs = encoder(variables, attrs)\n\n store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_save_mfdataset_save_mfdataset.if_mode_w_and_len_se.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_save_mfdataset_save_mfdataset.if_mode_w_and_len_se.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1120, "end_line": 1188, "span_ids": ["save_mfdataset"], "tokens": 725}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def save_mfdataset(\n datasets, paths, mode=\"w\", format=None, groups=None, engine=None, compute=True\n):\n \"\"\"Write multiple datasets to disk as netCDF files simultaneously.\n\n This function is intended for use with datasets consisting of dask.array\n objects, in which case it can write the multiple datasets to disk\n simultaneously using a shared thread pool.\n\n When not using dask, it is no different than calling ``to_netcdf``\n repeatedly.\n\n Parameters\n ----------\n datasets : list of xarray.Dataset\n List of datasets to save.\n paths : list of str or list of Paths\n List of paths to which to save each corresponding dataset.\n mode : {'w', 'a'}, optional\n Write ('w') or append ('a') mode. If mode='w', any existing file at\n these locations will be overwritten.\n format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT',\n 'NETCDF3_CLASSIC'}, optional\n\n File format for the resulting netCDF file:\n\n * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API\n features.\n * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only\n netCDF 3 compatible API features.\n * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format,\n which fully supports 2+ GB files, but is only compatible with\n clients linked against netCDF version 3.6.0 or later.\n * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not\n handle 2+ GB files very well.\n\n All formats are supported by the netCDF4-python library.\n scipy.io.netcdf only supports the last two formats.\n\n The default format is NETCDF4 if you are saving a file to disk and\n have the netCDF4-python library available. Otherwise, xarray falls\n back to using scipy to write netCDF files and defaults to the\n NETCDF3_64BIT format (scipy does not support netCDF4).\n groups : list of str, optional\n Paths to the netCDF4 group in each corresponding file to which to save\n datasets (only works for format='NETCDF4'). The groups will be created\n if necessary.\n engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional\n Engine to use when writing netCDF files. If not provided, the\n default engine is chosen based on available dependencies, with a\n preference for 'netcdf4' if writing to a file on disk.\n See `Dataset.to_netcdf` for additional information.\n compute: boolean\n If true compute immediately, otherwise return a\n ``dask.delayed.Delayed`` object that can be computed later.\n\n Examples\n --------\n\n Save a dataset into one netCDF per year of data:\n\n >>> years, datasets = zip(*ds.groupby('time.year'))\n >>> paths = ['%s.nc' % y for y in years]\n >>> xr.save_mfdataset(datasets, paths)\n \"\"\"\n if mode == \"w\" and len(set(paths)) < len(paths):\n raise ValueError(\n \"cannot use mode='w' when writing multiple \" \"datasets to the same path\"\n )\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_save_mfdataset.for_obj_in_datasets__save_mfdataset.if_not_compute_.return.dask_delayed_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_save_mfdataset.for_obj_in_datasets__save_mfdataset.if_not_compute_.return.dask_delayed_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1190, "end_line": 1228, "span_ids": ["save_mfdataset"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def save_mfdataset(\n datasets, paths, mode=\"w\", format=None, groups=None, engine=None, compute=True\n):\n # ... other code\n\n for obj in datasets:\n if not isinstance(obj, Dataset):\n raise TypeError(\n \"save_mfdataset only supports writing Dataset \"\n \"objects, received type %s\" % type(obj)\n )\n\n if groups is None:\n groups = [None] * len(datasets)\n\n if len({len(datasets), len(paths), len(groups)}) > 1:\n raise ValueError(\n \"must supply lists of the same length for the \"\n \"datasets, paths and groups arguments to \"\n \"save_mfdataset\"\n )\n\n writers, stores = zip(\n *[\n to_netcdf(\n ds, path, mode, format, group, engine, compute=compute, multifile=True\n )\n for ds, path, group in zip(datasets, paths, groups)\n ]\n )\n\n try:\n writes = [w.sync(compute=compute) for w in writers]\n finally:\n if compute:\n for store in stores:\n store.close()\n\n if not compute:\n import dask\n\n return dask.delayed(\n [dask.delayed(_finalize_store)(w, s) for w, s in zip(writes, stores)]\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_datatypes_for_zarr_append__validate_datatypes_for_zarr_append.for_k_in_dataset_data_var.check_dtype_k_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_datatypes_for_zarr_append__validate_datatypes_for_zarr_append.for_k_in_dataset_data_var.check_dtype_k_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1231, "end_line": 1249, "span_ids": ["_validate_datatypes_for_zarr_append"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _validate_datatypes_for_zarr_append(dataset):\n \"\"\"DataArray.name and Dataset keys must be a string or None\"\"\"\n\n def check_dtype(var):\n if (\n not np.issubdtype(var.dtype, np.number)\n and not coding.strings.is_unicode_dtype(var.dtype)\n and not var.dtype == object\n ):\n # and not re.match('^bytes[1-9]+$', var.dtype.name)):\n raise ValueError(\n \"Invalid dtype for data variable: {} \"\n \"dtype must be a subtype of number, \"\n \"a fixed sized string, a fixed size \"\n \"unicode string or an object\".format(var)\n )\n\n for k in dataset.data_vars.values():\n check_dtype(k)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_append_dim_and_encoding__validate_append_dim_and_encoding.for_data_var_in_ds_to_app.if_data_var_in_ds_.if_data_var_in_encoding_k.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py__validate_append_dim_and_encoding__validate_append_dim_and_encoding.for_data_var_in_ds_to_app.if_data_var_in_ds_.if_data_var_in_encoding_k.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1252, "end_line": 1275, "span_ids": ["_validate_append_dim_and_encoding"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _validate_append_dim_and_encoding(\n ds_to_append, store, append_dim, encoding, **open_kwargs\n):\n try:\n ds = backends.zarr.open_zarr(store, **open_kwargs)\n except ValueError: # store empty\n return\n if append_dim:\n if append_dim not in ds.dims:\n raise ValueError(\n \"{} not a valid dimension in the Dataset\".format(append_dim)\n )\n for data_var in ds_to_append:\n if data_var in ds:\n if append_dim is None:\n raise ValueError(\n \"variable '{}' already exists, but append_dim \"\n \"was not set\".format(data_var)\n )\n if data_var in encoding.keys():\n raise ValueError(\n \"variable '{}' already exists, but encoding was\"\n \"provided\".format(data_var)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_zarr_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/api.py_to_zarr_", "embedding": null, "metadata": {"file_path": "xarray/backends/api.py", "file_name": "api.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1278, "end_line": 1335, "span_ids": ["to_zarr"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_zarr(\n dataset,\n store=None,\n mode=None,\n synchronizer=None,\n group=None,\n encoding=None,\n compute=True,\n consolidated=False,\n append_dim=None,\n):\n \"\"\"This function creates an appropriate datastore for writing a dataset to\n a zarr ztore\n\n See `Dataset.to_zarr` for full API docs.\n \"\"\"\n if isinstance(store, Path):\n store = str(store)\n if encoding is None:\n encoding = {}\n\n # validate Dataset keys, DataArray names, and attr keys/values\n _validate_dataset_names(dataset)\n _validate_attrs(dataset)\n\n if mode == \"a\":\n _validate_datatypes_for_zarr_append(dataset)\n _validate_append_dim_and_encoding(\n dataset,\n store,\n append_dim,\n group=group,\n consolidated=consolidated,\n encoding=encoding,\n )\n\n zstore = backends.ZarrStore.open_group(\n store=store,\n mode=mode,\n synchronizer=synchronizer,\n group=group,\n consolidate_on_close=consolidated,\n )\n zstore.append_dim = append_dim\n writer = ArrayWriter()\n # TODO: figure out how to properly handle unlimited_dims\n dump_to_store(dataset, zstore, writer, encoding=encoding)\n writes = writer.sync(compute=compute)\n\n if compute:\n _finalize_store(writes, zstore)\n else:\n import dask\n\n return dask.delayed(_finalize_store)(writes, zstore)\n\n return zstore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/cfgrib_.py_np_CfGribArrayWrapper._getitem.with_self_datastore_lock_.return.self_array_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/cfgrib_.py_np_CfGribArrayWrapper._getitem.with_self_datastore_lock_.return.self_array_key_", "embedding": null, "metadata": {"file_path": "xarray/backends/cfgrib_.py", "file_name": "cfgrib_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 29, "span_ids": ["impl", "CfGribArrayWrapper", "CfGribArrayWrapper._getitem", "CfGribArrayWrapper.__getitem__", "imports"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom .. import Variable\nfrom ..core import indexing\nfrom ..core.utils import Frozen, FrozenDict\nfrom .common import AbstractDataStore, BackendArray\nfrom .locks import SerializableLock, ensure_lock\n\n# FIXME: Add a dedicated lock, even if ecCodes is supposed to be thread-safe\n# in most circumstances. See:\n# https://confluence.ecmwf.int/display/ECC/Frequently+Asked+Questions\nECCODES_LOCK = SerializableLock()\n\n\nclass CfGribArrayWrapper(BackendArray):\n def __init__(self, datastore, array):\n self.datastore = datastore\n self.shape = array.shape\n self.dtype = array.dtype\n self.array = array\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.OUTER, self._getitem\n )\n\n def _getitem(self, key):\n with self.datastore.lock:\n return self.array[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/cfgrib_.py_CfGribDataStore_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/cfgrib_.py_CfGribDataStore_", "embedding": null, "metadata": {"file_path": "xarray/backends/cfgrib_.py", "file_name": "cfgrib_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 72, "span_ids": ["CfGribDataStore.open_store_variable", "CfGribDataStore", "CfGribDataStore.get_dimensions", "CfGribDataStore.get_variables", "CfGribDataStore.get_encoding", "CfGribDataStore.get_attrs"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CfGribDataStore(AbstractDataStore):\n \"\"\"\n Implements the ``xr.AbstractDataStore`` read-only API for a GRIB file.\n \"\"\"\n\n def __init__(self, filename, lock=None, **backend_kwargs):\n import cfgrib\n\n if lock is None:\n lock = ECCODES_LOCK\n self.lock = ensure_lock(lock)\n self.ds = cfgrib.open_file(filename, **backend_kwargs)\n\n def open_store_variable(self, name, var):\n if isinstance(var.data, np.ndarray):\n data = var.data\n else:\n wrapped_array = CfGribArrayWrapper(self, var.data)\n data = indexing.LazilyOuterIndexedArray(wrapped_array)\n\n encoding = self.ds.encoding.copy()\n encoding[\"original_shape\"] = var.data.shape\n\n return Variable(var.dimensions, data, var.attributes, encoding)\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n\n def get_attrs(self):\n return Frozen(self.ds.attributes)\n\n def get_dimensions(self):\n return Frozen(self.ds.dimensions)\n\n def get_encoding(self):\n dims = self.get_dimensions()\n encoding = {\"unlimited_dims\": {k for k, v in dims.items() if v is None}}\n return encoding", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_logging_find_root_and_group.return.ds_group": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_logging_find_root_and_group.return.ds_group", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 40, "span_ids": ["impl", "find_root_and_group", "_decode_variable_name", "imports", "_encode_variable_name"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport time\nimport traceback\nimport warnings\nfrom collections.abc import Mapping\n\nimport numpy as np\n\nfrom ..conventions import cf_encoder\nfrom ..core import indexing\nfrom ..core.pycompat import dask_array_type\nfrom ..core.utils import FrozenDict, NdimSizeLenMixin\n\n# Create a logger object, but don't add any handlers. Leave that to user code.\nlogger = logging.getLogger(__name__)\n\n\nNONE_VAR_NAME = \"__values__\"\n\n\ndef _encode_variable_name(name):\n if name is None:\n name = NONE_VAR_NAME\n return name\n\n\ndef _decode_variable_name(name):\n if name == NONE_VAR_NAME:\n name = None\n return name\n\n\ndef find_root_and_group(ds):\n \"\"\"Find the root and group name of a netCDF4/h5netcdf dataset.\"\"\"\n hierarchy = ()\n while ds.parent is not None:\n hierarchy = (ds.name,) + hierarchy\n ds = ds.parent\n group = \"/\" + \"/\".join(hierarchy)\n return ds, group", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_robust_getitem_BackendArray.__array__.return.np_asarray_self_key_dty": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_robust_getitem_BackendArray.__array__.return.np_asarray_self_key_dty", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 74, "span_ids": ["robust_getitem", "BackendArray.__array__", "BackendArray"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500):\n \"\"\"\n Robustly index an array, using retry logic with exponential backoff if any\n of the errors ``catch`` are raised. The initial_delay is measured in ms.\n\n With the default settings, the maximum delay will be in the range of 32-64\n seconds.\n \"\"\"\n assert max_retries >= 0\n for n in range(max_retries + 1):\n try:\n return array[key]\n except catch:\n if n == max_retries:\n raise\n base_delay = initial_delay * 2 ** n\n next_delay = base_delay + np.random.randint(base_delay)\n msg = (\n \"getitem failed, waiting %s ms before trying again \"\n \"(%s tries remaining). Full traceback: %s\"\n % (next_delay, max_retries - n, traceback.format_exc())\n )\n logger.debug(msg)\n time.sleep(1e-3 * next_delay)\n\n\nclass BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed):\n __slots__ = ()\n\n def __array__(self, dtype=None):\n key = indexing.BasicIndexer((slice(None),) * self.ndim)\n return np.asarray(self[key], dtype=dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractDataStore_AbstractDataStore.load.return.variables_attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractDataStore_AbstractDataStore.load.return.variables_attributes", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 126, "span_ids": ["AbstractDataStore", "AbstractDataStore.__iter__", "AbstractDataStore.get_dimensions", "AbstractDataStore.get_encoding", "AbstractDataStore.__getitem__", "AbstractDataStore.get_variables", "AbstractDataStore.__len__", "AbstractDataStore.load", "AbstractDataStore.get_attrs"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractDataStore(Mapping):\n __slots__ = ()\n\n def __iter__(self):\n return iter(self.variables)\n\n def __getitem__(self, key):\n return self.variables[key]\n\n def __len__(self):\n return len(self.variables)\n\n def get_dimensions(self): # pragma: no cover\n raise NotImplementedError()\n\n def get_attrs(self): # pragma: no cover\n raise NotImplementedError()\n\n def get_variables(self): # pragma: no cover\n raise NotImplementedError()\n\n def get_encoding(self):\n return {}\n\n def load(self):\n \"\"\"\n This loads the variables and attributes simultaneously.\n A centralized loading function makes it easier to create\n data stores that do automatic encoding/decoding.\n\n For example::\n\n class SuffixAppendingDataStore(AbstractDataStore):\n\n def load(self):\n variables, attributes = AbstractDataStore.load(self)\n variables = {'%s_suffix' % k: v\n for k, v in variables.items()}\n attributes = {'%s_suffix' % k: v\n for k, v in attributes.items()}\n return variables, attributes\n\n This function will be called anytime variables or attributes\n are requested, so care should be taken to make sure its fast.\n \"\"\"\n variables = FrozenDict(\n (_decode_variable_name(k), v) for k, v in self.get_variables().items()\n )\n attributes = FrozenDict(self.get_attrs())\n return variables, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractDataStore.variables_AbstractDataStore.__exit__.self_close_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractDataStore.variables_AbstractDataStore.__exit__.self_close_", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 167, "span_ids": ["AbstractDataStore.__enter__", "AbstractDataStore.attrs", "AbstractDataStore.dimensions", "AbstractDataStore.variables", "AbstractDataStore.close", "AbstractDataStore.__exit__"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractDataStore(Mapping):\n\n @property\n def variables(self): # pragma: no cover\n warnings.warn(\n \"The ``variables`` property has been deprecated and \"\n \"will be removed in xarray v0.11.\",\n FutureWarning,\n stacklevel=2,\n )\n variables, _ = self.load()\n return variables\n\n @property\n def attrs(self): # pragma: no cover\n warnings.warn(\n \"The ``attrs`` property has been deprecated and \"\n \"will be removed in xarray v0.11.\",\n FutureWarning,\n stacklevel=2,\n )\n _, attrs = self.load()\n return attrs\n\n @property\n def dimensions(self): # pragma: no cover\n warnings.warn(\n \"The ``dimensions`` property has been deprecated and \"\n \"will be removed in xarray v0.11.\",\n FutureWarning,\n stacklevel=2,\n )\n return self.get_dimensions()\n\n def close(self):\n pass\n\n def __enter__(self):\n return self\n\n def __exit__(self, exception_type, exception_value, traceback):\n self.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_ArrayWriter_ArrayWriter.sync.if_self_sources_.return.delayed_store": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_ArrayWriter_ArrayWriter.sync.if_self_sources_.return.delayed_store", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 170, "end_line": 209, "span_ids": ["ArrayWriter.add", "ArrayWriter", "ArrayWriter.sync"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ArrayWriter:\n __slots__ = (\"sources\", \"targets\", \"regions\", \"lock\")\n\n def __init__(self, lock=None):\n self.sources = []\n self.targets = []\n self.regions = []\n self.lock = lock\n\n def add(self, source, target, region=None):\n if isinstance(source, dask_array_type):\n self.sources.append(source)\n self.targets.append(target)\n self.regions.append(region)\n else:\n if region:\n target[region] = source\n else:\n target[...] = source\n\n def sync(self, compute=True):\n if self.sources:\n import dask.array as da\n\n # TODO: consider wrapping targets with dask.delayed, if this makes\n # for any discernable difference in perforance, e.g.,\n # targets = [dask.delayed(t) for t in self.targets]\n\n delayed_store = da.store(\n self.sources,\n self.targets,\n lock=self.lock,\n compute=compute,\n flush=True,\n regions=self.regions,\n )\n self.sources = []\n self.targets = []\n self.regions = []\n return delayed_store", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore_AbstractWritableDataStore.encode.return.variables_attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore_AbstractWritableDataStore.encode.return.variables_attributes", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 212, "end_line": 234, "span_ids": ["AbstractWritableDataStore", "AbstractWritableDataStore.encode"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractWritableDataStore(AbstractDataStore):\n __slots__ = ()\n\n def encode(self, variables, attributes):\n \"\"\"\n Encode the variables and attributes in this store\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n attributes : dict-like\n Dictionary of key/value (attribute name / attribute) pairs\n\n Returns\n -------\n variables : dict-like\n attributes : dict-like\n\n \"\"\"\n variables = {k: self.encode_variable(v) for k, v in variables.items()}\n attributes = {k: self.encode_attribute(v) for k, v in attributes.items()}\n return variables, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.encode_variable_AbstractWritableDataStore.store_dataset.self_store_dataset_datas": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.encode_variable_AbstractWritableDataStore.store_dataset.self_store_dataset_datas", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 260, "span_ids": ["AbstractWritableDataStore.set_variable", "AbstractWritableDataStore.set_dimension", "AbstractWritableDataStore.set_attribute", "AbstractWritableDataStore.store_dataset", "AbstractWritableDataStore.encode_attribute", "AbstractWritableDataStore.encode_variable"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractWritableDataStore(AbstractDataStore):\n\n def encode_variable(self, v):\n \"\"\"encode one variable\"\"\"\n return v\n\n def encode_attribute(self, a):\n \"\"\"encode one attribute\"\"\"\n return a\n\n def set_dimension(self, d, l): # pragma: no cover\n raise NotImplementedError()\n\n def set_attribute(self, k, v): # pragma: no cover\n raise NotImplementedError()\n\n def set_variable(self, k, v): # pragma: no cover\n raise NotImplementedError()\n\n def store_dataset(self, dataset):\n \"\"\"\n in stores, variables are all variables AND coordinates\n in xarray.Dataset variables are variables NOT coordinates,\n so here we pass the whole dataset in instead of doing\n dataset.variables\n \"\"\"\n self.store(dataset, dataset.attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.store_AbstractWritableDataStore.store.self_set_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.store_AbstractWritableDataStore.store.self_set_variables_", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 262, "end_line": 299, "span_ids": ["AbstractWritableDataStore.store"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractWritableDataStore(AbstractDataStore):\n\n def store(\n self,\n variables,\n attributes,\n check_encoding_set=frozenset(),\n writer=None,\n unlimited_dims=None,\n ):\n \"\"\"\n Top level method for putting data on this store, this method:\n - encodes variables/attributes\n - sets dimensions\n - sets variables\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n attributes : dict-like\n Dictionary of key/value (attribute name / attribute) pairs\n check_encoding_set : list-like\n List of variables that should be checked for invalid encoding\n values\n writer : ArrayWriter\n unlimited_dims : list-like\n List of dimension names that should be treated as unlimited\n dimensions.\n \"\"\"\n if writer is None:\n writer = ArrayWriter()\n\n variables, attributes = self.encode(variables, attributes)\n\n self.set_attributes(attributes)\n self.set_dimensions(variables, unlimited_dims=unlimited_dims)\n self.set_variables(\n variables, check_encoding_set, writer, unlimited_dims=unlimited_dims\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.set_attributes_AbstractWritableDataStore.set_variables.for_vn_v_in_variables_it.writer_add_source_target": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.set_attributes_AbstractWritableDataStore.set_variables.for_vn_v_in_variables_it.writer_add_source_target", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 301, "end_line": 339, "span_ids": ["AbstractWritableDataStore.set_attributes", "AbstractWritableDataStore.set_variables"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractWritableDataStore(AbstractDataStore):\n\n def set_attributes(self, attributes):\n \"\"\"\n This provides a centralized method to set the dataset attributes on the\n data store.\n\n Parameters\n ----------\n attributes : dict-like\n Dictionary of key/value (attribute name / attribute) pairs\n \"\"\"\n for k, v in attributes.items():\n self.set_attribute(k, v)\n\n def set_variables(self, variables, check_encoding_set, writer, unlimited_dims=None):\n \"\"\"\n This provides a centralized method to set the variables on the data\n store.\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n check_encoding_set : list-like\n List of variables that should be checked for invalid encoding\n values\n writer : ArrayWriter\n unlimited_dims : list-like\n List of dimension names that should be treated as unlimited\n dimensions.\n \"\"\"\n\n for vn, v in variables.items():\n name = _encode_variable_name(vn)\n check = vn in check_encoding_set\n target, source = self.prepare_variable(\n name, v, check, unlimited_dims=unlimited_dims\n )\n\n writer.add(source, target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.set_dimensions_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/common.py_AbstractWritableDataStore.set_dimensions_", "embedding": null, "metadata": {"file_path": "xarray/backends/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 341, "end_line": 386, "span_ids": ["WritableCFDataStore.encode", "AbstractWritableDataStore.set_dimensions", "WritableCFDataStore"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractWritableDataStore(AbstractDataStore):\n\n def set_dimensions(self, variables, unlimited_dims=None):\n \"\"\"\n This provides a centralized method to set the dimensions on the data\n store.\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n unlimited_dims : list-like\n List of dimension names that should be treated as unlimited\n dimensions.\n \"\"\"\n if unlimited_dims is None:\n unlimited_dims = set()\n\n existing_dims = self.get_dimensions()\n\n dims = {}\n for v in unlimited_dims: # put unlimited_dims first\n dims[v] = None\n for v in variables.values():\n dims.update(dict(zip(v.dims, v.shape)))\n\n for dim, length in dims.items():\n if dim in existing_dims and length != existing_dims[dim]:\n raise ValueError(\n \"Unable to update size for existing dimension\"\n \"%r (%d != %d)\" % (dim, length, existing_dims[dim])\n )\n elif dim not in existing_dims:\n is_unlimited = dim in unlimited_dims\n self.set_dimension(dim, length, is_unlimited)\n\n\nclass WritableCFDataStore(AbstractWritableDataStore):\n __slots__ = ()\n\n def encode(self, variables, attributes):\n # All NetCDF files get CF encoded by default, without this attempting\n # to write times, for example, would fail.\n variables, attributes = cf_encoder(variables, attributes)\n variables = {k: self.encode_variable(v) for k, v in variables.items()}\n attributes = {k: self.encode_attribute(v) for k, v in attributes.items()}\n return variables, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_contextlib__DEFAULT_MODE.utils_ReprObject_unused": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_contextlib__DEFAULT_MODE.utils_ReprObject_unused", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 21, "span_ids": ["impl", "imports"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import contextlib\nimport io\nimport threading\nimport warnings\nfrom typing import Any, Dict, cast\n\nfrom ..core import utils\nfrom ..core.options import OPTIONS\nfrom .locks import acquire\nfrom .lru_cache import LRUCache\n\n# Global cache for storing open files.\nFILE_CACHE: LRUCache[str, io.IOBase] = LRUCache(\n maxsize=cast(int, OPTIONS[\"file_cache_maxsize\"]), on_evict=lambda k, v: v.close()\n)\nassert FILE_CACHE.maxsize, \"file cache must be at least size one\"\n\n\nREF_COUNTS: Dict[Any, int] = {}\n\n_DEFAULT_MODE = utils.ReprObject(\"\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_FileManager_FileManager.close.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_FileManager_FileManager.close.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 24, "end_line": 47, "span_ids": ["FileManager.close", "FileManager", "FileManager.acquire_context", "FileManager.acquire"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FileManager:\n \"\"\"Manager for acquiring and closing a file object.\n\n Use FileManager subclasses (CachingFileManager in particular) on backend\n storage classes to automatically handle issues related to keeping track of\n many open files and transferring them between multiple processes.\n \"\"\"\n\n def acquire(self, needs_lock=True):\n \"\"\"Acquire the file object from this manager.\"\"\"\n raise NotImplementedError()\n\n def acquire_context(self, needs_lock=True):\n \"\"\"Context manager for acquiring a file. Yields a file object.\n\n The context manager unwinds any actions taken as part of acquisition\n (i.e., removes it from any cache) if an exception is raised from the\n context. It *does not* automatically close the file.\n \"\"\"\n raise NotImplementedError()\n\n def close(self, needs_lock=True):\n \"\"\"Close the file object associated with this manager, if needed.\"\"\"\n raise NotImplementedError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager_CachingFileManager._Wrapper_for_automatica": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager_CachingFileManager._Wrapper_for_automatica", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 50, "end_line": 76, "span_ids": ["CachingFileManager"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n \"\"\"Wrapper for automatically opening and closing file objects.\n\n Unlike files, CachingFileManager objects can be safely pickled and passed\n between processes. They should be explicitly closed to release resources,\n but a per-process least-recently-used cache for open files ensures that you\n can safely create arbitrarily large numbers of FileManager objects.\n\n Don't directly close files acquired from a FileManager. Instead, call\n FileManager.close(), which ensures that closed files are removed from the\n cache as well.\n\n Example usage:\n\n manager = FileManager(open, 'example.txt', mode='w')\n f = manager.acquire()\n f.write(...)\n manager.close() # ensures file is closed\n\n Note that as long as previous files are still cached, acquiring a file\n multiple times from the same FileManager is essentially free:\n\n f1 = manager.acquire()\n f2 = manager.acquire()\n assert f1 is f2\n\n \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__init___CachingFileManager._optional_lock.if_needs_lock_.else_.yield": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__init___CachingFileManager._optional_lock.if_needs_lock_.else_.yield", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 164, "span_ids": ["CachingFileManager._make_key", "CachingFileManager._optional_lock", "CachingFileManager"], "tokens": 719}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n\n def __init__(\n self,\n opener,\n *args,\n mode=_DEFAULT_MODE,\n kwargs=None,\n lock=None,\n cache=None,\n ref_counts=None\n ):\n \"\"\"Initialize a FileManager.\n\n The cache and ref_counts arguments exist solely to facilitate\n dependency injection, and should only be set for tests.\n\n Parameters\n ----------\n opener : callable\n Function that when called like ``opener(*args, **kwargs)`` returns\n an open file object. The file object must implement a ``close()``\n method.\n *args\n Positional arguments for opener. A ``mode`` argument should be\n provided as a keyword argument (see below). All arguments must be\n hashable.\n mode : optional\n If provided, passed as a keyword argument to ``opener`` along with\n ``**kwargs``. ``mode='w' `` has special treatment: after the first\n call it is replaced by ``mode='a'`` in all subsequent function to\n avoid overriding the newly created file.\n kwargs : dict, optional\n Keyword arguments for opener, excluding ``mode``. All values must\n be hashable.\n lock : duck-compatible threading.Lock, optional\n Lock to use when modifying the cache inside acquire() and close().\n By default, uses a new threading.Lock() object. If set, this object\n should be pickleable.\n cache : MutableMapping, optional\n Mapping to use as a cache for open files. By default, uses xarray's\n global LRU file cache. Because ``cache`` typically points to a\n global variable and contains non-picklable file objects, an\n unpickled FileManager objects will be restored with the default\n cache.\n ref_counts : dict, optional\n Optional dict to use for keeping track the number of references to\n the same file.\n \"\"\"\n self._opener = opener\n self._args = args\n self._mode = mode\n self._kwargs = {} if kwargs is None else dict(kwargs)\n\n self._default_lock = lock is None or lock is False\n self._lock = threading.Lock() if self._default_lock else lock\n\n # cache[self._key] stores the file associated with this object.\n if cache is None:\n cache = FILE_CACHE\n self._cache = cache\n self._key = self._make_key()\n\n # ref_counts[self._key] stores the number of CachingFileManager objects\n # in memory referencing this same file. We use this to know if we can\n # close a file when the manager is deallocated.\n if ref_counts is None:\n ref_counts = REF_COUNTS\n self._ref_counter = _RefCounter(ref_counts)\n self._ref_counter.increment(self._key)\n\n def _make_key(self):\n \"\"\"Make a key for caching files in the LRU cache.\"\"\"\n value = (\n self._opener,\n self._args,\n \"a\" if self._mode == \"w\" else self._mode,\n tuple(sorted(self._kwargs.items())),\n )\n return _HashedSequence(value)\n\n @contextlib.contextmanager\n def _optional_lock(self, needs_lock):\n \"\"\"Context manager for optionally acquiring a lock.\"\"\"\n if needs_lock:\n with self._lock:\n yield\n else:\n yield", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.acquire_CachingFileManager.acquire_context.try_.except_Exception_.raise": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.acquire_CachingFileManager.acquire_context.try_.except_Exception_.raise", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 192, "span_ids": ["CachingFileManager.acquire", "CachingFileManager.acquire_context"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n\n def acquire(self, needs_lock=True):\n \"\"\"Acquire a file object from the manager.\n\n A new file is only opened if it has expired from the\n least-recently-used cache.\n\n This method uses a lock, which ensures that it is thread-safe. You can\n safely acquire a file in multiple threads at the same time, as long as\n the underlying file object is thread-safe.\n\n Returns\n -------\n An open file object, as returned by ``opener(*args, **kwargs)``.\n \"\"\"\n file, _ = self._acquire_with_cache_info(needs_lock)\n return file\n\n @contextlib.contextmanager\n def acquire_context(self, needs_lock=True):\n \"\"\"Context manager for acquiring a file.\"\"\"\n file, cached = self._acquire_with_cache_info(needs_lock)\n try:\n yield file\n except Exception:\n if not cached:\n self.close(needs_lock)\n raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager._acquire_with_cache_info_CachingFileManager.close.with_self__optional_lock_.if_file_is_not_None_.file_close_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager._acquire_with_cache_info_CachingFileManager.close.with_self__optional_lock_.if_file_is_not_None_.file_close_", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 194, "end_line": 221, "span_ids": ["CachingFileManager._acquire_with_cache_info", "CachingFileManager.close"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n\n def _acquire_with_cache_info(self, needs_lock=True):\n \"\"\"Acquire a file, returning the file and whether it was cached.\"\"\"\n with self._optional_lock(needs_lock):\n try:\n file = self._cache[self._key]\n except KeyError:\n kwargs = self._kwargs\n if self._mode is not _DEFAULT_MODE:\n kwargs = kwargs.copy()\n kwargs[\"mode\"] = self._mode\n file = self._opener(*self._args, **kwargs)\n if self._mode == \"w\":\n # ensure file doesn't get overriden when opened again\n self._mode = \"a\"\n self._cache[self._key] = file\n return file, False\n else:\n return file, True\n\n def close(self, needs_lock=True):\n \"\"\"Explicitly close any associated file object (if necessary).\"\"\"\n # TODO: remove needs_lock if/when we have a reentrant lock in\n # dask.distributed: https://github.com/dask/dask/issues/3832\n with self._optional_lock(needs_lock):\n default = None\n file = self._cache.pop(self._key, default)\n if file is not None:\n file.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__del___CachingFileManager.__del__.if_not_ref_count_and_self.if_OPTIONS_warn_for_uncl.warnings_warn_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__del___CachingFileManager.__del__.if_not_ref_count_and_self.if_OPTIONS_warn_for_uncl.warnings_warn_", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 223, "end_line": 253, "span_ids": ["CachingFileManager.__del__"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n\n def __del__(self):\n # If we're the only CachingFileManger referencing a unclosed file, we\n # should remove it from the cache upon garbage collection.\n #\n # Keeping our own count of file references might seem like overkill,\n # but it's actually pretty common to reopen files with the same\n # variable name in a notebook or command line environment, e.g., to\n # fix the parameters used when opening a file:\n # >>> ds = xarray.open_dataset('myfile.nc')\n # >>> ds = xarray.open_dataset('myfile.nc', decode_times=False)\n # This second assignment to \"ds\" drops CPython's ref-count on the first\n # \"ds\" argument to zero, which can trigger garbage collections. So if\n # we didn't check whether another object is referencing 'myfile.nc',\n # the newly opened file would actually be immediately closed!\n ref_count = self._ref_counter.decrement(self._key)\n\n if not ref_count and self._key in self._cache:\n if acquire(self._lock, blocking=False):\n # Only close files if we can do so immediately.\n try:\n self.close(needs_lock=False)\n finally:\n self._lock.release()\n\n if OPTIONS[\"warn_for_unclosed_files\"]:\n warnings.warn(\n \"deallocating {}, but file is not already closed. \"\n \"This may indicate a bug.\".format(self),\n RuntimeWarning,\n stacklevel=2,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__getstate___CachingFileManager.__repr__.return._r_kwargs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py_CachingFileManager.__getstate___CachingFileManager.__repr__.return._r_kwargs_", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 273, "span_ids": ["CachingFileManager.__getstate__", "CachingFileManager.__setstate__", "CachingFileManager.__repr__"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CachingFileManager(FileManager):\n\n def __getstate__(self):\n \"\"\"State for pickling.\"\"\"\n # cache and ref_counts are intentionally omitted: we don't want to try\n # to serialize these global objects.\n lock = None if self._default_lock else self._lock\n return (self._opener, self._args, self._mode, self._kwargs, lock)\n\n def __setstate__(self, state):\n \"\"\"Restore from a pickle.\"\"\"\n opener, args, mode, kwargs, lock = state\n self.__init__(opener, *args, mode=mode, kwargs=kwargs, lock=lock)\n\n def __repr__(self):\n args_string = \", \".join(map(repr, self._args))\n if self._mode is not _DEFAULT_MODE:\n args_string += \", mode={!r}\".format(self._mode)\n return \"{}({!r}, {}, kwargs={})\".format(\n type(self).__name__, self._opener, args_string, self._kwargs\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py__RefCounter__RefCounter.decrement.return.count": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py__RefCounter__RefCounter.decrement.return.count", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 276, "end_line": 295, "span_ids": ["_RefCounter", "_RefCounter.increment", "_RefCounter.decrement"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _RefCounter:\n \"\"\"Class for keeping track of reference counts.\"\"\"\n\n def __init__(self, counts):\n self._counts = counts\n self._lock = threading.Lock()\n\n def increment(self, name):\n with self._lock:\n count = self._counts[name] = self._counts.get(name, 0) + 1\n return count\n\n def decrement(self, name):\n with self._lock:\n count = self._counts[name] - 1\n if count:\n self._counts[name] = count\n else:\n del self._counts[name]\n return count", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py__HashedSequence_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/file_manager.py__HashedSequence_", "embedding": null, "metadata": {"file_path": "xarray/backends/file_manager.py", "file_name": "file_manager.py", "file_type": "text/x-python", "category": "implementation", "start_line": 298, "end_line": 334, "span_ids": ["DummyFileManager.close", "_HashedSequence", "DummyFileManager", "DummyFileManager.acquire_context", "_HashedSequence.__hash__", "DummyFileManager.acquire"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _HashedSequence(list):\n \"\"\"Speedup repeated look-ups by caching hash values.\n\n Based on what Python uses internally in functools.lru_cache.\n\n Python doesn't perform this optimization automatically:\n https://bugs.python.org/issue1462796\n \"\"\"\n\n def __init__(self, tuple_value):\n self[:] = tuple_value\n self.hashvalue = hash(tuple_value)\n\n def __hash__(self):\n return self.hashvalue\n\n\nclass DummyFileManager(FileManager):\n \"\"\"FileManager that simply wraps an open file in the FileManager interface.\n \"\"\"\n\n def __init__(self, value):\n self._value = value\n\n def acquire(self, needs_lock=True):\n del needs_lock # ignored\n return self._value\n\n @contextlib.contextmanager\n def acquire_context(self, needs_lock=True):\n del needs_lock\n yield self._value\n\n def close(self, needs_lock=True):\n del needs_lock # ignored\n self._value.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_functools_BaseNetCDF4Array": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_functools_BaseNetCDF4Array", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 17, "span_ids": ["imports"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\n\nimport numpy as np\n\nfrom .. import Variable\nfrom ..core import indexing\nfrom ..core.utils import FrozenDict\nfrom .common import WritableCFDataStore\nfrom .file_manager import CachingFileManager\nfrom .locks import HDF5_LOCK, combine_locks, ensure_lock, get_write_lock\nfrom .netCDF4_ import (\n BaseNetCDF4Array,\n _encode_nc4_variable,\n _extract_nc4_variable_encoding,\n _get_datatype,\n _nc4_require_group,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFArrayWrapper_H5NetCDFArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFArrayWrapper_H5NetCDFArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 20, "end_line": 37, "span_ids": ["H5NetCDFArrayWrapper", "H5NetCDFArrayWrapper.get_array", "H5NetCDFArrayWrapper.__getitem__", "H5NetCDFArrayWrapper._getitem"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class H5NetCDFArrayWrapper(BaseNetCDF4Array):\n def get_array(self, needs_lock=True):\n ds = self.datastore._acquire(needs_lock)\n variable = ds.variables[self.variable_name]\n return variable\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.OUTER_1VECTOR, self._getitem\n )\n\n def _getitem(self, key):\n # h5py requires using lists for fancy indexing:\n # https://github.com/h5py/h5py/issues/992\n key = tuple(list(k) if isinstance(k, np.ndarray) else k for k in key)\n with self.datastore.lock:\n array = self.get_array(needs_lock=False)\n return array[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_maybe_decode_bytes__h5netcdf_create_group.return.dataset_create_group_name": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_maybe_decode_bytes__h5netcdf_create_group.return.dataset_create_group_name", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 65, "span_ids": ["impl", "maybe_decode_bytes", "_read_attributes", "_h5netcdf_create_group"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maybe_decode_bytes(txt):\n if isinstance(txt, bytes):\n return txt.decode(\"utf-8\")\n else:\n return txt\n\n\ndef _read_attributes(h5netcdf_var):\n # GH451\n # to ensure conventions decoding works properly on Python 3, decode all\n # bytes attributes to strings\n attrs = {}\n for k, v in h5netcdf_var.attrs.items():\n if k not in [\"_FillValue\", \"missing_value\"]:\n v = maybe_decode_bytes(v)\n attrs[k] = v\n return attrs\n\n\n_extract_h5nc_encoding = functools.partial(\n _extract_nc4_variable_encoding, lsd_okay=False, h5py_okay=True, backend=\"h5netcdf\"\n)\n\n\ndef _h5netcdf_create_group(dataset, name):\n return dataset.create_group(name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore_H5NetCDFStore.ds.return.self__acquire_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore_H5NetCDFStore.ds.return.self__acquire_", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 68, "end_line": 115, "span_ids": ["H5NetCDFStore", "H5NetCDFStore.ds", "H5NetCDFStore._acquire"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class H5NetCDFStore(WritableCFDataStore):\n \"\"\"Store for reading and writing data via h5netcdf\n \"\"\"\n\n def __init__(\n self,\n filename,\n mode=\"r\",\n format=None,\n group=None,\n lock=None,\n autoclose=False,\n invalid_netcdf=None,\n ):\n import h5netcdf\n\n if format not in [None, \"NETCDF4\"]:\n raise ValueError(\"invalid format for h5netcdf backend\")\n\n kwargs = {\"invalid_netcdf\": invalid_netcdf}\n\n self._manager = CachingFileManager(\n h5netcdf.File, filename, mode=mode, kwargs=kwargs\n )\n\n if lock is None:\n if mode == \"r\":\n lock = HDF5_LOCK\n else:\n lock = combine_locks([HDF5_LOCK, get_write_lock(filename)])\n\n self._group = group\n self.format = format\n self._filename = filename\n self._mode = mode\n self.lock = ensure_lock(lock)\n self.autoclose = autoclose\n\n def _acquire(self, needs_lock=True):\n with self._manager.acquire_context(needs_lock) as root:\n ds = _nc4_require_group(\n root, self._group, self._mode, create_group=_h5netcdf_create_group\n )\n return ds\n\n @property\n def ds(self):\n return self._acquire()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.open_store_variable_H5NetCDFStore.open_store_variable.return.Variable_dimensions_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.open_store_variable_H5NetCDFStore.open_store_variable.return.Variable_dimensions_data", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 117, "end_line": 152, "span_ids": ["H5NetCDFStore.open_store_variable"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class H5NetCDFStore(WritableCFDataStore):\n\n def open_store_variable(self, name, var):\n import h5py\n\n dimensions = var.dimensions\n data = indexing.LazilyOuterIndexedArray(H5NetCDFArrayWrapper(name, self))\n attrs = _read_attributes(var)\n\n # netCDF4 specific encoding\n encoding = {\n \"chunksizes\": var.chunks,\n \"fletcher32\": var.fletcher32,\n \"shuffle\": var.shuffle,\n }\n # Convert h5py-style compression options to NetCDF4-Python\n # style, if possible\n if var.compression == \"gzip\":\n encoding[\"zlib\"] = True\n encoding[\"complevel\"] = var.compression_opts\n elif var.compression is not None:\n encoding[\"compression\"] = var.compression\n encoding[\"compression_opts\"] = var.compression_opts\n\n # save source so __repr__ can detect if it's local or not\n encoding[\"source\"] = self._filename\n encoding[\"original_shape\"] = var.shape\n\n vlen_dtype = h5py.check_dtype(vlen=var.dtype)\n if vlen_dtype is str:\n encoding[\"dtype\"] = str\n elif vlen_dtype is not None: # pragma: no cover\n # xarray doesn't support writing arbitrary vlen dtypes yet.\n pass\n else:\n encoding[\"dtype\"] = var.dtype\n\n return Variable(dimensions, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.get_variables_H5NetCDFStore.encode_variable.return._encode_nc4_variable_vari": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.get_variables_H5NetCDFStore.encode_variable.return._encode_nc4_variable_vari", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 154, "end_line": 183, "span_ids": ["H5NetCDFStore.get_dimensions", "H5NetCDFStore.get_variables", "H5NetCDFStore.encode_variable", "H5NetCDFStore.get_encoding", "H5NetCDFStore.get_attrs", "H5NetCDFStore.set_dimension", "H5NetCDFStore.set_attribute"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class H5NetCDFStore(WritableCFDataStore):\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n\n def get_attrs(self):\n return FrozenDict(_read_attributes(self.ds))\n\n def get_dimensions(self):\n return self.ds.dimensions\n\n def get_encoding(self):\n encoding = {}\n encoding[\"unlimited_dims\"] = {\n k for k, v in self.ds.dimensions.items() if v is None\n }\n return encoding\n\n def set_dimension(self, name, length, is_unlimited=False):\n if is_unlimited:\n self.ds.dimensions[name] = None\n self.ds.resize_dimension(name, length)\n else:\n self.ds.dimensions[name] = length\n\n def set_attribute(self, key, value):\n self.ds.attrs[key] = value\n\n def encode_variable(self, variable):\n return _encode_nc4_variable(variable)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.prepare_variable_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/h5netcdf_.py_H5NetCDFStore.prepare_variable_", "embedding": null, "metadata": {"file_path": "xarray/backends/h5netcdf_.py", "file_name": "h5netcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 265, "span_ids": ["H5NetCDFStore.prepare_variable", "H5NetCDFStore.close", "H5NetCDFStore.sync"], "tokens": 607}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class H5NetCDFStore(WritableCFDataStore):\n\n def prepare_variable(\n self, name, variable, check_encoding=False, unlimited_dims=None\n ):\n import h5py\n\n attrs = variable.attrs.copy()\n dtype = _get_datatype(variable, raise_on_invalid_encoding=check_encoding)\n\n fillvalue = attrs.pop(\"_FillValue\", None)\n if dtype is str and fillvalue is not None:\n raise NotImplementedError(\n \"h5netcdf does not yet support setting a fill value for \"\n \"variable-length strings \"\n \"(https://github.com/shoyer/h5netcdf/issues/37). \"\n \"Either remove '_FillValue' from encoding on variable %r \"\n \"or set {'dtype': 'S1'} in encoding to use the fixed width \"\n \"NC_CHAR type.\" % name\n )\n\n if dtype is str:\n dtype = h5py.special_dtype(vlen=str)\n\n encoding = _extract_h5nc_encoding(variable, raise_on_invalid=check_encoding)\n kwargs = {}\n\n # Convert from NetCDF4-Python style compression settings to h5py style\n # If both styles are used together, h5py takes precedence\n # If set_encoding=True, raise ValueError in case of mismatch\n if encoding.pop(\"zlib\", False):\n if check_encoding and encoding.get(\"compression\") not in (None, \"gzip\"):\n raise ValueError(\"'zlib' and 'compression' encodings mismatch\")\n encoding.setdefault(\"compression\", \"gzip\")\n\n if (\n check_encoding\n and \"complevel\" in encoding\n and \"compression_opts\" in encoding\n and encoding[\"complevel\"] != encoding[\"compression_opts\"]\n ):\n raise ValueError(\"'complevel' and 'compression_opts' encodings \" \"mismatch\")\n complevel = encoding.pop(\"complevel\", 0)\n if complevel != 0:\n encoding.setdefault(\"compression_opts\", complevel)\n\n encoding[\"chunks\"] = encoding.pop(\"chunksizes\", None)\n\n # Do not apply compression, filters or chunking to scalars.\n if variable.shape:\n for key in [\n \"compression\",\n \"compression_opts\",\n \"shuffle\",\n \"chunks\",\n \"fletcher32\",\n ]:\n if key in encoding:\n kwargs[key] = encoding[key]\n if name not in self.ds:\n nc4_var = self.ds.create_variable(\n name,\n dtype=dtype,\n dimensions=variable.dims,\n fillvalue=fillvalue,\n **kwargs\n )\n else:\n nc4_var = self.ds[name]\n\n for k, v in attrs.items():\n nc4_var.attrs[k] = v\n\n target = H5NetCDFArrayWrapper(name, self)\n\n return target, variable.data\n\n def sync(self):\n self.ds.sync()\n\n def close(self, **kwargs):\n self._manager.close(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_multiprocessing__get_lock_maker.return._LOCK_MAKERS_scheduler_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_multiprocessing__get_lock_maker.return._LOCK_MAKERS_scheduler_", "embedding": null, "metadata": {"file_path": "xarray/backends/locks.py", "file_name": "locks.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 62, "span_ids": ["imports:5", "impl", "imports:6", "impl:2", "_get_multiprocessing_lock", "_get_lock_maker", "impl:6", "_get_threaded_lock", "impl:15", "imports"], "tokens": 334}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import multiprocessing\nimport threading\nimport weakref\nfrom typing import Any, MutableMapping\n\ntry:\n from dask.utils import SerializableLock\nexcept ImportError:\n # no need to worry about serializing the lock\n SerializableLock = threading.Lock\n\ntry:\n from dask.distributed import Lock as DistributedLock\nexcept ImportError:\n DistributedLock = None\n\n\n# Locks used by multiple backends.\n# Neither HDF5 nor the netCDF-C library are thread-safe.\nHDF5_LOCK = SerializableLock()\nNETCDFC_LOCK = SerializableLock()\n\n\n_FILE_LOCKS: MutableMapping[Any, threading.Lock] = weakref.WeakValueDictionary()\n\n\ndef _get_threaded_lock(key):\n try:\n lock = _FILE_LOCKS[key]\n except KeyError:\n lock = _FILE_LOCKS[key] = threading.Lock()\n return lock\n\n\ndef _get_multiprocessing_lock(key):\n # TODO: make use of the key -- maybe use locket.py?\n # https://github.com/mwilliamson/locket.py\n del key # unused\n return multiprocessing.Lock()\n\n\n_LOCK_MAKERS = {\n None: _get_threaded_lock,\n \"threaded\": _get_threaded_lock,\n \"multiprocessing\": _get_multiprocessing_lock,\n \"distributed\": DistributedLock,\n}\n\n\ndef _get_lock_maker(scheduler=None):\n \"\"\"Returns an appropriate function for creating resource locks.\n\n Parameters\n ----------\n scheduler : str or None\n Dask scheduler being used.\n\n See Also\n --------\n dask.utils.get_scheduler_lock\n \"\"\"\n return _LOCK_MAKERS[scheduler]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py__get_scheduler_get_write_lock.return.lock_maker_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py__get_scheduler_get_write_lock.return.lock_maker_key_", "embedding": null, "metadata": {"file_path": "xarray/backends/locks.py", "file_name": "locks.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 109, "span_ids": ["get_write_lock", "_get_scheduler"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_scheduler(get=None, collection=None):\n \"\"\"Determine the dask scheduler that is being used.\n\n None is returned if no dask scheduler is active.\n\n See also\n --------\n dask.base.get_scheduler\n \"\"\"\n try:\n import dask # noqa: F401\n except ImportError:\n return None\n\n actual_get = dask.base.get_scheduler(get, collection)\n\n try:\n from dask.distributed import Client\n\n if isinstance(actual_get.__self__, Client):\n return \"distributed\"\n except (ImportError, AttributeError):\n pass\n\n if actual_get is dask.multiprocessing.get:\n return \"multiprocessing\"\n else:\n return \"threaded\"\n\n\ndef get_write_lock(key):\n \"\"\"Get a scheduler appropriate lock for writing to the given resource.\n\n Parameters\n ----------\n key : str\n Name of the resource for which to acquire a lock. Typically a filename.\n\n Returns\n -------\n Lock object that can be used like a threading.Lock object.\n \"\"\"\n scheduler = _get_scheduler()\n lock_maker = _get_lock_maker(scheduler)\n return lock_maker(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_acquire_acquire.if_blocking_.else_.return.lock_acquire_blocking_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_acquire_acquire.if_blocking_.else_.return.lock_acquire_blocking_", "embedding": null, "metadata": {"file_path": "xarray/backends/locks.py", "file_name": "locks.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 130, "span_ids": ["acquire"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def acquire(lock, blocking=True):\n \"\"\"Acquire a lock, possibly in a non-blocking fashion.\n\n Includes backwards compatibility hacks for old versions of Python, dask\n and dask-distributed.\n \"\"\"\n if blocking:\n # no arguments needed\n return lock.acquire()\n elif DistributedLock is not None and isinstance(lock, DistributedLock):\n # distributed.Lock doesn't support the blocking argument yet:\n # https://github.com/dask/distributed/pull/2412\n return lock.acquire(timeout=0)\n else:\n # \"blocking\" keyword argument not supported for:\n # - threading.Lock on Python 2.\n # - dask.SerializableLock with dask v1.0.0 or earlier.\n # - multiprocessing.Lock calls the argument \"block\" instead.\n return lock.acquire(blocking)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_CombinedLock_CombinedLock.__repr__.return._CombinedLock_r_list": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_CombinedLock_CombinedLock.__repr__.return._CombinedLock_r_list", "embedding": null, "metadata": {"file_path": "xarray/backends/locks.py", "file_name": "locks.py", "file_type": "text/x-python", "category": "implementation", "start_line": 133, "end_line": 162, "span_ids": ["CombinedLock", "CombinedLock.__exit__", "CombinedLock.locked", "CombinedLock.release", "CombinedLock.__enter__", "CombinedLock.__repr__", "CombinedLock.acquire"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CombinedLock:\n \"\"\"A combination of multiple locks.\n\n Like a locked door, a CombinedLock is locked if any of its constituent\n locks are locked.\n \"\"\"\n\n def __init__(self, locks):\n self.locks = tuple(set(locks)) # remove duplicates\n\n def acquire(self, blocking=True):\n return all(acquire(lock, blocking=blocking) for lock in self.locks)\n\n def release(self):\n for lock in self.locks:\n lock.release()\n\n def __enter__(self):\n for lock in self.locks:\n lock.__enter__()\n\n def __exit__(self, *args):\n for lock in self.locks:\n lock.__exit__(*args)\n\n def locked(self):\n return any(lock.locked for lock in self.locks)\n\n def __repr__(self):\n return \"CombinedLock(%r)\" % list(self.locks)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_DummyLock_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/locks.py_DummyLock_", "embedding": null, "metadata": {"file_path": "xarray/backends/locks.py", "file_name": "locks.py", "file_type": "text/x-python", "category": "implementation", "start_line": 165, "end_line": 207, "span_ids": ["DummyLock.__enter__", "DummyLock.acquire", "DummyLock.release", "ensure_lock", "combine_locks", "DummyLock.__exit__", "DummyLock.locked", "DummyLock"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DummyLock:\n \"\"\"DummyLock provides the lock API without any actual locking.\"\"\"\n\n def acquire(self, blocking=True):\n pass\n\n def release(self):\n pass\n\n def __enter__(self):\n pass\n\n def __exit__(self, *args):\n pass\n\n def locked(self):\n return False\n\n\ndef combine_locks(locks):\n \"\"\"Combine a sequence of locks into a single lock.\"\"\"\n all_locks = []\n for lock in locks:\n if isinstance(lock, CombinedLock):\n all_locks.extend(lock.locks)\n elif lock is not None:\n all_locks.append(lock)\n\n num_locks = len(all_locks)\n if num_locks > 1:\n return CombinedLock(all_locks)\n elif num_locks == 1:\n return all_locks[0]\n else:\n return DummyLock()\n\n\ndef ensure_lock(lock):\n \"\"\"Ensure that the given object is a lock.\"\"\"\n if lock is None or lock is False:\n return DummyLock()\n return lock", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_threading_LRUCache._enforce_size_limit.while_len_self__cache_.if_self__on_evict_is_not_.self__on_evict_key_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_threading_LRUCache._enforce_size_limit.while_len_self__cache_.if_self__on_evict_is_not_.self__on_evict_key_value", "embedding": null, "metadata": {"file_path": "xarray/backends/lru_cache.py", "file_name": "lru_cache.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 63, "span_ids": ["impl", "LRUCache.__getitem__", "LRUCache._enforce_size_limit", "imports", "LRUCache"], "tokens": 563}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import threading\nfrom collections import OrderedDict\nfrom typing import Any, Callable, Iterator, MutableMapping, Optional, TypeVar\n\nK = TypeVar(\"K\")\nV = TypeVar(\"V\")\n\n\nclass LRUCache(MutableMapping[K, V]):\n \"\"\"Thread-safe LRUCache based on an OrderedDict.\n\n All dict operations (__getitem__, __setitem__, __contains__) update the\n priority of the relevant key and take O(1) time. The dict is iterated over\n in order from the oldest to newest key, which means that a complete pass\n over the dict should not affect the order of any entries.\n\n When a new item is set and the maximum size of the cache is exceeded, the\n oldest item is dropped and called with ``on_evict(key, value)``.\n\n The ``maxsize`` property can be used to view or adjust the capacity of\n the cache, e.g., ``cache.maxsize = new_size``.\n \"\"\"\n\n _cache: \"OrderedDict[K, V]\"\n _maxsize: int\n _lock: threading.RLock\n _on_evict: Optional[Callable[[K, V], Any]]\n\n __slots__ = (\"_cache\", \"_lock\", \"_maxsize\", \"_on_evict\")\n\n def __init__(self, maxsize: int, on_evict: Callable[[K, V], Any] = None):\n \"\"\"\n Parameters\n ----------\n maxsize : int\n Integer maximum number of items to hold in the cache.\n on_evict: callable, optional\n Function to call like ``on_evict(key, value)`` when items are\n evicted.\n \"\"\"\n if not isinstance(maxsize, int):\n raise TypeError(\"maxsize must be an integer\")\n if maxsize < 0:\n raise ValueError(\"maxsize must be non-negative\")\n self._maxsize = maxsize\n self._cache = OrderedDict()\n self._lock = threading.RLock()\n self._on_evict = on_evict\n\n def __getitem__(self, key: K) -> V:\n # record recent use of the key by moving it to the front of the list\n with self._lock:\n value = self._cache[key]\n self._cache.move_to_end(key)\n return value\n\n def _enforce_size_limit(self, capacity: int) -> None:\n \"\"\"Shrink the cache if necessary, evicting the oldest items.\n \"\"\"\n while len(self._cache) > capacity:\n key, value = self._cache.popitem(last=False)\n if self._on_evict is not None:\n self._on_evict(key, value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_LRUCache.__setitem___LRUCache.__setitem__.with_self__lock_.if_key_in_self__cache_.elif_self__on_evict_is_no.self__on_evict_key_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_LRUCache.__setitem___LRUCache.__setitem__.with_self__lock_.if_key_in_self__cache_.elif_self__on_evict_is_no.self__on_evict_key_value", "embedding": null, "metadata": {"file_path": "xarray/backends/lru_cache.py", "file_name": "lru_cache.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 77, "span_ids": ["LRUCache.__setitem__"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LRUCache(MutableMapping[K, V]):\n\n def __setitem__(self, key: K, value: V) -> None:\n with self._lock:\n if key in self._cache:\n # insert the new value at the end\n del self._cache[key]\n self._cache[key] = value\n elif self._maxsize:\n # make room if necessary\n self._enforce_size_limit(self._maxsize - 1)\n self._cache[key] = value\n elif self._on_evict is not None:\n # not saving, immediately evict\n self._on_evict(key, value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_LRUCache.__delitem___": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/lru_cache.py_LRUCache.__delitem___", "embedding": null, "metadata": {"file_path": "xarray/backends/lru_cache.py", "file_name": "lru_cache.py", "file_type": "text/x-python", "category": "implementation", "start_line": 79, "end_line": 103, "span_ids": ["LRUCache.maxsize_7", "LRUCache.__delitem__", "LRUCache.__iter__", "LRUCache.__len__", "LRUCache.maxsize"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LRUCache(MutableMapping[K, V]):\n\n def __delitem__(self, key: K) -> None:\n del self._cache[key]\n\n def __iter__(self) -> Iterator[K]:\n # create a list, so accessing the cache during iteration cannot change\n # the iteration order\n return iter(list(self._cache))\n\n def __len__(self) -> int:\n return len(self._cache)\n\n @property\n def maxsize(self) -> int:\n \"\"\"Maximum number of items can be held in the cache.\"\"\"\n return self._maxsize\n\n @maxsize.setter\n def maxsize(self, size: int) -> None:\n \"\"\"Resize the cache, evicting the oldest items if necessary.\"\"\"\n if size < 0:\n raise ValueError(\"maxsize must be non-negative\")\n with self._lock:\n self._enforce_size_limit(size)\n self._maxsize = size", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/memory.py_copy_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/memory.py_copy_", "embedding": null, "metadata": {"file_path": "xarray/backends/memory.py", "file_name": "memory.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 46, "span_ids": ["InMemoryDataStore", "InMemoryDataStore.set_attribute", "InMemoryDataStore.get_variables", "InMemoryDataStore.get_dimensions", "InMemoryDataStore.get_attrs", "imports", "InMemoryDataStore.prepare_variable", "InMemoryDataStore.set_dimension"], "tokens": 289}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\n\nimport numpy as np\n\nfrom ..core.variable import Variable\nfrom .common import AbstractWritableDataStore\n\n\nclass InMemoryDataStore(AbstractWritableDataStore):\n \"\"\"\n Stores dimensions, variables and attributes in ordered dictionaries, making\n this store fast compared to stores which save to disk.\n\n This store exists purely for internal testing purposes.\n \"\"\"\n\n def __init__(self, variables=None, attributes=None):\n self._variables = {} if variables is None else variables\n self._attributes = {} if attributes is None else attributes\n\n def get_attrs(self):\n return self._attributes\n\n def get_variables(self):\n return self._variables\n\n def get_dimensions(self):\n dims = {}\n for v in self._variables.values():\n for d, s in v.dims.items():\n dims[d] = s\n return dims\n\n def prepare_variable(self, k, v, *args, **kwargs):\n new_var = Variable(v.dims, np.empty_like(v), v.attrs)\n self._variables[k] = new_var\n return new_var, v.data\n\n def set_attribute(self, k, v):\n # copy to imitate writing to disk.\n self._attributes[k] = copy.deepcopy(v)\n\n def set_dimension(self, d, l, unlimited_dims=None):\n # in this model, dimensions are accounted for in the variables\n pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_functools_NETCDF4_PYTHON_LOCK.combine_locks_NETCDFC_LO": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_functools_NETCDF4_PYTHON_LOCK.combine_locks_NETCDFC_LO", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 26, "span_ids": ["impl", "imports"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\nimport operator\nfrom contextlib import suppress\n\nimport numpy as np\n\nfrom .. import Variable, coding\nfrom ..coding.variables import pop_to\nfrom ..core import indexing\nfrom ..core.utils import FrozenDict, is_remote_uri\nfrom .common import (\n BackendArray,\n WritableCFDataStore,\n find_root_and_group,\n robust_getitem,\n)\nfrom .file_manager import CachingFileManager, DummyFileManager\nfrom .locks import HDF5_LOCK, NETCDFC_LOCK, combine_locks, ensure_lock, get_write_lock\nfrom .netcdf3 import encode_nc3_attr_value, encode_nc3_variable\n\n# This lookup table maps from dtype.byteorder to a readable endian\n# string used by netCDF4.\n_endian_lookup = {\"=\": \"native\", \">\": \"big\", \"<\": \"little\", \"|\": \"native\"}\n\n\nNETCDF4_PYTHON_LOCK = combine_locks([NETCDFC_LOCK, HDF5_LOCK])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_BaseNetCDF4Array_BaseNetCDF4Array.get_array.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_BaseNetCDF4Array_BaseNetCDF4Array.get_array.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 29, "end_line": 55, "span_ids": ["BaseNetCDF4Array", "BaseNetCDF4Array.get_array", "BaseNetCDF4Array.__setitem__"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BaseNetCDF4Array(BackendArray):\n __slots__ = (\"datastore\", \"dtype\", \"shape\", \"variable_name\")\n\n def __init__(self, variable_name, datastore):\n self.datastore = datastore\n self.variable_name = variable_name\n\n array = self.get_array()\n self.shape = array.shape\n\n dtype = array.dtype\n if dtype is str:\n # use object dtype because that's the only way in numpy to\n # represent variable length strings; it also prevents automatic\n # string concatenation via conventions.decode_cf_variable\n dtype = np.dtype(\"O\")\n self.dtype = dtype\n\n def __setitem__(self, key, value):\n with self.datastore.lock:\n data = self.get_array(needs_lock=False)\n data[key] = value\n if self.datastore.autoclose:\n self.datastore.close(needs_lock=False)\n\n def get_array(self, needs_lock=True):\n raise NotImplementedError(\"Virtual Method\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4ArrayWrapper_NetCDF4ArrayWrapper.__getitem__.return.indexing_explicit_indexin": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4ArrayWrapper_NetCDF4ArrayWrapper.__getitem__.return.indexing_explicit_indexin", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 73, "span_ids": ["NetCDF4ArrayWrapper.get_array", "NetCDF4ArrayWrapper", "NetCDF4ArrayWrapper.__getitem__"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4ArrayWrapper(BaseNetCDF4Array):\n __slots__ = ()\n\n def get_array(self, needs_lock=True):\n ds = self.datastore._acquire(needs_lock)\n variable = ds.variables[self.variable_name]\n variable.set_auto_maskandscale(False)\n # only added in netCDF4-python v1.2.8\n with suppress(AttributeError):\n variable.set_auto_chartostring(False)\n return variable\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.OUTER, self._getitem\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4ArrayWrapper._getitem_NetCDF4ArrayWrapper._getitem.return.array": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4ArrayWrapper._getitem_NetCDF4ArrayWrapper._getitem.return.array", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 95, "span_ids": ["NetCDF4ArrayWrapper._getitem"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4ArrayWrapper(BaseNetCDF4Array):\n\n def _getitem(self, key):\n if self.datastore.is_remote: # pragma: no cover\n getitem = functools.partial(robust_getitem, catch=RuntimeError)\n else:\n getitem = operator.getitem\n\n try:\n with self.datastore.lock:\n original_array = self.get_array(needs_lock=False)\n array = getitem(original_array, key)\n except IndexError:\n # Catch IndexError in netCDF4 and return a more informative\n # error message. This is most often called when an unsorted\n # indexer is used before the data is loaded from disk.\n msg = (\n \"The indexing operation you are attempting to perform \"\n \"is not valid on netCDF4.Variable object. Try loading \"\n \"your data into memory first by calling .load().\"\n )\n raise IndexError(msg)\n return array", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__encode_nc4_variable__check_encoding_dtype_is_vlen_string.if_dtype_is_not_str_.raise_AssertionError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__encode_nc4_variable__check_encoding_dtype_is_vlen_string.if_dtype_is_not_str_.raise_AssertionError_", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 98, "end_line": 112, "span_ids": ["_encode_nc4_variable", "_check_encoding_dtype_is_vlen_string"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _encode_nc4_variable(var):\n for coder in [\n coding.strings.EncodedStringCoder(allows_unicode=True),\n coding.strings.CharacterArrayCoder(),\n ]:\n var = coder.encode(var)\n return var\n\n\ndef _check_encoding_dtype_is_vlen_string(dtype):\n if dtype is not str:\n raise AssertionError( # pragma: no cover\n \"unexpected dtype encoding %r. This shouldn't happen: please \"\n \"file a bug report at github.com/pydata/xarray\" % dtype\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__get_datatype__get_datatype.return.datatype": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__get_datatype__get_datatype.return.datatype", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 115, "end_line": 128, "span_ids": ["_get_datatype"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_datatype(var, nc_format=\"NETCDF4\", raise_on_invalid_encoding=False):\n if nc_format == \"NETCDF4\":\n datatype = _nc4_dtype(var)\n else:\n if \"dtype\" in var.encoding:\n encoded_dtype = var.encoding[\"dtype\"]\n _check_encoding_dtype_is_vlen_string(encoded_dtype)\n if raise_on_invalid_encoding:\n raise ValueError(\n \"encoding dtype=str for vlen strings is only supported \"\n \"with format='NETCDF4'.\"\n )\n datatype = var.dtype\n return datatype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__nc4_dtype__netcdf4_create_group.return.dataset_createGroup_name_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__nc4_dtype__netcdf4_create_group.return.dataset_createGroup_name_", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 131, "end_line": 145, "span_ids": ["_netcdf4_create_group", "_nc4_dtype"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _nc4_dtype(var):\n if \"dtype\" in var.encoding:\n dtype = var.encoding.pop(\"dtype\")\n _check_encoding_dtype_is_vlen_string(dtype)\n elif coding.strings.is_unicode_dtype(var.dtype):\n dtype = str\n elif var.dtype.kind in [\"i\", \"u\", \"f\", \"c\", \"S\"]:\n dtype = var.dtype\n else:\n raise ValueError(\"unsupported dtype for netCDF4 variable: {}\".format(var.dtype))\n return dtype\n\n\ndef _netcdf4_create_group(dataset, name):\n return dataset.createGroup(name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__nc4_require_group__ensure_fill_value_valid.if_data_dtype_kind_S_.attributes__FillValue_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__nc4_require_group__ensure_fill_value_valid.if_data_dtype_kind_S_.attributes__FillValue_", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 174, "span_ids": ["_ensure_fill_value_valid", "_nc4_require_group"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):\n if group in {None, \"\", \"/\"}:\n # use the root group\n return ds\n else:\n # make sure it's a string\n if not isinstance(group, str):\n raise ValueError(\"group must be a string or None\")\n # support path-like syntax\n path = group.strip(\"/\").split(\"/\")\n for key in path:\n try:\n ds = ds.groups[key]\n except KeyError as e:\n if mode != \"r\":\n ds = create_group(ds, key)\n else:\n # wrap error to provide slightly more helpful message\n raise OSError(\"group not found: %s\" % key, e)\n return ds\n\n\ndef _ensure_fill_value_valid(data, attributes):\n # work around for netCDF4/scipy issue where _FillValue has the wrong type:\n # https://github.com/Unidata/netcdf4-python/issues/271\n if data.dtype.kind == \"S\" and \"_FillValue\" in attributes:\n attributes[\"_FillValue\"] = np.string_(attributes[\"_FillValue\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__force_native_endianness__force_native_endianness.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__force_native_endianness__force_native_endianness.return.var", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 177, "end_line": 197, "span_ids": ["_force_native_endianness"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _force_native_endianness(var):\n # possible values for byteorder are:\n # = native\n # < little-endian\n # > big-endian\n # | not applicable\n # Below we check if the data type is not native or NA\n if var.dtype.byteorder not in [\"=\", \"|\"]:\n # if endianness is specified explicitly, convert to the native type\n data = var.data.astype(var.dtype.newbyteorder(\"=\"))\n var = Variable(var.dims, data, var.attrs, var.encoding)\n # if endian exists, remove it from the encoding.\n var.encoding.pop(\"endian\", None)\n # check to see if encoding has a value for endian its 'native'\n if not var.encoding.get(\"endian\", \"native\") == \"native\":\n raise NotImplementedError(\n \"Attempt to write non-native endian type, \"\n \"this is not supported by the netCDF4 \"\n \"python library.\"\n )\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__extract_nc4_variable_encoding__extract_nc4_variable_encoding.return.encoding": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__extract_nc4_variable_encoding__extract_nc4_variable_encoding.return.encoding", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 200, "end_line": 266, "span_ids": ["_extract_nc4_variable_encoding"], "tokens": 488}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _extract_nc4_variable_encoding(\n variable,\n raise_on_invalid=False,\n lsd_okay=True,\n h5py_okay=False,\n backend=\"netCDF4\",\n unlimited_dims=None,\n):\n if unlimited_dims is None:\n unlimited_dims = ()\n\n encoding = variable.encoding.copy()\n\n safe_to_drop = {\"source\", \"original_shape\"}\n valid_encodings = {\n \"zlib\",\n \"complevel\",\n \"fletcher32\",\n \"contiguous\",\n \"chunksizes\",\n \"shuffle\",\n \"_FillValue\",\n \"dtype\",\n }\n if lsd_okay:\n valid_encodings.add(\"least_significant_digit\")\n if h5py_okay:\n valid_encodings.add(\"compression\")\n valid_encodings.add(\"compression_opts\")\n\n if not raise_on_invalid and encoding.get(\"chunksizes\") is not None:\n # It's possible to get encoded chunksizes larger than a dimension size\n # if the original file had an unlimited dimension. This is problematic\n # if the new file no longer has an unlimited dimension.\n chunksizes = encoding[\"chunksizes\"]\n chunks_too_big = any(\n c > d and dim not in unlimited_dims\n for c, d, dim in zip(chunksizes, variable.shape, variable.dims)\n )\n has_original_shape = \"original_shape\" in encoding\n changed_shape = (\n has_original_shape and encoding.get(\"original_shape\") != variable.shape\n )\n if chunks_too_big or changed_shape:\n del encoding[\"chunksizes\"]\n\n var_has_unlim_dim = any(dim in unlimited_dims for dim in variable.dims)\n if not raise_on_invalid and var_has_unlim_dim and \"contiguous\" in encoding.keys():\n del encoding[\"contiguous\"]\n\n for k in safe_to_drop:\n if k in encoding:\n del encoding[k]\n\n if raise_on_invalid:\n invalid = [k for k in encoding if k not in valid_encodings]\n if invalid:\n raise ValueError(\n \"unexpected encoding parameters for %r backend: %r. Valid \"\n \"encodings are: %r\" % (backend, invalid, valid_encodings)\n )\n else:\n for k in list(encoding):\n if k not in valid_encodings:\n del encoding[k]\n\n return encoding", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__is_list_of_strings_NetCDF4DataStore.__init__.self.autoclose.autoclose": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py__is_list_of_strings_NetCDF4DataStore.__init__.self.autoclose.autoclose", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 317, "span_ids": ["NetCDF4DataStore", "_is_list_of_strings"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _is_list_of_strings(value):\n if np.asarray(value).dtype.kind in [\"U\", \"S\"] and np.asarray(value).size > 1:\n return True\n else:\n return False\n\n\nclass NetCDF4DataStore(WritableCFDataStore):\n \"\"\"Store for reading and writing data via the Python-NetCDF4 library.\n\n This store supports NetCDF3, NetCDF4 and OpenDAP datasets.\n \"\"\"\n\n __slots__ = (\n \"autoclose\",\n \"format\",\n \"is_remote\",\n \"lock\",\n \"_filename\",\n \"_group\",\n \"_manager\",\n \"_mode\",\n )\n\n def __init__(\n self, manager, group=None, mode=None, lock=NETCDF4_PYTHON_LOCK, autoclose=False\n ):\n import netCDF4\n\n if isinstance(manager, netCDF4.Dataset):\n if group is None:\n root, group = find_root_and_group(manager)\n else:\n if not type(manager) is netCDF4.Dataset:\n raise ValueError(\n \"must supply a root netCDF4.Dataset if the group \"\n \"argument is provided\"\n )\n root = manager\n manager = DummyFileManager(root)\n\n self._manager = manager\n self._group = group\n self._mode = mode\n self.format = self.ds.data_model\n self._filename = self.ds.filepath()\n self.is_remote = is_remote_uri(self._filename)\n self.lock = ensure_lock(lock)\n self.autoclose = autoclose", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.open_NetCDF4DataStore.ds.return.self__acquire_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.open_NetCDF4DataStore.ds.return.self__acquire_", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 319, "end_line": 366, "span_ids": ["NetCDF4DataStore.ds", "NetCDF4DataStore.open", "NetCDF4DataStore._acquire"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4DataStore(WritableCFDataStore):\n\n @classmethod\n def open(\n cls,\n filename,\n mode=\"r\",\n format=\"NETCDF4\",\n group=None,\n clobber=True,\n diskless=False,\n persist=False,\n lock=None,\n lock_maker=None,\n autoclose=False,\n ):\n import netCDF4\n\n if format is None:\n format = \"NETCDF4\"\n\n if lock is None:\n if mode == \"r\":\n if is_remote_uri(filename):\n lock = NETCDFC_LOCK\n else:\n lock = NETCDF4_PYTHON_LOCK\n else:\n if format is None or format.startswith(\"NETCDF4\"):\n base_lock = NETCDF4_PYTHON_LOCK\n else:\n base_lock = NETCDFC_LOCK\n lock = combine_locks([base_lock, get_write_lock(filename)])\n\n kwargs = dict(\n clobber=clobber, diskless=diskless, persist=persist, format=format\n )\n manager = CachingFileManager(\n netCDF4.Dataset, filename, mode=mode, kwargs=kwargs\n )\n return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)\n\n def _acquire(self, needs_lock=True):\n with self._manager.acquire_context(needs_lock) as root:\n ds = _nc4_require_group(root, self._group, self._mode)\n return ds\n\n @property\n def ds(self):\n return self._acquire()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.open_store_variable_NetCDF4DataStore.open_store_variable.return.Variable_dimensions_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.open_store_variable_NetCDF4DataStore.open_store_variable.return.Variable_dimensions_data", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 368, "end_line": 395, "span_ids": ["NetCDF4DataStore.open_store_variable"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4DataStore(WritableCFDataStore):\n\n def open_store_variable(self, name, var):\n dimensions = var.dimensions\n data = indexing.LazilyOuterIndexedArray(NetCDF4ArrayWrapper(name, self))\n attributes = {k: var.getncattr(k) for k in var.ncattrs()}\n _ensure_fill_value_valid(data, attributes)\n # netCDF4 specific encoding; save _FillValue for later\n encoding = {}\n filters = var.filters()\n if filters is not None:\n encoding.update(filters)\n chunking = var.chunking()\n if chunking is not None:\n if chunking == \"contiguous\":\n encoding[\"contiguous\"] = True\n encoding[\"chunksizes\"] = None\n else:\n encoding[\"contiguous\"] = False\n encoding[\"chunksizes\"] = tuple(chunking)\n # TODO: figure out how to round-trip \"endian-ness\" without raising\n # warnings from netCDF4\n # encoding['endian'] = var.endian()\n pop_to(attributes, encoding, \"least_significant_digit\")\n # save source so __repr__ can detect if it's local or not\n encoding[\"source\"] = self._filename\n encoding[\"original_shape\"] = var.shape\n encoding[\"dtype\"] = var.dtype\n\n return Variable(dimensions, data, attributes, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.get_variables_NetCDF4DataStore.encode_variable.return.variable": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.get_variables_NetCDF4DataStore.encode_variable.return.variable", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 437, "span_ids": ["NetCDF4DataStore.encode_variable", "NetCDF4DataStore.set_dimension", "NetCDF4DataStore.get_dimensions", "NetCDF4DataStore.get_attrs", "NetCDF4DataStore.get_encoding", "NetCDF4DataStore.set_attribute", "NetCDF4DataStore.get_variables"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4DataStore(WritableCFDataStore):\n\n def get_variables(self):\n dsvars = FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n return dsvars\n\n def get_attrs(self):\n attrs = FrozenDict((k, self.ds.getncattr(k)) for k in self.ds.ncattrs())\n return attrs\n\n def get_dimensions(self):\n dims = FrozenDict((k, len(v)) for k, v in self.ds.dimensions.items())\n return dims\n\n def get_encoding(self):\n encoding = {}\n encoding[\"unlimited_dims\"] = {\n k for k, v in self.ds.dimensions.items() if v.isunlimited()\n }\n return encoding\n\n def set_dimension(self, name, length, is_unlimited=False):\n dim_length = length if not is_unlimited else None\n self.ds.createDimension(name, size=dim_length)\n\n def set_attribute(self, key, value):\n if self.format != \"NETCDF4\":\n value = encode_nc3_attr_value(value)\n if _is_list_of_strings(value):\n # encode as NC_STRING if attr is list of strings\n self.ds.setncattr_string(key, value)\n else:\n self.ds.setncattr(key, value)\n\n def encode_variable(self, variable):\n variable = _force_native_endianness(variable)\n if self.format == \"NETCDF4\":\n variable = _encode_nc4_variable(variable)\n else:\n variable = encode_nc3_variable(variable)\n return variable", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.prepare_variable_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netCDF4_.py_NetCDF4DataStore.prepare_variable_", "embedding": null, "metadata": {"file_path": "xarray/backends/netCDF4_.py", "file_name": "netCDF4_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 439, "end_line": 492, "span_ids": ["NetCDF4DataStore.sync", "NetCDF4DataStore.close", "NetCDF4DataStore.prepare_variable"], "tokens": 405}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF4DataStore(WritableCFDataStore):\n\n def prepare_variable(\n self, name, variable, check_encoding=False, unlimited_dims=None\n ):\n datatype = _get_datatype(\n variable, self.format, raise_on_invalid_encoding=check_encoding\n )\n attrs = variable.attrs.copy()\n\n fill_value = attrs.pop(\"_FillValue\", None)\n\n if datatype is str and fill_value is not None:\n raise NotImplementedError(\n \"netCDF4 does not yet support setting a fill value for \"\n \"variable-length strings \"\n \"(https://github.com/Unidata/netcdf4-python/issues/730). \"\n \"Either remove '_FillValue' from encoding on variable %r \"\n \"or set {'dtype': 'S1'} in encoding to use the fixed width \"\n \"NC_CHAR type.\" % name\n )\n\n encoding = _extract_nc4_variable_encoding(\n variable, raise_on_invalid=check_encoding, unlimited_dims=unlimited_dims\n )\n\n if name in self.ds.variables:\n nc4_var = self.ds.variables[name]\n else:\n nc4_var = self.ds.createVariable(\n varname=name,\n datatype=datatype,\n dimensions=variable.dims,\n zlib=encoding.get(\"zlib\", False),\n complevel=encoding.get(\"complevel\", 4),\n shuffle=encoding.get(\"shuffle\", True),\n fletcher32=encoding.get(\"fletcher32\", False),\n contiguous=encoding.get(\"contiguous\", False),\n chunksizes=encoding.get(\"chunksizes\"),\n endian=\"native\",\n least_significant_digit=encoding.get(\"least_significant_digit\"),\n fill_value=fill_value,\n )\n\n nc4_var.setncatts(attrs)\n\n target = NetCDF4ArrayWrapper(name, self)\n\n return target, variable.data\n\n def sync(self):\n self.ds.sync()\n\n def close(self, **kwargs):\n self._manager.close(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_unicodedata_STRING_ENCODING._utf_8_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_unicodedata_STRING_ENCODING._utf_8_", "embedding": null, "metadata": {"file_path": "xarray/backends/netcdf3.py", "file_name": "netcdf3.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 33, "span_ids": ["impl", "imports"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unicodedata\n\nimport numpy as np\n\nfrom .. import Variable, coding\n\n# Special characters that are permitted in netCDF names except in the\n# 0th position of the string\n_specialchars = '_.@+- !\"#$%&\\\\()*,:;<=>?[]^`{|}~'\n\n# The following are reserved names in CDL and may not be used as names of\n# variables, dimension, attributes\n_reserved_names = {\n \"byte\",\n \"char\",\n \"short\",\n \"ushort\",\n \"int\",\n \"uint\",\n \"int64\",\n \"uint64\",\n \"float\" \"real\",\n \"double\",\n \"bool\",\n \"string\",\n}\n\n# These data-types aren't supported by netCDF3, so they are automatically\n# coerced instead as indicated by the \"coerce_nc3_dtype\" function\n_nc3_dtype_coercions = {\"int64\": \"int32\", \"bool\": \"int8\"}\n\n# encode all strings as UTF-8\nSTRING_ENCODING = \"utf-8\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_coerce_nc3_dtype_coerce_nc3_dtype.return.arr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_coerce_nc3_dtype_coerce_nc3_dtype.return.arr", "embedding": null, "metadata": {"file_path": "xarray/backends/netcdf3.py", "file_name": "netcdf3.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 56, "span_ids": ["coerce_nc3_dtype"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def coerce_nc3_dtype(arr):\n \"\"\"Coerce an array to a data type that can be stored in a netCDF-3 file\n\n This function performs the following dtype conversions:\n int64 -> int32\n bool -> int8\n\n Data is checked for equality, or equivalence (non-NaN values) with\n `np.allclose` with the default keyword arguments.\n \"\"\"\n dtype = str(arr.dtype)\n if dtype in _nc3_dtype_coercions:\n new_dtype = _nc3_dtype_coercions[dtype]\n # TODO: raise a warning whenever casting the data-type instead?\n cast_arr = arr.astype(new_dtype)\n if not (cast_arr == arr).all():\n raise ValueError(\n \"could not safely cast array from dtype %s to %s\" % (dtype, new_dtype)\n )\n arr = cast_arr\n return arr", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_encode_nc3_attr_value__isalnumMUTF8.return.c_isalnum_or_len_c_enc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_encode_nc3_attr_value__isalnumMUTF8.return.c_isalnum_or_len_c_enc", "embedding": null, "metadata": {"file_path": "xarray/backends/netcdf3.py", "file_name": "netcdf3.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 92, "span_ids": ["encode_nc3_variable", "encode_nc3_attr_value", "encode_nc3_attrs", "_isalnumMUTF8"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def encode_nc3_attr_value(value):\n if isinstance(value, bytes):\n pass\n elif isinstance(value, str):\n value = value.encode(STRING_ENCODING)\n else:\n value = coerce_nc3_dtype(np.atleast_1d(value))\n if value.ndim > 1:\n raise ValueError(\"netCDF attributes must be 1-dimensional\")\n return value\n\n\ndef encode_nc3_attrs(attrs):\n return {k: encode_nc3_attr_value(v) for k, v in attrs.items()}\n\n\ndef encode_nc3_variable(var):\n for coder in [\n coding.strings.EncodedStringCoder(allows_unicode=False),\n coding.strings.CharacterArrayCoder(),\n ]:\n var = coder.encode(var)\n data = coerce_nc3_dtype(var.data)\n attrs = encode_nc3_attrs(var.attrs)\n return Variable(var.dims, data, attrs, var.encoding)\n\n\ndef _isalnumMUTF8(c):\n \"\"\"Return True if the given UTF-8 encoded character is alphanumeric\n or multibyte.\n\n Input is not checked!\n \"\"\"\n return c.isalnum() or (len(c.encode(\"utf-8\")) > 1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_is_valid_nc3_name_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/netcdf3.py_is_valid_nc3_name_", "embedding": null, "metadata": {"file_path": "xarray/backends/netcdf3.py", "file_name": "netcdf3.py", "file_type": "text/x-python", "category": "implementation", "start_line": 95, "end_line": 126, "span_ids": ["is_valid_nc3_name"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_valid_nc3_name(s):\n \"\"\"Test whether an object can be validly converted to a netCDF-3\n dimension, variable or attribute name\n\n Earlier versions of the netCDF C-library reference implementation\n enforced a more restricted set of characters in creating new names,\n but permitted reading names containing arbitrary bytes. This\n specification extends the permitted characters in names to include\n multi-byte UTF-8 encoded Unicode and additional printing characters\n from the US-ASCII alphabet. The first character of a name must be\n alphanumeric, a multi-byte UTF-8 character, or '_' (reserved for\n special names with meaning to implementations, such as the\n \"_FillValue\" attribute). Subsequent characters may also include\n printing special characters, except for '/' which is not allowed in\n names. Names that have trailing space characters are also not\n permitted.\n \"\"\"\n if not isinstance(s, str):\n return False\n if not isinstance(s, str):\n s = s.decode(\"utf-8\")\n num_bytes = len(s.encode(\"utf-8\"))\n return (\n (unicodedata.normalize(\"NFC\", s) == s)\n and (s not in _reserved_names)\n and (num_bytes >= 0)\n and (\"/\" not in s)\n and (s[-1] != \" \")\n and (_isalnumMUTF8(s[0]) or (s[0] == \"_\"))\n and all(_isalnumMUTF8(c) or c in _specialchars for c in s)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pseudonetcdf_.py_np_PncArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pseudonetcdf_.py_np_PncArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_", "embedding": null, "metadata": {"file_path": "xarray/backends/pseudonetcdf_.py", "file_name": "pseudonetcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 34, "span_ids": ["impl", "PncArrayWrapper.__getitem__", "PncArrayWrapper", "PncArrayWrapper.get_array", "PncArrayWrapper._getitem", "imports"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom .. import Variable\nfrom ..core import indexing\nfrom ..core.utils import Frozen, FrozenDict\nfrom .common import AbstractDataStore, BackendArray\nfrom .file_manager import CachingFileManager\nfrom .locks import HDF5_LOCK, NETCDFC_LOCK, combine_locks, ensure_lock\n\n# psuedonetcdf can invoke netCDF libraries internally\nPNETCDF_LOCK = combine_locks([HDF5_LOCK, NETCDFC_LOCK])\n\n\nclass PncArrayWrapper(BackendArray):\n def __init__(self, variable_name, datastore):\n self.datastore = datastore\n self.variable_name = variable_name\n array = self.get_array()\n self.shape = array.shape\n self.dtype = np.dtype(array.dtype)\n\n def get_array(self, needs_lock=True):\n ds = self.datastore._manager.acquire(needs_lock)\n return ds.variables[self.variable_name]\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.OUTER_1VECTOR, self._getitem\n )\n\n def _getitem(self, key):\n with self.datastore.lock:\n array = self.get_array(needs_lock=False)\n return array[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pseudonetcdf_.py_PseudoNetCDFDataStore_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pseudonetcdf_.py_PseudoNetCDFDataStore_", "embedding": null, "metadata": {"file_path": "xarray/backends/pseudonetcdf_.py", "file_name": "pseudonetcdf_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 89, "span_ids": ["PseudoNetCDFDataStore.close", "PseudoNetCDFDataStore.open_store_variable", "PseudoNetCDFDataStore.get_variables", "PseudoNetCDFDataStore.get_dimensions", "PseudoNetCDFDataStore.get_encoding", "PseudoNetCDFDataStore.open", "PseudoNetCDFDataStore", "PseudoNetCDFDataStore.__init__", "PseudoNetCDFDataStore.get_attrs", "PseudoNetCDFDataStore.ds"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PseudoNetCDFDataStore(AbstractDataStore):\n \"\"\"Store for accessing datasets via PseudoNetCDF\n \"\"\"\n\n @classmethod\n def open(cls, filename, lock=None, mode=None, **format_kwargs):\n from PseudoNetCDF import pncopen\n\n keywords = {\"kwargs\": format_kwargs}\n # only include mode if explicitly passed\n if mode is not None:\n keywords[\"mode\"] = mode\n\n if lock is None:\n lock = PNETCDF_LOCK\n\n manager = CachingFileManager(pncopen, filename, lock=lock, **keywords)\n return cls(manager, lock)\n\n def __init__(self, manager, lock=None):\n self._manager = manager\n self.lock = ensure_lock(lock)\n\n @property\n def ds(self):\n return self._manager.acquire()\n\n def open_store_variable(self, name, var):\n data = indexing.LazilyOuterIndexedArray(PncArrayWrapper(name, self))\n attrs = {k: getattr(var, k) for k in var.ncattrs()}\n return Variable(var.dimensions, data, attrs)\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n\n def get_attrs(self):\n return Frozen({k: getattr(self.ds, k) for k in self.ds.ncattrs()})\n\n def get_dimensions(self):\n return Frozen(self.ds.dimensions)\n\n def get_encoding(self):\n return {\n \"unlimited_dims\": {\n k for k in self.ds.dimensions if self.ds.dimensions[k].isunlimited()\n }\n }\n\n def close(self):\n self._manager.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py_np_PydapArrayWrapper._getitem.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py_np_PydapArrayWrapper._getitem.return.result", "embedding": null, "metadata": {"file_path": "xarray/backends/pydap_.py", "file_name": "pydap_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 37, "span_ids": ["PydapArrayWrapper.__getitem__", "PydapArrayWrapper.shape", "imports", "PydapArrayWrapper.dtype", "PydapArrayWrapper", "PydapArrayWrapper._getitem"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom .. import Variable\nfrom ..core import indexing\nfrom ..core.pycompat import integer_types\nfrom ..core.utils import Frozen, FrozenDict, is_dict_like\nfrom .common import AbstractDataStore, BackendArray, robust_getitem\n\n\nclass PydapArrayWrapper(BackendArray):\n def __init__(self, array):\n self.array = array\n\n @property\n def shape(self):\n return self.array.shape\n\n @property\n def dtype(self):\n return self.array.dtype\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.BASIC, self._getitem\n )\n\n def _getitem(self, key):\n # pull the data from the array attribute if possible, to avoid\n # downloading coordinate data twice\n array = getattr(self.array, \"array\", self.array)\n result = robust_getitem(array, key, catch=ValueError)\n # in some cases, pydap doesn't squeeze axes automatically like numpy\n axis = tuple(n for n, k in enumerate(key) if isinstance(k, integer_types))\n if result.ndim + len(axis) != array.ndim and len(axis) > 0:\n result = np.squeeze(result, axis)\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py__fix_attributes__fix_attributes.return.attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py__fix_attributes__fix_attributes.return.attributes", "embedding": null, "metadata": {"file_path": "xarray/backends/pydap_.py", "file_name": "pydap_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 56, "span_ids": ["_fix_attributes"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _fix_attributes(attributes):\n attributes = dict(attributes)\n for k in list(attributes):\n if k.lower() == \"global\" or k.lower().endswith(\"_global\"):\n # move global attributes to the top level, like the netcdf-C\n # DAP client\n attributes.update(attributes.pop(k))\n elif is_dict_like(attributes[k]):\n # Make Hierarchical attributes to a single level with a\n # dot-separated key\n attributes.update(\n {\n \"{}.{}\".format(k, k_child): v_child\n for k_child, v_child in attributes.pop(k).items()\n }\n )\n return attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py_PydapDataStore_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pydap_.py_PydapDataStore_", "embedding": null, "metadata": {"file_path": "xarray/backends/pydap_.py", "file_name": "pydap_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 95, "span_ids": ["PydapDataStore.open_store_variable", "PydapDataStore.open", "PydapDataStore", "PydapDataStore.get_attrs", "PydapDataStore.get_dimensions", "PydapDataStore.get_variables"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PydapDataStore(AbstractDataStore):\n \"\"\"Store for accessing OpenDAP datasets with pydap.\n\n This store provides an alternative way to access OpenDAP datasets that may\n be useful if the netCDF4 library is not available.\n \"\"\"\n\n def __init__(self, ds):\n \"\"\"\n Parameters\n ----------\n ds : pydap DatasetType\n \"\"\"\n self.ds = ds\n\n @classmethod\n def open(cls, url, session=None):\n import pydap.client\n\n ds = pydap.client.open_url(url, session=session)\n return cls(ds)\n\n def open_store_variable(self, var):\n data = indexing.LazilyOuterIndexedArray(PydapArrayWrapper(var))\n return Variable(var.dimensions, data, _fix_attributes(var.attributes))\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(self.ds[k])) for k in self.ds.keys()\n )\n\n def get_attrs(self):\n return Frozen(_fix_attributes(self.ds.attributes))\n\n def get_dimensions(self):\n return Frozen(self.ds.dimensions)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_np_PYNIO_LOCK.combine_locks_HDF5_LOCK_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_np_PYNIO_LOCK.combine_locks_HDF5_LOCK_", "embedding": null, "metadata": {"file_path": "xarray/backends/pynio_.py", "file_name": "pynio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 13, "span_ids": ["impl", "imports"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom .. import Variable\nfrom ..core import indexing\nfrom ..core.utils import Frozen, FrozenDict\nfrom .common import AbstractDataStore, BackendArray\nfrom .file_manager import CachingFileManager\nfrom .locks import HDF5_LOCK, NETCDFC_LOCK, SerializableLock, combine_locks, ensure_lock\n\n# PyNIO can invoke netCDF libraries internally\n# Add a dedicated lock just in case NCL as well isn't thread-safe.\nNCL_LOCK = SerializableLock()\nPYNIO_LOCK = combine_locks([HDF5_LOCK, NETCDFC_LOCK, NCL_LOCK])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_NioArrayWrapper_NioArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_NioArrayWrapper_NioArrayWrapper._getitem.with_self_datastore_lock_.return.array_key_", "embedding": null, "metadata": {"file_path": "xarray/backends/pynio_.py", "file_name": "pynio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 40, "span_ids": ["NioArrayWrapper.__getitem__", "NioArrayWrapper.get_array", "NioArrayWrapper", "NioArrayWrapper._getitem"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NioArrayWrapper(BackendArray):\n def __init__(self, variable_name, datastore):\n self.datastore = datastore\n self.variable_name = variable_name\n array = self.get_array()\n self.shape = array.shape\n self.dtype = np.dtype(array.typecode())\n\n def get_array(self, needs_lock=True):\n ds = self.datastore._manager.acquire(needs_lock)\n return ds.variables[self.variable_name]\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.BASIC, self._getitem\n )\n\n def _getitem(self, key):\n with self.datastore.lock:\n array = self.get_array(needs_lock=False)\n\n if key == () and self.ndim == 0:\n return array.get_value()\n\n return array[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_NioDataStore_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/pynio_.py_NioDataStore_", "embedding": null, "metadata": {"file_path": "xarray/backends/pynio_.py", "file_name": "pynio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 86, "span_ids": ["NioDataStore.get_variables", "NioDataStore.get_dimensions", "NioDataStore.get_encoding", "NioDataStore.open_store_variable", "NioDataStore.close", "NioDataStore", "NioDataStore.get_attrs", "NioDataStore.ds"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NioDataStore(AbstractDataStore):\n \"\"\"Store for accessing datasets via PyNIO\n \"\"\"\n\n def __init__(self, filename, mode=\"r\", lock=None, **kwargs):\n import Nio\n\n if lock is None:\n lock = PYNIO_LOCK\n self.lock = ensure_lock(lock)\n self._manager = CachingFileManager(\n Nio.open_file, filename, lock=lock, mode=mode, kwargs=kwargs\n )\n # xarray provides its own support for FillValue,\n # so turn off PyNIO's support for the same.\n self.ds.set_option(\"MaskedArrayMode\", \"MaskedNever\")\n\n @property\n def ds(self):\n return self._manager.acquire()\n\n def open_store_variable(self, name, var):\n data = indexing.LazilyOuterIndexedArray(NioArrayWrapper(name, self))\n return Variable(var.dimensions, data, var.attributes)\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n\n def get_attrs(self):\n return Frozen(self.ds.attributes)\n\n def get_dimensions(self):\n return Frozen(self.ds.dimensions)\n\n def get_encoding(self):\n return {\n \"unlimited_dims\": {k for k in self.ds.dimensions if self.ds.unlimited(k)}\n }\n\n def close(self):\n self._manager.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_os__ERROR_MSG._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_os__ERROR_MSG._", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 20, "span_ids": ["impl", "imports"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport warnings\n\nimport numpy as np\n\nfrom .. import DataArray\nfrom ..core import indexing\nfrom ..core.utils import is_scalar\nfrom .common import BackendArray\nfrom .file_manager import CachingFileManager\nfrom .locks import SerializableLock\n\n# TODO: should this be GDAL_LOCK instead?\nRASTERIO_LOCK = SerializableLock()\n\n_ERROR_MSG = (\n \"The kind of indexing operation you are trying to do is not \"\n \"valid on rasterio files. Try to load your data with ds.load()\"\n \"first.\"\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper_RasterioArrayWrapper.shape.return.self__shape": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper_RasterioArrayWrapper.shape.return.self__shape", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 23, "end_line": 50, "span_ids": ["RasterioArrayWrapper.shape", "RasterioArrayWrapper.dtype", "RasterioArrayWrapper"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RasterioArrayWrapper(BackendArray):\n \"\"\"A wrapper around rasterio dataset objects\"\"\"\n\n def __init__(self, manager, lock, vrt_params=None):\n from rasterio.vrt import WarpedVRT\n\n self.manager = manager\n self.lock = lock\n\n # cannot save riods as an attribute: this would break pickleability\n riods = manager.acquire()\n if vrt_params is not None:\n riods = WarpedVRT(riods, **vrt_params)\n self.vrt_params = vrt_params\n self._shape = (riods.count, riods.height, riods.width)\n\n dtypes = riods.dtypes\n if not np.all(np.asarray(dtypes) == dtypes[0]):\n raise ValueError(\"All bands should have the same dtype\")\n self._dtype = np.dtype(dtypes[0])\n\n @property\n def dtype(self):\n return self._dtype\n\n @property\n def shape(self):\n return self._shape", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper._get_indexer_RasterioArrayWrapper._get_indexer.return.band_key_tuple_window_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper._get_indexer_RasterioArrayWrapper._get_indexer.return.band_key_tuple_window_", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 52, "end_line": 107, "span_ids": ["RasterioArrayWrapper._get_indexer"], "tokens": 492}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RasterioArrayWrapper(BackendArray):\n\n def _get_indexer(self, key):\n \"\"\" Get indexer for rasterio array.\n\n Parameter\n ---------\n key: tuple of int\n\n Returns\n -------\n band_key: an indexer for the 1st dimension\n window: two tuples. Each consists of (start, stop).\n squeeze_axis: axes to be squeezed\n np_ind: indexer for loaded numpy array\n\n See also\n --------\n indexing.decompose_indexer\n \"\"\"\n assert len(key) == 3, \"rasterio datasets should always be 3D\"\n\n # bands cannot be windowed but they can be listed\n band_key = key[0]\n np_inds = []\n # bands (axis=0) cannot be windowed but they can be listed\n if isinstance(band_key, slice):\n start, stop, step = band_key.indices(self.shape[0])\n band_key = np.arange(start, stop, step)\n # be sure we give out a list\n band_key = (np.asarray(band_key) + 1).tolist()\n if isinstance(band_key, list): # if band_key is not a scalar\n np_inds.append(slice(None))\n\n # but other dims can only be windowed\n window = []\n squeeze_axis = []\n for i, (k, n) in enumerate(zip(key[1:], self.shape[1:])):\n if isinstance(k, slice):\n # step is always positive. see indexing.decompose_indexer\n start, stop, step = k.indices(n)\n np_inds.append(slice(None, None, step))\n elif is_scalar(k):\n # windowed operations will always return an array\n # we will have to squeeze it later\n squeeze_axis.append(-(2 - i))\n start = k\n stop = k + 1\n else:\n start, stop = np.min(k), np.max(k) + 1\n np_inds.append(k - start)\n window.append((start, stop))\n\n if isinstance(key[1], np.ndarray) and isinstance(key[2], np.ndarray):\n # do outer-style indexing\n np_inds[-2:] = np.ix_(*np_inds[-2:])\n\n return band_key, tuple(window), tuple(squeeze_axis), tuple(np_inds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper._getitem_RasterioArrayWrapper.__getitem__.return.indexing_explicit_indexin": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_RasterioArrayWrapper._getitem_RasterioArrayWrapper.__getitem__.return.indexing_explicit_indexin", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 109, "end_line": 132, "span_ids": ["RasterioArrayWrapper._getitem", "RasterioArrayWrapper.__getitem__"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RasterioArrayWrapper(BackendArray):\n\n def _getitem(self, key):\n from rasterio.vrt import WarpedVRT\n\n band_key, window, squeeze_axis, np_inds = self._get_indexer(key)\n\n if not band_key or any(start == stop for (start, stop) in window):\n # no need to do IO\n shape = (len(band_key),) + tuple(stop - start for (start, stop) in window)\n out = np.zeros(shape, dtype=self.dtype)\n else:\n with self.lock:\n riods = self.manager.acquire(needs_lock=False)\n if self.vrt_params is not None:\n riods = WarpedVRT(riods, **self.vrt_params)\n out = riods.read(band_key, window=window)\n\n if squeeze_axis:\n out = np.squeeze(out, axis=squeeze_axis)\n return out[np_inds]\n\n def __getitem__(self, key):\n return indexing.explicit_indexing_adapter(\n key, self.shape, indexing.IndexingSupport.OUTER, self._getitem\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py__parse_envi__parse_envi.return.parsed_meta": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py__parse_envi__parse_envi.return.parsed_meta", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 135, "end_line": 162, "span_ids": ["_parse_envi"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _parse_envi(meta):\n \"\"\"Parse ENVI metadata into Python data structures.\n\n See the link for information on the ENVI header file format:\n http://www.harrisgeospatial.com/docs/enviheaderfiles.html\n\n Parameters\n ----------\n meta : dict\n Dictionary of keys and str values to parse, as returned by the rasterio\n tags(ns='ENVI') call.\n\n Returns\n -------\n parsed_meta : dict\n Dictionary containing the original keys and the parsed values\n\n \"\"\"\n\n def parsevec(s):\n return np.fromstring(s.strip(\"{}\"), dtype=\"float\", sep=\",\")\n\n def default(s):\n return s.strip(\"{}\")\n\n parse = {\"wavelength\": parsevec, \"fwhm\": parsevec}\n parsed_meta = {k: parse.get(k, default)(v) for k, v in meta.items()}\n return parsed_meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio_open_rasterio.riods.manager_acquire_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio_open_rasterio.riods.manager_acquire_", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 165, "end_line": 239, "span_ids": ["open_rasterio"], "tokens": 771}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_rasterio(filename, parse_coordinates=None, chunks=None, cache=None, lock=None):\n \"\"\"Open a file with rasterio (experimental).\n\n This should work with any file that rasterio can open (most often:\n geoTIFF). The x and y coordinates are generated automatically from the\n file's geoinformation, shifted to the center of each pixel (see\n `\"PixelIsArea\" Raster Space\n `_\n for more information).\n\n You can generate 2D coordinates from the file's attributes with::\n\n from affine import Affine\n da = xr.open_rasterio('path_to_file.tif')\n transform = Affine.from_gdal(*da.attrs['transform'])\n nx, ny = da.sizes['x'], da.sizes['y']\n x, y = np.meshgrid(np.arange(nx)+0.5, np.arange(ny)+0.5) * transform\n\n\n Parameters\n ----------\n filename : str, rasterio.DatasetReader, or rasterio.WarpedVRT\n Path to the file to open. Or already open rasterio dataset.\n parse_coordinates : bool, optional\n Whether to parse the x and y coordinates out of the file's\n ``transform`` attribute or not. The default is to automatically\n parse the coordinates only if they are rectilinear (1D).\n It can be useful to set ``parse_coordinates=False``\n if your files are very large or if you don't need the coordinates.\n chunks : int, tuple or dict, optional\n Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or\n ``{'x': 5, 'y': 5}``. If chunks is provided, it used to load the new\n DataArray into a dask array.\n cache : bool, optional\n If True, cache data loaded from the underlying datastore in memory as\n NumPy arrays when accessed to avoid reading from the underlying data-\n store multiple times. Defaults to True unless you specify the `chunks`\n argument to use dask, in which case it defaults to False.\n lock : False, True or threading.Lock, optional\n If chunks is provided, this argument is passed on to\n :py:func:`dask.array.from_array`. By default, a global lock is\n used to avoid issues with concurrent access to the same file when using\n dask's multithreaded backend.\n\n Returns\n -------\n data : DataArray\n The newly created DataArray.\n \"\"\"\n import rasterio\n from rasterio.vrt import WarpedVRT\n\n vrt_params = None\n if isinstance(filename, rasterio.io.DatasetReader):\n filename = filename.name\n elif isinstance(filename, rasterio.vrt.WarpedVRT):\n vrt = filename\n filename = vrt.src_dataset.name\n vrt_params = dict(\n crs=vrt.crs.to_string(),\n resampling=vrt.resampling,\n src_nodata=vrt.src_nodata,\n dst_nodata=vrt.dst_nodata,\n tolerance=vrt.tolerance,\n transform=vrt.transform,\n width=vrt.width,\n height=vrt.height,\n warp_extras=vrt.warp_extras,\n )\n\n if lock is None:\n lock = RASTERIO_LOCK\n\n manager = CachingFileManager(rasterio.open, filename, lock=lock, mode=\"r\")\n riods = manager.acquire()\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio.if_vrt_params_is_not_None_open_rasterio._Parse_extra_metadata_fr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio.if_vrt_params_is_not_None_open_rasterio._Parse_extra_metadata_fr", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 240, "end_line": 318, "span_ids": ["open_rasterio"], "tokens": 842}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_rasterio(filename, parse_coordinates=None, chunks=None, cache=None, lock=None):\n # ... other code\n if vrt_params is not None:\n riods = WarpedVRT(riods, **vrt_params)\n\n if cache is None:\n cache = chunks is None\n\n coords = {}\n\n # Get bands\n if riods.count < 1:\n raise ValueError(\"Unknown dims\")\n coords[\"band\"] = np.asarray(riods.indexes)\n\n # Get coordinates\n if riods.transform.is_rectilinear:\n # 1d coordinates\n parse = True if parse_coordinates is None else parse_coordinates\n if parse:\n nx, ny = riods.width, riods.height\n # xarray coordinates are pixel centered\n x, _ = (np.arange(nx) + 0.5, np.zeros(nx) + 0.5) * riods.transform\n _, y = (np.zeros(ny) + 0.5, np.arange(ny) + 0.5) * riods.transform\n coords[\"y\"] = y\n coords[\"x\"] = x\n else:\n # 2d coordinates\n parse = False if (parse_coordinates is None) else parse_coordinates\n if parse:\n warnings.warn(\n \"The file coordinates' transformation isn't \"\n \"rectilinear: xarray won't parse the coordinates \"\n \"in this case. Set `parse_coordinates=False` to \"\n \"suppress this warning.\",\n RuntimeWarning,\n stacklevel=3,\n )\n\n # Attributes\n attrs = {}\n # Affine transformation matrix (always available)\n # This describes coefficients mapping pixel coordinates to CRS\n # For serialization store as tuple of 6 floats, the last row being\n # always (0, 0, 1) per definition (see\n # https://github.com/sgillies/affine)\n attrs[\"transform\"] = tuple(riods.transform)[:6]\n if hasattr(riods, \"crs\") and riods.crs:\n # CRS is a dict-like object specific to rasterio\n # If CRS is not None, we convert it back to a PROJ4 string using\n # rasterio itself\n try:\n attrs[\"crs\"] = riods.crs.to_proj4()\n except AttributeError:\n attrs[\"crs\"] = riods.crs.to_string()\n if hasattr(riods, \"res\"):\n # (width, height) tuple of pixels in units of CRS\n attrs[\"res\"] = riods.res\n if hasattr(riods, \"is_tiled\"):\n # Is the TIF tiled? (bool)\n # We cast it to an int for netCDF compatibility\n attrs[\"is_tiled\"] = np.uint8(riods.is_tiled)\n if hasattr(riods, \"nodatavals\"):\n # The nodata values for the raster bands\n attrs[\"nodatavals\"] = tuple(\n np.nan if nodataval is None else nodataval for nodataval in riods.nodatavals\n )\n if hasattr(riods, \"scales\"):\n # The scale values for the raster bands\n attrs[\"scales\"] = riods.scales\n if hasattr(riods, \"offsets\"):\n # The offset values for the raster bands\n attrs[\"offsets\"] = riods.offsets\n if hasattr(riods, \"descriptions\") and any(riods.descriptions):\n # Descriptions for each dataset band\n attrs[\"descriptions\"] = riods.descriptions\n if hasattr(riods, \"units\") and any(riods.units):\n # A list of units string for each dataset band\n attrs[\"units\"] = riods.units\n\n # Parse extra metadata from tags, if supported\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio.parsers_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/rasterio_.py_open_rasterio.parsers_", "embedding": null, "metadata": {"file_path": "xarray/backends/rasterio_.py", "file_name": "rasterio_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 319, "end_line": 364, "span_ids": ["open_rasterio"], "tokens": 387}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_rasterio(filename, parse_coordinates=None, chunks=None, cache=None, lock=None):\n # ... other code\n parsers = {\"ENVI\": _parse_envi, \"GTiff\": lambda m: m}\n\n driver = riods.driver\n if driver in parsers:\n if driver == \"GTiff\":\n meta = parsers[driver](riods.tags())\n else:\n meta = parsers[driver](riods.tags(ns=driver))\n\n for k, v in meta.items():\n # Add values as coordinates if they match the band count,\n # as attributes otherwise\n if isinstance(v, (list, np.ndarray)) and len(v) == riods.count:\n coords[k] = (\"band\", np.asarray(v))\n else:\n attrs[k] = v\n\n data = indexing.LazilyOuterIndexedArray(\n RasterioArrayWrapper(manager, lock, vrt_params)\n )\n\n # this lets you write arrays loaded with rasterio\n data = indexing.CopyOnWriteArray(data)\n if cache and chunks is None:\n data = indexing.MemoryCachedArray(data)\n\n result = DataArray(data=data, dims=(\"band\", \"y\", \"x\"), coords=coords, attrs=attrs)\n\n if chunks is not None:\n from dask.base import tokenize\n\n # augment the token with the file modification time\n try:\n mtime = os.path.getmtime(filename)\n except OSError:\n # the filename is probably an s3 bucket rather than a regular file\n mtime = None\n token = tokenize(filename, mtime, chunks)\n name_prefix = \"open_rasterio-%s\" % token\n result = result.chunk(chunks, name_prefix=name_prefix, token=token)\n\n # Make the file closeable\n result._file_obj = manager\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_from_io_import_BytesIO__decode_attrs.return._k_v_if_k__FillValue": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_from_io_import_BytesIO__decode_attrs.return._k_v_if_k__FillValue", "embedding": null, "metadata": {"file_path": "xarray/backends/scipy_.py", "file_name": "scipy_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 23, "span_ids": ["_decode_attrs", "_decode_string", "imports"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from io import BytesIO\n\nimport numpy as np\n\nfrom .. import Variable\nfrom ..core.indexing import NumpyIndexingAdapter\nfrom ..core.utils import Frozen, FrozenDict\nfrom .common import BackendArray, WritableCFDataStore\nfrom .file_manager import CachingFileManager, DummyFileManager\nfrom .locks import ensure_lock, get_write_lock\nfrom .netcdf3 import encode_nc3_attr_value, encode_nc3_variable, is_valid_nc3_name\n\n\ndef _decode_string(s):\n if isinstance(s, bytes):\n return s.decode(\"utf-8\", \"replace\")\n return s\n\n\ndef _decode_attrs(d):\n # don't decode _FillValue from bytes -> unicode, because we want to ensure\n # that its type matches the data exactly\n return {k: v if k == \"_FillValue\" else _decode_string(v) for (k, v) in d.items()}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyArrayWrapper_ScipyArrayWrapper.__setitem__.with_self_datastore_lock_.try_.except_TypeError_.if_key_is_Ellipsis_.else_.raise": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyArrayWrapper_ScipyArrayWrapper.__setitem__.with_self_datastore_lock_.try_.except_TypeError_.if_key_is_Ellipsis_.else_.raise", "embedding": null, "metadata": {"file_path": "xarray/backends/scipy_.py", "file_name": "scipy_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 26, "end_line": 56, "span_ids": ["ScipyArrayWrapper.get_variable", "ScipyArrayWrapper", "ScipyArrayWrapper.__getitem__", "ScipyArrayWrapper.__setitem__"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScipyArrayWrapper(BackendArray):\n def __init__(self, variable_name, datastore):\n self.datastore = datastore\n self.variable_name = variable_name\n array = self.get_variable().data\n self.shape = array.shape\n self.dtype = np.dtype(array.dtype.kind + str(array.dtype.itemsize))\n\n def get_variable(self, needs_lock=True):\n ds = self.datastore._manager.acquire(needs_lock)\n return ds.variables[self.variable_name]\n\n def __getitem__(self, key):\n data = NumpyIndexingAdapter(self.get_variable().data)[key]\n # Copy data if the source file is mmapped. This makes things consistent\n # with the netCDF4 library by ensuring we can safely read arrays even\n # after closing associated files.\n copy = self.datastore.ds.use_mmap\n return np.array(data, dtype=self.dtype, copy=copy)\n\n def __setitem__(self, key, value):\n with self.datastore.lock:\n data = self.get_variable(needs_lock=False)\n try:\n data[key] = value\n except TypeError:\n if key is Ellipsis:\n # workaround for GH: scipy/scipy#6880\n data[:] = value\n else:\n raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py__open_scipy_netcdf__open_scipy_netcdf.try_.except_TypeError_as_e_.if_is_not_a_valid_NetCDF.else_.raise": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py__open_scipy_netcdf__open_scipy_netcdf.try_.except_TypeError_as_e_.if_is_not_a_valid_NetCDF.else_.raise", "embedding": null, "metadata": {"file_path": "xarray/backends/scipy_.py", "file_name": "scipy_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 96, "span_ids": ["_open_scipy_netcdf"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _open_scipy_netcdf(filename, mode, mmap, version):\n import scipy.io\n import gzip\n\n # if the string ends with .gz, then gunzip and open as netcdf file\n if isinstance(filename, str) and filename.endswith(\".gz\"):\n try:\n return scipy.io.netcdf_file(\n gzip.open(filename), mode=mode, mmap=mmap, version=version\n )\n except TypeError as e:\n # TODO: gzipped loading only works with NetCDF3 files.\n if \"is not a valid NetCDF 3 file\" in e.message:\n raise ValueError(\n \"gzipped file loading only supports \" \"NetCDF 3 files.\"\n )\n else:\n raise\n\n if isinstance(filename, bytes) and filename.startswith(b\"CDF\"):\n # it's a NetCDF3 bytestring\n filename = BytesIO(filename)\n\n try:\n return scipy.io.netcdf_file(filename, mode=mode, mmap=mmap, version=version)\n except TypeError as e: # netcdf3 message is obscure in this case\n errmsg = e.args[0]\n if \"is not a valid NetCDF 3 file\" in errmsg:\n msg = \"\"\"\n If this is a NetCDF4 file, you may need to install the\n netcdf4 library, e.g.,\n\n $ pip install netcdf4\n \"\"\"\n errmsg += msg\n raise TypeError(errmsg)\n else:\n raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyDataStore_ScipyDataStore.encode_variable.return.variable": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyDataStore_ScipyDataStore.encode_variable.return.variable", "embedding": null, "metadata": {"file_path": "xarray/backends/scipy_.py", "file_name": "scipy_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 99, "end_line": 192, "span_ids": ["ScipyDataStore.get_dimensions", "ScipyDataStore.get_variables", "ScipyDataStore.ds", "ScipyDataStore.get_attrs", "ScipyDataStore.open_store_variable", "ScipyDataStore", "ScipyDataStore.set_dimension", "ScipyDataStore.get_encoding", "ScipyDataStore.encode_variable", "ScipyDataStore.set_attribute", "ScipyDataStore._validate_attr_key"], "tokens": 643}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScipyDataStore(WritableCFDataStore):\n \"\"\"Store for reading and writing data via scipy.io.netcdf.\n\n This store has the advantage of being able to be initialized with a\n StringIO object, allow for serialization without writing to disk.\n\n It only supports the NetCDF3 file-format.\n \"\"\"\n\n def __init__(\n self, filename_or_obj, mode=\"r\", format=None, group=None, mmap=None, lock=None\n ):\n if group is not None:\n raise ValueError(\n \"cannot save to a group with the \" \"scipy.io.netcdf backend\"\n )\n\n if format is None or format == \"NETCDF3_64BIT\":\n version = 2\n elif format == \"NETCDF3_CLASSIC\":\n version = 1\n else:\n raise ValueError(\"invalid format for scipy.io.netcdf backend: %r\" % format)\n\n if lock is None and mode != \"r\" and isinstance(filename_or_obj, str):\n lock = get_write_lock(filename_or_obj)\n\n self.lock = ensure_lock(lock)\n\n if isinstance(filename_or_obj, str):\n manager = CachingFileManager(\n _open_scipy_netcdf,\n filename_or_obj,\n mode=mode,\n lock=lock,\n kwargs=dict(mmap=mmap, version=version),\n )\n else:\n scipy_dataset = _open_scipy_netcdf(\n filename_or_obj, mode=mode, mmap=mmap, version=version\n )\n manager = DummyFileManager(scipy_dataset)\n\n self._manager = manager\n\n @property\n def ds(self):\n return self._manager.acquire()\n\n def open_store_variable(self, name, var):\n return Variable(\n var.dimensions,\n ScipyArrayWrapper(name, self),\n _decode_attrs(var._attributes),\n )\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n )\n\n def get_attrs(self):\n return Frozen(_decode_attrs(self.ds._attributes))\n\n def get_dimensions(self):\n return Frozen(self.ds.dimensions)\n\n def get_encoding(self):\n encoding = {}\n encoding[\"unlimited_dims\"] = {\n k for k, v in self.ds.dimensions.items() if v is None\n }\n return encoding\n\n def set_dimension(self, name, length, is_unlimited=False):\n if name in self.ds.dimensions:\n raise ValueError(\n \"%s does not support modifying dimensions\" % type(self).__name__\n )\n dim_length = length if not is_unlimited else None\n self.ds.createDimension(name, dim_length)\n\n def _validate_attr_key(self, key):\n if not is_valid_nc3_name(key):\n raise ValueError(\"Not a valid attribute name\")\n\n def set_attribute(self, key, value):\n self._validate_attr_key(key)\n value = encode_nc3_attr_value(value)\n setattr(self.ds, key, value)\n\n def encode_variable(self, variable):\n variable = encode_nc3_variable(variable)\n return variable", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyDataStore.prepare_variable_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/scipy_.py_ScipyDataStore.prepare_variable_", "embedding": null, "metadata": {"file_path": "xarray/backends/scipy_.py", "file_name": "scipy_.py", "file_type": "text/x-python", "category": "implementation", "start_line": 194, "end_line": 224, "span_ids": ["ScipyDataStore.close", "ScipyDataStore.sync", "ScipyDataStore.prepare_variable"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScipyDataStore(WritableCFDataStore):\n\n def prepare_variable(\n self, name, variable, check_encoding=False, unlimited_dims=None\n ):\n if check_encoding and variable.encoding:\n if variable.encoding != {\"_FillValue\": None}:\n raise ValueError(\n \"unexpected encoding for scipy backend: %r\"\n % list(variable.encoding)\n )\n\n data = variable.data\n # nb. this still creates a numpy array in all memory, even though we\n # don't write the data yet; scipy.io.netcdf does not not support\n # incremental writes.\n if name not in self.ds.variables:\n self.ds.createVariable(name, data.dtype, variable.dims)\n scipy_var = self.ds.variables[name]\n for k, v in variable.attrs.items():\n self._validate_attr_key(k)\n setattr(scipy_var, k, v)\n\n target = ScipyArrayWrapper(name, self)\n\n return target, data\n\n def sync(self):\n self.ds.sync()\n\n def close(self):\n self._manager.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_warnings__encode_zarr_attr_value.return.encoded": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_warnings__encode_zarr_attr_value.return.encoded", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 26, "span_ids": ["impl", "_encode_zarr_attr_value", "imports"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\n\nimport numpy as np\n\nfrom .. import Variable, coding, conventions\nfrom ..core import indexing\nfrom ..core.pycompat import integer_types\nfrom ..core.utils import FrozenDict, HiddenKeyDict\nfrom .common import AbstractWritableDataStore, BackendArray, _encode_variable_name\n\n# need some special secret attributes to tell us the dimensions\n_DIMENSION_KEY = \"_ARRAY_DIMENSIONS\"\n\n\n# zarr attributes have to be serializable as json\n# many xarray datasets / variables have numpy arrays and values\n# these functions handle encoding / decoding of such items\ndef _encode_zarr_attr_value(value):\n if isinstance(value, np.ndarray):\n encoded = value.tolist()\n # this checks if it's a scalar number\n elif isinstance(value, np.generic):\n encoded = value.item()\n else:\n encoded = value\n return encoded", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrArrayWrapper_ZarrArrayWrapper.__getitem__._could_possibly_have_a_w": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrArrayWrapper_ZarrArrayWrapper.__getitem__._could_possibly_have_a_w", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 29, "end_line": 57, "span_ids": ["ZarrArrayWrapper", "ZarrArrayWrapper.get_array", "ZarrArrayWrapper.__getitem__"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrArrayWrapper(BackendArray):\n __slots__ = (\"datastore\", \"dtype\", \"shape\", \"variable_name\")\n\n def __init__(self, variable_name, datastore):\n self.datastore = datastore\n self.variable_name = variable_name\n\n array = self.get_array()\n self.shape = array.shape\n\n dtype = array.dtype\n self.dtype = dtype\n\n def get_array(self):\n return self.datastore.ds[self.variable_name]\n\n def __getitem__(self, key):\n array = self.get_array()\n if isinstance(key, indexing.BasicIndexer):\n return array[key.tuple]\n elif isinstance(key, indexing.VectorizedIndexer):\n return array.vindex[\n indexing._arrayize_vectorized_indexer(key.tuple, self.shape).tuple\n ]\n else:\n assert isinstance(key, indexing.OuterIndexer)\n return array.oindex[key.tuple]\n # if self.ndim == 0:\n # could possibly have a work-around for 0d data here", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__determine_zarr_chunks__determine_zarr_chunks._threads": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__determine_zarr_chunks__determine_zarr_chunks._threads", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 131, "span_ids": ["_determine_zarr_chunks"], "tokens": 749}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _determine_zarr_chunks(enc_chunks, var_chunks, ndim):\n \"\"\"\n Given encoding chunks (possibly None) and variable chunks (possibly None)\n \"\"\"\n\n # zarr chunk spec:\n # chunks : int or tuple of ints, optional\n # Chunk shape. If not provided, will be guessed from shape and dtype.\n\n # if there are no chunks in encoding and the variable data is a numpy\n # array, then we let zarr use its own heuristics to pick the chunks\n if var_chunks is None and enc_chunks is None:\n return None\n\n # if there are no chunks in encoding but there are dask chunks, we try to\n # use the same chunks in zarr\n # However, zarr chunks needs to be uniform for each array\n # http://zarr.readthedocs.io/en/latest/spec/v1.html#chunks\n # while dask chunks can be variable sized\n # http://dask.pydata.org/en/latest/array-design.html#chunks\n if var_chunks and enc_chunks is None:\n if any(len(set(chunks[:-1])) > 1 for chunks in var_chunks):\n raise ValueError(\n \"Zarr requires uniform chunk sizes except for final chunk.\"\n \" Variable dask chunks %r are incompatible. Consider \"\n \"rechunking using `chunk()`.\" % (var_chunks,)\n )\n if any((chunks[0] < chunks[-1]) for chunks in var_chunks):\n raise ValueError(\n \"Final chunk of Zarr array must be the same size or smaller \"\n \"than the first. Variable Dask chunks %r are incompatible. \"\n \"Consider rechunking using `chunk()`.\" % var_chunks\n )\n # return the first chunk for each dimension\n return tuple(chunk[0] for chunk in var_chunks)\n\n # from here on, we are dealing with user-specified chunks in encoding\n # zarr allows chunks to be an integer, in which case it uses the same chunk\n # size on each dimension.\n # Here we re-implement this expansion ourselves. That makes the logic of\n # checking chunk compatibility easier\n\n if isinstance(enc_chunks, integer_types):\n enc_chunks_tuple = ndim * (enc_chunks,)\n else:\n enc_chunks_tuple = tuple(enc_chunks)\n\n if len(enc_chunks_tuple) != ndim:\n # throw away encoding chunks, start over\n return _determine_zarr_chunks(None, var_chunks, ndim)\n\n for x in enc_chunks_tuple:\n if not isinstance(x, int):\n raise TypeError(\n \"zarr chunks must be an int or a tuple of ints. \"\n \"Instead found %r\" % (enc_chunks_tuple,)\n )\n\n # if there are chunks in encoding and the variable data is a numpy array,\n # we use the specified chunks\n if var_chunks is None:\n return enc_chunks_tuple\n\n # the hard case\n # DESIGN CHOICE: do not allow multiple dask chunks on a single zarr chunk\n # this avoids the need to get involved in zarr synchronization / locking\n # From zarr docs:\n # \"If each worker in a parallel computation is writing to a separate\n # region of the array, and if region boundaries are perfectly aligned\n # with chunk boundaries, then no synchronization is required.\"\n # TODO: incorporate synchronizer to allow writes from multiple dask\n # threads\n if var_chunks and enc_chunks_tuple:\n # ... other code\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__determine_zarr_chunks.None_6__determine_zarr_chunks.raise_AssertionError_We_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__determine_zarr_chunks.None_6__determine_zarr_chunks.raise_AssertionError_We_", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 153, "span_ids": ["_determine_zarr_chunks"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _determine_zarr_chunks(enc_chunks, var_chunks, ndim):\n\n # if there are no chunks in encoding but there are dask chunks, we try to\n # ... other code\n if var_chunks and enc_chunks_tuple:\n for zchunk, dchunks in zip(enc_chunks_tuple, var_chunks):\n for dchunk in dchunks[:-1]:\n if dchunk % zchunk:\n raise NotImplementedError(\n \"Specified zarr chunks %r would overlap multiple dask \"\n \"chunks %r. This is not implemented in xarray yet. \"\n \" Consider rechunking the data using \"\n \"`chunk()` or specifying different chunks in encoding.\"\n % (enc_chunks_tuple, var_chunks)\n )\n if dchunks[-1] > zchunk:\n raise ValueError(\n \"Final chunk of Zarr array must be the same size or \"\n \"smaller than the first. The specified Zarr chunk \"\n \"encoding is %r, but %r in variable Dask chunks %r is \"\n \"incompatible. Consider rechunking using `chunk()`.\"\n % (enc_chunks_tuple, dchunks, var_chunks)\n )\n return enc_chunks_tuple\n\n raise AssertionError(\"We should never get here. Function logic must be wrong.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__get_zarr_dims_and_attrs__get_zarr_dims_and_attrs.return.dimensions_attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__get_zarr_dims_and_attrs__get_zarr_dims_and_attrs.return.dimensions_attributes", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 169, "span_ids": ["_get_zarr_dims_and_attrs"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_zarr_dims_and_attrs(zarr_obj, dimension_key):\n # Zarr arrays do not have dimenions. To get around this problem, we add\n # an attribute that specifies the dimension. We have to hide this attribute\n # when we send the attributes to the user.\n # zarr_obj can be either a zarr group or zarr array\n try:\n dimensions = zarr_obj.attrs[dimension_key]\n except KeyError:\n raise KeyError(\n \"Zarr object is missing the attribute `%s`, which is \"\n \"required for xarray to determine variable dimensions.\" % (dimension_key)\n )\n attributes = HiddenKeyDict(zarr_obj.attrs, [dimension_key])\n return dimensions, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__extract_zarr_variable_encoding__extract_zarr_variable_encoding.return.encoding": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__extract_zarr_variable_encoding__extract_zarr_variable_encoding.return.encoding", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 172, "end_line": 192, "span_ids": ["_extract_zarr_variable_encoding"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _extract_zarr_variable_encoding(variable, raise_on_invalid=False):\n encoding = variable.encoding.copy()\n\n valid_encodings = {\"chunks\", \"compressor\", \"filters\", \"cache_metadata\"}\n\n if raise_on_invalid:\n invalid = [k for k in encoding if k not in valid_encodings]\n if invalid:\n raise ValueError(\n \"unexpected encoding parameters for zarr \" \"backend: %r\" % invalid\n )\n else:\n for k in list(encoding):\n if k not in valid_encodings:\n del encoding[k]\n\n chunks = _determine_zarr_chunks(\n encoding.get(\"chunks\"), variable.chunks, variable.ndim\n )\n encoding[\"chunks\"] = chunks\n return encoding", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__Function_below_is_copie_encode_zarr_variable.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py__Function_below_is_copie_encode_zarr_variable.return.var", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 195, "end_line": 227, "span_ids": ["encode_zarr_variable", "_extract_zarr_variable_encoding"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Function below is copied from conventions.encode_cf_variable.\n# The only change is to raise an error for object dtypes.\ndef encode_zarr_variable(var, needs_copy=True, name=None):\n \"\"\"\n Converts an Variable into an Variable which follows some\n of the CF conventions:\n\n - Nans are masked using _FillValue (or the deprecated missing_value)\n - Rescaling via: scale_factor and add_offset\n - datetimes are converted to the CF 'units since time' format\n - dtype encodings are enforced.\n\n Parameters\n ----------\n var : xarray.Variable\n A variable holding un-encoded data.\n\n Returns\n -------\n out : xarray.Variable\n A variable which has been encoded as described above.\n \"\"\"\n\n var = conventions.encode_cf_variable(var, name=name)\n\n # zarr allows unicode, but not variable-length strings, so it's both\n # simpler and more compact to always encode as UTF-8 explicitly.\n # TODO: allow toggling this explicitly via dtype in encoding.\n coder = coding.strings.EncodedStringCoder(allows_unicode=True)\n var = coder.encode(var, name=name)\n var = coding.strings.ensure_fixed_length_bytes(var)\n\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore_ZarrStore.open_group.return.cls_zarr_group_consolida": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore_ZarrStore.open_group.return.cls_zarr_group_consolida", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 230, "end_line": 261, "span_ids": ["ZarrStore", "ZarrStore.open_group"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrStore(AbstractWritableDataStore):\n \"\"\"Store for reading and writing data via zarr\n \"\"\"\n\n __slots__ = (\n \"append_dim\",\n \"ds\",\n \"_consolidate_on_close\",\n \"_group\",\n \"_read_only\",\n \"_synchronizer\",\n )\n\n @classmethod\n def open_group(\n cls,\n store,\n mode=\"r\",\n synchronizer=None,\n group=None,\n consolidated=False,\n consolidate_on_close=False,\n ):\n import zarr\n\n open_kwargs = dict(mode=mode, synchronizer=synchronizer, path=group)\n if consolidated:\n # TODO: an option to pass the metadata_key keyword\n zarr_group = zarr.open_consolidated(store, **open_kwargs)\n else:\n zarr_group = zarr.open_group(store, **open_kwargs)\n return cls(zarr_group, consolidate_on_close)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.__init___ZarrStore.open_store_variable.return.Variable_dimensions_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.__init___ZarrStore.open_store_variable.return.Variable_dimensions_data", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 263, "end_line": 285, "span_ids": ["ZarrStore.__init__", "ZarrStore.open_store_variable"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrStore(AbstractWritableDataStore):\n\n def __init__(self, zarr_group, consolidate_on_close=False):\n self.ds = zarr_group\n self._read_only = self.ds.read_only\n self._synchronizer = self.ds.synchronizer\n self._group = self.ds.path\n self._consolidate_on_close = consolidate_on_close\n self.append_dim = None\n\n def open_store_variable(self, name, zarr_array):\n data = indexing.LazilyOuterIndexedArray(ZarrArrayWrapper(name, self))\n dimensions, attributes = _get_zarr_dims_and_attrs(zarr_array, _DIMENSION_KEY)\n attributes = dict(attributes)\n encoding = {\n \"chunks\": zarr_array.chunks,\n \"compressor\": zarr_array.compressor,\n \"filters\": zarr_array.filters,\n }\n # _FillValue needs to be in attributes, not encoding, so it will get\n # picked up by decode_cf\n if getattr(zarr_array, \"fill_value\") is not None:\n attributes[\"_FillValue\"] = zarr_array.fill_value\n\n return Variable(dimensions, data, attributes, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.get_variables_ZarrStore.encode_attribute.return._encode_zarr_attr_value_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.get_variables_ZarrStore.encode_attribute.return._encode_zarr_attr_value_a", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 287, "end_line": 330, "span_ids": ["ZarrStore.get_dimensions", "ZarrStore.encode_variable", "ZarrStore.get_variables", "ZarrStore.get_attrs", "ZarrStore.set_dimensions", "ZarrStore.encode_attribute", "ZarrStore.set_attributes"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrStore(AbstractWritableDataStore):\n\n def get_variables(self):\n return FrozenDict(\n (k, self.open_store_variable(k, v)) for k, v in self.ds.arrays()\n )\n\n def get_attrs(self):\n attributes = dict(self.ds.attrs.asdict())\n return attributes\n\n def get_dimensions(self):\n dimensions = {}\n for k, v in self.ds.arrays():\n try:\n for d, s in zip(v.attrs[_DIMENSION_KEY], v.shape):\n if d in dimensions and dimensions[d] != s:\n raise ValueError(\n \"found conflicting lengths for dimension %s \"\n \"(%d != %d)\" % (d, s, dimensions[d])\n )\n dimensions[d] = s\n\n except KeyError:\n raise KeyError(\n \"Zarr object is missing the attribute `%s`, \"\n \"which is required for xarray to determine \"\n \"variable dimensions.\" % (_DIMENSION_KEY)\n )\n return dimensions\n\n def set_dimensions(self, variables, unlimited_dims=None):\n if unlimited_dims is not None:\n raise NotImplementedError(\n \"Zarr backend doesn't know how to handle unlimited dimensions\"\n )\n\n def set_attributes(self, attributes):\n self.ds.attrs.put(attributes)\n\n def encode_variable(self, variable):\n variable = encode_zarr_variable(variable)\n return variable\n\n def encode_attribute(self, a):\n return _encode_zarr_attr_value(a)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.store_ZarrStore.sync.pass": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.store_ZarrStore.sync.pass", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 332, "end_line": 390, "span_ids": ["ZarrStore.sync", "ZarrStore.store"], "tokens": 429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrStore(AbstractWritableDataStore):\n\n def store(\n self,\n variables,\n attributes,\n check_encoding_set=frozenset(),\n writer=None,\n unlimited_dims=None,\n ):\n \"\"\"\n Top level method for putting data on this store, this method:\n - encodes variables/attributes\n - sets dimensions\n - sets variables\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n attributes : dict-like\n Dictionary of key/value (attribute name / attribute) pairs\n check_encoding_set : list-like\n List of variables that should be checked for invalid encoding\n values\n writer : ArrayWriter\n unlimited_dims : list-like\n List of dimension names that should be treated as unlimited\n dimensions.\n dimension on which the zarray will be appended\n only needed in append mode\n \"\"\"\n\n existing_variables = {\n vn for vn in variables if _encode_variable_name(vn) in self.ds\n }\n new_variables = set(variables) - existing_variables\n variables_without_encoding = {vn: variables[vn] for vn in new_variables}\n variables_encoded, attributes = self.encode(\n variables_without_encoding, attributes\n )\n\n if len(existing_variables) > 0:\n # there are variables to append\n # their encoding must be the same as in the store\n ds = open_zarr(self.ds.store, chunks=None)\n variables_with_encoding = {}\n for vn in existing_variables:\n variables_with_encoding[vn] = variables[vn].copy(deep=False)\n variables_with_encoding[vn].encoding = ds[vn].encoding\n variables_with_encoding, _ = self.encode(variables_with_encoding, {})\n variables_encoded.update(variables_with_encoding)\n\n self.set_attributes(attributes)\n self.set_dimensions(variables_encoded, unlimited_dims=unlimited_dims)\n self.set_variables(\n variables_encoded, check_encoding_set, writer, unlimited_dims=unlimited_dims\n )\n\n def sync(self):\n pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.set_variables_ZarrStore.close.if_self__consolidate_on_c.zarr_consolidate_metadata": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_ZarrStore.set_variables_ZarrStore.close.if_self__consolidate_on_c.zarr_consolidate_metadata", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 392, "end_line": 454, "span_ids": ["ZarrStore.close", "ZarrStore.set_variables"], "tokens": 517}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZarrStore(AbstractWritableDataStore):\n\n def set_variables(self, variables, check_encoding_set, writer, unlimited_dims=None):\n \"\"\"\n This provides a centralized method to set the variables on the data\n store.\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of key/value (variable name / xr.Variable) pairs\n check_encoding_set : list-like\n List of variables that should be checked for invalid encoding\n values\n writer :\n unlimited_dims : list-like\n List of dimension names that should be treated as unlimited\n dimensions.\n \"\"\"\n\n for vn, v in variables.items():\n name = _encode_variable_name(vn)\n check = vn in check_encoding_set\n attrs = v.attrs.copy()\n dims = v.dims\n dtype = v.dtype\n shape = v.shape\n\n fill_value = attrs.pop(\"_FillValue\", None)\n if v.encoding == {\"_FillValue\": None} and fill_value is None:\n v.encoding = {}\n if name in self.ds:\n zarr_array = self.ds[name]\n if self.append_dim in dims:\n # this is the DataArray that has append_dim as a\n # dimension\n append_axis = dims.index(self.append_dim)\n new_shape = list(zarr_array.shape)\n new_shape[append_axis] += v.shape[append_axis]\n new_region = [slice(None)] * len(new_shape)\n new_region[append_axis] = slice(zarr_array.shape[append_axis], None)\n zarr_array.resize(new_shape)\n writer.add(v.data, zarr_array, region=tuple(new_region))\n else:\n # new variable\n encoding = _extract_zarr_variable_encoding(v, raise_on_invalid=check)\n encoded_attrs = {}\n # the magic for storing the hidden dimension data\n encoded_attrs[_DIMENSION_KEY] = dims\n for k2, v2 in attrs.items():\n encoded_attrs[k2] = self.encode_attribute(v2)\n\n if coding.strings.check_vlen_dtype(dtype) == str:\n dtype = str\n zarr_array = self.ds.create(\n name, shape=shape, dtype=dtype, fill_value=fill_value, **encoding\n )\n zarr_array.attrs.put(encoded_attrs)\n writer.add(v.data, zarr_array)\n\n def close(self):\n if self._consolidate_on_close:\n import zarr\n\n zarr.consolidate_metadata(self.ds.store)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr_open_zarr._Load_and_decode_a_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr_open_zarr._Load_and_decode_a_data", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 457, "end_line": 542, "span_ids": ["open_zarr"], "tokens": 763}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_zarr(\n store,\n group=None,\n synchronizer=None,\n chunks=\"auto\",\n decode_cf=True,\n mask_and_scale=True,\n decode_times=True,\n concat_characters=True,\n decode_coords=True,\n drop_variables=None,\n consolidated=False,\n overwrite_encoded_chunks=False,\n **kwargs\n):\n \"\"\"Load and decode a dataset from a Zarr store.\n\n .. note:: Experimental\n The Zarr backend is new and experimental. Please report any\n unexpected behavior via github issues.\n\n The `store` object should be a valid store for a Zarr group. `store`\n variables must contain dimension metadata encoded in the\n `_ARRAY_DIMENSIONS` attribute.\n\n Parameters\n ----------\n store : MutableMapping or str\n A MutableMapping where a Zarr Group has been stored or a path to a\n directory in file system where a Zarr DirectoryStore has been stored.\n synchronizer : object, optional\n Array synchronizer provided to zarr\n group : str, obtional\n Group path. (a.k.a. `path` in zarr terminology.)\n chunks : int or dict or tuple or {None, 'auto'}, optional\n Chunk sizes along each dimension, e.g., ``5`` or\n ``{'x': 5, 'y': 5}``. If `chunks='auto'`, dask chunks are created\n based on the variable's zarr chunks. If `chunks=None`, zarr array\n data will lazily convert to numpy arrays upon access. This accepts\n all the chunk specifications as Dask does.\n overwrite_encoded_chunks: bool, optional\n Whether to drop the zarr chunks encoded for each variable when a\n dataset is loaded with specified chunk sizes (default: False)\n decode_cf : bool, optional\n Whether to decode these variables, assuming they were saved according\n to CF conventions.\n mask_and_scale : bool, optional\n If True, replace array values equal to `_FillValue` with NA and scale\n values according to the formula `original_values * scale_factor +\n add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are\n taken from variable attributes (if they exist). If the `_FillValue` or\n `missing_value` attribute contains multiple values a warning will be\n issued and all array values matching one of the multiple values will\n be replaced by NA.\n decode_times : bool, optional\n If True, decode times encoded in the standard NetCDF datetime format\n into datetime objects. Otherwise, leave them encoded as numbers.\n concat_characters : bool, optional\n If True, concatenate along the last dimension of character arrays to\n form string arrays. Dimensions will only be concatenated over (and\n removed) if they have no corresponding variable and if they are only\n used as the last dimension of character arrays.\n decode_coords : bool, optional\n If True, decode the 'coordinates' attribute to identify coordinates in\n the resulting dataset.\n drop_variables : string or iterable, optional\n A variable or list of variables to exclude from being parsed from the\n dataset. This may be useful to drop variables with problems or\n inconsistent values.\n consolidated : bool, optional\n Whether to open the store using zarr's consolidated metadata\n capability. Only works for stores that have already been consolidated.\n\n Returns\n -------\n dataset : Dataset\n The newly created dataset.\n\n See Also\n --------\n open_dataset\n\n References\n ----------\n http://zarr.readthedocs.io/\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.if_auto_chunk_in_kwargs_open_zarr.if_isinstance_chunks_tup.chunks.dict_zip_ds_dims_chunks_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.if_auto_chunk_in_kwargs_open_zarr.if_isinstance_chunks_tup.chunks.dict_zip_ds_dims_chunks_", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 543, "end_line": 611, "span_ids": ["open_zarr"], "tokens": 536}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_zarr(\n store,\n group=None,\n synchronizer=None,\n chunks=\"auto\",\n decode_cf=True,\n mask_and_scale=True,\n decode_times=True,\n concat_characters=True,\n decode_coords=True,\n drop_variables=None,\n consolidated=False,\n overwrite_encoded_chunks=False,\n **kwargs\n):\n if \"auto_chunk\" in kwargs:\n auto_chunk = kwargs.pop(\"auto_chunk\")\n if auto_chunk:\n chunks = \"auto\" # maintain backwards compatibility\n else:\n chunks = None\n\n warnings.warn(\n \"auto_chunk is deprecated. Use chunks='auto' instead.\",\n FutureWarning,\n stacklevel=2,\n )\n\n if kwargs:\n raise TypeError(\n \"open_zarr() got unexpected keyword arguments \" + \",\".join(kwargs.keys())\n )\n\n if not isinstance(chunks, (int, dict)):\n if chunks != \"auto\" and chunks is not None:\n raise ValueError(\n \"chunks must be an int, dict, 'auto', or None. \"\n \"Instead found %s. \" % chunks\n )\n\n if not decode_cf:\n mask_and_scale = False\n decode_times = False\n concat_characters = False\n decode_coords = False\n\n def maybe_decode_store(store, lock=False):\n ds = conventions.decode_cf(\n store,\n mask_and_scale=mask_and_scale,\n decode_times=decode_times,\n concat_characters=concat_characters,\n decode_coords=decode_coords,\n drop_variables=drop_variables,\n )\n\n # TODO: this is where we would apply caching\n\n return ds\n\n # Zarr supports a wide range of access modes, but for now xarray either\n # reads or writes from a store, never both. For open_zarr, we only read\n mode = \"r\"\n zarr_store = ZarrStore.open_group(\n store,\n mode=mode,\n synchronizer=synchronizer,\n group=group,\n consolidated=consolidated,\n )\n ds = maybe_decode_store(zarr_store)\n\n # auto chunking needs to be here and not in ZarrStore because variable\n # chunks do not survive decode_cf\n # return trivial case\n if not chunks:\n return ds\n\n # adapted from Dataset.Chunk()\n if isinstance(chunks, int):\n chunks = dict.fromkeys(ds.dims, chunks)\n\n if isinstance(chunks, tuple) and len(chunks) == len(ds.dims):\n chunks = dict(zip(ds.dims, chunks))\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.get_chunk_open_zarr.get_chunk.return.chunk_spec": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.get_chunk_open_zarr.get_chunk.return.chunk_spec", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 613, "end_line": 640, "span_ids": ["open_zarr"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_zarr(\n store,\n group=None,\n synchronizer=None,\n chunks=\"auto\",\n decode_cf=True,\n mask_and_scale=True,\n decode_times=True,\n concat_characters=True,\n decode_coords=True,\n drop_variables=None,\n consolidated=False,\n overwrite_encoded_chunks=False,\n **kwargs\n):\n # ... other code\n\n def get_chunk(name, var, chunks):\n chunk_spec = dict(zip(var.dims, var.encoding.get(\"chunks\")))\n\n # Coordinate labels aren't chunked\n if var.ndim == 1 and var.dims[0] == name:\n return chunk_spec\n\n if chunks == \"auto\":\n return chunk_spec\n\n for dim in var.dims:\n if dim in chunks:\n spec = chunks[dim]\n if isinstance(spec, int):\n spec = (spec,)\n if isinstance(spec, (tuple, list)) and chunk_spec[dim]:\n if any(s % chunk_spec[dim] for s in spec):\n warnings.warn(\n \"Specified Dask chunks %r would \"\n \"separate Zarr chunk shape %r for \"\n \"dimension %r. This significantly \"\n \"degrades performance. Consider \"\n \"rechunking after loading instead.\"\n % (chunks[dim], chunk_spec[dim], dim),\n stacklevel=2,\n )\n chunk_spec[dim] = chunks[dim]\n return chunk_spec\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.maybe_chunk_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/backends/zarr.py_open_zarr.maybe_chunk_", "embedding": null, "metadata": {"file_path": "xarray/backends/zarr.py", "file_name": "zarr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 642, "end_line": 660, "span_ids": ["open_zarr"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def open_zarr(\n store,\n group=None,\n synchronizer=None,\n chunks=\"auto\",\n decode_cf=True,\n mask_and_scale=True,\n decode_times=True,\n concat_characters=True,\n decode_coords=True,\n drop_variables=None,\n consolidated=False,\n overwrite_encoded_chunks=False,\n **kwargs\n):\n # ... other code\n\n def maybe_chunk(name, var, chunks):\n from dask.base import tokenize\n\n chunk_spec = get_chunk(name, var, chunks)\n\n if (var.ndim > 0) and (chunk_spec is not None):\n # does this cause any data to be read?\n token2 = tokenize(name, var._data)\n name2 = \"zarr-%s\" % token2\n var = var.chunk(chunk_spec, name=name2, lock=None)\n if overwrite_encoded_chunks and var.chunks is not None:\n var.encoding[\"chunks\"] = tuple(x[0] for x in var.chunks)\n return var\n else:\n return var\n\n variables = {k: maybe_chunk(k, v, chunks) for k, v in ds.variables.items()}\n return ds._replace_vars_and_dims(variables)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__Time_offset_classes_fo_format_cftime_datetime": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__Time_offset_classes_fo_format_cftime_datetime", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 52, "span_ids": ["docstring", "imports"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Time offset classes for use with cftime.datetime objects\"\"\"\n# The offset classes and mechanisms for generating time ranges defined in\n# this module were copied/adapted from those defined in pandas. See in\n# particular the objects and methods defined in pandas.tseries.offsets\n# and pandas.core.indexes.datetimes.\n\nimport re\nfrom datetime import timedelta\nfrom functools import partial\nfrom typing import ClassVar, Optional\n\nimport numpy as np\n\nfrom ..core.pdcompat import count_not_none\nfrom .cftimeindex import CFTimeIndex, _parse_iso8601_with_reso\nfrom .times import format_cftime_datetime", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_get_date_type_get_date_type.try_.else_.return.calendars_calendar_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_get_date_type_get_date_type.try_.else_.return.calendars_calendar_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 55, "end_line": 73, "span_ids": ["get_date_type"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_date_type(calendar):\n \"\"\"Return the cftime date type for a given calendar name.\"\"\"\n try:\n import cftime\n except ImportError:\n raise ImportError(\"cftime is required for dates with non-standard calendars\")\n else:\n calendars = {\n \"noleap\": cftime.DatetimeNoLeap,\n \"360_day\": cftime.Datetime360Day,\n \"365_day\": cftime.DatetimeNoLeap,\n \"366_day\": cftime.DatetimeAllLeap,\n \"gregorian\": cftime.DatetimeGregorian,\n \"proleptic_gregorian\": cftime.DatetimeProlepticGregorian,\n \"julian\": cftime.DatetimeJulian,\n \"all_leap\": cftime.DatetimeAllLeap,\n \"standard\": cftime.DatetimeGregorian,\n }\n return calendars[calendar]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_BaseCFTimeOffset_BaseCFTimeOffset._get_offset_day.return._get_day_of_month_other_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_BaseCFTimeOffset_BaseCFTimeOffset._get_offset_day.return._get_day_of_month_other_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 76, "end_line": 157, "span_ids": ["BaseCFTimeOffset._get_offset_day", "BaseCFTimeOffset.rollback", "BaseCFTimeOffset.__rmul__", "BaseCFTimeOffset.onOffset", "BaseCFTimeOffset.__str__", "BaseCFTimeOffset.__apply__", "BaseCFTimeOffset.__ne__", "BaseCFTimeOffset.__radd__", "BaseCFTimeOffset.__mul__", "BaseCFTimeOffset.__repr__", "BaseCFTimeOffset.rollforward", "BaseCFTimeOffset.__add__", "BaseCFTimeOffset.__neg__", "BaseCFTimeOffset.__sub__", "BaseCFTimeOffset.__rsub__", "BaseCFTimeOffset.__eq__", "BaseCFTimeOffset.rule_code", "BaseCFTimeOffset"], "tokens": 560}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BaseCFTimeOffset:\n _freq: ClassVar[Optional[str]] = None\n _day_option: ClassVar[Optional[str]] = None\n\n def __init__(self, n=1):\n if not isinstance(n, int):\n raise TypeError(\n \"The provided multiple 'n' must be an integer. \"\n \"Instead a value of type {!r} was provided.\".format(type(n))\n )\n self.n = n\n\n def rule_code(self):\n return self._freq\n\n def __eq__(self, other):\n return self.n == other.n and self.rule_code() == other.rule_code()\n\n def __ne__(self, other):\n return not self == other\n\n def __add__(self, other):\n return self.__apply__(other)\n\n def __sub__(self, other):\n import cftime\n\n if isinstance(other, cftime.datetime):\n raise TypeError(\"Cannot subtract a cftime.datetime \" \"from a time offset.\")\n elif type(other) == type(self):\n return type(self)(self.n - other.n)\n else:\n return NotImplemented\n\n def __mul__(self, other):\n return type(self)(n=other * self.n)\n\n def __neg__(self):\n return self * -1\n\n def __rmul__(self, other):\n return self.__mul__(other)\n\n def __radd__(self, other):\n return self.__add__(other)\n\n def __rsub__(self, other):\n if isinstance(other, BaseCFTimeOffset) and type(self) != type(other):\n raise TypeError(\"Cannot subtract cftime offsets of differing \" \"types\")\n return -self + other\n\n def __apply__(self):\n return NotImplemented\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n test_date = (self + date) - self\n return date == test_date\n\n def rollforward(self, date):\n if self.onOffset(date):\n return date\n else:\n return date + type(self)()\n\n def rollback(self, date):\n if self.onOffset(date):\n return date\n else:\n return date - type(self)()\n\n def __str__(self):\n return \"<{}: n={}>\".format(type(self).__name__, self.n)\n\n def __repr__(self):\n return str(self)\n\n def _get_offset_day(self, other):\n # subclass must implement `_day_option`; calling from the base class\n # will raise NotImplementedError.\n return _get_day_of_month(other, self._day_option)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__get_day_of_month__get_day_of_month.if_day_option_start_.else_.raise_ValueError_day_opti": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__get_day_of_month__get_day_of_month.if_day_option_start_.else_.raise_ValueError_day_opti", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 160, "end_line": 187, "span_ids": ["_get_day_of_month"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_day_of_month(other, day_option):\n \"\"\"Find the day in `other`'s month that satisfies a BaseCFTimeOffset's\n onOffset policy, as described by the `day_option` argument.\n\n Parameters\n ----------\n other : cftime.datetime\n day_option : 'start', 'end'\n 'start': returns 1\n 'end': returns last day of the month\n\n Returns\n -------\n day_of_month : int\n\n \"\"\"\n\n if day_option == \"start\":\n return 1\n elif day_option == \"end\":\n days_in_month = _days_in_month(other)\n return days_in_month\n elif day_option is None:\n # Note: unlike `_shift_month`, _get_day_of_month does not\n # allow day_option = None\n raise NotImplementedError()\n else:\n raise ValueError(day_option)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__days_in_month__adjust_n_years.return.n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__days_in_month__adjust_n_years.return.n", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 219, "span_ids": ["_adjust_n_years", "_adjust_n_months", "_days_in_month"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _days_in_month(date):\n \"\"\"The number of days in the month of the given date\"\"\"\n if date.month == 12:\n reference = type(date)(date.year + 1, 1, 1)\n else:\n reference = type(date)(date.year, date.month + 1, 1)\n return (reference - timedelta(days=1)).day\n\n\ndef _adjust_n_months(other_day, n, reference_day):\n \"\"\"Adjust the number of times a monthly offset is applied based\n on the day of a given date, and the reference day provided.\n \"\"\"\n if n > 0 and other_day < reference_day:\n n = n - 1\n elif n <= 0 and other_day > reference_day:\n n = n + 1\n return n\n\n\ndef _adjust_n_years(other, n, month, reference_day):\n \"\"\"Adjust the number of times an annual offset is applied based on\n another date, and the reference day provided\"\"\"\n if n > 0:\n if other.month < month or (other.month == month and other.day < reference_day):\n n -= 1\n else:\n if other.month > month or (other.month == month and other.day > reference_day):\n n += 1\n return n", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__shift_month__shift_month.return.date_replace_year_year_m": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__shift_month__shift_month.return.date_replace_year_year_m", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 222, "end_line": 244, "span_ids": ["_shift_month"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _shift_month(date, months, day_option=\"start\"):\n \"\"\"Shift the date to a month start or end a given number of months away.\n \"\"\"\n delta_year = (date.month + months) // 12\n month = (date.month + months) % 12\n\n if month == 0:\n month = 12\n delta_year = delta_year - 1\n year = date.year + delta_year\n\n if day_option == \"start\":\n day = 1\n elif day_option == \"end\":\n reference = type(date)(year, month, 1)\n day = _days_in_month(reference)\n else:\n raise ValueError(day_option)\n # dayofwk=-1 is required to update the dayofwk and dayofyr attributes of\n # the returned date object in versions of cftime between 1.0.2 and\n # 1.0.3.4. It can be removed for versions of cftime greater than\n # 1.0.3.4.\n return date.replace(year=year, month=month, day=day, dayofwk=-1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_roll_qtrday_roll_qtrday.return.n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_roll_qtrday_roll_qtrday.return.n", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 247, "end_line": 285, "span_ids": ["roll_qtrday"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def roll_qtrday(other, n, month, day_option, modby=3):\n \"\"\"Possibly increment or decrement the number of periods to shift\n based on rollforward/rollbackward conventions.\n\n Parameters\n ----------\n other : cftime.datetime\n n : number of periods to increment, before adjusting for rolling\n month : int reference month giving the first month of the year\n day_option : 'start', 'end'\n The convention to use in finding the day in a given month against\n which to compare for rollforward/rollbackward decisions.\n modby : int 3 for quarters, 12 for years\n\n Returns\n -------\n n : int number of periods to increment\n\n See Also\n --------\n _get_day_of_month : Find the day in a month provided an offset.\n \"\"\"\n\n months_since = other.month % modby - month % modby\n\n if n > 0:\n if months_since < 0 or (\n months_since == 0 and other.day < _get_day_of_month(other, day_option)\n ):\n # pretend to roll back if on same month but\n # before compare_day\n n -= 1\n else:\n if months_since > 0 or (\n months_since == 0 and other.day > _get_day_of_month(other, day_option)\n ):\n # make sure to roll forward, so negate\n n += 1\n return n", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__validate_month__validate_month.return.result_month": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__validate_month__validate_month.return.result_month", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 288, "end_line": 305, "span_ids": ["_validate_month"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _validate_month(month, default_month):\n if month is None:\n result_month = default_month\n else:\n result_month = month\n if not isinstance(result_month, int):\n raise TypeError(\n \"'self.month' must be an integer value between 1 \"\n \"and 12. Instead, it was set to a value of \"\n \"{!r}\".format(result_month)\n )\n elif not (1 <= result_month <= 12):\n raise ValueError(\n \"'self.month' must be an integer value between 1 \"\n \"and 12. Instead, it was set to a value of \"\n \"{!r}\".format(result_month)\n )\n return result_month", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_MonthBegin__MONTH_ABBREVIATIONS._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_MonthBegin__MONTH_ABBREVIATIONS._", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 308, "end_line": 347, "span_ids": ["impl", "MonthBegin.onOffset", "MonthEnd.__apply__", "MonthBegin.__apply__", "MonthBegin", "MonthEnd.onOffset", "MonthEnd"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MonthBegin(BaseCFTimeOffset):\n _freq = \"MS\"\n\n def __apply__(self, other):\n n = _adjust_n_months(other.day, self.n, 1)\n return _shift_month(other, n, \"start\")\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n return date.day == 1\n\n\nclass MonthEnd(BaseCFTimeOffset):\n _freq = \"M\"\n\n def __apply__(self, other):\n n = _adjust_n_months(other.day, self.n, _days_in_month(other))\n return _shift_month(other, n, \"end\")\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n return date.day == _days_in_month(date)\n\n\n_MONTH_ABBREVIATIONS = {\n 1: \"JAN\",\n 2: \"FEB\",\n 3: \"MAR\",\n 4: \"APR\",\n 5: \"MAY\",\n 6: \"JUN\",\n 7: \"JUL\",\n 8: \"AUG\",\n 9: \"SEP\",\n 10: \"OCT\",\n 11: \"NOV\",\n 12: \"DEC\",\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterOffset_QuarterOffset.__apply__.return._shift_month_other_month": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterOffset_QuarterOffset.__apply__.return._shift_month_other_month", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 350, "end_line": 372, "span_ids": ["QuarterOffset.__apply__", "QuarterOffset"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class QuarterOffset(BaseCFTimeOffset):\n \"\"\"Quarter representation copied off of pandas/tseries/offsets.py\n \"\"\"\n\n _freq: ClassVar[str]\n _default_month: ClassVar[int]\n\n def __init__(self, n=1, month=None):\n BaseCFTimeOffset.__init__(self, n)\n self.month = _validate_month(month, self._default_month)\n\n def __apply__(self, other):\n # months_since: find the calendar quarter containing other.month,\n # e.g. if other.month == 8, the calendar quarter is [Jul, Aug, Sep].\n # Then find the month in that quarter containing an onOffset date for\n # self. `months_since` is the number of months to shift other.month\n # to get to this on-offset month.\n months_since = other.month % 3 - self.month % 3\n qtrs = roll_qtrday(\n other, self.n, self.month, day_option=self._day_option, modby=3\n )\n months = qtrs * 3 - months_since\n return _shift_month(other, months, self._day_option)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterOffset.onOffset_QuarterOffset.__str__.return._n_month_fo": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterOffset.onOffset_QuarterOffset.__str__.return._n_month_fo", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 374, "end_line": 397, "span_ids": ["QuarterOffset.rule_code", "QuarterOffset.__str__", "QuarterOffset.__sub__", "QuarterOffset.onOffset", "QuarterOffset.__mul__"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class QuarterOffset(BaseCFTimeOffset):\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n mod_month = (date.month - self.month) % 3\n return mod_month == 0 and date.day == self._get_offset_day(date)\n\n def __sub__(self, other):\n import cftime\n\n if isinstance(other, cftime.datetime):\n raise TypeError(\"Cannot subtract cftime.datetime from offset.\")\n elif type(other) == type(self) and other.month == self.month:\n return type(self)(self.n - other.n, month=self.month)\n else:\n return NotImplemented\n\n def __mul__(self, other):\n return type(self)(n=other * self.n, month=self.month)\n\n def rule_code(self):\n return \"{}-{}\".format(self._freq, _MONTH_ABBREVIATIONS[self.month])\n\n def __str__(self):\n return \"<{}: n={}, month={}>\".format(type(self).__name__, self.n, self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterBegin_QuarterBegin.rollback.if_self_onOffset_date_.else_.return.date_QuarterBegin_month": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterBegin_QuarterBegin.rollback.if_self_onOffset_date_.else_.return.date_QuarterBegin_month", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 400, "end_line": 422, "span_ids": ["QuarterBegin.rollback", "QuarterBegin", "QuarterBegin.rollforward"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class QuarterBegin(QuarterOffset):\n # When converting a string to an offset, pandas converts\n # 'QS' to a QuarterBegin offset starting in the month of\n # January. When creating a QuarterBegin offset directly\n # from the constructor, however, the default month is March.\n # We follow that behavior here.\n _default_month = 3\n _freq = \"QS\"\n _day_option = \"start\"\n\n def rollforward(self, date):\n \"\"\"Roll date forward to nearest start of quarter\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date + QuarterBegin(month=self.month)\n\n def rollback(self, date):\n \"\"\"Roll date backward to nearest start of quarter\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date - QuarterBegin(month=self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterEnd_QuarterEnd.rollback.if_self_onOffset_date_.else_.return.date_QuarterEnd_month_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_QuarterEnd_QuarterEnd.rollback.if_self_onOffset_date_.else_.return.date_QuarterEnd_month_s", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 425, "end_line": 447, "span_ids": ["QuarterEnd.rollforward", "QuarterEnd.rollback", "QuarterEnd"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class QuarterEnd(QuarterOffset):\n # When converting a string to an offset, pandas converts\n # 'Q' to a QuarterEnd offset starting in the month of\n # December. When creating a QuarterEnd offset directly\n # from the constructor, however, the default month is March.\n # We follow that behavior here.\n _default_month = 3\n _freq = \"Q\"\n _day_option = \"end\"\n\n def rollforward(self, date):\n \"\"\"Roll date forward to nearest end of quarter\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date + QuarterEnd(month=self.month)\n\n def rollback(self, date):\n \"\"\"Roll date backward to nearest end of quarter\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date - QuarterEnd(month=self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearOffset_YearOffset.__str__.return._n_month_fo": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearOffset_YearOffset.__str__.return._n_month_fo", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 450, "end_line": 482, "span_ids": ["YearOffset.__apply__", "YearOffset.rule_code", "YearOffset.__mul__", "YearOffset.__str__", "YearOffset.__sub__", "YearOffset"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class YearOffset(BaseCFTimeOffset):\n _freq: ClassVar[str]\n _day_option: ClassVar[str]\n _default_month: ClassVar[int]\n\n def __init__(self, n=1, month=None):\n BaseCFTimeOffset.__init__(self, n)\n self.month = _validate_month(month, self._default_month)\n\n def __apply__(self, other):\n reference_day = _get_day_of_month(other, self._day_option)\n years = _adjust_n_years(other, self.n, self.month, reference_day)\n months = years * 12 + (self.month - other.month)\n return _shift_month(other, months, self._day_option)\n\n def __sub__(self, other):\n import cftime\n\n if isinstance(other, cftime.datetime):\n raise TypeError(\"Cannot subtract cftime.datetime from offset.\")\n elif type(other) == type(self) and other.month == self.month:\n return type(self)(self.n - other.n, month=self.month)\n else:\n return NotImplemented\n\n def __mul__(self, other):\n return type(self)(n=other * self.n, month=self.month)\n\n def rule_code(self):\n return \"{}-{}\".format(self._freq, _MONTH_ABBREVIATIONS[self.month])\n\n def __str__(self):\n return \"<{}: n={}, month={}>\".format(type(self).__name__, self.n, self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearBegin_YearBegin.rollback.if_self_onOffset_date_.else_.return.date_YearBegin_month_se": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearBegin_YearBegin.rollback.if_self_onOffset_date_.else_.return.date_YearBegin_month_se", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 485, "end_line": 507, "span_ids": ["YearBegin", "YearBegin.rollback", "YearBegin.onOffset", "YearBegin.rollforward"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class YearBegin(YearOffset):\n _freq = \"AS\"\n _day_option = \"start\"\n _default_month = 1\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n return date.day == 1 and date.month == self.month\n\n def rollforward(self, date):\n \"\"\"Roll date forward to nearest start of year\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date + YearBegin(month=self.month)\n\n def rollback(self, date):\n \"\"\"Roll date backward to nearest start of year\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date - YearBegin(month=self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearEnd_YearEnd.rollback.if_self_onOffset_date_.else_.return.date_YearEnd_month_self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_YearEnd_YearEnd.rollback.if_self_onOffset_date_.else_.return.date_YearEnd_month_self", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 510, "end_line": 532, "span_ids": ["YearEnd.onOffset", "YearEnd.rollback", "YearEnd.rollforward", "YearEnd"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class YearEnd(YearOffset):\n _freq = \"A\"\n _day_option = \"end\"\n _default_month = 12\n\n def onOffset(self, date):\n \"\"\"Check if the given date is in the set of possible dates created\n using a length-one version of this offset class.\"\"\"\n return date.day == _days_in_month(date) and date.month == self.month\n\n def rollforward(self, date):\n \"\"\"Roll date forward to nearest end of year\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date + YearEnd(month=self.month)\n\n def rollback(self, date):\n \"\"\"Roll date backward to nearest end of year\"\"\"\n if self.onOffset(date):\n return date\n else:\n return date - YearEnd(month=self.month)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_Day_Second.__apply__.return.other_self_as_timedelta": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_Day_Second.__apply__.return.other_self_as_timedelta", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 535, "end_line": 572, "span_ids": ["Hour", "Hour.as_timedelta", "Minute.as_timedelta", "Second", "Hour.__apply__", "Second.as_timedelta", "Minute", "Day", "Second.__apply__", "Day.__apply__", "Day.as_timedelta", "Minute.__apply__"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Day(BaseCFTimeOffset):\n _freq = \"D\"\n\n def as_timedelta(self):\n return timedelta(days=self.n)\n\n def __apply__(self, other):\n return other + self.as_timedelta()\n\n\nclass Hour(BaseCFTimeOffset):\n _freq = \"H\"\n\n def as_timedelta(self):\n return timedelta(hours=self.n)\n\n def __apply__(self, other):\n return other + self.as_timedelta()\n\n\nclass Minute(BaseCFTimeOffset):\n _freq = \"T\"\n\n def as_timedelta(self):\n return timedelta(minutes=self.n)\n\n def __apply__(self, other):\n return other + self.as_timedelta()\n\n\nclass Second(BaseCFTimeOffset):\n _freq = \"S\"\n\n def as_timedelta(self):\n return timedelta(seconds=self.n)\n\n def __apply__(self, other):\n return other + self.as_timedelta()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__FREQUENCIES__FREQUENCIES._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__FREQUENCIES__FREQUENCIES._", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 575, "end_line": 637, "span_ids": ["impl:3"], "tokens": 832}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_FREQUENCIES = {\n \"A\": YearEnd,\n \"AS\": YearBegin,\n \"Y\": YearEnd,\n \"YS\": YearBegin,\n \"Q\": partial(QuarterEnd, month=12),\n \"QS\": partial(QuarterBegin, month=1),\n \"M\": MonthEnd,\n \"MS\": MonthBegin,\n \"D\": Day,\n \"H\": Hour,\n \"T\": Minute,\n \"min\": Minute,\n \"S\": Second,\n \"AS-JAN\": partial(YearBegin, month=1),\n \"AS-FEB\": partial(YearBegin, month=2),\n \"AS-MAR\": partial(YearBegin, month=3),\n \"AS-APR\": partial(YearBegin, month=4),\n \"AS-MAY\": partial(YearBegin, month=5),\n \"AS-JUN\": partial(YearBegin, month=6),\n \"AS-JUL\": partial(YearBegin, month=7),\n \"AS-AUG\": partial(YearBegin, month=8),\n \"AS-SEP\": partial(YearBegin, month=9),\n \"AS-OCT\": partial(YearBegin, month=10),\n \"AS-NOV\": partial(YearBegin, month=11),\n \"AS-DEC\": partial(YearBegin, month=12),\n \"A-JAN\": partial(YearEnd, month=1),\n \"A-FEB\": partial(YearEnd, month=2),\n \"A-MAR\": partial(YearEnd, month=3),\n \"A-APR\": partial(YearEnd, month=4),\n \"A-MAY\": partial(YearEnd, month=5),\n \"A-JUN\": partial(YearEnd, month=6),\n \"A-JUL\": partial(YearEnd, month=7),\n \"A-AUG\": partial(YearEnd, month=8),\n \"A-SEP\": partial(YearEnd, month=9),\n \"A-OCT\": partial(YearEnd, month=10),\n \"A-NOV\": partial(YearEnd, month=11),\n \"A-DEC\": partial(YearEnd, month=12),\n \"QS-JAN\": partial(QuarterBegin, month=1),\n \"QS-FEB\": partial(QuarterBegin, month=2),\n \"QS-MAR\": partial(QuarterBegin, month=3),\n \"QS-APR\": partial(QuarterBegin, month=4),\n \"QS-MAY\": partial(QuarterBegin, month=5),\n \"QS-JUN\": partial(QuarterBegin, month=6),\n \"QS-JUL\": partial(QuarterBegin, month=7),\n \"QS-AUG\": partial(QuarterBegin, month=8),\n \"QS-SEP\": partial(QuarterBegin, month=9),\n \"QS-OCT\": partial(QuarterBegin, month=10),\n \"QS-NOV\": partial(QuarterBegin, month=11),\n \"QS-DEC\": partial(QuarterBegin, month=12),\n \"Q-JAN\": partial(QuarterEnd, month=1),\n \"Q-FEB\": partial(QuarterEnd, month=2),\n \"Q-MAR\": partial(QuarterEnd, month=3),\n \"Q-APR\": partial(QuarterEnd, month=4),\n \"Q-MAY\": partial(QuarterEnd, month=5),\n \"Q-JUN\": partial(QuarterEnd, month=6),\n \"Q-JUL\": partial(QuarterEnd, month=7),\n \"Q-AUG\": partial(QuarterEnd, month=8),\n \"Q-SEP\": partial(QuarterEnd, month=9),\n \"Q-OCT\": partial(QuarterEnd, month=10),\n \"Q-NOV\": partial(QuarterEnd, month=11),\n \"Q-DEC\": partial(QuarterEnd, month=12),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__FREQUENCY_CONDITION_to_offset.return._FREQUENCIES_freq_n_mult": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__FREQUENCY_CONDITION_to_offset.return._FREQUENCIES_freq_n_mult", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 640, "end_line": 667, "span_ids": ["to_offset", "impl:5"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_FREQUENCY_CONDITION = \"|\".join(_FREQUENCIES.keys())\n_PATTERN = r\"^((?P\\d+)|())(?P({}))$\".format(_FREQUENCY_CONDITION)\n\n\n# pandas defines these offsets as \"Tick\" objects, which for instance have\n# distinct behavior from monthly or longer frequencies in resample.\nCFTIME_TICKS = (Day, Hour, Minute, Second)\n\n\ndef to_offset(freq):\n \"\"\"Convert a frequency string to the appropriate subclass of\n BaseCFTimeOffset.\"\"\"\n if isinstance(freq, BaseCFTimeOffset):\n return freq\n else:\n try:\n freq_data = re.match(_PATTERN, freq).groupdict()\n except AttributeError:\n raise ValueError(\"Invalid frequency string provided\")\n\n freq = freq_data[\"freq\"]\n multiples = freq_data[\"multiple\"]\n if multiples is None:\n multiples = 1\n else:\n multiples = int(multiples)\n\n return _FREQUENCIES[freq](n=multiples)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_to_cftime_datetime_to_cftime_datetime.if_isinstance_date_str_or.else_.raise_TypeError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_to_cftime_datetime_to_cftime_datetime.if_isinstance_date_str_or.else_.raise_TypeError_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 670, "end_line": 688, "span_ids": ["to_cftime_datetime"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_cftime_datetime(date_str_or_date, calendar=None):\n import cftime\n\n if isinstance(date_str_or_date, str):\n if calendar is None:\n raise ValueError(\n \"If converting a string to a cftime.datetime object, \"\n \"a calendar type must be provided\"\n )\n date, _ = _parse_iso8601_with_reso(get_date_type(calendar), date_str_or_date)\n return date\n elif isinstance(date_str_or_date, cftime.datetime):\n return date_str_or_date\n else:\n raise TypeError(\n \"date_str_or_date must be a string or a \"\n \"subclass of cftime.datetime. Instead got \"\n \"{!r}.\".format(date_str_or_date)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_normalize_date__generate_linear_range.return.cftime_num2date_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_normalize_date__generate_linear_range.return.cftime_num2date_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 691, "end_line": 715, "span_ids": ["_generate_linear_range", "normalize_date", "_maybe_normalize_date"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def normalize_date(date):\n \"\"\"Round datetime down to midnight.\"\"\"\n return date.replace(hour=0, minute=0, second=0, microsecond=0)\n\n\ndef _maybe_normalize_date(date, normalize):\n \"\"\"Round datetime down to midnight if normalize is True.\"\"\"\n if normalize:\n return normalize_date(date)\n else:\n return date\n\n\ndef _generate_linear_range(start, end, periods):\n \"\"\"Generate an equally-spaced sequence of cftime.datetime objects between\n and including two dates (whose length equals the number of periods).\"\"\"\n import cftime\n\n total_seconds = (end - start).total_seconds()\n values = np.linspace(0.0, total_seconds, periods, endpoint=True)\n units = \"seconds since {}\".format(format_cftime_datetime(start))\n calendar = start.calendar\n return cftime.num2date(\n values, units=units, calendar=calendar, only_use_cftime_datetimes=True\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__generate_range_cftime_range": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py__generate_range_cftime_range", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 718, "end_line": 993, "span_ids": ["cftime_range", "_generate_range"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _generate_range(start, end, periods, offset):\n \"\"\"Generate a regular range of cftime.datetime objects with a\n given time offset.\n\n Adapted from pandas.tseries.offsets.generate_range.\n\n Parameters\n ----------\n start : cftime.datetime, or None\n Start of range\n end : cftime.datetime, or None\n End of range\n periods : int, or None\n Number of elements in the sequence\n offset : BaseCFTimeOffset\n An offset class designed for working with cftime.datetime objects\n\n Returns\n -------\n A generator object\n \"\"\"\n if start:\n start = offset.rollforward(start)\n\n if end:\n end = offset.rollback(end)\n\n if periods is None and end < start:\n end = None\n periods = 0\n\n if end is None:\n end = start + (periods - 1) * offset\n\n if start is None:\n start = end - (periods - 1) * offset\n\n current = start\n if offset.n >= 0:\n while current <= end:\n yield current\n\n next_date = current + offset\n if next_date <= current:\n raise ValueError(\n \"Offset {offset} did not increment date\".format(offset=offset)\n )\n current = next_date\n else:\n while current >= end:\n yield current\n\n next_date = current + offset\n if next_date >= current:\n raise ValueError(\n \"Offset {offset} did not decrement date\".format(offset=offset)\n )\n current = next_date\n\n\ndef cftime_range(\n start=None,\n end=None,\n periods=None,\n freq=\"D\",\n normalize=False,\n name=None,\n closed=None,\n calendar=\"standard\",\n):\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_cftime_range._Return_a_fixed_frequen_cftime_range._Return_a_fixed_frequen": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_cftime_range._Return_a_fixed_frequen_cftime_range._Return_a_fixed_frequen", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 788, "end_line": 954, "span_ids": ["cftime_range"], "tokens": 1911}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def cftime_range(\n start=None,\n end=None,\n periods=None,\n freq=\"D\",\n normalize=False,\n name=None,\n closed=None,\n calendar=\"standard\",\n):\n \"\"\"Return a fixed frequency CFTimeIndex.\n\n Parameters\n ----------\n start : str or cftime.datetime, optional\n Left bound for generating dates.\n end : str or cftime.datetime, optional\n Right bound for generating dates.\n periods : integer, optional\n Number of periods to generate.\n freq : str, default 'D', BaseCFTimeOffset, or None\n Frequency strings can have multiples, e.g. '5H'.\n normalize : bool, default False\n Normalize start/end dates to midnight before generating date range.\n name : str, default None\n Name of the resulting index\n closed : {None, 'left', 'right'}, optional\n Make the interval closed with respect to the given frequency to the\n 'left', 'right', or both sides (None, the default).\n calendar : str\n Calendar type for the datetimes (default 'standard').\n\n Returns\n -------\n CFTimeIndex\n\n Notes\n -----\n\n This function is an analog of ``pandas.date_range`` for use in generating\n sequences of ``cftime.datetime`` objects. It supports most of the\n features of ``pandas.date_range`` (e.g. specifying how the index is\n ``closed`` on either side, or whether or not to ``normalize`` the start and\n end bounds); however, there are some notable exceptions:\n\n - You cannot specify a ``tz`` (time zone) argument.\n - Start or end dates specified as partial-datetime strings must use the\n `ISO-8601 format `_.\n - It supports many, but not all, frequencies supported by\n ``pandas.date_range``. For example it does not currently support any of\n the business-related, semi-monthly, or sub-second frequencies.\n - Compound sub-monthly frequencies are not supported, e.g. '1H1min', as\n these can easily be written in terms of the finest common resolution,\n e.g. '61min'.\n\n Valid simple frequency strings for use with ``cftime``-calendars include\n any multiples of the following.\n\n +--------+--------------------------+\n | Alias | Description |\n +========+==========================+\n | A, Y | Year-end frequency |\n +--------+--------------------------+\n | AS, YS | Year-start frequency |\n +--------+--------------------------+\n | Q | Quarter-end frequency |\n +--------+--------------------------+\n | QS | Quarter-start frequency |\n +--------+--------------------------+\n | M | Month-end frequency |\n +--------+--------------------------+\n | MS | Month-start frequency |\n +--------+--------------------------+\n | D | Day frequency |\n +--------+--------------------------+\n | H | Hour frequency |\n +--------+--------------------------+\n | T, min | Minute frequency |\n +--------+--------------------------+\n | S | Second frequency |\n +--------+--------------------------+\n\n Any multiples of the following anchored offsets are also supported.\n\n +----------+--------------------------------------------------------------------+\n | Alias | Description |\n +==========+====================================================================+\n | A(S)-JAN | Annual frequency, anchored at the end (or beginning) of January |\n +----------+--------------------------------------------------------------------+\n | A(S)-FEB | Annual frequency, anchored at the end (or beginning) of February |\n +----------+--------------------------------------------------------------------+\n | A(S)-MAR | Annual frequency, anchored at the end (or beginning) of March |\n +----------+--------------------------------------------------------------------+\n | A(S)-APR | Annual frequency, anchored at the end (or beginning) of April |\n +----------+--------------------------------------------------------------------+\n | A(S)-MAY | Annual frequency, anchored at the end (or beginning) of May |\n +----------+--------------------------------------------------------------------+\n | A(S)-JUN | Annual frequency, anchored at the end (or beginning) of June |\n +----------+--------------------------------------------------------------------+\n | A(S)-JUL | Annual frequency, anchored at the end (or beginning) of July |\n +----------+--------------------------------------------------------------------+\n | A(S)-AUG | Annual frequency, anchored at the end (or beginning) of August |\n +----------+--------------------------------------------------------------------+\n | A(S)-SEP | Annual frequency, anchored at the end (or beginning) of September |\n +----------+--------------------------------------------------------------------+\n | A(S)-OCT | Annual frequency, anchored at the end (or beginning) of October |\n +----------+--------------------------------------------------------------------+\n | A(S)-NOV | Annual frequency, anchored at the end (or beginning) of November |\n +----------+--------------------------------------------------------------------+\n | A(S)-DEC | Annual frequency, anchored at the end (or beginning) of December |\n +----------+--------------------------------------------------------------------+\n | Q(S)-JAN | Quarter frequency, anchored at the end (or beginning) of January |\n +----------+--------------------------------------------------------------------+\n | Q(S)-FEB | Quarter frequency, anchored at the end (or beginning) of February |\n +----------+--------------------------------------------------------------------+\n | Q(S)-MAR | Quarter frequency, anchored at the end (or beginning) of March |\n +----------+--------------------------------------------------------------------+\n | Q(S)-APR | Quarter frequency, anchored at the end (or beginning) of April |\n +----------+--------------------------------------------------------------------+\n | Q(S)-MAY | Quarter frequency, anchored at the end (or beginning) of May |\n +----------+--------------------------------------------------------------------+\n | Q(S)-JUN | Quarter frequency, anchored at the end (or beginning) of June |\n +----------+--------------------------------------------------------------------+\n | Q(S)-JUL | Quarter frequency, anchored at the end (or beginning) of July |\n +----------+--------------------------------------------------------------------+\n | Q(S)-AUG | Quarter frequency, anchored at the end (or beginning) of August |\n +----------+--------------------------------------------------------------------+\n | Q(S)-SEP | Quarter frequency, anchored at the end (or beginning) of September |\n +----------+--------------------------------------------------------------------+\n | Q(S)-OCT | Quarter frequency, anchored at the end (or beginning) of October |\n +----------+--------------------------------------------------------------------+\n | Q(S)-NOV | Quarter frequency, anchored at the end (or beginning) of November |\n +----------+--------------------------------------------------------------------+\n | Q(S)-DEC | Quarter frequency, anchored at the end (or beginning) of December |\n +----------+--------------------------------------------------------------------+\n\n\n Finally, the following calendar aliases are supported.\n\n +--------------------------------+---------------------------------------+\n | Alias | Date type |\n +================================+=======================================+\n | standard, gregorian | ``cftime.DatetimeGregorian`` |\n +--------------------------------+---------------------------------------+\n | proleptic_gregorian | ``cftime.DatetimeProlepticGregorian`` |\n +--------------------------------+---------------------------------------+\n | noleap, 365_day | ``cftime.DatetimeNoLeap`` |\n +--------------------------------+---------------------------------------+\n | all_leap, 366_day | ``cftime.DatetimeAllLeap`` |\n +--------------------------------+---------------------------------------+\n | 360_day | ``cftime.Datetime360Day`` |\n +--------------------------------+---------------------------------------+\n | julian | ``cftime.DatetimeJulian`` |\n +--------------------------------+---------------------------------------+\n\n Examples\n --------\n\n This function returns a ``CFTimeIndex``, populated with ``cftime.datetime``\n objects associated with the specified calendar type, e.g.\n\n >>> xr.cftime_range(start='2000', periods=6, freq='2MS', calendar='noleap')\n CFTimeIndex([2000-01-01 00:00:00, 2000-03-01 00:00:00, 2000-05-01 00:00:00,\n 2000-07-01 00:00:00, 2000-09-01 00:00:00, 2000-11-01 00:00:00],\n dtype='object')\n\n As in the standard pandas function, three of the ``start``, ``end``,\n ``periods``, or ``freq`` arguments must be specified at a given time, with\n the other set to ``None``. See the `pandas documentation\n `_\n for more examples of the behavior of ``date_range`` with each of the\n parameters.\n\n See Also\n --------\n pandas.date_range\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_cftime_range._Adapted_from_pandas_cor_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftime_offsets.py_cftime_range._Adapted_from_pandas_cor_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftime_offsets.py", "file_name": "cftime_offsets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 955, "end_line": 994, "span_ids": ["cftime_range"], "tokens": 359}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def cftime_range(\n start=None,\n end=None,\n periods=None,\n freq=\"D\",\n normalize=False,\n name=None,\n closed=None,\n calendar=\"standard\",\n):\n # Adapted from pandas.core.indexes.datetimes._generate_range.\n if count_not_none(start, end, periods, freq) != 3:\n raise ValueError(\n \"Of the arguments 'start', 'end', 'periods', and 'freq', three \"\n \"must be specified at a time.\"\n )\n\n if start is not None:\n start = to_cftime_datetime(start, calendar)\n start = _maybe_normalize_date(start, normalize)\n if end is not None:\n end = to_cftime_datetime(end, calendar)\n end = _maybe_normalize_date(end, normalize)\n\n if freq is None:\n dates = _generate_linear_range(start, end, periods)\n else:\n offset = to_offset(freq)\n dates = np.array(list(_generate_range(start, end, periods, offset)))\n\n left_closed = False\n right_closed = False\n\n if closed is None:\n left_closed = True\n right_closed = True\n elif closed == \"left\":\n left_closed = True\n elif closed == \"right\":\n right_closed = True\n else:\n raise ValueError(\"Closed must be either 'left', 'right' or None\")\n\n if not left_closed and len(dates) and start is not None and dates[0] == start:\n dates = dates[1:]\n if not right_closed and len(dates) and end is not None and dates[-1] == end:\n dates = dates[:-1]\n\n return CFTimeIndex(dates, name=name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__DatetimeIndex_analog_f_trailing_optional.return.xs_0_optional_trailing": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__DatetimeIndex_analog_f_trailing_optional.return.xs_0_optional_trailing", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 66, "span_ids": ["named", "optional", "docstring", "trailing_optional", "imports"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"DatetimeIndex analog for cftime.datetime objects\"\"\"\n# The pandas.Index subclass defined here was copied and adapted for\n# use with cftime.datetime objects based on the source code defining\n# pandas.DatetimeIndex.\n\nimport re\nimport warnings\nfrom datetime import timedelta\nfrom distutils.version import LooseVersion\n\nimport numpy as np\nimport pandas as pd\n\nfrom xarray.core.utils import is_scalar\n\nfrom .times import _STANDARD_CALENDARS, cftime_to_nptime, infer_calendar_name\n\n\ndef named(name, pattern):\n return \"(?P<\" + name + \">\" + pattern + \")\"\n\n\ndef optional(x):\n return \"(?:\" + x + \")?\"\n\n\ndef trailing_optional(xs):\n if not xs:\n return \"\"\n return xs[0] + optional(trailing_optional(xs[1:]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_build_pattern_parse_iso8601.raise_ValueError_no_ISO_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_build_pattern_parse_iso8601.raise_ValueError_no_ISO_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 95, "span_ids": ["impl", "parse_iso8601", "build_pattern"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def build_pattern(date_sep=r\"\\-\", datetime_sep=r\"T\", time_sep=r\"\\:\"):\n pieces = [\n (None, \"year\", r\"\\d{4}\"),\n (date_sep, \"month\", r\"\\d{2}\"),\n (date_sep, \"day\", r\"\\d{2}\"),\n (datetime_sep, \"hour\", r\"\\d{2}\"),\n (time_sep, \"minute\", r\"\\d{2}\"),\n (time_sep, \"second\", r\"\\d{2}\"),\n ]\n pattern_list = []\n for sep, name, sub_pattern in pieces:\n pattern_list.append((sep if sep else \"\") + named(name, sub_pattern))\n # TODO: allow timezone offsets?\n return \"^\" + trailing_optional(pattern_list) + \"$\"\n\n\n_BASIC_PATTERN = build_pattern(date_sep=\"\", time_sep=\"\")\n_EXTENDED_PATTERN = build_pattern()\n_PATTERNS = [_BASIC_PATTERN, _EXTENDED_PATTERN]\n\n\ndef parse_iso8601(datetime_string):\n for pattern in _PATTERNS:\n match = re.match(pattern, datetime_string)\n if match:\n return match.groupdict()\n raise ValueError(\"no ISO-8601 match for string: %s\" % datetime_string)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parse_iso8601_with_reso__parse_iso8601_with_reso.return.default_replace_replace": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parse_iso8601_with_reso__parse_iso8601_with_reso.return.default_replace_replace", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 98, "end_line": 116, "span_ids": ["_parse_iso8601_with_reso"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _parse_iso8601_with_reso(date_type, timestr):\n default = date_type(1, 1, 1)\n result = parse_iso8601(timestr)\n replace = {}\n\n for attr in [\"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\"]:\n value = result.get(attr, None)\n if value is not None:\n # Note ISO8601 conventions allow for fractional seconds.\n # TODO: Consider adding support for sub-second resolution?\n replace[attr] = int(value)\n resolution = attr\n\n # dayofwk=-1 is required to update the dayofwk and dayofyr attributes of\n # the returned date object in versions of cftime between 1.0.2 and\n # 1.0.3.4. It can be removed for versions of cftime greater than\n # 1.0.3.4.\n replace[\"dayofwk\"] = -1\n return default.replace(**replace), resolution", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parsed_string_to_bounds__parsed_string_to_bounds.if_resolution_year_.else_.raise_KeyError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parsed_string_to_bounds__parsed_string_to_bounds.if_resolution_year_.else_.raise_KeyError", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 119, "end_line": 160, "span_ids": ["_parsed_string_to_bounds"], "tokens": 363}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _parsed_string_to_bounds(date_type, resolution, parsed):\n \"\"\"Generalization of\n pandas.tseries.index.DatetimeIndex._parsed_string_to_bounds\n for use with non-standard calendars and cftime.datetime\n objects.\n \"\"\"\n if resolution == \"year\":\n return (\n date_type(parsed.year, 1, 1),\n date_type(parsed.year + 1, 1, 1) - timedelta(microseconds=1),\n )\n elif resolution == \"month\":\n if parsed.month == 12:\n end = date_type(parsed.year + 1, 1, 1) - timedelta(microseconds=1)\n else:\n end = date_type(parsed.year, parsed.month + 1, 1) - timedelta(\n microseconds=1\n )\n return date_type(parsed.year, parsed.month, 1), end\n elif resolution == \"day\":\n start = date_type(parsed.year, parsed.month, parsed.day)\n return start, start + timedelta(days=1, microseconds=-1)\n elif resolution == \"hour\":\n start = date_type(parsed.year, parsed.month, parsed.day, parsed.hour)\n return start, start + timedelta(hours=1, microseconds=-1)\n elif resolution == \"minute\":\n start = date_type(\n parsed.year, parsed.month, parsed.day, parsed.hour, parsed.minute\n )\n return start, start + timedelta(minutes=1, microseconds=-1)\n elif resolution == \"second\":\n start = date_type(\n parsed.year,\n parsed.month,\n parsed.day,\n parsed.hour,\n parsed.minute,\n parsed.second,\n )\n return start, start + timedelta(seconds=1, microseconds=-1)\n else:\n raise KeyError", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_get_date_field__field_accessor.return.property_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_get_date_field__field_accessor.return.property_f_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 163, "end_line": 187, "span_ids": ["get_date_field", "_field_accessor"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_date_field(datetimes, field):\n \"\"\"Adapted from pandas.tslib.get_date_field\"\"\"\n return np.array([getattr(date, field) for date in datetimes])\n\n\ndef _field_accessor(name, docstring=None, min_cftime_version=\"0.0\"):\n \"\"\"Adapted from pandas.tseries.index._field_accessor\"\"\"\n\n def f(self, min_cftime_version=min_cftime_version):\n import cftime\n\n version = cftime.__version__\n\n if LooseVersion(version) >= LooseVersion(min_cftime_version):\n return get_date_field(self._data, name)\n else:\n raise ImportError(\n \"The {!r} accessor requires a minimum \"\n \"version of cftime of {}. Found an \"\n \"installed version of {}.\".format(name, min_cftime_version, version)\n )\n\n f.__name__ = name\n f.__doc__ = docstring\n return property(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_get_date_type_assert_all_valid_date_type.if_len_data_0_.if_not_all_isinstance_val.raise_TypeError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_get_date_type_assert_all_valid_date_type.if_len_data_0_.if_not_all_isinstance_val.raise_TypeError_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 212, "span_ids": ["get_date_type", "assert_all_valid_date_type"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_date_type(self):\n if self._data.size:\n return type(self._data[0])\n else:\n return None\n\n\ndef assert_all_valid_date_type(data):\n import cftime\n\n if len(data) > 0:\n sample = data[0]\n date_type = type(sample)\n if not isinstance(sample, cftime.datetime):\n raise TypeError(\n \"CFTimeIndex requires cftime.datetime \"\n \"objects. Got object of {}.\".format(date_type)\n )\n if not all(isinstance(value, date_type) for value in data):\n raise TypeError(\n \"CFTimeIndex requires using datetime \"\n \"objects of all the same type. Got\\n{}.\".format(data)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex_CFTimeIndex.__new__.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex_CFTimeIndex.__new__.return.result", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 253, "span_ids": ["CFTimeIndex", "CFTimeIndex.__new__"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n \"\"\"Custom Index for working with CF calendars and dates\n\n All elements of a CFTimeIndex must be cftime.datetime objects.\n\n Parameters\n ----------\n data : array or CFTimeIndex\n Sequence of cftime.datetime objects to use in index\n name : str, default None\n Name of the resulting index\n\n See Also\n --------\n cftime_range\n \"\"\"\n\n year = _field_accessor(\"year\", \"The year of the datetime\")\n month = _field_accessor(\"month\", \"The month of the datetime\")\n day = _field_accessor(\"day\", \"The days of the datetime\")\n hour = _field_accessor(\"hour\", \"The hours of the datetime\")\n minute = _field_accessor(\"minute\", \"The minutes of the datetime\")\n second = _field_accessor(\"second\", \"The seconds of the datetime\")\n microsecond = _field_accessor(\"microsecond\", \"The microseconds of the datetime\")\n dayofyear = _field_accessor(\n \"dayofyr\", \"The ordinal day of year of the datetime\", \"1.0.2.1\"\n )\n dayofweek = _field_accessor(\"dayofwk\", \"The day of week of the datetime\", \"1.0.2.1\")\n date_type = property(get_date_type)\n\n def __new__(cls, data, name=None):\n assert_all_valid_date_type(data)\n if name is None and hasattr(data, \"name\"):\n name = data.name\n\n result = object.__new__(cls)\n result._data = np.array(data, dtype=\"O\")\n result.name = name\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._partial_date_slice_CFTimeIndex._partial_date_slice.return.np_flatnonzero_lhs_mask_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._partial_date_slice_CFTimeIndex._partial_date_slice.return.np_flatnonzero_lhs_mask_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 316, "span_ids": ["CFTimeIndex._partial_date_slice"], "tokens": 614}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def _partial_date_slice(self, resolution, parsed):\n \"\"\"Adapted from\n pandas.tseries.index.DatetimeIndex._partial_date_slice\n\n Note that when using a CFTimeIndex, if a partial-date selection\n returns a single element, it will never be converted to a scalar\n coordinate; this is in slight contrast to the behavior when using\n a DatetimeIndex, which sometimes will return a DataArray with a scalar\n coordinate depending on the resolution of the datetimes used in\n defining the index. For example:\n\n >>> from cftime import DatetimeNoLeap\n >>> import pandas as pd\n >>> import xarray as xr\n >>> da = xr.DataArray([1, 2],\n coords=[[DatetimeNoLeap(2001, 1, 1),\n DatetimeNoLeap(2001, 2, 1)]],\n dims=['time'])\n >>> da.sel(time='2001-01-01')\n \n array([1])\n Coordinates:\n * time (time) object 2001-01-01 00:00:00\n >>> da = xr.DataArray([1, 2],\n coords=[[pd.Timestamp(2001, 1, 1),\n pd.Timestamp(2001, 2, 1)]],\n dims=['time'])\n >>> da.sel(time='2001-01-01')\n \n array(1)\n Coordinates:\n time datetime64[ns] 2001-01-01\n >>> da = xr.DataArray([1, 2],\n coords=[[pd.Timestamp(2001, 1, 1, 1),\n pd.Timestamp(2001, 2, 1)]],\n dims=['time'])\n >>> da.sel(time='2001-01-01')\n \n array([1])\n Coordinates:\n * time (time) datetime64[ns] 2001-01-01T01:00:00\n \"\"\"\n start, end = _parsed_string_to_bounds(self.date_type, resolution, parsed)\n\n times = self._data\n\n if self.is_monotonic:\n if len(times) and (\n (start < times[0] and end < times[0])\n or (start > times[-1] and end > times[-1])\n ):\n # we are out of range\n raise KeyError\n\n # a monotonic (sorted) series can be sliced\n left = times.searchsorted(start, side=\"left\")\n right = times.searchsorted(end, side=\"right\")\n return slice(left, right)\n\n lhs_mask = times >= start\n rhs_mask = times <= end\n return np.flatnonzero(lhs_mask & rhs_mask)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._get_string_slice_CFTimeIndex.get_loc.if_isinstance_key_str_.else_.return.pd_Index_get_loc_self_ke": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._get_string_slice_CFTimeIndex.get_loc.if_isinstance_key_str_.else_.return.pd_Index_get_loc_self_ke", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 318, "end_line": 332, "span_ids": ["CFTimeIndex._get_string_slice", "CFTimeIndex.get_loc"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def _get_string_slice(self, key):\n \"\"\"Adapted from pandas.tseries.index.DatetimeIndex._get_string_slice\"\"\"\n parsed, resolution = _parse_iso8601_with_reso(self.date_type, key)\n try:\n loc = self._partial_date_slice(resolution, parsed)\n except KeyError:\n raise KeyError(key)\n return loc\n\n def get_loc(self, key, method=None, tolerance=None):\n \"\"\"Adapted from pandas.tseries.index.DatetimeIndex.get_loc\"\"\"\n if isinstance(key, str):\n return self._get_string_slice(key)\n else:\n return pd.Index.get_loc(self, key, method=method, tolerance=tolerance)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._maybe_cast_slice_bound_CFTimeIndex._maybe_cast_slice_bound.if_isinstance_label_str_.else_.return.label": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._maybe_cast_slice_bound_CFTimeIndex._maybe_cast_slice_bound.if_isinstance_label_str_.else_.return.label", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 334, "end_line": 344, "span_ids": ["CFTimeIndex._maybe_cast_slice_bound"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def _maybe_cast_slice_bound(self, label, side, kind):\n \"\"\"Adapted from\n pandas.tseries.index.DatetimeIndex._maybe_cast_slice_bound\"\"\"\n if isinstance(label, str):\n parsed, resolution = _parse_iso8601_with_reso(self.date_type, label)\n start, end = _parsed_string_to_bounds(self.date_type, resolution, parsed)\n if self.is_monotonic_decreasing and len(self) > 1:\n return end if side == \"left\" else start\n return start if side == \"left\" else end\n else:\n return label", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._TODO_Add_ability_to_us_CFTimeIndex.contains.return.self___contains___key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex._TODO_Add_ability_to_us_CFTimeIndex.contains.return.self___contains___key_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 346, "end_line": 372, "span_ids": ["CFTimeIndex.get_value", "CFTimeIndex._maybe_cast_slice_bound", "CFTimeIndex.__contains__", "CFTimeIndex.contains"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n # TODO: Add ability to use integer range outside of iloc?\n # e.g. series[1:5].\n def get_value(self, series, key):\n \"\"\"Adapted from pandas.tseries.index.DatetimeIndex.get_value\"\"\"\n if np.asarray(key).dtype == np.dtype(bool):\n return series.iloc[key]\n elif isinstance(key, slice):\n return series.iloc[self.slice_indexer(key.start, key.stop, key.step)]\n else:\n return series.iloc[self.get_loc(key)]\n\n def __contains__(self, key):\n \"\"\"Adapted from\n pandas.tseries.base.DatetimeIndexOpsMixin.__contains__\"\"\"\n try:\n result = self.get_loc(key)\n return (\n is_scalar(result)\n or type(result) == slice\n or (isinstance(result, np.ndarray) and result.size)\n )\n except (KeyError, TypeError, ValueError):\n return False\n\n def contains(self, key):\n \"\"\"Needed for .loc based partial-string indexing\"\"\"\n return self.__contains__(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.shift_CFTimeIndex.shift.if_isinstance_freq_timed.else_.raise_TypeError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.shift_CFTimeIndex.shift.if_isinstance_freq_timed.else_.raise_TypeError_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 374, "end_line": 415, "span_ids": ["CFTimeIndex.shift"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def shift(self, n, freq):\n \"\"\"Shift the CFTimeIndex a multiple of the given frequency.\n\n See the documentation for :py:func:`~xarray.cftime_range` for a\n complete listing of valid frequency strings.\n\n Parameters\n ----------\n n : int\n Periods to shift by\n freq : str or datetime.timedelta\n A frequency string or datetime.timedelta object to shift by\n\n Returns\n -------\n CFTimeIndex\n\n See also\n --------\n pandas.DatetimeIndex.shift\n\n Examples\n --------\n >>> index = xr.cftime_range('2000', periods=1, freq='M')\n >>> index\n CFTimeIndex([2000-01-31 00:00:00], dtype='object')\n >>> index.shift(1, 'M')\n CFTimeIndex([2000-02-29 00:00:00], dtype='object')\n \"\"\"\n from .cftime_offsets import to_offset\n\n if not isinstance(n, int):\n raise TypeError(\"'n' must be an int, got {}.\".format(n))\n if isinstance(freq, timedelta):\n return self + n * freq\n elif isinstance(freq, str):\n return self + n * to_offset(freq)\n else:\n raise TypeError(\n \"'freq' must be of type \"\n \"str or datetime.timedelta, got {}.\".format(freq)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.__add___CFTimeIndex.__rsub__.return.pd_TimedeltaIndex_other_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.__add___CFTimeIndex.__rsub__.return.pd_TimedeltaIndex_other_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 417, "end_line": 438, "span_ids": ["CFTimeIndex.__sub__", "CFTimeIndex.__add__", "CFTimeIndex.__radd__", "CFTimeIndex.__rsub__"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def __add__(self, other):\n if isinstance(other, pd.TimedeltaIndex):\n other = other.to_pytimedelta()\n return CFTimeIndex(np.array(self) + other)\n\n def __radd__(self, other):\n if isinstance(other, pd.TimedeltaIndex):\n other = other.to_pytimedelta()\n return CFTimeIndex(other + np.array(self))\n\n def __sub__(self, other):\n import cftime\n\n if isinstance(other, (CFTimeIndex, cftime.datetime)):\n return pd.TimedeltaIndex(np.array(self) - np.array(other))\n elif isinstance(other, pd.TimedeltaIndex):\n return CFTimeIndex(np.array(self) - other.to_pytimedelta())\n else:\n return CFTimeIndex(np.array(self) - other)\n\n def __rsub__(self, other):\n return pd.TimedeltaIndex(other - np.array(self))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.to_datetimeindex_CFTimeIndex.to_datetimeindex.return.pd_DatetimeIndex_nptimes_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.to_datetimeindex_CFTimeIndex.to_datetimeindex.return.pd_DatetimeIndex_nptimes_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 440, "end_line": 491, "span_ids": ["CFTimeIndex.to_datetimeindex"], "tokens": 429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def to_datetimeindex(self, unsafe=False):\n \"\"\"If possible, convert this index to a pandas.DatetimeIndex.\n\n Parameters\n ----------\n unsafe : bool\n Flag to turn off warning when converting from a CFTimeIndex with\n a non-standard calendar to a DatetimeIndex (default ``False``).\n\n Returns\n -------\n pandas.DatetimeIndex\n\n Raises\n ------\n ValueError\n If the CFTimeIndex contains dates that are not possible in the\n standard calendar or outside the pandas.Timestamp-valid range.\n\n Warns\n -----\n RuntimeWarning\n If converting from a non-standard calendar to a DatetimeIndex.\n\n Warnings\n --------\n Note that for non-standard calendars, this will change the calendar\n type of the index. In that case the result of this method should be\n used with caution.\n\n Examples\n --------\n >>> import xarray as xr\n >>> times = xr.cftime_range('2000', periods=2, calendar='gregorian')\n >>> times\n CFTimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00], dtype='object')\n >>> times.to_datetimeindex()\n DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)\n \"\"\"\n nptimes = cftime_to_nptime(self)\n calendar = infer_calendar_name(self)\n if calendar not in _STANDARD_CALENDARS and not unsafe:\n warnings.warn(\n \"Converting a CFTimeIndex with dates from a non-standard \"\n \"calendar, {!r}, to a pandas.DatetimeIndex, which uses dates \"\n \"from the standard calendar. This may lead to subtle errors \"\n \"in operations that depend on the length of time between \"\n \"dates.\".format(calendar),\n RuntimeWarning,\n stacklevel=2,\n )\n return pd.DatetimeIndex(nptimes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.strftime_CFTimeIndex.strftime.return.pd_Index_date_strftime_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py_CFTimeIndex.strftime_CFTimeIndex.strftime.return.pd_Index_date_strftime_d", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 493, "end_line": 520, "span_ids": ["CFTimeIndex.strftime"], "tokens": 286}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeIndex(pd.Index):\n\n def strftime(self, date_format):\n \"\"\"\n Return an Index of formatted strings specified by date_format, which\n supports the same string format as the python standard library. Details\n of the string format can be found in `python string format doc\n `__\n\n Parameters\n ----------\n date_format : str\n Date format string (e.g. \"%Y-%m-%d\")\n\n Returns\n -------\n Index\n Index of formatted strings\n\n Examples\n --------\n >>> rng = xr.cftime_range(start='2000', periods=5, freq='2MS',\n ... calendar='noleap')\n >>> rng.strftime('%B %d, %Y, %r')\n Index(['January 01, 2000, 12:00:00 AM', 'March 01, 2000, 12:00:00 AM',\n 'May 01, 2000, 12:00:00 AM', 'July 01, 2000, 12:00:00 AM',\n 'September 01, 2000, 12:00:00 AM'],\n dtype='object')\n \"\"\"\n return pd.Index([date.strftime(date_format) for date in self._data])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parse_iso8601_without_reso_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/cftimeindex.py__parse_iso8601_without_reso_", "embedding": null, "metadata": {"file_path": "xarray/coding/cftimeindex.py", "file_name": "cftimeindex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 523, "end_line": 548, "span_ids": ["_parse_array_of_cftime_strings", "_parse_iso8601_without_reso"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _parse_iso8601_without_reso(date_type, datetime_str):\n date, _ = _parse_iso8601_with_reso(date_type, datetime_str)\n return date\n\n\ndef _parse_array_of_cftime_strings(strings, date_type):\n \"\"\"Create a numpy array from an array of strings.\n\n For use in generating dates from strings for use with interp. Assumes the\n array is either 0-dimensional or 1-dimensional.\n\n Parameters\n ----------\n strings : array of strings\n Strings to convert to dates\n date_type : cftime.datetime type\n Calendar type to use for dates\n\n Returns\n -------\n np.array\n \"\"\"\n return np.array(\n [_parse_iso8601_without_reso(date_type, s) for s in strings.ravel()]\n ).reshape(strings.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py__Coders_for_strings__is_bytes_dtype.return.dtype_kind_S_or_chec": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py__Coders_for_strings__is_bytes_dtype.return.dtype_kind_S_or_chec", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 36, "span_ids": ["create_vlen_dtype", "docstring", "check_vlen_dtype", "is_unicode_dtype", "imports", "is_bytes_dtype"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Coders for strings.\"\"\"\nfrom functools import partial\n\nimport numpy as np\n\nfrom ..core import indexing\nfrom ..core.pycompat import dask_array_type\nfrom ..core.variable import Variable\nfrom .variables import (\n VariableCoder,\n lazy_elemwise_func,\n pop_to,\n safe_setitem,\n unpack_for_decoding,\n unpack_for_encoding,\n)\n\n\ndef create_vlen_dtype(element_type):\n # based on h5py.special_dtype\n return np.dtype(\"O\", metadata={\"element_type\": element_type})\n\n\ndef check_vlen_dtype(dtype):\n if dtype.kind != \"O\" or dtype.metadata is None:\n return None\n else:\n return dtype.metadata.get(\"element_type\")\n\n\ndef is_unicode_dtype(dtype):\n return dtype.kind == \"U\" or check_vlen_dtype(dtype) == str\n\n\ndef is_bytes_dtype(dtype):\n return dtype.kind == \"S\" or check_vlen_dtype(dtype) == bytes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_EncodedStringCoder_EncodedStringCoder.decode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_EncodedStringCoder_EncodedStringCoder.decode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 39, "end_line": 77, "span_ids": ["EncodedStringCoder", "EncodedStringCoder.decode", "EncodedStringCoder.encode"], "tokens": 345}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EncodedStringCoder(VariableCoder):\n \"\"\"Transforms between unicode strings and fixed-width UTF-8 bytes.\"\"\"\n\n def __init__(self, allows_unicode=True):\n self.allows_unicode = allows_unicode\n\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n\n contains_unicode = is_unicode_dtype(data.dtype)\n encode_as_char = encoding.get(\"dtype\") == \"S1\"\n\n if encode_as_char:\n del encoding[\"dtype\"] # no longer relevant\n\n if contains_unicode and (encode_as_char or not self.allows_unicode):\n if \"_FillValue\" in attrs:\n raise NotImplementedError(\n \"variable {!r} has a _FillValue specified, but \"\n \"_FillValue is not yet supported on unicode strings: \"\n \"https://github.com/pydata/xarray/issues/1647\".format(name)\n )\n\n string_encoding = encoding.pop(\"_Encoding\", \"utf-8\")\n safe_setitem(attrs, \"_Encoding\", string_encoding, name=name)\n # TODO: figure out how to handle this in a lazy way with dask\n data = encode_string_array(data, string_encoding)\n\n return Variable(dims, data, attrs, encoding)\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if \"_Encoding\" in attrs:\n string_encoding = pop_to(attrs, encoding, \"_Encoding\")\n func = partial(decode_bytes_array, encoding=string_encoding)\n data = lazy_elemwise_func(data, func, np.dtype(object))\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_decode_bytes_array_ensure_fixed_length_bytes.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_decode_bytes_array_ensure_fixed_length_bytes.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 99, "span_ids": ["ensure_fixed_length_bytes", "decode_bytes_array", "encode_string_array"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decode_bytes_array(bytes_array, encoding=\"utf-8\"):\n # This is faster than using np.char.decode() or np.vectorize()\n bytes_array = np.asarray(bytes_array)\n decoded = [x.decode(encoding) for x in bytes_array.ravel()]\n return np.array(decoded, dtype=object).reshape(bytes_array.shape)\n\n\ndef encode_string_array(string_array, encoding=\"utf-8\"):\n string_array = np.asarray(string_array)\n encoded = [x.encode(encoding) for x in string_array.ravel()]\n return np.array(encoded, dtype=bytes).reshape(string_array.shape)\n\n\ndef ensure_fixed_length_bytes(var):\n \"\"\"Ensure that a variable with vlen bytes is converted to fixed width.\"\"\"\n dims, data, attrs, encoding = unpack_for_encoding(var)\n if check_vlen_dtype(data.dtype) == bytes:\n # TODO: figure out how to handle this with dask\n data = np.asarray(data, dtype=np.string_)\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_CharacterArrayCoder_CharacterArrayCoder.decode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_CharacterArrayCoder_CharacterArrayCoder.decode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 125, "span_ids": ["CharacterArrayCoder.decode", "CharacterArrayCoder.encode", "CharacterArrayCoder"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CharacterArrayCoder(VariableCoder):\n \"\"\"Transforms between arrays containing bytes and character arrays.\"\"\"\n\n def encode(self, variable, name=None):\n variable = ensure_fixed_length_bytes(variable)\n\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n if data.dtype.kind == \"S\" and encoding.get(\"dtype\") is not str:\n data = bytes_to_char(data)\n if \"char_dim_name\" in encoding.keys():\n char_dim_name = encoding.pop(\"char_dim_name\")\n else:\n char_dim_name = \"string%s\" % data.shape[-1]\n dims = dims + (char_dim_name,)\n return Variable(dims, data, attrs, encoding)\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if data.dtype == \"S1\" and dims:\n encoding[\"char_dim_name\"] = dims[-1]\n dims = dims[:-1]\n data = char_to_bytes(data)\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_bytes_to_char__numpy_bytes_to_char.return.arr_reshape_arr_shape_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_bytes_to_char__numpy_bytes_to_char.return.arr_reshape_arr_shape_", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 152, "span_ids": ["_numpy_bytes_to_char", "bytes_to_char"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def bytes_to_char(arr):\n \"\"\"Convert numpy/dask arrays from fixed width bytes to characters.\"\"\"\n if arr.dtype.kind != \"S\":\n raise ValueError(\"argument must have a fixed-width bytes dtype\")\n\n if isinstance(arr, dask_array_type):\n import dask.array as da\n\n return da.map_blocks(\n _numpy_bytes_to_char,\n arr,\n dtype=\"S1\",\n chunks=arr.chunks + ((arr.dtype.itemsize,)),\n new_axis=[arr.ndim],\n )\n else:\n return _numpy_bytes_to_char(arr)\n\n\ndef _numpy_bytes_to_char(arr):\n \"\"\"Like netCDF4.stringtochar, but faster and more flexible.\n \"\"\"\n # ensure the array is contiguous\n arr = np.array(arr, copy=False, order=\"C\", dtype=np.string_)\n return arr.reshape(arr.shape + (1,)).view(\"S1\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_char_to_bytes__numpy_char_to_bytes.return.arr_view_dtype_reshape_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_char_to_bytes__numpy_char_to_bytes.return.arr_view_dtype_reshape_a", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 155, "end_line": 197, "span_ids": ["_numpy_char_to_bytes", "char_to_bytes"], "tokens": 308}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def char_to_bytes(arr):\n \"\"\"Convert numpy/dask arrays from characters to fixed width bytes.\"\"\"\n if arr.dtype != \"S1\":\n raise ValueError(\"argument must have dtype='S1'\")\n\n if not arr.ndim:\n # no dimension to concatenate along\n return arr\n\n size = arr.shape[-1]\n\n if not size:\n # can't make an S0 dtype\n return np.zeros(arr.shape[:-1], dtype=np.string_)\n\n if isinstance(arr, dask_array_type):\n import dask.array as da\n\n if len(arr.chunks[-1]) > 1:\n raise ValueError(\n \"cannot stacked dask character array with \"\n \"multiple chunks in the last dimension: {}\".format(arr)\n )\n\n dtype = np.dtype(\"S\" + str(arr.shape[-1]))\n return da.map_blocks(\n _numpy_char_to_bytes,\n arr,\n dtype=dtype,\n chunks=arr.chunks[:-1],\n drop_axis=[arr.ndim - 1],\n )\n else:\n return StackedBytesArray(arr)\n\n\ndef _numpy_char_to_bytes(arr):\n \"\"\"Like netCDF4.chartostring, but faster and more flexible.\n \"\"\"\n # based on: http://stackoverflow.com/a/10984878/809705\n arr = np.array(arr, copy=False, order=\"C\")\n dtype = \"S\" + str(arr.shape[-1])\n return arr.view(dtype).reshape(arr.shape[:-1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_StackedBytesArray_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/strings.py_StackedBytesArray_", "embedding": null, "metadata": {"file_path": "xarray/coding/strings.py", "file_name": "strings.py", "file_type": "text/x-python", "category": "implementation", "start_line": 200, "end_line": 239, "span_ids": ["StackedBytesArray.dtype", "StackedBytesArray.__getitem__", "StackedBytesArray", "StackedBytesArray.shape", "StackedBytesArray.__repr__"], "tokens": 285}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StackedBytesArray(indexing.ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrapper around array-like objects to create a new indexable object where\n values, when accessed, are automatically stacked along the last dimension.\n\n >>> StackedBytesArray(np.array(['a', 'b', 'c']))[:]\n array('abc',\n dtype='|S3')\n \"\"\"\n\n def __init__(self, array):\n \"\"\"\n Parameters\n ----------\n array : array-like\n Original array of values to wrap.\n \"\"\"\n if array.dtype != \"S1\":\n raise ValueError(\n \"can only use StackedBytesArray if argument has dtype='S1'\"\n )\n self.array = indexing.as_indexable(array)\n\n @property\n def dtype(self):\n return np.dtype(\"S\" + str(self.array.shape[-1]))\n\n @property\n def shape(self):\n return self.array.shape[:-1]\n\n def __repr__(self):\n return \"%s(%r)\" % (type(self).__name__, self.array)\n\n def __getitem__(self, key):\n # require slicing the last dimension completely\n key = type(key)(indexing.expanded_indexer(key.tuple, self.array.ndim))\n if key.tuple[-1] != slice(None):\n raise IndexError(\"too many indices\")\n return _numpy_char_to_bytes(self.array[key])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_re__unpack_netcdf_time_units.return.delta_units_ref_date": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_re__unpack_netcdf_time_units.return.delta_units_ref_date", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 92, "span_ids": ["impl", "_netcdf_to_numpy_timeunit", "_unpack_netcdf_time_units", "_import_cftime", "_require_standalone_cftime", "imports"], "tokens": 618}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import re\nimport warnings\nfrom datetime import datetime\nfrom distutils.version import LooseVersion\nfrom functools import partial\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.errors import OutOfBoundsDatetime\n\nfrom ..core import indexing\nfrom ..core.common import contains_cftime_datetimes\nfrom ..core.formatting import first_n_items, format_timestamp, last_item\nfrom ..core.variable import Variable\nfrom .variables import (\n SerializationWarning,\n VariableCoder,\n lazy_elemwise_func,\n pop_to,\n safe_setitem,\n unpack_for_decoding,\n unpack_for_encoding,\n)\n\n# standard calendars recognized by cftime\n_STANDARD_CALENDARS = {\"standard\", \"gregorian\", \"proleptic_gregorian\"}\n\n_NS_PER_TIME_DELTA = {\n \"us\": int(1e3),\n \"ms\": int(1e6),\n \"s\": int(1e9),\n \"m\": int(1e9) * 60,\n \"h\": int(1e9) * 60 * 60,\n \"D\": int(1e9) * 60 * 60 * 24,\n}\n\nTIME_UNITS = frozenset(\n [\"days\", \"hours\", \"minutes\", \"seconds\", \"milliseconds\", \"microseconds\"]\n)\n\n\ndef _import_cftime():\n \"\"\"\n helper function handle the transition to netcdftime/cftime\n as a stand-alone package\n \"\"\"\n try:\n import cftime\n except ImportError:\n # in netCDF4 the num2date/date2num function are top-level api\n try:\n import netCDF4 as cftime\n except ImportError:\n raise ImportError(\"Failed to import cftime\")\n return cftime\n\n\ndef _require_standalone_cftime():\n \"\"\"Raises an ImportError if the standalone cftime is not found\"\"\"\n try:\n import cftime # noqa: F401\n except ImportError:\n raise ImportError(\n \"Decoding times with non-standard calendars \"\n \"or outside the pandas.Timestamp-valid range \"\n \"requires the standalone cftime package.\"\n )\n\n\ndef _netcdf_to_numpy_timeunit(units):\n units = units.lower()\n if not units.endswith(\"s\"):\n units = \"%ss\" % units\n return {\n \"microseconds\": \"us\",\n \"milliseconds\": \"ms\",\n \"seconds\": \"s\",\n \"minutes\": \"m\",\n \"hours\": \"h\",\n \"days\": \"D\",\n }[units]\n\n\ndef _unpack_netcdf_time_units(units):\n # CF datetime units follow the format: \"UNIT since DATE\"\n # this parses out the unit and date allowing for extraneous\n # whitespace.\n matches = re.match(\"(.+) since (.+)\", units)\n if not matches:\n raise ValueError(\"invalid time units: %s\" % units)\n delta_units, ref_date = [s.strip() for s in matches.groups()]\n return delta_units, ref_date", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_cf_datetime_dtype__decode_cf_datetime_dtype.return.dtype": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_cf_datetime_dtype__decode_cf_datetime_dtype.return.dtype", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 95, "end_line": 118, "span_ids": ["_decode_cf_datetime_dtype"], "tokens": 223}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decode_cf_datetime_dtype(data, units, calendar, use_cftime):\n # Verify that at least the first and last date can be decoded\n # successfully. Otherwise, tracebacks end up swallowed by\n # Dataset.__repr__ when users try to view their lazily decoded array.\n values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))\n example_value = np.concatenate(\n [first_n_items(values, 1) or [0], last_item(values) or [0]]\n )\n\n try:\n result = decode_cf_datetime(example_value, units, calendar, use_cftime)\n except Exception:\n calendar_msg = (\n \"the default calendar\" if calendar is None else \"calendar %r\" % calendar\n )\n msg = (\n \"unable to decode time units %r with %s. Try \"\n \"opening your dataset with decode_times=False.\" % (units, calendar_msg)\n )\n raise ValueError(msg)\n else:\n dtype = getattr(result, \"dtype\", np.dtype(\"object\"))\n\n return dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_datetime_with_cftime__decode_datetime_with_cftime.if_cftime___name___cf.else_.return.np_asarray_cftime_num2dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_datetime_with_cftime__decode_datetime_with_cftime.if_cftime___name___cf.else_.return.np_asarray_cftime_num2dat", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 121, "end_line": 131, "span_ids": ["_decode_datetime_with_cftime"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decode_datetime_with_cftime(num_dates, units, calendar):\n cftime = _import_cftime()\n\n if cftime.__name__ == \"cftime\":\n return np.asarray(\n cftime.num2date(num_dates, units, calendar, only_use_cftime_datetimes=True)\n )\n else:\n # Must be using num2date from an old version of netCDF4 which\n # does not have the only_use_cftime_datetimes option.\n return np.asarray(cftime.num2date(num_dates, units, calendar))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_datetime_with_pandas__decode_datetime_with_pandas.return._pd_to_timedelta_flat_num": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__decode_datetime_with_pandas__decode_datetime_with_pandas.return._pd_to_timedelta_flat_num", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 165, "span_ids": ["_decode_datetime_with_pandas"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decode_datetime_with_pandas(flat_num_dates, units, calendar):\n if calendar not in _STANDARD_CALENDARS:\n raise OutOfBoundsDatetime(\n \"Cannot decode times from a non-standard calendar, {!r}, using \"\n \"pandas.\".format(calendar)\n )\n\n delta, ref_date = _unpack_netcdf_time_units(units)\n delta = _netcdf_to_numpy_timeunit(delta)\n try:\n ref_date = pd.Timestamp(ref_date)\n except ValueError:\n # ValueError is raised by pd.Timestamp for non-ISO timestamp\n # strings, in which case we fall back to using cftime\n raise OutOfBoundsDatetime\n\n # fixes: https://github.com/pydata/pandas/issues/14068\n # these lines check if the the lowest or the highest value in dates\n # cause an OutOfBoundsDatetime (Overflow) error\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"invalid value encountered\", RuntimeWarning)\n pd.to_timedelta(flat_num_dates.min(), delta) + ref_date\n pd.to_timedelta(flat_num_dates.max(), delta) + ref_date\n\n # Cast input dates to integers of nanoseconds because `pd.to_datetime`\n # works much faster when dealing with integers\n # make _NS_PER_TIME_DELTA an array to ensure type upcasting\n flat_num_dates_ns_int = (\n flat_num_dates.astype(np.float64) * _NS_PER_TIME_DELTA[delta]\n ).astype(np.int64)\n\n return (pd.to_timedelta(flat_num_dates_ns_int, \"ns\") + ref_date).values", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_decode_cf_datetime_decode_cf_datetime.return.dates_reshape_num_dates_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_decode_cf_datetime_decode_cf_datetime.return.dates_reshape_num_dates_s", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 219, "span_ids": ["decode_cf_datetime"], "tokens": 408}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):\n \"\"\"Given an array of numeric dates in netCDF format, convert it into a\n numpy array of date time objects.\n\n For standard (Gregorian) calendars, this function uses vectorized\n operations, which makes it much faster than cftime.num2date. In such a\n case, the returned array will be of type np.datetime64.\n\n Note that time unit in `units` must not be smaller than microseconds and\n not larger than days.\n\n See also\n --------\n cftime.num2date\n \"\"\"\n num_dates = np.asarray(num_dates)\n flat_num_dates = num_dates.ravel()\n if calendar is None:\n calendar = \"standard\"\n\n if use_cftime is None:\n try:\n dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)\n except (OutOfBoundsDatetime, OverflowError):\n dates = _decode_datetime_with_cftime(\n flat_num_dates.astype(np.float), units, calendar\n )\n\n if (\n dates[np.nanargmin(num_dates)].year < 1678\n or dates[np.nanargmax(num_dates)].year >= 2262\n ):\n if calendar in _STANDARD_CALENDARS:\n warnings.warn(\n \"Unable to decode time axis into full \"\n \"numpy.datetime64 objects, continuing using \"\n \"cftime.datetime objects instead, reason: dates out \"\n \"of range\",\n SerializationWarning,\n stacklevel=3,\n )\n else:\n if calendar in _STANDARD_CALENDARS:\n dates = cftime_to_nptime(dates)\n elif use_cftime:\n dates = _decode_datetime_with_cftime(\n flat_num_dates.astype(np.float), units, calendar\n )\n else:\n dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)\n\n return dates.reshape(num_dates.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_to_timedelta_unboxed_infer_calendar_name.if_np_asarray_dates_dtyp.else_.return.np_asarray_dates_ravel_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_to_timedelta_unboxed_infer_calendar_name.if_np_asarray_dates_dtyp.else_.return.np_asarray_dates_ravel_", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 222, "end_line": 265, "span_ids": ["to_timedelta_unboxed", "to_datetime_unboxed", "_infer_time_units_from_diff", "decode_cf_timedelta", "infer_calendar_name"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_timedelta_unboxed(value, **kwargs):\n if LooseVersion(pd.__version__) < \"0.25.0\":\n result = pd.to_timedelta(value, **kwargs, box=False)\n else:\n result = pd.to_timedelta(value, **kwargs).to_numpy()\n assert result.dtype == \"timedelta64[ns]\"\n return result\n\n\ndef to_datetime_unboxed(value, **kwargs):\n if LooseVersion(pd.__version__) < \"0.25.0\":\n result = pd.to_datetime(value, **kwargs, box=False)\n else:\n result = pd.to_datetime(value, **kwargs).to_numpy()\n assert result.dtype == \"datetime64[ns]\"\n return result\n\n\ndef decode_cf_timedelta(num_timedeltas, units):\n \"\"\"Given an array of numeric timedeltas in netCDF format, convert it into a\n numpy timedelta64[ns] array.\n \"\"\"\n num_timedeltas = np.asarray(num_timedeltas)\n units = _netcdf_to_numpy_timeunit(units)\n result = to_timedelta_unboxed(num_timedeltas.ravel(), unit=units)\n return result.reshape(num_timedeltas.shape)\n\n\ndef _infer_time_units_from_diff(unique_timedeltas):\n for time_unit in [\"days\", \"hours\", \"minutes\", \"seconds\"]:\n delta_ns = _NS_PER_TIME_DELTA[_netcdf_to_numpy_timeunit(time_unit)]\n unit_delta = np.timedelta64(delta_ns, \"ns\")\n diffs = unique_timedeltas / unit_delta\n if np.all(diffs == diffs.astype(int)):\n return time_unit\n return \"seconds\"\n\n\ndef infer_calendar_name(dates):\n \"\"\"Given an array of datetimes, infer the CF calendar name\"\"\"\n if np.asarray(dates).dtype == \"datetime64[ns]\":\n return \"proleptic_gregorian\"\n else:\n return np.asarray(dates).ravel()[0].calendar", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_infer_datetime_units_infer_datetime_units.return._s_since_s_units_r": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_infer_datetime_units_infer_datetime_units.return._s_since_s_units_r", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 289, "span_ids": ["infer_datetime_units"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def infer_datetime_units(dates):\n \"\"\"Given an array of datetimes, returns a CF compatible time-unit string of\n the form \"{time_unit} since {date[0]}\", where `time_unit` is 'days',\n 'hours', 'minutes' or 'seconds' (the first one that can evenly divide all\n unique time deltas in `dates`)\n \"\"\"\n dates = np.asarray(dates).ravel()\n if np.asarray(dates).dtype == \"datetime64[ns]\":\n dates = to_datetime_unboxed(dates)\n dates = dates[pd.notnull(dates)]\n reference_date = dates[0] if len(dates) > 0 else \"1970-01-01\"\n reference_date = pd.Timestamp(reference_date)\n else:\n reference_date = dates[0] if len(dates) > 0 else \"1970-01-01\"\n reference_date = format_cftime_datetime(reference_date)\n unique_timedeltas = np.unique(np.diff(dates))\n if unique_timedeltas.dtype == np.dtype(\"O\"):\n # Convert to np.timedelta64 objects using pandas to work around a\n # NumPy casting bug: https://github.com/numpy/numpy/issues/11096\n unique_timedeltas = to_timedelta_unboxed(unique_timedeltas)\n units = _infer_time_units_from_diff(unique_timedeltas)\n return \"%s since %s\" % (units, reference_date)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_format_cftime_datetime_format_cftime_datetime.return._04d_02d_02d_0": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_format_cftime_datetime_format_cftime_datetime.return._04d_02d_02d_0", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 292, "end_line": 304, "span_ids": ["format_cftime_datetime"], "tokens": 103}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def format_cftime_datetime(date):\n \"\"\"Converts a cftime.datetime object to a string with the format:\n YYYY-MM-DD HH:MM:SS.UUUUUU\n \"\"\"\n return \"{:04d}-{:02d}-{:02d} {:02d}:{:02d}:{:02d}.{:06d}\".format(\n date.year,\n date.month,\n date.day,\n date.hour,\n date.minute,\n date.second,\n date.microsecond,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_infer_timedelta_units_infer_timedelta_units.return.units": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_infer_timedelta_units_infer_timedelta_units.return.units", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 315, "span_ids": ["infer_timedelta_units"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def infer_timedelta_units(deltas):\n \"\"\"Given an array of timedeltas, returns a CF compatible time-unit from\n {'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly\n divide all unique time deltas in `deltas`)\n \"\"\"\n deltas = to_timedelta_unboxed(np.asarray(deltas).ravel())\n unique_timedeltas = np.unique(deltas[pd.notnull(deltas)])\n units = _infer_time_units_from_diff(unique_timedeltas)\n return units", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_cftime_to_nptime_cftime_to_nptime.return.new": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_cftime_to_nptime_cftime_to_nptime.return.new", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 318, "end_line": 338, "span_ids": ["cftime_to_nptime"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def cftime_to_nptime(times):\n \"\"\"Given an array of cftime.datetime objects, return an array of\n numpy.datetime64 objects of the same size\"\"\"\n times = np.asarray(times)\n new = np.empty(times.shape, dtype=\"M8[ns]\")\n for i, t in np.ndenumerate(times):\n try:\n # Use pandas.Timestamp in place of datetime.datetime, because\n # NumPy casts it safely it np.datetime64[ns] for dates outside\n # 1678 to 2262 (this is not currently the case for\n # datetime.datetime).\n dt = pd.Timestamp(\n t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond\n )\n except ValueError as e:\n raise ValueError(\n \"Cannot convert date {} to a date in the \"\n \"standard calendar. Reason: {}.\".format(t, e)\n )\n new[i] = np.datetime64(dt)\n return new", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__cleanup_netcdf_time_units_cast_to_int_if_safe.return.num": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py__cleanup_netcdf_time_units_cast_to_int_if_safe.return.num", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 341, "end_line": 373, "span_ids": ["_cleanup_netcdf_time_units", "cast_to_int_if_safe", "_encode_datetime_with_cftime"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _cleanup_netcdf_time_units(units):\n delta, ref_date = _unpack_netcdf_time_units(units)\n try:\n units = \"%s since %s\" % (delta, format_timestamp(ref_date))\n except OutOfBoundsDatetime:\n # don't worry about reifying the units if they're out of bounds\n pass\n return units\n\n\ndef _encode_datetime_with_cftime(dates, units, calendar):\n \"\"\"Fallback method for encoding dates using cftime.\n\n This method is more flexible than xarray's parsing using datetime64[ns]\n arrays but also slower because it loops over each element.\n \"\"\"\n cftime = _import_cftime()\n\n if np.issubdtype(dates.dtype, np.datetime64):\n # numpy's broken datetime conversion only works for us precision\n dates = dates.astype(\"M8[us]\").astype(datetime)\n\n def encode_datetime(d):\n return np.nan if d is None else cftime.date2num(d, units, calendar)\n\n return np.vectorize(encode_datetime)(dates)\n\n\ndef cast_to_int_if_safe(num):\n int_num = np.array(num, dtype=np.int64)\n if (num == int_num).all():\n num = int_num\n return num", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_encode_cf_datetime_encode_cf_datetime.return._num_units_calendar_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_encode_cf_datetime_encode_cf_datetime.return._num_units_calendar_", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 376, "end_line": 422, "span_ids": ["encode_cf_datetime"], "tokens": 402}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def encode_cf_datetime(dates, units=None, calendar=None):\n \"\"\"Given an array of datetime objects, returns the tuple `(num, units,\n calendar)` suitable for a CF compliant time variable.\n\n Unlike `date2num`, this function can handle datetime64 arrays.\n\n See also\n --------\n cftime.date2num\n \"\"\"\n dates = np.asarray(dates)\n\n if units is None:\n units = infer_datetime_units(dates)\n else:\n units = _cleanup_netcdf_time_units(units)\n\n if calendar is None:\n calendar = infer_calendar_name(dates)\n\n delta, ref_date = _unpack_netcdf_time_units(units)\n try:\n if calendar not in _STANDARD_CALENDARS or dates.dtype.kind == \"O\":\n # parse with cftime instead\n raise OutOfBoundsDatetime\n assert dates.dtype == \"datetime64[ns]\"\n\n delta_units = _netcdf_to_numpy_timeunit(delta)\n time_delta = np.timedelta64(1, delta_units).astype(\"timedelta64[ns]\")\n ref_date = pd.Timestamp(ref_date)\n\n # If the ref_date Timestamp is timezone-aware, convert to UTC and\n # make it timezone-naive (GH 2649).\n if ref_date.tz is not None:\n ref_date = ref_date.tz_convert(None)\n\n # Wrap the dates in a DatetimeIndex to do the subtraction to ensure\n # an OverflowError is raised if the ref_date is too far away from\n # dates to be encoded (GH 2272).\n num = (pd.DatetimeIndex(dates.ravel()) - ref_date) / time_delta\n num = num.values.reshape(dates.shape)\n\n except (OutOfBoundsDatetime, OverflowError):\n num = _encode_datetime_with_cftime(dates, units, calendar)\n\n num = cast_to_int_if_safe(num)\n return (num, units, calendar)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_encode_cf_timedelta_CFDatetimeCoder.encode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_encode_cf_timedelta_CFDatetimeCoder.encode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 425, "end_line": 451, "span_ids": ["encode_cf_timedelta", "CFDatetimeCoder.encode", "CFDatetimeCoder"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def encode_cf_timedelta(timedeltas, units=None):\n if units is None:\n units = infer_timedelta_units(timedeltas)\n\n np_unit = _netcdf_to_numpy_timeunit(units)\n num = 1.0 * timedeltas / np.timedelta64(1, np_unit)\n num = np.where(pd.isnull(timedeltas), np.nan, num)\n num = cast_to_int_if_safe(num)\n return (num, units)\n\n\nclass CFDatetimeCoder(VariableCoder):\n def __init__(self, use_cftime=None):\n self.use_cftime = use_cftime\n\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n if np.issubdtype(data.dtype, np.datetime64) or contains_cftime_datetimes(\n variable\n ):\n (data, units, calendar) = encode_cf_datetime(\n data, encoding.pop(\"units\", None), encoding.pop(\"calendar\", None)\n )\n safe_setitem(attrs, \"units\", units, name=name)\n safe_setitem(attrs, \"calendar\", calendar, name=name)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_CFDatetimeCoder.decode_CFDatetimeCoder.decode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_CFDatetimeCoder.decode_CFDatetimeCoder.decode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 453, "end_line": 468, "span_ids": ["CFDatetimeCoder.decode"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFDatetimeCoder(VariableCoder):\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if \"units\" in attrs and \"since\" in attrs[\"units\"]:\n units = pop_to(attrs, encoding, \"units\")\n calendar = pop_to(attrs, encoding, \"calendar\")\n dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n transform = partial(\n decode_cf_datetime,\n units=units,\n calendar=calendar,\n use_cftime=self.use_cftime,\n )\n data = lazy_elemwise_func(data, transform, dtype)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_CFTimedeltaCoder_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/times.py_CFTimedeltaCoder_", "embedding": null, "metadata": {"file_path": "xarray/coding/times.py", "file_name": "times.py", "file_type": "text/x-python", "category": "implementation", "start_line": 471, "end_line": 491, "span_ids": ["CFTimedeltaCoder.decode", "CFTimedeltaCoder", "CFTimedeltaCoder.encode"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimedeltaCoder(VariableCoder):\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n\n if np.issubdtype(data.dtype, np.timedelta64):\n data, units = encode_cf_timedelta(data, encoding.pop(\"units\", None))\n safe_setitem(attrs, \"units\", units, name=name)\n\n return Variable(dims, data, attrs, encoding)\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if \"units\" in attrs and attrs[\"units\"] in TIME_UNITS:\n units = pop_to(attrs, encoding, \"units\")\n transform = partial(decode_cf_timedelta, units=units)\n dtype = np.dtype(\"timedelta64[ns]\")\n data = lazy_elemwise_func(data, transform, dtype=dtype)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__Coders_for_individual__VariableCoder.decode.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__Coders_for_individual__VariableCoder.decode.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 48, "span_ids": ["VariableCoder.encode", "docstring", "VariableCoder", "imports", "VariableCoder.decode", "SerializationWarning"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Coders for individual Variable objects.\"\"\"\nimport warnings\nfrom functools import partial\nfrom typing import Any, Hashable\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..core import dtypes, duck_array_ops, indexing\nfrom ..core.pycompat import dask_array_type\nfrom ..core.utils import equivalent\nfrom ..core.variable import Variable\n\n\nclass SerializationWarning(RuntimeWarning):\n \"\"\"Warnings about encoding/decoding issues in serialization.\"\"\"\n\n\nclass VariableCoder:\n \"\"\"Base class for encoding and decoding transformations on variables.\n\n We use coders for transforming variables between xarray's data model and\n a format suitable for serialization. For example, coders apply CF\n conventions for how data should be represented in netCDF files.\n\n Subclasses should implement encode() and decode(), which should satisfy\n the identity ``coder.decode(coder.encode(variable)) == variable``. If any\n options are necessary, they should be implemented as arguments to the\n __init__ method.\n\n The optional name argument to encode() and decode() exists solely for the\n sake of better error messages, and should correspond to the name of\n variables in the underlying store.\n \"\"\"\n\n def encode(\n self, variable: Variable, name: Hashable = None\n ) -> Variable: # pragma: no cover\n \"\"\"Convert an encoded variable to a decoded variable\n \"\"\"\n raise NotImplementedError()\n\n def decode(\n self, variable: Variable, name: Hashable = None\n ) -> Variable: # pragma: no cover\n \"\"\"Convert an decoded variable to a encoded variable\n \"\"\"\n raise NotImplementedError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__ElementwiseFunctionArray__ElementwiseFunctionArray.__repr__.return._s_r_func_r_dtype_r": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__ElementwiseFunctionArray__ElementwiseFunctionArray.__repr__.return._s_r_func_r_dtype_r", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 51, "end_line": 81, "span_ids": ["_ElementwiseFunctionArray.__array__", "_ElementwiseFunctionArray.__repr__", "_ElementwiseFunctionArray.__getitem__", "_ElementwiseFunctionArray.dtype", "_ElementwiseFunctionArray"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _ElementwiseFunctionArray(indexing.ExplicitlyIndexedNDArrayMixin):\n \"\"\"Lazily computed array holding values of elemwise-function.\n\n Do not construct this object directly: call lazy_elemwise_func instead.\n\n Values are computed upon indexing or coercion to a NumPy array.\n \"\"\"\n\n def __init__(self, array, func, dtype):\n assert not isinstance(array, dask_array_type)\n self.array = indexing.as_indexable(array)\n self.func = func\n self._dtype = dtype\n\n @property\n def dtype(self):\n return np.dtype(self._dtype)\n\n def __getitem__(self, key):\n return type(self)(self.array[key], self.func, self.dtype)\n\n def __array__(self, dtype=None):\n return self.func(self.array)\n\n def __repr__(self):\n return \"%s(%r, func=%r, dtype=%r)\" % (\n type(self).__name__,\n self.array,\n self.func,\n self.dtype,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_lazy_elemwise_func_lazy_elemwise_func.if_isinstance_array_dask.else_.return._ElementwiseFunctionArray": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_lazy_elemwise_func_lazy_elemwise_func.if_isinstance_array_dask.else_.return._ElementwiseFunctionArray", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 103, "span_ids": ["lazy_elemwise_func"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def lazy_elemwise_func(array, func, dtype):\n \"\"\"Lazily apply an element-wise function to an array.\n\n Parameters\n ----------\n array : any valid value of Variable._data\n func : callable\n Function to apply to indexed slices of an array. For use with dask,\n this should be a pickle-able object.\n dtype : coercible to np.dtype\n Dtype for the result of this function.\n\n Returns\n -------\n Either a dask.array.Array or _ElementwiseFunctionArray.\n \"\"\"\n if isinstance(array, dask_array_type):\n return array.map_blocks(func, dtype=dtype)\n else:\n return _ElementwiseFunctionArray(array, func, dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_unpack_for_encoding_safe_setitem.dest_key_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_unpack_for_encoding_safe_setitem.dest_key_value", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 123, "span_ids": ["unpack_for_decoding", "safe_setitem", "unpack_for_encoding"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unpack_for_encoding(var):\n return var.dims, var.data, var.attrs.copy(), var.encoding.copy()\n\n\ndef unpack_for_decoding(var):\n return var.dims, var._data, var.attrs.copy(), var.encoding.copy()\n\n\ndef safe_setitem(dest, key, value, name=None):\n if key in dest:\n var_str = \" on variable {!r}\".format(name) if name else \"\"\n raise ValueError(\n \"failed to prevent overwriting existing key {} in attrs{}. \"\n \"This is probably an encoding field used by xarray to describe \"\n \"how a variable is serialized. To proceed, remove this key from \"\n \"the variable's attributes manually.\".format(key, var_str)\n )\n dest[key] = value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_pop_to__apply_mask.return.np_where_condition_decod": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_pop_to__apply_mask.return.np_where_condition_decod", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 146, "span_ids": ["pop_to", "_apply_mask"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def pop_to(source, dest, key, name=None):\n \"\"\"\n A convenience function which pops a key k from source to dest.\n None values are not passed on. If k already exists in dest an\n error is raised.\n \"\"\"\n value = source.pop(key, None)\n if value is not None:\n safe_setitem(dest, key, value, name=name)\n return value\n\n\ndef _apply_mask(\n data: np.ndarray, encoded_fill_values: list, decoded_fill_value: Any, dtype: Any\n) -> np.ndarray:\n \"\"\"Mask all matching values in a NumPy arrays.\"\"\"\n data = np.asarray(data, dtype=dtype)\n condition = False\n for fv in encoded_fill_values:\n condition |= data == fv\n return np.where(condition, decoded_fill_value, data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFMaskCoder_CFMaskCoder.encode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFMaskCoder_CFMaskCoder.encode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 149, "end_line": 174, "span_ids": ["CFMaskCoder", "CFMaskCoder.encode"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFMaskCoder(VariableCoder):\n \"\"\"Mask or unmask fill values according to CF conventions.\"\"\"\n\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n\n fv = encoding.get(\"_FillValue\")\n mv = encoding.get(\"missing_value\")\n\n if fv is not None and mv is not None and not equivalent(fv, mv):\n raise ValueError(\n \"Variable {!r} has multiple fill values {}. \"\n \"Cannot encode data. \".format(name, [fv, mv])\n )\n\n if fv is not None:\n fill_value = pop_to(encoding, attrs, \"_FillValue\", name=name)\n if not pd.isnull(fill_value):\n data = duck_array_ops.fillna(data, fill_value)\n\n if mv is not None:\n fill_value = pop_to(encoding, attrs, \"missing_value\", name=name)\n if not pd.isnull(fill_value) and fv is None:\n data = duck_array_ops.fillna(data, fill_value)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFMaskCoder.decode_CFMaskCoder.decode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFMaskCoder.decode_CFMaskCoder.decode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 210, "span_ids": ["CFMaskCoder.decode"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFMaskCoder(VariableCoder):\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n raw_fill_values = [\n pop_to(attrs, encoding, attr, name=name)\n for attr in (\"missing_value\", \"_FillValue\")\n ]\n if raw_fill_values:\n encoded_fill_values = {\n fv\n for option in raw_fill_values\n for fv in np.ravel(option)\n if not pd.isnull(fv)\n }\n\n if len(encoded_fill_values) > 1:\n warnings.warn(\n \"variable {!r} has multiple fill values {}, \"\n \"decoding all values to NaN.\".format(name, encoded_fill_values),\n SerializationWarning,\n stacklevel=3,\n )\n\n dtype, decoded_fill_value = dtypes.maybe_promote(data.dtype)\n\n if encoded_fill_values:\n transform = partial(\n _apply_mask,\n encoded_fill_values=encoded_fill_values,\n decoded_fill_value=decoded_fill_value,\n dtype=dtype,\n )\n data = lazy_elemwise_func(data, transform, dtype)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__scale_offset_decoding__choose_float_dtype.return.np_float64": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py__scale_offset_decoding__choose_float_dtype.return.np_float64", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 238, "span_ids": ["_choose_float_dtype", "_scale_offset_decoding"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _scale_offset_decoding(data, scale_factor, add_offset, dtype):\n data = np.array(data, dtype=dtype, copy=True)\n if scale_factor is not None:\n data *= scale_factor\n if add_offset is not None:\n data += add_offset\n return data\n\n\ndef _choose_float_dtype(dtype, has_offset):\n \"\"\"Return a float dtype that can losslessly represent `dtype` values.\"\"\"\n # Keep float32 as-is. Upcast half-precision to single-precision,\n # because float16 is \"intended for storage but not computation\"\n if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating):\n return np.float32\n # float32 can exactly represent all integers up to 24 bits\n if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer):\n # A scale factor is entirely safe (vanishing into the mantissa),\n # but a large integer offset could lead to loss of precision.\n # Sensitivity analysis can be tricky, so we just use a float64\n # if there's any offset at all - better unoptimised than wrong!\n if not has_offset:\n return np.float32\n # For all other types and circumstances, we just use float64.\n # (safe because eg. complex numbers are not supported in NetCDF)\n return np.float64", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFScaleOffsetCoder_CFScaleOffsetCoder.encode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFScaleOffsetCoder_CFScaleOffsetCoder.encode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 241, "end_line": 259, "span_ids": ["CFScaleOffsetCoder.encode", "CFScaleOffsetCoder"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFScaleOffsetCoder(VariableCoder):\n \"\"\"Scale and offset variables according to CF conventions.\n\n Follows the formula:\n decode_values = encoded_values * scale_factor + add_offset\n \"\"\"\n\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n\n if \"scale_factor\" in encoding or \"add_offset\" in encoding:\n dtype = _choose_float_dtype(data.dtype, \"add_offset\" in encoding)\n data = data.astype(dtype=dtype, copy=True)\n if \"add_offset\" in encoding:\n data -= pop_to(encoding, attrs, \"add_offset\", name=name)\n if \"scale_factor\" in encoding:\n data /= pop_to(encoding, attrs, \"scale_factor\", name=name)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFScaleOffsetCoder.decode_CFScaleOffsetCoder.decode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_CFScaleOffsetCoder.decode_CFScaleOffsetCoder.decode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 261, "end_line": 276, "span_ids": ["CFScaleOffsetCoder.decode"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFScaleOffsetCoder(VariableCoder):\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if \"scale_factor\" in attrs or \"add_offset\" in attrs:\n scale_factor = pop_to(attrs, encoding, \"scale_factor\", name=name)\n add_offset = pop_to(attrs, encoding, \"add_offset\", name=name)\n dtype = _choose_float_dtype(data.dtype, \"add_offset\" in attrs)\n transform = partial(\n _scale_offset_decoding,\n scale_factor=scale_factor,\n add_offset=add_offset,\n dtype=dtype,\n )\n data = lazy_elemwise_func(data, transform, dtype)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_UnsignedIntegerCoder_UnsignedIntegerCoder.encode.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_UnsignedIntegerCoder_UnsignedIntegerCoder.encode.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 279, "end_line": 295, "span_ids": ["UnsignedIntegerCoder.encode", "UnsignedIntegerCoder"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnsignedIntegerCoder(VariableCoder):\n def encode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_encoding(variable)\n\n # from netCDF best practices\n # https://www.unidata.ucar.edu/software/netcdf/docs/BestPractices.html\n # \"_Unsigned = \"true\" to indicate that\n # integer data should be treated as unsigned\"\n if encoding.get(\"_Unsigned\", \"false\") == \"true\":\n pop_to(encoding, attrs, \"_Unsigned\")\n signed_dtype = np.dtype(\"i%s\" % data.dtype.itemsize)\n if \"_FillValue\" in attrs:\n new_fill = signed_dtype.type(attrs[\"_FillValue\"])\n attrs[\"_FillValue\"] = new_fill\n data = duck_array_ops.around(data).astype(signed_dtype)\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_UnsignedIntegerCoder.decode_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/coding/variables.py_UnsignedIntegerCoder.decode_", "embedding": null, "metadata": {"file_path": "xarray/coding/variables.py", "file_name": "variables.py", "file_type": "text/x-python", "category": "implementation", "start_line": 297, "end_line": 320, "span_ids": ["UnsignedIntegerCoder.decode"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnsignedIntegerCoder(VariableCoder):\n\n def decode(self, variable, name=None):\n dims, data, attrs, encoding = unpack_for_decoding(variable)\n\n if \"_Unsigned\" in attrs:\n unsigned = pop_to(attrs, encoding, \"_Unsigned\")\n\n if data.dtype.kind == \"i\":\n if unsigned == \"true\":\n unsigned_dtype = np.dtype(\"u%s\" % data.dtype.itemsize)\n transform = partial(np.asarray, dtype=unsigned_dtype)\n data = lazy_elemwise_func(data, transform, unsigned_dtype)\n if \"_FillValue\" in attrs:\n new_fill = unsigned_dtype.type(attrs[\"_FillValue\"])\n attrs[\"_FillValue\"] = new_fill\n else:\n warnings.warn(\n \"variable %r has _Unsigned attribute but is not \"\n \"of integer type. Ignoring attribute.\" % name,\n SerializationWarning,\n stacklevel=3,\n )\n\n return Variable(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_warnings_NativeEndiannessArray.__getitem__.return.np_asarray_self_array_key": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_warnings_NativeEndiannessArray.__getitem__.return.np_asarray_self_array_key", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 44, "span_ids": ["NativeEndiannessArray.dtype", "NativeEndiannessArray.__getitem__", "NativeEndiannessArray", "imports"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom collections import defaultdict\n\nimport numpy as np\nimport pandas as pd\n\nfrom .coding import strings, times, variables\nfrom .coding.variables import SerializationWarning\nfrom .core import duck_array_ops, indexing\nfrom .core.common import contains_cftime_datetimes\nfrom .core.pycompat import dask_array_type\nfrom .core.variable import IndexVariable, Variable, as_variable\n\n\nclass NativeEndiannessArray(indexing.ExplicitlyIndexedNDArrayMixin):\n \"\"\"Decode arrays on the fly from non-native to native endianness\n\n This is useful for decoding arrays from netCDF3 files (which are all\n big endian) into native endianness, so they can be used with Cython\n functions, such as those found in bottleneck and pandas.\n\n >>> x = np.arange(5, dtype='>i2')\n\n >>> x.dtype\n dtype('>i2')\n\n >>> NativeEndianArray(x).dtype\n dtype('int16')\n\n >>> NativeEndianArray(x)[:].dtype\n dtype('int16')\n \"\"\"\n\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n self.array = indexing.as_indexable(array)\n\n @property\n def dtype(self):\n return np.dtype(self.array.dtype.kind + str(self.array.dtype.itemsize))\n\n def __getitem__(self, key):\n return np.asarray(self.array[key], dtype=self.dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_BoolTypeArray__var_as_tuple.return.var_dims_var_data_var_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_BoolTypeArray__var_as_tuple.return.var_dims_var_data_var_a", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 47, "end_line": 79, "span_ids": ["BoolTypeArray.dtype", "BoolTypeArray", "BoolTypeArray.__getitem__", "_var_as_tuple"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BoolTypeArray(indexing.ExplicitlyIndexedNDArrayMixin):\n \"\"\"Decode arrays on the fly from integer to boolean datatype\n\n This is useful for decoding boolean arrays from integer typed netCDF\n variables.\n\n >>> x = np.array([1, 0, 1, 1, 0], dtype='i1')\n\n >>> x.dtype\n dtype('>i2')\n\n >>> BoolTypeArray(x).dtype\n dtype('bool')\n\n >>> BoolTypeArray(x)[:].dtype\n dtype('bool')\n \"\"\"\n\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n self.array = indexing.as_indexable(array)\n\n @property\n def dtype(self):\n return np.dtype(\"bool\")\n\n def __getitem__(self, key):\n return np.asarray(self.array[key], dtype=self.dtype)\n\n\ndef _var_as_tuple(var):\n return var.dims, var.data, var.attrs.copy(), var.encoding.copy()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_maybe_encode_nonstring_dtype_maybe_encode_nonstring_dtype.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_maybe_encode_nonstring_dtype_maybe_encode_nonstring_dtype.return.var", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 103, "span_ids": ["maybe_encode_nonstring_dtype"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maybe_encode_nonstring_dtype(var, name=None):\n if \"dtype\" in var.encoding and var.encoding[\"dtype\"] not in (\"S1\", str):\n dims, data, attrs, encoding = _var_as_tuple(var)\n dtype = np.dtype(encoding.pop(\"dtype\"))\n if dtype != var.dtype:\n if np.issubdtype(dtype, np.integer):\n if (\n np.issubdtype(var.dtype, np.floating)\n and \"_FillValue\" not in var.attrs\n and \"missing_value\" not in var.attrs\n ):\n warnings.warn(\n \"saving variable %s with floating \"\n \"point data as an integer dtype without \"\n \"any _FillValue to use for NaNs\" % name,\n SerializationWarning,\n stacklevel=10,\n )\n data = duck_array_ops.around(data)[...]\n data = data.astype(dtype=dtype)\n var = Variable(dims, data, attrs, encoding)\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_maybe_default_fill_value_maybe_encode_bools.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_maybe_default_fill_value_maybe_encode_bools.return.var", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 127, "span_ids": ["maybe_encode_bools", "maybe_default_fill_value"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maybe_default_fill_value(var):\n # make NaN the fill value for float types:\n if (\n \"_FillValue\" not in var.attrs\n and \"_FillValue\" not in var.encoding\n and np.issubdtype(var.dtype, np.floating)\n ):\n var.attrs[\"_FillValue\"] = var.dtype.type(np.nan)\n return var\n\n\ndef maybe_encode_bools(var):\n if (\n (var.dtype == np.bool)\n and (\"dtype\" not in var.encoding)\n and (\"dtype\" not in var.attrs)\n ):\n dims, data, attrs, encoding = _var_as_tuple(var)\n attrs[\"dtype\"] = \"bool\"\n data = data.astype(dtype=\"i1\", copy=True)\n var = Variable(dims, data, attrs, encoding)\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__infer_dtype__infer_dtype.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__infer_dtype__infer_dtype.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 130, "end_line": 151, "span_ids": ["_infer_dtype"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_dtype(array, name=None):\n \"\"\"Given an object array with no missing values, infer its dtype from its\n first element\n \"\"\"\n if array.dtype.kind != \"O\":\n raise TypeError(\"infer_type must be called on a dtype=object array\")\n\n if array.size == 0:\n return np.dtype(float)\n\n element = array[(0,) * array.ndim]\n if isinstance(element, (bytes, str)):\n return strings.create_vlen_dtype(type(element))\n\n dtype = np.array(element).dtype\n if dtype.kind != \"O\":\n return dtype\n\n raise ValueError(\n \"unable to infer dtype on variable {!r}; xarray \"\n \"cannot serialize arbitrary Python objects\".format(name)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_ensure_not_multiindex__copy_with_dtype.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_ensure_not_multiindex__copy_with_dtype.return.result", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 154, "end_line": 173, "span_ids": ["_copy_with_dtype", "ensure_not_multiindex"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ensure_not_multiindex(var, name=None):\n if isinstance(var, IndexVariable) and isinstance(var.to_index(), pd.MultiIndex):\n raise NotImplementedError(\n \"variable {!r} is a MultiIndex, which cannot yet be \"\n \"serialized to netCDF files \"\n \"(https://github.com/pydata/xarray/issues/1077). Use \"\n \"reset_index() to convert MultiIndex levels into coordinate \"\n \"variables instead.\".format(name)\n )\n\n\ndef _copy_with_dtype(data, dtype):\n \"\"\"Create a copy of an array with the given dtype.\n\n We use this instead of np.array() to ensure that custom object dtypes end\n up on the resulting array.\n \"\"\"\n result = np.empty(data.shape, dtype)\n result[...] = data\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_ensure_dtype_not_object_ensure_dtype_not_object.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_ensure_dtype_not_object_ensure_dtype_not_object.return.var", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 218, "span_ids": ["ensure_dtype_not_object"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ensure_dtype_not_object(var, name=None):\n # TODO: move this from conventions to backends? (it's not CF related)\n if var.dtype.kind == \"O\":\n dims, data, attrs, encoding = _var_as_tuple(var)\n\n if isinstance(data, dask_array_type):\n warnings.warn(\n \"variable {} has data in the form of a dask array with \"\n \"dtype=object, which means it is being loaded into memory \"\n \"to determine a data type that can be safely stored on disk. \"\n \"To avoid this, coerce this variable to a fixed-size dtype \"\n \"with astype() before saving it.\".format(name),\n SerializationWarning,\n )\n data = data.compute()\n\n missing = pd.isnull(data)\n if missing.any():\n # nb. this will fail for dask.array data\n non_missing_values = data[~missing]\n inferred_dtype = _infer_dtype(non_missing_values, name)\n\n # There is no safe bit-pattern for NA in typical binary string\n # formats, we so can't set a fill_value. Unfortunately, this means\n # we can't distinguish between missing values and empty strings.\n if strings.is_bytes_dtype(inferred_dtype):\n fill_value = b\"\"\n elif strings.is_unicode_dtype(inferred_dtype):\n fill_value = \"\"\n else:\n # insist on using float for numeric values\n if not np.issubdtype(inferred_dtype, np.floating):\n inferred_dtype = np.dtype(float)\n fill_value = inferred_dtype.type(np.nan)\n\n data = _copy_with_dtype(data, dtype=inferred_dtype)\n data[missing] = fill_value\n else:\n data = _copy_with_dtype(data, dtype=_infer_dtype(data, name))\n\n assert data.dtype.kind != \"O\" or data.dtype.metadata\n var = Variable(dims, data, attrs, encoding)\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_encode_cf_variable_encode_cf_variable.return.var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_encode_cf_variable_encode_cf_variable.return.var", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 221, "end_line": 257, "span_ids": ["encode_cf_variable"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def encode_cf_variable(var, needs_copy=True, name=None):\n \"\"\"\n Converts an Variable into an Variable which follows some\n of the CF conventions:\n\n - Nans are masked using _FillValue (or the deprecated missing_value)\n - Rescaling via: scale_factor and add_offset\n - datetimes are converted to the CF 'units since time' format\n - dtype encodings are enforced.\n\n Parameters\n ----------\n var : xarray.Variable\n A variable holding un-encoded data.\n\n Returns\n -------\n out : xarray.Variable\n A variable which has been encoded as described above.\n \"\"\"\n ensure_not_multiindex(var, name=name)\n\n for coder in [\n times.CFDatetimeCoder(),\n times.CFTimedeltaCoder(),\n variables.CFScaleOffsetCoder(),\n variables.CFMaskCoder(),\n variables.UnsignedIntegerCoder(),\n ]:\n var = coder.encode(var, name=name)\n\n # TODO(shoyer): convert all of these to use coders, too:\n var = maybe_encode_nonstring_dtype(var, name=name)\n var = maybe_default_fill_value(var)\n var = maybe_encode_bools(var)\n var = ensure_dtype_not_object(var, name=name)\n return var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_variable_decode_cf_variable.return.Variable_dimensions_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_variable_decode_cf_variable.return.Variable_dimensions_data", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 260, "end_line": 355, "span_ids": ["decode_cf_variable"], "tokens": 797}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decode_cf_variable(\n name,\n var,\n concat_characters=True,\n mask_and_scale=True,\n decode_times=True,\n decode_endianness=True,\n stack_char_dim=True,\n use_cftime=None,\n):\n \"\"\"\n Decodes a variable which may hold CF encoded information.\n\n This includes variables that have been masked and scaled, which\n hold CF style time variables (this is almost always the case if\n the dataset has been serialized) and which have strings encoded\n as character arrays.\n\n Parameters\n ----------\n name: str\n Name of the variable. Used for better error messages.\n var : Variable\n A variable holding potentially CF encoded information.\n concat_characters : bool\n Should character arrays be concatenated to strings, for\n example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'\n mask_and_scale: bool\n Lazily scale (using scale_factor and add_offset) and mask\n (using _FillValue). If the _Unsigned attribute is present\n treat integer arrays as unsigned.\n decode_times : bool\n Decode cf times ('hours since 2000-01-01') to np.datetime64.\n decode_endianness : bool\n Decode arrays from non-native to native endianness.\n stack_char_dim : bool\n Whether to stack characters into bytes along the last dimension of this\n array. Passed as an argument because we need to look at the full\n dataset to figure out if this is appropriate.\n use_cftime: bool, optional\n Only relevant if encoded dates come from a standard calendar\n (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not\n specified). If None (default), attempt to decode times to\n ``np.datetime64[ns]`` objects; if this is not possible, decode times to\n ``cftime.datetime`` objects. If True, always decode times to\n ``cftime.datetime`` objects, regardless of whether or not they can be\n represented using ``np.datetime64[ns]`` objects. If False, always\n decode times to ``np.datetime64[ns]`` objects; if this is not possible\n raise an error.\n\n Returns\n -------\n out : Variable\n A variable holding the decoded equivalent of var.\n \"\"\"\n var = as_variable(var)\n original_dtype = var.dtype\n\n if concat_characters:\n if stack_char_dim:\n var = strings.CharacterArrayCoder().decode(var, name=name)\n var = strings.EncodedStringCoder().decode(var)\n\n if mask_and_scale:\n for coder in [\n variables.UnsignedIntegerCoder(),\n variables.CFMaskCoder(),\n variables.CFScaleOffsetCoder(),\n ]:\n var = coder.decode(var, name=name)\n\n if decode_times:\n for coder in [\n times.CFTimedeltaCoder(),\n times.CFDatetimeCoder(use_cftime=use_cftime),\n ]:\n var = coder.decode(var, name=name)\n\n dimensions, data, attributes, encoding = variables.unpack_for_decoding(var)\n # TODO(shoyer): convert everything below to use coders\n\n if decode_endianness and not data.dtype.isnative:\n # do this last, so it's only done if we didn't already unmask/scale\n data = NativeEndiannessArray(data)\n original_dtype = data.dtype\n\n encoding.setdefault(\"dtype\", original_dtype)\n\n if \"dtype\" in attributes and attributes[\"dtype\"] == \"bool\":\n del attributes[\"dtype\"]\n data = BoolTypeArray(data)\n\n if not isinstance(data, dask_array_type):\n data = indexing.LazilyOuterIndexedArray(data)\n\n return Variable(dimensions, data, attributes, encoding=encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__update_bounds_attributes__update_bounds_attributes.for_v_in_variables_values.if_has_date_units_and_bo.if_attrs_bounds_in_var.if_calendar_in_attrs_.bounds_attrs_setdefault_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__update_bounds_attributes__update_bounds_attributes.for_v_in_variables_values.if_has_date_units_and_bo.if_attrs_bounds_in_var.if_calendar_in_attrs_.bounds_attrs_setdefault_", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 358, "end_line": 383, "span_ids": ["_update_bounds_attributes"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _update_bounds_attributes(variables):\n \"\"\"Adds time attributes to time bounds variables.\n\n Variables handling time bounds (\"Cell boundaries\" in the CF\n conventions) do not necessarily carry the necessary attributes to be\n decoded. This copies the attributes from the time variable to the\n associated boundaries.\n\n See Also:\n\n http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/\n cf-conventions.html#cell-boundaries\n\n https://github.com/pydata/xarray/issues/2565\n \"\"\"\n\n # For all time variables with bounds\n for v in variables.values():\n attrs = v.attrs\n has_date_units = \"units\" in attrs and \"since\" in attrs[\"units\"]\n if has_date_units and \"bounds\" in attrs:\n if attrs[\"bounds\"] in variables:\n bounds_attrs = variables[attrs[\"bounds\"]].attrs\n bounds_attrs.setdefault(\"units\", attrs[\"units\"])\n if \"calendar\" in attrs:\n bounds_attrs.setdefault(\"calendar\", attrs[\"calendar\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__update_bounds_encoding__update_bounds_encoding.for_v_in_variables_values.if_has_date_units_and_bo.if_attrs_bounds_in_var.if_calendar_in_encoding.bounds_encoding_setdefaul": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__update_bounds_encoding__update_bounds_encoding.for_v_in_variables_values.if_has_date_units_and_bo.if_attrs_bounds_in_var.if_calendar_in_encoding.bounds_encoding_setdefaul", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 386, "end_line": 433, "span_ids": ["_update_bounds_encoding"], "tokens": 404}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _update_bounds_encoding(variables):\n \"\"\"Adds time encoding to time bounds variables.\n\n Variables handling time bounds (\"Cell boundaries\" in the CF\n conventions) do not necessarily carry the necessary attributes to be\n decoded. This copies the encoding from the time variable to the\n associated bounds variable so that we write CF-compliant files.\n\n See Also:\n\n http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/\n cf-conventions.html#cell-boundaries\n\n https://github.com/pydata/xarray/issues/2565\n \"\"\"\n\n # For all time variables with bounds\n for v in variables.values():\n attrs = v.attrs\n encoding = v.encoding\n has_date_units = \"units\" in encoding and \"since\" in encoding[\"units\"]\n is_datetime_type = np.issubdtype(\n v.dtype, np.datetime64\n ) or contains_cftime_datetimes(v)\n\n if (\n is_datetime_type\n and not has_date_units\n and \"bounds\" in attrs\n and attrs[\"bounds\"] in variables\n ):\n warnings.warn(\n \"Variable '{0}' has datetime type and a \"\n \"bounds variable but {0}.encoding does not have \"\n \"units specified. The units encodings for '{0}' \"\n \"and '{1}' will be determined independently \"\n \"and may not be equal, counter to CF-conventions. \"\n \"If this is a concern, specify a units encoding for \"\n \"'{0}' before writing to a file.\".format(v.name, attrs[\"bounds\"]),\n UserWarning,\n )\n\n if has_date_units and \"bounds\" in attrs:\n if attrs[\"bounds\"] in variables:\n bounds_encoding = variables[attrs[\"bounds\"]].encoding\n bounds_encoding.setdefault(\"units\", encoding[\"units\"])\n if \"calendar\" in encoding:\n bounds_encoding.setdefault(\"calendar\", encoding[\"calendar\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_variables_decode_cf_variables.return.new_vars_attributes_coo": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_variables_decode_cf_variables.return.new_vars_attributes_coo", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 436, "end_line": 510, "span_ids": ["decode_cf_variables"], "tokens": 465}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decode_cf_variables(\n variables,\n attributes,\n concat_characters=True,\n mask_and_scale=True,\n decode_times=True,\n decode_coords=True,\n drop_variables=None,\n use_cftime=None,\n):\n \"\"\"\n Decode several CF encoded variables.\n\n See: decode_cf_variable\n \"\"\"\n dimensions_used_by = defaultdict(list)\n for v in variables.values():\n for d in v.dims:\n dimensions_used_by[d].append(v)\n\n def stackable(dim):\n # figure out if a dimension can be concatenated over\n if dim in variables:\n return False\n for v in dimensions_used_by[dim]:\n if v.dtype.kind != \"S\" or dim != v.dims[-1]:\n return False\n return True\n\n coord_names = set()\n\n if isinstance(drop_variables, str):\n drop_variables = [drop_variables]\n elif drop_variables is None:\n drop_variables = []\n drop_variables = set(drop_variables)\n\n # Time bounds coordinates might miss the decoding attributes\n if decode_times:\n _update_bounds_attributes(variables)\n\n new_vars = {}\n for k, v in variables.items():\n if k in drop_variables:\n continue\n stack_char_dim = (\n concat_characters\n and v.dtype == \"S1\"\n and v.ndim > 0\n and stackable(v.dims[-1])\n )\n new_vars[k] = decode_cf_variable(\n k,\n v,\n concat_characters=concat_characters,\n mask_and_scale=mask_and_scale,\n decode_times=decode_times,\n stack_char_dim=stack_char_dim,\n use_cftime=use_cftime,\n )\n if decode_coords:\n var_attrs = new_vars[k].attrs\n if \"coordinates\" in var_attrs:\n coord_str = var_attrs[\"coordinates\"]\n var_coord_names = coord_str.split()\n if all(k in variables for k in var_coord_names):\n new_vars[k].encoding[\"coordinates\"] = coord_str\n del var_attrs[\"coordinates\"]\n coord_names.update(var_coord_names)\n\n if decode_coords and \"coordinates\" in attributes:\n attributes = dict(attributes)\n coord_names.update(attributes.pop(\"coordinates\").split())\n\n return new_vars, attributes, coord_names", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_decode_cf.return.ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_decode_cf_decode_cf.return.ds", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 513, "end_line": 592, "span_ids": ["decode_cf"], "tokens": 637}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decode_cf(\n obj,\n concat_characters=True,\n mask_and_scale=True,\n decode_times=True,\n decode_coords=True,\n drop_variables=None,\n use_cftime=None,\n):\n \"\"\"Decode the given Dataset or Datastore according to CF conventions into\n a new Dataset.\n\n Parameters\n ----------\n obj : Dataset or DataStore\n Object to decode.\n concat_characters : bool, optional\n Should character arrays be concatenated to strings, for\n example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'\n mask_and_scale: bool, optional\n Lazily scale (using scale_factor and add_offset) and mask\n (using _FillValue).\n decode_times : bool, optional\n Decode cf times (e.g., integers since 'hours since 2000-01-01') to\n np.datetime64.\n decode_coords : bool, optional\n Use the 'coordinates' attribute on variable (or the dataset itself) to\n identify coordinates.\n drop_variables: string or iterable, optional\n A variable or list of variables to exclude from being parsed from the\n dataset. This may be useful to drop variables with problems or\n inconsistent values.\n use_cftime: bool, optional\n Only relevant if encoded dates come from a standard calendar\n (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not\n specified). If None (default), attempt to decode times to\n ``np.datetime64[ns]`` objects; if this is not possible, decode times to\n ``cftime.datetime`` objects. If True, always decode times to\n ``cftime.datetime`` objects, regardless of whether or not they can be\n represented using ``np.datetime64[ns]`` objects. If False, always\n decode times to ``np.datetime64[ns]`` objects; if this is not possible\n raise an error.\n\n Returns\n -------\n decoded : Dataset\n \"\"\"\n from .core.dataset import Dataset\n from .backends.common import AbstractDataStore\n\n if isinstance(obj, Dataset):\n vars = obj._variables\n attrs = obj.attrs\n extra_coords = set(obj.coords)\n file_obj = obj._file_obj\n encoding = obj.encoding\n elif isinstance(obj, AbstractDataStore):\n vars, attrs = obj.load()\n extra_coords = set()\n file_obj = obj\n encoding = obj.get_encoding()\n else:\n raise TypeError(\"can only decode Dataset or DataStore objects\")\n\n vars, attrs, coord_names = decode_cf_variables(\n vars,\n attrs,\n concat_characters,\n mask_and_scale,\n decode_times,\n decode_coords,\n drop_variables=drop_variables,\n use_cftime=use_cftime,\n )\n ds = Dataset(vars, attrs=attrs)\n ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars))\n ds._file_obj = file_obj\n ds.encoding = encoding\n\n return ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_cf_decoder_cf_decoder.return.variables_attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_cf_decoder_cf_decoder.return.variables_attributes", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 595, "end_line": 634, "span_ids": ["cf_decoder"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def cf_decoder(\n variables,\n attributes,\n concat_characters=True,\n mask_and_scale=True,\n decode_times=True,\n):\n \"\"\"\n Decode a set of CF encoded variables and attributes.\n\n Parameters\n ----------\n variables : dict\n A dictionary mapping from variable name to xarray.Variable\n attributes : dict\n A dictionary mapping from attribute name to value\n concat_characters : bool\n Should character arrays be concatenated to strings, for\n example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'\n mask_and_scale: bool\n Lazily scale (using scale_factor and add_offset) and mask\n (using _FillValue).\n decode_times : bool\n Decode cf times ('hours since 2000-01-01') to np.datetime64.\n\n Returns\n -------\n decoded_variables : dict\n A dictionary mapping from variable name to xarray.Variable objects.\n decoded_attributes : dict\n A dictionary mapping from attribute name to values.\n\n See also\n --------\n decode_cf_variable\n \"\"\"\n variables, attributes, _ = decode_cf_variables(\n variables, attributes, concat_characters, mask_and_scale, decode_times\n )\n return variables, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__encode_coordinates__encode_coordinates.return.variables_attributes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py__encode_coordinates__encode_coordinates.return.variables_attributes", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 637, "end_line": 692, "span_ids": ["_encode_coordinates"], "tokens": 478}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _encode_coordinates(variables, attributes, non_dim_coord_names):\n # calculate global and variable specific coordinates\n non_dim_coord_names = set(non_dim_coord_names)\n\n for name in list(non_dim_coord_names):\n if isinstance(name, str) and \" \" in name:\n warnings.warn(\n \"coordinate {!r} has a space in its name, which means it \"\n \"cannot be marked as a coordinate on disk and will be \"\n \"saved as a data variable instead\".format(name),\n SerializationWarning,\n stacklevel=6,\n )\n non_dim_coord_names.discard(name)\n\n global_coordinates = non_dim_coord_names.copy()\n variable_coordinates = defaultdict(set)\n for coord_name in non_dim_coord_names:\n target_dims = variables[coord_name].dims\n for k, v in variables.items():\n if (\n k not in non_dim_coord_names\n and k not in v.dims\n and set(target_dims) <= set(v.dims)\n ):\n variable_coordinates[k].add(coord_name)\n global_coordinates.discard(coord_name)\n\n variables = {k: v.copy(deep=False) for k, v in variables.items()}\n\n # These coordinates are saved according to CF conventions\n for var_name, coord_names in variable_coordinates.items():\n attrs = variables[var_name].attrs\n if \"coordinates\" in attrs:\n raise ValueError(\n \"cannot serialize coordinates because variable \"\n \"%s already has an attribute 'coordinates'\" % var_name\n )\n attrs[\"coordinates\"] = \" \".join(map(str, coord_names))\n\n # These coordinates are not associated with any particular variables, so we\n # save them under a global 'coordinates' attribute so xarray can roundtrip\n # the dataset faithfully. Because this serialization goes beyond CF\n # conventions, only do it if necessary.\n # Reference discussion:\n # http://mailman.cgd.ucar.edu/pipermail/cf-metadata/2014/057771.html\n if global_coordinates:\n attributes = dict(attributes)\n if \"coordinates\" in attributes:\n raise ValueError(\n \"cannot serialize coordinates because the global \"\n \"attribute 'coordinates' already exists\"\n )\n attributes[\"coordinates\"] = \" \".join(map(str, global_coordinates))\n\n return variables, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_encode_dataset_coordinates_encode_dataset_coordinates.return._encode_coordinates_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_encode_dataset_coordinates_encode_dataset_coordinates.return._encode_coordinates_", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 695, "end_line": 714, "span_ids": ["encode_dataset_coordinates"], "tokens": 110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def encode_dataset_coordinates(dataset):\n \"\"\"Encode coordinates on the given dataset object into variable specific\n and global attributes.\n\n When possible, this is done according to CF conventions.\n\n Parameters\n ----------\n dataset : Dataset\n Object to encode.\n\n Returns\n -------\n variables : dict\n attrs : dict\n \"\"\"\n non_dim_coord_names = set(dataset.coords) - set(dataset.dims)\n return _encode_coordinates(\n dataset._variables, dataset.attrs, non_dim_coord_names=non_dim_coord_names\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_cf_encoder_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/conventions.py_cf_encoder_", "embedding": null, "metadata": {"file_path": "xarray/conventions.py", "file_name": "conventions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 717, "end_line": 771, "span_ids": ["cf_encoder"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def cf_encoder(variables, attributes):\n \"\"\"\n Encode a set of CF encoded variables and attributes.\n Takes a dicts of variables and attributes and encodes them\n to conform to CF conventions as much as possible.\n This includes masking, scaling, character array handling,\n and CF-time encoding.\n\n\n Parameters\n ----------\n variables : dict\n A dictionary mapping from variable name to xarray.Variable\n attributes : dict\n A dictionary mapping from attribute name to value\n\n Returns\n -------\n encoded_variables : dict\n A dictionary mapping from variable name to xarray.Variable,\n encoded_attributes : dict\n A dictionary mapping from attribute name to value\n\n See also\n --------\n decode_cf_variable, encode_cf_variable\n \"\"\"\n\n # add encoding for time bounds variables if present.\n _update_bounds_encoding(variables)\n\n new_vars = {k: encode_cf_variable(v, name=k) for k, v in variables.items()}\n\n # Remove attrs from bounds variables (issue #2921)\n for var in new_vars.values():\n bounds = var.attrs[\"bounds\"] if \"bounds\" in var.attrs else None\n if bounds and bounds in new_vars:\n # see http://cfconventions.org/cf-conventions/cf-conventions.html#cell-boundaries\n for attr in [\n \"units\",\n \"standard_name\",\n \"axis\",\n \"positive\",\n \"calendar\",\n \"long_name\",\n \"leap_month\",\n \"leap_year\",\n \"month_lengths\",\n ]:\n if attr in new_vars[bounds].attrs and attr in var.attrs:\n if new_vars[bounds].attrs[attr] == var.attrs[attr]:\n new_vars[bounds].attrs.pop(attr)\n\n return new_vars, attributes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__Functions_for_converti__filter_attrs.return._k_v_for_k_v_in_attrs_i": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__Functions_for_converti__filter_attrs.return._k_v_for_k_v_in_attrs_i", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 60, "span_ids": ["impl", "_filter_attrs", "docstring", "imports", "encode"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Functions for converting to and from xarray objects\n\"\"\"\nfrom collections import Counter\n\nimport numpy as np\nimport pandas as pd\n\nfrom .coding.times import CFDatetimeCoder, CFTimedeltaCoder\nfrom .conventions import decode_cf\nfrom .core import duck_array_ops\nfrom .core.dataarray import DataArray\nfrom .core.dtypes import get_fill_value\n\ncdms2_ignored_attrs = {\"name\", \"tileIndex\"}\niris_forbidden_keys = {\n \"standard_name\",\n \"long_name\",\n \"units\",\n \"bounds\",\n \"axis\",\n \"calendar\",\n \"leap_month\",\n \"leap_year\",\n \"month_lengths\",\n \"coordinates\",\n \"grid_mapping\",\n \"climatology\",\n \"cell_methods\",\n \"formula_terms\",\n \"compress\",\n \"missing_value\",\n \"add_offset\",\n \"scale_factor\",\n \"valid_max\",\n \"valid_min\",\n \"valid_range\",\n \"_FillValue\",\n}\ncell_methods_strings = {\n \"point\",\n \"sum\",\n \"maximum\",\n \"median\",\n \"mid_range\",\n \"minimum\",\n \"mean\",\n \"mode\",\n \"standard_deviation\",\n \"variance\",\n}\n\n\ndef encode(var):\n return CFTimedeltaCoder().encode(CFDatetimeCoder().encode(var.variable))\n\n\ndef _filter_attrs(attrs, ignored_attrs):\n \"\"\" Return attrs that are not in ignored_attrs\n \"\"\"\n return {k: v for k, v in attrs.items() if k not in ignored_attrs}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_from_cdms2_from_cdms2.return.decode_cf_dataarray_to_da": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_from_cdms2_from_cdms2.return.decode_cf_dataarray_to_da", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 63, "end_line": 88, "span_ids": ["from_cdms2"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def from_cdms2(variable):\n \"\"\"Convert a cdms2 variable into an DataArray\n \"\"\"\n values = np.asarray(variable)\n name = variable.id\n dims = variable.getAxisIds()\n coords = {}\n for axis in variable.getAxisList():\n coords[axis.id] = DataArray(\n np.asarray(axis),\n dims=[axis.id],\n attrs=_filter_attrs(axis.attributes, cdms2_ignored_attrs),\n )\n grid = variable.getGrid()\n if grid is not None:\n ids = [a.id for a in grid.getAxisList()]\n for axis in grid.getLongitude(), grid.getLatitude():\n if axis.id not in variable.getAxisIds():\n coords[axis.id] = DataArray(\n np.asarray(axis[:]),\n dims=ids,\n attrs=_filter_attrs(axis.attributes, cdms2_ignored_attrs),\n )\n attrs = _filter_attrs(variable.attributes, cdms2_ignored_attrs)\n dataarray = DataArray(values, dims=dims, coords=coords, name=name, attrs=attrs)\n return decode_cf(dataarray.to_dataset())[dataarray.name]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_to_cdms2_to_cdms2.return.cdms2_var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_to_cdms2_to_cdms2.return.cdms2_var", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 150, "span_ids": ["to_cdms2"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_cdms2(dataarray, copy=True):\n \"\"\"Convert a DataArray into a cdms2 variable\n \"\"\"\n # we don't want cdms2 to be a hard dependency\n import cdms2\n\n def set_cdms2_attrs(var, attrs):\n for k, v in attrs.items():\n setattr(var, k, v)\n\n # 1D axes\n axes = []\n for dim in dataarray.dims:\n coord = encode(dataarray.coords[dim])\n axis = cdms2.createAxis(coord.values, id=dim)\n set_cdms2_attrs(axis, coord.attrs)\n axes.append(axis)\n\n # Data\n var = encode(dataarray)\n cdms2_var = cdms2.createVariable(\n var.values, axes=axes, id=dataarray.name, mask=pd.isnull(var.values), copy=copy\n )\n\n # Attributes\n set_cdms2_attrs(cdms2_var, var.attrs)\n\n # Curvilinear and unstructured grids\n if dataarray.name not in dataarray.coords:\n\n cdms2_axes = {}\n for coord_name in set(dataarray.coords.keys()) - set(dataarray.dims):\n\n coord_array = dataarray.coords[coord_name].to_cdms2()\n\n cdms2_axis_cls = (\n cdms2.coord.TransientAxis2D\n if coord_array.ndim\n else cdms2.auxcoord.TransientAuxAxis1D\n )\n cdms2_axis = cdms2_axis_cls(coord_array)\n if cdms2_axis.isLongitude():\n cdms2_axes[\"lon\"] = cdms2_axis\n elif cdms2_axis.isLatitude():\n cdms2_axes[\"lat\"] = cdms2_axis\n\n if \"lon\" in cdms2_axes and \"lat\" in cdms2_axes:\n if len(cdms2_axes[\"lon\"].shape) == 2:\n cdms2_grid = cdms2.hgrid.TransientCurveGrid(\n cdms2_axes[\"lat\"], cdms2_axes[\"lon\"]\n )\n else:\n cdms2_grid = cdms2.gengrid.AbstractGenericGrid(\n cdms2_axes[\"lat\"], cdms2_axes[\"lon\"]\n )\n for axis in cdms2_grid.getAxisList():\n cdms2_var.setAxis(cdms2_var.getAxisIds().index(axis.id), axis)\n cdms2_var.setGrid(cdms2_grid)\n\n return cdms2_var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__pick_attrs__get_iris_args.return.args": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__pick_attrs__get_iris_args.return.args", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 153, "end_line": 170, "span_ids": ["_pick_attrs", "_get_iris_args"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _pick_attrs(attrs, keys):\n \"\"\" Return attrs with keys in keys list\n \"\"\"\n return {k: v for k, v in attrs.items() if k in keys}\n\n\ndef _get_iris_args(attrs):\n \"\"\" Converts the xarray attrs into args that can be passed into Iris\n \"\"\"\n # iris.unit is deprecated in Iris v1.9\n import cf_units\n\n args = {\"attributes\": _filter_attrs(attrs, iris_forbidden_keys)}\n args.update(_pick_attrs(attrs, (\"standard_name\", \"long_name\")))\n unit_args = _pick_attrs(attrs, (\"calendar\",))\n if \"units\" in attrs:\n args[\"units\"] = cf_units.Unit(attrs[\"units\"], **unit_args)\n return args", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__TODO_Add_converting_bo_to_iris.return.cube": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__TODO_Add_converting_bo_to_iris.return.cube", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 173, "end_line": 212, "span_ids": ["to_iris", "_get_iris_args"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# TODO: Add converting bounds from xarray to Iris and back\ndef to_iris(dataarray):\n \"\"\" Convert a DataArray into a Iris Cube\n \"\"\"\n # Iris not a hard dependency\n import iris\n from iris.fileformats.netcdf import parse_cell_methods\n\n dim_coords = []\n aux_coords = []\n\n for coord_name in dataarray.coords:\n coord = encode(dataarray.coords[coord_name])\n coord_args = _get_iris_args(coord.attrs)\n coord_args[\"var_name\"] = coord_name\n axis = None\n if coord.dims:\n axis = dataarray.get_axis_num(coord.dims)\n if coord_name in dataarray.dims:\n try:\n iris_coord = iris.coords.DimCoord(coord.values, **coord_args)\n dim_coords.append((iris_coord, axis))\n except ValueError:\n iris_coord = iris.coords.AuxCoord(coord.values, **coord_args)\n aux_coords.append((iris_coord, axis))\n else:\n iris_coord = iris.coords.AuxCoord(coord.values, **coord_args)\n aux_coords.append((iris_coord, axis))\n\n args = _get_iris_args(dataarray.attrs)\n args[\"var_name\"] = dataarray.name\n args[\"dim_coords_and_dims\"] = dim_coords\n args[\"aux_coords_and_dims\"] = aux_coords\n if \"cell_methods\" in dataarray.attrs:\n args[\"cell_methods\"] = parse_cell_methods(dataarray.attrs[\"cell_methods\"])\n\n masked_data = duck_array_ops.masked_invalid(dataarray.data)\n cube = iris.cube.Cube(masked_data, **args)\n\n return cube", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__iris_obj_to_attrs__name.return.iris_obj_var_name_or_iris": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py__iris_obj_to_attrs__name.return.iris_obj_var_name_or_iris", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 252, "span_ids": ["_name", "_iris_cell_methods_to_str", "_iris_obj_to_attrs"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _iris_obj_to_attrs(obj):\n \"\"\" Return a dictionary of attrs when given a Iris object\n \"\"\"\n attrs = {\"standard_name\": obj.standard_name, \"long_name\": obj.long_name}\n if obj.units.calendar:\n attrs[\"calendar\"] = obj.units.calendar\n if obj.units.origin != \"1\" and not obj.units.is_unknown():\n attrs[\"units\"] = obj.units.origin\n attrs.update(obj.attributes)\n return {k: v for k, v in attrs.items() if v is not None}\n\n\ndef _iris_cell_methods_to_str(cell_methods_obj):\n \"\"\" Converts a Iris cell methods into a string\n \"\"\"\n cell_methods = []\n for cell_method in cell_methods_obj:\n names = \"\".join([\"{}: \".format(n) for n in cell_method.coord_names])\n intervals = \" \".join(\n [\"interval: {}\".format(interval) for interval in cell_method.intervals]\n )\n comments = \" \".join(\n [\"comment: {}\".format(comment) for comment in cell_method.comments]\n )\n extra = \" \".join([intervals, comments]).strip()\n if extra:\n extra = \" ({})\".format(extra)\n cell_methods.append(names + cell_method.method + extra)\n return \" \".join(cell_methods)\n\n\ndef _name(iris_obj, default=\"unknown\"):\n \"\"\" Mimicks `iris_obj.name()` but with different name resolution order.\n\n Similar to iris_obj.name() method, but using iris_obj.var_name first to\n enable roundtripping.\n \"\"\"\n return iris_obj.var_name or iris_obj.standard_name or iris_obj.long_name or default", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_from_iris_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/convert.py_from_iris_", "embedding": null, "metadata": {"file_path": "xarray/convert.py", "file_name": "convert.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 309, "span_ids": ["from_iris"], "tokens": 453}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def from_iris(cube):\n \"\"\" Convert a Iris cube into an DataArray\n \"\"\"\n import iris.exceptions\n from xarray.core.pycompat import dask_array_type\n\n name = _name(cube)\n if name == \"unknown\":\n name = None\n dims = []\n for i in range(cube.ndim):\n try:\n dim_coord = cube.coord(dim_coords=True, dimensions=(i,))\n dims.append(_name(dim_coord))\n except iris.exceptions.CoordinateNotFoundError:\n dims.append(\"dim_{}\".format(i))\n\n if len(set(dims)) != len(dims):\n duplicates = [k for k, v in Counter(dims).items() if v > 1]\n raise ValueError(\"Duplicate coordinate name {}.\".format(duplicates))\n\n coords = {}\n\n for coord in cube.coords():\n coord_attrs = _iris_obj_to_attrs(coord)\n coord_dims = [dims[i] for i in cube.coord_dims(coord)]\n if coord_dims:\n coords[_name(coord)] = (coord_dims, coord.points, coord_attrs)\n else:\n coords[_name(coord)] = ((), coord.points.item(), coord_attrs)\n\n array_attrs = _iris_obj_to_attrs(cube)\n cell_methods = _iris_cell_methods_to_str(cube.cell_methods)\n if cell_methods:\n array_attrs[\"cell_methods\"] = cell_methods\n\n # Deal with iris 1.* and 2.*\n cube_data = cube.core_data() if hasattr(cube, \"core_data\") else cube.data\n\n # Deal with dask and numpy masked arrays\n if isinstance(cube_data, dask_array_type):\n from dask.array import ma as dask_ma\n\n filled_data = dask_ma.filled(cube_data, get_fill_value(cube.dtype))\n elif isinstance(cube_data, np.ma.MaskedArray):\n filled_data = np.ma.filled(cube_data, get_fill_value(cube.dtype))\n else:\n filled_data = cube_data\n\n dataarray = DataArray(\n filled_data, coords=coords, name=name, attrs=array_attrs, dims=dims\n )\n decoded_ds = decode_cf(dataarray._to_temp_dataset())\n return dataarray._from_temp_dataset(decoded_ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_np__season_from_months.return.seasons_months_3_4": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_np__season_from_months.return.seasons_months_3_4", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 14, "span_ids": ["_season_from_months", "imports"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\n\nfrom .common import _contains_datetime_like_objects, is_np_datetime_like\nfrom .pycompat import dask_array_type\n\n\ndef _season_from_months(months):\n \"\"\"Compute season (DJF, MAM, JJA, SON) from month ordinal\n \"\"\"\n # TODO: Move \"season\" accessor upstream into pandas\n seasons = np.array([\"DJF\", \"MAM\", \"JJA\", \"SON\"])\n months = np.asarray(months)\n return seasons[(months // 3) % 4]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__access_through_cftimeindex__access_through_series.return.field_values_reshape_valu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__access_through_cftimeindex__access_through_series.return.field_values_reshape_valu", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 17, "end_line": 42, "span_ids": ["_access_through_series", "_access_through_cftimeindex"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _access_through_cftimeindex(values, name):\n \"\"\"Coerce an array of datetime-like values to a CFTimeIndex\n and access requested datetime component\n \"\"\"\n from ..coding.cftimeindex import CFTimeIndex\n\n values_as_cftimeindex = CFTimeIndex(values.ravel())\n if name == \"season\":\n months = values_as_cftimeindex.month\n field_values = _season_from_months(months)\n else:\n field_values = getattr(values_as_cftimeindex, name)\n return field_values.reshape(values.shape)\n\n\ndef _access_through_series(values, name):\n \"\"\"Coerce an array of datetime-like values to a pandas Series and\n access requested datetime component\n \"\"\"\n values_as_series = pd.Series(values.ravel())\n if name == \"season\":\n months = values_as_series.dt.month.values\n field_values = _season_from_months(months)\n else:\n field_values = getattr(values_as_series.dt, name).values\n return field_values.reshape(values.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__get_date_field__get_date_field.if_isinstance_values_das.else_.return.access_method_values_nam": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__get_date_field__get_date_field.if_isinstance_values_das.else_.return.access_method_values_nam", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 74, "span_ids": ["_get_date_field"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_date_field(values, name, dtype):\n \"\"\"Indirectly access pandas' libts.get_date_field by wrapping data\n as a Series and calling through `.dt` attribute.\n\n Parameters\n ----------\n values : np.ndarray or dask.array-like\n Array-like container of datetime-like values\n name : str\n Name of datetime field to access\n dtype : dtype-like\n dtype for output date field values\n\n Returns\n -------\n datetime_fields : same type as values\n Array-like of datetime fields accessed for each element in values\n\n \"\"\"\n if is_np_datetime_like(values.dtype):\n access_method = _access_through_series\n else:\n access_method = _access_through_cftimeindex\n\n if isinstance(values, dask_array_type):\n from dask.array import map_blocks\n\n return map_blocks(access_method, values, name, dtype=dtype)\n else:\n return access_method(values, name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__round_series__round_field.if_isinstance_values_das.else_.return._round_series_values_nam": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__round_series__round_field.if_isinstance_values_das.else_.return._round_series_values_nam", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 111, "span_ids": ["_round_field", "_round_series"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _round_series(values, name, freq):\n \"\"\"Coerce an array of datetime-like values to a pandas Series and\n apply requested rounding\n \"\"\"\n values_as_series = pd.Series(values.ravel())\n method = getattr(values_as_series.dt, name)\n field_values = method(freq=freq).values\n\n return field_values.reshape(values.shape)\n\n\ndef _round_field(values, name, freq):\n \"\"\"Indirectly access pandas rounding functions by wrapping data\n as a Series and calling through `.dt` attribute.\n\n Parameters\n ----------\n values : np.ndarray or dask.array-like\n Array-like container of datetime-like values\n name : str (ceil, floor, round)\n Name of rounding function\n freq : a freq string indicating the rounding resolution\n\n Returns\n -------\n rounded timestamps : same type as values\n Array-like of datetime fields accessed for each element in values\n\n \"\"\"\n if isinstance(values, dask_array_type):\n from dask.array import map_blocks\n\n return map_blocks(_round_series, values, name, freq=freq, dtype=np.datetime64)\n else:\n return _round_series(values, name, freq)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__strftime_through_cftimeindex__strftime.if_isinstance_values_das.else_.return.access_method_values_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py__strftime_through_cftimeindex__strftime.if_isinstance_values_das.else_.return.access_method_values_dat", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 114, "end_line": 145, "span_ids": ["_strftime", "_strftime_through_cftimeindex", "_strftime_through_series"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _strftime_through_cftimeindex(values, date_format):\n \"\"\"Coerce an array of cftime-like values to a CFTimeIndex\n and access requested datetime component\n \"\"\"\n from ..coding.cftimeindex import CFTimeIndex\n\n values_as_cftimeindex = CFTimeIndex(values.ravel())\n\n field_values = values_as_cftimeindex.strftime(date_format)\n return field_values.values.reshape(values.shape)\n\n\ndef _strftime_through_series(values, date_format):\n \"\"\"Coerce an array of datetime-like values to a pandas Series and\n apply string formatting\n \"\"\"\n values_as_series = pd.Series(values.ravel())\n strs = values_as_series.dt.strftime(date_format)\n return strs.values.reshape(values.shape)\n\n\ndef _strftime(values, date_format):\n if is_np_datetime_like(values.dtype):\n access_method = _strftime_through_series\n else:\n access_method = _strftime_through_cftimeindex\n if isinstance(values, dask_array_type):\n from dask.array import map_blocks\n\n return map_blocks(access_method, values, date_format)\n else:\n return access_method(values, date_format)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor_DatetimeAccessor.quarter._tslib_field_accessor_qu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor_DatetimeAccessor.quarter._tslib_field_accessor_qu", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 225, "span_ids": ["DatetimeAccessor._tslib_field_accessor", "DatetimeAccessor", "DatetimeAccessor:3"], "tokens": 796}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatetimeAccessor:\n \"\"\"Access datetime fields for DataArrays with datetime-like dtypes.\n\n Similar to pandas, fields can be accessed through the `.dt` attribute\n for applicable DataArrays:\n\n >>> ds = xarray.Dataset({'time': pd.date_range(start='2000/01/01',\n ... freq='D', periods=100)})\n >>> ds.time.dt\n \n >>> ds.time.dt.dayofyear[:5]\n \n array([1, 2, 3, 4, 5], dtype=int32)\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n\n All of the pandas fields are accessible here. Note that these fields are\n not calendar-aware; if your datetimes are encoded with a non-Gregorian\n calendar (e.g. a 360-day calendar) using cftime, then some fields like\n `dayofyear` may not be accurate.\n\n \"\"\"\n\n def __init__(self, obj):\n if not _contains_datetime_like_objects(obj):\n raise TypeError(\n \"'dt' accessor only available for \"\n \"DataArray with datetime64 timedelta64 dtype or \"\n \"for arrays containing cftime datetime \"\n \"objects.\"\n )\n self._obj = obj\n\n def _tslib_field_accessor(name, docstring=None, dtype=None):\n def f(self, dtype=dtype):\n if dtype is None:\n dtype = self._obj.dtype\n obj_type = type(self._obj)\n result = _get_date_field(self._obj.data, name, dtype)\n return obj_type(\n result, name=name, coords=self._obj.coords, dims=self._obj.dims\n )\n\n f.__name__ = name\n f.__doc__ = docstring\n return property(f)\n\n year = _tslib_field_accessor(\"year\", \"The year of the datetime\", np.int64)\n month = _tslib_field_accessor(\n \"month\", \"The month as January=1, December=12\", np.int64\n )\n day = _tslib_field_accessor(\"day\", \"The days of the datetime\", np.int64)\n hour = _tslib_field_accessor(\"hour\", \"The hours of the datetime\", np.int64)\n minute = _tslib_field_accessor(\"minute\", \"The minutes of the datetime\", np.int64)\n second = _tslib_field_accessor(\"second\", \"The seconds of the datetime\", np.int64)\n microsecond = _tslib_field_accessor(\n \"microsecond\", \"The microseconds of the datetime\", np.int64\n )\n nanosecond = _tslib_field_accessor(\n \"nanosecond\", \"The nanoseconds of the datetime\", np.int64\n )\n weekofyear = _tslib_field_accessor(\n \"weekofyear\", \"The week ordinal of the year\", np.int64\n )\n week = weekofyear\n dayofweek = _tslib_field_accessor(\n \"dayofweek\", \"The day of the week with Monday=0, Sunday=6\", np.int64\n )\n weekday = dayofweek\n\n weekday_name = _tslib_field_accessor(\n \"weekday_name\", \"The name of day in a week (ex: Friday)\", object\n )\n\n dayofyear = _tslib_field_accessor(\n \"dayofyear\", \"The ordinal day of the year\", np.int64\n )\n quarter = _tslib_field_accessor(\"quarter\", \"The quarter of the date\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor.days_in_month_DatetimeAccessor.round.return.self__tslib_round_accesso": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor.days_in_month_DatetimeAccessor.round.return.self__tslib_round_accesso", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 226, "end_line": 289, "span_ids": ["DatetimeAccessor.round", "DatetimeAccessor.floor", "DatetimeAccessor._tslib_round_accessor", "DatetimeAccessor.ceil", "DatetimeAccessor:3"], "tokens": 416}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatetimeAccessor:\n days_in_month = _tslib_field_accessor(\n \"days_in_month\", \"The number of days in the month\", np.int64\n )\n daysinmonth = days_in_month\n\n season = _tslib_field_accessor(\"season\", \"Season of the year (ex: DJF)\", object)\n\n time = _tslib_field_accessor(\n \"time\", \"Timestamps corresponding to datetimes\", object\n )\n\n def _tslib_round_accessor(self, name, freq):\n obj_type = type(self._obj)\n result = _round_field(self._obj.data, name, freq)\n return obj_type(result, name=name, coords=self._obj.coords, dims=self._obj.dims)\n\n def floor(self, freq):\n \"\"\"\n Round timestamps downward to specified frequency resolution.\n\n Parameters\n ----------\n freq : a freq string indicating the rounding resolution\n e.g. 'D' for daily resolution\n\n Returns\n -------\n floor-ed timestamps : same type as values\n Array-like of datetime fields accessed for each element in values\n \"\"\"\n\n return self._tslib_round_accessor(\"floor\", freq)\n\n def ceil(self, freq):\n \"\"\"\n Round timestamps upward to specified frequency resolution.\n\n Parameters\n ----------\n freq : a freq string indicating the rounding resolution\n e.g. 'D' for daily resolution\n\n Returns\n -------\n ceil-ed timestamps : same type as values\n Array-like of datetime fields accessed for each element in values\n \"\"\"\n return self._tslib_round_accessor(\"ceil\", freq)\n\n def round(self, freq):\n \"\"\"\n Round timestamps to specified frequency resolution.\n\n Parameters\n ----------\n freq : a freq string indicating the rounding resolution\n e.g. 'D' for daily resolution\n\n Returns\n -------\n rounded timestamps : same type as values\n Array-like of datetime fields accessed for each element in values\n \"\"\"\n return self._tslib_round_accessor(\"round\", freq)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor.strftime_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_dt.py_DatetimeAccessor.strftime_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_dt.py", "file_name": "accessor_dt.py", "file_type": "text/x-python", "category": "implementation", "start_line": 291, "end_line": 324, "span_ids": ["DatetimeAccessor.strftime"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatetimeAccessor:\n\n def strftime(self, date_format):\n '''\n Return an array of formatted strings specified by date_format, which\n supports the same string format as the python standard library. Details\n of the string format can be found in `python string format doc\n `__\n\n Parameters\n ----------\n date_format : str\n date format string (e.g. \"%Y-%m-%d\")\n\n Returns\n -------\n formatted strings : same type as values\n Array-like of strings formatted for each element in values\n\n Examples\n --------\n >>> rng = xr.Dataset({'time': datetime.datetime(2000, 1, 1)})\n >>> rng['time'].dt.strftime('%B %d, %Y, %r')\n \n array('January 01, 2000, 12:00:00 AM', dtype=object)\n \"\"\"\n\n '''\n obj_type = type(self._obj)\n\n result = _strftime(self._obj.data, date_format)\n\n return obj_type(\n result, name=\"strftime\", coords=self._obj.coords, dims=self._obj.dims\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py__The_StringAccessor_clas__is_str_like.return.isinstance_x_str_or_isi": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py__The_StringAccessor_clas__is_str_like.return.isinstance_x_str_or_isi", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 61, "span_ids": ["impl", "_is_str_like", "docstring", "imports"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# The StringAccessor class defined below is an adaptation of the\n# pandas string methods source code (see pd.core.strings)\n\nimport codecs\nimport re\nimport textwrap\n\nimport numpy as np\n\nfrom .computation import apply_ufunc\n\n_cpython_optimized_encoders = (\n \"utf-8\",\n \"utf8\",\n \"latin-1\",\n \"latin1\",\n \"iso-8859-1\",\n \"mbcs\",\n \"ascii\",\n)\n_cpython_optimized_decoders = _cpython_optimized_encoders + (\"utf-16\", \"utf-32\")\n\n\ndef _is_str_like(x):\n return isinstance(x, str) or isinstance(x, bytes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor_StringAccessor.__getitem__.if_isinstance_key_slice_.else_.return.self_get_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor_StringAccessor.__getitem__.if_isinstance_key_slice_.else_.return.self_get_key_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 105, "span_ids": ["StringAccessor.len", "StringAccessor", "StringAccessor._apply", "StringAccessor.__getitem__"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n \"\"\"Vectorized string functions for string-like arrays.\n\n Similar to pandas, fields can be accessed through the `.str` attribute\n for applicable DataArrays.\n\n >>> da = xr.DataArray(['some', 'text', 'in', 'an', 'array'])\n >>> ds.str.len()\n \n array([4, 4, 2, 2, 5])\n Dimensions without coordinates: dim_0\n\n \"\"\"\n\n __slots__ = (\"_obj\",)\n\n def __init__(self, obj):\n self._obj = obj\n\n def _apply(self, f, dtype=None):\n # TODO handling of na values ?\n if dtype is None:\n dtype = self._obj.dtype\n\n g = np.vectorize(f, otypes=[dtype])\n return apply_ufunc(g, self._obj, dask=\"parallelized\", output_dtypes=[dtype])\n\n def len(self):\n \"\"\"\n Compute the length of each element in the array.\n\n Returns\n -------\n lengths array : array of int\n \"\"\"\n return self._apply(len, dtype=int)\n\n def __getitem__(self, key):\n if isinstance(key, slice):\n return self.slice(start=key.start, stop=key.stop, step=key.step)\n else:\n return self.get(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.get_StringAccessor.get.return.self__apply_lambda_x_x_o": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.get_StringAccessor.get.return.self__apply_lambda_x_x_o", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 124, "span_ids": ["StringAccessor.get"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def get(self, i):\n \"\"\"\n Extract element from indexable in each element in the array.\n\n Parameters\n ----------\n i : int\n Position of element to extract.\n default : optional\n Value for out-of-range index. If not specified (None) defaults to\n an empty string.\n\n Returns\n -------\n items : array of objects\n \"\"\"\n obj = slice(-1, None) if i == -1 else slice(i, i + 1)\n return self._apply(lambda x: x[obj])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.slice_StringAccessor.slice.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.slice_StringAccessor.slice.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 145, "span_ids": ["StringAccessor.slice"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def slice(self, start=None, stop=None, step=None):\n \"\"\"\n Slice substrings from each element in the array.\n\n Parameters\n ----------\n start : int, optional\n Start position for slice operation.\n stop : int, optional\n Stop position for slice operation.\n step : int, optional\n Step size for slice operation.\n\n Returns\n -------\n sliced strings : same type as values\n \"\"\"\n s = slice(start, stop, step)\n f = lambda x: x[s]\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.slice_replace_StringAccessor.slice_replace.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.slice_replace_StringAccessor.slice_replace.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 147, "end_line": 184, "span_ids": ["StringAccessor.slice_replace"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def slice_replace(self, start=None, stop=None, repl=\"\"):\n \"\"\"\n Replace a positional slice of a string with another value.\n\n Parameters\n ----------\n start : int, optional\n Left index position to use for the slice. If not specified (None),\n the slice is unbounded on the left, i.e. slice from the start\n of the string.\n stop : int, optional\n Right index position to use for the slice. If not specified (None),\n the slice is unbounded on the right, i.e. slice until the\n end of the string.\n repl : str, optional\n String for replacement. If not specified, the sliced region\n is replaced with an empty string.\n\n Returns\n -------\n replaced : same type as values\n \"\"\"\n repl = self._obj.dtype.type(repl)\n\n def f(x):\n if len(x[start:stop]) == 0:\n local_stop = start\n else:\n local_stop = stop\n y = self._obj.dtype.type(\"\")\n if start is not None:\n y += x[:start]\n y += repl\n if stop is not None:\n y += x[local_stop:]\n return y\n\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.capitalize_StringAccessor.istitle.return.self__apply_lambda_x_x_i": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.capitalize_StringAccessor.istitle.return.self__apply_lambda_x_x_i", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 186, "end_line": 322, "span_ids": ["StringAccessor.istitle", "StringAccessor.isdecimal", "StringAccessor.isnumeric", "StringAccessor.islower", "StringAccessor.isspace", "StringAccessor.swapcase", "StringAccessor.lower", "StringAccessor.isalnum", "StringAccessor.capitalize", "StringAccessor.upper", "StringAccessor.isalpha", "StringAccessor.isdigit", "StringAccessor.title"], "tokens": 729}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def capitalize(self):\n \"\"\"\n Convert strings in the array to be capitalized.\n\n Returns\n -------\n capitalized : same type as values\n \"\"\"\n return self._apply(lambda x: x.capitalize())\n\n def lower(self):\n \"\"\"\n Convert strings in the array to lowercase.\n\n Returns\n -------\n lowerd : same type as values\n \"\"\"\n return self._apply(lambda x: x.lower())\n\n def swapcase(self):\n \"\"\"\n Convert strings in the array to be swapcased.\n\n Returns\n -------\n swapcased : same type as values\n \"\"\"\n return self._apply(lambda x: x.swapcase())\n\n def title(self):\n \"\"\"\n Convert strings in the array to titlecase.\n\n Returns\n -------\n titled : same type as values\n \"\"\"\n return self._apply(lambda x: x.title())\n\n def upper(self):\n \"\"\"\n Convert strings in the array to uppercase.\n\n Returns\n -------\n uppered : same type as values\n \"\"\"\n return self._apply(lambda x: x.upper())\n\n def isalnum(self):\n \"\"\"\n Check whether all characters in each string are alphanumeric.\n\n Returns\n -------\n isalnum : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isalnum(), dtype=bool)\n\n def isalpha(self):\n \"\"\"\n Check whether all characters in each string are alphabetic.\n\n Returns\n -------\n isalpha : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isalpha(), dtype=bool)\n\n def isdecimal(self):\n \"\"\"\n Check whether all characters in each string are decimal.\n\n Returns\n -------\n isdecimal : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isdecimal(), dtype=bool)\n\n def isdigit(self):\n \"\"\"\n Check whether all characters in each string are digits.\n\n Returns\n -------\n isdigit : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isdigit(), dtype=bool)\n\n def islower(self):\n \"\"\"\n Check whether all characters in each string are lowercase.\n\n Returns\n -------\n islower : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.islower(), dtype=bool)\n\n def isnumeric(self):\n \"\"\"\n Check whether all characters in each string are numeric.\n\n Returns\n -------\n isnumeric : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isnumeric(), dtype=bool)\n\n def isspace(self):\n \"\"\"\n Check whether all characters in each string are spaces.\n\n Returns\n -------\n isspace : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isspace(), dtype=bool)\n\n def istitle(self):\n \"\"\"\n Check whether all characters in each string are titlecase.\n\n Returns\n -------\n istitle : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.istitle(), dtype=bool)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.isupper_StringAccessor.count.return.self__apply_f_dtype_int_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.isupper_StringAccessor.count.return.self__apply_f_dtype_int_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 324, "end_line": 358, "span_ids": ["StringAccessor.count", "StringAccessor.isupper"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def isupper(self):\n \"\"\"\n Check whether all characters in each string are uppercase.\n\n Returns\n -------\n isupper : array of bool\n Array of boolean values with the same shape as the original array.\n \"\"\"\n return self._apply(lambda x: x.isupper(), dtype=bool)\n\n def count(self, pat, flags=0):\n \"\"\"\n Count occurrences of pattern in each string of the array.\n\n This function is used to count the number of times a particular regex\n pattern is repeated in each of the string elements of the\n :class:`~xarray.DatArray`.\n\n Parameters\n ----------\n pat : str\n Valid regular expression.\n flags : int, default 0, meaning no flags\n Flags for the `re` module. For a complete list, `see here\n `_.\n\n Returns\n -------\n counts : array of int\n \"\"\"\n pat = self._obj.dtype.type(pat)\n regex = re.compile(pat, flags=flags)\n f = lambda x: len(regex.findall(x))\n return self._apply(f, dtype=int)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.startswith_StringAccessor.startswith.return.self__apply_f_dtype_bool": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.startswith_StringAccessor.startswith.return.self__apply_f_dtype_bool", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 360, "end_line": 377, "span_ids": ["StringAccessor.startswith"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def startswith(self, pat):\n \"\"\"\n Test if the start of each string element matches a pattern.\n\n Parameters\n ----------\n pat : str\n Character sequence. Regular expressions are not accepted.\n\n Returns\n -------\n startswith : array of bool\n An array of booleans indicating whether the given pattern matches\n the start of each string element.\n \"\"\"\n pat = self._obj.dtype.type(pat)\n f = lambda x: x.startswith(pat)\n return self._apply(f, dtype=bool)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.endswith_StringAccessor.endswith.return.self__apply_f_dtype_bool": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.endswith_StringAccessor.endswith.return.self__apply_f_dtype_bool", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 379, "end_line": 396, "span_ids": ["StringAccessor.endswith"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def endswith(self, pat):\n \"\"\"\n Test if the end of each string element matches a pattern.\n\n Parameters\n ----------\n pat : str\n Character sequence. Regular expressions are not accepted.\n\n Returns\n -------\n endswith : array of bool\n A Series of booleans indicating whether the given pattern matches\n the end of each string element.\n \"\"\"\n pat = self._obj.dtype.type(pat)\n f = lambda x: x.endswith(pat)\n return self._apply(f, dtype=bool)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.pad_StringAccessor.pad.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.pad_StringAccessor.pad.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 398, "end_line": 431, "span_ids": ["StringAccessor.pad"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def pad(self, width, side=\"left\", fillchar=\" \"):\n \"\"\"\n Pad strings in the array up to width.\n\n Parameters\n ----------\n width : int\n Minimum width of resulting string; additional characters will be\n filled with character defined in `fillchar`.\n side : {'left', 'right', 'both'}, default 'left'\n Side from which to fill resulting string.\n fillchar : str, default ' '\n Additional character for filling, default is whitespace.\n\n Returns\n -------\n filled : same type as values\n Array with a minimum number of char in each element.\n \"\"\"\n width = int(width)\n fillchar = self._obj.dtype.type(fillchar)\n if len(fillchar) != 1:\n raise TypeError(\"fillchar must be a character, not str\")\n\n if side == \"left\":\n f = lambda s: s.rjust(width, fillchar)\n elif side == \"right\":\n f = lambda s: s.ljust(width, fillchar)\n elif side == \"both\":\n f = lambda s: s.center(width, fillchar)\n else: # pragma: no cover\n raise ValueError(\"Invalid side\")\n\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.center_StringAccessor.center.return.self_pad_width_side_bot": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.center_StringAccessor.center.return.self_pad_width_side_bot", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 433, "end_line": 450, "span_ids": ["StringAccessor.center"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def center(self, width, fillchar=\" \"):\n \"\"\"\n Filling left and right side of strings in the array with an\n additional character.\n\n Parameters\n ----------\n width : int\n Minimum width of resulting string; additional characters will be\n filled with ``fillchar``\n fillchar : str\n Additional character for filling, default is whitespace\n\n Returns\n -------\n filled : same type as values\n \"\"\"\n return self.pad(width, side=\"both\", fillchar=fillchar)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.ljust_StringAccessor.ljust.return.self_pad_width_side_rig": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.ljust_StringAccessor.ljust.return.self_pad_width_side_rig", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 452, "end_line": 469, "span_ids": ["StringAccessor.ljust"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def ljust(self, width, fillchar=\" \"):\n \"\"\"\n Filling right side of strings in the array with an additional\n character.\n\n Parameters\n ----------\n width : int\n Minimum width of resulting string; additional characters will be\n filled with ``fillchar``\n fillchar : str\n Additional character for filling, default is whitespace\n\n Returns\n -------\n filled : same type as values\n \"\"\"\n return self.pad(width, side=\"right\", fillchar=fillchar)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rjust_StringAccessor.rjust.return.self_pad_width_side_lef": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rjust_StringAccessor.rjust.return.self_pad_width_side_lef", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 471, "end_line": 487, "span_ids": ["StringAccessor.rjust"], "tokens": 110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def rjust(self, width, fillchar=\" \"):\n \"\"\"\n Filling left side of strings in the array with an additional character.\n\n Parameters\n ----------\n width : int\n Minimum width of resulting string; additional characters will be\n filled with ``fillchar``\n fillchar : str\n Additional character for filling, default is whitespace\n\n Returns\n -------\n filled : same type as values\n \"\"\"\n return self.pad(width, side=\"left\", fillchar=fillchar)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.zfill_StringAccessor.zfill.return.self_pad_width_side_lef": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.zfill_StringAccessor.zfill.return.self_pad_width_side_lef", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 489, "end_line": 507, "span_ids": ["StringAccessor.zfill"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def zfill(self, width):\n \"\"\"\n Pad strings in the array by prepending '0' characters.\n\n Strings in the array are padded with '0' characters on the\n left of the string to reach a total string length `width`. Strings\n in the array with length greater or equal to `width` are unchanged.\n\n Parameters\n ----------\n width : int\n Minimum length of resulting string; strings with length less\n than `width` be prepended with '0' characters.\n\n Returns\n -------\n filled : same type as values\n \"\"\"\n return self.pad(width, side=\"left\", fillchar=\"0\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.contains_StringAccessor.contains.return.self__apply_f_dtype_bool": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.contains_StringAccessor.contains.return.self__apply_f_dtype_bool", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 509, "end_line": 553, "span_ids": ["StringAccessor.contains"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def contains(self, pat, case=True, flags=0, regex=True):\n \"\"\"\n Test if pattern or regex is contained within a string of the array.\n\n Return boolean array based on whether a given pattern or regex is\n contained within a string of the array.\n\n Parameters\n ----------\n pat : str\n Character sequence or regular expression.\n case : bool, default True\n If True, case sensitive.\n flags : int, default 0 (no flags)\n Flags to pass through to the re module, e.g. re.IGNORECASE.\n regex : bool, default True\n If True, assumes the pat is a regular expression.\n If False, treats the pat as a literal string.\n\n Returns\n -------\n contains : array of bool\n An array of boolean values indicating whether the\n given pattern is contained within the string of each element\n of the array.\n \"\"\"\n pat = self._obj.dtype.type(pat)\n if regex:\n if not case:\n flags |= re.IGNORECASE\n\n regex = re.compile(pat, flags=flags)\n\n if regex.groups > 0: # pragma: no cover\n raise ValueError(\"This pattern has match groups.\")\n\n f = lambda x: bool(regex.search(x))\n else:\n if case:\n f = lambda x: pat in x\n else:\n uppered = self._obj.str.upper()\n return uppered.str.contains(pat.upper(), regex=False)\n\n return self._apply(f, dtype=bool)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.match_StringAccessor.match.return.self__apply_f_dtype_bool": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.match_StringAccessor.match.return.self__apply_f_dtype_bool", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 555, "end_line": 578, "span_ids": ["StringAccessor.match"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def match(self, pat, case=True, flags=0):\n \"\"\"\n Determine if each string matches a regular expression.\n\n Parameters\n ----------\n pat : string\n Character sequence or regular expression\n case : boolean, default True\n If True, case sensitive\n flags : int, default 0 (no flags)\n re module flags, e.g. re.IGNORECASE\n\n Returns\n -------\n matched : array of bool\n \"\"\"\n if not case:\n flags |= re.IGNORECASE\n\n pat = self._obj.dtype.type(pat)\n regex = re.compile(pat, flags=flags)\n f = lambda x: bool(regex.match(x))\n return self._apply(f, dtype=bool)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.strip_StringAccessor.strip.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.strip_StringAccessor.strip.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 612, "span_ids": ["StringAccessor.strip"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def strip(self, to_strip=None, side=\"both\"):\n \"\"\"\n Remove leading and trailing characters.\n\n Strip whitespaces (including newlines) or a set of specified characters\n from each string in the array from left and/or right sides.\n\n Parameters\n ----------\n to_strip : str or None, default None\n Specifying the set of characters to be removed.\n All combinations of this set of characters will be stripped.\n If None then whitespaces are removed.\n side : {'left', 'right', 'both'}, default 'left'\n Side from which to strip.\n\n Returns\n -------\n stripped : same type as values\n \"\"\"\n if to_strip is not None:\n to_strip = self._obj.dtype.type(to_strip)\n\n if side == \"both\":\n f = lambda x: x.strip(to_strip)\n elif side == \"left\":\n f = lambda x: x.lstrip(to_strip)\n elif side == \"right\":\n f = lambda x: x.rstrip(to_strip)\n else: # pragma: no cover\n raise ValueError(\"Invalid side\")\n\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.lstrip_StringAccessor.lstrip.return.self_strip_to_strip_side": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.lstrip_StringAccessor.lstrip.return.self_strip_to_strip_side", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 614, "end_line": 632, "span_ids": ["StringAccessor.lstrip"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def lstrip(self, to_strip=None):\n \"\"\"\n Remove leading and trailing characters.\n\n Strip whitespaces (including newlines) or a set of specified characters\n from each string in the array from the left side.\n\n Parameters\n ----------\n to_strip : str or None, default None\n Specifying the set of characters to be removed.\n All combinations of this set of characters will be stripped.\n If None then whitespaces are removed.\n\n Returns\n -------\n stripped : same type as values\n \"\"\"\n return self.strip(to_strip, side=\"left\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rstrip_StringAccessor.rstrip.return.self_strip_to_strip_side": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rstrip_StringAccessor.rstrip.return.self_strip_to_strip_side", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 634, "end_line": 652, "span_ids": ["StringAccessor.rstrip"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def rstrip(self, to_strip=None):\n \"\"\"\n Remove leading and trailing characters.\n\n Strip whitespaces (including newlines) or a set of specified characters\n from each string in the array from the right side.\n\n Parameters\n ----------\n to_strip : str or None, default None\n Specifying the set of characters to be removed.\n All combinations of this set of characters will be stripped.\n If None then whitespaces are removed.\n\n Returns\n -------\n stripped : same type as values\n \"\"\"\n return self.strip(to_strip, side=\"right\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.wrap_StringAccessor.wrap.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.wrap_StringAccessor.wrap.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 654, "end_line": 693, "span_ids": ["StringAccessor.wrap"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def wrap(self, width, **kwargs):\n \"\"\"\n Wrap long strings in the array to be formatted in paragraphs with\n length less than a given width.\n\n This method has the same keyword parameters and defaults as\n :class:`textwrap.TextWrapper`.\n\n Parameters\n ----------\n width : int\n Maximum line-width\n expand_tabs : bool, optional\n If true, tab characters will be expanded to spaces (default: True)\n replace_whitespace : bool, optional\n If true, each whitespace character (as defined by\n string.whitespace) remaining after tab expansion will be replaced\n by a single space (default: True)\n drop_whitespace : bool, optional\n If true, whitespace that, after wrapping, happens to end up at the\n beginning or end of a line is dropped (default: True)\n break_long_words : bool, optional\n If true, then words longer than width will be broken in order to\n ensure that no lines are longer than width. If it is false, long\n words will not be broken, and some lines may be longer than width.\n (default: True)\n break_on_hyphens : bool, optional\n If true, wrapping will occur preferably on whitespace and right\n after hyphens in compound words, as it is customary in English. If\n false, only whitespaces will be considered as potentially good\n places for line breaks, but you need to set break_long_words to\n false if you want truly insecable words. (default: True)\n\n Returns\n -------\n wrapped : same type as values\n \"\"\"\n tw = textwrap.TextWrapper(width=width)\n f = lambda x: \"\\n\".join(tw.wrap(x))\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.translate_StringAccessor.repeat.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.translate_StringAccessor.repeat.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 695, "end_line": 729, "span_ids": ["StringAccessor.translate", "StringAccessor.repeat"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def translate(self, table):\n \"\"\"\n Map all characters in the string through the given mapping table.\n\n Parameters\n ----------\n table : dict\n A a mapping of Unicode ordinals to Unicode ordinals, strings,\n or None. Unmapped characters are left untouched. Characters mapped\n to None are deleted. :meth:`str.maketrans` is a helper function for\n making translation tables.\n\n Returns\n -------\n translated : same type as values\n \"\"\"\n f = lambda x: x.translate(table)\n return self._apply(f)\n\n def repeat(self, repeats):\n \"\"\"\n Duplicate each string in the array.\n\n Parameters\n ----------\n repeats : int\n Number of repetitions.\n\n Returns\n -------\n repeated : same type as values\n Array of repeated string objects.\n \"\"\"\n f = lambda x: repeats * x\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.find_StringAccessor.find.return.self__apply_f_dtype_int_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.find_StringAccessor.find.return.self__apply_f_dtype_int_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 731, "end_line": 766, "span_ids": ["StringAccessor.find"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def find(self, sub, start=0, end=None, side=\"left\"):\n \"\"\"\n Return lowest or highest indexes in each strings in the array\n where the substring is fully contained between [start:end].\n Return -1 on failure.\n\n Parameters\n ----------\n sub : str\n Substring being searched\n start : int\n Left edge index\n end : int\n Right edge index\n side : {'left', 'right'}, default 'left'\n Starting side for search.\n\n Returns\n -------\n found : array of integer values\n \"\"\"\n sub = self._obj.dtype.type(sub)\n\n if side == \"left\":\n method = \"find\"\n elif side == \"right\":\n method = \"rfind\"\n else: # pragma: no cover\n raise ValueError(\"Invalid side\")\n\n if end is None:\n f = lambda x: getattr(x, method)(sub, start)\n else:\n f = lambda x: getattr(x, method)(sub, start, end)\n\n return self._apply(f, dtype=int)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rfind_StringAccessor.rfind.return.self_find_sub_start_star": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rfind_StringAccessor.rfind.return.self_find_sub_start_star", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 768, "end_line": 787, "span_ids": ["StringAccessor.rfind"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def rfind(self, sub, start=0, end=None):\n \"\"\"\n Return highest indexes in each strings in the array\n where the substring is fully contained between [start:end].\n Return -1 on failure.\n\n Parameters\n ----------\n sub : str\n Substring being searched\n start : int\n Left edge index\n end : int\n Right edge index\n\n Returns\n -------\n found : array of integer values\n \"\"\"\n return self.find(sub, start=start, end=end, side=\"right\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.index_StringAccessor.index.return.self__apply_f_dtype_int_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.index_StringAccessor.index.return.self__apply_f_dtype_int_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 789, "end_line": 825, "span_ids": ["StringAccessor.index"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def index(self, sub, start=0, end=None, side=\"left\"):\n \"\"\"\n Return lowest or highest indexes in each strings where the substring is\n fully contained between [start:end]. This is the same as\n ``str.find`` except instead of returning -1, it raises a ValueError\n when the substring is not found.\n\n Parameters\n ----------\n sub : str\n Substring being searched\n start : int\n Left edge index\n end : int\n Right edge index\n side : {'left', 'right'}, default 'left'\n Starting side for search.\n\n Returns\n -------\n found : array of integer values\n \"\"\"\n sub = self._obj.dtype.type(sub)\n\n if side == \"left\":\n method = \"index\"\n elif side == \"right\":\n method = \"rindex\"\n else: # pragma: no cover\n raise ValueError(\"Invalid side\")\n\n if end is None:\n f = lambda x: getattr(x, method)(sub, start)\n else:\n f = lambda x: getattr(x, method)(sub, start, end)\n\n return self._apply(f, dtype=int)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rindex_StringAccessor.rindex.return.self_index_sub_start_sta": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.rindex_StringAccessor.rindex.return.self_index_sub_start_sta", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 827, "end_line": 847, "span_ids": ["StringAccessor.rindex"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def rindex(self, sub, start=0, end=None):\n \"\"\"\n Return highest indexes in each strings where the substring is\n fully contained between [start:end]. This is the same as\n ``str.rfind`` except instead of returning -1, it raises a ValueError\n when the substring is not found.\n\n Parameters\n ----------\n sub : str\n Substring being searched\n start : int\n Left edge index\n end : int\n Right edge index\n\n Returns\n -------\n found : array of integer values\n \"\"\"\n return self.index(sub, start=start, end=end, side=\"right\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.replace_StringAccessor.replace.return.self__apply_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.replace_StringAccessor.replace.return.self__apply_f_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 849, "end_line": 926, "span_ids": ["StringAccessor.replace"], "tokens": 659}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def replace(self, pat, repl, n=-1, case=None, flags=0, regex=True):\n \"\"\"\n Replace occurrences of pattern/regex in the array with some string.\n\n Parameters\n ----------\n pat : string or compiled regex\n String can be a character sequence or regular expression.\n\n repl : string or callable\n Replacement string or a callable. The callable is passed the regex\n match object and must return a replacement string to be used.\n See :func:`re.sub`.\n\n n : int, default -1 (all)\n Number of replacements to make from start\n case : boolean, default None\n - If True, case sensitive (the default if `pat` is a string)\n - Set to False for case insensitive\n - Cannot be set if `pat` is a compiled regex\n flags : int, default 0 (no flags)\n - re module flags, e.g. re.IGNORECASE\n - Cannot be set if `pat` is a compiled regex\n regex : boolean, default True\n - If True, assumes the passed-in pattern is a regular expression.\n - If False, treats the pattern as a literal string\n - Cannot be set to False if `pat` is a compiled regex or `repl` is\n a callable.\n\n Returns\n -------\n replaced : same type as values\n A copy of the object with all matching occurrences of `pat`\n replaced by `repl`.\n \"\"\"\n if not (_is_str_like(repl) or callable(repl)): # pragma: no cover\n raise TypeError(\"repl must be a string or callable\")\n\n if _is_str_like(pat):\n pat = self._obj.dtype.type(pat)\n\n if _is_str_like(repl):\n repl = self._obj.dtype.type(repl)\n\n is_compiled_re = isinstance(pat, type(re.compile(\"\")))\n if regex:\n if is_compiled_re:\n if (case is not None) or (flags != 0):\n raise ValueError(\n \"case and flags cannot be set\" \" when pat is a compiled regex\"\n )\n else:\n # not a compiled regex\n # set default case\n if case is None:\n case = True\n\n # add case flag, if provided\n if case is False:\n flags |= re.IGNORECASE\n if is_compiled_re or len(pat) > 1 or flags or callable(repl):\n n = n if n >= 0 else 0\n compiled = re.compile(pat, flags=flags)\n f = lambda x: compiled.sub(repl=repl, string=x, count=n)\n else:\n f = lambda x: x.replace(pat, repl, n)\n else:\n if is_compiled_re:\n raise ValueError(\n \"Cannot use a compiled regex as replacement \"\n \"pattern with regex=False\"\n )\n if callable(repl):\n raise ValueError(\n \"Cannot use a callable replacement when \" \"regex=False\"\n )\n f = lambda x: x.replace(pat, repl, n)\n return self._apply(f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.decode_StringAccessor.decode.return.self__apply_f_dtype_np_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.decode_StringAccessor.decode.return.self__apply_f_dtype_np_s", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 928, "end_line": 946, "span_ids": ["StringAccessor.decode"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def decode(self, encoding, errors=\"strict\"):\n \"\"\"\n Decode character string in the array using indicated encoding.\n\n Parameters\n ----------\n encoding : str\n errors : str, optional\n\n Returns\n -------\n decoded : same type as values\n \"\"\"\n if encoding in _cpython_optimized_decoders:\n f = lambda x: x.decode(encoding, errors)\n else:\n decoder = codecs.getdecoder(encoding)\n f = lambda x: decoder(x, errors)[0]\n return self._apply(f, dtype=np.str_)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.encode_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/accessor_str.py_StringAccessor.encode_", "embedding": null, "metadata": {"file_path": "xarray/core/accessor_str.py", "file_name": "accessor_str.py", "file_type": "text/x-python", "category": "implementation", "start_line": 948, "end_line": 967, "span_ids": ["StringAccessor.encode"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StringAccessor:\n\n def encode(self, encoding, errors=\"strict\"):\n \"\"\"\n Encode character string in the array using indicated encoding.\n\n Parameters\n ----------\n encoding : str\n errors : str, optional\n\n Returns\n -------\n encoded : same type as values\n \"\"\"\n if encoding in _cpython_optimized_encoders:\n f = lambda x: x.encode(encoding, errors)\n else:\n encoder = codecs.getencoder(encoding)\n f = lambda x: encoder(x, errors)[0]\n return self._apply(f, dtype=np.bytes_)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_functools__get_joiner.if_join_outer_.else_.raise_ValueError_invalid": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_functools__get_joiner.if_join_outer_.else_.raise_ValueError_invalid", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 37, "span_ids": ["imports:12", "impl", "_get_joiner", "imports"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\nimport operator\nfrom collections import defaultdict\nfrom contextlib import suppress\nfrom typing import TYPE_CHECKING, Any, Dict, Hashable, Mapping, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import dtypes, utils\nfrom .indexing import get_indexer_nd\nfrom .utils import is_dict_like, is_full_slice\nfrom .variable import IndexVariable, Variable\n\nif TYPE_CHECKING:\n from .dataarray import DataArray\n from .dataset import Dataset\n\n\ndef _get_joiner(join):\n if join == \"outer\":\n return functools.partial(functools.reduce, operator.or_)\n elif join == \"inner\":\n return functools.partial(functools.reduce, operator.and_)\n elif join == \"left\":\n return operator.itemgetter(0)\n elif join == \"right\":\n return operator.itemgetter(-1)\n elif join == \"exact\":\n # We cannot return a function to \"align\" in this case, because it needs\n # access to the dimension name to give a good error message.\n return None\n elif join == \"override\":\n # We rewrite all indexes and then use join='left'\n return operator.itemgetter(0)\n else:\n raise ValueError(\"invalid value for join: %s\" % join)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py__override_indexes_align": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py__override_indexes_align", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 337, "span_ids": ["_override_indexes", "align"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _override_indexes(objects, all_indexes, exclude):\n for dim, dim_indexes in all_indexes.items():\n if dim not in exclude:\n lengths = {index.size for index in dim_indexes}\n if len(lengths) != 1:\n raise ValueError(\n \"Indexes along dimension %r don't have the same length.\"\n \" Cannot use join='override'.\" % dim\n )\n\n objects = list(objects)\n for idx, obj in enumerate(objects[1:]):\n new_indexes = {}\n for dim in obj.dims:\n if dim not in exclude:\n new_indexes[dim] = all_indexes[dim][0]\n objects[idx + 1] = obj._overwrite_indexes(new_indexes)\n\n return objects\n\n\ndef align(\n *objects,\n join=\"inner\",\n copy=True,\n indexes=None,\n exclude=frozenset(),\n fill_value=dtypes.NA\n):\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_align.__align._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_align.__align._", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 249, "span_ids": ["align"], "tokens": 1864}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def align(\n *objects,\n join=\"inner\",\n copy=True,\n indexes=None,\n exclude=frozenset(),\n fill_value=dtypes.NA\n):\n \"\"\"\n Given any number of Dataset and/or DataArray objects, returns new\n objects with aligned indexes and dimension sizes.\n\n Array from the aligned objects are suitable as input to mathematical\n operators, because along each dimension they have the same index and size.\n\n Missing values (if ``join != 'inner'``) are filled with ``fill_value``.\n The default fill value is NaN.\n\n Parameters\n ----------\n *objects : Dataset or DataArray\n Objects to align.\n join : {'outer', 'inner', 'left', 'right', 'exact', 'override'}, optional\n Method for joining the indexes of the passed objects along each\n dimension:\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n copy : bool, optional\n If ``copy=True``, data in the return values is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed with\n only slice operations, then the output may share memory with the input.\n In either case, new xarray objects are always returned.\n indexes : dict-like, optional\n Any indexes explicitly provided with the `indexes` argument should be\n used in preference to the aligned indexes.\n exclude : sequence of str, optional\n Dimensions that must be excluded from alignment\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n aligned : same as *objects\n Tuple of objects with aligned coordinates.\n\n Raises\n ------\n ValueError\n If any dimensions without labels on the arguments have different sizes,\n or a different size than the size of the aligned dimension labels.\n\n Examples\n --------\n\n >>> import xarray as xr\n >>> x = xr.DataArray([[25, 35], [10, 24]], dims=('lat', 'lon'),\n ... coords={'lat': [35., 40.], 'lon': [100., 120.]})\n >>> y = xr.DataArray([[20, 5], [7, 13]], dims=('lat', 'lon'),\n ... coords={'lat': [35., 42.], 'lon': [100., 120.]})\n\n >>> x\n \n array([[25, 35],\n [10, 24]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n\n >>> y\n \n array([[20, 5],\n [ 7, 13]])\n Coordinates:\n * lat (lat) float64 35.0 42.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y)\n >>> a\n \n array([[25, 35]])\n Coordinates:\n * lat (lat) float64 35.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[20, 5]])\n Coordinates:\n * lat (lat) float64 35.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y, join='outer')\n >>> a\n \n array([[25., 35.],\n [10., 24.],\n [nan, nan]])\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[20., 5.],\n [nan, nan],\n [ 7., 13.]])\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y, join='outer', fill_value=-999)\n >>> a\n \n array([[ 25, 35],\n [ 10, 24],\n [-999, -999]])\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[ 20, 5],\n [-999, -999],\n [ 7, 13]])\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y, join='left')\n >>> a\n \n array([[25, 35],\n [10, 24]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[20., 5.],\n [nan, nan]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y, join='right')\n >>> a\n \n array([[25., 35.],\n [nan, nan]])\n Coordinates:\n * lat (lat) float64 35.0 42.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[20, 5],\n [ 7, 13]])\n Coordinates:\n * lat (lat) float64 35.0 42.0\n * lon (lon) float64 100.0 120.0\n\n >>> a, b = xr.align(x, y, join='exact')\n Traceback (most recent call last):\n ...\n \"indexes along dimension {!r} are not equal\".format(dim)\n ValueError: indexes along dimension 'lat' are not equal\n\n >>> a, b = xr.align(x, y, join='override')\n >>> a\n \n array([[25, 35],\n [10, 24]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n >>> b\n \n array([[20, 5],\n [ 7, 13]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_align.if_indexes_is_None__align.return.tuple_result_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_align.if_indexes_is_None__align.return.tuple_result_", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 250, "end_line": 337, "span_ids": ["align"], "tokens": 725}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def align(\n *objects,\n join=\"inner\",\n copy=True,\n indexes=None,\n exclude=frozenset(),\n fill_value=dtypes.NA\n):\n if indexes is None:\n indexes = {}\n\n if not indexes and len(objects) == 1:\n # fast path for the trivial case\n obj, = objects\n return (obj.copy(deep=copy),)\n\n all_indexes = defaultdict(list)\n unlabeled_dim_sizes = defaultdict(set)\n for obj in objects:\n for dim in obj.dims:\n if dim not in exclude:\n try:\n index = obj.indexes[dim]\n except KeyError:\n unlabeled_dim_sizes[dim].add(obj.sizes[dim])\n else:\n all_indexes[dim].append(index)\n\n if join == \"override\":\n objects = _override_indexes(objects, all_indexes, exclude)\n\n # We don't reindex over dimensions with all equal indexes for two reasons:\n # - It's faster for the usual case (already aligned objects).\n # - It ensures it's possible to do operations that don't require alignment\n # on indexes with duplicate values (which cannot be reindexed with\n # pandas). This is useful, e.g., for overwriting such duplicate indexes.\n joiner = _get_joiner(join)\n joined_indexes = {}\n for dim, matching_indexes in all_indexes.items():\n if dim in indexes:\n index = utils.safe_cast_to_index(indexes[dim])\n if (\n any(not index.equals(other) for other in matching_indexes)\n or dim in unlabeled_dim_sizes\n ):\n joined_indexes[dim] = index\n else:\n if (\n any(\n not matching_indexes[0].equals(other)\n for other in matching_indexes[1:]\n )\n or dim in unlabeled_dim_sizes\n ):\n if join == \"exact\":\n raise ValueError(\n \"indexes along dimension {!r} are not equal\".format(dim)\n )\n index = joiner(matching_indexes)\n joined_indexes[dim] = index\n else:\n index = matching_indexes[0]\n\n if dim in unlabeled_dim_sizes:\n unlabeled_sizes = unlabeled_dim_sizes[dim]\n labeled_size = index.size\n if len(unlabeled_sizes | {labeled_size}) > 1:\n raise ValueError(\n \"arguments without labels along dimension %r cannot be \"\n \"aligned because they have different dimension size(s) %r \"\n \"than the size of the aligned dimension labels: %r\"\n % (dim, unlabeled_sizes, labeled_size)\n )\n\n for dim in unlabeled_dim_sizes:\n if dim not in all_indexes:\n sizes = unlabeled_dim_sizes[dim]\n if len(sizes) > 1:\n raise ValueError(\n \"arguments without labels along dimension %r cannot be \"\n \"aligned because they have different dimension sizes: %r\"\n % (dim, sizes)\n )\n\n result = []\n for obj in objects:\n valid_indexers = {k: v for k, v in joined_indexes.items() if k in obj.dims}\n if not valid_indexers:\n # fast path for no reindexing necessary\n new_obj = obj.copy(deep=copy)\n else:\n new_obj = obj.reindex(copy=copy, fill_value=fill_value, **valid_indexers)\n new_obj.encoding = obj.encoding\n result.append(new_obj)\n\n return tuple(result)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_deep_align_deep_align.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_deep_align_deep_align.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 340, "end_line": 420, "span_ids": ["deep_align"], "tokens": 521}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def deep_align(\n objects,\n join=\"inner\",\n copy=True,\n indexes=None,\n exclude=frozenset(),\n raise_on_invalid=True,\n fill_value=dtypes.NA,\n):\n \"\"\"Align objects for merging, recursing into dictionary values.\n\n This function is not public API.\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n\n if indexes is None:\n indexes = {}\n\n def is_alignable(obj):\n return isinstance(obj, (DataArray, Dataset))\n\n positions = []\n keys = []\n out = []\n targets = []\n no_key = object()\n not_replaced = object()\n for position, variables in enumerate(objects):\n if is_alignable(variables):\n positions.append(position)\n keys.append(no_key)\n targets.append(variables)\n out.append(not_replaced)\n elif is_dict_like(variables):\n current_out = {}\n for k, v in variables.items():\n if is_alignable(v) and k not in indexes:\n # Skip variables in indexes for alignment, because these\n # should to be overwritten instead:\n # https://github.com/pydata/xarray/issues/725\n # https://github.com/pydata/xarray/issues/3377\n # TODO(shoyer): doing this here feels super-hacky -- can we\n # move it explicitly into merge instead?\n positions.append(position)\n keys.append(k)\n targets.append(v)\n current_out[k] = not_replaced\n else:\n current_out[k] = v\n out.append(current_out)\n elif raise_on_invalid:\n raise ValueError(\n \"object to align is neither an xarray.Dataset, \"\n \"an xarray.DataArray nor a dictionary: {!r}\".format(variables)\n )\n else:\n out.append(variables)\n\n aligned = align(\n *targets,\n join=join,\n copy=copy,\n indexes=indexes,\n exclude=exclude,\n fill_value=fill_value\n )\n\n for position, key, aligned_obj in zip(positions, keys, aligned):\n if key is no_key:\n out[position] = aligned_obj\n else:\n out[position][key] = aligned_obj\n\n # something went wrong: we should have replaced all sentinel values\n for arg in out:\n assert arg is not not_replaced\n if is_dict_like(arg):\n assert all(value is not not_replaced for value in arg.values())\n\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_like_indexers_reindex_like_indexers.return.indexers": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_like_indexers_reindex_like_indexers.return.indexers", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 423, "end_line": 459, "span_ids": ["reindex_like_indexers"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def reindex_like_indexers(\n target: \"Union[DataArray, Dataset]\", other: \"Union[DataArray, Dataset]\"\n) -> Dict[Hashable, pd.Index]:\n \"\"\"Extract indexers to align target with other.\n\n Not public API.\n\n Parameters\n ----------\n target : Dataset or DataArray\n Object to be aligned.\n other : Dataset or DataArray\n Object to be aligned with.\n\n Returns\n -------\n Dict[Hashable, pandas.Index] providing indexes for reindex keyword\n arguments.\n\n Raises\n ------\n ValueError\n If any dimensions without labels have different sizes.\n \"\"\"\n indexers = {k: v for k, v in other.indexes.items() if k in target.dims}\n\n for dim in other.dims:\n if dim not in indexers and dim in target.dims:\n other_size = other.sizes[dim]\n target_size = target.sizes[dim]\n if other_size != target_size:\n raise ValueError(\n \"different size for unlabeled \"\n \"dimension on argument %r: %r vs %r\"\n % (dim, other_size, target_size)\n )\n return indexers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_variables_reindex_variables.unchanged_dims.set_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_variables_reindex_variables.unchanged_dims.set_", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 462, "end_line": 525, "span_ids": ["reindex_variables"], "tokens": 566}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def reindex_variables(\n variables: Mapping[Any, Variable],\n sizes: Mapping[Any, int],\n indexes: Mapping[Any, pd.Index],\n indexers: Mapping,\n method: Optional[str] = None,\n tolerance: Any = None,\n copy: bool = True,\n fill_value: Optional[Any] = dtypes.NA,\n) -> Tuple[Dict[Hashable, Variable], Dict[Hashable, pd.Index]]:\n \"\"\"Conform a dictionary of aligned variables onto a new set of variables,\n filling in missing values with NaN.\n\n Not public API.\n\n Parameters\n ----------\n variables : dict-like\n Dictionary of xarray.Variable objects.\n sizes : dict-like\n Dictionary from dimension names to integer sizes.\n indexes : dict-like\n Dictionary of indexes associated with variables.\n indexers : dict\n Dictionary with keys given by dimension names and values given by\n arrays of coordinates tick labels. Any mis-matched coordinate values\n will be filled in with NaN, and any mis-matched dimension names will\n simply be ignored.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for filling index values in ``indexers`` not found in\n this dataset:\n * None (default): don't fill gaps\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact matches.\n The values of the index at the matching locations must satisfy the\n equation ``abs(index[indexer] - target) <= tolerance``.\n copy : bool, optional\n If ``copy=True``, data in the return values is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed\n with only slice operations, then the output may share memory with\n the input. In either case, new xarray objects are always returned.\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n reindexed : dict\n Dict of reindexed variables.\n new_indexes : dict\n Dict of indexes associated with the reindexed variables.\n \"\"\"\n from .dataarray import DataArray\n\n # create variables for the new dataset\n reindexed: Dict[Hashable, Variable] = {}\n\n # build up indexers for assignment along each dimension\n int_indexers = {}\n new_indexes = dict(indexes)\n masked_dims = set()\n unchanged_dims = set()\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_variables.for_dim_indexer_in_index_reindex_variables.return.reindexed_new_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_reindex_variables.for_dim_indexer_in_index_reindex_variables.return.reindexed_new_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 527, "end_line": 594, "span_ids": ["reindex_variables"], "tokens": 639}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def reindex_variables(\n variables: Mapping[Any, Variable],\n sizes: Mapping[Any, int],\n indexes: Mapping[Any, pd.Index],\n indexers: Mapping,\n method: Optional[str] = None,\n tolerance: Any = None,\n copy: bool = True,\n fill_value: Optional[Any] = dtypes.NA,\n) -> Tuple[Dict[Hashable, Variable], Dict[Hashable, pd.Index]]:\n # ... other code\n\n for dim, indexer in indexers.items():\n if isinstance(indexer, DataArray) and indexer.dims != (dim,):\n raise ValueError(\n \"Indexer has dimensions {:s} that are different \"\n \"from that to be indexed along {:s}\".format(str(indexer.dims), dim)\n )\n\n target = new_indexes[dim] = utils.safe_cast_to_index(indexers[dim])\n\n if dim in indexes:\n index = indexes[dim]\n\n if not index.is_unique:\n raise ValueError(\n \"cannot reindex or align along dimension %r because the \"\n \"index has duplicate values\" % dim\n )\n\n int_indexer = get_indexer_nd(index, target, method, tolerance)\n\n # We uses negative values from get_indexer_nd to signify\n # values that are missing in the index.\n if (int_indexer < 0).any():\n masked_dims.add(dim)\n elif np.array_equal(int_indexer, np.arange(len(index))):\n unchanged_dims.add(dim)\n\n int_indexers[dim] = int_indexer\n\n if dim in variables:\n var = variables[dim]\n args: tuple = (var.attrs, var.encoding)\n else:\n args = ()\n reindexed[dim] = IndexVariable((dim,), target, *args)\n\n for dim in sizes:\n if dim not in indexes and dim in indexers:\n existing_size = sizes[dim]\n new_size = indexers[dim].size\n if existing_size != new_size:\n raise ValueError(\n \"cannot reindex or align along dimension %r without an \"\n \"index because its size %r is different from the size of \"\n \"the new index %r\" % (dim, existing_size, new_size)\n )\n\n for name, var in variables.items():\n if name not in indexers:\n key = tuple(\n slice(None) if d in unchanged_dims else int_indexers.get(d, slice(None))\n for d in var.dims\n )\n needs_masking = any(d in masked_dims for d in var.dims)\n\n if needs_masking:\n new_var = var._getitem_with_mask(key, fill_value=fill_value)\n elif all(is_full_slice(k) for k in key):\n # no reindexing necessary\n # here we need to manually deal with copying data, since\n # we neither created a new ndarray nor used fancy indexing\n new_var = var.copy(deep=copy)\n else:\n new_var = var[key]\n\n reindexed[name] = new_var\n\n return reindexed, new_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py__get_broadcast_dims_map_common_coords__broadcast_helper.if_isinstance_arg_DataAr.else_.raise_ValueError_all_inp": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py__get_broadcast_dims_map_common_coords__broadcast_helper.if_isinstance_arg_DataAr.else_.raise_ValueError_all_inp", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 597, "end_line": 643, "span_ids": ["_broadcast_helper", "_get_broadcast_dims_map_common_coords"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_broadcast_dims_map_common_coords(args, exclude):\n\n common_coords = {}\n dims_map = {}\n for arg in args:\n for dim in arg.dims:\n if dim not in common_coords and dim not in exclude:\n dims_map[dim] = arg.sizes[dim]\n if dim in arg.coords:\n common_coords[dim] = arg.coords[dim].variable\n\n return dims_map, common_coords\n\n\ndef _broadcast_helper(arg, exclude, dims_map, common_coords):\n\n from .dataarray import DataArray\n from .dataset import Dataset\n\n def _set_dims(var):\n # Add excluded dims to a copy of dims_map\n var_dims_map = dims_map.copy()\n for dim in exclude:\n with suppress(ValueError):\n # ignore dim not in var.dims\n var_dims_map[dim] = var.shape[var.dims.index(dim)]\n\n return var.set_dims(var_dims_map)\n\n def _broadcast_array(array):\n data = _set_dims(array.variable)\n coords = dict(array.coords)\n coords.update(common_coords)\n return DataArray(data, coords, data.dims, name=array.name, attrs=array.attrs)\n\n def _broadcast_dataset(ds):\n data_vars = {k: _set_dims(ds.variables[k]) for k in ds.data_vars}\n coords = dict(ds.coords)\n coords.update(common_coords)\n return Dataset(data_vars, coords, ds.attrs)\n\n if isinstance(arg, DataArray):\n return _broadcast_array(arg)\n elif isinstance(arg, Dataset):\n return _broadcast_dataset(arg)\n else:\n raise ValueError(\"all input must be Dataset or DataArray objects\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_broadcast_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/alignment.py_broadcast_", "embedding": null, "metadata": {"file_path": "xarray/core/alignment.py", "file_name": "alignment.py", "file_type": "text/x-python", "category": "implementation", "start_line": 646, "end_line": 729, "span_ids": ["broadcast"], "tokens": 708}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def broadcast(*args, exclude=None):\n \"\"\"Explicitly broadcast any number of DataArray or Dataset objects against\n one another.\n\n xarray objects automatically broadcast against each other in arithmetic\n operations, so this function should not be necessary for normal use.\n\n If no change is needed, the input data is returned to the output without\n being copied.\n\n Parameters\n ----------\n *args : DataArray or Dataset objects\n Arrays to broadcast against each other.\n exclude : sequence of str, optional\n Dimensions that must not be broadcasted\n\n Returns\n -------\n broadcast : tuple of xarray objects\n The same data as the input arrays, but with additional dimensions\n inserted so that all data arrays have the same dimensions and shape.\n\n Examples\n --------\n\n Broadcast two data arrays against one another to fill out their dimensions:\n\n >>> a = xr.DataArray([1, 2, 3], dims='x')\n >>> b = xr.DataArray([5, 6], dims='y')\n >>> a\n \n array([1, 2, 3])\n Coordinates:\n * x (x) int64 0 1 2\n >>> b\n \n array([5, 6])\n Coordinates:\n * y (y) int64 0 1\n >>> a2, b2 = xr.broadcast(a, b)\n >>> a2\n \n array([[1, 1],\n [2, 2],\n [3, 3]])\n Coordinates:\n * x (x) int64 0 1 2\n * y (y) int64 0 1\n >>> b2\n \n array([[5, 6],\n [5, 6],\n [5, 6]])\n Coordinates:\n * y (y) int64 0 1\n * x (x) int64 0 1 2\n\n Fill out the dimensions of all data variables in a dataset:\n\n >>> ds = xr.Dataset({'a': a, 'b': b})\n >>> ds2, = xr.broadcast(ds) # use tuple unpacking to extract one dataset\n >>> ds2\n \n Dimensions: (x: 3, y: 2)\n Coordinates:\n * x (x) int64 0 1 2\n * y (y) int64 0 1\n Data variables:\n a (x, y) int64 1 1 2 2 3 3\n b (x, y) int64 5 6 5 6 5 6\n \"\"\"\n\n if exclude is None:\n exclude = set()\n args = align(*args, join=\"outer\", copy=False, exclude=exclude)\n\n dims_map, common_coords = _get_broadcast_dims_map_common_coords(args, exclude)\n result = []\n for arg in args:\n result.append(_broadcast_helper(arg, exclude, dims_map, common_coords))\n\n return tuple(result)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py__Base_classes_implement_SupportsArithmetic._HANDLED_TYPES._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py__Base_classes_implement_SupportsArithmetic._HANDLED_TYPES._", "embedding": null, "metadata": {"file_path": "xarray/core/arithmetic.py", "file_name": "arithmetic.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 30, "span_ids": ["SupportsArithmetic", "docstring", "imports"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Base classes implementing arithmetic for xarray objects.\"\"\"\nimport numbers\n\nimport numpy as np\n\nfrom .options import OPTIONS\nfrom .pycompat import dask_array_type\nfrom .utils import not_implemented\n\n\nclass SupportsArithmetic:\n \"\"\"Base class for xarray types that support arithmetic.\n\n Used by Dataset, DataArray, Variable and GroupBy.\n \"\"\"\n\n __slots__ = ()\n\n # TODO: implement special methods for arithmetic here rather than injecting\n # them in xarray/core/ops.py. Ideally, do so by inheriting from\n # numpy.lib.mixins.NDArrayOperatorsMixin.\n\n # TODO: allow extending this with some sort of registration system\n _HANDLED_TYPES = (\n np.ndarray,\n np.generic,\n numbers.Number,\n bytes,\n str,\n ) + dask_array_type", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py_SupportsArithmetic.__array_ufunc___SupportsArithmetic.__array_ufunc__.return.apply_ufunc_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py_SupportsArithmetic.__array_ufunc___SupportsArithmetic.__array_ufunc__.return.apply_ufunc_", "embedding": null, "metadata": {"file_path": "xarray/core/arithmetic.py", "file_name": "arithmetic.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 80, "span_ids": ["SupportsArithmetic.__array_ufunc__"], "tokens": 448}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupportsArithmetic:\n\n def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):\n from .computation import apply_ufunc\n\n # See the docstring example for numpy.lib.mixins.NDArrayOperatorsMixin.\n out = kwargs.get(\"out\", ())\n for x in inputs + out:\n if not isinstance(x, self._HANDLED_TYPES + (SupportsArithmetic,)):\n return NotImplemented\n\n if ufunc.signature is not None:\n raise NotImplementedError(\n \"{} not supported: xarray objects do not directly implement \"\n \"generalized ufuncs. Instead, use xarray.apply_ufunc or \"\n \"explicitly convert to xarray objects to NumPy arrays \"\n \"(e.g., with `.values`).\".format(ufunc)\n )\n\n if method != \"__call__\":\n # TODO: support other methods, e.g., reduce and accumulate.\n raise NotImplementedError(\n \"{} method for ufunc {} is not implemented on xarray objects, \"\n \"which currently only support the __call__ method. As an \"\n \"alternative, consider explicitly converting xarray objects \"\n \"to NumPy arrays (e.g., with `.values`).\".format(method, ufunc)\n )\n\n if any(isinstance(o, SupportsArithmetic) for o in out):\n # TODO: implement this with logic like _inplace_binary_op. This\n # will be necessary to use NDArrayOperatorsMixin.\n raise NotImplementedError(\n \"xarray objects are not yet supported in the `out` argument \"\n \"for ufuncs. As an alternative, consider explicitly \"\n \"converting xarray objects to NumPy arrays (e.g., with \"\n \"`.values`).\"\n )\n\n join = dataset_join = OPTIONS[\"arithmetic_join\"]\n\n return apply_ufunc(\n ufunc,\n *inputs,\n input_core_dims=((),) * ufunc.nin,\n output_core_dims=((),) * ufunc.nout,\n join=join,\n dataset_join=dataset_join,\n dataset_fill_value=np.nan,\n kwargs=kwargs,\n dask=\"allowed\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py_SupportsArithmetic._this_has_no_runtime_fun_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/arithmetic.py_SupportsArithmetic._this_has_no_runtime_fun_", "embedding": null, "metadata": {"file_path": "xarray/core/arithmetic.py", "file_name": "arithmetic.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 105, "span_ids": ["SupportsArithmetic.__array_ufunc__", "SupportsArithmetic:7"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupportsArithmetic:\n\n # this has no runtime function - these are listed so IDEs know these\n # methods are defined and don't warn on these operations\n __lt__ = (\n __le__\n ) = (\n __ge__\n ) = (\n __gt__\n ) = (\n __add__\n ) = (\n __sub__\n ) = (\n __mul__\n ) = (\n __truediv__\n ) = (\n __floordiv__\n ) = (\n __mod__\n ) = (\n __pow__\n ) = __and__ = __xor__ = __or__ = __div__ = __eq__ = __ne__ = not_implemented", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_itertools__infer_tile_ids_from_nested_list.if_isinstance_entry_list.else_.yield_current_pos_entry": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_itertools__infer_tile_ids_from_nested_list.if_isinstance_entry_list.else_.yield_current_pos_entry", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 46, "span_ids": ["_infer_tile_ids_from_nested_list", "_infer_concat_order_from_positions", "imports"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\nimport warnings\nfrom collections import Counter\nfrom textwrap import dedent\n\nimport pandas as pd\n\nfrom . import dtypes\nfrom .concat import concat\nfrom .dataarray import DataArray\nfrom .dataset import Dataset\nfrom .merge import merge\n\n\ndef _infer_concat_order_from_positions(datasets):\n combined_ids = dict(_infer_tile_ids_from_nested_list(datasets, ()))\n return combined_ids\n\n\ndef _infer_tile_ids_from_nested_list(entry, current_pos):\n \"\"\"\n Given a list of lists (of lists...) of objects, returns a iterator\n which returns a tuple containing the index of each object in the nested\n list structure as the key, and the object. This can then be called by the\n dict constructor to create a dictionary of the objects organised by their\n position in the original nested list.\n\n Recursively traverses the given structure, while keeping track of the\n current position. Should work for any type of object which isn't a list.\n\n Parameters\n ----------\n entry : list[list[obj, obj, ...], ...]\n List of lists of arbitrary depth, containing objects in the order\n they are to be concatenated.\n\n Returns\n -------\n combined_tile_ids : dict[tuple(int, ...), obj]\n \"\"\"\n\n if isinstance(entry, list):\n for i, item in enumerate(entry):\n yield from _infer_tile_ids_from_nested_list(item, current_pos + (i,))\n else:\n yield current_pos, entry", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__infer_concat_order_from_coords__infer_concat_order_from_coords.return.combined_ids_concat_dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__infer_concat_order_from_coords__infer_concat_order_from_coords.return.combined_ids_concat_dims", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 49, "end_line": 115, "span_ids": ["_infer_concat_order_from_coords"], "tokens": 529}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_concat_order_from_coords(datasets):\n\n concat_dims = []\n tile_ids = [() for ds in datasets]\n\n # All datasets have same variables because they've been grouped as such\n ds0 = datasets[0]\n for dim in ds0.dims:\n\n # Check if dim is a coordinate dimension\n if dim in ds0:\n\n # Need to read coordinate values to do ordering\n indexes = [ds.indexes.get(dim) for ds in datasets]\n if any(index is None for index in indexes):\n raise ValueError(\n \"Every dimension needs a coordinate for \"\n \"inferring concatenation order\"\n )\n\n # If dimension coordinate values are same on every dataset then\n # should be leaving this dimension alone (it's just a \"bystander\")\n if not all(index.equals(indexes[0]) for index in indexes[1:]):\n\n # Infer order datasets should be arranged in along this dim\n concat_dims.append(dim)\n\n if all(index.is_monotonic_increasing for index in indexes):\n ascending = True\n elif all(index.is_monotonic_decreasing for index in indexes):\n ascending = False\n else:\n raise ValueError(\n \"Coordinate variable {} is neither \"\n \"monotonically increasing nor \"\n \"monotonically decreasing on all datasets\".format(dim)\n )\n\n # Assume that any two datasets whose coord along dim starts\n # with the same value have the same coord values throughout.\n if any(index.size == 0 for index in indexes):\n raise ValueError(\"Cannot handle size zero dimensions\")\n first_items = pd.Index([index.take([0]) for index in indexes])\n\n # Sort datasets along dim\n # We want rank but with identical elements given identical\n # position indices - they should be concatenated along another\n # dimension, not along this one\n series = first_items.to_series()\n rank = series.rank(method=\"dense\", ascending=ascending)\n order = rank.astype(int).values - 1\n\n # Append positions along extra dimension to structure which\n # encodes the multi-dimensional concatentation order\n tile_ids = [\n tile_id + (position,) for tile_id, position in zip(tile_ids, order)\n ]\n\n if len(datasets) > 1 and not concat_dims:\n raise ValueError(\n \"Could not find any dimension coordinates to use to \"\n \"order the datasets for concatenation\"\n )\n\n combined_ids = dict(zip(tile_ids, datasets))\n\n return combined_ids, concat_dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__check_shape_tile_ids__check_shape_tile_ids.for_dim_in_range_nesting_.if_len_set_occurrences_va.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__check_shape_tile_ids__check_shape_tile_ids.for_dim_in_range_nesting_.if_len_set_occurrences_va.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 141, "span_ids": ["_check_shape_tile_ids"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _check_shape_tile_ids(combined_tile_ids):\n tile_ids = combined_tile_ids.keys()\n\n # Check all tuples are the same length\n # i.e. check that all lists are nested to the same depth\n nesting_depths = [len(tile_id) for tile_id in tile_ids]\n if not nesting_depths:\n nesting_depths = [0]\n if not set(nesting_depths) == {nesting_depths[0]}:\n raise ValueError(\n \"The supplied objects do not form a hypercube because\"\n \" sub-lists do not have consistent depths\"\n )\n\n # Check all lists along one dimension are same length\n for dim in range(nesting_depths[0]):\n indices_along_dim = [tile_id[dim] for tile_id in tile_ids]\n occurrences = Counter(indices_along_dim)\n if len(set(occurrences.values())) != 1:\n raise ValueError(\n \"The supplied objects do not form a hypercube \"\n \"because sub-lists do not have consistent \"\n \"lengths along dimension\" + str(dim)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_nd__combine_nd.return.combined_ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_nd__combine_nd.return.combined_ds", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 201, "span_ids": ["_combine_nd"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _combine_nd(\n combined_ids,\n concat_dims,\n data_vars=\"all\",\n coords=\"different\",\n compat=\"no_conflicts\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"\n Combines an N-dimensional structure of datasets into one by applying a\n series of either concat and merge operations along each dimension.\n\n No checks are performed on the consistency of the datasets, concat_dims or\n tile_IDs, because it is assumed that this has already been done.\n\n Parameters\n ----------\n combined_ids : Dict[Tuple[int, ...]], xarray.Dataset]\n Structure containing all datasets to be concatenated with \"tile_IDs\" as\n keys, which specify position within the desired final combined result.\n concat_dims : sequence of str\n The dimensions along which the datasets should be concatenated. Must be\n in order, and the length must match the length of the tuples used as\n keys in combined_ids. If the string is a dimension name then concat\n along that dimension, if it is None then merge.\n\n Returns\n -------\n combined_ds : xarray.Dataset\n \"\"\"\n\n example_tile_id = next(iter(combined_ids.keys()))\n\n n_dims = len(example_tile_id)\n if len(concat_dims) != n_dims:\n raise ValueError(\n \"concat_dims has length {} but the datasets \"\n \"passed are nested in a {}-dimensional structure\".format(\n len(concat_dims), n_dims\n )\n )\n\n # Each iteration of this loop reduces the length of the tile_ids tuples\n # by one. It always combines along the first dimension, removing the first\n # element of the tuple\n for concat_dim in concat_dims:\n combined_ids = _combine_all_along_first_dim(\n combined_ids,\n dim=concat_dim,\n data_vars=data_vars,\n coords=coords,\n compat=compat,\n fill_value=fill_value,\n join=join,\n )\n (combined_ds,) = combined_ids.values()\n return combined_ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_all_along_first_dim__combine_all_along_first_dim.return.new_combined_ids": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_all_along_first_dim__combine_all_along_first_dim.return.new_combined_ids", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 204, "end_line": 222, "span_ids": ["_combine_all_along_first_dim"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _combine_all_along_first_dim(\n combined_ids, dim, data_vars, coords, compat, fill_value=dtypes.NA, join=\"outer\"\n):\n\n # Group into lines of datasets which must be combined along dim\n # need to sort by _new_tile_id first for groupby to work\n # TODO: is the sorted need?\n combined_ids = dict(sorted(combined_ids.items(), key=_new_tile_id))\n grouped = itertools.groupby(combined_ids.items(), key=_new_tile_id)\n\n # Combine all of these datasets along dim\n new_combined_ids = {}\n for new_id, group in grouped:\n combined_ids = dict(sorted(group))\n datasets = combined_ids.values()\n new_combined_ids[new_id] = _combine_1d(\n datasets, dim, compat, data_vars, coords, fill_value, join\n )\n return new_combined_ids", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_1d__combine_1d.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__combine_1d__combine_1d.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 225, "end_line": 264, "span_ids": ["_combine_1d"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _combine_1d(\n datasets,\n concat_dim,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"\n Applies either concat or merge to 1D list of datasets depending on value\n of concat_dim\n \"\"\"\n\n if concat_dim is not None:\n try:\n combined = concat(\n datasets,\n dim=concat_dim,\n data_vars=data_vars,\n coords=coords,\n compat=compat,\n fill_value=fill_value,\n join=join,\n )\n except ValueError as err:\n if \"encountered unexpected variable\" in str(err):\n raise ValueError(\n \"These objects cannot be combined using only \"\n \"xarray.combine_nested, instead either use \"\n \"xarray.combine_by_coords, or do it manually \"\n \"with xarray.concat, xarray.merge and \"\n \"xarray.align\"\n )\n else:\n raise\n else:\n combined = merge(datasets, compat=compat, fill_value=fill_value, join=join)\n\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__new_tile_id__nested_combine.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__new_tile_id__nested_combine.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 267, "end_line": 309, "span_ids": ["_nested_combine", "_new_tile_id"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _new_tile_id(single_id_ds_pair):\n tile_id, ds = single_id_ds_pair\n return tile_id[1:]\n\n\ndef _nested_combine(\n datasets,\n concat_dims,\n compat,\n data_vars,\n coords,\n ids,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n\n if len(datasets) == 0:\n return Dataset()\n\n # Arrange datasets for concatenation\n # Use information from the shape of the user input\n if not ids:\n # Determine tile_IDs by structure of input in N-D\n # (i.e. ordering in list-of-lists)\n combined_ids = _infer_concat_order_from_positions(datasets)\n else:\n # Already sorted so just use the ids already passed\n combined_ids = dict(zip(ids, datasets))\n\n # Check that the inferred shape is combinable\n _check_shape_tile_ids(combined_ids)\n\n # Apply series of concatenate or merge operations along each dimension\n combined = _combine_nd(\n combined_ids,\n concat_dims,\n compat=compat,\n data_vars=data_vars,\n coords=coords,\n fill_value=fill_value,\n join=join,\n )\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_nested_combine_by_coords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_nested_combine_by_coords", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 312, "end_line": 697, "span_ids": ["combine_by_coords", "combine_nested", "vars_as_keys"], "tokens": 1542}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def combine_nested(\n datasets,\n concat_dim,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"\n Explicitly combine an N-dimensional grid of datasets into one by using a\n succession of concat and merge operations along each dimension of the grid.\n\n Does not sort the supplied datasets under any circumstances, so the\n datasets must be passed in the order you wish them to be concatenated. It\n does align coordinates, but different variables on datasets can cause it to\n fail under some scenarios. In complex cases, you may need to clean up your\n data and use concat/merge explicitly.\n\n To concatenate along multiple dimensions the datasets must be passed as a\n nested list-of-lists, with a depth equal to the length of ``concat_dims``.\n ``manual_combine`` will concatenate along the top-level list first.\n\n Useful for combining datasets from a set of nested directories, or for\n collecting the output of a simulation parallelized along multiple\n dimensions.\n\n Parameters\n ----------\n datasets : list or nested list of xarray.Dataset objects.\n Dataset objects to combine.\n If concatenation or merging along more than one dimension is desired,\n then datasets must be supplied in a nested list-of-lists.\n concat_dim : str, or list of str, DataArray, Index or None\n Dimensions along which to concatenate variables, as used by\n :py:func:`xarray.concat`.\n Set ``concat_dim=[..., None, ...]`` explicitly to disable concatenation\n and merge instead along a particular dimension.\n The position of ``None`` in the list specifies the dimension of the\n nested-list input along which to merge.\n Must be the same length as the depth of the list passed to\n ``datasets``.\n compat : {'identical', 'equals', 'broadcast_equals',\n 'no_conflicts', 'override'}, optional\n String indicating how to compare variables of the same name for\n potential merge conflicts:\n\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n - 'override': skip comparing and pick variable from first dataset\n data_vars : {'minimal', 'different', 'all' or list of str}, optional\n Details are in the documentation of concat\n coords : {'minimal', 'different', 'all' or list of str}, optional\n Details are in the documentation of concat\n fill_value : scalar, optional\n Value to use for newly missing values\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n String indicating how to combine differing indexes\n (excluding concat_dim) in objects\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n\n Returns\n -------\n combined : xarray.Dataset\n\n Examples\n --------\n\n A common task is collecting data from a parallelized simulation in which\n each process wrote out to a separate file. A domain which was decomposed\n into 4 parts, 2 each along both the x and y axes, requires organising the\n datasets into a doubly-nested list, e.g:\n\n >>> x1y1\n \n Dimensions: (x: 2, y: 2)\n Dimensions without coordinates: x, y\n Data variables:\n temperature (x, y) float64 11.04 23.57 20.77 ...\n precipitation (x, y) float64 5.904 2.453 3.404 ...\n\n >>> ds_grid = [[x1y1, x1y2], [x2y1, x2y2]]\n >>> combined = xr.combine_nested(ds_grid, concat_dim=['x', 'y'])\n \n Dimensions: (x: 4, y: 4)\n Dimensions without coordinates: x, y\n Data variables:\n temperature (x, y) float64 11.04 23.57 20.77 ...\n precipitation (x, y) float64 5.904 2.453 3.404 ...\n\n ``manual_combine`` can also be used to explicitly merge datasets with\n different variables. For example if we have 4 datasets, which are divided\n along two times, and contain two different variables, we can pass ``None``\n to ``concat_dim`` to specify the dimension of the nested list over which\n we wish to use ``merge`` instead of ``concat``:\n\n >>> t1temp\n \n Dimensions: (t: 5)\n Dimensions without coordinates: t\n Data variables:\n temperature (t) float64 11.04 23.57 20.77 ...\n\n >>> t1precip\n \n Dimensions: (t: 5)\n Dimensions without coordinates: t\n Data variables:\n precipitation (t) float64 5.904 2.453 3.404 ...\n\n >>> ds_grid = [[t1temp, t1precip], [t2temp, t2precip]]\n >>> combined = xr.combine_nested(ds_grid, concat_dim=['t', None])\n \n Dimensions: (t: 10)\n Dimensions without coordinates: t\n Data variables:\n temperature (t) float64 11.04 23.57 20.77 ...\n precipitation (t) float64 5.904 2.453 3.404 ...\n\n See also\n --------\n concat\n merge\n auto_combine\n \"\"\"\n if isinstance(concat_dim, (str, DataArray)) or concat_dim is None:\n concat_dim = [concat_dim]\n\n # The IDs argument tells _manual_combine that datasets aren't yet sorted\n return _nested_combine(\n datasets,\n concat_dims=concat_dim,\n compat=compat,\n data_vars=data_vars,\n coords=coords,\n ids=False,\n fill_value=fill_value,\n join=join,\n )\n\n\ndef vars_as_keys(ds):\n return tuple(sorted(ds))\n\n\ndef combine_by_coords(\n datasets,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_by_coords.__combine_by_coords._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_by_coords.__combine_by_coords._", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 480, "end_line": 655, "span_ids": ["combine_by_coords"], "tokens": 2137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def combine_by_coords(\n datasets,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"\n Attempt to auto-magically combine the given datasets into one by using\n dimension coordinates.\n\n This method attempts to combine a group of datasets along any number of\n dimensions into a single entity by inspecting coords and metadata and using\n a combination of concat and merge.\n\n Will attempt to order the datasets such that the values in their dimension\n coordinates are monotonic along all dimensions. If it cannot determine the\n order in which to concatenate the datasets, it will raise a ValueError.\n Non-coordinate dimensions will be ignored, as will any coordinate\n dimensions which do not vary between each dataset.\n\n Aligns coordinates, but different variables on datasets can cause it\n to fail under some scenarios. In complex cases, you may need to clean up\n your data and use concat/merge explicitly (also see `manual_combine`).\n\n Works well if, for example, you have N years of data and M data variables,\n and each combination of a distinct time period and set of data variables is\n saved as its own dataset. Also useful for if you have a simulation which is\n parallelized in multiple dimensions, but has global coordinates saved in\n each file specifying the positions of points within the global domain.\n\n Parameters\n ----------\n datasets : sequence of xarray.Dataset\n Dataset objects to combine.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts:\n\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n - 'override': skip comparing and pick variable from first dataset\n data_vars : {'minimal', 'different', 'all' or list of str}, optional\n These data variables will be concatenated together:\n\n * 'minimal': Only data variables in which the dimension already\n appears are included.\n * 'different': Data variables which are not equal (ignoring\n attributes) across all datasets are also concatenated (as well as\n all for which dimension already appears). Beware: this option may\n load the data payload of data variables into memory if they are not\n already loaded.\n * 'all': All data variables will be concatenated.\n * list of str: The listed data variables will be concatenated, in\n addition to the 'minimal' data variables.\n If objects are DataArrays, `data_vars` must be 'all'.\n coords : {'minimal', 'different', 'all' or list of str}, optional\n As per the 'data_vars' kwarg, but for coordinate variables.\n fill_value : scalar, optional\n Value to use for newly missing values\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n String indicating how to combine differing indexes\n (excluding concat_dim) in objects\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n\n Returns\n -------\n combined : xarray.Dataset\n\n See also\n --------\n concat\n merge\n combine_nested\n\n Examples\n --------\n\n Combining two datasets using their common dimension coordinates. Notice\n they are concatenated based on the values in their dimension coordinates,\n not on their position in the list passed to `combine_by_coords`.\n\n >>> import numpy as np\n >>> import xarray as xr\n\n >>> x1 = xr.Dataset(\n ... {\n ... \"temperature\": ((\"y\", \"x\"), 20 * np.random.rand(6).reshape(2, 3)),\n ... \"precipitation\": ((\"y\", \"x\"), np.random.rand(6).reshape(2, 3)),\n ... },\n ... coords={\"y\": [0, 1], \"x\": [10, 20, 30]},\n ... )\n >>> x2 = xr.Dataset(\n ... {\n ... \"temperature\": ((\"y\", \"x\"), 20 * np.random.rand(6).reshape(2, 3)),\n ... \"precipitation\": ((\"y\", \"x\"), np.random.rand(6).reshape(2, 3)),\n ... },\n ... coords={\"y\": [2, 3], \"x\": [10, 20, 30]},\n ... )\n >>> x3 = xr.Dataset(\n ... {\n ... \"temperature\": ((\"y\", \"x\"), 20 * np.random.rand(6).reshape(2, 3)),\n ... \"precipitation\": ((\"y\", \"x\"), np.random.rand(6).reshape(2, 3)),\n ... },\n ... coords={\"y\": [2, 3], \"x\": [40, 50, 60]},\n ... )\n\n >>> x1\n \n Dimensions: (x: 3, y: 2)\n Coordinates:\n * y (y) int64 0 1\n * x (x) int64 10 20 30\n Data variables:\n temperature (y, x) float64 1.654 10.63 7.015 2.543 13.93 9.436\n precipitation (y, x) float64 0.2136 0.9974 0.7603 0.4679 0.3115 0.945\n\n >>> x2\n \n Dimensions: (x: 3, y: 2)\n Coordinates:\n * y (y) int64 2 3\n * x (x) int64 10 20 30\n Data variables:\n temperature (y, x) float64 9.341 0.1251 6.269 7.709 8.82 2.316\n precipitation (y, x) float64 0.1728 0.1178 0.03018 0.6509 0.06938 0.3792\n\n >>> x3\n \n Dimensions: (x: 3, y: 2)\n Coordinates:\n * y (y) int64 2 3\n * x (x) int64 40 50 60\n Data variables:\n temperature (y, x) float64 2.789 2.446 6.551 12.46 2.22 15.96\n precipitation (y, x) float64 0.4804 0.1902 0.2457 0.6125 0.4654 0.5953\n\n >>> xr.combine_by_coords([x2, x1])\n \n Dimensions: (x: 3, y: 4)\n Coordinates:\n * x (x) int64 10 20 30\n * y (y) int64 0 1 2 3\n Data variables:\n temperature (y, x) float64 1.654 10.63 7.015 2.543 ... 7.709 8.82 2.316\n precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6509 0.06938 0.3792\n\n >>> xr.combine_by_coords([x3, x1])\n \n Dimensions: (x: 6, y: 4)\n Coordinates:\n * x (x) int64 10 20 30 40 50 60\n * y (y) int64 0 1 2 3\n Data variables:\n temperature (y, x) float64 1.654 10.63 7.015 nan ... nan 12.46 2.22 15.96\n precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6125 0.4654 0.5953\n\n >>> xr.combine_by_coords([x3, x1], join='override')\n \n Dimensions: (x: 3, y: 4)\n Coordinates:\n * x (x) int64 10 20 30\n * y (y) int64 0 1 2 3\n Data variables:\n temperature (y, x) float64 1.654 10.63 7.015 2.543 ... 12.46 2.22 15.96\n precipitation (y, x) float64 0.2136 0.9974 0.7603 ... 0.6125 0.4654 0.5953\n\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_by_coords._Group_by_data_vars__CONCAT_DIM_DEFAULT.___infer_concat_dim___": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_combine_by_coords._Group_by_data_vars__CONCAT_DIM_DEFAULT.___infer_concat_dim___", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 657, "end_line": 704, "span_ids": ["impl", "combine_by_coords"], "tokens": 370}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def combine_by_coords(\n datasets,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n\n # Group by data vars\n sorted_datasets = sorted(datasets, key=vars_as_keys)\n grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys)\n\n # Perform the multidimensional combine on each group of data variables\n # before merging back together\n concatenated_grouped_by_data_vars = []\n for vars, datasets_with_same_vars in grouped_by_vars:\n combined_ids, concat_dims = _infer_concat_order_from_coords(\n list(datasets_with_same_vars)\n )\n\n _check_shape_tile_ids(combined_ids)\n\n # Concatenate along all of concat_dims one by one to create single ds\n concatenated = _combine_nd(\n combined_ids,\n concat_dims=concat_dims,\n data_vars=data_vars,\n coords=coords,\n compat=compat,\n fill_value=fill_value,\n join=join,\n )\n\n # Check the overall coordinates are monotonically increasing\n for dim in concat_dims:\n indexes = concatenated.indexes.get(dim)\n if not (indexes.is_monotonic_increasing or indexes.is_monotonic_decreasing):\n raise ValueError(\n \"Resulting object does not have monotonic\"\n \" global indexes along dimension {}\".format(dim)\n )\n concatenated_grouped_by_data_vars.append(concatenated)\n\n return merge(\n concatenated_grouped_by_data_vars,\n compat=compat,\n fill_value=fill_value,\n join=join,\n )\n\n\n# Everything beyond here is only needed until the deprecation cycle in #2616\n# is completed\n\n\n_CONCAT_DIM_DEFAULT = \"__infer_concat_dim__\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_auto_combine_auto_combine._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_auto_combine_auto_combine._", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 707, "end_line": 787, "span_ids": ["auto_combine"], "tokens": 786}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def auto_combine(\n datasets,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n from_openmfds=False,\n):\n \"\"\"\n Attempt to auto-magically combine the given datasets into one.\n\n This entire function is deprecated in favour of ``combine_nested`` and\n ``combine_by_coords``.\n\n This method attempts to combine a list of datasets into a single entity by\n inspecting metadata and using a combination of concat and merge.\n It does not concatenate along more than one dimension or sort data under\n any circumstances. It does align coordinates, but different variables on\n datasets can cause it to fail under some scenarios. In complex cases, you\n may need to clean up your data and use ``concat``/``merge`` explicitly.\n ``auto_combine`` works well if you have N years of data and M data\n variables, and each combination of a distinct time period and set of data\n variables is saved its own dataset.\n\n Parameters\n ----------\n datasets : sequence of xarray.Dataset\n Dataset objects to merge.\n concat_dim : str or DataArray or Index, optional\n Dimension along which to concatenate variables, as used by\n :py:func:`xarray.concat`. You only need to provide this argument if\n the dimension along which you want to concatenate is not a dimension\n in the original datasets, e.g., if you want to stack a collection of\n 2D arrays along a third dimension.\n By default, xarray attempts to infer this argument by examining\n component files. Set ``concat_dim=None`` explicitly to disable\n concatenation.\n compat : {'identical', 'equals', 'broadcast_equals',\n 'no_conflicts', 'override'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts:\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n - 'override': skip comparing and pick variable from first dataset\n data_vars : {'minimal', 'different', 'all' or list of str}, optional\n Details are in the documentation of concat\n coords : {'minimal', 'different', 'all' o list of str}, optional\n Details are in the documentation of concat\n fill_value : scalar, optional\n Value to use for newly missing values\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n String indicating how to combine differing indexes\n (excluding concat_dim) in objects\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n\n Returns\n -------\n combined : xarray.Dataset\n\n See also\n --------\n concat\n Dataset.merge\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_auto_combine.if_not_from_openmfds__auto_combine.return._old_auto_combine_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py_auto_combine.if_not_from_openmfds__auto_combine.return._old_auto_combine_", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 789, "end_line": 854, "span_ids": ["auto_combine"], "tokens": 613}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def auto_combine(\n datasets,\n concat_dim=\"_not_supplied\",\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n from_openmfds=False,\n):\n\n if not from_openmfds:\n basic_msg = dedent(\n \"\"\"\\\n In xarray version 0.15 `auto_combine` will be deprecated. See\n http://xarray.pydata.org/en/stable/combining.html#combining-multi\"\"\"\n )\n warnings.warn(basic_msg, FutureWarning, stacklevel=2)\n\n if concat_dim == \"_not_supplied\":\n concat_dim = _CONCAT_DIM_DEFAULT\n message = \"\"\n else:\n message = dedent(\n \"\"\"\\\n Also `open_mfdataset` will no longer accept a `concat_dim` argument.\n To get equivalent behaviour from now on please use the new\n `combine_nested` function instead (or the `combine='nested'` option to\n `open_mfdataset`).\"\"\"\n )\n\n if _dimension_coords_exist(datasets):\n message += dedent(\n \"\"\"\\\n The datasets supplied have global dimension coordinates. You may want\n to use the new `combine_by_coords` function (or the\n `combine='by_coords'` option to `open_mfdataset`) to order the datasets\n before concatenation. Alternatively, to continue concatenating based\n on the order the datasets are supplied in future, please use the new\n `combine_nested` function (or the `combine='nested'` option to\n open_mfdataset).\"\"\"\n )\n else:\n message += dedent(\n \"\"\"\\\n The datasets supplied do not have global dimension coordinates. In\n future, to continue concatenating without supplying dimension\n coordinates, please use the new `combine_nested` function (or the\n `combine='nested'` option to open_mfdataset.\"\"\"\n )\n\n if _requires_concat_and_merge(datasets):\n manual_dims = [concat_dim].append(None)\n message += dedent(\n \"\"\"\\\n The datasets supplied require both concatenation and merging. From\n xarray version 0.15 this will operation will require either using the\n new `combine_nested` function (or the `combine='nested'` option to\n open_mfdataset), with a nested list structure such that you can combine\n along the dimensions {}. Alternatively if your datasets have global\n dimension coordinates then you can use the new `combine_by_coords`\n function.\"\"\".format(\n manual_dims\n )\n )\n\n warnings.warn(message, FutureWarning, stacklevel=2)\n\n return _old_auto_combine(\n datasets,\n concat_dim=concat_dim,\n compat=compat,\n data_vars=data_vars,\n coords=coords,\n fill_value=fill_value,\n join=join,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__dimension_coords_exist__requires_concat_and_merge.return.len_list_grouped_by_vars_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__dimension_coords_exist__requires_concat_and_merge.return.len_list_grouped_by_vars_", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 857, "end_line": 889, "span_ids": ["_dimension_coords_exist", "_requires_concat_and_merge"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dimension_coords_exist(datasets):\n \"\"\"\n Check if the datasets have consistent global dimension coordinates\n which would in future be used by `auto_combine` for concatenation ordering.\n \"\"\"\n\n # Group by data vars\n sorted_datasets = sorted(datasets, key=vars_as_keys)\n grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys)\n\n # Simulates performing the multidimensional combine on each group of data\n # variables before merging back together\n try:\n for vars, datasets_with_same_vars in grouped_by_vars:\n _infer_concat_order_from_coords(list(datasets_with_same_vars))\n return True\n except ValueError:\n # ValueError means datasets don't have global dimension coordinates\n # Or something else went wrong in trying to determine them\n return False\n\n\ndef _requires_concat_and_merge(datasets):\n \"\"\"\n Check if the datasets require the use of both xarray.concat and\n xarray.merge, which in future might require the user to use\n `manual_combine` instead.\n \"\"\"\n # Group by data vars\n sorted_datasets = sorted(datasets, key=vars_as_keys)\n grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys)\n\n return len(list(grouped_by_vars)) > 1", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__old_auto_combine__old_auto_combine.return.merged": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__old_auto_combine__old_auto_combine.return.merged", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 892, "end_line": 922, "span_ids": ["_old_auto_combine"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _old_auto_combine(\n datasets,\n concat_dim=_CONCAT_DIM_DEFAULT,\n compat=\"no_conflicts\",\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n if concat_dim is not None:\n dim = None if concat_dim is _CONCAT_DIM_DEFAULT else concat_dim\n\n sorted_datasets = sorted(datasets, key=vars_as_keys)\n grouped = itertools.groupby(sorted_datasets, key=vars_as_keys)\n\n concatenated = [\n _auto_concat(\n list(datasets),\n dim=dim,\n data_vars=data_vars,\n coords=coords,\n compat=compat,\n fill_value=fill_value,\n join=join,\n )\n for vars, datasets in grouped\n ]\n else:\n concatenated = datasets\n merged = merge(concatenated, compat=compat, fill_value=fill_value, join=join)\n return merged", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__auto_concat_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/combine.py__auto_concat_", "embedding": null, "metadata": {"file_path": "xarray/core/combine.py", "file_name": "combine.py", "file_type": "text/x-python", "category": "implementation", "start_line": 925, "end_line": 966, "span_ids": ["_auto_concat"], "tokens": 311}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _auto_concat(\n datasets,\n dim=None,\n data_vars=\"all\",\n coords=\"different\",\n fill_value=dtypes.NA,\n join=\"outer\",\n compat=\"no_conflicts\",\n):\n if len(datasets) == 1 and dim is None:\n # There is nothing more to combine, so kick out early.\n return datasets[0]\n else:\n if dim is None:\n ds0 = datasets[0]\n ds1 = datasets[1]\n concat_dims = set(ds0.dims)\n if ds0.dims != ds1.dims:\n dim_tuples = set(ds0.dims.items()) - set(ds1.dims.items())\n concat_dims = {i for i, _ in dim_tuples}\n if len(concat_dims) > 1:\n concat_dims = {d for d in concat_dims if not ds0[d].equals(ds1[d])}\n if len(concat_dims) > 1:\n raise ValueError(\n \"too many different dimensions to \" \"concatenate: %s\" % concat_dims\n )\n elif len(concat_dims) == 0:\n raise ValueError(\n \"cannot infer dimension to concatenate: \"\n \"supply the ``concat_dim`` argument \"\n \"explicitly\"\n )\n dim, = concat_dims\n return concat(\n datasets,\n dim=dim,\n data_vars=data_vars,\n coords=coords,\n fill_value=fill_value,\n compat=compat,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_warnings_T.TypeVar_T_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_warnings_T.TypeVar_T_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 34, "span_ids": ["impl", "imports"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom contextlib import suppress\nfrom textwrap import dedent\nfrom typing import (\n Any,\n Callable,\n Dict,\n Hashable,\n Iterable,\n Iterator,\n List,\n Mapping,\n Tuple,\n TypeVar,\n Union,\n)\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import dtypes, duck_array_ops, formatting, ops\nfrom .arithmetic import SupportsArithmetic\nfrom .npcompat import DTypeLike\nfrom .options import _get_keep_attrs\nfrom .pycompat import dask_array_type\nfrom .rolling_exp import RollingExp\nfrom .utils import Frozen, ReprObject, either_dict_or_kwargs\n\n# Used as a sentinel value to indicate a all dimensions\nALL_DIMS = ReprObject(\"\")\n\n\nC = TypeVar(\"C\")\nT = TypeVar(\"T\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsArrayReduce_ImplementsArrayReduce._reduce_method.return.wrapped_func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsArrayReduce_ImplementsArrayReduce._reduce_method.return.wrapped_func", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 54, "span_ids": ["ImplementsArrayReduce", "ImplementsArrayReduce._reduce_method"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImplementsArrayReduce:\n __slots__ = ()\n\n @classmethod\n def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):\n if include_skipna:\n\n def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs):\n return self.reduce(\n func, dim, axis, skipna=skipna, allow_lazy=True, **kwargs\n )\n\n else:\n\n def wrapped_func(self, dim=None, axis=None, **kwargs): # type: ignore\n return self.reduce(func, dim, axis, allow_lazy=True, **kwargs)\n\n return wrapped_func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsArrayReduce._reduce_extra_args_docstring_ImplementsArrayReduce._cum_extra_args_docstring.dedent_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsArrayReduce._reduce_extra_args_docstring_ImplementsArrayReduce._cum_extra_args_docstring.dedent_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 56, "end_line": 73, "span_ids": ["ImplementsArrayReduce:4"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImplementsArrayReduce:\n\n _reduce_extra_args_docstring = dedent(\n \"\"\"\\\n dim : str or sequence of str, optional\n Dimension(s) over which to apply `{name}`.\n axis : int or sequence of int, optional\n Axis(es) over which to apply `{name}`. Only one of the 'dim'\n and 'axis' arguments can be supplied. If neither are supplied, then\n `{name}` is calculated over axes.\"\"\"\n )\n\n _cum_extra_args_docstring = dedent(\n \"\"\"\\\n dim : str or sequence of str, optional\n Dimension over which to apply `{name}`.\n axis : int or sequence of int, optional\n Axis over which to apply `{name}`. Only one of the 'dim'\n and 'axis' arguments can be supplied.\"\"\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsDatasetReduce_ImplementsDatasetReduce._reduce_method.return.wrapped_func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsDatasetReduce_ImplementsDatasetReduce._reduce_method.return.wrapped_func", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 76, "end_line": 100, "span_ids": ["ImplementsDatasetReduce", "ImplementsDatasetReduce._reduce_method"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImplementsDatasetReduce:\n __slots__ = ()\n\n @classmethod\n def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):\n if include_skipna:\n\n def wrapped_func(self, dim=None, skipna=None, **kwargs):\n return self.reduce(\n func,\n dim,\n skipna=skipna,\n numeric_only=numeric_only,\n allow_lazy=True,\n **kwargs\n )\n\n else:\n\n def wrapped_func(self, dim=None, **kwargs): # type: ignore\n return self.reduce(\n func, dim, numeric_only=numeric_only, allow_lazy=True, **kwargs\n )\n\n return wrapped_func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsDatasetReduce._reduce_extra_args_docstring_ImplementsDatasetReduce._cum_extra_args_docstring._dim_str_or_sequence_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ImplementsDatasetReduce._reduce_extra_args_docstring_ImplementsDatasetReduce._cum_extra_args_docstring._dim_str_or_sequence_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 110, "span_ids": ["ImplementsDatasetReduce:4"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImplementsDatasetReduce:\n\n _reduce_extra_args_docstring = \"\"\"dim : str or sequence of str, optional\n Dimension(s) over which to apply `{name}`. By default `{name}` is\n applied over all dimensions.\"\"\"\n\n _cum_extra_args_docstring = \"\"\"dim : str or sequence of str, optional\n Dimension over which to apply `{name}`.\n axis : int or sequence of int, optional\n Axis over which to apply `{name}`. Only one of the 'dim'\n and 'axis' arguments can be supplied.\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray_AbstractArray.__iter__.return.self__iter_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray_AbstractArray.__iter__.return.self__iter_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 113, "end_line": 144, "span_ids": ["AbstractArray.__int__", "AbstractArray.__complex__", "AbstractArray.__array__", "AbstractArray._iter", "AbstractArray.__iter__", "AbstractArray", "AbstractArray.__float__", "AbstractArray.__repr__", "AbstractArray.__bool__"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractArray(ImplementsArrayReduce):\n \"\"\"Shared base class for DataArray and Variable.\n \"\"\"\n\n __slots__ = ()\n\n def __bool__(self: Any) -> bool:\n return bool(self.values)\n\n def __float__(self: Any) -> float:\n return float(self.values)\n\n def __int__(self: Any) -> int:\n return int(self.values)\n\n def __complex__(self: Any) -> complex:\n return complex(self.values)\n\n def __array__(self: Any, dtype: DTypeLike = None) -> np.ndarray:\n return np.asarray(self.values, dtype=dtype)\n\n def __repr__(self) -> str:\n return formatting.array_repr(self)\n\n def _iter(self: Any) -> Iterator[Any]:\n for n in range(len(self)):\n yield self[n]\n\n def __iter__(self: Any) -> Iterator[Any]:\n if self.ndim == 0:\n raise TypeError(\"iteration over a 0-d array\")\n return self._iter()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray.get_axis_num_AbstractArray.get_axis_num.if_isinstance_dim_Iterab.else_.return.self__get_axis_num_dim_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray.get_axis_num_AbstractArray.get_axis_num.if_isinstance_dim_Iterab.else_.return.self__get_axis_num_dim_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 146, "end_line": 164, "span_ids": ["AbstractArray.get_axis_num"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractArray(ImplementsArrayReduce):\n\n def get_axis_num(\n self, dim: Union[Hashable, Iterable[Hashable]]\n ) -> Union[int, Tuple[int, ...]]:\n \"\"\"Return axis number(s) corresponding to dimension(s) in this array.\n\n Parameters\n ----------\n dim : str or iterable of str\n Dimension name(s) for which to lookup axes.\n\n Returns\n -------\n int or tuple of int\n Axis number or numbers corresponding to the given dimensions.\n \"\"\"\n if isinstance(dim, Iterable) and not isinstance(dim, str):\n return tuple(self._get_axis_num(d) for d in dim)\n else:\n return self._get_axis_num(dim)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray._get_axis_num_AbstractArray.sizes.return.Frozen_dict_zip_self_dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AbstractArray._get_axis_num_AbstractArray.sizes.return.Frozen_dict_zip_self_dims", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 182, "span_ids": ["AbstractArray.sizes", "AbstractArray._get_axis_num"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AbstractArray(ImplementsArrayReduce):\n\n def _get_axis_num(self: Any, dim: Hashable) -> int:\n try:\n return self.dims.index(dim)\n except ValueError:\n raise ValueError(\"%r not found in array dimensions %r\" % (dim, self.dims))\n\n @property\n def sizes(self: Any) -> Mapping[Hashable, int]:\n \"\"\"Ordered mapping from dimension names to lengths.\n\n Immutable.\n\n See also\n --------\n Dataset.sizes\n \"\"\"\n return Frozen(dict(zip(self.dims, self.shape)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin_AttrAccessMixin.__init_subclass__.if_not_hasattr_object___n.else_.warnings_warn_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin_AttrAccessMixin.__init_subclass__.if_not_hasattr_object___n.else_.warnings_warn_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 206, "span_ids": ["AttrAccessMixin", "AttrAccessMixin.__init_subclass__"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AttrAccessMixin:\n \"\"\"Mixin class that allows getting keys with attribute access\n \"\"\"\n\n __slots__ = ()\n\n def __init_subclass__(cls):\n \"\"\"Verify that all subclasses explicitly define ``__slots__``. If they don't,\n raise error in the core xarray module and a FutureWarning in third-party\n extensions.\n \"\"\"\n if not hasattr(object.__new__(cls), \"__dict__\"):\n pass\n elif cls.__module__.startswith(\"xarray.\"):\n raise AttributeError(\"%s must explicitly define __slots__\" % cls.__name__)\n else:\n cls.__setattr__ = cls._setattr_dict\n warnings.warn(\n \"xarray subclass %s should explicitly define __slots__\" % cls.__name__,\n FutureWarning,\n stacklevel=2,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin._attr_sources_AttrAccessMixin._FutureWarning_can_be_ch": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin._attr_sources_AttrAccessMixin._FutureWarning_can_be_ch", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 235, "span_ids": ["AttrAccessMixin.__getattr__", "AttrAccessMixin._attr_sources", "AttrAccessMixin._item_sources"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AttrAccessMixin:\n\n @property\n def _attr_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for attribute-style access\n \"\"\"\n return []\n\n @property\n def _item_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for key-autocompletion\n \"\"\"\n return []\n\n def __getattr__(self, name: str) -> Any:\n if name not in {\"__dict__\", \"__setstate__\"}:\n # this avoids an infinite loop when pickle looks for the\n # __setstate__ attribute before the xarray object is initialized\n for source in self._attr_sources:\n with suppress(KeyError):\n return source[name]\n raise AttributeError(\n \"%r object has no attribute %r\" % (type(self).__name__, name)\n )\n\n # This complicated two-method design boosts overall performance of simple operations\n # - particularly DataArray methods that perform a _to_temp_dataset() round-trip - by\n # a whopping 8% compared to a single method that checks hasattr(self, \"__dict__\") at\n # runtime before every single assignment. All of this is just temporary until the\n # FutureWarning can be changed into a hard crash.", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin._setattr_dict_AttrAccessMixin._setattr_dict.if_name_in_self___dict___.warnings_warn_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin._setattr_dict_AttrAccessMixin._setattr_dict.if_name_in_self___dict___.warnings_warn_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 249, "span_ids": ["AttrAccessMixin._setattr_dict"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AttrAccessMixin:\n def _setattr_dict(self, name: str, value: Any) -> None:\n \"\"\"Deprecated third party subclass (see ``__init_subclass__`` above)\n \"\"\"\n object.__setattr__(self, name, value)\n if name in self.__dict__:\n # Custom, non-slotted attr, or improperly assigned variable?\n warnings.warn(\n \"Setting attribute %r on a %r object. Explicitly define __slots__ \"\n \"to suppress this warning for legitimate custom attributes and \"\n \"raise an error when attempting variables assignments.\"\n % (name, type(self).__name__),\n FutureWarning,\n stacklevel=2,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin.__setattr___AttrAccessMixin.__setattr__.try_.except_AttributeError_as_.raise_AttributeError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin.__setattr___AttrAccessMixin.__setattr__.try_.except_AttributeError_as_.raise_AttributeError_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 251, "end_line": 267, "span_ids": ["AttrAccessMixin.__setattr__"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AttrAccessMixin:\n\n def __setattr__(self, name: str, value: Any) -> None:\n \"\"\"Objects with ``__slots__`` raise AttributeError if you try setting an\n undeclared attribute. This is desirable, but the error message could use some\n improvement.\n \"\"\"\n try:\n object.__setattr__(self, name, value)\n except AttributeError as e:\n # Don't accidentally shadow custom AttributeErrors, e.g.\n # DataArray.dims.setter\n if str(e) != \"%r object has no attribute %r\" % (type(self).__name__, name):\n raise\n raise AttributeError(\n \"cannot set attribute %r on a %r object. Use __setitem__ style\"\n \"assignment (e.g., `ds['name'] = ...`) instead of assigning variables.\"\n % (name, type(self).__name__)\n ) from e", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin.__dir___AttrAccessMixin._ipython_key_completions_.return.list_set_item_lists_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_AttrAccessMixin.__dir___AttrAccessMixin._ipython_key_completions_.return.list_set_item_lists_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 292, "span_ids": ["AttrAccessMixin._ipython_key_completions_", "AttrAccessMixin.__dir__"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AttrAccessMixin:\n\n def __dir__(self) -> List[str]:\n \"\"\"Provide method name lookup and completion. Only provide 'public'\n methods.\n \"\"\"\n extra_attrs = [\n item\n for sublist in self._attr_sources\n for item in sublist\n if isinstance(item, str)\n ]\n return sorted(set(dir(type(self)) + extra_attrs))\n\n def _ipython_key_completions_(self) -> List[str]:\n \"\"\"Provide method for the key-autocompletions in IPython.\n See http://ipython.readthedocs.io/en/stable/config/integrating.html#tab-completion\n For the details.\n \"\"\"\n item_lists = [\n item\n for sublist in self._item_sources\n for item in sublist\n if isinstance(item, str)\n ]\n return list(set(item_lists))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_get_squeeze_dims_get_squeeze_dims.return.dim": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_get_squeeze_dims_get_squeeze_dims.return.dim", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 295, "end_line": 326, "span_ids": ["get_squeeze_dims"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_squeeze_dims(\n xarray_obj,\n dim: Union[Hashable, Iterable[Hashable], None] = None,\n axis: Union[int, Iterable[int], None] = None,\n) -> List[Hashable]:\n \"\"\"Get a list of dimensions to squeeze out.\n \"\"\"\n if dim is not None and axis is not None:\n raise ValueError(\"cannot use both parameters `axis` and `dim`\")\n if dim is None and axis is None:\n return [d for d, s in xarray_obj.sizes.items() if s == 1]\n\n if isinstance(dim, Iterable) and not isinstance(dim, str):\n dim = list(dim)\n elif dim is not None:\n dim = [dim]\n else:\n assert axis is not None\n if isinstance(axis, int):\n axis = [axis]\n axis = list(axis)\n if any(not isinstance(a, int) for a in axis):\n raise TypeError(\"parameter `axis` must be int or iterable of int.\")\n alldims = list(xarray_obj.sizes.keys())\n dim = [alldims[a] for a in axis]\n\n if any(xarray_obj.sizes[k] > 1 for k in dim):\n raise ValueError(\n \"cannot select a dimension to squeeze out \"\n \"which has length greater than one\"\n )\n return dim", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords_DataWithCoords.squeeze.return.self_isel_drop_drop_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords_DataWithCoords.squeeze.return.self_isel_drop_drop_d", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 329, "end_line": 367, "span_ids": ["DataWithCoords.squeeze", "DataWithCoords"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n \"\"\"Shared base class for Dataset and DataArray.\"\"\"\n\n __slots__ = ()\n\n _rolling_exp_cls = RollingExp\n\n def squeeze(\n self,\n dim: Union[Hashable, Iterable[Hashable], None] = None,\n drop: bool = False,\n axis: Union[int, Iterable[int], None] = None,\n ):\n \"\"\"Return a new object with squeezed data.\n\n Parameters\n ----------\n dim : None or Hashable or iterable of Hashable, optional\n Selects a subset of the length one dimensions. If a dimension is\n selected with length greater than one, an error is raised. If\n None, all length one dimensions are squeezed.\n drop : bool, optional\n If ``drop=True``, drop squeezed coordinates instead of making them\n scalar.\n axis : None or int or iterable of int, optional\n Like dim, but positional.\n\n Returns\n -------\n squeezed : same type as caller\n This object, but with with all or a subset of the dimensions of\n length 1 removed.\n\n See Also\n --------\n numpy.squeeze\n \"\"\"\n dims = get_squeeze_dims(self, dim, axis)\n return self.isel(drop=drop, **{d: 0 for d in dims})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.get_index_DataWithCoords._calc_assign_results.return._k_v_self_if_callable_v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.get_index_DataWithCoords._calc_assign_results.return._k_v_self_if_callable_v", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 369, "end_line": 384, "span_ids": ["DataWithCoords._calc_assign_results", "DataWithCoords.get_index"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def get_index(self, key: Hashable) -> pd.Index:\n \"\"\"Get an index for a dimension, with fall-back to a default RangeIndex\n \"\"\"\n if key not in self.dims:\n raise KeyError(key)\n\n try:\n return self.indexes[key]\n except KeyError:\n # need to ensure dtype=int64 in case range is empty on Python 2\n return pd.Index(range(self.sizes[key]), name=key, dtype=np.int64)\n\n def _calc_assign_results(\n self: C, kwargs: Mapping[Hashable, Union[T, Callable[[C], T]]]\n ) -> Dict[Hashable, T]:\n return {k: v(self) if callable(v) else v for k, v in kwargs.items()}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.assign_coords_DataWithCoords.assign_coords.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.assign_coords_DataWithCoords.assign_coords.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 386, "end_line": 453, "span_ids": ["DataWithCoords.assign_coords"], "tokens": 626}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def assign_coords(self, coords=None, **coords_kwargs):\n \"\"\"Assign new coordinates to this object.\n\n Returns a new object with all the original data in addition to the new\n coordinates.\n\n Parameters\n ----------\n coords : dict, optional\n A dict with keys which are variables names. If the values are\n callable, they are computed on this object and assigned to new\n coordinate variables. If the values are not callable,\n (e.g. a ``DataArray``, scalar, or array), they are simply assigned.\n\n **coords_kwargs : keyword, value pairs, optional\n The keyword arguments form of ``coords``.\n One of ``coords`` or ``coords_kwargs`` must be provided.\n\n Returns\n -------\n assigned : same type as caller\n A new object with the new coordinates in addition to the existing\n data.\n\n Examples\n --------\n Convert longitude coordinates from 0-359 to -180-179:\n\n >>> da = xr.DataArray(np.random.rand(4),\n ... coords=[np.array([358, 359, 0, 1])],\n ... dims='lon')\n >>> da\n \n array([0.28298 , 0.667347, 0.657938, 0.177683])\n Coordinates:\n * lon (lon) int64 358 359 0 1\n >>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))\n \n array([0.28298 , 0.667347, 0.657938, 0.177683])\n Coordinates:\n * lon (lon) int64 -2 -1 0 1\n\n The function also accepts dictionary arguments:\n\n >>> da.assign_coords({'lon': (((da.lon + 180) % 360) - 180)})\n \n array([0.28298 , 0.667347, 0.657938, 0.177683])\n Coordinates:\n * lon (lon) int64 -2 -1 0 1\n\n Notes\n -----\n Since ``coords_kwargs`` is a dictionary, the order of your arguments may\n not be preserved, and so the order of the new variables is not well\n defined. Assigning multiple variables within the same ``assign_coords``\n is possible, but you cannot reference other variables created within\n the same ``assign_coords`` call.\n\n See also\n --------\n Dataset.assign\n Dataset.swap_dims\n \"\"\"\n coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, \"assign_coords\")\n data = self.copy(deep=False)\n results = self._calc_assign_results(coords_kwargs)\n data.coords.update(results)\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.assign_attrs_DataWithCoords.assign_attrs.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.assign_attrs_DataWithCoords.assign_attrs.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 455, "end_line": 476, "span_ids": ["DataWithCoords.assign_attrs"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def assign_attrs(self, *args, **kwargs):\n \"\"\"Assign new attrs to this object.\n\n Returns a new object equivalent to self.attrs.update(*args, **kwargs).\n\n Parameters\n ----------\n args : positional arguments passed into ``attrs.update``.\n kwargs : keyword arguments passed into ``attrs.update``.\n\n Returns\n -------\n assigned : same type as caller\n A new object with the new attrs in addition to the existing data.\n\n See also\n --------\n Dataset.assign\n \"\"\"\n out = self.copy(deep=False)\n out.attrs.update(*args, **kwargs)\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.pipe_DataWithCoords.pipe.if_isinstance_func_tuple.else_.return.func_self_args_kwarg": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.pipe_DataWithCoords.pipe.if_isinstance_func_tuple.else_.return.func_self_args_kwarg", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 478, "end_line": 607, "span_ids": ["DataWithCoords.pipe"], "tokens": 1172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def pipe(\n self,\n func: Union[Callable[..., T], Tuple[Callable[..., T], str]],\n *args,\n **kwargs\n ) -> T:\n \"\"\"\n Apply func(self, *args, **kwargs)\n\n This method replicates the pandas method of the same name.\n\n Parameters\n ----------\n func : function\n function to apply to this xarray object (Dataset/DataArray).\n ``args``, and ``kwargs`` are passed into ``func``.\n Alternatively a ``(callable, data_keyword)`` tuple where\n ``data_keyword`` is a string indicating the keyword of\n ``callable`` that expects the xarray object.\n args : positional arguments passed into ``func``.\n kwargs : a dictionary of keyword arguments passed into ``func``.\n\n Returns\n -------\n object : the return type of ``func``.\n\n Notes\n -----\n\n Use ``.pipe`` when chaining together functions that expect\n xarray or pandas objects, e.g., instead of writing\n\n >>> f(g(h(ds), arg1=a), arg2=b, arg3=c)\n\n You can write\n\n >>> (ds.pipe(h)\n ... .pipe(g, arg1=a)\n ... .pipe(f, arg2=b, arg3=c)\n ... )\n\n If you have a function that takes the data as (say) the second\n argument, pass a tuple indicating which keyword expects the\n data. For example, suppose ``f`` takes its data as ``arg2``:\n\n >>> (ds.pipe(h)\n ... .pipe(g, arg1=a)\n ... .pipe((f, 'arg2'), arg1=a, arg3=c)\n ... )\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xr\n >>> x = xr.Dataset(\n ... {\n ... \"temperature_c\": ((\"lat\", \"lon\"), 20 * np.random.rand(4).reshape(2, 2)),\n ... \"precipitation\": ((\"lat\", \"lon\"), np.random.rand(4).reshape(2, 2)),\n ... },\n ... coords={\"lat\": [10, 20], \"lon\": [150, 160]},\n ... )\n >>> x\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lat (lat) int64 10 20\n * lon (lon) int64 150 160\n Data variables:\n temperature_c (lat, lon) float64 14.53 11.85 19.27 16.37\n precipitation (lat, lon) float64 0.7315 0.7189 0.8481 0.4671\n\n >>> def adder(data, arg):\n ... return data + arg\n ...\n >>> def div(data, arg):\n ... return data / arg\n ...\n >>> def sub_mult(data, sub_arg, mult_arg):\n ... return (data * mult_arg) - sub_arg\n ...\n >>> x.pipe(adder, 2)\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lon (lon) int64 150 160\n * lat (lat) int64 10 20\n Data variables:\n temperature_c (lat, lon) float64 16.53 13.85 21.27 18.37\n precipitation (lat, lon) float64 2.731 2.719 2.848 2.467\n\n >>> x.pipe(adder, arg=2)\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lon (lon) int64 150 160\n * lat (lat) int64 10 20\n Data variables:\n temperature_c (lat, lon) float64 16.53 13.85 21.27 18.37\n precipitation (lat, lon) float64 2.731 2.719 2.848 2.467\n\n >>> (\n ... x\n ... .pipe(adder, arg=2)\n ... .pipe(div, arg=2)\n ... .pipe(sub_mult, sub_arg=2, mult_arg=2)\n ... )\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lon (lon) int64 150 160\n * lat (lat) int64 10 20\n Data variables:\n temperature_c (lat, lon) float64 14.53 11.85 19.27 16.37\n precipitation (lat, lon) float64 0.7315 0.7189 0.8481 0.4671\n\n See Also\n --------\n pandas.DataFrame.pipe\n \"\"\"\n if isinstance(func, tuple):\n func, target = func\n if target in kwargs:\n raise ValueError(\n \"%s is both the pipe target and a keyword \" \"argument\" % target\n )\n kwargs[target] = self\n return func(*args, **kwargs)\n else:\n return func(self, *args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.groupby_DataWithCoords.groupby.return.self__groupby_cls_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.groupby_DataWithCoords.groupby.return.self__groupby_cls_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 609, "end_line": 658, "span_ids": ["DataWithCoords.groupby"], "tokens": 604}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def groupby(self, group, squeeze: bool = True, restore_coord_dims: bool = None):\n \"\"\"Returns a GroupBy object for performing grouped operations.\n\n Parameters\n ----------\n group : str, DataArray or IndexVariable\n Array whose unique values should be used to group this array. If a\n string, must be the name of a variable contained in this dataset.\n squeeze : boolean, optional\n If \"group\" is a dimension of any arrays in this dataset, `squeeze`\n controls whether the subarrays have a dimension of length 1 along\n that dimension or if the dimension is squeezed out.\n restore_coord_dims : bool, optional\n If True, also restore the dimension order of multi-dimensional\n coordinates.\n\n Returns\n -------\n grouped : GroupBy\n A `GroupBy` object patterned after `pandas.GroupBy` that can be\n iterated over in the form of `(unique_value, grouped_array)` pairs.\n\n Examples\n --------\n Calculate daily anomalies for daily data:\n\n >>> da = xr.DataArray(np.linspace(0, 1826, num=1827),\n ... coords=[pd.date_range('1/1/2000', '31/12/2004',\n ... freq='D')],\n ... dims='time')\n >>> da\n \n array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03, 1.826e+03])\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n >>> da.groupby('time.dayofyear') - da.groupby('time.dayofyear').mean('time')\n \n array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5])\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n dayofyear (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...\n\n See Also\n --------\n core.groupby.DataArrayGroupBy\n core.groupby.DatasetGroupBy\n \"\"\"\n return self._groupby_cls(\n self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.groupby_bins_DataWithCoords.groupby_bins.return.self__groupby_cls_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.groupby_bins_DataWithCoords.groupby_bins.return.self__groupby_cls_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 660, "end_line": 731, "span_ids": ["DataWithCoords.groupby_bins"], "tokens": 662}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def groupby_bins(\n self,\n group,\n bins,\n right: bool = True,\n labels=None,\n precision: int = 3,\n include_lowest: bool = False,\n squeeze: bool = True,\n restore_coord_dims: bool = None,\n ):\n \"\"\"Returns a GroupBy object for performing grouped operations.\n\n Rather than using all unique values of `group`, the values are discretized\n first by applying `pandas.cut` [1]_ to `group`.\n\n Parameters\n ----------\n group : str, DataArray or IndexVariable\n Array whose binned values should be used to group this array. If a\n string, must be the name of a variable contained in this dataset.\n bins : int or array of scalars\n If bins is an int, it defines the number of equal-width bins in the\n range of x. However, in this case, the range of x is extended by .1%\n on each side to include the min or max values of x. If bins is a\n sequence it defines the bin edges allowing for non-uniform bin\n width. No extension of the range of x is done in this case.\n right : boolean, optional\n Indicates whether the bins include the rightmost edge or not. If\n right == True (the default), then the bins [1,2,3,4] indicate\n (1,2], (2,3], (3,4].\n labels : array or boolean, default None\n Used as labels for the resulting bins. Must be of the same length as\n the resulting bins. If False, string bin labels are assigned by\n `pandas.cut`.\n precision : int\n The precision at which to store and display the bins labels.\n include_lowest : bool\n Whether the first interval should be left-inclusive or not.\n squeeze : boolean, optional\n If \"group\" is a dimension of any arrays in this dataset, `squeeze`\n controls whether the subarrays have a dimension of length 1 along\n that dimension or if the dimension is squeezed out.\n restore_coord_dims : bool, optional\n If True, also restore the dimension order of multi-dimensional\n coordinates.\n\n Returns\n -------\n grouped : GroupBy\n A `GroupBy` object patterned after `pandas.GroupBy` that can be\n iterated over in the form of `(unique_value, grouped_array)` pairs.\n The name of the group has the added suffix `_bins` in order to\n distinguish it from the original variable.\n\n References\n ----------\n .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html\n \"\"\"\n return self._groupby_cls(\n self,\n group,\n squeeze=squeeze,\n bins=bins,\n restore_coord_dims=restore_coord_dims,\n cut_kwargs={\n \"right\": right,\n \"labels\": labels,\n \"precision\": precision,\n \"include_lowest\": include_lowest,\n },\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.rolling_DataWithCoords.rolling.return.self__rolling_cls_self_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.rolling_DataWithCoords.rolling.return.self__rolling_cls_self_d", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 733, "end_line": 796, "span_ids": ["DataWithCoords.rolling"], "tokens": 708}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def rolling(\n self,\n dim: Mapping[Hashable, int] = None,\n min_periods: int = None,\n center: bool = False,\n **window_kwargs: int\n ):\n \"\"\"\n Rolling window object.\n\n Parameters\n ----------\n dim: dict, optional\n Mapping from the dimension name to create the rolling iterator\n along (e.g. `time`) to its moving window size.\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA). The default, None, is equivalent to\n setting min_periods equal to the size of the window.\n center : boolean, default False\n Set the labels at the center of the window.\n **window_kwargs : optional\n The keyword arguments form of ``dim``.\n One of dim or window_kwargs must be provided.\n\n Returns\n -------\n Rolling object (core.rolling.DataArrayRolling for DataArray,\n core.rolling.DatasetRolling for Dataset.)\n\n Examples\n --------\n Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:\n\n >>> da = xr.DataArray(np.linspace(0, 11, num=12),\n ... coords=[pd.date_range('15/12/1999',\n ... periods=12, freq=pd.DateOffset(months=1))],\n ... dims='time')\n >>> da\n \n array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...\n >>> da.rolling(time=3, center=True).mean()\n \n array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...\n\n Remove the NaNs using ``dropna()``:\n\n >>> da.rolling(time=3, center=True).mean().dropna('time')\n \n array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])\n Coordinates:\n * time (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ...\n\n See Also\n --------\n core.rolling.DataArrayRolling\n core.rolling.DatasetRolling\n \"\"\"\n dim = either_dict_or_kwargs(dim, window_kwargs, \"rolling\")\n return self._rolling_cls(self, dim, min_periods=min_periods, center=center)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.rolling_exp_DataWithCoords.rolling_exp.return.self__rolling_exp_cls_sel": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.rolling_exp_DataWithCoords.rolling_exp.return.self__rolling_exp_cls_sel", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 798, "end_line": 835, "span_ids": ["DataWithCoords.rolling_exp"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def rolling_exp(\n self,\n window: Mapping[Hashable, int] = None,\n window_type: str = \"span\",\n **window_kwargs\n ):\n \"\"\"\n Exponentially-weighted moving window.\n Similar to EWM in pandas\n\n Requires the optional Numbagg dependency.\n\n Parameters\n ----------\n window : A single mapping from a dimension name to window value,\n optional\n dim : str\n Name of the dimension to create the rolling exponential window\n along (e.g., `time`).\n window : int\n Size of the moving window. The type of this is specified in\n `window_type`\n window_type : str, one of ['span', 'com', 'halflife', 'alpha'],\n default 'span'\n The format of the previously supplied window. Each is a simple\n numerical transformation of the others. Described in detail:\n https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.ewm.html\n **window_kwargs : optional\n The keyword arguments form of ``window``.\n One of window or window_kwargs must be provided.\n\n See Also\n --------\n core.rolling_exp.RollingExp\n \"\"\"\n window = either_dict_or_kwargs(window, window_kwargs, \"rolling_exp\")\n\n return self._rolling_exp_cls(self, window, window_type)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.coarsen_DataWithCoords.coarsen.return.self__coarsen_cls_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.coarsen_DataWithCoords.coarsen.return.self__coarsen_cls_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 837, "end_line": 901, "span_ids": ["DataWithCoords.coarsen"], "tokens": 646}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def coarsen(\n self,\n dim: Mapping[Hashable, int] = None,\n boundary: str = \"exact\",\n side: Union[str, Mapping[Hashable, str]] = \"left\",\n coord_func: str = \"mean\",\n **window_kwargs: int\n ):\n \"\"\"\n Coarsen object.\n\n Parameters\n ----------\n dim: dict, optional\n Mapping from the dimension name to the window size.\n dim : str\n Name of the dimension to create the rolling iterator\n along (e.g., `time`).\n window : int\n Size of the moving window.\n boundary : 'exact' | 'trim' | 'pad'\n If 'exact', a ValueError will be raised if dimension size is not a\n multiple of the window size. If 'trim', the excess entries are\n dropped. If 'pad', NA will be padded.\n side : 'left' or 'right' or mapping from dimension to 'left' or 'right'\n coord_func: function (name) that is applied to the coordintes,\n or a mapping from coordinate name to function (name).\n\n Returns\n -------\n Coarsen object (core.rolling.DataArrayCoarsen for DataArray,\n core.rolling.DatasetCoarsen for Dataset.)\n\n Examples\n --------\n Coarsen the long time series by averaging over every four days.\n\n >>> da = xr.DataArray(np.linspace(0, 364, num=364),\n ... dims='time',\n ... coords={'time': pd.date_range(\n ... '15/12/1999', periods=364)})\n >>> da\n \n array([ 0. , 1.002755, 2.00551 , ..., 361.99449 , 362.997245,\n 364. ])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12\n >>>\n >>> da.coarsen(time=3, boundary='trim').mean()\n \n array([ 1.002755, 4.011019, 7.019284, ..., 358.986226,\n 361.99449 ])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10\n >>>\n\n See Also\n --------\n core.rolling.DataArrayCoarsen\n core.rolling.DatasetCoarsen\n \"\"\"\n dim = either_dict_or_kwargs(dim, window_kwargs, \"coarsen\")\n return self._coarsen_cls(\n self, dim, boundary=boundary, side=side, coord_func=coord_func\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.resample_DataWithCoords.resample._Returns_a_Resample_obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.resample_DataWithCoords.resample._Returns_a_Resample_obj", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 903, "end_line": 991, "span_ids": ["DataWithCoords.resample"], "tokens": 957}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def resample(\n self,\n indexer: Mapping[Hashable, str] = None,\n skipna=None,\n closed: str = None,\n label: str = None,\n base: int = 0,\n keep_attrs: bool = None,\n loffset=None,\n restore_coord_dims: bool = None,\n **indexer_kwargs: str\n ):\n \"\"\"Returns a Resample object for performing resampling operations.\n\n Handles both downsampling and upsampling. If any intervals contain no\n values from the original object, they will be given the value ``NaN``.\n\n Parameters\n ----------\n indexer : {dim: freq}, optional\n Mapping from the dimension name to resample frequency.\n skipna : bool, optional\n Whether to skip missing values when aggregating in downsampling.\n closed : 'left' or 'right', optional\n Side of each interval to treat as closed.\n label : 'left or 'right', optional\n Side of each interval to use for labeling.\n base : int, optional\n For frequencies that evenly subdivide 1 day, the \"origin\" of the\n aggregated intervals. For example, for '24H' frequency, base could\n range from 0 through 23.\n loffset : timedelta or str, optional\n Offset used to adjust the resampled time labels. Some pandas date\n offset strings are supported.\n keep_attrs : bool, optional\n If True, the object's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n restore_coord_dims : bool, optional\n If True, also restore the dimension order of multi-dimensional\n coordinates.\n **indexer_kwargs : {dim: freq}\n The keyword arguments form of ``indexer``.\n One of indexer or indexer_kwargs must be provided.\n\n Returns\n -------\n resampled : same type as caller\n This object resampled.\n\n Examples\n --------\n Downsample monthly time-series data to seasonal data:\n\n >>> da = xr.DataArray(np.linspace(0, 11, num=12),\n ... coords=[pd.date_range('15/12/1999',\n ... periods=12, freq=pd.DateOffset(months=1))],\n ... dims='time')\n >>> da\n \n array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...\n >>> da.resample(time=\"QS-DEC\").mean()\n \n array([ 1., 4., 7., 10.])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01\n\n Upsample monthly time-series data to daily data:\n\n >>> da.resample(time='1D').interpolate('linear')\n \n array([ 0. , 0.032258, 0.064516, ..., 10.935484, 10.967742, 11. ])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ...\n\n Limit scope of upsampling method\n >>> da.resample(time='1D').nearest(tolerance='1D')\n \n array([ 0., 0., nan, ..., nan, 11., 11.])\n Coordinates:\n * time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15\n\n References\n ----------\n\n .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.resample._TODO_support_non_string_DataWithCoords.resample.return.resampler": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.resample._TODO_support_non_string_DataWithCoords.resample.return.resampler", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 992, "end_line": 1041, "span_ids": ["DataWithCoords.resample"], "tokens": 513}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def resample(\n self,\n indexer: Mapping[Hashable, str] = None,\n skipna=None,\n closed: str = None,\n label: str = None,\n base: int = 0,\n keep_attrs: bool = None,\n loffset=None,\n restore_coord_dims: bool = None,\n **indexer_kwargs: str\n ):\n # TODO support non-string indexer after removing the old API.\n\n from .dataarray import DataArray\n from .resample import RESAMPLE_DIM\n from ..coding.cftimeindex import CFTimeIndex\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n\n # note: the second argument (now 'skipna') use to be 'dim'\n if (\n (skipna is not None and not isinstance(skipna, bool))\n or (\"how\" in indexer_kwargs and \"how\" not in self.dims)\n or (\"dim\" in indexer_kwargs and \"dim\" not in self.dims)\n ):\n raise TypeError(\n \"resample() no longer supports the `how` or \"\n \"`dim` arguments. Instead call methods on resample \"\n \"objects, e.g., data.resample(time='1D').mean()\"\n )\n\n indexer = either_dict_or_kwargs(indexer, indexer_kwargs, \"resample\")\n if len(indexer) != 1:\n raise ValueError(\"Resampling only supported along single dimensions.\")\n dim, freq = next(iter(indexer.items()))\n\n dim_name = dim\n dim_coord = self[dim]\n\n if isinstance(self.indexes[dim_name], CFTimeIndex):\n from .resample_cftime import CFTimeGrouper\n\n grouper = CFTimeGrouper(freq, closed, label, base, loffset)\n else:\n grouper = pd.Grouper(\n freq=freq, closed=closed, label=label, base=base, loffset=loffset\n )\n group = DataArray(\n dim_coord, coords=dim_coord.coords, dims=dim_coord.dims, name=RESAMPLE_DIM\n )\n resampler = self._resample_cls(\n self,\n group=group,\n dim=dim_name,\n grouper=grouper,\n resample_dim=RESAMPLE_DIM,\n restore_coord_dims=restore_coord_dims,\n )\n\n return resampler", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.where_DataWithCoords.where.Dataset": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.where_DataWithCoords.where.Dataset", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1043, "end_line": 1101, "span_ids": ["DataWithCoords.where"], "tokens": 643}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def where(self, cond, other=dtypes.NA, drop: bool = False):\n \"\"\"Filter elements from this object according to a condition.\n\n This operation follows the normal broadcasting and alignment rules that\n xarray uses for binary arithmetic.\n\n Parameters\n ----------\n cond : DataArray or Dataset with boolean dtype\n Locations at which to preserve this object's values.\n other : scalar, DataArray or Dataset, optional\n Value to use for locations in this object where ``cond`` is False.\n By default, these locations filled with NA.\n drop : boolean, optional\n If True, coordinate labels that only correspond to False values of\n the condition are dropped from the result. Mutually exclusive with\n ``other``.\n\n Returns\n -------\n Same type as caller.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=('x', 'y'))\n >>> a.where(a.x + a.y < 4)\n \n array([[ 0., 1., 2., 3., nan],\n [ 5., 6., 7., nan, nan],\n [ 10., 11., nan, nan, nan],\n [ 15., nan, nan, nan, nan],\n [ nan, nan, nan, nan, nan]])\n Dimensions without coordinates: x, y\n >>> a.where(a.x + a.y < 5, -1)\n \n array([[ 0, 1, 2, 3, 4],\n [ 5, 6, 7, 8, -1],\n [10, 11, 12, -1, -1],\n [15, 16, -1, -1, -1],\n [20, -1, -1, -1, -1]])\n Dimensions without coordinates: x, y\n >>> a.where(a.x + a.y < 4, drop=True)\n \n array([[ 0., 1., 2., 3.],\n [ 5., 6., 7., nan],\n [ 10., 11., nan, nan],\n [ 15., nan, nan, nan]])\n Dimensions without coordinates: x, y\n\n See also\n --------\n numpy.where : corresponding numpy function\n where : equivalent function\n \"\"\"\n from .alignment import align\n from .dataarray import DataArray\n from .dataset import Dataset\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.where.if_drop__DataWithCoords.close.self._file_obj.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.where.if_drop__DataWithCoords.close.self._file_obj.None", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1103, "end_line": 1136, "span_ids": ["DataWithCoords.close", "DataWithCoords.where"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def where(self, cond, other=dtypes.NA, drop: bool = False):\n # ... other code\n\n if drop:\n if other is not dtypes.NA:\n raise ValueError(\"cannot set `other` if drop=True\")\n\n if not isinstance(cond, (Dataset, DataArray)):\n raise TypeError(\n \"cond argument is %r but must be a %r or %r\"\n % (cond, Dataset, DataArray)\n )\n\n # align so we can use integer indexing\n self, cond = align(self, cond)\n\n # get cond with the minimal size needed for the Dataset\n if isinstance(cond, Dataset):\n clipcond = cond.to_array().any(\"variable\")\n else:\n clipcond = cond\n\n # clip the data corresponding to coordinate dims that are not used\n nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values))\n indexers = {k: np.unique(v) for k, v in nonzeros}\n\n self = self.isel(**indexers)\n cond = cond.isel(**indexers)\n\n return ops.where_method(self, cond, other)\n\n def close(self: Any) -> None:\n \"\"\"Close any files linked to this object\n \"\"\"\n if self._file_obj is not None:\n self._file_obj.close()\n self._file_obj = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.isin_DataWithCoords.__getitem__.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_DataWithCoords.isin_DataWithCoords.__getitem__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1138, "end_line": 1197, "span_ids": ["DataWithCoords.__exit__", "DataWithCoords.isin", "DataWithCoords.__getitem__", "DataWithCoords.__enter__"], "tokens": 396}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataWithCoords(SupportsArithmetic, AttrAccessMixin):\n\n def isin(self, test_elements):\n \"\"\"Tests each value in the array for whether it is in test elements.\n\n Parameters\n ----------\n test_elements : array_like\n The values against which to test each value of `element`.\n This argument is flattened if an array or array_like.\n See numpy notes for behavior with non-array-like parameters.\n\n Returns\n -------\n isin : same as object, bool\n Has the same shape as this object.\n\n Examples\n --------\n\n >>> array = xr.DataArray([1, 2, 3], dims='x')\n >>> array.isin([1, 3])\n \n array([ True, False, True])\n Dimensions without coordinates: x\n\n See also\n --------\n numpy.isin\n \"\"\"\n from .computation import apply_ufunc\n from .dataset import Dataset\n from .dataarray import DataArray\n from .variable import Variable\n\n if isinstance(test_elements, Dataset):\n raise TypeError(\n \"isin() argument must be convertible to an array: {}\".format(\n test_elements\n )\n )\n elif isinstance(test_elements, (Variable, DataArray)):\n # need to explicitly pull out data to support dask arrays as the\n # second argument\n test_elements = test_elements.data\n\n return apply_ufunc(\n duck_array_ops.isin,\n self,\n kwargs=dict(test_elements=test_elements),\n dask=\"allowed\",\n )\n\n def __enter__(self: T) -> T:\n return self\n\n def __exit__(self, exc_type, exc_value, traceback) -> None:\n self.close()\n\n def __getitem__(self, value):\n # implementations of this class should implement this method\n raise NotImplementedError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_full_like_full_like.Variable": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_full_like_full_like.Variable", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1200, "end_line": 1277, "span_ids": ["full_like"], "tokens": 706}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def full_like(other, fill_value, dtype: DTypeLike = None):\n \"\"\"Return a new object with the same shape and type as a given object.\n\n Parameters\n ----------\n other : DataArray, Dataset, or Variable\n The reference object in input\n fill_value : scalar\n Value to fill the new object with before returning it.\n dtype : dtype, optional\n dtype of the new array. If omitted, it defaults to other.dtype.\n\n Returns\n -------\n out : same as object\n New object with the same shape and type as other, with the data\n filled with fill_value. Coords will be copied from other.\n If other is based on dask, the new one will be as well, and will be\n split in the same chunks.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xr\n >>> x = xr.DataArray(np.arange(6).reshape(2, 3),\n ... dims=['lat', 'lon'],\n ... coords={'lat': [1, 2], 'lon': [0, 1, 2]})\n >>> x\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.full_like(x, 1)\n \n array([[1, 1, 1],\n [1, 1, 1]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.full_like(x, 0.5)\n \n array([[0, 0, 0],\n [0, 0, 0]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.full_like(x, 0.5, dtype=np.double)\n \n array([[0.5, 0.5, 0.5],\n [0.5, 0.5, 0.5]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.full_like(x, np.nan, dtype=np.double)\n \n array([[nan, nan, nan],\n [nan, nan, nan]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n See also\n --------\n\n zeros_like\n ones_like\n\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n from .variable import Variable\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_full_like.if_isinstance_other_Data_full_like.if_isinstance_other_Data.else_.raise_TypeError_Expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_full_like.if_isinstance_other_Data_full_like.if_isinstance_other_Data.else_.raise_TypeError_Expected", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1279, "end_line": 1296, "span_ids": ["full_like"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def full_like(other, fill_value, dtype: DTypeLike = None):\n # ... other code\n\n if isinstance(other, Dataset):\n data_vars = {\n k: _full_like_variable(v, fill_value, dtype)\n for k, v in other.data_vars.items()\n }\n return Dataset(data_vars, coords=other.coords, attrs=other.attrs)\n elif isinstance(other, DataArray):\n return DataArray(\n _full_like_variable(other.variable, fill_value, dtype),\n dims=other.dims,\n coords=other.coords,\n attrs=other.attrs,\n name=other.name,\n )\n elif isinstance(other, Variable):\n return _full_like_variable(other, fill_value, dtype)\n else:\n raise TypeError(\"Expected DataArray, Dataset, or Variable\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py__full_like_variable__full_like_variable.return.Variable_dims_other_dims_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py__full_like_variable__full_like_variable.return.Variable_dims_other_dims_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1299, "end_line": 1315, "span_ids": ["_full_like_variable"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _full_like_variable(other, fill_value, dtype: DTypeLike = None):\n \"\"\"Inner function of full_like, where other must be a variable\n \"\"\"\n from .variable import Variable\n\n if isinstance(other.data, dask_array_type):\n import dask.array\n\n if dtype is None:\n dtype = other.dtype\n data = dask.array.full(\n other.shape, fill_value, dtype=dtype, chunks=other.data.chunks\n )\n else:\n data = np.full_like(other, fill_value, dtype=dtype)\n\n return Variable(dims=other.dims, data=data, attrs=other.attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_zeros_like_zeros_like.return.full_like_other_0_dtype": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_zeros_like_zeros_like.return.full_like_other_0_dtype", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1318, "end_line": 1373, "span_ids": ["zeros_like"], "tokens": 460}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def zeros_like(other, dtype: DTypeLike = None):\n \"\"\"Return a new object of zeros with the same shape and\n type as a given dataarray or dataset.\n\n Parameters\n ----------\n other : DataArray, Dataset, or Variable\n The reference object. The output will have the same dimensions and coordinates as this object.\n dtype : dtype, optional\n dtype of the new array. If omitted, it defaults to other.dtype.\n\n Returns\n -------\n out : same as object\n New object of zeros with the same shape and type as other.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xr\n >>> x = xr.DataArray(np.arange(6).reshape(2, 3),\n ... dims=['lat', 'lon'],\n ... coords={'lat': [1, 2], 'lon': [0, 1, 2]})\n >>> x\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.zeros_like(x)\n \n array([[0, 0, 0],\n [0, 0, 0]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> xr.zeros_like(x, dtype=np.float)\n \n array([[0., 0., 0.],\n [0., 0., 0.]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n See also\n --------\n\n ones_like\n full_like\n\n \"\"\"\n return full_like(other, 0, dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ones_like_ones_like.return.full_like_other_1_dtype": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_ones_like_ones_like.return.full_like_other_1_dtype", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1376, "end_line": 1423, "span_ids": ["ones_like"], "tokens": 377}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ones_like(other, dtype: DTypeLike = None):\n \"\"\"Return a new object of ones with the same shape and\n type as a given dataarray or dataset.\n\n Parameters\n ----------\n other : DataArray, Dataset, or Variable\n The reference object. The output will have the same dimensions and coordinates as this object.\n dtype : dtype, optional\n dtype of the new array. If omitted, it defaults to other.dtype.\n\n Returns\n -------\n out : same as object\n New object of ones with the same shape and type as other.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xr\n >>> x = xr.DataArray(np.arange(6).reshape(2, 3),\n ... dims=['lat', 'lon'],\n ... coords={'lat': [1, 2], 'lon': [0, 1, 2]})\n >>> x\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n >>> >>> xr.ones_like(x)\n \n array([[1, 1, 1],\n [1, 1, 1]])\n Coordinates:\n * lat (lat) int64 1 2\n * lon (lon) int64 0 1 2\n\n See also\n --------\n\n zeros_like\n full_like\n\n \"\"\"\n return full_like(other, 1, dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_is_np_datetime_like_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/common.py_is_np_datetime_like_", "embedding": null, "metadata": {"file_path": "xarray/core/common.py", "file_name": "common.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1426, "end_line": 1462, "span_ids": ["_contains_datetime_like_objects", "is_np_datetime_like", "_contains_cftime_datetimes", "contains_cftime_datetimes"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_np_datetime_like(dtype: DTypeLike) -> bool:\n \"\"\"Check if a dtype is a subclass of the numpy datetime types\n \"\"\"\n return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)\n\n\ndef _contains_cftime_datetimes(array) -> bool:\n \"\"\"Check if an array contains cftime.datetime objects\n \"\"\"\n try:\n from cftime import datetime as cftime_datetime\n except ImportError:\n return False\n else:\n if array.dtype == np.dtype(\"O\") and array.size > 0:\n sample = array.ravel()[0]\n if isinstance(sample, dask_array_type):\n sample = sample.compute()\n if isinstance(sample, np.ndarray):\n sample = sample.item()\n return isinstance(sample, cftime_datetime)\n else:\n return False\n\n\ndef contains_cftime_datetimes(var) -> bool:\n \"\"\"Check if an xarray.Variable contains cftime.datetime objects\n \"\"\"\n return _contains_cftime_datetimes(var.data)\n\n\ndef _contains_datetime_like_objects(var) -> bool:\n \"\"\"Check if a variable contains datetime like objects (either\n np.datetime64, np.timedelta64, or cftime.datetime)\n \"\"\"\n return is_np_datetime_like(var.dtype) or contains_cftime_datetimes(var)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py____JOINS_WITHOUT_FILL_VALUES.frozenset_inner_exac": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py____JOINS_WITHOUT_FILL_VALUES.frozenset_inner_exac", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 39, "span_ids": ["impl", "impl:2", "docstring", "imports:14", "docstring:2", "imports", "imports:13"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"\nFunctions for applying functions that act on arrays to xarray's labeled data.\n\"\"\"\nimport functools\nimport itertools\nimport operator\nfrom collections import Counter\nfrom typing import (\n TYPE_CHECKING,\n AbstractSet,\n Any,\n Callable,\n Dict,\n Hashable,\n Iterable,\n List,\n Mapping,\n Optional,\n Sequence,\n Tuple,\n Union,\n)\n\nimport numpy as np\n\nfrom . import duck_array_ops, utils\nfrom .alignment import deep_align\nfrom .merge import merge_coordinates_without_align\nfrom .pycompat import dask_array_type\nfrom .utils import is_dict_like\nfrom .variable import Variable\n\nif TYPE_CHECKING:\n from .coordinates import Coordinates # noqa\n from .dataset import Dataset\n\n_NO_FILL_VALUE = utils.ReprObject(\"\")\n_DEFAULT_NAME = utils.ReprObject(\"\")\n_JOINS_WITHOUT_FILL_VALUES = frozenset({\"inner\", \"exact\"})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__UFuncSignature__UFuncSignature.__str__.return._format_lhs_rhs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__UFuncSignature__UFuncSignature.__str__.return._format_lhs_rhs_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 122, "span_ids": ["_UFuncSignature.__eq__", "_UFuncSignature.num_inputs", "_UFuncSignature.all_input_core_dims", "_UFuncSignature.__ne__", "_UFuncSignature", "_UFuncSignature.__repr__", "_UFuncSignature.__str__", "_UFuncSignature.all_output_core_dims", "_UFuncSignature.num_outputs", "_UFuncSignature.all_core_dims"], "tokens": 569}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _UFuncSignature:\n \"\"\"Core dimensions signature for a given function.\n\n Based on the signature provided by generalized ufuncs in NumPy.\n\n Attributes\n ----------\n input_core_dims : tuple[tuple]\n Core dimension names on each input variable.\n output_core_dims : tuple[tuple]\n Core dimension names on each output variable.\n \"\"\"\n\n __slots__ = (\n \"input_core_dims\",\n \"output_core_dims\",\n \"_all_input_core_dims\",\n \"_all_output_core_dims\",\n \"_all_core_dims\",\n )\n\n def __init__(self, input_core_dims, output_core_dims=((),)):\n self.input_core_dims = tuple(tuple(a) for a in input_core_dims)\n self.output_core_dims = tuple(tuple(a) for a in output_core_dims)\n self._all_input_core_dims = None\n self._all_output_core_dims = None\n self._all_core_dims = None\n\n @property\n def all_input_core_dims(self):\n if self._all_input_core_dims is None:\n self._all_input_core_dims = frozenset(\n dim for dims in self.input_core_dims for dim in dims\n )\n return self._all_input_core_dims\n\n @property\n def all_output_core_dims(self):\n if self._all_output_core_dims is None:\n self._all_output_core_dims = frozenset(\n dim for dims in self.output_core_dims for dim in dims\n )\n return self._all_output_core_dims\n\n @property\n def all_core_dims(self):\n if self._all_core_dims is None:\n self._all_core_dims = self.all_input_core_dims | self.all_output_core_dims\n return self._all_core_dims\n\n @property\n def num_inputs(self):\n return len(self.input_core_dims)\n\n @property\n def num_outputs(self):\n return len(self.output_core_dims)\n\n def __eq__(self, other):\n try:\n return (\n self.input_core_dims == other.input_core_dims\n and self.output_core_dims == other.output_core_dims\n )\n except AttributeError:\n return False\n\n def __ne__(self, other):\n return not self == other\n\n def __repr__(self):\n return \"%s(%r, %r)\" % (\n type(self).__name__,\n list(self.input_core_dims),\n list(self.output_core_dims),\n )\n\n def __str__(self):\n lhs = \",\".join(\"({})\".format(\",\".join(dims)) for dims in self.input_core_dims)\n rhs = \",\".join(\"({})\".format(\",\".join(dims)) for dims in self.output_core_dims)\n return \"{}->{}\".format(lhs, rhs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__UFuncSignature.to_gufunc_string__UFuncSignature.to_gufunc_string.return.str_alt_signature_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__UFuncSignature.to_gufunc_string__UFuncSignature.to_gufunc_string.return.str_alt_signature_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 124, "end_line": 141, "span_ids": ["_UFuncSignature.to_gufunc_string"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _UFuncSignature:\n\n def to_gufunc_string(self):\n \"\"\"Create an equivalent signature string for a NumPy gufunc.\n\n Unlike __str__, handles dimensions that don't map to Python\n identifiers.\n \"\"\"\n all_dims = self.all_core_dims\n dims_map = dict(zip(sorted(all_dims), range(len(all_dims))))\n input_core_dims = [\n [\"dim%d\" % dims_map[dim] for dim in core_dims]\n for core_dims in self.input_core_dims\n ]\n output_core_dims = [\n [\"dim%d\" % dims_map[dim] for dim in core_dims]\n for core_dims in self.output_core_dims\n ]\n alt_signature = type(self)(input_core_dims, output_core_dims)\n return str(alt_signature)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_result_name__get_coords_list.return.coords_list": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_result_name__get_coords_list.return.coords_list", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 165, "span_ids": ["_get_coords_list", "result_name"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def result_name(objects: list) -> Any:\n # use the same naming heuristics as pandas:\n # https://github.com/blaze/blaze/issues/458#issuecomment-51936356\n names = {getattr(obj, \"name\", _DEFAULT_NAME) for obj in objects}\n names.discard(_DEFAULT_NAME)\n if len(names) == 1:\n name, = names\n else:\n name = None\n return name\n\n\ndef _get_coords_list(args) -> List[\"Coordinates\"]:\n coords_list = []\n for arg in args:\n try:\n coords = arg.coords\n except AttributeError:\n pass # skip this argument\n else:\n coords_list.append(coords)\n return coords_list", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_build_output_coords_build_output_coords.return.output_coords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_build_output_coords_build_output_coords.return.output_coords", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 211, "span_ids": ["build_output_coords"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def build_output_coords(\n args: list, signature: _UFuncSignature, exclude_dims: AbstractSet = frozenset()\n) -> \"List[Dict[Any, Variable]]\":\n \"\"\"Build output coordinates for an operation.\n\n Parameters\n ----------\n args : list\n List of raw operation arguments. Any valid types for xarray operations\n are OK, e.g., scalars, Variable, DataArray, Dataset.\n signature : _UfuncSignature\n Core dimensions signature for the operation.\n exclude_dims : optional set\n Dimensions excluded from the operation. Coordinates along these\n dimensions are dropped.\n\n Returns\n -------\n Dictionary of Variable objects with merged coordinates.\n \"\"\"\n coords_list = _get_coords_list(args)\n\n if len(coords_list) == 1 and not exclude_dims:\n # we can skip the expensive merge\n unpacked_coords, = coords_list\n merged_vars = dict(unpacked_coords.variables)\n else:\n # TODO: save these merged indexes, instead of re-computing them later\n merged_vars, unused_indexes = merge_coordinates_without_align(\n coords_list, exclude_dims=exclude_dims\n )\n\n output_coords = []\n for output_dims in signature.output_core_dims:\n dropped_dims = signature.all_input_core_dims - set(output_dims)\n if dropped_dims:\n filtered = {\n k: v for k, v in merged_vars.items() if dropped_dims.isdisjoint(v.dims)\n }\n else:\n filtered = merged_vars\n output_coords.append(filtered)\n\n return output_coords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dataarray_vfunc_apply_dataarray_vfunc.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dataarray_vfunc_apply_dataarray_vfunc.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 245, "span_ids": ["apply_dataarray_vfunc"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_dataarray_vfunc(\n func, *args, signature, join=\"inner\", exclude_dims=frozenset(), keep_attrs=False\n):\n \"\"\"Apply a variable level function over DataArray, Variable and/or ndarray\n objects.\n \"\"\"\n from .dataarray import DataArray\n\n if len(args) > 1:\n args = deep_align(\n args, join=join, copy=False, exclude=exclude_dims, raise_on_invalid=False\n )\n\n if keep_attrs and hasattr(args[0], \"name\"):\n name = args[0].name\n else:\n name = result_name(args)\n result_coords = build_output_coords(args, signature, exclude_dims)\n\n data_vars = [getattr(a, \"variable\", a) for a in args]\n result_var = func(*data_vars)\n\n if signature.num_outputs > 1:\n out = tuple(\n DataArray(variable, coords, name=name, fastpath=True)\n for variable, coords in zip(result_var, result_coords)\n )\n else:\n coords, = result_coords\n out = DataArray(result_var, coords, name=name, fastpath=True)\n\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_ordered_set_union__unpack_dict_tuples.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_ordered_set_union__unpack_dict_tuples.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 248, "end_line": 313, "span_ids": ["_unpack_dict_tuples", "impl:8", "ordered_set_intersection", "_as_variables_or_variable", "ordered_set_union", "assert_and_return_exact_match", "join_dict_keys", "collect_dict_values"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ordered_set_union(all_keys: List[Iterable]) -> Iterable:\n return {key: None for keys in all_keys for key in keys}.keys()\n\n\ndef ordered_set_intersection(all_keys: List[Iterable]) -> Iterable:\n intersection = set(all_keys[0])\n for keys in all_keys[1:]:\n intersection.intersection_update(keys)\n return [key for key in all_keys[0] if key in intersection]\n\n\ndef assert_and_return_exact_match(all_keys):\n first_keys = all_keys[0]\n for keys in all_keys[1:]:\n if keys != first_keys:\n raise ValueError(\n \"exact match required for all data variable names, \"\n \"but %r != %r\" % (keys, first_keys)\n )\n return first_keys\n\n\n_JOINERS = {\n \"inner\": ordered_set_intersection,\n \"outer\": ordered_set_union,\n \"left\": operator.itemgetter(0),\n \"right\": operator.itemgetter(-1),\n \"exact\": assert_and_return_exact_match,\n}\n\n\ndef join_dict_keys(\n objects: Iterable[Union[Mapping, Any]], how: str = \"inner\"\n) -> Iterable:\n joiner = _JOINERS[how]\n all_keys = [obj.keys() for obj in objects if hasattr(obj, \"keys\")]\n return joiner(all_keys)\n\n\ndef collect_dict_values(\n objects: Iterable[Union[Mapping, Any]], keys: Iterable, fill_value: object = None\n) -> List[list]:\n return [\n [obj.get(key, fill_value) if is_dict_like(obj) else obj for obj in objects]\n for key in keys\n ]\n\n\ndef _as_variables_or_variable(arg):\n try:\n return arg.variables\n except AttributeError:\n try:\n return arg.variable\n except AttributeError:\n return arg\n\n\ndef _unpack_dict_tuples(\n result_vars: Mapping[Hashable, Tuple[Variable, ...]], num_outputs: int\n) -> Tuple[Dict[Hashable, Variable], ...]:\n out = tuple({} for _ in range(num_outputs)) # type: ignore\n for name, values in result_vars.items():\n for value, results_dict in zip(values, out):\n results_dict[name] = value\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dict_of_variables_vfunc__fast_dataset.return.Dataset__from_vars_and_co": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dict_of_variables_vfunc__fast_dataset.return.Dataset__from_vars_and_co", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 316, "end_line": 347, "span_ids": ["_fast_dataset", "apply_dict_of_variables_vfunc"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_dict_of_variables_vfunc(\n func, *args, signature, join=\"inner\", fill_value=None\n):\n \"\"\"Apply a variable level function over dicts of DataArray, DataArray,\n Variable and ndarray objects.\n \"\"\"\n args = [_as_variables_or_variable(arg) for arg in args]\n names = join_dict_keys(args, how=join)\n grouped_by_name = collect_dict_values(args, names, fill_value)\n\n result_vars = {}\n for name, variable_args in zip(names, grouped_by_name):\n result_vars[name] = func(*variable_args)\n\n if signature.num_outputs > 1:\n return _unpack_dict_tuples(result_vars, signature.num_outputs)\n else:\n return result_vars\n\n\ndef _fast_dataset(\n variables: Dict[Hashable, Variable], coord_variables: Mapping[Hashable, Variable]\n) -> \"Dataset\":\n \"\"\"Create a dataset as quickly as possible.\n\n Beware: the `variables` dict is modified INPLACE.\n \"\"\"\n from .dataset import Dataset\n\n variables.update(coord_variables)\n coord_names = set(coord_variables)\n return Dataset._from_vars_and_coord_names(variables, coord_names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dataset_vfunc__iter_over_selections.for_value_in_values_.yield_obj_sel": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_dataset_vfunc__iter_over_selections.for_value_in_values_.yield_obj_sel", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 350, "end_line": 412, "span_ids": ["apply_dataset_vfunc", "_iter_over_selections"], "tokens": 471}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_dataset_vfunc(\n func,\n *args,\n signature,\n join=\"inner\",\n dataset_join=\"exact\",\n fill_value=_NO_FILL_VALUE,\n exclude_dims=frozenset(),\n keep_attrs=False\n):\n \"\"\"Apply a variable level function over Dataset, dict of DataArray,\n DataArray, Variable and/or ndarray objects.\n \"\"\"\n from .dataset import Dataset\n\n first_obj = args[0] # we'll copy attrs from this in case keep_attrs=True\n\n if dataset_join not in _JOINS_WITHOUT_FILL_VALUES and fill_value is _NO_FILL_VALUE:\n raise TypeError(\n \"to apply an operation to datasets with different \"\n \"data variables with apply_ufunc, you must supply the \"\n \"dataset_fill_value argument.\"\n )\n\n if len(args) > 1:\n args = deep_align(\n args, join=join, copy=False, exclude=exclude_dims, raise_on_invalid=False\n )\n\n list_of_coords = build_output_coords(args, signature, exclude_dims)\n args = [getattr(arg, \"data_vars\", arg) for arg in args]\n\n result_vars = apply_dict_of_variables_vfunc(\n func, *args, signature=signature, join=dataset_join, fill_value=fill_value\n )\n\n if signature.num_outputs > 1:\n out = tuple(_fast_dataset(*args) for args in zip(result_vars, list_of_coords))\n else:\n coord_vars, = list_of_coords\n out = _fast_dataset(result_vars, coord_vars)\n\n if keep_attrs and isinstance(first_obj, Dataset):\n if isinstance(out, tuple):\n out = tuple(ds._copy_attrs_from(first_obj) for ds in out)\n else:\n out._copy_attrs_from(first_obj)\n return out\n\n\ndef _iter_over_selections(obj, dim, values):\n \"\"\"Iterate over selections of an xarray object in the provided order.\"\"\"\n from .groupby import _dummy_copy\n\n dummy = None\n for value in values:\n try:\n obj_sel = obj.sel(**{dim: value})\n except (KeyError, IndexError):\n if dummy is None:\n dummy = _dummy_copy(obj)\n obj_sel = dummy\n yield obj_sel", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_groupby_func_apply_groupby_func.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_groupby_func_apply_groupby_func.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 415, "end_line": 458, "span_ids": ["apply_groupby_func"], "tokens": 387}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_groupby_func(func, *args):\n \"\"\"Apply a dataset or datarray level function over GroupBy, Dataset,\n DataArray, Variable and/or ndarray objects.\n \"\"\"\n from .groupby import GroupBy, peek_at\n from .variable import Variable\n\n groupbys = [arg for arg in args if isinstance(arg, GroupBy)]\n assert groupbys, \"must have at least one groupby to iterate over\"\n first_groupby = groupbys[0]\n if any(not first_groupby._group.equals(gb._group) for gb in groupbys[1:]):\n raise ValueError(\n \"apply_ufunc can only perform operations over \"\n \"multiple GroupBy objets at once if they are all \"\n \"grouped the same way\"\n )\n\n grouped_dim = first_groupby._group.name\n unique_values = first_groupby._unique_coord.values\n\n iterators = []\n for arg in args:\n if isinstance(arg, GroupBy):\n iterator = (value for _, value in arg)\n elif hasattr(arg, \"dims\") and grouped_dim in arg.dims:\n if isinstance(arg, Variable):\n raise ValueError(\n \"groupby operations cannot be performed with \"\n \"xarray.Variable objects that share a dimension with \"\n \"the grouped dimension\"\n )\n iterator = _iter_over_selections(arg, grouped_dim, unique_values)\n else:\n iterator = itertools.repeat(arg)\n iterators.append(iterator)\n\n applied = (func(*zipped_args) for zipped_args in zip(*iterators))\n applied_example, applied = peek_at(applied)\n combine = first_groupby._combine\n if isinstance(applied_example, tuple):\n combined = tuple(combine(output) for output in zip(*applied))\n else:\n combined = combine(applied)\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_unified_dim_sizes_SLICE_NONE.slice_None_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_unified_dim_sizes_SLICE_NONE.slice_None_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 461, "end_line": 486, "span_ids": ["unified_dim_sizes", "impl:10"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unified_dim_sizes(\n variables: Iterable[Variable], exclude_dims: AbstractSet = frozenset()\n) -> Dict[Hashable, int]:\n\n dim_sizes: Dict[Hashable, int] = {}\n\n for var in variables:\n if len(set(var.dims)) < len(var.dims):\n raise ValueError(\n \"broadcasting cannot handle duplicate \"\n \"dimensions on a variable: %r\" % list(var.dims)\n )\n for dim, size in zip(var.dims, var.shape):\n if dim not in exclude_dims:\n if dim not in dim_sizes:\n dim_sizes[dim] = size\n elif dim_sizes[dim] != size:\n raise ValueError(\n \"operands cannot be broadcast together \"\n \"with mismatched lengths for dimension \"\n \"%r: %s vs %s\" % (dim, dim_sizes[dim], size)\n )\n return dim_sizes\n\n\nSLICE_NONE = slice(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_broadcast_compat_data_broadcast_compat_data.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_broadcast_compat_data_broadcast_compat_data.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 489, "end_line": 540, "span_ids": ["broadcast_compat_data"], "tokens": 401}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def broadcast_compat_data(\n variable: Variable,\n broadcast_dims: Tuple[Hashable, ...],\n core_dims: Tuple[Hashable, ...],\n) -> Any:\n data = variable.data\n\n old_dims = variable.dims\n new_dims = broadcast_dims + core_dims\n\n if new_dims == old_dims:\n # optimize for the typical case\n return data\n\n set_old_dims = set(old_dims)\n missing_core_dims = [d for d in core_dims if d not in set_old_dims]\n if missing_core_dims:\n raise ValueError(\n \"operand to apply_ufunc has required core dimensions {}, but \"\n \"some of these dimensions are absent on an input variable: {}\".format(\n list(core_dims), missing_core_dims\n )\n )\n\n set_new_dims = set(new_dims)\n unexpected_dims = [d for d in old_dims if d not in set_new_dims]\n if unexpected_dims:\n raise ValueError(\n \"operand to apply_ufunc encountered unexpected \"\n \"dimensions %r on an input variable: these are core \"\n \"dimensions on other input or output variables\" % unexpected_dims\n )\n\n # for consistency with numpy, keep broadcast dimensions to the left\n old_broadcast_dims = tuple(d for d in broadcast_dims if d in set_old_dims)\n reordered_dims = old_broadcast_dims + core_dims\n if reordered_dims != old_dims:\n order = tuple(old_dims.index(d) for d in reordered_dims)\n data = duck_array_ops.transpose(data, order)\n\n if new_dims != reordered_dims:\n key_parts = []\n for dim in new_dims:\n if dim in set_old_dims:\n key_parts.append(SLICE_NONE)\n elif key_parts:\n # no need to insert new axes at the beginning that are already\n # handled by broadcasting\n key_parts.append(np.newaxis)\n data = data[tuple(key_parts)]\n\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_variable_ufunc_apply_variable_ufunc.None_3.else_.return.tuple_output_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_variable_ufunc_apply_variable_ufunc.None_3.else_.return.tuple_output_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 543, "end_line": 648, "span_ids": ["apply_variable_ufunc"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_variable_ufunc(\n func,\n *args,\n signature,\n exclude_dims=frozenset(),\n dask=\"forbidden\",\n output_dtypes=None,\n output_sizes=None,\n keep_attrs=False\n):\n \"\"\"Apply a ndarray level function over Variable and/or ndarray objects.\n \"\"\"\n from .variable import Variable, as_compatible_data\n\n dim_sizes = unified_dim_sizes(\n (a for a in args if hasattr(a, \"dims\")), exclude_dims=exclude_dims\n )\n broadcast_dims = tuple(\n dim for dim in dim_sizes if dim not in signature.all_core_dims\n )\n output_dims = [broadcast_dims + out for out in signature.output_core_dims]\n\n input_data = [\n broadcast_compat_data(arg, broadcast_dims, core_dims)\n if isinstance(arg, Variable)\n else arg\n for arg, core_dims in zip(args, signature.input_core_dims)\n ]\n\n if any(isinstance(array, dask_array_type) for array in input_data):\n if dask == \"forbidden\":\n raise ValueError(\n \"apply_ufunc encountered a dask array on an \"\n \"argument, but handling for dask arrays has not \"\n \"been enabled. Either set the ``dask`` argument \"\n \"or load your data into memory first with \"\n \"``.load()`` or ``.compute()``\"\n )\n elif dask == \"parallelized\":\n input_dims = [broadcast_dims + dims for dims in signature.input_core_dims]\n numpy_func = func\n\n def func(*arrays):\n return _apply_blockwise(\n numpy_func,\n arrays,\n input_dims,\n output_dims,\n signature,\n output_dtypes,\n output_sizes,\n )\n\n elif dask == \"allowed\":\n pass\n else:\n raise ValueError(\n \"unknown setting for dask array handling in \"\n \"apply_ufunc: {}\".format(dask)\n )\n result_data = func(*input_data)\n\n if signature.num_outputs == 1:\n result_data = (result_data,)\n elif (\n not isinstance(result_data, tuple) or len(result_data) != signature.num_outputs\n ):\n raise ValueError(\n \"applied function does not have the number of \"\n \"outputs specified in the ufunc signature. \"\n \"Result is not a tuple of {} elements: {!r}\".format(\n signature.num_outputs, result_data\n )\n )\n\n output = []\n for dims, data in zip(output_dims, result_data):\n data = as_compatible_data(data)\n if data.ndim != len(dims):\n raise ValueError(\n \"applied function returned data with unexpected \"\n \"number of dimensions: {} vs {}, for dimensions {}\".format(\n data.ndim, len(dims), dims\n )\n )\n\n var = Variable(dims, data, fastpath=True)\n for dim, new_size in var.sizes.items():\n if dim in dim_sizes and new_size != dim_sizes[dim]:\n raise ValueError(\n \"size of dimension {!r} on inputs was unexpectedly \"\n \"changed by applied function from {} to {}. Only \"\n \"dimensions specified in ``exclude_dims`` with \"\n \"xarray.apply_ufunc are allowed to change size.\".format(\n dim, dim_sizes[dim], new_size\n )\n )\n\n if keep_attrs and isinstance(args[0], Variable):\n var.attrs.update(args[0].attrs)\n output.append(var)\n\n if signature.num_outputs == 1:\n return output[0]\n else:\n return tuple(output)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__apply_blockwise__apply_blockwise.return.dask_array_blockwise_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py__apply_blockwise__apply_blockwise.return.dask_array_blockwise_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 651, "end_line": 724, "span_ids": ["_apply_blockwise"], "tokens": 605}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _apply_blockwise(\n func, args, input_dims, output_dims, signature, output_dtypes, output_sizes=None\n):\n import dask.array\n\n if signature.num_outputs > 1:\n raise NotImplementedError(\n \"multiple outputs from apply_ufunc not yet \"\n \"supported with dask='parallelized'\"\n )\n\n if output_dtypes is None:\n raise ValueError(\n \"output dtypes (output_dtypes) must be supplied to \"\n \"apply_func when using dask='parallelized'\"\n )\n if not isinstance(output_dtypes, list):\n raise TypeError(\n \"output_dtypes must be a list of objects coercible to \"\n \"numpy dtypes, got {}\".format(output_dtypes)\n )\n if len(output_dtypes) != signature.num_outputs:\n raise ValueError(\n \"apply_ufunc arguments output_dtypes and \"\n \"output_core_dims must have the same length: {} vs {}\".format(\n len(output_dtypes), signature.num_outputs\n )\n )\n (dtype,) = output_dtypes\n\n if output_sizes is None:\n output_sizes = {}\n\n new_dims = signature.all_output_core_dims - signature.all_input_core_dims\n if any(dim not in output_sizes for dim in new_dims):\n raise ValueError(\n \"when using dask='parallelized' with apply_ufunc, \"\n \"output core dimensions not found on inputs must \"\n \"have explicitly set sizes with ``output_sizes``: {}\".format(new_dims)\n )\n\n for n, (data, core_dims) in enumerate(zip(args, signature.input_core_dims)):\n if isinstance(data, dask_array_type):\n # core dimensions cannot span multiple chunks\n for axis, dim in enumerate(core_dims, start=-len(core_dims)):\n if len(data.chunks[axis]) != 1:\n raise ValueError(\n \"dimension {!r} on {}th function argument to \"\n \"apply_ufunc with dask='parallelized' consists of \"\n \"multiple chunks, but is also a core dimension. To \"\n \"fix, rechunk into a single dask array chunk along \"\n \"this dimension, i.e., ``.chunk({})``, but beware \"\n \"that this may significantly increase memory usage.\".format(\n dim, n, {dim: -1}\n )\n )\n\n (out_ind,) = output_dims\n\n blockwise_args = []\n for arg, dims in zip(args, input_dims):\n # skip leading dimensions that are implicitly added by broadcasting\n ndim = getattr(arg, \"ndim\", 0)\n trimmed_dims = dims[-ndim:] if ndim else ()\n blockwise_args.extend([arg, trimmed_dims])\n\n return dask.array.blockwise(\n func,\n out_ind,\n *blockwise_args,\n dtype=dtype,\n concatenate=True,\n new_axes=output_sizes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_array_ufunc_apply_array_ufunc.return.func_args_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_array_ufunc_apply_array_ufunc.return.func_args_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 727, "end_line": 747, "span_ids": ["apply_array_ufunc"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_array_ufunc(func, *args, dask=\"forbidden\"):\n \"\"\"Apply a ndarray level function over ndarray objects.\"\"\"\n if any(isinstance(arg, dask_array_type) for arg in args):\n if dask == \"forbidden\":\n raise ValueError(\n \"apply_ufunc encountered a dask array on an \"\n \"argument, but handling for dask arrays has not \"\n \"been enabled. Either set the ``dask`` argument \"\n \"or load your data into memory first with \"\n \"``.load()`` or ``.compute()``\"\n )\n elif dask == \"parallelized\":\n raise ValueError(\n \"cannot use dask='parallelized' for apply_ufunc \"\n \"unless at least one input is an xarray object\"\n )\n elif dask == \"allowed\":\n pass\n else:\n raise ValueError(\"unknown setting for dask array handling: {}\".format(dask))\n return func(*args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc_apply_ufunc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc_apply_ufunc", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 750, "end_line": 1049, "span_ids": ["apply_ufunc"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_ufunc(\n func: Callable,\n *args: Any,\n input_core_dims: Sequence[Sequence] = None,\n output_core_dims: Optional[Sequence[Sequence]] = ((),),\n exclude_dims: AbstractSet = frozenset(),\n vectorize: bool = False,\n join: str = \"exact\",\n dataset_join: str = \"exact\",\n dataset_fill_value: object = _NO_FILL_VALUE,\n keep_attrs: bool = False,\n kwargs: Mapping = None,\n dask: str = \"forbidden\",\n output_dtypes: Sequence = None,\n output_sizes: Mapping[Any, int] = None\n) -> Any:\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc._Apply_a_vectorized_fun_apply_ufunc._Apply_a_vectorized_fun": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc._Apply_a_vectorized_fun_apply_ufunc._Apply_a_vectorized_fun", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 766, "end_line": 965, "span_ids": ["apply_ufunc"], "tokens": 2229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_ufunc(\n func: Callable,\n *args: Any,\n input_core_dims: Sequence[Sequence] = None,\n output_core_dims: Optional[Sequence[Sequence]] = ((),),\n exclude_dims: AbstractSet = frozenset(),\n vectorize: bool = False,\n join: str = \"exact\",\n dataset_join: str = \"exact\",\n dataset_fill_value: object = _NO_FILL_VALUE,\n keep_attrs: bool = False,\n kwargs: Mapping = None,\n dask: str = \"forbidden\",\n output_dtypes: Sequence = None,\n output_sizes: Mapping[Any, int] = None\n) -> Any:\n \"\"\"Apply a vectorized function for unlabeled arrays on xarray objects.\n\n The function will be mapped over the data variable(s) of the input\n arguments using xarray's standard rules for labeled computation, including\n alignment, broadcasting, looping over GroupBy/Dataset variables, and\n merging of coordinates.\n\n Parameters\n ----------\n func : callable\n Function to call like ``func(*args, **kwargs)`` on unlabeled arrays\n (``.data``) that returns an array or tuple of arrays. If multiple\n arguments with non-matching dimensions are supplied, this function is\n expected to vectorize (broadcast) over axes of positional arguments in\n the style of NumPy universal functions [1]_ (if this is not the case,\n set ``vectorize=True``). If this function returns multiple outputs, you\n must set ``output_core_dims`` as well.\n *args : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars\n Mix of labeled and/or unlabeled arrays to which to apply the function.\n input_core_dims : Sequence[Sequence], optional\n List of the same length as ``args`` giving the list of core dimensions\n on each input argument that should not be broadcast. By default, we\n assume there are no core dimensions on any input arguments.\n\n For example, ``input_core_dims=[[], ['time']]`` indicates that all\n dimensions on the first argument and all dimensions other than 'time'\n on the second argument should be broadcast.\n\n Core dimensions are automatically moved to the last axes of input\n variables before applying ``func``, which facilitates using NumPy style\n generalized ufuncs [2]_.\n output_core_dims : List[tuple], optional\n List of the same length as the number of output arguments from\n ``func``, giving the list of core dimensions on each output that were\n not broadcast on the inputs. By default, we assume that ``func``\n outputs exactly one array, with axes corresponding to each broadcast\n dimension.\n\n Core dimensions are assumed to appear as the last dimensions of each\n output in the provided order.\n exclude_dims : set, optional\n Core dimensions on the inputs to exclude from alignment and\n broadcasting entirely. Any input coordinates along these dimensions\n will be dropped. Each excluded dimension must also appear in\n ``input_core_dims`` for at least one argument. Only dimensions listed\n here are allowed to change size between input and output objects.\n vectorize : bool, optional\n If True, then assume ``func`` only takes arrays defined over core\n dimensions as input and vectorize it automatically with\n :py:func:`numpy.vectorize`. This option exists for convenience, but is\n almost always slower than supplying a pre-vectorized function.\n Using this option requires NumPy version 1.12 or newer.\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n Method for joining the indexes of the passed objects along each\n dimension, and the variables of Dataset objects with mismatched\n data variables:\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': raise `ValueError` instead of aligning when indexes to be\n aligned are not equal\n dataset_join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n Method for joining variables of Dataset objects with mismatched\n data variables.\n\n - 'outer': take variables from both Dataset objects\n - 'inner': take only overlapped variables\n - 'left': take only variables from the first object\n - 'right': take only variables from the last object\n - 'exact': data variables on all Dataset objects must match exactly\n dataset_fill_value : optional\n Value used in place of missing variables on Dataset inputs when the\n datasets do not share the exact same ``data_vars``. Required if\n ``dataset_join not in {'inner', 'exact'}``, otherwise ignored.\n keep_attrs: boolean, Optional\n Whether to copy attributes from the first argument to the output.\n kwargs: dict, optional\n Optional keyword arguments passed directly on to call ``func``.\n dask: 'forbidden', 'allowed' or 'parallelized', optional\n How to handle applying to objects containing lazy data in the form of\n dask arrays:\n\n - 'forbidden' (default): raise an error if a dask array is encountered.\n - 'allowed': pass dask arrays directly on to ``func``.\n - 'parallelized': automatically parallelize ``func`` if any of the\n inputs are a dask array. If used, the ``output_dtypes`` argument must\n also be provided. Multiple output arguments are not yet supported.\n output_dtypes : list of dtypes, optional\n Optional list of output dtypes. Only used if dask='parallelized'.\n output_sizes : dict, optional\n Optional mapping from dimension names to sizes for outputs. Only used\n if dask='parallelized' and new dimensions (not found on inputs) appear\n on outputs.\n\n Returns\n -------\n Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or\n numpy.ndarray, the first type on that list to appear on an input.\n\n Examples\n --------\n\n Calculate the vector magnitude of two arguments:\n\n >>> def magnitude(a, b):\n ... func = lambda x, y: np.sqrt(x ** 2 + y ** 2)\n ... return xr.apply_ufunc(func, a, b)\n\n You can now apply ``magnitude()`` to ``xr.DataArray`` and ``xr.Dataset``\n objects, with automatically preserved dimensions and coordinates, e.g.,\n\n >>> array = xr.DataArray([1, 2, 3], coords=[('x', [0.1, 0.2, 0.3])])\n >>> magnitude(array, -array)\n \n array([1.414214, 2.828427, 4.242641])\n Coordinates:\n * x (x) float64 0.1 0.2 0.3\n\n Plain scalars, numpy arrays and a mix of these with xarray objects is also\n supported:\n\n >>> magnitude(4, 5)\n 5.0\n >>> magnitude(3, np.array([0, 4]))\n array([3., 5.])\n >>> magnitude(array, 0)\n \n array([1., 2., 3.])\n Coordinates:\n * x (x) float64 0.1 0.2 0.3\n\n Other examples of how you could use ``apply_ufunc`` to write functions to\n (very nearly) replicate existing xarray functionality:\n\n Compute the mean (``.mean``) over one dimension::\n\n def mean(obj, dim):\n # note: apply always moves core dimensions to the end\n return apply_ufunc(np.mean, obj,\n input_core_dims=[[dim]],\n kwargs={'axis': -1})\n\n Inner product over a specific dimension (like ``xr.dot``)::\n\n def _inner(x, y):\n result = np.matmul(x[..., np.newaxis, :], y[..., :, np.newaxis])\n return result[..., 0, 0]\n\n def inner_product(a, b, dim):\n return apply_ufunc(_inner, a, b, input_core_dims=[[dim], [dim]])\n\n Stack objects along a new dimension (like ``xr.concat``)::\n\n def stack(objects, dim, new_coord):\n # note: this version does not stack coordinates\n func = lambda *x: np.stack(x, axis=-1)\n result = apply_ufunc(func, *objects,\n output_core_dims=[[dim]],\n join='outer',\n dataset_fill_value=np.nan)\n result[dim] = new_coord\n return result\n\n If your function is not vectorized but can be applied only to core\n dimensions, you can use ``vectorize=True`` to turn into a vectorized\n function. This wraps :py:func:`numpy.vectorize`, so the operation isn't\n terribly fast. Here we'll use it to calculate the distance between\n empirical samples from two probability distributions, using a scipy\n function that needs to be applied to vectors::\n\n import scipy.stats\n\n def earth_mover_distance(first_samples,\n second_samples,\n dim='ensemble'):\n return apply_ufunc(scipy.stats.wasserstein_distance,\n first_samples, second_samples,\n input_core_dims=[[dim], [dim]],\n vectorize=True)\n\n Most of NumPy's builtin functions already broadcast their inputs\n appropriately for use in `apply`. You may find helper functions such as\n numpy.broadcast_arrays helpful in writing your function. `apply_ufunc` also\n works well with numba's vectorize and guvectorize. Further explanation with\n examples are provided in the xarray documentation [3].\n\n See also\n --------\n numpy.broadcast_arrays\n numba.vectorize\n numba.guvectorize\n\n References\n ----------\n .. [1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html\n .. [2] http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html\n .. [3] http://xarray.pydata.org/en/stable/computation.html#wrapping-custom-computation\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc.GroupBy_apply_ufunc.if_any_isinstance_a_Grou.else_.return.apply_array_ufunc_func_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_apply_ufunc.GroupBy_apply_ufunc.if_any_isinstance_a_Grou.else_.return.apply_array_ufunc_func_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 966, "end_line": 1049, "span_ids": ["apply_ufunc"], "tokens": 718}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_ufunc(\n func: Callable,\n *args: Any,\n input_core_dims: Sequence[Sequence] = None,\n output_core_dims: Optional[Sequence[Sequence]] = ((),),\n exclude_dims: AbstractSet = frozenset(),\n vectorize: bool = False,\n join: str = \"exact\",\n dataset_join: str = \"exact\",\n dataset_fill_value: object = _NO_FILL_VALUE,\n keep_attrs: bool = False,\n kwargs: Mapping = None,\n dask: str = \"forbidden\",\n output_dtypes: Sequence = None,\n output_sizes: Mapping[Any, int] = None\n) -> Any:\n from .groupby import GroupBy\n from .dataarray import DataArray\n from .variable import Variable\n\n if input_core_dims is None:\n input_core_dims = ((),) * (len(args))\n elif len(input_core_dims) != len(args):\n raise ValueError(\n \"input_core_dims must be None or a tuple with the length same to \"\n \"the number of arguments. Given input_core_dims: {}, \"\n \"number of args: {}.\".format(input_core_dims, len(args))\n )\n\n if kwargs is None:\n kwargs = {}\n\n signature = _UFuncSignature(input_core_dims, output_core_dims)\n\n if exclude_dims and not exclude_dims <= signature.all_core_dims:\n raise ValueError(\n \"each dimension in `exclude_dims` must also be a \"\n \"core dimension in the function signature\"\n )\n\n if kwargs:\n func = functools.partial(func, **kwargs)\n\n if vectorize:\n if signature.all_core_dims:\n func = np.vectorize(\n func, otypes=output_dtypes, signature=signature.to_gufunc_string()\n )\n else:\n func = np.vectorize(func, otypes=output_dtypes)\n\n variables_vfunc = functools.partial(\n apply_variable_ufunc,\n func,\n signature=signature,\n exclude_dims=exclude_dims,\n keep_attrs=keep_attrs,\n dask=dask,\n output_dtypes=output_dtypes,\n output_sizes=output_sizes,\n )\n\n if any(isinstance(a, GroupBy) for a in args):\n this_apply = functools.partial(\n apply_ufunc,\n func,\n input_core_dims=input_core_dims,\n output_core_dims=output_core_dims,\n exclude_dims=exclude_dims,\n join=join,\n dataset_join=dataset_join,\n dataset_fill_value=dataset_fill_value,\n keep_attrs=keep_attrs,\n dask=dask,\n )\n return apply_groupby_func(this_apply, *args)\n elif any(is_dict_like(a) for a in args):\n return apply_dataset_vfunc(\n variables_vfunc,\n *args,\n signature=signature,\n join=join,\n exclude_dims=exclude_dims,\n dataset_join=dataset_join,\n fill_value=dataset_fill_value,\n keep_attrs=keep_attrs\n )\n elif any(isinstance(a, DataArray) for a in args):\n return apply_dataarray_vfunc(\n variables_vfunc,\n *args,\n signature=signature,\n join=join,\n exclude_dims=exclude_dims,\n keep_attrs=keep_attrs\n )\n elif any(isinstance(a, Variable) for a in args):\n return variables_vfunc(*args)\n else:\n return apply_array_ufunc(func, *args, dask=dask)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_dot_dot.all_dims._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_dot_dot.all_dims._", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1052, "end_line": 1139, "span_ids": ["dot"], "tokens": 768}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dot(*arrays, dims=None, **kwargs):\n \"\"\"Generalized dot product for xarray objects. Like np.einsum, but\n provides a simpler interface based on array dimensions.\n\n Parameters\n ----------\n arrays: DataArray (or Variable) objects\n Arrays to compute.\n dims: str or tuple of strings, optional\n Which dimensions to sum over.\n If not speciified, then all the common dimensions are summed over.\n **kwargs: dict\n Additional keyword arguments passed to numpy.einsum or\n dask.array.einsum\n\n Returns\n -------\n dot: DataArray\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xp\n >>> da_a = xr.DataArray(np.arange(3 * 2).reshape(3, 2), dims=['a', 'b'])\n >>> da_b = xr.DataArray(np.arange(3 * 2 * 2).reshape(3, 2, 2),\n ... dims=['a', 'b', 'c'])\n >>> da_c = xr.DataArray(np.arange(2 * 3).reshape(2, 3), dims=['c', 'd'])\n\n >>> da_a\n \n array([[0, 1],\n [2, 3],\n [4, 5]])\n Dimensions without coordinates: a, b\n\n >>> da_b\n \n array([[[ 0, 1],\n [ 2, 3]],\n [[ 4, 5],\n [ 6, 7]],\n [[ 8, 9],\n [10, 11]]])\n Dimensions without coordinates: a, b, c\n\n >>> da_c\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Dimensions without coordinates: c, d\n\n >>> xr.dot(da_a, da_b, dims=['a', 'b'])\n \n array([110, 125])\n Dimensions without coordinates: c\n\n >>> xr.dot(da_a, da_b, dims=['a'])\n \n array([[40, 46],\n [70, 79]])\n Dimensions without coordinates: b, c\n\n >>> xr.dot(da_a, da_b, da_c, dims=['b', 'c'])\n \n array([[ 9, 14, 19],\n [ 93, 150, 207],\n [273, 446, 619]])\n Dimensions without coordinates: a, d\n\n \"\"\"\n from .dataarray import DataArray\n from .variable import Variable\n\n if any(not isinstance(arr, (Variable, DataArray)) for arr in arrays):\n raise TypeError(\n \"Only xr.DataArray and xr.Variable are supported.\"\n \"Given {}.\".format([type(arr) for arr in arrays])\n )\n\n if len(arrays) == 0:\n raise TypeError(\"At least one array should be given.\")\n\n if isinstance(dims, str):\n dims = (dims,)\n\n common_dims = set.intersection(*[set(arr.dims) for arr in arrays])\n all_dims = []\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_dot.for_arr_in_arrays__dot.return.result_transpose_d_for_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_dot.for_arr_in_arrays__dot.return.result_transpose_d_for_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1140, "end_line": 1180, "span_ids": ["dot"], "tokens": 436}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dot(*arrays, dims=None, **kwargs):\n # ... other code\n for arr in arrays:\n all_dims += [d for d in arr.dims if d not in all_dims]\n\n einsum_axes = \"abcdefghijklmnopqrstuvwxyz\"\n dim_map = {d: einsum_axes[i] for i, d in enumerate(all_dims)}\n\n if dims is None:\n # find dimensions that occur more than one times\n dim_counts = Counter()\n for arr in arrays:\n dim_counts.update(arr.dims)\n dims = tuple(d for d, c in dim_counts.items() if c > 1)\n\n dims = tuple(dims) # make dims a tuple\n\n # dimensions to be parallelized\n broadcast_dims = tuple(d for d in all_dims if d in common_dims and d not in dims)\n input_core_dims = [\n [d for d in arr.dims if d not in broadcast_dims] for arr in arrays\n ]\n output_core_dims = [tuple(d for d in all_dims if d not in dims + broadcast_dims)]\n\n # construct einsum subscripts, such as '...abc,...ab->...c'\n # Note: input_core_dims are always moved to the last position\n subscripts_list = [\n \"...\" + \"\".join([dim_map[d] for d in ds]) for ds in input_core_dims\n ]\n subscripts = \",\".join(subscripts_list)\n subscripts += \"->...\" + \"\".join([dim_map[d] for d in output_core_dims[0]])\n\n # subscripts should be passed to np.einsum as arg, not as kwargs. We need\n # to construct a partial function for apply_ufunc to work.\n func = functools.partial(duck_array_ops.einsum, subscripts, **kwargs)\n result = apply_ufunc(\n func,\n *arrays,\n input_core_dims=input_core_dims,\n output_core_dims=output_core_dims,\n dask=\"allowed\"\n )\n return result.transpose(*[d for d in all_dims if d in result.dims])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_where_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/computation.py_where_", "embedding": null, "metadata": {"file_path": "xarray/core/computation.py", "file_name": "computation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1183, "end_line": 1266, "span_ids": ["where"], "tokens": 883}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def where(cond, x, y):\n \"\"\"Return elements from `x` or `y` depending on `cond`.\n\n Performs xarray-like broadcasting across input arguments.\n\n Parameters\n ----------\n cond : scalar, array, Variable, DataArray or Dataset with boolean dtype\n When True, return values from `x`, otherwise returns values from `y`.\n x, y : scalar, array, Variable, DataArray or Dataset\n Values from which to choose. All dimension coordinates on these objects\n must be aligned with each other and with `cond`.\n\n Returns\n -------\n In priority order: Dataset, DataArray, Variable or array, whichever\n type appears as an input argument.\n\n Examples\n --------\n >>> import xarray as xr\n >>> import numpy as np\n >>> x = xr.DataArray(0.1 * np.arange(10), dims=['lat'],\n ... coords={'lat': np.arange(10)}, name='sst')\n >>> x\n \n array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n Coordinates:\n * lat (lat) int64 0 1 2 3 4 5 6 7 8 9\n\n >>> xr.where(x < 0.5, x, 100*x)\n \n array([ 0. , 0.1, 0.2, 0.3, 0.4, 50. , 60. , 70. , 80. , 90. ])\n Coordinates:\n * lat (lat) int64 0 1 2 3 4 5 6 7 8 9\n\n >>> >>> y = xr.DataArray(\n ... 0.1 * np.arange(9).reshape(3, 3),\n ... dims=[\"lat\", \"lon\"],\n ... coords={\"lat\": np.arange(3), \"lon\": 10 + np.arange(3)},\n ... name=\"sst\",\n ... )\n >>> y\n \n array([[0. , 0.1, 0.2],\n [0.3, 0.4, 0.5],\n [0.6, 0.7, 0.8]])\n Coordinates:\n * lat (lat) int64 0 1 2\n * lon (lon) int64 10 11 12\n\n >>> xr.where(y.lat < 1, y, -1)\n \n array([[ 0. , 0.1, 0.2],\n [-1. , -1. , -1. ],\n [-1. , -1. , -1. ]])\n Coordinates:\n * lat (lat) int64 0 1 2\n * lon (lon) int64 10 11 12\n\n >>> cond = xr.DataArray([True, False], dims=['x'])\n >>> x = xr.DataArray([1, 2], dims=['y'])\n >>> xr.where(cond, x, 0)\n \n array([[1, 2],\n [0, 0]])\n Dimensions without coordinates: x, y\n\n See also\n --------\n numpy.where : corresponding numpy function\n Dataset.where, DataArray.where : equivalent methods\n \"\"\"\n # alignment for three arguments is complicated, so don't support it yet\n return apply_ufunc(\n duck_array_ops.where,\n cond,\n x,\n y,\n join=\"exact\",\n dataset_join=\"exact\",\n dask=\"allowed\",\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py_pd_concat._Concatenate_xarray_obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py_pd_concat._Concatenate_xarray_obj", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 104, "span_ids": ["impl", "concat", "imports"], "tokens": 1001}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import pandas as pd\n\nfrom . import dtypes, utils\nfrom .alignment import align\nfrom .merge import _VALID_COMPAT, unique_variable\nfrom .variable import IndexVariable, Variable, as_variable\nfrom .variable import concat as concat_vars\n\n\ndef concat(\n objs,\n dim,\n data_vars=\"all\",\n coords=\"different\",\n compat=\"equals\",\n positions=None,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"Concatenate xarray objects along a new or existing dimension.\n\n Parameters\n ----------\n objs : sequence of Dataset and DataArray objects\n xarray objects to concatenate together. Each object is expected to\n consist of variables and coordinates with matching shapes except for\n along the concatenated dimension.\n dim : str or DataArray or pandas.Index\n Name of the dimension to concatenate along. This can either be a new\n dimension name, in which case it is added along axis=0, or an existing\n dimension name, in which case the location of the dimension is\n unchanged. If dimension is provided as a DataArray or Index, its name\n is used as the dimension to concatenate along and the values are added\n as a coordinate.\n data_vars : {'minimal', 'different', 'all' or list of str}, optional\n These data variables will be concatenated together:\n * 'minimal': Only data variables in which the dimension already\n appears are included.\n * 'different': Data variables which are not equal (ignoring\n attributes) across all datasets are also concatenated (as well as\n all for which dimension already appears). Beware: this option may\n load the data payload of data variables into memory if they are not\n already loaded.\n * 'all': All data variables will be concatenated.\n * list of str: The listed data variables will be concatenated, in\n addition to the 'minimal' data variables.\n If objects are DataArrays, data_vars must be 'all'.\n coords : {'minimal', 'different', 'all' or list of str}, optional\n These coordinate variables will be concatenated together:\n * 'minimal': Only coordinates in which the dimension already appears\n are included.\n * 'different': Coordinates which are not equal (ignoring attributes)\n across all datasets are also concatenated (as well as all for which\n dimension already appears). Beware: this option may load the data\n payload of coordinate variables into memory if they are not already\n loaded.\n * 'all': All coordinate variables will be concatenated, except\n those corresponding to other dimensions.\n * list of str: The listed coordinate variables will be concatenated,\n in addition to the 'minimal' coordinates.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional\n String indicating how to compare non-concatenated variables of the same name for\n potential conflicts. This is passed down to merge.\n\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n - 'override': skip comparing and pick variable from first dataset\n positions : None or list of integer arrays, optional\n List of integer arrays which specifies the integer positions to which\n to assign each dataset along the concatenated dimension. If not\n supplied, objects are concatenated in the provided order.\n fill_value : scalar, optional\n Value to use for newly missing values\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n String indicating how to combine differing indexes\n (excluding dim) in objects\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n\n indexers, mode, concat_over : deprecated\n\n Returns\n -------\n concatenated : type of objs\n\n See also\n --------\n merge\n auto_combine\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py_concat._TODO_add_ignore_index__concat.return.f_objs_dim_data_vars_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py_concat._TODO_add_ignore_index__concat.return.f_objs_dim_data_vars_c", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 131, "span_ids": ["concat"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def concat(\n objs,\n dim,\n data_vars=\"all\",\n coords=\"different\",\n compat=\"equals\",\n positions=None,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n # TODO: add ignore_index arguments copied from pandas.concat\n # TODO: support concatenating scalar coordinates even if the concatenated\n # dimension already exists\n from .dataset import Dataset\n from .dataarray import DataArray\n\n try:\n first_obj, objs = utils.peek_at(objs)\n except StopIteration:\n raise ValueError(\"must supply at least one object to concatenate\")\n\n if compat not in _VALID_COMPAT:\n raise ValueError(\n \"compat=%r invalid: must be 'broadcast_equals', 'equals', 'identical', 'no_conflicts' or 'override'\"\n % compat\n )\n\n if isinstance(first_obj, DataArray):\n f = _dataarray_concat\n elif isinstance(first_obj, Dataset):\n f = _dataset_concat\n else:\n raise TypeError(\n \"can only concatenate xarray Dataset and DataArray \"\n \"objects, got %s\" % type(first_obj)\n )\n return f(objs, dim, data_vars, coords, compat, positions, fill_value, join)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_dim_coord__calc_concat_dim_coord.return.dim_coord": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_dim_coord__calc_concat_dim_coord.return.dim_coord", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 155, "span_ids": ["_calc_concat_dim_coord"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _calc_concat_dim_coord(dim):\n \"\"\"\n Infer the dimension name and 1d coordinate variable (if appropriate)\n for concatenating along the new dimension.\n \"\"\"\n from .dataarray import DataArray\n\n if isinstance(dim, str):\n coord = None\n elif not isinstance(dim, (DataArray, Variable)):\n dim_name = getattr(dim, \"name\", None)\n if dim_name is None:\n dim_name = \"concat_dim\"\n coord = IndexVariable(dim_name, dim)\n dim = dim_name\n elif not isinstance(dim, DataArray):\n coord = as_variable(dim).to_index_variable()\n dim, = coord.dims\n else:\n coord = dim\n dim, = coord.dims\n return dim, coord", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_over__calc_concat_over.for_ds_in_datasets_.concat_dim_lengths_append": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_over__calc_concat_over.for_ds_in_datasets_.concat_dim_lengths_append", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 158, "end_line": 179, "span_ids": ["_calc_concat_over"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _calc_concat_over(datasets, dim, dim_names, data_vars, coords, compat):\n \"\"\"\n Determine which dataset variables need to be concatenated in the result,\n \"\"\"\n # Return values\n concat_over = set()\n equals = {}\n\n if dim in dim_names:\n concat_over_existing_dim = True\n concat_over.add(dim)\n else:\n concat_over_existing_dim = False\n\n concat_dim_lengths = []\n for ds in datasets:\n if concat_over_existing_dim:\n if dim not in ds.dims:\n if dim in ds:\n ds = ds.set_coords(dim)\n concat_over.update(k for k, v in ds.variables.items() if dim in v.dims)\n concat_dim_lengths.append(ds.dims.get(dim, 1))\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_over.process_subset_opt__calc_concat_over.return.concat_over_equals_conc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__calc_concat_over.process_subset_opt__calc_concat_over.return.concat_over_equals_conc", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 181, "end_line": 238, "span_ids": ["_calc_concat_over"], "tokens": 507}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _calc_concat_over(datasets, dim, dim_names, data_vars, coords, compat):\n # ... other code\n\n def process_subset_opt(opt, subset):\n if isinstance(opt, str):\n if opt == \"different\":\n if compat == \"override\":\n raise ValueError(\n \"Cannot specify both %s='different' and compat='override'.\"\n % subset\n )\n # all nonindexes that are not the same in each dataset\n for k in getattr(datasets[0], subset):\n if k not in concat_over:\n # Compare the variable of all datasets vs. the one\n # of the first dataset. Perform the minimum amount of\n # loads in order to avoid multiple loads from disk\n # while keeping the RAM footprint low.\n v_lhs = datasets[0].variables[k].load()\n # We'll need to know later on if variables are equal.\n computed = []\n for ds_rhs in datasets[1:]:\n v_rhs = ds_rhs.variables[k].compute()\n computed.append(v_rhs)\n if not getattr(v_lhs, compat)(v_rhs):\n concat_over.add(k)\n equals[k] = False\n # computed variables are not to be re-computed\n # again in the future\n for ds, v in zip(datasets[1:], computed):\n ds.variables[k].data = v.data\n break\n else:\n equals[k] = True\n\n elif opt == \"all\":\n concat_over.update(\n set(getattr(datasets[0], subset)) - set(datasets[0].dims)\n )\n elif opt == \"minimal\":\n pass\n else:\n raise ValueError(\"unexpected value for %s: %s\" % (subset, opt))\n else:\n invalid_vars = [k for k in opt if k not in getattr(datasets[0], subset)]\n if invalid_vars:\n if subset == \"coords\":\n raise ValueError(\n \"some variables in coords are not coordinates on \"\n \"the first dataset: %s\" % (invalid_vars,)\n )\n else:\n raise ValueError(\n \"some variables in data_vars are not data variables \"\n \"on the first dataset: %s\" % (invalid_vars,)\n )\n concat_over.update(opt)\n\n process_subset_opt(data_vars, \"data_vars\")\n process_subset_opt(coords, \"coords\")\n return concat_over, equals, concat_dim_lengths", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__determine_dimensional_c__parse_datasets.return.dim_coords_dims_sizes_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__determine_dimensional_c__parse_datasets.return.dim_coords_dims_sizes_a", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 241, "end_line": 260, "span_ids": ["_parse_datasets", "_calc_concat_over"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# determine dimensional coordinate names and a dict mapping name to DataArray\ndef _parse_datasets(datasets):\n\n dims = set()\n all_coord_names = set()\n data_vars = set() # list of data_vars\n dim_coords = {} # maps dim name to variable\n dims_sizes = {} # shared dimension sizes to expand variables\n\n for ds in datasets:\n dims_sizes.update(ds.dims)\n all_coord_names.update(ds.coords)\n data_vars.update(ds.data_vars)\n\n for dim in set(ds.dims) - dims:\n if dim not in dim_coords:\n dim_coords[dim] = ds.coords[dim].variable\n dims = dims | set(ds.dims)\n\n return dim_coords, dims_sizes, all_coord_names, data_vars", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat__dataset_concat._shared_dimension_sizes_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat__dataset_concat._shared_dimension_sizes_", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 263, "end_line": 343, "span_ids": ["_dataset_concat"], "tokens": 636}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dataset_concat(\n datasets,\n dim,\n data_vars,\n coords,\n compat,\n positions,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n \"\"\"\n Concatenate a sequence of datasets along a new or existing dimension\n \"\"\"\n from .dataset import Dataset\n\n dim, coord = _calc_concat_dim_coord(dim)\n # Make sure we're working on a copy (we'll be loading variables)\n datasets = [ds.copy() for ds in datasets]\n datasets = align(\n *datasets, join=join, copy=False, exclude=[dim], fill_value=fill_value\n )\n\n dim_coords, dims_sizes, coord_names, data_names = _parse_datasets(datasets)\n dim_names = set(dim_coords)\n unlabeled_dims = dim_names - coord_names\n\n both_data_and_coords = coord_names & data_names\n if both_data_and_coords:\n raise ValueError(\n \"%r is a coordinate in some datasets but not others.\" % both_data_and_coords\n )\n # we don't want the concat dimension in the result dataset yet\n dim_coords.pop(dim, None)\n dims_sizes.pop(dim, None)\n\n # case where concat dimension is a coordinate or data_var but not a dimension\n if (dim in coord_names or dim in data_names) and dim not in dim_names:\n datasets = [ds.expand_dims(dim) for ds in datasets]\n\n # determine which variables to concatentate\n concat_over, equals, concat_dim_lengths = _calc_concat_over(\n datasets, dim, dim_names, data_vars, coords, compat\n )\n\n # determine which variables to merge, and then merge them according to compat\n variables_to_merge = (coord_names | data_names) - concat_over - dim_names\n\n result_vars = {}\n if variables_to_merge:\n to_merge = {var: [] for var in variables_to_merge}\n\n for ds in datasets:\n absent_merge_vars = variables_to_merge - set(ds.variables)\n if absent_merge_vars:\n raise ValueError(\n \"variables %r are present in some datasets but not others. \"\n % absent_merge_vars\n )\n\n for var in variables_to_merge:\n to_merge[var].append(ds.variables[var])\n\n for var in variables_to_merge:\n result_vars[var] = unique_variable(\n var, to_merge[var], compat=compat, equals=equals.get(var, None)\n )\n else:\n result_vars = {}\n result_vars.update(dim_coords)\n\n # assign attrs and encoding from first dataset\n result_attrs = datasets[0].attrs\n result_encoding = datasets[0].encoding\n\n # check that global attributes are fixed across all datasets if necessary\n for ds in datasets[1:]:\n if compat == \"identical\" and not utils.dict_equiv(ds.attrs, result_attrs):\n raise ValueError(\"Dataset global attributes not equal.\")\n\n # we've already verified everything is consistent; now, calculate\n # shared dimension sizes so we can expand the necessary variables\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat.ensure_common_dims__dataset_concat.ensure_common_dims.for_var_dim_len_in_zip_v.yield_var": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat.ensure_common_dims__dataset_concat.ensure_common_dims.for_var_dim_len_in_zip_v.yield_var", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 344, "end_line": 355, "span_ids": ["_dataset_concat"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dataset_concat(\n datasets,\n dim,\n data_vars,\n coords,\n compat,\n positions,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n # ... other code\n def ensure_common_dims(vars):\n # ensure each variable with the given name shares the same\n # dimensions and the same shape for all of them except along the\n # concat dimension\n common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n if dim not in common_dims:\n common_dims = (dim,) + common_dims\n for var, dim_len in zip(vars, concat_dim_lengths):\n if var.dims != common_dims:\n common_shape = tuple(dims_sizes.get(d, dim_len) for d in common_dims)\n var = var.set_dims(common_dims, common_shape)\n yield var\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat._stack_up_each_variable___dataset_concat.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataset_concat._stack_up_each_variable___dataset_concat.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 357, "end_line": 385, "span_ids": ["_dataset_concat"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dataset_concat(\n datasets,\n dim,\n data_vars,\n coords,\n compat,\n positions,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n\n # stack up each variable to fill-out the dataset (in order)\n # n.b. this loop preserves variable order, needed for groupby.\n for k in datasets[0].variables:\n if k in concat_over:\n try:\n vars = ensure_common_dims([ds.variables[k] for ds in datasets])\n except KeyError:\n raise ValueError(\"%r is not present in all datasets.\" % k)\n combined = concat_vars(vars, dim, positions)\n assert isinstance(combined, Variable)\n result_vars[k] = combined\n\n result = Dataset(result_vars, attrs=result_attrs)\n absent_coord_names = coord_names - set(result.variables)\n if absent_coord_names:\n raise ValueError(\n \"Variables %r are coordinates in some datasets but not others.\"\n % absent_coord_names\n )\n result = result.set_coords(coord_names)\n result.encoding = result_encoding\n\n result = result.drop(unlabeled_dims, errors=\"ignore\")\n\n if coord is not None:\n # add concat dimension last to ensure that its in the final Dataset\n result[coord.name] = coord\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataarray_concat_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/concat.py__dataarray_concat_", "embedding": null, "metadata": {"file_path": "xarray/core/concat.py", "file_name": "concat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 388, "end_line": 427, "span_ids": ["_dataarray_concat"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dataarray_concat(\n arrays,\n dim,\n data_vars,\n coords,\n compat,\n positions,\n fill_value=dtypes.NA,\n join=\"outer\",\n):\n arrays = list(arrays)\n\n if data_vars != \"all\":\n raise ValueError(\n \"data_vars is not a valid argument when concatenating DataArray objects\"\n )\n\n datasets = []\n for n, arr in enumerate(arrays):\n if n == 0:\n name = arr.name\n elif name != arr.name:\n if compat == \"identical\":\n raise ValueError(\"array names not identical\")\n else:\n arr = arr.rename(name)\n datasets.append(arr._to_temp_dataset())\n\n ds = _dataset_concat(\n datasets,\n dim,\n data_vars,\n coords,\n compat,\n positions,\n fill_value=fill_value,\n join=join,\n )\n return arrays[0]._from_temp_dataset(ds, name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_from_contextlib_import_co__THIS_ARRAY.ReprObject_this_array_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_from_contextlib_import_co__THIS_ARRAY.ReprObject_this_array_", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 30, "span_ids": ["imports:9", "impl", "imports", "impl:2"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from contextlib import contextmanager\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Dict,\n Hashable,\n Iterator,\n Mapping,\n Sequence,\n Set,\n Tuple,\n Union,\n cast,\n)\n\nimport pandas as pd\n\nfrom . import formatting, indexing\nfrom .indexes import Indexes\nfrom .merge import merge_coordinates_without_align, merge_coords\nfrom .utils import Frozen, ReprObject, either_dict_or_kwargs\nfrom .variable import Variable\n\nif TYPE_CHECKING:\n from .dataarray import DataArray\n from .dataset import Dataset\n\n# Used as the key corresponding to a DataArray's variable when converting\n# arbitrary DataArray objects to datasets\n_THIS_ARRAY = ReprObject(\"\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates_Coordinates.to_dataset.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates_Coordinates.to_dataset.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 77, "span_ids": ["Coordinates._names", "Coordinates.dims", "Coordinates.__repr__", "Coordinates.to_dataset", "Coordinates._update_coords", "Coordinates.__contains__", "Coordinates.__getitem__", "Coordinates.indexes", "Coordinates.__setitem__", "Coordinates", "Coordinates.__len__", "Coordinates.variables", "Coordinates.__iter__"], "tokens": 289}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coordinates(Mapping[Hashable, \"DataArray\"]):\n __slots__ = ()\n\n def __getitem__(self, key: Hashable) -> \"DataArray\":\n raise NotImplementedError()\n\n def __setitem__(self, key: Hashable, value: Any) -> None:\n self.update({key: value})\n\n @property\n def _names(self) -> Set[Hashable]:\n raise NotImplementedError()\n\n @property\n def dims(self) -> Union[Mapping[Hashable, int], Tuple[Hashable, ...]]:\n raise NotImplementedError()\n\n @property\n def indexes(self) -> Indexes:\n return self._data.indexes # type: ignore\n\n @property\n def variables(self):\n raise NotImplementedError()\n\n def _update_coords(self, coords, indexes):\n raise NotImplementedError()\n\n def __iter__(self) -> Iterator[\"Hashable\"]:\n # needs to be in the same order as the dataset variables\n for k in self.variables:\n if k in self._names:\n yield k\n\n def __len__(self) -> int:\n return len(self._names)\n\n def __contains__(self, key: Hashable) -> bool:\n return key in self._names\n\n def __repr__(self) -> str:\n return formatting.coords_repr(self)\n\n def to_dataset(self) -> \"Dataset\":\n raise NotImplementedError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.to_index_Coordinates.to_index.if_len_ordered_dims_0.else_.return.pd_MultiIndex_from_produc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.to_index_Coordinates.to_index.if_len_ordered_dims_0.else_.return.pd_MultiIndex_from_produc", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 79, "end_line": 111, "span_ids": ["Coordinates.to_index"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coordinates(Mapping[Hashable, \"DataArray\"]):\n\n def to_index(self, ordered_dims: Sequence[Hashable] = None) -> pd.Index:\n \"\"\"Convert all index coordinates into a :py:class:`pandas.Index`.\n\n Parameters\n ----------\n ordered_dims : sequence of hashable, optional\n Possibly reordered version of this object's dimensions indicating\n the order in which dimensions should appear on the result.\n\n Returns\n -------\n pandas.Index\n Index subclass corresponding to the outer-product of all dimension\n coordinates. This will be a MultiIndex if this object is has more\n than more dimension.\n \"\"\"\n if ordered_dims is None:\n ordered_dims = list(self.dims)\n elif set(ordered_dims) != set(self.dims):\n raise ValueError(\n \"ordered_dims must match dims, but does not: \"\n \"{} vs {}\".format(ordered_dims, self.dims)\n )\n\n if len(ordered_dims) == 0:\n raise ValueError(\"no valid index for a 0-dimensional object\")\n elif len(ordered_dims) == 1:\n (dim,) = ordered_dims\n return self._data.get_index(dim) # type: ignore\n else:\n indexes = [self._data.get_index(k) for k in ordered_dims] # type: ignore\n names = list(ordered_dims)\n return pd.MultiIndex.from_product(indexes, names=names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.update_Coordinates._merge_raw.return.variables_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.update_Coordinates._merge_raw.return.variables_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 113, "end_line": 127, "span_ids": ["Coordinates._merge_raw", "Coordinates.update"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coordinates(Mapping[Hashable, \"DataArray\"]):\n\n def update(self, other: Mapping[Hashable, Any]) -> None:\n other_vars = getattr(other, \"variables\", other)\n coords, indexes = merge_coords(\n [self.variables, other_vars], priority_arg=1, indexes=self.indexes\n )\n self._update_coords(coords, indexes)\n\n def _merge_raw(self, other):\n \"\"\"For use with binary arithmetic.\"\"\"\n if other is None:\n variables = dict(self.variables)\n indexes = dict(self.indexes)\n else:\n variables, indexes = merge_coordinates_without_align([self, other])\n return variables, indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates._merge_inplace_Coordinates._merge_inplace.if_other_is_None_.else_.self__update_coords_varia": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates._merge_inplace_Coordinates._merge_inplace.if_other_is_None_.else_.self__update_coords_varia", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 129, "end_line": 144, "span_ids": ["Coordinates._merge_inplace"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coordinates(Mapping[Hashable, \"DataArray\"]):\n\n @contextmanager\n def _merge_inplace(self, other):\n \"\"\"For use with in-place binary arithmetic.\"\"\"\n if other is None:\n yield\n else:\n # don't include indexes in prioritized, because we didn't align\n # first and we want indexes to be checked\n prioritized = {\n k: (v, None) for k, v in self.variables.items() if k not in self.indexes\n }\n variables, indexes = merge_coordinates_without_align(\n [self, other], prioritized\n )\n yield\n self._update_coords(variables, indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.merge_Coordinates.merge.return.merged": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_Coordinates.merge_Coordinates.merge.return.merged", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 146, "end_line": 181, "span_ids": ["Coordinates.merge"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coordinates(Mapping[Hashable, \"DataArray\"]):\n\n def merge(self, other: \"Coordinates\") -> \"Dataset\":\n \"\"\"Merge two sets of coordinates to create a new Dataset\n\n The method implements the logic used for joining coordinates in the\n result of a binary operation performed on xarray objects:\n\n - If two index coordinates conflict (are not equal), an exception is\n raised. You must align your data before passing it to this method.\n - If an index coordinate and a non-index coordinate conflict, the non-\n index coordinate is dropped.\n - If two non-index coordinates conflict, both are dropped.\n\n Parameters\n ----------\n other : DatasetCoordinates or DataArrayCoordinates\n The coordinates from another dataset or data array.\n\n Returns\n -------\n merged : Dataset\n A new Dataset with merged coordinates.\n \"\"\"\n from .dataset import Dataset\n\n if other is None:\n return self.to_dataset()\n\n if not isinstance(other, Coordinates):\n other = Dataset(coords=other).coords\n\n coords, indexes = merge_coordinates_without_align([self, other])\n coord_names = set(coords)\n merged = Dataset._construct_direct(\n variables=coords, coord_names=coord_names, indexes=indexes\n )\n return merged", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DatasetCoordinates_DatasetCoordinates.to_dataset.return.self__data__copy_listed_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DatasetCoordinates_DatasetCoordinates.to_dataset.return.self__data__copy_listed_s", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 184, "end_line": 219, "span_ids": ["DatasetCoordinates.dims", "DatasetCoordinates.__getitem__", "DatasetCoordinates", "DatasetCoordinates._names", "DatasetCoordinates.to_dataset", "DatasetCoordinates.variables"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetCoordinates(Coordinates):\n \"\"\"Dictionary like container for Dataset coordinates.\n\n Essentially an immutable dictionary with keys given by the array's\n dimensions and the values given by the corresponding xarray.Coordinate\n objects.\n \"\"\"\n\n __slots__ = (\"_data\",)\n\n def __init__(self, dataset: \"Dataset\"):\n self._data = dataset\n\n @property\n def _names(self) -> Set[Hashable]:\n return self._data._coord_names\n\n @property\n def dims(self) -> Mapping[Hashable, int]:\n return self._data.dims\n\n @property\n def variables(self) -> Mapping[Hashable, Variable]:\n return Frozen(\n {k: v for k, v in self._data.variables.items() if k in self._names}\n )\n\n def __getitem__(self, key: Hashable) -> \"DataArray\":\n if key in self._data.data_vars:\n raise KeyError(key)\n return cast(\"DataArray\", self._data[key])\n\n def to_dataset(self) -> \"Dataset\":\n \"\"\"Convert these coordinates into a new Dataset\n \"\"\"\n return self._data._copy_listed(self._names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DatasetCoordinates._update_coords_DatasetCoordinates._ipython_key_completions_.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DatasetCoordinates._update_coords_DatasetCoordinates._ipython_key_completions_.return._", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 221, "end_line": 258, "span_ids": ["DatasetCoordinates._ipython_key_completions_", "DatasetCoordinates._update_coords", "DatasetCoordinates.__delitem__"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetCoordinates(Coordinates):\n\n def _update_coords(\n self, coords: Dict[Hashable, Variable], indexes: Mapping[Hashable, pd.Index]\n ) -> None:\n from .dataset import calculate_dimensions\n\n variables = self._data._variables.copy()\n variables.update(coords)\n\n # check for inconsistent state *before* modifying anything in-place\n dims = calculate_dimensions(variables)\n new_coord_names = set(coords)\n for dim, size in dims.items():\n if dim in variables:\n new_coord_names.add(dim)\n\n self._data._variables = variables\n self._data._coord_names.update(new_coord_names)\n self._data._dims = dims\n\n # TODO(shoyer): once ._indexes is always populated by a dict, modify\n # it to update inplace instead.\n original_indexes = dict(self._data.indexes)\n original_indexes.update(indexes)\n self._data._indexes = original_indexes\n\n def __delitem__(self, key: Hashable) -> None:\n if key in self:\n del self._data[key]\n else:\n raise KeyError(key)\n\n def _ipython_key_completions_(self):\n \"\"\"Provide method for the key-autocompletions in IPython. \"\"\"\n return [\n key\n for key in self._data._ipython_key_completions_()\n if key not in self._data.data_vars\n ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates_DataArrayCoordinates.__getitem__.return.self__data__getitem_coord": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates_DataArrayCoordinates.__getitem__.return.self__data__getitem_coord", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 261, "end_line": 282, "span_ids": ["DataArrayCoordinates._names", "DataArrayCoordinates.__getitem__", "DataArrayCoordinates", "DataArrayCoordinates.dims"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayCoordinates(Coordinates):\n \"\"\"Dictionary like container for DataArray coordinates.\n\n Essentially a dict with keys given by the array's\n dimensions and the values given by corresponding DataArray objects.\n \"\"\"\n\n __slots__ = (\"_data\",)\n\n def __init__(self, dataarray: \"DataArray\"):\n self._data = dataarray\n\n @property\n def dims(self) -> Tuple[Hashable, ...]:\n return self._data.dims\n\n @property\n def _names(self) -> Set[Hashable]:\n return set(self._data._coords)\n\n def __getitem__(self, key: Hashable) -> \"DataArray\":\n return self._data._getitem_coord(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates._update_coords_DataArrayCoordinates._update_coords.self._data._indexes.original_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates._update_coords_DataArrayCoordinates._update_coords.self._data._indexes.original_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 284, "end_line": 302, "span_ids": ["DataArrayCoordinates._update_coords"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayCoordinates(Coordinates):\n\n def _update_coords(\n self, coords: Dict[Hashable, Variable], indexes: Mapping[Hashable, pd.Index]\n ) -> None:\n from .dataset import calculate_dimensions\n\n coords_plus_data = coords.copy()\n coords_plus_data[_THIS_ARRAY] = self._data.variable\n dims = calculate_dimensions(coords_plus_data)\n if not set(dims) <= set(self.dims):\n raise ValueError(\n \"cannot add coordinates with new dimensions to \" \"a DataArray\"\n )\n self._data._coords = coords\n\n # TODO(shoyer): once ._indexes is always populated by a dict, modify\n # it to update inplace instead.\n original_indexes = dict(self._data.indexes)\n original_indexes.update(indexes)\n self._data._indexes = original_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates.variables_DataArrayCoordinates._ipython_key_completions_.return.self__data__ipython_key_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_DataArrayCoordinates.variables_DataArrayCoordinates._ipython_key_completions_.return.self__data__ipython_key_c", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 304, "end_line": 319, "span_ids": ["DataArrayCoordinates._ipython_key_completions_", "DataArrayCoordinates.to_dataset", "DataArrayCoordinates.variables", "DataArrayCoordinates.__delitem__"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayCoordinates(Coordinates):\n\n @property\n def variables(self):\n return Frozen(self._data._coords)\n\n def to_dataset(self) -> \"Dataset\":\n from .dataset import Dataset\n\n coords = {k: v.copy(deep=False) for k, v in self._data._coords.items()}\n return Dataset._from_vars_and_coord_names(coords, set(coords))\n\n def __delitem__(self, key: Hashable) -> None:\n del self._data._coords[key]\n\n def _ipython_key_completions_(self):\n \"\"\"Provide method for the key-autocompletions in IPython. \"\"\"\n return self._data._ipython_key_completions_()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_LevelCoordinatesSource_LevelCoordinatesSource.__len__.return.len_self__data__level_coo": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_LevelCoordinatesSource_LevelCoordinatesSource.__len__.return.len_self__data__level_coo", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 322, "end_line": 342, "span_ids": ["LevelCoordinatesSource.__iter__", "LevelCoordinatesSource.__getitem__", "LevelCoordinatesSource.__len__", "LevelCoordinatesSource"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LevelCoordinatesSource(Mapping[Hashable, Any]):\n \"\"\"Iterator for MultiIndex level coordinates.\n\n Used for attribute style lookup with AttrAccessMixin. Not returned directly\n by any public methods.\n \"\"\"\n\n __slots__ = (\"_data\",)\n\n def __init__(self, data_object: \"Union[DataArray, Dataset]\"):\n self._data = data_object\n\n def __getitem__(self, key):\n # not necessary -- everything here can already be found in coords.\n raise KeyError()\n\n def __iter__(self) -> Iterator[Hashable]:\n return iter(self._data._level_coords)\n\n def __len__(self) -> int:\n return len(self._data._level_coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_assert_coordinate_consistent_assert_coordinate_consistent.for_k_in_obj_dims_.if_k_in_coords_and_k_in_o.if_not_coords_k_equals_o.raise_IndexError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_assert_coordinate_consistent_assert_coordinate_consistent.for_k_in_obj_dims_.if_k_in_coords_and_k_in_o.if_not_coords_k_equals_o.raise_IndexError_", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 345, "end_line": 362, "span_ids": ["assert_coordinate_consistent"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_coordinate_consistent(\n obj: Union[\"DataArray\", \"Dataset\"], coords: Mapping[Hashable, Variable]\n) -> None:\n \"\"\"Make sure the dimension coordinate of obj is consistent with coords.\n\n obj: DataArray or Dataset\n coords: Dict-like of variables\n \"\"\"\n for k in obj.dims:\n # make sure there are no conflict in dimension coordinates\n if k in coords and k in obj.coords:\n if not coords[k].equals(obj[k].variable):\n raise IndexError(\n \"dimension coordinate {!r} conflicts between \"\n \"indexed and indexing objects:\\n{}\\nvs.\\n{}\".format(\n k, obj[k], coords[k]\n )\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_remap_label_indexers_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/coordinates.py_remap_label_indexers_", "embedding": null, "metadata": {"file_path": "xarray/core/coordinates.py", "file_name": "coordinates.py", "file_type": "text/x-python", "category": "implementation", "start_line": 365, "end_line": 404, "span_ids": ["remap_label_indexers"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def remap_label_indexers(\n obj: Union[\"DataArray\", \"Dataset\"],\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance=None,\n **indexers_kwargs: Any\n) -> Tuple[dict, dict]: # TODO more precise return type after annotations in indexing\n \"\"\"Remap indexers from obj.coords.\n If indexer is an instance of DataArray and it has coordinate, then this coordinate\n will be attached to pos_indexers.\n\n Returns\n -------\n pos_indexers: Same type of indexers.\n np.ndarray or Variable or DataArray\n new_indexes: mapping of new dimensional-coordinate.\n \"\"\"\n from .dataarray import DataArray\n\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"remap_label_indexers\")\n\n v_indexers = {\n k: v.variable.data if isinstance(v, DataArray) else v\n for k, v in indexers.items()\n }\n\n pos_indexers, new_indexes = indexing.remap_label_indexers(\n obj, v_indexers, method=method, tolerance=tolerance\n )\n # attach indexer's coordinate to pos_indexers\n for k, v in indexers.items():\n if isinstance(v, Variable):\n pos_indexers[k] = Variable(v.dims, pos_indexers[k])\n elif isinstance(v, DataArray):\n # drop coordinates found in indexers since .sel() already\n # ensures alignments\n coords = {k: var for k, var in v._coords.items() if k not in indexers}\n pos_indexers[k] = DataArray(pos_indexers[k], coords=coords, dims=v.dims)\n return pos_indexers, new_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_compat.py_from_distutils_version_im_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_compat.py_from_distutils_version_im_", "embedding": null, "metadata": {"file_path": "xarray/core/dask_array_compat.py", "file_name": "dask_array_compat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 92, "span_ids": ["imports:5", "impl", "if_LooseVersion_dask_vers.else_.meta_from_array:2", "imports", "if_LooseVersion_dask_vers.else_.meta_from_array", "if_LooseVersion_dask_vers.else_.meta_from_array:docstring"], "tokens": 673}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from distutils.version import LooseVersion\n\nimport dask.array as da\nimport numpy as np\nfrom dask import __version__ as dask_version\n\nif LooseVersion(dask_version) >= LooseVersion(\"2.0.0\"):\n meta_from_array = da.utils.meta_from_array\nelse:\n # Copied from dask v2.4.0\n # Used under the terms of Dask's license, see licenses/DASK_LICENSE.\n import numbers\n\n def meta_from_array(x, ndim=None, dtype=None):\n \"\"\" Normalize an array to appropriate meta object\n\n Parameters\n ----------\n x: array-like, callable\n Either an object that looks sufficiently like a Numpy array,\n or a callable that accepts shape and dtype keywords\n ndim: int\n Number of dimensions of the array\n dtype: Numpy dtype\n A valid input for ``np.dtype``\n\n Returns\n -------\n array-like with zero elements of the correct dtype\n \"\"\"\n # If using x._meta, x must be a Dask Array, some libraries (e.g. zarr)\n # implement a _meta attribute that are incompatible with Dask Array._meta\n if hasattr(x, \"_meta\") and isinstance(x, da.Array):\n x = x._meta\n\n if dtype is None and x is None:\n raise ValueError(\"You must specify the meta or dtype of the array\")\n\n if np.isscalar(x):\n x = np.array(x)\n\n if x is None:\n x = np.ndarray\n\n if isinstance(x, type):\n x = x(shape=(0,) * (ndim or 0), dtype=dtype)\n\n if (\n not hasattr(x, \"shape\")\n or not hasattr(x, \"dtype\")\n or not isinstance(x.shape, tuple)\n ):\n return x\n\n if isinstance(x, list) or isinstance(x, tuple):\n ndims = [\n 0\n if isinstance(a, numbers.Number)\n else a.ndim\n if hasattr(a, \"ndim\")\n else len(a)\n for a in x\n ]\n a = [a if nd == 0 else meta_from_array(a, nd) for a, nd in zip(x, ndims)]\n return a if isinstance(x, list) else tuple(x)\n\n if ndim is None:\n ndim = x.ndim\n\n try:\n meta = x[tuple(slice(0, 0, None) for _ in range(x.ndim))]\n if meta.ndim != ndim:\n if ndim > x.ndim:\n meta = meta[\n (Ellipsis,) + tuple(None for _ in range(ndim - meta.ndim))\n ]\n meta = meta[tuple(slice(0, 0, None) for _ in range(meta.ndim))]\n elif ndim == 0:\n meta = meta.sum()\n else:\n meta = meta.reshape((0,) * ndim)\n except Exception:\n meta = np.empty((0,) * ndim, dtype=dtype or x.dtype)\n\n if np.isscalar(meta):\n meta = np.array(meta)\n\n if dtype and meta.dtype != dtype:\n meta = meta.astype(dtype)\n\n return meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_ops.py_np_dask_rolling_wrapper.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_ops.py_np_dask_rolling_wrapper.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/dask_array_ops.py", "file_name": "dask_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 27, "span_ids": ["dask_rolling_wrapper", "imports"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom . import dtypes, nputils\n\n\ndef dask_rolling_wrapper(moving_func, a, window, min_count=None, axis=-1):\n \"\"\"Wrapper to apply bottleneck moving window funcs on dask arrays\n \"\"\"\n import dask.array as da\n\n dtype, fill_value = dtypes.maybe_promote(a.dtype)\n a = a.astype(dtype)\n # inputs for overlap\n if axis < 0:\n axis = a.ndim + axis\n depth = {d: 0 for d in range(a.ndim)}\n depth[axis] = (window + 1) // 2\n boundary = {d: fill_value for d in range(a.ndim)}\n # Create overlap array.\n ag = da.overlap.overlap(a, depth=depth, boundary=boundary)\n # apply rolling func\n out = ag.map_blocks(\n moving_func, window, min_count=min_count, axis=axis, dtype=a.dtype\n )\n # trim array\n result = da.overlap.trim_internal(out, depth)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_ops.py_rolling_window_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dask_array_ops.py_rolling_window_", "embedding": null, "metadata": {"file_path": "xarray/core/dask_array_ops.py", "file_name": "dask_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 30, "end_line": 98, "span_ids": ["rolling_window"], "tokens": 671}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def rolling_window(a, axis, window, center, fill_value):\n \"\"\"Dask's equivalence to np.utils.rolling_window\n \"\"\"\n import dask.array as da\n\n orig_shape = a.shape\n if axis < 0:\n axis = a.ndim + axis\n depth = {d: 0 for d in range(a.ndim)}\n depth[axis] = int(window / 2)\n # For evenly sized window, we need to crop the first point of each block.\n offset = 1 if window % 2 == 0 else 0\n\n if depth[axis] > min(a.chunks[axis]):\n raise ValueError(\n \"For window size %d, every chunk should be larger than %d, \"\n \"but the smallest chunk size is %d. Rechunk your array\\n\"\n \"with a larger chunk size or a chunk size that\\n\"\n \"more evenly divides the shape of your array.\"\n % (window, depth[axis], min(a.chunks[axis]))\n )\n\n # Although da.overlap pads values to boundaries of the array,\n # the size of the generated array is smaller than what we want\n # if center == False.\n if center:\n start = int(window / 2) # 10 -> 5, 9 -> 4\n end = window - 1 - start\n else:\n start, end = window - 1, 0\n pad_size = max(start, end) + offset - depth[axis]\n drop_size = 0\n # pad_size becomes more than 0 when the overlapped array is smaller than\n # needed. In this case, we need to enlarge the original array by padding\n # before overlapping.\n if pad_size > 0:\n if pad_size < depth[axis]:\n # overlapping requires each chunk larger than depth. If pad_size is\n # smaller than the depth, we enlarge this and truncate it later.\n drop_size = depth[axis] - pad_size\n pad_size = depth[axis]\n shape = list(a.shape)\n shape[axis] = pad_size\n chunks = list(a.chunks)\n chunks[axis] = (pad_size,)\n fill_array = da.full(shape, fill_value, dtype=a.dtype, chunks=chunks)\n a = da.concatenate([fill_array, a], axis=axis)\n\n boundary = {d: fill_value for d in range(a.ndim)}\n\n # create overlap arrays\n ag = da.overlap.overlap(a, depth=depth, boundary=boundary)\n\n # apply rolling func\n def func(x, window, axis=-1):\n x = np.asarray(x)\n rolling = nputils._rolling_window(x, window, axis)\n return rolling[(slice(None),) * axis + (slice(offset, None),)]\n\n chunks = list(a.chunks)\n chunks.append(window)\n out = ag.map_blocks(\n func, dtype=a.dtype, new_axis=a.ndim, chunks=chunks, window=window, axis=axis\n )\n\n # crop boundary.\n index = (slice(None),) * axis + (slice(drop_size, drop_size + orig_shape[axis]),)\n return out[index]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_functools_if_TYPE_CHECKING_.None_2.except_ImportError_.iris_Cube.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_functools_if_TYPE_CHECKING_.None_2.except_ImportError_.iris_Cube.None", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 79, "span_ids": ["impl", "imports:22", "imports:20", "imports:21", "impl:9", "imports", "impl:13", "impl:5"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\nimport warnings\nfrom numbers import Number\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n Hashable,\n Iterable,\n List,\n Mapping,\n Optional,\n Sequence,\n Tuple,\n TypeVar,\n Union,\n cast,\n overload,\n)\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..plot.plot import _PlotMethods\nfrom . import (\n computation,\n dtypes,\n groupby,\n indexing,\n ops,\n pdcompat,\n resample,\n rolling,\n utils,\n)\nfrom .accessor_dt import DatetimeAccessor\nfrom .accessor_str import StringAccessor\nfrom .alignment import (\n _broadcast_helper,\n _get_broadcast_dims_map_common_coords,\n align,\n reindex_like_indexers,\n)\nfrom .common import AbstractArray, DataWithCoords\nfrom .coordinates import (\n DataArrayCoordinates,\n LevelCoordinatesSource,\n assert_coordinate_consistent,\n remap_label_indexers,\n)\nfrom .dataset import Dataset, merge_indexes, split_indexes\nfrom .formatting import format_item\nfrom .indexes import Indexes, default_indexes\nfrom .options import OPTIONS\nfrom .utils import ReprObject, _check_inplace, either_dict_or_kwargs\nfrom .variable import (\n IndexVariable,\n Variable,\n as_compatible_data,\n as_variable,\n assert_unique_multiindex_level_names,\n)\n\nif TYPE_CHECKING:\n T_DSorDA = TypeVar(\"T_DSorDA\", \"DataArray\", Dataset)\n\n try:\n from dask.delayed import Delayed\n except ImportError:\n Delayed = None\n try:\n from cdms2 import Variable as cdms2_Variable\n except ImportError:\n cdms2_Variable = None\n try:\n from iris.cube import Cube as iris_Cube\n except ImportError:\n iris_Cube = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__infer_coords_and_dims__infer_coords_and_dims.return.new_coords_dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__infer_coords_and_dims__infer_coords_and_dims.return.new_coords_dims", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 164, "span_ids": ["_infer_coords_and_dims"], "tokens": 691}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_coords_and_dims(\n shape, coords, dims\n) -> \"Tuple[Dict[Any, Variable], Tuple[Hashable, ...]]\":\n \"\"\"All the logic for creating a new DataArray\"\"\"\n\n if (\n coords is not None\n and not utils.is_dict_like(coords)\n and len(coords) != len(shape)\n ):\n raise ValueError(\n \"coords is not dict-like, but it has %s items, \"\n \"which does not match the %s dimensions of the \"\n \"data\" % (len(coords), len(shape))\n )\n\n if isinstance(dims, str):\n dims = (dims,)\n\n if dims is None:\n dims = [\"dim_%s\" % n for n in range(len(shape))]\n if coords is not None and len(coords) == len(shape):\n # try to infer dimensions from coords\n if utils.is_dict_like(coords):\n # deprecated in GH993, removed in GH1539\n raise ValueError(\n \"inferring DataArray dimensions from \"\n \"dictionary like ``coords`` is no longer \"\n \"supported. Use an explicit list of \"\n \"``dims`` instead.\"\n )\n for n, (dim, coord) in enumerate(zip(dims, coords)):\n coord = as_variable(coord, name=dims[n]).to_index_variable()\n dims[n] = coord.name\n dims = tuple(dims)\n elif len(dims) != len(shape):\n raise ValueError(\n \"different number of dimensions on data \"\n \"and dims: %s vs %s\" % (len(shape), len(dims))\n )\n else:\n for d in dims:\n if not isinstance(d, str):\n raise TypeError(\"dimension %s is not a string\" % d)\n\n new_coords: Dict[Any, Variable] = {}\n\n if utils.is_dict_like(coords):\n for k, v in coords.items():\n new_coords[k] = as_variable(v, name=k)\n elif coords is not None:\n for dim, coord in zip(dims, coords):\n var = as_variable(coord, name=dim)\n var.dims = (dim,)\n new_coords[dim] = var.to_index_variable()\n\n sizes = dict(zip(dims, shape))\n for k, v in new_coords.items():\n if any(d not in dims for d in v.dims):\n raise ValueError(\n \"coordinate %s has dimensions %s, but these \"\n \"are not a subset of the DataArray \"\n \"dimensions %s\" % (k, v.dims, dims)\n )\n\n for d, s in zip(v.dims, v.shape):\n if s != sizes[d]:\n raise ValueError(\n \"conflicting sizes for dimension %r: \"\n \"length %s on the data but length %s on \"\n \"coordinate %r\" % (d, sizes[d], s, k)\n )\n\n if k in sizes and v.shape != (sizes[k],):\n raise ValueError(\n \"coordinate %r is a DataArray dimension, but \"\n \"it has shape %r rather than expected shape %r \"\n \"matching the dimension size\" % (k, v.shape, (sizes[k],))\n )\n\n assert_unique_multiindex_level_names(new_coords)\n\n return new_coords, dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__check_data_shape__check_data_shape.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__check_data_shape__check_data_shape.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 167, "end_line": 182, "span_ids": ["_check_data_shape"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _check_data_shape(data, coords, dims):\n if data is dtypes.NA:\n data = np.nan\n if coords is not None and utils.is_scalar(data, include_0d=False):\n if utils.is_dict_like(coords):\n if dims is None:\n return data\n else:\n data_shape = tuple(\n as_variable(coords[k], k).size if k in coords.keys() else 1\n for k in dims\n )\n else:\n data_shape = tuple(as_variable(coord, \"foo\").size for coord in coords)\n data = np.full(data_shape, data)\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__LocIndexer__THIS_ARRAY.ReprObject_this_array_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py__LocIndexer__THIS_ARRAY.ReprObject_this_array_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 210, "span_ids": ["impl:16", "_LocIndexer.__setitem__", "_LocIndexer.__getitem__", "_LocIndexer"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _LocIndexer:\n __slots__ = (\"data_array\",)\n\n def __init__(self, data_array: \"DataArray\"):\n self.data_array = data_array\n\n def __getitem__(self, key) -> \"DataArray\":\n if not utils.is_dict_like(key):\n # expand the indexer so we can handle Ellipsis\n labels = indexing.expanded_indexer(key, self.data_array.ndim)\n key = dict(zip(self.data_array.dims, labels))\n return self.data_array.sel(**key)\n\n def __setitem__(self, key, value) -> None:\n if not utils.is_dict_like(key):\n # expand the indexer so we can handle Ellipsis\n labels = indexing.expanded_indexer(key, self.data_array.ndim)\n key = dict(zip(self.data_array.dims, labels))\n\n pos_indexers, _ = remap_label_indexers(self.data_array, key)\n self.data_array[pos_indexers] = value\n\n\n# Used as the key corresponding to a DataArray's variable when converting\n# arbitrary DataArray objects to datasets\n_THIS_ARRAY = ReprObject(\"\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray_DataArray.dt.property_DatetimeAccessor": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray_DataArray.dt.property_DatetimeAccessor", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 275, "span_ids": ["DataArray"], "tokens": 503}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n \"\"\"N-dimensional array with labeled coordinates and dimensions.\n\n DataArray provides a wrapper around numpy ndarrays that uses labeled\n dimensions and coordinates to support metadata aware operations. The API is\n similar to that for the pandas Series or DataFrame, but DataArray objects\n can have any number of dimensions, and their contents have fixed data\n types.\n\n Additional features over raw numpy arrays:\n\n - Apply operations over dimensions by name: ``x.sum('time')``.\n - Select or assign values by integer location (like numpy): ``x[:10]``\n or by label (like pandas): ``x.loc['2014-01-01']`` or\n ``x.sel(time='2014-01-01')``.\n - Mathematical operations (e.g., ``x - y``) vectorize across multiple\n dimensions (known in numpy as \"broadcasting\") based on dimension names,\n regardless of their original order.\n - Keep track of arbitrary metadata in the form of a Python dictionary:\n ``x.attrs``\n - Convert to a pandas Series: ``x.to_series()``.\n\n Getting items from or doing mathematical operations with a DataArray\n always returns another DataArray.\n\n Attributes\n ----------\n dims : tuple\n Dimension names associated with this array.\n values : np.ndarray\n Access or modify DataArray values as a numpy array.\n coords : dict-like\n Dictionary of DataArray objects that label values along each dimension.\n name : str or None\n Name of this array.\n attrs : dict\n Dictionary for holding arbitrary metadata.\n \"\"\"\n\n _accessors: Optional[Dict[str, Any]]\n _coords: Dict[Any, Variable]\n _indexes: Optional[Dict[Hashable, pd.Index]]\n _name: Optional[Hashable]\n _variable: Variable\n\n __slots__ = (\n \"_accessors\",\n \"_coords\",\n \"_file_obj\",\n \"_indexes\",\n \"_name\",\n \"_variable\",\n \"__weakref__\",\n )\n\n _groupby_cls = groupby.DataArrayGroupBy\n _rolling_cls = rolling.DataArrayRolling\n _coarsen_cls = rolling.DataArrayCoarsen\n _resample_cls = resample.DataArrayResample\n\n __default = ReprObject(\"\")\n\n dt = property(DatetimeAccessor)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__init___DataArray.__init__.self._file_obj.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__init___DataArray.__init__.self._file_obj.None", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 277, "end_line": 384, "span_ids": ["DataArray"], "tokens": 954}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def __init__(\n self,\n data: Any = dtypes.NA,\n coords: Union[Sequence[Tuple], Mapping[Hashable, Any], None] = None,\n dims: Union[Hashable, Sequence[Hashable], None] = None,\n name: Hashable = None,\n attrs: Mapping = None,\n # deprecated parameters\n encoding=None,\n # internal parameters\n indexes: Dict[Hashable, pd.Index] = None,\n fastpath: bool = False,\n ):\n \"\"\"\n Parameters\n ----------\n data : array_like\n Values for this array. Must be an ``numpy.ndarray``, ndarray like,\n or castable to an ``ndarray``. If a self-described xarray or pandas\n object, attempts are made to use this array's metadata to fill in\n other unspecified arguments. A view of the array's data is used\n instead of a copy if possible.\n coords : sequence or dict of array_like objects, optional\n Coordinates (tick labels) to use for indexing along each dimension.\n The following notations are accepted:\n\n - mapping {dimension name: array-like}\n - sequence of tuples that are valid arguments for xarray.Variable()\n - (dims, data)\n - (dims, data, attrs)\n - (dims, data, attrs, encoding)\n\n Additionally, it is possible to define a coord whose name\n does not match the dimension name, or a coord based on multiple\n dimensions, with one of the following notations:\n\n - mapping {coord name: DataArray}\n - mapping {coord name: Variable}\n - mapping {coord name: (dimension name, array-like)}\n - mapping {coord name: (tuple of dimension names, array-like)}\n\n dims : hashable or sequence of hashable, optional\n Name(s) of the data dimension(s). Must be either a hashable (only\n for 1D data) or a sequence of hashables with length equal to the\n number of dimensions. If this argument is omitted, dimension names\n are taken from ``coords`` (if possible) and otherwise default to\n ``['dim_0', ... 'dim_n']``.\n name : str or None, optional\n Name of this array.\n attrs : dict_like or None, optional\n Attributes to assign to the new instance. By default, an empty\n attribute dictionary is initialized.\n \"\"\"\n if encoding is not None:\n warnings.warn(\n \"The `encoding` argument to `DataArray` is deprecated, and . \"\n \"will be removed in 0.15. \"\n \"Instead, specify the encoding when writing to disk or \"\n \"set the `encoding` attribute directly.\",\n FutureWarning,\n stacklevel=2,\n )\n if fastpath:\n variable = data\n assert dims is None\n assert attrs is None\n assert encoding is None\n else:\n # try to fill in arguments from data if they weren't supplied\n if coords is None:\n\n if isinstance(data, DataArray):\n coords = data.coords\n elif isinstance(data, pd.Series):\n coords = [data.index]\n elif isinstance(data, pd.DataFrame):\n coords = [data.index, data.columns]\n elif isinstance(data, (pd.Index, IndexVariable)):\n coords = [data]\n elif isinstance(data, pdcompat.Panel):\n coords = [data.items, data.major_axis, data.minor_axis]\n\n if dims is None:\n dims = getattr(data, \"dims\", getattr(coords, \"dims\", None))\n if name is None:\n name = getattr(data, \"name\", None)\n if attrs is None:\n attrs = getattr(data, \"attrs\", None)\n if encoding is None:\n encoding = getattr(data, \"encoding\", None)\n\n data = _check_data_shape(data, coords, dims)\n data = as_compatible_data(data)\n coords, dims = _infer_coords_and_dims(data.shape, coords, dims)\n variable = Variable(dims, data, attrs, encoding, fastpath=True)\n\n # These fully describe a DataArray\n self._variable = variable\n assert isinstance(coords, dict)\n self._coords = coords\n self._name = name\n self._accessors = None\n\n # TODO(shoyer): document this argument, once it becomes part of the\n # public interface.\n self._indexes = indexes\n\n self._file_obj = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._replace_DataArray._replace_maybe_drop_dims.return.self__replace_variable_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._replace_DataArray._replace_maybe_drop_dims.return.self__replace_variable_c", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 386, "end_line": 418, "span_ids": ["DataArray._replace_maybe_drop_dims", "DataArray._replace"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _replace(\n self,\n variable: Variable = None,\n coords=None,\n name: Optional[Hashable] = __default,\n ) -> \"DataArray\":\n if variable is None:\n variable = self.variable\n if coords is None:\n coords = self._coords\n if name is self.__default:\n name = self.name\n return type(self)(variable, coords, name=name, fastpath=True)\n\n def _replace_maybe_drop_dims(\n self, variable: Variable, name: Optional[Hashable] = __default\n ) -> \"DataArray\":\n if variable.dims == self.dims and variable.shape == self.shape:\n coords = self._coords.copy()\n elif variable.dims == self.dims:\n # Shape has changed (e.g. from reduce(..., keepdims=True)\n new_sizes = dict(zip(self.dims, variable.shape))\n coords = {\n k: v\n for k, v in self._coords.items()\n if v.shape == tuple(new_sizes[d] for d in v.dims)\n }\n else:\n allowed_dims = set(variable.dims)\n coords = {\n k: v for k, v in self._coords.items() if set(v.dims) <= allowed_dims\n }\n return self._replace(variable, coords, name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._overwrite_indexes_DataArray._overwrite_indexes.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._overwrite_indexes_DataArray._overwrite_indexes.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 420, "end_line": 435, "span_ids": ["DataArray._overwrite_indexes"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _overwrite_indexes(self, indexes: Mapping[Hashable, Any]) -> \"DataArray\":\n if not len(indexes):\n return self\n coords = self._coords.copy()\n for name, idx in indexes.items():\n coords[name] = IndexVariable(name, idx)\n obj = self._replace(coords=coords)\n\n # switch from dimension to level names, if necessary\n dim_names: Dict[Any, str] = {}\n for dim, idx in indexes.items():\n if not isinstance(idx, pd.MultiIndex) and idx.name != dim:\n dim_names[dim] = idx.name\n if dim_names:\n obj = obj.rename(dim_names)\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._to_temp_dataset_DataArray._to_dataset_split.return.Dataset_variables_coords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._to_temp_dataset_DataArray._to_dataset_split.return.Dataset_variables_coords", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 437, "end_line": 460, "span_ids": ["DataArray._to_dataset_split", "DataArray._to_temp_dataset", "DataArray._from_temp_dataset"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _to_temp_dataset(self) -> Dataset:\n return self._to_dataset_whole(name=_THIS_ARRAY, shallow_copy=False)\n\n def _from_temp_dataset(\n self, dataset: Dataset, name: Hashable = __default\n ) -> \"DataArray\":\n variable = dataset._variables.pop(_THIS_ARRAY)\n coords = dataset._variables\n return self._replace(variable, coords, name)\n\n def _to_dataset_split(self, dim: Hashable) -> Dataset:\n def subset(dim, label):\n array = self.loc[{dim: label}]\n if dim in array.coords:\n del array.coords[dim]\n array.attrs = {}\n return array\n\n variables = {label: subset(dim, label) for label in self.get_index(dim)}\n\n coords = self.coords.to_dataset()\n if dim in coords:\n del coords[dim]\n return Dataset(variables, coords, self.attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._to_dataset_whole_DataArray._to_dataset_whole.return.dataset": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._to_dataset_whole_DataArray._to_dataset_whole.return.dataset", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 462, "end_line": 486, "span_ids": ["DataArray._to_dataset_whole"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _to_dataset_whole(\n self, name: Hashable = None, shallow_copy: bool = True\n ) -> Dataset:\n if name is None:\n name = self.name\n if name is None:\n raise ValueError(\n \"unable to convert unnamed DataArray to a \"\n \"Dataset without providing an explicit name\"\n )\n if name in self.coords:\n raise ValueError(\n \"cannot create a Dataset from a DataArray with \"\n \"the same name as one of its coordinates\"\n )\n # use private APIs for speed: this is called by _to_temp_dataset(),\n # which is used in the guts of a lot of operations (e.g., reindex)\n variables = self._coords.copy()\n variables[name] = self.variable\n if shallow_copy:\n for k in variables:\n variables[k] = variables[k].copy(deep=False)\n coord_names = set(self._coords)\n dataset = Dataset._from_vars_and_coord_names(variables, coord_names)\n return dataset", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dataset_DataArray.to_dataset.if_dim_is_not_None_.else_.return.self__to_dataset_whole_na": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dataset_DataArray.to_dataset.if_dim_is_not_None_.else_.return.self__to_dataset_whole_na", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 488, "end_line": 515, "span_ids": ["DataArray.to_dataset"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_dataset(self, dim: Hashable = None, *, name: Hashable = None) -> Dataset:\n \"\"\"Convert a DataArray to a Dataset.\n\n Parameters\n ----------\n dim : hashable, optional\n Name of the dimension on this array along which to split this array\n into separate variables. If not provided, this array is converted\n into a Dataset of one variable.\n name : hashable, optional\n Name to substitute for this array's name. Only valid if ``dim`` is\n not provided.\n\n Returns\n -------\n dataset : Dataset\n \"\"\"\n if dim is not None and dim not in self.dims:\n raise TypeError(\n \"{} is not a dim. If supplying a ``name``, pass as a kwarg.\".format(dim)\n )\n\n if dim is not None:\n if name is not None:\n raise TypeError(\"cannot supply both dim and name arguments\")\n return self._to_dataset_split(dim)\n else:\n return self._to_dataset_whole(name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.name_DataArray._item_key_to_dict.if_utils_is_dict_like_key.else_.return.dict_zip_self_dims_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.name_DataArray._item_key_to_dict.if_utils_is_dict_like_key.else_.return.dict_zip_self_dims_key_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 517, "end_line": 606, "span_ids": ["DataArray._item_key_to_dict", "DataArray.name_9", "DataArray.__len__", "DataArray.variable", "DataArray.values", "DataArray.name", "DataArray.dims_24", "DataArray.data_18", "DataArray.shape", "DataArray.data", "DataArray.to_index", "DataArray.values_20", "DataArray.dims", "DataArray.nbytes", "DataArray.ndim", "DataArray.dtype", "DataArray.size", "DataArray._in_memory"], "tokens": 562}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @property\n def name(self) -> Optional[Hashable]:\n \"\"\"The name of this array.\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, value: Optional[Hashable]) -> None:\n self._name = value\n\n @property\n def variable(self) -> Variable:\n \"\"\"Low level interface to the Variable object for this DataArray.\"\"\"\n return self._variable\n\n @property\n def dtype(self) -> np.dtype:\n return self.variable.dtype\n\n @property\n def shape(self) -> Tuple[int, ...]:\n return self.variable.shape\n\n @property\n def size(self) -> int:\n return self.variable.size\n\n @property\n def nbytes(self) -> int:\n return self.variable.nbytes\n\n @property\n def ndim(self) -> int:\n return self.variable.ndim\n\n def __len__(self) -> int:\n return len(self.variable)\n\n @property\n def data(self) -> Any:\n \"\"\"The array's data as a dask or numpy array\n \"\"\"\n return self.variable.data\n\n @data.setter\n def data(self, value: Any) -> None:\n self.variable.data = value\n\n @property\n def values(self) -> np.ndarray:\n \"\"\"The array's data as a numpy.ndarray\"\"\"\n return self.variable.values\n\n @values.setter\n def values(self, value: Any) -> None:\n self.variable.values = value\n\n @property\n def _in_memory(self) -> bool:\n return self.variable._in_memory\n\n def to_index(self) -> pd.Index:\n \"\"\"Convert this variable to a pandas.Index. Only possible for 1D\n arrays.\n \"\"\"\n return self.variable.to_index()\n\n @property\n def dims(self) -> Tuple[Hashable, ...]:\n \"\"\"Tuple of dimension names associated with this array.\n\n Note that the type of this property is inconsistent with\n `Dataset.dims`. See `Dataset.sizes` and `DataArray.sizes` for\n consistently named properties.\n \"\"\"\n return self.variable.dims\n\n @dims.setter\n def dims(self, value):\n raise AttributeError(\n \"you cannot assign dims on a DataArray. Use \"\n \".rename() or .swap_dims() instead.\"\n )\n\n def _item_key_to_dict(self, key: Any) -> Mapping[Hashable, Any]:\n if utils.is_dict_like(key):\n return key\n else:\n key = indexing.expanded_indexer(key, self.ndim)\n return dict(zip(self.dims, key))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._level_coords_DataArray._level_coords.return.level_coords": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._level_coords_DataArray._level_coords.return.level_coords", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 608, "end_line": 621, "span_ids": ["DataArray._level_coords"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @property\n def _level_coords(self) -> Dict[Hashable, Hashable]:\n \"\"\"Return a mapping of all MultiIndex levels and their corresponding\n coordinate name.\n \"\"\"\n level_coords: Dict[Hashable, Hashable] = {}\n\n for cname, var in self._coords.items():\n if var.ndim == 1 and isinstance(var, IndexVariable):\n level_names = var.level_names\n if level_names is not None:\n dim, = var.dims\n level_coords.update({lname: dim for lname in level_names})\n return level_coords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._getitem_coord_DataArray.__getitem__.if_isinstance_key_str_.else_.return.self_isel_indexers_self__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._getitem_coord_DataArray.__getitem__.if_isinstance_key_str_.else_.return.self_isel_indexers_self__", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 623, "end_line": 641, "span_ids": ["DataArray.__getitem__", "DataArray._getitem_coord"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _getitem_coord(self, key):\n from .dataset import _get_virtual_variable\n\n try:\n var = self._coords[key]\n except KeyError:\n dim_sizes = dict(zip(self.dims, self.shape))\n _, key, var = _get_virtual_variable(\n self._coords, key, self._level_coords, dim_sizes\n )\n\n return self._replace_maybe_drop_dims(var, name=key)\n\n def __getitem__(self, key: Any) -> \"DataArray\":\n if isinstance(key, str):\n return self._getitem_coord(key)\n else:\n # xarray-style array indexing\n return self.isel(indexers=self._item_key_to_dict(key))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__setitem___DataArray.__setitem__.if_isinstance_key_str_.else_.self_variable_key_valu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__setitem___DataArray.__setitem__.if_isinstance_key_str_.else_.self_variable_key_valu", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 643, "end_line": 658, "span_ids": ["DataArray.__setitem__"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def __setitem__(self, key: Any, value: Any) -> None:\n if isinstance(key, str):\n self.coords[key] = value\n else:\n # Coordinates in key, value and self[key] should be consistent.\n # TODO Coordinate consistency in key is checked here, but it\n # causes unnecessary indexing. It should be optimized.\n obj = self[key]\n if isinstance(value, DataArray):\n assert_coordinate_consistent(value, obj.coords.variables)\n # DataArray key -> Variable key\n key = {\n k: v.variable if isinstance(v, DataArray) else v\n for k, v in self._item_key_to_dict(key).items()\n }\n self.variable[key] = value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__delitem___DataArray.coords.return.DataArrayCoordinates_self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__delitem___DataArray.coords.return.DataArrayCoordinates_self", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 660, "end_line": 720, "span_ids": ["DataArray._attr_sources", "DataArray.attrs", "DataArray.coords", "DataArray.__delitem__", "DataArray.encoding", "DataArray._item_sources", "DataArray.__contains__", "DataArray.encoding_38", "DataArray.indexes", "DataArray.loc", "DataArray.attrs_36"], "tokens": 448}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def __delitem__(self, key: Any) -> None:\n del self.coords[key]\n\n @property\n def _attr_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for attribute-style access\n \"\"\"\n return self._item_sources + [self.attrs]\n\n @property\n def _item_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for key-completion\n \"\"\"\n return [\n self.coords,\n {d: self.coords[d] for d in self.dims},\n LevelCoordinatesSource(self),\n ]\n\n def __contains__(self, key: Any) -> bool:\n return key in self.data\n\n @property\n def loc(self) -> _LocIndexer:\n \"\"\"Attribute for location based indexing like pandas.\n \"\"\"\n return _LocIndexer(self)\n\n @property\n def attrs(self) -> Dict[Hashable, Any]:\n \"\"\"Dictionary storing arbitrary metadata with this array.\"\"\"\n return self.variable.attrs\n\n @attrs.setter\n def attrs(self, value: Mapping[Hashable, Any]) -> None:\n # Disable type checking to work around mypy bug - see mypy#4167\n self.variable.attrs = value # type: ignore\n\n @property\n def encoding(self) -> Dict[Hashable, Any]:\n \"\"\"Dictionary of format-specific settings for how this array should be\n serialized.\"\"\"\n return self.variable.encoding\n\n @encoding.setter\n def encoding(self, value: Mapping[Hashable, Any]) -> None:\n self.variable.encoding = value\n\n @property\n def indexes(self) -> Indexes:\n \"\"\"Mapping of pandas.Index objects used for label based indexing\n \"\"\"\n if self._indexes is None:\n self._indexes = default_indexes(self._coords, self.dims)\n return Indexes(self._indexes)\n\n @property\n def coords(self) -> DataArrayCoordinates:\n \"\"\"Dictionary-like container of coordinate arrays.\n \"\"\"\n return DataArrayCoordinates(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reset_coords_DataArray.reset_coords.if_drop_.else_.return.dataset": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reset_coords_DataArray.reset_coords.if_drop_.else_.return.dataset", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 722, "end_line": 755, "span_ids": ["DataArray.reset_coords"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reset_coords(\n self,\n names: Union[Iterable[Hashable], Hashable, None] = None,\n drop: bool = False,\n inplace: bool = None,\n ) -> Union[None, \"DataArray\", Dataset]:\n \"\"\"Given names of coordinates, reset them to become variables.\n\n Parameters\n ----------\n names : hashable or iterable of hashables, optional\n Name(s) of non-index coordinates in this dataset to reset into\n variables. By default, all non-index coordinates are reset.\n drop : bool, optional\n If True, remove coordinates instead of converting them into\n variables.\n\n Returns\n -------\n Dataset, or DataArray if ``drop == True``\n \"\"\"\n _check_inplace(inplace)\n if names is None:\n names = set(self.coords) - set(self.dims)\n dataset = self.coords.to_dataset().reset_coords(names, drop)\n if drop:\n return self._replace(coords=dataset._variables)\n else:\n if self.name is None:\n raise ValueError(\n \"cannot reset_coords with drop=False on an unnamed DataArrray\"\n )\n dataset[self.name] = self.variable\n return dataset", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__dask_graph___DataArray._dask_finalize.return.DataArray_variable_coord": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.__dask_graph___DataArray._dask_finalize.return.DataArray_variable_coord", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 757, "end_line": 787, "span_ids": ["DataArray.__dask_graph__", "DataArray.__dask_postpersist__", "DataArray.__dask_layers__", "DataArray.__dask_keys__", "DataArray.__dask_scheduler__", "DataArray._dask_finalize", "DataArray.__dask_postcompute__", "DataArray.__dask_optimize__"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def __dask_graph__(self):\n return self._to_temp_dataset().__dask_graph__()\n\n def __dask_keys__(self):\n return self._to_temp_dataset().__dask_keys__()\n\n def __dask_layers__(self):\n return self._to_temp_dataset().__dask_layers__()\n\n @property\n def __dask_optimize__(self):\n return self._to_temp_dataset().__dask_optimize__\n\n @property\n def __dask_scheduler__(self):\n return self._to_temp_dataset().__dask_scheduler__\n\n def __dask_postcompute__(self):\n func, args = self._to_temp_dataset().__dask_postcompute__()\n return self._dask_finalize, (func, args, self.name)\n\n def __dask_postpersist__(self):\n func, args = self._to_temp_dataset().__dask_postpersist__()\n return self._dask_finalize, (func, args, self.name)\n\n @staticmethod\n def _dask_finalize(results, func, args, name):\n ds = func(results, *args)\n variable = ds._variables.pop(_THIS_ARRAY)\n coords = ds._variables\n return DataArray(variable, coords, name=name, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.load_DataArray.load.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.load_DataArray.load.return.self", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 789, "end_line": 811, "span_ids": ["DataArray.load"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def load(self, **kwargs) -> \"DataArray\":\n \"\"\"Manually trigger loading of this array's data from disk or a\n remote source into memory and return this array.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically. However, this method can be necessary when\n working with many file objects on disk.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n ds = self._to_temp_dataset().load(**kwargs)\n new = self._from_temp_dataset(ds)\n self._variable = new._variable\n self._coords = new._coords\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.compute_DataArray.compute.return.new_load_kwargs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.compute_DataArray.compute.return.new_load_kwargs_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 813, "end_line": 833, "span_ids": ["DataArray.compute"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def compute(self, **kwargs) -> \"DataArray\":\n \"\"\"Manually trigger loading of this array's data from disk or a\n remote source into memory and return a new array. The original is\n left unaltered.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically. However, this method can be necessary when\n working with many file objects on disk.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n new = self.copy(deep=False)\n return new.load(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.persist_DataArray.persist.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.persist_DataArray.persist.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 835, "end_line": 852, "span_ids": ["DataArray.persist"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def persist(self, **kwargs) -> \"DataArray\":\n \"\"\" Trigger computation in constituent dask arrays\n\n This keeps them as dask arrays but encourages them to keep data in\n memory. This is particularly useful when on a distributed machine.\n When on a single machine consider using ``.compute()`` instead.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.persist``.\n\n See Also\n --------\n dask.persist\n \"\"\"\n ds = self._to_temp_dataset().persist(**kwargs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.copy_DataArray.copy.return.self__replace_variable_c": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.copy_DataArray.copy.return.self__replace_variable_c", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 854, "end_line": 926, "span_ids": ["DataArray.copy"], "tokens": 668}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def copy(self, deep: bool = True, data: Any = None) -> \"DataArray\":\n \"\"\"Returns a copy of this array.\n\n If `deep=True`, a deep copy is made of the data array.\n Otherwise, a shallow copy is made, so each variable in the new\n array's dataset is also a variable in this array's dataset.\n\n Use `data` to create a new object with the same structure as\n original but entirely new data.\n\n Parameters\n ----------\n deep : bool, optional\n Whether the data array and its coordinates are loaded into memory\n and copied onto the new object. Default is True.\n data : array_like, optional\n Data to use in the new object. Must have same shape as original.\n When `data` is used, `deep` is ignored for all data variables,\n and only used for coords.\n\n Returns\n -------\n object : DataArray\n New object with dimensions, attributes, coordinates, name,\n encoding, and optionally data copied from original.\n\n Examples\n --------\n\n Shallow versus deep copy\n\n >>> array = xr.DataArray([1, 2, 3], dims='x',\n ... coords={'x': ['a', 'b', 'c']})\n >>> array.copy()\n \n array([1, 2, 3])\n Coordinates:\n * x (x) >> array_0 = array.copy(deep=False)\n >>> array_0[0] = 7\n >>> array_0\n \n array([7, 2, 3])\n Coordinates:\n * x (x) >> array\n \n array([7, 2, 3])\n Coordinates:\n * x (x) >> array.copy(data=[0.1, 0.2, 0.3])\n \n array([ 0.1, 0.2, 0.3])\n Coordinates:\n * x (x) >> array\n \n array([1, 2, 3])\n Coordinates:\n * x (x) \"DataArray\":\n return self.copy(deep=False)\n\n def __deepcopy__(self, memo=None) -> \"DataArray\":\n # memo does nothing but is required for compatibility with\n # copy.deepcopy\n return self.copy(deep=True)\n\n # mutable objects should not be hashable\n # https://github.com/python/mypy/issues/4266\n __hash__ = None # type: ignore\n\n @property\n def chunks(self) -> Optional[Tuple[Tuple[int, ...], ...]]:\n \"\"\"Block dimensions for this array's data or None if it's not a dask\n array.\n \"\"\"\n return self.variable.chunks", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.chunk_DataArray.chunk.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.chunk_DataArray.chunk.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 947, "end_line": 993, "span_ids": ["DataArray.chunk"], "tokens": 403}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def chunk(\n self,\n chunks: Union[\n None,\n Number,\n Tuple[Number, ...],\n Tuple[Tuple[Number, ...], ...],\n Mapping[Hashable, Union[None, Number, Tuple[Number, ...]]],\n ] = None,\n name_prefix: str = \"xarray-\",\n token: str = None,\n lock: bool = False,\n ) -> \"DataArray\":\n \"\"\"Coerce this array's data into a dask arrays with the given chunks.\n\n If this variable is a non-dask array, it will be converted to dask\n array. If it's a dask array, it will be rechunked to the given chunk\n sizes.\n\n If neither chunks is not provided for one or more dimensions, chunk\n sizes along that dimension will not be updated; non-dask arrays will be\n converted into dask arrays with a single block.\n\n Parameters\n ----------\n chunks : int, tuple or mapping, optional\n Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or\n ``{'x': 5, 'y': 5}``.\n name_prefix : str, optional\n Prefix for the name of the new dask array.\n token : str, optional\n Token uniquely identifying this array.\n lock : optional\n Passed on to :py:func:`dask.array.from_array`, if the array is not\n already as dask array.\n\n Returns\n -------\n chunked : xarray.DataArray\n \"\"\"\n if isinstance(chunks, (tuple, list)):\n chunks = dict(zip(self.dims, chunks))\n\n ds = self._to_temp_dataset().chunk(\n chunks, name_prefix=name_prefix, token=token, lock=lock\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.isel_DataArray.isel.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.isel_DataArray.isel.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 995, "end_line": 1011, "span_ids": ["DataArray.isel"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def isel(\n self,\n indexers: Mapping[Hashable, Any] = None,\n drop: bool = False,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Return a new DataArray whose data is given by integer indexing\n along the specified dimension(s).\n\n See Also\n --------\n Dataset.isel\n DataArray.sel\n \"\"\"\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"isel\")\n ds = self._to_temp_dataset().isel(drop=drop, indexers=indexers)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.sel_DataArray.sel.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.sel_DataArray.sel.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1013, "end_line": 1049, "span_ids": ["DataArray.sel"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def sel(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance=None,\n drop: bool = False,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Return a new DataArray whose data is given by selecting index\n labels along the specified dimension(s).\n\n .. warning::\n\n Do not try to assign values when using any of the indexing methods\n ``isel`` or ``sel``::\n\n da = xr.DataArray([0, 1, 2, 3], dims=['x'])\n # DO NOT do this\n da.isel(x=[0, 1, 2])[1] = -1\n\n Assigning values with the chained indexing using ``.sel`` or\n ``.isel`` fails silently.\n\n See Also\n --------\n Dataset.sel\n DataArray.isel\n\n \"\"\"\n ds = self._to_temp_dataset().sel(\n indexers=indexers,\n drop=drop,\n method=method,\n tolerance=tolerance,\n **indexers_kwargs\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.head_DataArray.head.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.head_DataArray.head.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1051, "end_line": 1066, "span_ids": ["DataArray.head"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def head(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Return a new DataArray whose data is given by the the first `n`\n values along the specified dimension(s). Default `n` = 5\n\n See Also\n --------\n Dataset.head\n DataArray.tail\n DataArray.thin\n \"\"\"\n ds = self._to_temp_dataset().head(indexers, **indexers_kwargs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.tail_DataArray.tail.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.tail_DataArray.tail.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1068, "end_line": 1083, "span_ids": ["DataArray.tail"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def tail(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Return a new DataArray whose data is given by the the last `n`\n values along the specified dimension(s). Default `n` = 5\n\n See Also\n --------\n Dataset.tail\n DataArray.head\n DataArray.thin\n \"\"\"\n ds = self._to_temp_dataset().tail(indexers, **indexers_kwargs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.thin_DataArray.thin.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.thin_DataArray.thin.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1085, "end_line": 1100, "span_ids": ["DataArray.thin"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def thin(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Return a new DataArray whose data is given by each `n` value\n along the specified dimension(s). Default `n` = 5\n\n See Also\n --------\n Dataset.thin\n DataArray.head\n DataArray.tail\n \"\"\"\n ds = self._to_temp_dataset().thin(indexers, **indexers_kwargs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.broadcast_like_DataArray.broadcast_like.return._broadcast_helper_args_1_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.broadcast_like_DataArray.broadcast_like.return._broadcast_helper_args_1_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1102, "end_line": 1164, "span_ids": ["DataArray.broadcast_like"], "tokens": 585}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def broadcast_like(\n self, other: Union[\"DataArray\", Dataset], exclude: Iterable[Hashable] = None\n ) -> \"DataArray\":\n \"\"\"Broadcast this DataArray against another Dataset or DataArray.\n\n This is equivalent to xr.broadcast(other, self)[1]\n\n xarray objects are broadcast against each other in arithmetic\n operations, so this method is not be necessary for most uses.\n\n If no change is needed, the input data is returned to the output\n without being copied.\n\n If new coords are added by the broadcast, their values are\n NaN filled.\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object against which to broadcast this array.\n exclude : iterable of hashable, optional\n Dimensions that must not be broadcasted\n\n Returns\n -------\n new_da: xr.DataArray\n\n Examples\n --------\n\n >>> arr1\n \n array([[0.840235, 0.215216, 0.77917 ],\n [0.726351, 0.543824, 0.875115]])\n Coordinates:\n * x (x) >> arr2\n \n array([[0.612611, 0.125753],\n [0.853181, 0.948818],\n [0.180885, 0.33363 ]])\n Coordinates:\n * x (x) >> arr1.broadcast_like(arr2)\n \n array([[0.840235, 0.215216, 0.77917 ],\n [0.726351, 0.543824, 0.875115],\n [ nan, nan, nan]])\n Coordinates:\n * x (x) object 'a' 'b' 'c'\n * y (y) object 'a' 'b' 'c'\n \"\"\"\n if exclude is None:\n exclude = set()\n else:\n exclude = set(exclude)\n args = align(other, self, join=\"outer\", copy=False, exclude=exclude)\n\n dims_map, common_coords = _get_broadcast_dims_map_common_coords(args, exclude)\n\n return _broadcast_helper(args[1], exclude, dims_map, common_coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reindex_like_DataArray.reindex_like.return.self_reindex_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reindex_like_DataArray.reindex_like.return.self_reindex_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1166, "end_line": 1224, "span_ids": ["DataArray.reindex_like"], "tokens": 524}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reindex_like(\n self,\n other: Union[\"DataArray\", Dataset],\n method: str = None,\n tolerance=None,\n copy: bool = True,\n fill_value=dtypes.NA,\n ) -> \"DataArray\":\n \"\"\"Conform this object onto the indexes of another object, filling in\n missing values with ``fill_value``. The default fill value is NaN.\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object with an 'indexes' attribute giving a mapping from dimension\n names to pandas.Index objects, which provides coordinates upon\n which to index the variables in this dataset. The indexes on this\n other object need not be the same as the indexes on this\n dataset. Any mis-matched index values will be filled in with\n NaN, and any mis-matched dimension names will simply be ignored.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for filling index values from other not found on this\n data array:\n\n * None (default): don't fill gaps\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations must\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n copy : bool, optional\n If ``copy=True``, data in the return value is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed\n with only slice operations, then the output may share memory with\n the input. In either case, a new xarray object is always returned.\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n reindexed : DataArray\n Another dataset array, with this array's data but coordinates from\n the other object.\n\n See Also\n --------\n DataArray.reindex\n align\n \"\"\"\n indexers = reindex_like_indexers(self, other)\n return self.reindex(\n indexers=indexers,\n method=method,\n tolerance=tolerance,\n copy=copy,\n fill_value=fill_value,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reindex_DataArray.reindex.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reindex_DataArray.reindex.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1226, "end_line": 1288, "span_ids": ["DataArray.reindex"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reindex(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance=None,\n copy: bool = True,\n fill_value=dtypes.NA,\n **indexers_kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Conform this object onto the indexes of another object, filling in\n missing values with ``fill_value``. The default fill value is NaN.\n\n Parameters\n ----------\n indexers : dict, optional\n Dictionary with keys given by dimension names and values given by\n arrays of coordinates tick labels. Any mis-matched coordinate\n values will be filled in with NaN, and any mis-matched dimension\n names will simply be ignored.\n One of indexers or indexers_kwargs must be provided.\n copy : bool, optional\n If ``copy=True``, data in the return value is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed\n with only slice operations, then the output may share memory with\n the input. In either case, a new xarray object is always returned.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for filling index values in ``indexers`` not found on\n this data array:\n\n * None (default): don't fill gaps\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations must\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n fill_value : scalar, optional\n Value to use for newly missing values\n **indexers_kwarg : {dim: indexer, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n Returns\n -------\n reindexed : DataArray\n Another dataset array, with this array's data but replaced\n coordinates.\n\n See Also\n --------\n DataArray.reindex_like\n align\n \"\"\"\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"reindex\")\n ds = self._to_temp_dataset().reindex(\n indexers=indexers,\n method=method,\n tolerance=tolerance,\n copy=copy,\n fill_value=fill_value,\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interp_DataArray.interp.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interp_DataArray.interp.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1290, "end_line": 1353, "span_ids": ["DataArray.interp"], "tokens": 477}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def interp(\n self,\n coords: Mapping[Hashable, Any] = None,\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n **coords_kwargs: Any\n ) -> \"DataArray\":\n \"\"\" Multidimensional interpolation of variables.\n\n coords : dict, optional\n Mapping from dimension names to the new coordinates.\n new coordinate can be an scalar, array-like or DataArray.\n If DataArrays are passed as new coordates, their dimensions are\n used for the broadcasting.\n method: {'linear', 'nearest'} for multidimensional array,\n {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}\n for 1-dimensional array.\n assume_sorted: boolean, optional\n If False, values of x can be in any order and they are sorted\n first. If True, x has to be an array of monotonically increasing\n values.\n kwargs: dictionary\n Additional keyword passed to scipy's interpolator.\n **coords_kwarg : {dim: coordinate, ...}, optional\n The keyword arguments form of ``coords``.\n One of coords or coords_kwargs must be provided.\n\n Returns\n -------\n interpolated: xr.DataArray\n New dataarray on the new coordinates.\n\n Notes\n -----\n scipy is required.\n\n See Also\n --------\n scipy.interpolate.interp1d\n scipy.interpolate.interpn\n\n Examples\n --------\n >>> da = xr.DataArray([1, 3], [('x', np.arange(2))])\n >>> da.interp(x=0.5)\n \n array(2.0)\n Coordinates:\n x float64 0.5\n \"\"\"\n if self.dtype.kind not in \"uifc\":\n raise TypeError(\n \"interp only works for a numeric type array. \"\n \"Given {}.\".format(self.dtype)\n )\n ds = self._to_temp_dataset().interp(\n coords,\n method=method,\n kwargs=kwargs,\n assume_sorted=assume_sorted,\n **coords_kwargs\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interp_like_DataArray.interp_like.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interp_like_DataArray.interp_like.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1355, "end_line": 1408, "span_ids": ["DataArray.interp_like"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def interp_like(\n self,\n other: Union[\"DataArray\", Dataset],\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n ) -> \"DataArray\":\n \"\"\"Interpolate this object onto the coordinates of another object,\n filling out of range values with NaN.\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object with an 'indexes' attribute giving a mapping from dimension\n names to an 1d array-like, which provides coordinates upon\n which to index the variables in this dataset.\n method: string, optional.\n {'linear', 'nearest'} for multidimensional array,\n {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}\n for 1-dimensional array. 'linear' is used by default.\n assume_sorted: boolean, optional\n If False, values of coordinates that are interpolated over can be\n in any order and they are sorted first. If True, interpolated\n coordinates are assumed to be an array of monotonically increasing\n values.\n kwargs: dictionary, optional\n Additional keyword passed to scipy's interpolator.\n\n Returns\n -------\n interpolated: xr.DataArray\n Another dataarray by interpolating this dataarray's data along the\n coordinates of the other object.\n\n Notes\n -----\n scipy is required.\n If the dataarray has object-type coordinates, reindex is used for these\n coordinates instead of the interpolation.\n\n See Also\n --------\n DataArray.interp\n DataArray.reindex_like\n \"\"\"\n if self.dtype.kind not in \"uifc\":\n raise TypeError(\n \"interp only works for a numeric type array. \"\n \"Given {}.\".format(self.dtype)\n )\n ds = self._to_temp_dataset().interp_like(\n other, method=method, kwargs=kwargs, assume_sorted=assume_sorted\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.rename_DataArray.rename.if_names_or_utils_is_dict.else_.return.self__replace_name_new_na": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.rename_DataArray.rename.if_names_or_utils_is_dict.else_.return.self__replace_name_new_na", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1410, "end_line": 1447, "span_ids": ["DataArray.rename"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def rename(\n self,\n new_name_or_name_dict: Union[Hashable, Mapping[Hashable, Hashable]] = None,\n **names: Hashable\n ) -> \"DataArray\":\n \"\"\"Returns a new DataArray with renamed coordinates or a new name.\n\n Parameters\n ----------\n new_name_or_name_dict : str or dict-like, optional\n If the argument is dict-like, it used as a mapping from old\n names to new names for coordinates. Otherwise, use the argument\n as the new name for this array.\n **names: hashable, optional\n The keyword arguments form of a mapping from old names to\n new names for coordinates.\n One of new_name_or_name_dict or names must be provided.\n\n Returns\n -------\n renamed : DataArray\n Renamed array or array with renamed coordinates.\n\n See Also\n --------\n Dataset.rename\n DataArray.swap_dims\n \"\"\"\n if names or utils.is_dict_like(new_name_or_name_dict):\n new_name_or_name_dict = cast(\n Mapping[Hashable, Hashable], new_name_or_name_dict\n )\n name_dict = either_dict_or_kwargs(new_name_or_name_dict, names, \"rename\")\n dataset = self._to_temp_dataset().rename(name_dict)\n return self._from_temp_dataset(dataset)\n else:\n new_name_or_name_dict = cast(Hashable, new_name_or_name_dict)\n return self._replace(name=new_name_or_name_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.swap_dims_DataArray.swap_dims.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.swap_dims_DataArray.swap_dims.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1449, "end_line": 1488, "span_ids": ["DataArray.swap_dims"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def swap_dims(self, dims_dict: Mapping[Hashable, Hashable]) -> \"DataArray\":\n \"\"\"Returns a new DataArray with swapped dimensions.\n\n Parameters\n ----------\n dims_dict : dict-like\n Dictionary whose keys are current dimension names and whose values\n are new names. Each value must already be a coordinate on this\n array.\n\n Returns\n -------\n swapped : DataArray\n DataArray with swapped dimensions.\n\n Examples\n --------\n >>> arr = xr.DataArray(data=[0, 1], dims=\"x\",\n coords={\"x\": [\"a\", \"b\"], \"y\": (\"x\", [0, 1])})\n >>> arr\n \n array([0, 1])\n Coordinates:\n * x (x) >> arr.swap_dims({\"x\": \"y\"})\n \n array([0, 1])\n Coordinates:\n x (y) \"DataArray\":\n \"\"\"Return a new object with an additional axis (or axes) inserted at\n the corresponding position in the array shape. The new object is a\n view into the underlying array, not a copy.\n\n\n If dim is already a scalar coordinate, it will be promoted to a 1D\n coordinate consisting of a single value.\n\n Parameters\n ----------\n dim : hashable, sequence of hashable, dict, or None\n Dimensions to include on the new variable.\n If provided as str or sequence of str, then dimensions are inserted\n with length 1. If provided as a dict, then the keys are the new\n dimensions and the values are either integers (giving the length of\n the new dimensions) or sequence/ndarray (giving the coordinates of\n the new dimensions).\n axis : integer, list (or tuple) of integers, or None\n Axis position(s) where new axis is to be inserted (position(s) on\n the result array). If a list (or tuple) of integers is passed,\n multiple axes are inserted. In this case, dim arguments should be\n same length list. If axis=None is passed, all the axes will be\n inserted to the start of the result array.\n **dim_kwargs : int or sequence/ndarray\n The keywords are arbitrary dimensions being inserted and the values\n are either the lengths of the new dims (if int is given), or their\n coordinates. Note, this is an alternative to passing a dict to the\n dim kwarg and will only be used if dim is None.\n\n Returns\n -------\n expanded : same type as caller\n This object, but with an additional dimension(s).\n \"\"\"\n if isinstance(dim, int):\n raise TypeError(\"dim should be hashable or sequence/mapping of hashables\")\n elif isinstance(dim, Sequence) and not isinstance(dim, str):\n if len(dim) != len(set(dim)):\n raise ValueError(\"dims should not contain duplicate values.\")\n dim = dict.fromkeys(dim, 1)\n elif dim is not None and not isinstance(dim, Mapping):\n dim = {cast(Hashable, dim): 1}\n\n dim = either_dict_or_kwargs(dim, dim_kwargs, \"expand_dims\")\n ds = self._to_temp_dataset().expand_dims(dim, axis)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.set_index_DataArray.set_index.return.self__replace_coords_coor": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.set_index_DataArray.set_index.return.self__replace_coords_coor", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1543, "end_line": 1602, "span_ids": ["DataArray.set_index"], "tokens": 539}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def set_index(\n self,\n indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]] = None,\n append: bool = False,\n inplace: bool = None,\n **indexes_kwargs: Union[Hashable, Sequence[Hashable]]\n ) -> Optional[\"DataArray\"]:\n \"\"\"Set DataArray (multi-)indexes using one or more existing\n coordinates.\n\n Parameters\n ----------\n indexes : {dim: index, ...}\n Mapping from names matching dimensions and values given\n by (lists of) the names of existing coordinates or variables to set\n as new (multi-)index.\n append : bool, optional\n If True, append the supplied index(es) to the existing index(es).\n Otherwise replace the existing index(es) (default).\n **indexes_kwargs: optional\n The keyword arguments form of ``indexes``.\n One of indexes or indexes_kwargs must be provided.\n\n Returns\n -------\n obj : DataArray\n Another DataArray, with this data but replaced coordinates.\n\n Examples\n --------\n >>> arr = xr.DataArray(data=np.ones((2, 3)),\n ... dims=['x', 'y'],\n ... coords={'x':\n ... range(2), 'y':\n ... range(3), 'a': ('x', [3, 4])\n ... })\n >>> arr\n \n array([[1., 1., 1.],\n [1., 1., 1.]])\n Coordinates:\n * x (x) int64 0 1\n * y (y) int64 0 1 2\n a (x) int64 3 4\n >>> arr.set_index(x='a')\n \n array([[1., 1., 1.],\n [1., 1., 1.]])\n Coordinates:\n * x (x) int64 3 4\n * y (y) int64 0 1 2\n\n See Also\n --------\n DataArray.reset_index\n \"\"\"\n _check_inplace(inplace)\n indexes = either_dict_or_kwargs(indexes, indexes_kwargs, \"set_index\")\n coords, _ = merge_indexes(indexes, self._coords, set(), append=append)\n return self._replace(coords=coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reset_index_DataArray.reset_index.return.self__replace_coords_coor": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reset_index_DataArray.reset_index.return.self__replace_coords_coor", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1604, "end_line": 1635, "span_ids": ["DataArray.reset_index"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reset_index(\n self,\n dims_or_levels: Union[Hashable, Sequence[Hashable]],\n drop: bool = False,\n inplace: bool = None,\n ) -> Optional[\"DataArray\"]:\n \"\"\"Reset the specified index(es) or multi-index level(s).\n\n Parameters\n ----------\n dims_or_levels : hashable or sequence of hashables\n Name(s) of the dimension(s) and/or multi-index level(s) that will\n be reset.\n drop : bool, optional\n If True, remove the specified indexes and/or multi-index levels\n instead of extracting them as new coordinates (default: False).\n\n Returns\n -------\n obj : DataArray\n Another dataarray, with this dataarray's data but replaced\n coordinates.\n\n See Also\n --------\n DataArray.set_index\n \"\"\"\n _check_inplace(inplace)\n coords, _ = split_indexes(\n dims_or_levels, self._coords, set(), self._level_coords, drop=drop\n )\n return self._replace(coords=coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reorder_levels_DataArray.reorder_levels.return.self__replace_coords_coor": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reorder_levels_DataArray.reorder_levels.return.self__replace_coords_coor", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1637, "end_line": 1672, "span_ids": ["DataArray.reorder_levels"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reorder_levels(\n self,\n dim_order: Mapping[Hashable, Sequence[int]] = None,\n inplace: bool = None,\n **dim_order_kwargs: Sequence[int]\n ) -> \"DataArray\":\n \"\"\"Rearrange index levels using input order.\n\n Parameters\n ----------\n dim_order : optional\n Mapping from names matching dimensions and values given\n by lists representing new level orders. Every given dimension\n must have a multi-index.\n **dim_order_kwargs: optional\n The keyword arguments form of ``dim_order``.\n One of dim_order or dim_order_kwargs must be provided.\n\n Returns\n -------\n obj : DataArray\n Another dataarray, with this dataarray's data but replaced\n coordinates.\n \"\"\"\n _check_inplace(inplace)\n dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs, \"reorder_levels\")\n replace_coords = {}\n for dim, order in dim_order.items():\n coord = self._coords[dim]\n index = coord.to_index()\n if not isinstance(index, pd.MultiIndex):\n raise ValueError(\"coordinate %r has no MultiIndex\" % dim)\n replace_coords[dim] = IndexVariable(coord.dims, index.reorder_levels(order))\n coords = self._coords.copy()\n coords.update(replace_coords)\n return self._replace(coords=coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.stack_DataArray.stack.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.stack_DataArray.stack.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1674, "end_line": 1722, "span_ids": ["DataArray.stack"], "tokens": 427}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def stack(\n self,\n dimensions: Mapping[Hashable, Sequence[Hashable]] = None,\n **dimensions_kwargs: Sequence[Hashable]\n ) -> \"DataArray\":\n \"\"\"\n Stack any number of existing dimensions into a single new dimension.\n\n New dimensions will be added at the end, and the corresponding\n coordinate variables will be combined into a MultiIndex.\n\n Parameters\n ----------\n dimensions : Mapping of the form new_name=(dim1, dim2, ...)\n Names of new dimensions, and the existing dimensions that they\n replace.\n **dimensions_kwargs:\n The keyword arguments form of ``dimensions``.\n One of dimensions or dimensions_kwargs must be provided.\n\n Returns\n -------\n stacked : DataArray\n DataArray with stacked data.\n\n Examples\n --------\n\n >>> arr = DataArray(np.arange(6).reshape(2, 3),\n ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])])\n >>> arr\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Coordinates:\n * x (x) |S1 'a' 'b'\n * y (y) int64 0 1 2\n >>> stacked = arr.stack(z=('x', 'y'))\n >>> stacked.indexes['z']\n MultiIndex(levels=[['a', 'b'], [0, 1, 2]],\n codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n names=['x', 'y'])\n\n See also\n --------\n DataArray.unstack\n \"\"\"\n ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.unstack_DataArray.unstack.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.unstack_DataArray.unstack.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1724, "end_line": 1770, "span_ids": ["DataArray.unstack"], "tokens": 402}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def unstack(\n self, dim: Union[Hashable, Sequence[Hashable], None] = None\n ) -> \"DataArray\":\n \"\"\"\n Unstack existing dimensions corresponding to MultiIndexes into\n multiple new dimensions.\n\n New dimensions will be added at the end.\n\n Parameters\n ----------\n dim : hashable or sequence of hashable, optional\n Dimension(s) over which to unstack. By default unstacks all\n MultiIndexes.\n\n Returns\n -------\n unstacked : DataArray\n Array with unstacked data.\n\n Examples\n --------\n\n >>> arr = DataArray(np.arange(6).reshape(2, 3),\n ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])])\n >>> arr\n \n array([[0, 1, 2],\n [3, 4, 5]])\n Coordinates:\n * x (x) |S1 'a' 'b'\n * y (y) int64 0 1 2\n >>> stacked = arr.stack(z=('x', 'y'))\n >>> stacked.indexes['z']\n MultiIndex(levels=[['a', 'b'], [0, 1, 2]],\n codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n names=['x', 'y'])\n >>> roundtripped = stacked.unstack()\n >>> arr.identical(roundtripped)\n True\n\n See also\n --------\n DataArray.stack\n \"\"\"\n ds = self._to_temp_dataset().unstack(dim)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_unstacked_dataset_DataArray.to_unstacked_dataset.return.Dataset_data_dict_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_unstacked_dataset_DataArray.to_unstacked_dataset.return.Dataset_data_dict_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1772, "end_line": 1836, "span_ids": ["DataArray.to_unstacked_dataset"], "tokens": 562}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_unstacked_dataset(self, dim, level=0):\n \"\"\"Unstack DataArray expanding to Dataset along a given level of a\n stacked coordinate.\n\n This is the inverse operation of Dataset.to_stacked_array.\n\n Parameters\n ----------\n dim : str\n Name of existing dimension to unstack\n level : int or str\n The MultiIndex level to expand to a dataset along. Can either be\n the integer index of the level or its name.\n label : int, default 0\n Label of the level to expand dataset along. Overrides the label\n argument if given.\n\n Returns\n -------\n unstacked: Dataset\n\n Examples\n --------\n >>> import xarray as xr\n >>> arr = DataArray(np.arange(6).reshape(2, 3),\n ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])])\n >>> data = xr.Dataset({'a': arr, 'b': arr.isel(y=0)})\n >>> data\n \n Dimensions: (x: 2, y: 3)\n Coordinates:\n * x (x) >> stacked = data.to_stacked_array(\"z\", ['y'])\n >>> stacked.indexes['z']\n MultiIndex(levels=[['a', 'b'], [0, 1, 2]],\n labels=[[0, 0, 0, 1], [0, 1, 2, -1]],\n names=['variable', 'y'])\n >>> roundtripped = stacked.to_unstacked_dataset(dim='z')\n >>> data.identical(roundtripped)\n True\n\n See Also\n --------\n Dataset.to_stacked_array\n \"\"\"\n\n idx = self.indexes[dim]\n if not isinstance(idx, pd.MultiIndex):\n raise ValueError(\"'{}' is not a stacked coordinate\".format(dim))\n\n level_number = idx._get_level_number(level)\n variables = idx.levels[level_number]\n variable_dim = idx.names[level_number]\n\n # pull variables out of datarray\n data_dict = {}\n for k in variables:\n data_dict[k] = self.sel({variable_dim: k}).squeeze(drop=True)\n\n # unstacked dataset\n return Dataset(data_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.transpose_DataArray.transpose.if_transpose_coords_.else_.return.self__replace_variable_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.transpose_DataArray.transpose.if_transpose_coords_.else_.return.self__replace_variable_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1838, "end_line": 1889, "span_ids": ["DataArray.transpose"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def transpose(self, *dims: Hashable, transpose_coords: bool = None) -> \"DataArray\":\n \"\"\"Return a new DataArray object with transposed dimensions.\n\n Parameters\n ----------\n *dims : hashable, optional\n By default, reverse the dimensions. Otherwise, reorder the\n dimensions to this order.\n transpose_coords : boolean, optional\n If True, also transpose the coordinates of this DataArray.\n\n Returns\n -------\n transposed : DataArray\n The returned DataArray's array is transposed.\n\n Notes\n -----\n This operation returns a view of this array's data. It is\n lazy for dask-backed DataArrays but not for numpy-backed DataArrays\n -- the data will be fully loaded.\n\n See Also\n --------\n numpy.transpose\n Dataset.transpose\n \"\"\"\n if dims:\n if set(dims) ^ set(self.dims):\n raise ValueError(\n \"arguments to transpose (%s) must be \"\n \"permuted array dimensions (%s)\" % (dims, tuple(self.dims))\n )\n\n variable = self.variable.transpose(*dims)\n if transpose_coords:\n coords: Dict[Hashable, Variable] = {}\n for name, coord in self.coords.items():\n coord_dims = tuple(dim for dim in dims if dim in coord.dims)\n coords[name] = coord.variable.transpose(*coord_dims)\n return self._replace(variable, coords)\n else:\n if transpose_coords is None and any(self[c].ndim > 1 for c in self.coords):\n warnings.warn(\n \"This DataArray contains multi-dimensional \"\n \"coordinates. In the future, these coordinates \"\n \"will be transposed as well unless you specify \"\n \"transpose_coords=False.\",\n FutureWarning,\n stacklevel=2,\n )\n return self._replace(variable)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.T_DataArray.drop_80._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.T_DataArray.drop_80._", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1891, "end_line": 1907, "span_ids": ["DataArray.drop", "DataArray.drop_80", "DataArray.T"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @property\n def T(self) -> \"DataArray\":\n return self.transpose()\n\n # Drop coords\n @overload\n def drop(\n self, labels: Union[Hashable, Iterable[Hashable]], *, errors: str = \"raise\"\n ) -> \"DataArray\":\n ...\n\n # Drop index labels along dimension\n @overload # noqa: F811\n def drop(\n self, labels: Any, dim: Hashable, *, errors: str = \"raise\" # array-like\n ) -> \"DataArray\":\n ...", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.drop_81_DataArray.drop_81.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.drop_81_DataArray.drop_81.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1909, "end_line": 1930, "span_ids": ["DataArray.drop_81"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def drop(self, labels, dim=None, *, errors=\"raise\"): # noqa: F811\n \"\"\"Drop coordinates or index labels from this DataArray.\n\n Parameters\n ----------\n labels : hashable or sequence of hashables\n Name(s) of coordinates or index labels to drop.\n If dim is not None, labels can be any array-like.\n dim : hashable, optional\n Dimension along which to drop index labels. By default (if\n ``dim is None``), drops coordinates rather than index labels.\n errors: {'raise', 'ignore'}, optional\n If 'raise' (default), raises a ValueError error if\n any of the coordinates or index labels passed are not\n in the array. If 'ignore', any given labels that are in the\n array are dropped and no error is raised.\n Returns\n -------\n dropped : DataArray\n \"\"\"\n ds = self._to_temp_dataset().drop(labels, dim, errors=errors)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.dropna_DataArray.dropna.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.dropna_DataArray.dropna.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1932, "end_line": 1954, "span_ids": ["DataArray.dropna"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def dropna(\n self, dim: Hashable, how: str = \"any\", thresh: int = None\n ) -> \"DataArray\":\n \"\"\"Returns a new array with dropped labels for missing values along\n the provided dimension.\n\n Parameters\n ----------\n dim : hashable\n Dimension along which to drop missing values. Dropping along\n multiple dimensions simultaneously is not yet supported.\n how : {'any', 'all'}, optional\n * any : if any NA values are present, drop that label\n * all : if all values are NA, drop that label\n thresh : int, default None\n If supplied, require this many non-NA values.\n\n Returns\n -------\n DataArray\n \"\"\"\n ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.fillna_DataArray.fillna.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.fillna_DataArray.fillna.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1956, "end_line": 1981, "span_ids": ["DataArray.fillna"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def fillna(self, value: Any) -> \"DataArray\":\n \"\"\"Fill missing values in this object.\n\n This operation follows the normal broadcasting and alignment rules that\n xarray uses for binary arithmetic, except the result is aligned to this\n object (``join='left'``) instead of aligned to the intersection of\n index coordinates (``join='inner'``).\n\n Parameters\n ----------\n value : scalar, ndarray or DataArray\n Used to fill all matching missing values in this array. If the\n argument is a DataArray, it is first aligned with (reindexed to)\n this array.\n\n Returns\n -------\n DataArray\n \"\"\"\n if utils.is_dict_like(value):\n raise TypeError(\n \"cannot provide fill value as a dictionary with \"\n \"fillna on a DataArray\"\n )\n out = ops.fillna(self, value)\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interpolate_na_DataArray.interpolate_na.return.interp_na_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.interpolate_na_DataArray.interpolate_na.return.interp_na_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1983, "end_line": 2038, "span_ids": ["DataArray.interpolate_na"], "tokens": 470}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def interpolate_na(\n self,\n dim=None,\n method: str = \"linear\",\n limit: int = None,\n use_coordinate: Union[bool, str] = True,\n **kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Interpolate values according to different methods.\n\n Parameters\n ----------\n dim : str\n Specifies the dimension along which to interpolate.\n method : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',\n 'polynomial', 'barycentric', 'krog', 'pchip',\n 'spline', 'akima'}, optional\n String indicating which method to use for interpolation:\n\n - 'linear': linear interpolation (Default). Additional keyword\n arguments are passed to ``numpy.interp``\n - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',\n 'polynomial': are passed to ``scipy.interpolate.interp1d``. If\n method=='polynomial', the ``order`` keyword argument must also be\n provided.\n - 'barycentric', 'krog', 'pchip', 'spline', and `akima`: use their\n respective``scipy.interpolate`` classes.\n use_coordinate : boolean or str, default True\n Specifies which index to use as the x values in the interpolation\n formulated as `y = f(x)`. If False, values are treated as if\n eqaully-spaced along `dim`. If True, the IndexVariable `dim` is\n used. If use_coordinate is a string, it specifies the name of a\n coordinate variariable to use as the index.\n limit : int, default None\n Maximum number of consecutive NaNs to fill. Must be greater than 0\n or None for no limit.\n\n Returns\n -------\n DataArray\n\n See also\n --------\n numpy.interp\n scipy.interpolate\n \"\"\"\n from .missing import interp_na\n\n return interp_na(\n self,\n dim=dim,\n method=method,\n limit=limit,\n use_coordinate=use_coordinate,\n **kwargs\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.ffill_DataArray.ffill.return.ffill_self_dim_limit_li": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.ffill_DataArray.ffill.return.ffill_self_dim_limit_li", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2040, "end_line": 2062, "span_ids": ["DataArray.ffill"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def ffill(self, dim: Hashable, limit: int = None) -> \"DataArray\":\n \"\"\"Fill NaN values by propogating values forward\n\n *Requires bottleneck.*\n\n Parameters\n ----------\n dim : hashable\n Specifies the dimension along which to propagate values when\n filling.\n limit : int, default None\n The maximum number of consecutive NaN values to forward fill. In\n other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. Must be greater\n than 0 or None for no limit.\n\n Returns\n -------\n DataArray\n \"\"\"\n from .missing import ffill\n\n return ffill(self, dim, limit=limit)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.bfill_DataArray.bfill.return.bfill_self_dim_limit_li": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.bfill_DataArray.bfill.return.bfill_self_dim_limit_li", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2064, "end_line": 2086, "span_ids": ["DataArray.bfill"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def bfill(self, dim: Hashable, limit: int = None) -> \"DataArray\":\n \"\"\"Fill NaN values by propogating values backward\n\n *Requires bottleneck.*\n\n Parameters\n ----------\n dim : str\n Specifies the dimension along which to propagate values when\n filling.\n limit : int, default None\n The maximum number of consecutive NaN values to backward fill. In\n other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. Must be greater\n than 0 or None for no limit.\n\n Returns\n -------\n DataArray\n \"\"\"\n from .missing import bfill\n\n return bfill(self, dim, limit=limit)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.combine_first_DataArray.combine_first.return.ops_fillna_self_other_j": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.combine_first_DataArray.combine_first.return.ops_fillna_self_other_j", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2088, "end_line": 2104, "span_ids": ["DataArray.combine_first"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def combine_first(self, other: \"DataArray\") -> \"DataArray\":\n \"\"\"Combine two DataArray objects, with union of coordinates.\n\n This operation follows the normal broadcasting and alignment rules of\n ``join='outer'``. Default to non-null values of array calling the\n method. Use np.nan to fill in vacant cells after alignment.\n\n Parameters\n ----------\n other : DataArray\n Used to fill all matching missing values in this array.\n\n Returns\n -------\n DataArray\n \"\"\"\n return ops.fillna(self, other, join=\"outer\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reduce_DataArray.reduce.return.self__replace_maybe_drop_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.reduce_DataArray.reduce.return.self__replace_maybe_drop_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2106, "end_line": 2149, "span_ids": ["DataArray.reduce"], "tokens": 422}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def reduce(\n self,\n func: Callable[..., Any],\n dim: Union[None, Hashable, Sequence[Hashable]] = None,\n axis: Union[None, int, Sequence[int]] = None,\n keep_attrs: bool = None,\n keepdims: bool = False,\n **kwargs: Any\n ) -> \"DataArray\":\n \"\"\"Reduce this array by applying `func` along some dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `f(x, axis=axis, **kwargs)` to return the result of reducing an\n np.ndarray over an integer valued axis.\n dim : hashable or sequence of hashables, optional\n Dimension(s) over which to apply `func`.\n axis : int or sequence of int, optional\n Axis(es) over which to repeatedly apply `func`. Only one of the\n 'dim' and 'axis' arguments can be supplied. If neither are\n supplied, then the reduction is calculated over the flattened array\n (by calling `f(x)` without an axis argument).\n keep_attrs : bool, optional\n If True, the variable's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n keepdims : bool, default False\n If True, the dimensions which are reduced are left in the result\n as dimensions of size one. Coordinates that use these dimensions\n are removed.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : DataArray\n DataArray with this object's array replaced with an array with\n summarized data and the indicated dimension(s) removed.\n \"\"\"\n\n var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)\n return self._replace_maybe_drop_dims(var)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_pandas_DataArray.to_pandas.return.constructor_self_values_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_pandas_DataArray.to_pandas.return.constructor_self_values_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2151, "end_line": 2182, "span_ids": ["DataArray.to_pandas"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_pandas(self) -> Union[\"DataArray\", pd.Series, pd.DataFrame]:\n \"\"\"Convert this array into a pandas object with the same shape.\n\n The type of the returned object depends on the number of DataArray\n dimensions:\n\n * 0D -> `xarray.DataArray`\n * 1D -> `pandas.Series`\n * 2D -> `pandas.DataFrame`\n * 3D -> `pandas.Panel` *(deprecated)*\n\n Only works for arrays with 3 or fewer dimensions.\n\n The DataArray constructor performs the inverse transformation.\n \"\"\"\n # TODO: consolidate the info about pandas constructors and the\n # attributes that correspond to their indexes into a separate module?\n constructors = {\n 0: lambda x: x,\n 1: pd.Series,\n 2: pd.DataFrame,\n 3: pdcompat.Panel,\n }\n try:\n constructor = constructors[self.ndim]\n except KeyError:\n raise ValueError(\n \"cannot convert arrays with %s dimensions into \"\n \"pandas objects\" % self.ndim\n )\n indexes = [self.get_index(dim) for dim in self.dims]\n return constructor(self.values, *indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dataframe_DataArray.to_dataframe.return.df": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dataframe_DataArray.to_dataframe.return.df", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2184, "end_line": 2210, "span_ids": ["DataArray.to_dataframe"], "tokens": 286}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_dataframe(self, name: Hashable = None) -> pd.DataFrame:\n \"\"\"Convert this array and its coordinates into a tidy pandas.DataFrame.\n\n The DataFrame is indexed by the Cartesian product of index coordinates\n (in the form of a :py:class:`pandas.MultiIndex`).\n\n Other coordinates are included as columns in the DataFrame.\n \"\"\"\n if name is None:\n name = self.name\n if name is None:\n raise ValueError(\n \"cannot convert an unnamed DataArray to a \"\n \"DataFrame: use the ``name`` parameter\"\n )\n\n dims = dict(zip(self.dims, self.shape))\n # By using a unique name, we can convert a DataArray into a DataFrame\n # even if it shares a name with one of its coordinates.\n # I would normally use unique_name = object() but that results in a\n # dataframe with columns in the wrong order, for reasons I have not\n # been able to debug (possibly a pandas bug?).\n unique_name = \"__unique_name_identifier_z98xfz98xugfg73ho__\"\n ds = self._to_dataset_whole(name=unique_name)\n df = ds._to_dataframe(dims)\n df.columns = [name if c == unique_name else c for c in df.columns]\n return df", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_series_DataArray.to_masked_array.return.np_ma_MaskedArray_data_va": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_series_DataArray.to_masked_array.return.np_ma_MaskedArray_data_va", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2212, "end_line": 2237, "span_ids": ["DataArray.to_series", "DataArray.to_masked_array"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_series(self) -> pd.Series:\n \"\"\"Convert this array into a pandas.Series.\n\n The Series is indexed by the Cartesian product of index coordinates\n (in the form of a :py:class:`pandas.MultiIndex`).\n \"\"\"\n index = self.coords.to_index()\n return pd.Series(self.values.reshape(-1), index=index, name=self.name)\n\n def to_masked_array(self, copy: bool = True) -> np.ma.MaskedArray:\n \"\"\"Convert this array into a numpy.ma.MaskedArray\n\n Parameters\n ----------\n copy : bool\n If True (default) make a copy of the array in the result. If False,\n a MaskedArray view of DataArray.values is returned.\n\n Returns\n -------\n result : MaskedArray\n Masked where invalid values (nan or inf) occur.\n \"\"\"\n values = self.values # only compute lazy arrays once\n isnull = pd.isnull(values)\n return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_netcdf_DataArray.to_netcdf.return.dataset_to_netcdf_args_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_netcdf_DataArray.to_netcdf.return.dataset_to_netcdf_args_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2239, "end_line": 2266, "span_ids": ["DataArray.to_netcdf"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_netcdf(self, *args, **kwargs) -> Union[bytes, \"Delayed\", None]:\n \"\"\"Write DataArray contents to a netCDF file.\n\n All parameters are passed directly to `xarray.Dataset.to_netcdf`.\n\n Notes\n -----\n Only xarray.Dataset objects can be written to netCDF files, so\n the xarray.DataArray is converted to a xarray.Dataset object\n containing a single variable. If the DataArray has no name, or if the\n name is the same as a co-ordinate name, then it is given the name\n '__xarray_dataarray_variable__'.\n \"\"\"\n from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE\n\n if self.name is None:\n # If no name is set then use a generic xarray name\n dataset = self.to_dataset(name=DATAARRAY_VARIABLE)\n elif self.name in self.coords or self.name in self.dims:\n # The name is the same as one of the coords names, which netCDF\n # doesn't support, so rename it but keep track of the old name\n dataset = self.to_dataset(name=DATAARRAY_VARIABLE)\n dataset.attrs[DATAARRAY_NAME] = self.name\n else:\n # No problems with the name - so we're fine!\n dataset = self.to_dataset()\n\n return dataset.to_netcdf(*args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dict_DataArray.to_dict.return.d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_dict_DataArray.to_dict.return.d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2268, "end_line": 2291, "span_ids": ["DataArray.to_dict"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_dict(self, data: bool = True) -> dict:\n \"\"\"\n Convert this xarray.DataArray into a dictionary following xarray\n naming conventions.\n\n Converts all variables and attributes to native Python objects.\n Useful for coverting to json. To avoid datetime incompatibility\n use decode_times=False kwarg in xarrray.open_dataset.\n\n Parameters\n ----------\n data : bool, optional\n Whether to include the actual data in the dictionary. When set to\n False, returns just the schema.\n\n See also\n --------\n DataArray.from_dict\n \"\"\"\n d = self.variable.to_dict(data=data)\n d.update({\"coords\": {}, \"name\": self.name})\n for k in self.coords:\n d[\"coords\"][k] = self.coords[k].variable.to_dict(data=data)\n return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.from_dict_DataArray.from_dict.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.from_dict_DataArray.from_dict.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2293, "end_line": 2343, "span_ids": ["DataArray.from_dict"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @classmethod\n def from_dict(cls, d: dict) -> \"DataArray\":\n \"\"\"\n Convert a dictionary into an xarray.DataArray\n\n Input dict can take several forms::\n\n d = {'dims': ('t'), 'data': x}\n\n d = {'coords': {'t': {'dims': 't', 'data': t,\n 'attrs': {'units':'s'}}},\n 'attrs': {'title': 'air temperature'},\n 'dims': 't',\n 'data': x,\n 'name': 'a'}\n\n where 't' is the name of the dimesion, 'a' is the name of the array,\n and x and t are lists, numpy.arrays, or pandas objects.\n\n Parameters\n ----------\n d : dict, with a minimum structure of {'dims': [..], 'data': [..]}\n\n Returns\n -------\n obj : xarray.DataArray\n\n See also\n --------\n DataArray.to_dict\n Dataset.from_dict\n \"\"\"\n coords = None\n if \"coords\" in d:\n try:\n coords = {\n k: (v[\"dims\"], v[\"data\"], v.get(\"attrs\"))\n for k, v in d[\"coords\"].items()\n }\n except KeyError as e:\n raise ValueError(\n \"cannot convert dict when coords are missing the key \"\n \"'{dims_data}'\".format(dims_data=str(e.args[0]))\n )\n try:\n data = d[\"data\"]\n except KeyError:\n raise ValueError(\"cannot convert dict without the key 'data''\")\n else:\n obj = cls(data, coords, d.get(\"dims\"), d.get(\"name\"), d.get(\"attrs\"))\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.from_series_DataArray.from_series.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.from_series_DataArray.from_series.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2345, "end_line": 2366, "span_ids": ["DataArray.from_series"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @classmethod\n def from_series(cls, series: pd.Series, sparse: bool = False) -> \"DataArray\":\n \"\"\"Convert a pandas.Series into an xarray.DataArray.\n\n If the series's index is a MultiIndex, it will be expanded into a\n tensor product of one-dimensional coordinates (filling in missing\n values with NaN). Thus this operation should be the inverse of the\n `to_series` method.\n\n If sparse=True, creates a sparse array instead of a dense NumPy array.\n Requires the pydata/sparse package.\n\n See also\n --------\n xarray.Dataset.from_dataframe\n \"\"\"\n temp_name = \"__temporary_name\"\n df = pd.DataFrame({temp_name: series})\n ds = Dataset.from_dataframe(df, sparse=sparse)\n result = cast(DataArray, ds[temp_name])\n result.name = series.name\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_cdms2_DataArray.broadcast_equals.try_.except_TypeError_Attrib.return.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.to_cdms2_DataArray.broadcast_equals.try_.except_TypeError_Attrib.return.False", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2368, "end_line": 2422, "span_ids": ["DataArray.from_iris", "DataArray.to_iris", "DataArray.to_cdms2", "DataArray.broadcast_equals", "DataArray.from_cdms2", "DataArray._all_compat"], "tokens": 387}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def to_cdms2(self) -> \"cdms2_Variable\":\n \"\"\"Convert this array into a cdms2.Variable\n \"\"\"\n from ..convert import to_cdms2\n\n return to_cdms2(self)\n\n @classmethod\n def from_cdms2(cls, variable: \"cdms2_Variable\") -> \"DataArray\":\n \"\"\"Convert a cdms2.Variable into an xarray.DataArray\n \"\"\"\n from ..convert import from_cdms2\n\n return from_cdms2(variable)\n\n def to_iris(self) -> \"iris_Cube\":\n \"\"\"Convert this array into a iris.cube.Cube\n \"\"\"\n from ..convert import to_iris\n\n return to_iris(self)\n\n @classmethod\n def from_iris(cls, cube: \"iris_Cube\") -> \"DataArray\":\n \"\"\"Convert a iris.cube.Cube into an xarray.DataArray\n \"\"\"\n from ..convert import from_iris\n\n return from_iris(cube)\n\n def _all_compat(self, other: \"DataArray\", compat_str: str) -> bool:\n \"\"\"Helper function for equals, broadcast_equals, and identical\n \"\"\"\n\n def compat(x, y):\n return getattr(x.variable, compat_str)(y.variable)\n\n return utils.dict_equiv(self.coords, other.coords, compat=compat) and compat(\n self, other\n )\n\n def broadcast_equals(self, other: \"DataArray\") -> bool:\n \"\"\"Two DataArrays are broadcast equal if they are equal after\n broadcasting them against each other such that they have the same\n dimensions.\n\n See Also\n --------\n DataArray.equals\n DataArray.identical\n \"\"\"\n try:\n return self._all_compat(other, \"broadcast_equals\")\n except (TypeError, AttributeError):\n return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.equals_DataArray.equals.try_.except_TypeError_Attrib.return.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.equals_DataArray.equals.try_.except_TypeError_Attrib.return.False", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2424, "end_line": 2442, "span_ids": ["DataArray.equals"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def equals(self, other: \"DataArray\") -> bool:\n \"\"\"True if two DataArrays have the same dimensions, coordinates and\n values; otherwise False.\n\n DataArrays can still be equal (like pandas objects) if they have NaN\n values in the same locations.\n\n This method is necessary because `v1 == v2` for ``DataArray``\n does element-wise comparisons (like numpy.ndarrays).\n\n See Also\n --------\n DataArray.broadcast_equals\n DataArray.identical\n \"\"\"\n try:\n return self._all_compat(other, \"equals\")\n except (TypeError, AttributeError):\n return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.identical_DataArray._unary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.identical_DataArray._unary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2444, "end_line": 2488, "span_ids": ["DataArray.__rmatmul__", "DataArray.__matmul__", "DataArray.__array_wrap__", "DataArray._unary_op", "DataArray._result_name", "DataArray:24", "DataArray.identical"], "tokens": 370}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def identical(self, other: \"DataArray\") -> bool:\n \"\"\"Like equals, but also checks the array name and attributes, and\n attributes on all coordinates.\n\n See Also\n --------\n DataArray.broadcast_equals\n DataArray.equal\n \"\"\"\n try:\n return self.name == other.name and self._all_compat(other, \"identical\")\n except (TypeError, AttributeError):\n return False\n\n __default_name = object()\n\n def _result_name(self, other: Any = None) -> Optional[Hashable]:\n # use the same naming heuristics as pandas:\n # https://github.com/ContinuumIO/blaze/issues/458#issuecomment-51936356\n other_name = getattr(other, \"name\", self.__default_name)\n if other_name is self.__default_name or other_name == self.name:\n return self.name\n else:\n return None\n\n def __array_wrap__(self, obj, context=None) -> \"DataArray\":\n new_var = self.variable.__array_wrap__(obj, context)\n return self._replace(new_var)\n\n def __matmul__(self, obj):\n return self.dot(obj)\n\n def __rmatmul__(self, other):\n # currently somewhat duplicative, as only other DataArrays are\n # compatible with matmul\n return computation.dot(other, self)\n\n @staticmethod\n def _unary_op(f: Callable[..., Any]) -> Callable[..., \"DataArray\"]:\n @functools.wraps(f)\n def func(self, *args, **kwargs):\n with np.errstate(all=\"ignore\"):\n return self.__array_wrap__(f(self.variable.data, *args, **kwargs))\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._binary_op_DataArray._binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._binary_op_DataArray._binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2490, "end_line": 2517, "span_ids": ["DataArray._binary_op"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @staticmethod\n def _binary_op(\n f: Callable[..., Any],\n reflexive: bool = False,\n join: str = None, # see xarray.align\n **ignored_kwargs\n ) -> Callable[..., \"DataArray\"]:\n @functools.wraps(f)\n def func(self, other):\n if isinstance(other, (Dataset, groupby.GroupBy)):\n return NotImplemented\n if isinstance(other, DataArray):\n align_type = OPTIONS[\"arithmetic_join\"] if join is None else join\n self, other = align(self, other, join=align_type, copy=False)\n other_variable = getattr(other, \"variable\", other)\n other_coords = getattr(other, \"coords\", None)\n\n variable = (\n f(self.variable, other_variable)\n if not reflexive\n else f(other_variable, self.variable)\n )\n coords, indexes = self.coords._merge_raw(other_coords)\n name = self._result_name(other)\n\n return self._replace(variable, coords, name)\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._inplace_binary_op_DataArray._inplace_binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._inplace_binary_op_DataArray._inplace_binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2519, "end_line": 2538, "span_ids": ["DataArray._inplace_binary_op"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @staticmethod\n def _inplace_binary_op(f: Callable) -> Callable[..., \"DataArray\"]:\n @functools.wraps(f)\n def func(self, other):\n if isinstance(other, groupby.GroupBy):\n raise TypeError(\n \"in-place operations between a DataArray and \"\n \"a grouped object are not permitted\"\n )\n # n.b. we can't align other to self (with other.reindex_like(self))\n # because `other` may be converted into floats, which would cause\n # in-place arithmetic to fail unpredictably. Instead, we simply\n # don't support automatic alignment with in-place arithmetic.\n other_coords = getattr(other, \"coords\", None)\n other_variable = getattr(other, \"variable\", other)\n with self.coords._merge_inplace(other_coords):\n f(self.variable, other_variable)\n return self\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._copy_attrs_from_DataArray.plot.return._PlotMethods_self_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._copy_attrs_from_DataArray.plot.return._PlotMethods_self_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2540, "end_line": 2560, "span_ids": ["DataArray.plot", "DataArray._copy_attrs_from"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _copy_attrs_from(self, other: Union[\"DataArray\", Dataset, Variable]) -> None:\n self.attrs = other.attrs\n\n @property\n def plot(self) -> _PlotMethods:\n \"\"\"\n Access plotting functions\n\n >>> d = DataArray([[1, 2], [3, 4]])\n\n For convenience just call this directly\n\n >>> d.plot()\n\n Or use it as a namespace to use xarray.plot functions as\n DataArray methods\n\n >>> d.plot.imshow() # equivalent to xarray.plot.imshow(d)\n\n \"\"\"\n return _PlotMethods(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._title_for_slice_DataArray._title_for_slice.return.title": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray._title_for_slice_DataArray._title_for_slice.return.title", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2562, "end_line": 2589, "span_ids": ["DataArray._title_for_slice"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def _title_for_slice(self, truncate: int = 50) -> str:\n \"\"\"\n If the dataarray has 1 dimensional coordinates or comes from a slice\n we can show that info in the title\n\n Parameters\n ----------\n truncate : integer\n maximum number of characters for title\n\n Returns\n -------\n title : string\n Can be used for plot titles\n\n \"\"\"\n one_dims = []\n for dim, coord in self.coords.items():\n if coord.size == 1:\n one_dims.append(\n \"{dim} = {v}\".format(dim=dim, v=format_item(coord.values))\n )\n\n title = \", \".join(one_dims)\n if len(title) > truncate:\n title = title[: (truncate - 3)] + \"...\"\n\n return title", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.diff_DataArray.diff.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.diff_DataArray.diff.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2591, "end_line": 2630, "span_ids": ["DataArray.diff"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def diff(self, dim: Hashable, n: int = 1, label: Hashable = \"upper\") -> \"DataArray\":\n \"\"\"Calculate the n-th order discrete difference along given axis.\n\n Parameters\n ----------\n dim : hashable, optional\n Dimension over which to calculate the finite difference.\n n : int, optional\n The number of times values are differenced.\n label : hashable, optional\n The new coordinate in dimension ``dim`` will have the\n values of either the minuend's or subtrahend's coordinate\n for values 'upper' and 'lower', respectively. Other\n values are not supported.\n\n Returns\n -------\n difference : same type as caller\n The n-th order finite difference of this object.\n\n Examples\n --------\n >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ['x'])\n >>> arr.diff('x')\n \n array([0, 1, 0])\n Coordinates:\n * x (x) int64 2 3 4\n >>> arr.diff('x', 2)\n \n array([ 1, -1])\n Coordinates:\n * x (x) int64 3 4\n\n See Also\n --------\n DataArray.differentiate\n \"\"\"\n ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.shift_DataArray.shift.return.self__replace_variable_va": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.shift_DataArray.shift.return.self__replace_variable_va", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2632, "end_line": 2679, "span_ids": ["DataArray.shift"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def shift(\n self,\n shifts: Mapping[Hashable, int] = None,\n fill_value: Any = dtypes.NA,\n **shifts_kwargs: int\n ) -> \"DataArray\":\n \"\"\"Shift this array by an offset along one or more dimensions.\n\n Only the data is moved; coordinates stay in place. Values shifted from\n beyond array bounds are replaced by NaN. This is consistent with the\n behavior of ``shift`` in pandas.\n\n Parameters\n ----------\n shifts : Mapping with the form of {dim: offset}\n Integer offset to shift along each of the given dimensions.\n Positive offsets shift to the right; negative offsets shift to the\n left.\n fill_value: scalar, optional\n Value to use for newly missing values\n **shifts_kwargs:\n The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwarg must be provided.\n\n Returns\n -------\n shifted : DataArray\n DataArray with the same coordinates and attributes but shifted\n data.\n\n See also\n --------\n roll\n\n Examples\n --------\n\n >>> arr = xr.DataArray([5, 6, 7], dims='x')\n >>> arr.shift(x=1)\n \n array([ nan, 5., 6.])\n Coordinates:\n * x (x) int64 0 1 2\n \"\"\"\n variable = self.variable.shift(\n shifts=shifts, fill_value=fill_value, **shifts_kwargs\n )\n return self._replace(variable=variable)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.roll_DataArray.roll.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.roll_DataArray.roll.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2681, "end_line": 2729, "span_ids": ["DataArray.roll"], "tokens": 382}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def roll(\n self,\n shifts: Mapping[Hashable, int] = None,\n roll_coords: bool = None,\n **shifts_kwargs: int\n ) -> \"DataArray\":\n \"\"\"Roll this array by an offset along one or more dimensions.\n\n Unlike shift, roll may rotate all variables, including coordinates\n if specified. The direction of rotation is consistent with\n :py:func:`numpy.roll`.\n\n Parameters\n ----------\n shifts : Mapping with the form of {dim: offset}\n Integer offset to rotate each of the given dimensions.\n Positive offsets roll to the right; negative offsets roll to the\n left.\n roll_coords : bool\n Indicates whether to roll the coordinates by the offset\n The current default of roll_coords (None, equivalent to True) is\n deprecated and will change to False in a future version.\n Explicitly pass roll_coords to silence the warning.\n **shifts_kwargs : The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwarg must be provided.\n\n Returns\n -------\n rolled : DataArray\n DataArray with the same attributes but rolled data and coordinates.\n\n See also\n --------\n shift\n\n Examples\n --------\n\n >>> arr = xr.DataArray([5, 6, 7], dims='x')\n >>> arr.roll(x=1)\n \n array([7, 5, 6])\n Coordinates:\n * x (x) int64 2 0 1\n \"\"\"\n ds = self._to_temp_dataset().roll(\n shifts=shifts, roll_coords=roll_coords, **shifts_kwargs\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.real_DataArray.dot.return.computation_dot_self_oth": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.real_DataArray.dot.return.computation_dot_self_oth", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2731, "end_line": 2788, "span_ids": ["DataArray.real", "DataArray.dot", "DataArray.imag"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n @property\n def real(self) -> \"DataArray\":\n return self._replace(self.variable.real)\n\n @property\n def imag(self) -> \"DataArray\":\n return self._replace(self.variable.imag)\n\n def dot(\n self, other: \"DataArray\", dims: Union[Hashable, Sequence[Hashable], None] = None\n ) -> \"DataArray\":\n \"\"\"Perform dot product of two DataArrays along their shared dims.\n\n Equivalent to taking taking tensordot over all shared dims.\n\n Parameters\n ----------\n other : DataArray\n The other array with which the dot product is performed.\n dims: hashable or sequence of hashables, optional\n Along which dimensions to be summed over. Default all the common\n dimensions are summed over.\n\n Returns\n -------\n result : DataArray\n Array resulting from the dot product over all shared dimensions.\n\n See also\n --------\n dot\n numpy.tensordot\n\n Examples\n --------\n\n >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))\n >>> da = DataArray(da_vals, dims=['x', 'y', 'z'])\n >>> dm_vals = np.arange(4)\n >>> dm = DataArray(dm_vals, dims=['z'])\n\n >>> dm.dims\n ('z')\n >>> da.dims\n ('x', 'y', 'z')\n\n >>> dot_result = da.dot(dm)\n >>> dot_result.dims\n ('x', 'y')\n \"\"\"\n if isinstance(other, Dataset):\n raise NotImplementedError(\n \"dot products are not yet supported with Dataset objects.\"\n )\n if not isinstance(other, DataArray):\n raise TypeError(\"dot only operates on DataArrays.\")\n\n return computation.dot(self, other, dims=dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.sortby_DataArray.sortby.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.sortby_DataArray.sortby.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2790, "end_line": 2844, "span_ids": ["DataArray.sortby"], "tokens": 571}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def sortby(\n self,\n variables: Union[Hashable, \"DataArray\", Sequence[Union[Hashable, \"DataArray\"]]],\n ascending: bool = True,\n ) -> \"DataArray\":\n \"\"\"Sort object by labels or values (along an axis).\n\n Sorts the dataarray, either along specified dimensions,\n or according to values of 1-D dataarrays that share dimension\n with calling object.\n\n If the input variables are dataarrays, then the dataarrays are aligned\n (via left-join) to the calling object prior to sorting by cell values.\n NaNs are sorted to the end, following Numpy convention.\n\n If multiple sorts along the same dimension is\n given, numpy's lexsort is performed along that dimension:\n https://docs.scipy.org/doc/numpy/reference/generated/numpy.lexsort.html\n and the FIRST key in the sequence is used as the primary sort key,\n followed by the 2nd key, etc.\n\n Parameters\n ----------\n variables: hashable, DataArray, or sequence of either\n 1D DataArray objects or name(s) of 1D variable(s) in\n coords whose values are used to sort this array.\n ascending: boolean, optional\n Whether to sort by ascending or descending order.\n\n Returns\n -------\n sorted: DataArray\n A new dataarray where all the specified dims are sorted by dim\n labels.\n\n Examples\n --------\n\n >>> da = xr.DataArray(np.random.rand(5),\n ... coords=[pd.date_range('1/1/2000', periods=5)],\n ... dims='time')\n >>> da\n \n array([ 0.965471, 0.615637, 0.26532 , 0.270962, 0.552878])\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n\n >>> da.sortby(da)\n \n array([ 0.26532 , 0.270962, 0.552878, 0.615637, 0.965471])\n Coordinates:\n * time (time) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05 ...\n \"\"\"\n ds = self._to_temp_dataset().sortby(variables, ascending=ascending)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.quantile_DataArray.quantile.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.quantile_DataArray.quantile.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2846, "end_line": 2897, "span_ids": ["DataArray.quantile"], "tokens": 488}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def quantile(\n self,\n q: Any,\n dim: Union[Hashable, Sequence[Hashable], None] = None,\n interpolation: str = \"linear\",\n keep_attrs: bool = None,\n ) -> \"DataArray\":\n \"\"\"Compute the qth quantile of the data along the specified dimension.\n\n Returns the qth quantiles(s) of the array elements.\n\n Parameters\n ----------\n q : float in range of [0,1] or array-like of floats\n Quantile to compute, which must be between 0 and 1 inclusive.\n dim : hashable or sequence of hashable, optional\n Dimension(s) over which to apply quantile.\n interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n This optional parameter specifies the interpolation method to\n use when the desired quantile lies between two data points\n ``i < j``:\n\n - linear: ``i + (j - i) * fraction``, where ``fraction`` is\n the fractional part of the index surrounded by ``i`` and\n ``j``.\n - lower: ``i``.\n - higher: ``j``.\n - nearest: ``i`` or ``j``, whichever is nearest.\n - midpoint: ``(i + j) / 2``.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n\n Returns\n -------\n quantiles : DataArray\n If `q` is a single quantile, then the result\n is a scalar. If multiple percentiles are given, first axis of\n the result corresponds to the quantile and a quantile dimension\n is added to the return array. The other dimensions are the\n dimensions that remain after the reduction of the array.\n\n See Also\n --------\n numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile\n \"\"\"\n\n ds = self._to_temp_dataset().quantile(\n q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation\n )\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.rank_DataArray.rank.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.rank_DataArray.rank.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2899, "end_line": 2939, "span_ids": ["DataArray.rank"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def rank(\n self, dim: Hashable, pct: bool = False, keep_attrs: bool = None\n ) -> \"DataArray\":\n \"\"\"Ranks the data.\n\n Equal values are assigned a rank that is the average of the ranks that\n would have been otherwise assigned to all of the values within that\n set. Ranks begin at 1, not 0. If pct, computes percentage ranks.\n\n NaNs in the input array are returned as NaNs.\n\n The `bottleneck` library is required.\n\n Parameters\n ----------\n dim : hashable\n Dimension over which to compute rank.\n pct : bool, optional\n If True, compute percentage ranks, otherwise compute integer ranks.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n\n Returns\n -------\n ranked : DataArray\n DataArray with the same coordinates and dtype 'float64'.\n\n Examples\n --------\n\n >>> arr = xr.DataArray([5, 6, 7], dims='x')\n >>> arr.rank('x')\n \n array([ 1., 2., 3.])\n Dimensions without coordinates: x\n \"\"\"\n\n ds = self._to_temp_dataset().rank(dim, pct=pct, keep_attrs=keep_attrs)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.differentiate_DataArray.differentiate.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.differentiate_DataArray.differentiate.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2941, "end_line": 2995, "span_ids": ["DataArray.differentiate"], "tokens": 548}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def differentiate(\n self, coord: Hashable, edge_order: int = 1, datetime_unit: str = None\n ) -> \"DataArray\":\n \"\"\" Differentiate the array with the second order accurate central\n differences.\n\n .. note::\n This feature is limited to simple cartesian geometry, i.e. coord\n must be one dimensional.\n\n Parameters\n ----------\n coord: hashable\n The coordinate to be used to compute the gradient.\n edge_order: 1 or 2. Default 1\n N-th order accurate differences at the boundaries.\n datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',\n 'us', 'ns', 'ps', 'fs', 'as'}\n Unit to compute gradient. Only valid for datetime coordinate.\n\n Returns\n -------\n differentiated: DataArray\n\n See also\n --------\n numpy.gradient: corresponding numpy function\n\n Examples\n --------\n\n >>> da = xr.DataArray(np.arange(12).reshape(4, 3), dims=['x', 'y'],\n ... coords={'x': [0, 0.1, 1.1, 1.2]})\n >>> da\n \n array([[ 0, 1, 2],\n [ 3, 4, 5],\n [ 6, 7, 8],\n [ 9, 10, 11]])\n Coordinates:\n * x (x) float64 0.0 0.1 1.1 1.2\n Dimensions without coordinates: y\n >>>\n >>> da.differentiate('x')\n \n array([[30. , 30. , 30. ],\n [27.545455, 27.545455, 27.545455],\n [27.545455, 27.545455, 27.545455],\n [30. , 30. , 30. ]])\n Coordinates:\n * x (x) float64 0.0 0.1 1.1 1.2\n Dimensions without coordinates: y\n \"\"\"\n ds = self._to_temp_dataset().differentiate(coord, edge_order, datetime_unit)\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.integrate_DataArray.unify_chunks.return.self__from_temp_dataset_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.integrate_DataArray.unify_chunks.return.self__from_temp_dataset_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2997, "end_line": 3060, "span_ids": ["DataArray.unify_chunks", "DataArray.integrate"], "tokens": 526}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def integrate(\n self, dim: Union[Hashable, Sequence[Hashable]], datetime_unit: str = None\n ) -> \"DataArray\":\n \"\"\" integrate the array with the trapezoidal rule.\n\n .. note::\n This feature is limited to simple cartesian geometry, i.e. coord\n must be one dimensional.\n\n Parameters\n ----------\n dim: hashable, or a sequence of hashable\n Coordinate(s) used for the integration.\n datetime_unit: str, optional\n Can be used to specify the unit if datetime coordinate is used.\n One of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns',\n 'ps', 'fs', 'as'}\n\n Returns\n -------\n integrated: DataArray\n\n See also\n --------\n numpy.trapz: corresponding numpy function\n\n Examples\n --------\n\n >>> da = xr.DataArray(np.arange(12).reshape(4, 3), dims=['x', 'y'],\n ... coords={'x': [0, 0.1, 1.1, 1.2]})\n >>> da\n \n array([[ 0, 1, 2],\n [ 3, 4, 5],\n [ 6, 7, 8],\n [ 9, 10, 11]])\n Coordinates:\n * x (x) float64 0.0 0.1 1.1 1.2\n Dimensions without coordinates: y\n >>>\n >>> da.integrate('x')\n \n array([5.4, 6.6, 7.8])\n Dimensions without coordinates: y\n \"\"\"\n ds = self._to_temp_dataset().integrate(dim, datetime_unit)\n return self._from_temp_dataset(ds)\n\n def unify_chunks(self) -> \"DataArray\":\n \"\"\" Unify chunk size along all chunked dimensions of this DataArray.\n\n Returns\n -------\n\n DataArray with consistent chunk sizes for all dask-array variables\n\n See Also\n --------\n\n dask.array.core.unify_chunks\n \"\"\"\n ds = self._to_temp_dataset().unify_chunks()\n return self._from_temp_dataset(ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.map_blocks_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataarray.py_DataArray.map_blocks_", "embedding": null, "metadata": {"file_path": "xarray/core/dataarray.py", "file_name": "dataarray.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3062, "end_line": 3126, "span_ids": ["DataArray:26", "DataArray.map_blocks", "impl:18"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArray(AbstractArray, DataWithCoords):\n\n def map_blocks(\n self,\n func: \"Callable[..., T_DSorDA]\",\n args: Sequence[Any] = (),\n kwargs: Mapping[str, Any] = None,\n ) -> \"T_DSorDA\":\n \"\"\"\n Apply a function to each chunk of this DataArray. This method is experimental\n and its signature may change.\n\n Parameters\n ----------\n func: callable\n User-provided function that accepts a DataArray as its first parameter. The\n function will receive a subset of this DataArray, corresponding to one chunk\n along each chunked dimension. ``func`` will be executed as\n ``func(obj_subset, *args, **kwargs)``.\n\n The function will be first run on mocked-up data, that looks like this array\n but has sizes 0, to determine properties of the returned object such as\n dtype, variable names, new dimensions and new indexes (if any).\n\n This function must return either a single DataArray or a single Dataset.\n\n This function cannot change size of existing dimensions, or add new chunked\n dimensions.\n args: Sequence\n Passed verbatim to func after unpacking, after the sliced DataArray. xarray\n objects, if any, will not be split by chunks. Passing dask collections is\n not allowed.\n kwargs: Mapping\n Passed verbatim to func after unpacking. xarray objects, if any, will not be\n split by chunks. Passing dask collections is not allowed.\n\n Returns\n -------\n A single DataArray or Dataset with dask backend, reassembled from the outputs of\n the function.\n\n Notes\n -----\n This method is designed for when one needs to manipulate a whole xarray object\n within each chunk. In the more common case where one can work on numpy arrays,\n it is recommended to use apply_ufunc.\n\n If none of the variables in this DataArray is backed by dask, calling this\n method is equivalent to calling ``func(self, *args, **kwargs)``.\n\n See Also\n --------\n dask.array.map_blocks, xarray.apply_ufunc, xarray.map_blocks,\n xarray.Dataset.map_blocks\n \"\"\"\n from .parallel import map_blocks\n\n return map_blocks(func, self, args, kwargs)\n\n # this needs to be at the end, or mypy will confuse with `str`\n # https://mypy.readthedocs.io/en/latest/common_issues.html#dealing-with-conflicting-names\n str = property(StringAccessor)\n\n\n# priority most be higher than Variable to properly work with binary ufuncs\nops.inject_all_ops_and_reduce_methods(DataArray, priority=60)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_copy__DATETIMEINDEX_COMPONENTS._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_copy__DATETIMEINDEX_COMPONENTS._", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 113, "span_ids": ["imports:28", "impl", "imports:25", "impl:2", "imports", "impl:5"], "tokens": 564}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\nimport functools\nimport sys\nimport warnings\nfrom collections import defaultdict\nfrom numbers import Number\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n DefaultDict,\n Dict,\n Hashable,\n Iterable,\n Iterator,\n List,\n Mapping,\n MutableMapping,\n Optional,\n Sequence,\n Set,\n Tuple,\n TypeVar,\n Union,\n cast,\n overload,\n)\n\nimport numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom ..coding.cftimeindex import _parse_array_of_cftime_strings\nfrom ..plot.dataset_plot import _Dataset_PlotMethods\nfrom . import (\n alignment,\n dtypes,\n duck_array_ops,\n formatting,\n groupby,\n ops,\n resample,\n rolling,\n utils,\n)\nfrom .alignment import _broadcast_helper, _get_broadcast_dims_map_common_coords, align\nfrom .common import (\n ALL_DIMS,\n DataWithCoords,\n ImplementsDatasetReduce,\n _contains_datetime_like_objects,\n)\nfrom .coordinates import (\n DatasetCoordinates,\n LevelCoordinatesSource,\n assert_coordinate_consistent,\n remap_label_indexers,\n)\nfrom .duck_array_ops import datetime_to_numeric\nfrom .indexes import Indexes, default_indexes, isel_variable_and_index, roll_index\nfrom .merge import (\n dataset_merge_method,\n dataset_update_method,\n merge_coordinates_without_align,\n merge_data_and_coords,\n)\nfrom .options import OPTIONS, _get_keep_attrs\nfrom .pycompat import dask_array_type\nfrom .utils import (\n Frozen,\n SortedKeysDict,\n _check_inplace,\n decode_numpy_dict_values,\n either_dict_or_kwargs,\n hashable,\n is_dict_like,\n is_list_like,\n maybe_wrap_array,\n)\nfrom .variable import IndexVariable, Variable, as_variable, broadcast_variables\n\nif TYPE_CHECKING:\n from ..backends import AbstractDataStore, ZarrStore\n from .dataarray import DataArray\n from .merge import CoercibleMapping\n\n T_DSorDA = TypeVar(\"T_DSorDA\", DataArray, \"Dataset\")\n\n try:\n from dask.delayed import Delayed\n except ImportError:\n Delayed = None\n\n\n# list of attributes of pd.DatetimeIndex that are ndarrays of time info\n_DATETIMEINDEX_COMPONENTS = [\n \"year\",\n \"month\",\n \"day\",\n \"hour\",\n \"minute\",\n \"second\",\n \"microsecond\",\n \"nanosecond\",\n \"date\",\n \"time\",\n \"dayofyear\",\n \"weekofyear\",\n \"dayofweek\",\n \"quarter\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__get_virtual_variable__get_virtual_variable.return.ref_name_var_name_virtu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__get_virtual_variable__get_virtual_variable.return.ref_name_var_name_virtu", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 116, "end_line": 161, "span_ids": ["_get_virtual_variable"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_virtual_variable(\n variables, key: Hashable, level_vars: Mapping = None, dim_sizes: Mapping = None\n) -> Tuple[Hashable, Hashable, Variable]:\n \"\"\"Get a virtual variable (e.g., 'time.year' or a MultiIndex level)\n from a dict of xarray.Variable objects (if possible)\n \"\"\"\n if level_vars is None:\n level_vars = {}\n if dim_sizes is None:\n dim_sizes = {}\n\n if key in dim_sizes:\n data = pd.Index(range(dim_sizes[key]), name=key)\n variable = IndexVariable((key,), data)\n return key, key, variable\n\n if not isinstance(key, str):\n raise KeyError(key)\n\n split_key = key.split(\".\", 1)\n var_name: Optional[str]\n if len(split_key) == 2:\n ref_name, var_name = split_key\n elif len(split_key) == 1:\n ref_name, var_name = key, None\n else:\n raise KeyError(key)\n\n if ref_name in level_vars:\n dim_var = variables[level_vars[ref_name]]\n ref_var = dim_var.to_index_variable().get_level_variable(ref_name)\n else:\n ref_var = variables[ref_name]\n\n if var_name is None:\n virtual_var = ref_var\n var_name = key\n else:\n if _contains_datetime_like_objects(ref_var):\n ref_var = xr.DataArray(ref_var)\n data = getattr(ref_var.dt, var_name).data\n else:\n data = getattr(ref_var, var_name).data\n virtual_var = Variable(ref_var.dims, data)\n\n return ref_name, var_name, virtual_var", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_calculate_dimensions_calculate_dimensions.return.dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_calculate_dimensions_calculate_dimensions.return.dims", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 164, "end_line": 188, "span_ids": ["calculate_dimensions"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def calculate_dimensions(variables: Mapping[Hashable, Variable]) -> Dict[Hashable, int]:\n \"\"\"Calculate the dimensions corresponding to a set of variables.\n\n Returns dictionary mapping from dimension names to sizes. Raises ValueError\n if any of the dimension sizes conflict.\n \"\"\"\n dims: Dict[Hashable, int] = {}\n last_used = {}\n scalar_vars = {k for k, v in variables.items() if not v.dims}\n for k, var in variables.items():\n for dim, size in zip(var.dims, var.shape):\n if dim in scalar_vars:\n raise ValueError(\n \"dimension %r already exists as a scalar \" \"variable\" % dim\n )\n if dim not in dims:\n dims[dim] = size\n last_used[dim] = k\n elif dims[dim] != size:\n raise ValueError(\n \"conflicting sizes for dimension %r: \"\n \"length %s on %r and length %s on %r\"\n % (dim, size, k, dims[dim], last_used[dim])\n )\n return dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_merge_indexes_merge_indexes.return.new_variables_new_coord_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_merge_indexes_merge_indexes.return.new_variables_new_coord_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 191, "end_line": 265, "span_ids": ["merge_indexes"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_indexes(\n indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]],\n variables: Mapping[Hashable, Variable],\n coord_names: Set[Hashable],\n append: bool = False,\n) -> Tuple[Dict[Hashable, Variable], Set[Hashable]]:\n \"\"\"Merge variables into multi-indexes.\n\n Not public API. Used in Dataset and DataArray set_index\n methods.\n \"\"\"\n vars_to_replace: Dict[Hashable, Variable] = {}\n vars_to_remove: List[Hashable] = []\n error_msg = \"{} is not the name of an existing variable.\"\n\n for dim, var_names in indexes.items():\n if isinstance(var_names, str) or not isinstance(var_names, Sequence):\n var_names = [var_names]\n\n names: List[Hashable] = []\n codes: List[List[int]] = []\n levels: List[List[int]] = []\n current_index_variable = variables.get(dim)\n\n for n in var_names:\n try:\n var = variables[n]\n except KeyError:\n raise ValueError(error_msg.format(n))\n if (\n current_index_variable is not None\n and var.dims != current_index_variable.dims\n ):\n raise ValueError(\n \"dimension mismatch between %r %s and %r %s\"\n % (dim, current_index_variable.dims, n, var.dims)\n )\n\n if current_index_variable is not None and append:\n current_index = current_index_variable.to_index()\n if isinstance(current_index, pd.MultiIndex):\n names.extend(current_index.names)\n codes.extend(current_index.codes)\n levels.extend(current_index.levels)\n else:\n names.append(\"%s_level_0\" % dim)\n cat = pd.Categorical(current_index.values, ordered=True)\n codes.append(cat.codes)\n levels.append(cat.categories)\n\n if not len(names) and len(var_names) == 1:\n idx = pd.Index(variables[var_names[0]].values)\n\n else:\n for n in var_names:\n try:\n var = variables[n]\n except KeyError:\n raise ValueError(error_msg.format(n))\n names.append(n)\n cat = pd.Categorical(var.values, ordered=True)\n codes.append(cat.codes)\n levels.append(cat.categories)\n\n idx = pd.MultiIndex(levels, codes, names=names)\n\n vars_to_replace[dim] = IndexVariable(dim, idx)\n vars_to_remove.extend(var_names)\n\n new_variables = {k: v for k, v in variables.items() if k not in vars_to_remove}\n new_variables.update(vars_to_replace)\n new_coord_names = coord_names | set(vars_to_replace)\n new_coord_names -= set(vars_to_remove)\n\n return new_variables, new_coord_names", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_split_indexes_split_indexes.return.new_variables_new_coord_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_split_indexes_split_indexes.return.new_variables_new_coord_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 323, "span_ids": ["split_indexes"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def split_indexes(\n dims_or_levels: Union[Hashable, Sequence[Hashable]],\n variables: Mapping[Hashable, Variable],\n coord_names: Set[Hashable],\n level_coords: Mapping[Hashable, Hashable],\n drop: bool = False,\n) -> Tuple[Dict[Hashable, Variable], Set[Hashable]]:\n \"\"\"Extract (multi-)indexes (levels) as variables.\n\n Not public API. Used in Dataset and DataArray reset_index\n methods.\n \"\"\"\n if isinstance(dims_or_levels, str) or not isinstance(dims_or_levels, Sequence):\n dims_or_levels = [dims_or_levels]\n\n dim_levels: DefaultDict[Any, List[Hashable]] = defaultdict(list)\n dims = []\n for k in dims_or_levels:\n if k in level_coords:\n dim_levels[level_coords[k]].append(k)\n else:\n dims.append(k)\n\n vars_to_replace = {}\n vars_to_create: Dict[Hashable, Variable] = {}\n vars_to_remove = []\n\n for d in dims:\n index = variables[d].to_index()\n if isinstance(index, pd.MultiIndex):\n dim_levels[d] = index.names\n else:\n vars_to_remove.append(d)\n if not drop:\n vars_to_create[str(d) + \"_\"] = Variable(d, index)\n\n for d, levs in dim_levels.items():\n index = variables[d].to_index()\n if len(levs) == index.nlevels:\n vars_to_remove.append(d)\n else:\n vars_to_replace[d] = IndexVariable(d, index.droplevel(levs))\n\n if not drop:\n for lev in levs:\n idx = index.get_level_values(lev)\n vars_to_create[idx.name] = Variable(d, idx)\n\n new_variables = dict(variables)\n for v in set(vars_to_remove):\n del new_variables[v]\n new_variables.update(vars_to_replace)\n new_variables.update(vars_to_create)\n new_coord_names = (coord_names | set(vars_to_create)) - set(vars_to_remove)\n\n return new_variables, new_coord_names", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__assert_empty_as_dataset.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__assert_empty_as_dataset.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 326, "end_line": 341, "span_ids": ["_assert_empty", "as_dataset"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_empty(args: tuple, msg: str = \"%s\") -> None:\n if args:\n raise ValueError(msg % args)\n\n\ndef as_dataset(obj: Any) -> \"Dataset\":\n \"\"\"Cast the given object to a Dataset.\n\n Handles Datasets, DataArrays and dictionaries of variables. A new Dataset\n object is only created if the provided object is not already one.\n \"\"\"\n if hasattr(obj, \"to_dataset\"):\n obj = obj.to_dataset()\n if not isinstance(obj, Dataset):\n obj = Dataset(obj)\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_DataVariables_DataVariables._ipython_key_completions_.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_DataVariables_DataVariables._ipython_key_completions_.return._", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 344, "end_line": 382, "span_ids": ["DataVariables", "DataVariables.__repr__", "DataVariables.__contains__", "DataVariables.__iter__", "DataVariables.variables", "DataVariables._ipython_key_completions_", "DataVariables.__len__", "DataVariables.__getitem__"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataVariables(Mapping[Hashable, \"DataArray\"]):\n __slots__ = (\"_dataset\",)\n\n def __init__(self, dataset: \"Dataset\"):\n self._dataset = dataset\n\n def __iter__(self) -> Iterator[Hashable]:\n return (\n key\n for key in self._dataset._variables\n if key not in self._dataset._coord_names\n )\n\n def __len__(self) -> int:\n return len(self._dataset._variables) - len(self._dataset._coord_names)\n\n def __contains__(self, key: Hashable) -> bool:\n return key in self._dataset._variables and key not in self._dataset._coord_names\n\n def __getitem__(self, key: Hashable) -> \"DataArray\":\n if key not in self._dataset._coord_names:\n return cast(\"DataArray\", self._dataset[key])\n raise KeyError(key)\n\n def __repr__(self) -> str:\n return formatting.data_vars_repr(self)\n\n @property\n def variables(self) -> Mapping[Hashable, Variable]:\n all_variables = self._dataset.variables\n return Frozen({k: all_variables[k] for k in self})\n\n def _ipython_key_completions_(self):\n \"\"\"Provide method for the key-autocompletions in IPython. \"\"\"\n return [\n key\n for key in self._dataset._ipython_key_completions_()\n if key not in self._dataset._coord_names\n ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__LocIndexer_Dataset._resample_cls.resample_DatasetResample": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py__LocIndexer_Dataset._resample_cls.resample_DatasetResample", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 385, "end_line": 434, "span_ids": ["Dataset", "_LocIndexer.__getitem__", "_LocIndexer"], "tokens": 368}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _LocIndexer:\n __slots__ = (\"dataset\",)\n\n def __init__(self, dataset: \"Dataset\"):\n self.dataset = dataset\n\n def __getitem__(self, key: Mapping[Hashable, Any]) -> \"Dataset\":\n if not utils.is_dict_like(key):\n raise TypeError(\"can only lookup dictionaries from Dataset.loc\")\n return self.dataset.sel(key)\n\n\nclass Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n \"\"\"A multi-dimensional, in memory, array database.\n\n A dataset resembles an in-memory representation of a NetCDF file, and\n consists of variables, coordinates and attributes which together form a\n self describing dataset.\n\n Dataset implements the mapping interface with keys given by variable names\n and values given by DataArray objects for each variable name.\n\n One dimensional variables with name equal to their dimension are index\n coordinates used for label based indexing.\n \"\"\"\n\n _accessors: Optional[Dict[str, Any]]\n _attrs: Optional[Dict[Hashable, Any]]\n _coord_names: Set[Hashable]\n _dims: Dict[Hashable, int]\n _encoding: Optional[Dict[Hashable, Any]]\n _indexes: Optional[Dict[Hashable, pd.Index]]\n _variables: Dict[Hashable, Variable]\n\n __slots__ = (\n \"_accessors\",\n \"_attrs\",\n \"_coord_names\",\n \"_dims\",\n \"_encoding\",\n \"_file_obj\",\n \"_indexes\",\n \"_variables\",\n \"__weakref__\",\n )\n\n _groupby_cls = groupby.DatasetGroupBy\n _rolling_cls = rolling.DatasetRolling\n _coarsen_cls = rolling.DatasetCoarsen\n _resample_cls = resample.DatasetResample", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__init___Dataset.__init__.self._indexes.indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__init___Dataset.__init__.self._indexes.indexes", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 436, "end_line": 534, "span_ids": ["Dataset"], "tokens": 815}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __init__(\n self,\n # could make a VariableArgs to use more generally, and refine these\n # categories\n data_vars: Mapping[Hashable, Any] = None,\n coords: Mapping[Hashable, Any] = None,\n attrs: Mapping[Hashable, Any] = None,\n compat=None,\n ):\n \"\"\"To load data from a file or file-like object, use the `open_dataset`\n function.\n\n Parameters\n ----------\n data_vars : dict-like, optional\n A mapping from variable names to :py:class:`~xarray.DataArray`\n objects, :py:class:`~xarray.Variable` objects or to tuples of the\n form ``(dims, data[, attrs])`` which can be used as arguments to\n create a new ``Variable``. Each dimension must have the same length\n in all variables in which it appears.\n\n The following notations are accepted:\n\n - mapping {var name: DataArray}\n - mapping {var name: Variable}\n - mapping {var name: (dimension name, array-like)}\n - mapping {var name: (tuple of dimension names, array-like)}\n - mapping {dimension name: array-like}\n (it will be automatically moved to coords, see below)\n\n Each dimension must have the same length in all variables in which\n it appears.\n coords : dict-like, optional\n Another mapping in similar form as the `data_vars` argument,\n except the each item is saved on the dataset as a \"coordinate\".\n These variables have an associated meaning: they describe\n constant/fixed/independent quantities, unlike the\n varying/measured/dependent quantities that belong in `variables`.\n Coordinates values may be given by 1-dimensional arrays or scalars,\n in which case `dims` do not need to be supplied: 1D arrays will be\n assumed to give index values along the dimension with the same\n name.\n\n The following notations are accepted:\n\n - mapping {coord name: DataArray}\n - mapping {coord name: Variable}\n - mapping {coord name: (dimension name, array-like)}\n - mapping {coord name: (tuple of dimension names, array-like)}\n - mapping {dimension name: array-like}\n (the dimension name is implicitly set to be the same as the coord name)\n\n The last notation implies that the coord name is the same as the\n dimension name.\n\n attrs : dict-like, optional\n Global attributes to save on this dataset.\n compat : deprecated\n \"\"\"\n if compat is not None:\n warnings.warn(\n \"The `compat` argument to Dataset is deprecated and will be \"\n \"removed in 0.15.\"\n \"Instead, use `merge` to control how variables are combined\",\n FutureWarning,\n stacklevel=2,\n )\n else:\n compat = \"broadcast_equals\"\n\n # TODO(shoyer): expose indexes as a public argument in __init__\n\n if data_vars is None:\n data_vars = {}\n if coords is None:\n coords = {}\n\n both_data_and_coords = set(data_vars) & set(coords)\n if both_data_and_coords:\n raise ValueError(\n \"variables %r are found in both data_vars and coords\"\n % both_data_and_coords\n )\n\n if isinstance(coords, Dataset):\n coords = coords.variables\n\n variables, coord_names, dims, indexes = merge_data_and_coords(\n data_vars, coords, compat=compat\n )\n\n self._accessors = None\n self._attrs = dict(attrs) if attrs is not None else None\n self._file_obj = None\n self._encoding = None\n self._variables = variables\n self._coord_names = coord_names\n self._dims = dims\n self._indexes = indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.load_store_Dataset.sizes.return.self_dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.load_store_Dataset.sizes.return.self_dims", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 536, "end_line": 608, "span_ids": ["Dataset.attrs_3", "Dataset.sizes", "Dataset.encoding", "Dataset.dims", "Dataset.attrs", "Dataset.variables", "Dataset.encoding_5", "Dataset.load_store"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @classmethod\n def load_store(cls, store, decoder=None) -> \"Dataset\":\n \"\"\"Create a new dataset from the contents of a backends.*DataStore\n object\n \"\"\"\n variables, attributes = store.load()\n if decoder:\n variables, attributes = decoder(variables, attributes)\n obj = cls(variables, attrs=attributes)\n obj._file_obj = store\n return obj\n\n @property\n def variables(self) -> Mapping[Hashable, Variable]:\n \"\"\"Low level interface to Dataset contents as dict of Variable objects.\n\n This ordered dictionary is frozen to prevent mutation that could\n violate Dataset invariants. It contains all variable objects\n constituting the Dataset, including both data variables and\n coordinates.\n \"\"\"\n return Frozen(self._variables)\n\n @property\n def attrs(self) -> Dict[Hashable, Any]:\n \"\"\"Dictionary of global attributes on this dataset\n \"\"\"\n if self._attrs is None:\n self._attrs = {}\n return self._attrs\n\n @attrs.setter\n def attrs(self, value: Mapping[Hashable, Any]) -> None:\n self._attrs = dict(value)\n\n @property\n def encoding(self) -> Dict:\n \"\"\"Dictionary of global encoding attributes on this dataset\n \"\"\"\n if self._encoding is None:\n self._encoding = {}\n return self._encoding\n\n @encoding.setter\n def encoding(self, value: Mapping) -> None:\n self._encoding = dict(value)\n\n @property\n def dims(self) -> Mapping[Hashable, int]:\n \"\"\"Mapping from dimension names to lengths.\n\n Cannot be modified directly, but is updated when adding new variables.\n\n Note that type of this object differs from `DataArray.dims`.\n See `Dataset.sizes` and `DataArray.sizes` for consistently named\n properties.\n \"\"\"\n return Frozen(SortedKeysDict(self._dims))\n\n @property\n def sizes(self) -> Mapping[Hashable, int]:\n \"\"\"Mapping from dimension names to lengths.\n\n Cannot be modified directly, but is updated when adding new variables.\n\n This is an alias for `Dataset.dims` provided for the benefit of\n consistency with `DataArray.sizes`.\n\n See also\n --------\n DataArray.sizes\n \"\"\"\n return self.dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.load_Dataset.load.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.load_Dataset.load.return.self", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 610, "end_line": 649, "span_ids": ["Dataset.load"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def load(self, **kwargs) -> \"Dataset\":\n \"\"\"Manually trigger loading and/or computation of this dataset's data\n from disk or a remote source into memory and return this dataset.\n Unlike compute, the original dataset is modified and returned.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically. However, this method can be necessary when\n working with many file objects on disk.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n # access .data to coerce everything to numpy or dask arrays\n lazy_data = {\n k: v._data\n for k, v in self.variables.items()\n if isinstance(v._data, dask_array_type)\n }\n if lazy_data:\n import dask.array as da\n\n # evaluate all the dask arrays simultaneously\n evaluated_data = da.compute(*lazy_data.values(), **kwargs)\n\n for k, data in zip(lazy_data, evaluated_data):\n self.variables[k].data = data\n\n # load everything else sequentially\n for k, v in self.variables.items():\n if k not in lazy_data:\n v.load()\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_graph___Dataset.__dask_scheduler__.return.da_Array___dask_scheduler": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_graph___Dataset.__dask_scheduler__.return.da_Array___dask_scheduler", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 651, "end_line": 697, "span_ids": ["Dataset.__dask_layers__", "Dataset.__dask_keys__", "Dataset.__dask_scheduler__", "Dataset.__dask_optimize__", "Dataset.__dask_graph__"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __dask_graph__(self):\n graphs = {k: v.__dask_graph__() for k, v in self.variables.items()}\n graphs = {k: v for k, v in graphs.items() if v is not None}\n if not graphs:\n return None\n else:\n try:\n from dask.highlevelgraph import HighLevelGraph\n\n return HighLevelGraph.merge(*graphs.values())\n except ImportError:\n from dask import sharedict\n\n return sharedict.merge(*graphs.values())\n\n def __dask_keys__(self):\n import dask\n\n return [\n v.__dask_keys__()\n for v in self.variables.values()\n if dask.is_dask_collection(v)\n ]\n\n def __dask_layers__(self):\n import dask\n\n return sum(\n [\n v.__dask_layers__()\n for v in self.variables.values()\n if dask.is_dask_collection(v)\n ],\n (),\n )\n\n @property\n def __dask_optimize__(self):\n import dask.array as da\n\n return da.Array.__dask_optimize__\n\n @property\n def __dask_scheduler__(self):\n import dask.array as da\n\n return da.Array.__dask_scheduler__", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_postcompute___Dataset.__dask_postcompute__.return.self__dask_postcompute_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_postcompute___Dataset.__dask_postcompute__.return.self__dask_postcompute_a", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 699, "end_line": 717, "span_ids": ["Dataset.__dask_postcompute__"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __dask_postcompute__(self):\n import dask\n\n info = [\n (True, k, v.__dask_postcompute__())\n if dask.is_dask_collection(v)\n else (False, k, v)\n for k, v in self._variables.items()\n ]\n args = (\n info,\n self._coord_names,\n self._dims,\n self._attrs,\n self._indexes,\n self._encoding,\n self._file_obj,\n )\n return self._dask_postcompute, args", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_postpersist___Dataset.__dask_postpersist__.return.self__dask_postpersist_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__dask_postpersist___Dataset.__dask_postpersist__.return.self__dask_postpersist_a", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 719, "end_line": 737, "span_ids": ["Dataset.__dask_postpersist__"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __dask_postpersist__(self):\n import dask\n\n info = [\n (True, k, v.__dask_postpersist__())\n if dask.is_dask_collection(v)\n else (False, k, v)\n for k, v in self._variables.items()\n ]\n args = (\n info,\n self._coord_names,\n self._dims,\n self._attrs,\n self._indexes,\n self._encoding,\n self._file_obj,\n )\n return self._dask_postpersist, args", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._dask_postcompute_Dataset._dask_postpersist.return.Dataset__construct_direct": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._dask_postcompute_Dataset._dask_postpersist.return.Dataset__construct_direct", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 739, "end_line": 766, "span_ids": ["Dataset._dask_postpersist", "Dataset._dask_postcompute"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @staticmethod\n def _dask_postcompute(results, info, *args):\n variables = {}\n results2 = list(results[::-1])\n for is_dask, k, v in info:\n if is_dask:\n func, args2 = v\n r = results2.pop()\n result = func(r, *args2)\n else:\n result = v\n variables[k] = result\n\n final = Dataset._construct_direct(variables, *args)\n return final\n\n @staticmethod\n def _dask_postpersist(dsk, info, *args):\n variables = {}\n for is_dask, k, v in info:\n if is_dask:\n func, args2 = v\n result = func(dsk, *args2)\n else:\n result = v\n variables[k] = result\n\n return Dataset._construct_direct(variables, *args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.compute_Dataset.compute.return.new_load_kwargs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.compute_Dataset.compute.return.new_load_kwargs_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 768, "end_line": 788, "span_ids": ["Dataset.compute"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def compute(self, **kwargs) -> \"Dataset\":\n \"\"\"Manually trigger loading and/or computation of this dataset's data\n from disk or a remote source into memory and return a new dataset.\n Unlike load, the original dataset is left unaltered.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically. However, this method can be necessary when\n working with many file objects on disk.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n new = self.copy(deep=False)\n return new.load(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._persist_inplace_Dataset._persist_inplace.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._persist_inplace_Dataset._persist_inplace.return.self", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 790, "end_line": 808, "span_ids": ["Dataset._persist_inplace"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _persist_inplace(self, **kwargs) -> \"Dataset\":\n \"\"\"Persist all Dask arrays in memory\n \"\"\"\n # access .data to coerce everything to numpy or dask arrays\n lazy_data = {\n k: v._data\n for k, v in self.variables.items()\n if isinstance(v._data, dask_array_type)\n }\n if lazy_data:\n import dask\n\n # evaluate all the dask arrays simultaneously\n evaluated_data = dask.persist(*lazy_data.values(), **kwargs)\n\n for k, data in zip(lazy_data, evaluated_data):\n self.variables[k].data = data\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.persist_Dataset.persist.return.new__persist_inplace_kw": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.persist_Dataset.persist.return.new__persist_inplace_kw", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 810, "end_line": 829, "span_ids": ["Dataset.persist"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def persist(self, **kwargs) -> \"Dataset\":\n \"\"\" Trigger computation, keeping data as dask arrays\n\n This operation can be used to trigger computation on underlying dask\n arrays, similar to ``.compute()`` or ``.load()``. However this\n operation keeps the data as dask arrays. This is particularly useful\n when using the dask.distributed scheduler and you want to load a large\n amount of data into distributed memory.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.persist``.\n\n See Also\n --------\n dask.persist\n \"\"\"\n new = self.copy(deep=False)\n return new._persist_inplace(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._construct_direct_Dataset._https_github_com_pyth": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._construct_direct_Dataset._https_github_com_pyth", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 831, "end_line": 866, "span_ids": ["Dataset._from_vars_and_coord_names", "Dataset._construct_direct", "Dataset:20"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @classmethod\n def _construct_direct(\n cls,\n variables,\n coord_names,\n dims=None,\n attrs=None,\n indexes=None,\n encoding=None,\n file_obj=None,\n ):\n \"\"\"Shortcut around __init__ for internal use when we want to skip\n costly validation\n \"\"\"\n if dims is None:\n dims = calculate_dimensions(variables)\n obj = object.__new__(cls)\n obj._variables = variables\n obj._coord_names = coord_names\n obj._dims = dims\n obj._indexes = indexes\n obj._attrs = attrs\n obj._file_obj = file_obj\n obj._encoding = encoding\n obj._accessors = None\n return obj\n\n __default = object()\n\n @classmethod\n def _from_vars_and_coord_names(cls, variables, coord_names, attrs=None):\n return cls._construct_direct(variables, coord_names, attrs=attrs)\n\n # TODO(shoyer): renable type checking on this signature when pytype has a\n # good way to handle defaulting arguments to a sentinel value:\n # https://github.com/python/mypy/issues/1803", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_Dataset._replace.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_Dataset._replace.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 867, "end_line": 915, "span_ids": ["Dataset._replace"], "tokens": 408}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n def _replace( # type: ignore\n self,\n variables: Dict[Hashable, Variable] = None,\n coord_names: Set[Hashable] = None,\n dims: Dict[Any, int] = None,\n attrs: Optional[Dict[Hashable, Any]] = __default,\n indexes: Optional[Dict[Any, pd.Index]] = __default,\n encoding: Optional[dict] = __default,\n inplace: bool = False,\n ) -> \"Dataset\":\n \"\"\"Fastpath constructor for internal use.\n\n Returns an object with optionally with replaced attributes.\n\n Explicitly passed arguments are *not* copied when placed on the new\n dataset. It is up to the caller to ensure that they have the right type\n and are not used elsewhere.\n \"\"\"\n if inplace:\n if variables is not None:\n self._variables = variables\n if coord_names is not None:\n self._coord_names = coord_names\n if dims is not None:\n self._dims = dims\n if attrs is not self.__default:\n self._attrs = attrs\n if indexes is not self.__default:\n self._indexes = indexes\n if encoding is not self.__default:\n self._encoding = encoding\n obj = self\n else:\n if variables is None:\n variables = self._variables.copy()\n if coord_names is None:\n coord_names = self._coord_names.copy()\n if dims is None:\n dims = self._dims.copy()\n if attrs is self.__default:\n attrs = copy.copy(self._attrs)\n if indexes is self.__default:\n indexes = copy.copy(self._indexes)\n if encoding is self.__default:\n encoding = copy.copy(self._encoding)\n obj = self._construct_direct(\n variables, coord_names, dims, attrs, indexes, encoding\n )\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_with_new_dims_Dataset._replace_with_new_dims.return.self__replace_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_with_new_dims_Dataset._replace_with_new_dims.return.self__replace_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 917, "end_line": 929, "span_ids": ["Dataset._replace_with_new_dims"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _replace_with_new_dims( # type: ignore\n self,\n variables: Dict[Hashable, Variable],\n coord_names: set = None,\n attrs: Optional[Dict[Hashable, Any]] = __default,\n indexes: Dict[Hashable, pd.Index] = __default,\n inplace: bool = False,\n ) -> \"Dataset\":\n \"\"\"Replace variables with recalculated dimensions.\"\"\"\n dims = calculate_dimensions(variables)\n return self._replace(\n variables, coord_names, dims, attrs, indexes, inplace=inplace\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_vars_and_dims_Dataset._replace_vars_and_dims.return.self__replace_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._replace_vars_and_dims_Dataset._replace_vars_and_dims.return.self__replace_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 931, "end_line": 948, "span_ids": ["Dataset._replace_vars_and_dims"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _replace_vars_and_dims( # type: ignore\n self,\n variables: Dict[Hashable, Variable],\n coord_names: set = None,\n dims: Dict[Hashable, int] = None,\n attrs: Dict[Hashable, Any] = __default,\n inplace: bool = False,\n ) -> \"Dataset\":\n \"\"\"Deprecated version of _replace_with_new_dims().\n\n Unlike _replace_with_new_dims(), this method always recalculates\n indexes from variables.\n \"\"\"\n if dims is None:\n dims = calculate_dimensions(variables)\n return self._replace(\n variables, coord_names, dims, attrs, indexes=None, inplace=inplace\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._overwrite_indexes_Dataset._overwrite_indexes.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._overwrite_indexes_Dataset._overwrite_indexes.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 950, "end_line": 968, "span_ids": ["Dataset._overwrite_indexes"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _overwrite_indexes(self, indexes: Mapping[Any, pd.Index]) -> \"Dataset\":\n if not indexes:\n return self\n\n variables = self._variables.copy()\n new_indexes = dict(self.indexes)\n for name, idx in indexes.items():\n variables[name] = IndexVariable(name, idx)\n new_indexes[name] = idx\n obj = self._replace(variables, indexes=new_indexes)\n\n # switch from dimension to level names, if necessary\n dim_names: Dict[Hashable, str] = {}\n for dim, idx in indexes.items():\n if not isinstance(idx, pd.MultiIndex) and idx.name != dim:\n dim_names[dim] = idx.name\n if dim_names:\n obj = obj.rename(dim_names)\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.copy_Dataset.copy._Returns_a_copy_of_this": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.copy_Dataset.copy._Returns_a_copy_of_this", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 970, "end_line": 1062, "span_ids": ["Dataset.copy"], "tokens": 1038}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def copy(self, deep: bool = False, data: Mapping = None) -> \"Dataset\":\n \"\"\"Returns a copy of this dataset.\n\n If `deep=True`, a deep copy is made of each of the component variables.\n Otherwise, a shallow copy of each of the component variable is made, so\n that the underlying memory region of the new dataset is the same as in\n the original dataset.\n\n Use `data` to create a new object with the same structure as\n original but entirely new data.\n\n Parameters\n ----------\n deep : bool, optional\n Whether each component variable is loaded into memory and copied onto\n the new object. Default is False.\n data : dict-like, optional\n Data to use in the new object. Each item in `data` must have same\n shape as corresponding data variable in original. When `data` is\n used, `deep` is ignored for the data variables and only used for\n coords.\n\n Returns\n -------\n object : Dataset\n New object with dimensions, attributes, coordinates, name, encoding,\n and optionally data copied from original.\n\n Examples\n --------\n\n Shallow copy versus deep copy\n\n >>> da = xr.DataArray(np.random.randn(2, 3))\n >>> ds = xr.Dataset({'foo': da, 'bar': ('x', [-1, 2])},\n coords={'x': ['one', 'two']})\n >>> ds.copy()\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Coordinates:\n * x (x) >> ds_0 = ds.copy(deep=False)\n >>> ds_0['foo'][0, 0] = 7\n >>> ds_0\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Coordinates:\n * x (x) >> ds\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Coordinates:\n * x (x) >> ds.copy(data={'foo': np.arange(6).reshape(2, 3), 'bar': ['a', 'b']})\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Coordinates:\n * x (x) >> ds\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Coordinates:\n * x (x) \"Dataset\":\n if data is None:\n variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}\n elif not utils.is_dict_like(data):\n raise ValueError(\"Data must be dict-like\")\n else:\n var_keys = set(self.data_vars.keys())\n data_keys = set(data.keys())\n keys_not_in_vars = data_keys - var_keys\n if keys_not_in_vars:\n raise ValueError(\n \"Data must only contain variables in original \"\n \"dataset. Extra variables: {}\".format(keys_not_in_vars)\n )\n keys_missing_from_data = var_keys - data_keys\n if keys_missing_from_data:\n raise ValueError(\n \"Data must contain all variables in original \"\n \"dataset. Data is missing {}\".format(keys_missing_from_data)\n )\n variables = {\n k: v.copy(deep=deep, data=data.get(k))\n for k, v in self._variables.items()\n }\n\n attrs = copy.deepcopy(self._attrs) if deep else copy.copy(self._attrs)\n\n return self._replace(variables, attrs=attrs)\n\n @property\n def _level_coords(self) -> Dict[str, Hashable]:\n \"\"\"Return a mapping of all MultiIndex levels and their corresponding\n coordinate name.\n \"\"\"\n level_coords: Dict[str, Hashable] = {}\n for name, index in self.indexes.items():\n if isinstance(index, pd.MultiIndex):\n level_names = index.names\n (dim,) = self.variables[name].dims\n level_coords.update({lname: dim for lname in level_names})\n return level_coords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._copy_listed_Dataset._copy_listed.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._copy_listed_Dataset._copy_listed.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1104, "end_line": 1138, "span_ids": ["Dataset._copy_listed"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _copy_listed(self, names: Iterable[Hashable]) -> \"Dataset\":\n \"\"\"Create a new Dataset with the listed variables from this dataset and\n the all relevant coordinates. Skips all validation.\n \"\"\"\n variables: Dict[Hashable, Variable] = {}\n coord_names = set()\n indexes: Dict[Hashable, pd.Index] = {}\n\n for name in names:\n try:\n variables[name] = self._variables[name]\n except KeyError:\n ref_name, var_name, var = _get_virtual_variable(\n self._variables, name, self._level_coords, self.dims\n )\n variables[var_name] = var\n if ref_name in self._coord_names or ref_name in self.dims:\n coord_names.add(var_name)\n if (var_name,) == var.dims:\n indexes[var_name] = var.to_index()\n\n needed_dims: Set[Hashable] = set()\n for v in variables.values():\n needed_dims.update(v.dims)\n\n dims = {k: self.dims[k] for k in needed_dims}\n\n for k in self._coord_names:\n if set(self.variables[k].dims) <= needed_dims:\n variables[k] = self._variables[k]\n coord_names.add(k)\n if k in self.indexes:\n indexes[k] = self.indexes[k]\n\n return self._replace(variables, coord_names, dims, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._construct_dataarray_Dataset._construct_dataarray.return.DataArray_variable_coord": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._construct_dataarray_Dataset._construct_dataarray.return.DataArray_variable_coord", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1140, "end_line": 1164, "span_ids": ["Dataset._construct_dataarray"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _construct_dataarray(self, name: Hashable) -> \"DataArray\":\n \"\"\"Construct a DataArray by indexing this dataset\n \"\"\"\n from .dataarray import DataArray\n\n try:\n variable = self._variables[name]\n except KeyError:\n _, name, variable = _get_virtual_variable(\n self._variables, name, self._level_coords, self.dims\n )\n\n needed_dims = set(variable.dims)\n\n coords: Dict[Hashable, Variable] = {}\n for k in self.coords:\n if set(self.variables[k].dims) <= needed_dims:\n coords[k] = self.variables[k]\n\n if self._indexes is None:\n indexes = None\n else:\n indexes = {k: v for k, v in self._indexes.items() if k in coords}\n\n return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__copy___Dataset.loc.return._LocIndexer_self_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__copy___Dataset.loc.return._LocIndexer_self_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1166, "end_line": 1223, "span_ids": ["Dataset.__deepcopy__", "Dataset.__bool__", "Dataset.__contains__", "Dataset._attr_sources", "Dataset.__len__", "Dataset.__iter__", "Dataset.nbytes", "Dataset.__copy__", "Dataset.loc", "Dataset._item_sources", "Dataset.__array__"], "tokens": 444}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __copy__(self) -> \"Dataset\":\n return self.copy(deep=False)\n\n def __deepcopy__(self, memo=None) -> \"Dataset\":\n # memo does nothing but is required for compatibility with\n # copy.deepcopy\n return self.copy(deep=True)\n\n @property\n def _attr_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for attribute-style access\n \"\"\"\n return self._item_sources + [self.attrs]\n\n @property\n def _item_sources(self) -> List[Mapping[Hashable, Any]]:\n \"\"\"List of places to look-up items for key-completion\n \"\"\"\n return [\n self.data_vars,\n self.coords,\n {d: self[d] for d in self.dims},\n LevelCoordinatesSource(self),\n ]\n\n def __contains__(self, key: object) -> bool:\n \"\"\"The 'in' operator will return true or false depending on whether\n 'key' is an array in the dataset or not.\n \"\"\"\n return key in self._variables\n\n def __len__(self) -> int:\n return len(self.data_vars)\n\n def __bool__(self) -> bool:\n return bool(self.data_vars)\n\n def __iter__(self) -> Iterator[Hashable]:\n return iter(self.data_vars)\n\n def __array__(self, dtype=None):\n raise TypeError(\n \"cannot directly convert an xarray.Dataset into a \"\n \"numpy array. Instead, create an xarray.DataArray \"\n \"first, either with indexing on the Dataset or by \"\n \"invoking the `to_array()` method.\"\n )\n\n @property\n def nbytes(self) -> int:\n return sum(v.nbytes for v in self.variables.values())\n\n @property\n def loc(self) -> _LocIndexer:\n \"\"\"Attribute for location based indexing. Only supports __getitem__,\n and only when the key is a dict of the form {dim: labels}.\n \"\"\"\n return _LocIndexer(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__getitem___Dataset.__getitem__.if_hashable_key_.else_.return.self__copy_listed_np_asar": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__getitem___Dataset.__getitem__.if_hashable_key_.else_.return.self__copy_listed_np_asar", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1225, "end_line": 1238, "span_ids": ["Dataset.__getitem__"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __getitem__(self, key: Any) -> \"Union[DataArray, Dataset]\":\n \"\"\"Access variables or coordinates this dataset as a\n :py:class:`~xarray.DataArray`.\n\n Indexing with a list of names will return a new ``Dataset`` object.\n \"\"\"\n # TODO(shoyer): type this properly: https://github.com/python/mypy/issues/7328\n if utils.is_dict_like(key):\n return self.isel(**cast(Mapping, key))\n\n if hashable(key):\n return self._construct_dataarray(key)\n else:\n return self._copy_listed(np.asarray(key))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__setitem___Dataset.__setitem__.self_update_key_value_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__setitem___Dataset.__setitem__.self_update_key_value_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1240, "end_line": 1256, "span_ids": ["Dataset.__setitem__"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __setitem__(self, key: Hashable, value) -> None:\n \"\"\"Add an array to this dataset.\n\n If value is a `DataArray`, call its `select_vars()` method, rename it\n to `key` and merge the contents of the resulting dataset into this\n dataset.\n\n If value is an `Variable` object (or tuple of form\n ``(dims, data[, attrs])``), add it to this dataset as a new\n variable.\n \"\"\"\n if utils.is_dict_like(key):\n raise NotImplementedError(\n \"cannot yet use a dictionary as a key \" \"to set Dataset values\"\n )\n\n self.update({key: value})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__delitem___Dataset._all_compat.return.self__coord_names_othe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__delitem___Dataset._all_compat.return.self__coord_names_othe", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1258, "end_line": 1283, "span_ids": ["Dataset.__delitem__", "Dataset:22", "Dataset._all_compat"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __delitem__(self, key: Hashable) -> None:\n \"\"\"Remove a variable from this dataset.\n \"\"\"\n del self._variables[key]\n self._coord_names.discard(key)\n if key in self.indexes:\n assert self._indexes is not None\n del self._indexes[key]\n self._dims = calculate_dimensions(self._variables)\n\n # mutable objects should not be hashable\n # https://github.com/python/mypy/issues/4266\n __hash__ = None # type: ignore\n\n def _all_compat(self, other: \"Dataset\", compat_str: str) -> bool:\n \"\"\"Helper function for equals and identical\n \"\"\"\n\n # some stores (e.g., scipy) do not seem to preserve order, so don't\n # require matching order for equality\n def compat(x: Variable, y: Variable) -> bool:\n return getattr(x, compat_str)(y)\n\n return self._coord_names == other._coord_names and utils.dict_equiv(\n self._variables, other._variables, compat=compat\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.broadcast_equals_Dataset.broadcast_equals.try_.except_TypeError_Attrib.return.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.broadcast_equals_Dataset.broadcast_equals.try_.except_TypeError_Attrib.return.False", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1285, "end_line": 1301, "span_ids": ["Dataset.broadcast_equals"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def broadcast_equals(self, other: \"Dataset\") -> bool:\n \"\"\"Two Datasets are broadcast equal if they are equal after\n broadcasting all variables against each other.\n\n For example, variables that are scalar in one dataset but non-scalar in\n the other dataset can still be broadcast equal if the the non-scalar\n variable is a constant.\n\n See Also\n --------\n Dataset.equals\n Dataset.identical\n \"\"\"\n try:\n return self._all_compat(other, \"broadcast_equals\")\n except (TypeError, AttributeError):\n return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.equals_Dataset.equals.try_.except_TypeError_Attrib.return.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.equals_Dataset.equals.try_.except_TypeError_Attrib.return.False", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1303, "end_line": 1321, "span_ids": ["Dataset.equals"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def equals(self, other: \"Dataset\") -> bool:\n \"\"\"Two Datasets are equal if they have matching variables and\n coordinates, all of which are equal.\n\n Datasets can still be equal (like pandas objects) if they have NaN\n values in the same locations.\n\n This method is necessary because `v1 == v2` for ``Dataset``\n does element-wise comparisons (like numpy.ndarrays).\n\n See Also\n --------\n Dataset.broadcast_equals\n Dataset.identical\n \"\"\"\n try:\n return self._all_compat(other, \"equals\")\n except (TypeError, AttributeError):\n return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.identical_Dataset.data_vars.return.DataVariables_self_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.identical_Dataset.data_vars.return.DataVariables_self_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1323, "end_line": 1358, "span_ids": ["Dataset.data_vars", "Dataset.coords", "Dataset.identical", "Dataset.indexes"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def identical(self, other: \"Dataset\") -> bool:\n \"\"\"Like equals, but also checks all dataset attributes and the\n attributes on all variables and coordinates.\n\n See Also\n --------\n Dataset.broadcast_equals\n Dataset.equals\n \"\"\"\n try:\n return utils.dict_equiv(self.attrs, other.attrs) and self._all_compat(\n other, \"identical\"\n )\n except (TypeError, AttributeError):\n return False\n\n @property\n def indexes(self) -> Indexes:\n \"\"\"Mapping of pandas.Index objects used for label based indexing\n \"\"\"\n if self._indexes is None:\n self._indexes = default_indexes(self._variables, self._dims)\n return Indexes(self._indexes)\n\n @property\n def coords(self) -> DatasetCoordinates:\n \"\"\"Dictionary of xarray.DataArray objects corresponding to coordinate\n variables\n \"\"\"\n return DatasetCoordinates(self)\n\n @property\n def data_vars(self) -> DataVariables:\n \"\"\"Dictionary of DataArray objects corresponding to data variables\n \"\"\"\n return DataVariables(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.set_coords_Dataset.set_coords.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.set_coords_Dataset.set_coords.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1360, "end_line": 1390, "span_ids": ["Dataset.set_coords"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def set_coords(\n self, names: \"Union[Hashable, Iterable[Hashable]]\", inplace: bool = None\n ) -> \"Dataset\":\n \"\"\"Given names of one or more variables, set them as coordinates\n\n Parameters\n ----------\n names : hashable or iterable of hashables\n Name(s) of variables in this dataset to convert into coordinates.\n\n Returns\n -------\n Dataset\n\n See also\n --------\n Dataset.swap_dims\n \"\"\"\n # TODO: allow inserting new coordinates with this method, like\n # DataFrame.set_index?\n # nb. check in self._variables, not self.data_vars to insure that the\n # operation is idempotent\n _check_inplace(inplace)\n if isinstance(names, str) or not isinstance(names, Iterable):\n names = [names]\n else:\n names = list(names)\n self._assert_all_in_dataset(names)\n obj = self.copy()\n obj._coord_names.update(names)\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reset_coords_Dataset.dump_to_store.dump_to_store_self_store": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reset_coords_Dataset.dump_to_store.dump_to_store_self_store", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1392, "end_line": 1441, "span_ids": ["Dataset.dump_to_store", "Dataset.reset_coords"], "tokens": 378}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reset_coords(\n self,\n names: \"Union[Hashable, Iterable[Hashable], None]\" = None,\n drop: bool = False,\n inplace: bool = None,\n ) -> \"Dataset\":\n \"\"\"Given names of coordinates, reset them to become variables\n\n Parameters\n ----------\n names : hashable or iterable of hashables, optional\n Name(s) of non-index coordinates in this dataset to reset into\n variables. By default, all non-index coordinates are reset.\n drop : bool, optional\n If True, remove coordinates instead of converting them into\n variables.\n\n Returns\n -------\n Dataset\n \"\"\"\n _check_inplace(inplace)\n if names is None:\n names = self._coord_names - set(self.dims)\n else:\n if isinstance(names, str) or not isinstance(names, Iterable):\n names = [names]\n else:\n names = list(names)\n self._assert_all_in_dataset(names)\n bad_coords = set(names) & set(self.dims)\n if bad_coords:\n raise ValueError(\n \"cannot remove index coordinates with reset_coords: %s\" % bad_coords\n )\n obj = self.copy()\n obj._coord_names.difference_update(names)\n if drop:\n for name in names:\n del obj._variables[name]\n return obj\n\n def dump_to_store(self, store: \"AbstractDataStore\", **kwargs) -> None:\n \"\"\"Store dataset contents to a backends.*DataStore object.\n \"\"\"\n from ..backends.api import dump_to_store\n\n # TODO: rename and/or cleanup this method to make it more consistent\n # with to_netcdf()\n dump_to_store(self, store, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_netcdf_Dataset.to_netcdf.return.to_netcdf_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_netcdf_Dataset.to_netcdf.return.to_netcdf_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1443, "end_line": 1537, "span_ids": ["Dataset.to_netcdf"], "tokens": 1014}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_netcdf(\n self,\n path=None,\n mode: str = \"w\",\n format: str = None,\n group: str = None,\n engine: str = None,\n encoding: Mapping = None,\n unlimited_dims: Iterable[Hashable] = None,\n compute: bool = True,\n invalid_netcdf: bool = False,\n ) -> Union[bytes, \"Delayed\", None]:\n \"\"\"Write dataset contents to a netCDF file.\n\n Parameters\n ----------\n path : str, Path or file-like object, optional\n Path to which to save this dataset. File-like objects are only\n supported by the scipy engine. If no path is provided, this\n function returns the resulting netCDF file as bytes; in this case,\n we need to use scipy, which does not support netCDF version 4 (the\n default format becomes NETCDF3_64BIT).\n mode : {'w', 'a'}, optional\n Write ('w') or append ('a') mode. If mode='w', any existing file at\n this location will be overwritten. If mode='a', existing variables\n will be overwritten.\n format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT',\n 'NETCDF3_CLASSIC'}, optional\n File format for the resulting netCDF file:\n\n * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API\n features.\n * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only\n netCDF 3 compatible API features.\n * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format,\n which fully supports 2+ GB files, but is only compatible with\n clients linked against netCDF version 3.6.0 or later.\n * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not\n handle 2+ GB files very well.\n\n All formats are supported by the netCDF4-python library.\n scipy.io.netcdf only supports the last two formats.\n\n The default format is NETCDF4 if you are saving a file to disk and\n have the netCDF4-python library available. Otherwise, xarray falls\n back to using scipy to write netCDF files and defaults to the\n NETCDF3_64BIT format (scipy does not support netCDF4).\n group : str, optional\n Path to the netCDF4 group in the given file to open (only works for\n format='NETCDF4'). The group(s) will be created if necessary.\n engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional\n Engine to use when writing netCDF files. If not provided, the\n default engine is chosen based on available dependencies, with a\n preference for 'netcdf4' if writing to a file on disk.\n encoding : dict, optional\n Nested dictionary with variable names as keys and dictionaries of\n variable specific encodings as values, e.g.,\n ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1,\n 'zlib': True}, ...}``\n\n The `h5netcdf` engine supports both the NetCDF4-style compression\n encoding parameters ``{'zlib': True, 'complevel': 9}`` and the h5py\n ones ``{'compression': 'gzip', 'compression_opts': 9}``.\n This allows using any compression plugin installed in the HDF5\n library, e.g. LZF.\n\n unlimited_dims : iterable of hashable, optional\n Dimension(s) that should be serialized as unlimited dimensions.\n By default, no dimensions are treated as unlimited dimensions.\n Note that unlimited_dims may also be set via\n ``dataset.encoding['unlimited_dims']``.\n compute: boolean\n If true compute immediately, otherwise return a\n ``dask.delayed.Delayed`` object that can be computed later.\n invalid_netcdf: boolean\n Only valid along with engine='h5netcdf'. If True, allow writing\n hdf5 files which are valid netcdf as described in\n https://github.com/shoyer/h5netcdf. Default: False.\n \"\"\"\n if encoding is None:\n encoding = {}\n from ..backends.api import to_netcdf\n\n return to_netcdf(\n self,\n path,\n mode,\n format=format,\n group=group,\n engine=engine,\n encoding=encoding,\n unlimited_dims=unlimited_dims,\n compute=compute,\n invalid_netcdf=invalid_netcdf,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_zarr_Dataset.to_zarr.return.to_zarr_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_zarr_Dataset.to_zarr.return.to_zarr_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1539, "end_line": 1617, "span_ids": ["Dataset.to_zarr"], "tokens": 669}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_zarr(\n self,\n store: Union[MutableMapping, str, Path] = None,\n mode: str = None,\n synchronizer=None,\n group: str = None,\n encoding: Mapping = None,\n compute: bool = True,\n consolidated: bool = False,\n append_dim: Hashable = None,\n ) -> \"ZarrStore\":\n \"\"\"Write dataset contents to a zarr group.\n\n .. note:: Experimental\n The Zarr backend is new and experimental. Please report any\n unexpected behavior via github issues.\n\n Parameters\n ----------\n store : MutableMapping, str or Path, optional\n Store or path to directory in file system.\n mode : {'w', 'w-', 'a', None}\n Persistence mode: 'w' means create (overwrite if exists);\n 'w-' means create (fail if exists);\n 'a' means append (create if does not exist).\n If ``append_dim`` is set, ``mode`` can be omitted as it is\n internally set to ``'a'``. Otherwise, ``mode`` will default to\n `w-` if not set.\n synchronizer : object, optional\n Array synchronizer\n group : str, optional\n Group path. (a.k.a. `path` in zarr terminology.)\n encoding : dict, optional\n Nested dictionary with variable names as keys and dictionaries of\n variable specific encodings as values, e.g.,\n ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1,}, ...}``\n compute: bool, optional\n If True compute immediately, otherwise return a\n ``dask.delayed.Delayed`` object that can be computed later.\n consolidated: bool, optional\n If True, apply zarr's `consolidate_metadata` function to the store\n after writing.\n append_dim: hashable, optional\n If set, the dimension on which the data will be appended.\n\n References\n ----------\n https://zarr.readthedocs.io/\n \"\"\"\n if encoding is None:\n encoding = {}\n if (mode == \"a\") or (append_dim is not None):\n if mode is None:\n mode = \"a\"\n elif mode != \"a\":\n raise ValueError(\n \"append_dim was set along with mode='{}', either set \"\n \"mode='a' or don't set it.\".format(mode)\n )\n elif mode is None:\n mode = \"w-\"\n if mode not in [\"w\", \"w-\", \"a\"]:\n # TODO: figure out how to handle 'r+'\n raise ValueError(\n \"The only supported options for mode are 'w',\" \"'w-' and 'a'.\"\n )\n from ..backends.api import to_zarr\n\n return to_zarr(\n self,\n store=store,\n mode=mode,\n synchronizer=synchronizer,\n group=group,\n encoding=encoding,\n compute=compute,\n consolidated=consolidated,\n append_dim=append_dim,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__repr___Dataset.info.buf_write_n_join_lines": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.__repr___Dataset.info.buf_write_n_join_lines", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1619, "end_line": 1656, "span_ids": ["Dataset.__repr__", "Dataset.info"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def __repr__(self) -> str:\n return formatting.dataset_repr(self)\n\n def info(self, buf=None) -> None:\n \"\"\"\n Concise summary of a Dataset variables and attributes.\n\n Parameters\n ----------\n buf : writable buffer, defaults to sys.stdout\n\n See Also\n --------\n pandas.DataFrame.assign\n ncdump: netCDF's ncdump\n \"\"\"\n if buf is None: # pragma: no cover\n buf = sys.stdout\n\n lines = []\n lines.append(\"xarray.Dataset {\")\n lines.append(\"dimensions:\")\n for name, size in self.dims.items():\n lines.append(\"\\t{name} = {size} ;\".format(name=name, size=size))\n lines.append(\"\\nvariables:\")\n for name, da in self.variables.items():\n dims = \", \".join(da.dims)\n lines.append(\n \"\\t{type} {name}({dims}) ;\".format(type=da.dtype, name=name, dims=dims)\n )\n for k, v in da.attrs.items():\n lines.append(\"\\t\\t{name}:{k} = {v} ;\".format(name=name, k=k, v=v))\n lines.append(\"\\n// global attributes:\")\n for k, v in self.attrs.items():\n lines.append(\"\\t:{k} = {v} ;\".format(k=k, v=v))\n lines.append(\"}\")\n\n buf.write(\"\\n\".join(lines))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.chunks_Dataset.chunks.return.Frozen_SortedKeysDict_chu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.chunks_Dataset.chunks.return.Frozen_SortedKeysDict_chu", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1658, "end_line": 1673, "span_ids": ["Dataset.chunks"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @property\n def chunks(self) -> Mapping[Hashable, Tuple[int, ...]]:\n \"\"\"Block dimensions for this dataset's data or None if it's not a dask\n array.\n \"\"\"\n chunks: Dict[Hashable, Tuple[int, ...]] = {}\n for v in self.variables.values():\n if v.chunks is not None:\n for dim, c in zip(v.dims, v.chunks):\n if dim in chunks and c != chunks[dim]:\n raise ValueError(\n f\"Object has inconsistent chunks along dimension {dim}. \"\n \"This can be fixed by calling unify_chunks().\"\n )\n chunks[dim] = c\n return Frozen(SortedKeysDict(chunks))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.chunk_Dataset.chunk.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.chunk_Dataset.chunk.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1675, "end_line": 1741, "span_ids": ["Dataset.chunk"], "tokens": 552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def chunk(\n self,\n chunks: Union[\n None, Number, Mapping[Hashable, Union[None, Number, Tuple[Number, ...]]]\n ] = None,\n name_prefix: str = \"xarray-\",\n token: str = None,\n lock: bool = False,\n ) -> \"Dataset\":\n \"\"\"Coerce all arrays in this dataset into dask arrays with the given\n chunks.\n\n Non-dask arrays in this dataset will be converted to dask arrays. Dask\n arrays will be rechunked to the given chunk sizes.\n\n If neither chunks is not provided for one or more dimensions, chunk\n sizes along that dimension will not be updated; non-dask arrays will be\n converted into dask arrays with a single block.\n\n Parameters\n ----------\n chunks : int or mapping, optional\n Chunk sizes along each dimension, e.g., ``5`` or\n ``{'x': 5, 'y': 5}``.\n name_prefix : str, optional\n Prefix for the name of any new dask arrays.\n token : str, optional\n Token uniquely identifying this dataset.\n lock : optional\n Passed on to :py:func:`dask.array.from_array`, if the array is not\n already as dask array.\n\n Returns\n -------\n chunked : xarray.Dataset\n \"\"\"\n from dask.base import tokenize\n\n if isinstance(chunks, Number):\n chunks = dict.fromkeys(self.dims, chunks)\n\n if chunks is not None:\n bad_dims = chunks.keys() - self.dims.keys()\n if bad_dims:\n raise ValueError(\n \"some chunks keys are not dimensions on this \"\n \"object: %s\" % bad_dims\n )\n\n def selkeys(dict_, keys):\n if dict_ is None:\n return None\n return {d: dict_[d] for d in keys if d in dict_}\n\n def maybe_chunk(name, var, chunks):\n chunks = selkeys(chunks, var.dims)\n if not chunks:\n chunks = None\n if var.ndim > 0:\n token2 = tokenize(name, token if token else var._data)\n name2 = \"%s%s-%s\" % (name_prefix, name, token2)\n return var.chunk(chunks, name=name2, lock=lock)\n else:\n return var\n\n variables = {k: maybe_chunk(k, v, chunks) for k, v in self.variables.items()}\n return self._replace(variables)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._validate_indexers_Dataset._validate_indexers.for_k_v_in_indexers_item.if_isinstance_v_int_sl.else_.yield_k_v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._validate_indexers_Dataset._validate_indexers.for_k_v_in_indexers_item.if_isinstance_v_int_sl.else_.yield_k_v", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1743, "end_line": 1785, "span_ids": ["Dataset._validate_indexers"], "tokens": 388}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _validate_indexers(\n self, indexers: Mapping[Hashable, Any]\n ) -> Iterator[Tuple[Hashable, Union[int, slice, np.ndarray, Variable]]]:\n \"\"\" Here we make sure\n + indexer has a valid keys\n + indexer is in a valid data type\n + string indexers are cast to the appropriate date type if the\n associated index is a DatetimeIndex or CFTimeIndex\n \"\"\"\n from .dataarray import DataArray\n\n invalid = indexers.keys() - self.dims.keys()\n if invalid:\n raise ValueError(\"dimensions %r do not exist\" % invalid)\n\n # all indexers should be int, slice, np.ndarrays, or Variable\n for k, v in indexers.items():\n if isinstance(v, (int, slice, Variable)):\n yield k, v\n elif isinstance(v, DataArray):\n yield k, v.variable\n elif isinstance(v, tuple):\n yield k, as_variable(v)\n elif isinstance(v, Dataset):\n raise TypeError(\"cannot use a Dataset as an indexer\")\n elif isinstance(v, Sequence) and len(v) == 0:\n yield k, np.empty((0,), dtype=\"int64\")\n else:\n v = np.asarray(v)\n\n if v.dtype.kind in \"US\":\n index = self.indexes[k]\n if isinstance(index, pd.DatetimeIndex):\n v = v.astype(\"datetime64[ns]\")\n elif isinstance(index, xr.CFTimeIndex):\n v = _parse_array_of_cftime_strings(v, index.date_type)\n\n if v.ndim > 1:\n raise IndexError(\n \"Unlabeled multi-dimensional array cannot be \"\n \"used for indexing: {}\".format(k)\n )\n yield k, v", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._validate_interp_indexers_Dataset._validate_interp_indexers.for_k_v_in_self__validat.if_isinstance_v_Variable.else_.raise_TypeError_type_v_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._validate_interp_indexers_Dataset._validate_interp_indexers.for_k_v_in_self__validat.if_isinstance_v_Variable.else_.raise_TypeError_type_v_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1787, "end_line": 1808, "span_ids": ["Dataset._validate_interp_indexers"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _validate_interp_indexers(\n self, indexers: Mapping[Hashable, Any]\n ) -> Iterator[Tuple[Hashable, Variable]]:\n \"\"\"Variant of _validate_indexers to be used for interpolation\n \"\"\"\n for k, v in self._validate_indexers(indexers):\n if isinstance(v, Variable):\n if v.ndim == 1:\n yield k, v.to_index_variable()\n else:\n yield k, v\n elif isinstance(v, int):\n yield k, Variable((), v)\n elif isinstance(v, np.ndarray):\n if v.ndim == 0:\n yield k, Variable((), v)\n elif v.ndim == 1:\n yield k, IndexVariable((k,), v)\n else:\n raise AssertionError() # Already tested by _validate_indexers\n else:\n raise TypeError(type(v))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._get_indexers_coords_and_indexes_Dataset._get_indexers_coords_and_indexes.return.attached_coords_attached": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._get_indexers_coords_and_indexes_Dataset._get_indexers_coords_and_indexes.return.attached_coords_attached", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1810, "end_line": 1846, "span_ids": ["Dataset._get_indexers_coords_and_indexes"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _get_indexers_coords_and_indexes(self, indexers):\n \"\"\"Extract coordinates and indexes from indexers.\n\n Only coordinate with a name different from any of self.variables will\n be attached.\n \"\"\"\n from .dataarray import DataArray\n\n coords_list = []\n for k, v in indexers.items():\n if isinstance(v, DataArray):\n if v.dtype.kind == \"b\":\n if v.ndim != 1: # we only support 1-d boolean array\n raise ValueError(\n \"{:d}d-boolean array is used for indexing along \"\n \"dimension {!r}, but only 1d boolean arrays are \"\n \"supported.\".format(v.ndim, k)\n )\n # Make sure in case of boolean DataArray, its\n # coordinate also should be indexed.\n v_coords = v[v.values.nonzero()[0]].coords\n else:\n v_coords = v.coords\n coords_list.append(v_coords)\n\n # we don't need to call align() explicitly or check indexes for\n # alignment, because merge_variables already checks for exact alignment\n # between dimension coordinates\n coords, indexes = merge_coordinates_without_align(coords_list)\n assert_coordinate_consistent(self, coords)\n\n # silently drop the conflicted variables.\n attached_coords = {k: v for k, v in coords.items() if k not in self._variables}\n attached_indexes = {\n k: v for k, v in indexes.items() if k not in self._variables\n }\n return attached_coords, attached_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.isel_Dataset.isel.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.isel_Dataset.isel.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1848, "end_line": 1927, "span_ids": ["Dataset.isel"], "tokens": 724}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def isel(\n self,\n indexers: Mapping[Hashable, Any] = None,\n drop: bool = False,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Returns a new dataset with each array indexed along the specified\n dimension(s).\n\n This method selects values from each array using its `__getitem__`\n method, except this method does not require knowing the order of\n each array's dimensions.\n\n Parameters\n ----------\n indexers : dict, optional\n A dict with keys matching dimensions and values given\n by integers, slice objects or arrays.\n indexer can be a integer, slice, array-like or DataArray.\n If DataArrays are passed as indexers, xarray-style indexing will be\n carried out. See :ref:`indexing` for the details.\n One of indexers or indexers_kwargs must be provided.\n drop : bool, optional\n If ``drop=True``, drop coordinates variables indexed by integers\n instead of making them scalar.\n **indexers_kwarg : {dim: indexer, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n Returns\n -------\n obj : Dataset\n A new Dataset with the same contents as this dataset, except each\n array and dimension is indexed by the appropriate indexers.\n If indexer DataArrays have coordinates that do not conflict with\n this object, then these coordinates will be attached.\n In general, each array's data will be a view of the array's data\n in this dataset, unless vectorized indexing was triggered by using\n an array indexer, in which case the data will be a copy.\n\n See Also\n --------\n Dataset.sel\n DataArray.isel\n \"\"\"\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"isel\")\n # Note: we need to preserve the original indexers variable in order to merge the\n # coords below\n indexers_list = list(self._validate_indexers(indexers))\n\n variables: Dict[Hashable, Variable] = {}\n indexes: Dict[Hashable, pd.Index] = {}\n\n for name, var in self.variables.items():\n var_indexers = {k: v for k, v in indexers_list if k in var.dims}\n if drop and name in var_indexers:\n continue # drop this variable\n\n if name in self.indexes:\n new_var, new_index = isel_variable_and_index(\n name, var, self.indexes[name], var_indexers\n )\n if new_index is not None:\n indexes[name] = new_index\n elif var_indexers:\n new_var = var.isel(indexers=var_indexers)\n else:\n new_var = var.copy(deep=False)\n\n variables[name] = new_var\n\n coord_names = self._coord_names & variables.keys()\n selected = self._replace_with_new_dims(variables, coord_names, indexes)\n\n # Extract coordinates from indexers\n coord_vars, new_indexes = selected._get_indexers_coords_and_indexes(indexers)\n variables.update(coord_vars)\n indexes.update(new_indexes)\n coord_names = self._coord_names & variables.keys() | coord_vars.keys()\n return self._replace_with_new_dims(variables, coord_names, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.sel_Dataset.sel.return.result__overwrite_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.sel_Dataset.sel.return.result__overwrite_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1929, "end_line": 2003, "span_ids": ["Dataset.sel"], "tokens": 735}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def sel(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance: Number = None,\n drop: bool = False,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Returns a new dataset with each array indexed by tick labels\n along the specified dimension(s).\n\n In contrast to `Dataset.isel`, indexers for this method should use\n labels instead of integers.\n\n Under the hood, this method is powered by using pandas's powerful Index\n objects. This makes label based indexing essentially just as fast as\n using integer indexing.\n\n It also means this method uses pandas's (well documented) logic for\n indexing. This means you can use string shortcuts for datetime indexes\n (e.g., '2000-01' to select all values in January 2000). It also means\n that slices are treated as inclusive of both the start and stop values,\n unlike normal Python indexing.\n\n Parameters\n ----------\n indexers : dict, optional\n A dict with keys matching dimensions and values given\n by scalars, slices or arrays of tick labels. For dimensions with\n multi-index, the indexer may also be a dict-like object with keys\n matching index level names.\n If DataArrays are passed as indexers, xarray-style indexing will be\n carried out. See :ref:`indexing` for the details.\n One of indexers or indexers_kwargs must be provided.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for inexact matches:\n\n * None (default): only exact matches\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations must\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n drop : bool, optional\n If ``drop=True``, drop coordinates variables in `indexers` instead\n of making them scalar.\n **indexers_kwarg : {dim: indexer, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n Returns\n -------\n obj : Dataset\n A new Dataset with the same contents as this dataset, except each\n variable and dimension is indexed by the appropriate indexers.\n If indexer DataArrays have coordinates that do not conflict with\n this object, then these coordinates will be attached.\n In general, each array's data will be a view of the array's data\n in this dataset, unless vectorized indexing was triggered by using\n an array indexer, in which case the data will be a copy.\n\n\n See Also\n --------\n Dataset.isel\n DataArray.sel\n \"\"\"\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"sel\")\n pos_indexers, new_indexes = remap_label_indexers(\n self, indexers=indexers, method=method, tolerance=tolerance\n )\n result = self.isel(indexers=pos_indexers, drop=drop)\n return result._overwrite_indexes(new_indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.head_Dataset.head.return.self_isel_indexers_slices": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.head_Dataset.head.return.self_isel_indexers_slices", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2005, "end_line": 2050, "span_ids": ["Dataset.head"], "tokens": 406}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def head(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Returns a new dataset with the first `n` values of each array\n for the specified dimension(s).\n\n Parameters\n ----------\n indexers : dict or int, default: 5\n A dict with keys matching dimensions and integer values `n`\n or a single integer `n` applied over all dimensions.\n One of indexers or indexers_kwargs must be provided.\n **indexers_kwargs : {dim: n, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n\n See Also\n --------\n Dataset.tail\n Dataset.thin\n DataArray.head\n \"\"\"\n if not indexers_kwargs:\n if indexers is None:\n indexers = 5\n if not isinstance(indexers, int) and not is_dict_like(indexers):\n raise TypeError(\"indexers must be either dict-like or a single integer\")\n if isinstance(indexers, int):\n indexers = {dim: indexers for dim in self.dims}\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"head\")\n for k, v in indexers.items():\n if not isinstance(v, int):\n raise TypeError(\n \"expected integer type indexer for \"\n \"dimension %r, found %r\" % (k, type(v))\n )\n elif v < 0:\n raise ValueError(\n \"expected positive integer as indexer \"\n \"for dimension %r, found %s\" % (k, v)\n )\n indexers_slices = {k: slice(val) for k, val in indexers.items()}\n return self.isel(indexers_slices)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.tail_Dataset.tail.return.self_isel_indexers_slices": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.tail_Dataset.tail.return.self_isel_indexers_slices", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2052, "end_line": 2100, "span_ids": ["Dataset.tail"], "tokens": 422}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def tail(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Returns a new dataset with the last `n` values of each array\n for the specified dimension(s).\n\n Parameters\n ----------\n indexers : dict or int, default: 5\n A dict with keys matching dimensions and integer values `n`\n or a single integer `n` applied over all dimensions.\n One of indexers or indexers_kwargs must be provided.\n **indexers_kwargs : {dim: n, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n\n See Also\n --------\n Dataset.head\n Dataset.thin\n DataArray.tail\n \"\"\"\n if not indexers_kwargs:\n if indexers is None:\n indexers = 5\n if not isinstance(indexers, int) and not is_dict_like(indexers):\n raise TypeError(\"indexers must be either dict-like or a single integer\")\n if isinstance(indexers, int):\n indexers = {dim: indexers for dim in self.dims}\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"tail\")\n for k, v in indexers.items():\n if not isinstance(v, int):\n raise TypeError(\n \"expected integer type indexer for \"\n \"dimension %r, found %r\" % (k, type(v))\n )\n elif v < 0:\n raise ValueError(\n \"expected positive integer as indexer \"\n \"for dimension %r, found %s\" % (k, v)\n )\n indexers_slices = {\n k: slice(-val, None) if val != 0 else slice(val)\n for k, val in indexers.items()\n }\n return self.isel(indexers_slices)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.thin_Dataset.thin.return.self_isel_indexers_slices": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.thin_Dataset.thin.return.self_isel_indexers_slices", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2102, "end_line": 2150, "span_ids": ["Dataset.thin"], "tokens": 418}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def thin(\n self,\n indexers: Union[Mapping[Hashable, int], int] = None,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Returns a new dataset with each array indexed along every `n`th\n value for the specified dimension(s)\n\n Parameters\n ----------\n indexers : dict or int, default: 5\n A dict with keys matching dimensions and integer values `n`\n or a single integer `n` applied over all dimensions.\n One of indexers or indexers_kwargs must be provided.\n **indexers_kwargs : {dim: n, ...}, optional\n The keyword arguments form of ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n\n See Also\n --------\n Dataset.head\n Dataset.tail\n DataArray.thin\n \"\"\"\n if (\n not indexers_kwargs\n and not isinstance(indexers, int)\n and not is_dict_like(indexers)\n ):\n raise TypeError(\"indexers must be either dict-like or a single integer\")\n if isinstance(indexers, int):\n indexers = {dim: indexers for dim in self.dims}\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"thin\")\n for k, v in indexers.items():\n if not isinstance(v, int):\n raise TypeError(\n \"expected integer type indexer for \"\n \"dimension %r, found %r\" % (k, type(v))\n )\n elif v < 0:\n raise ValueError(\n \"expected positive integer as indexer \"\n \"for dimension %r, found %s\" % (k, v)\n )\n elif v == 0:\n raise ValueError(\"step cannot be zero\")\n indexers_slices = {k: slice(None, None, val) for k, val in indexers.items()}\n return self.isel(indexers_slices)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.broadcast_like_Dataset.broadcast_like.return._broadcast_helper_args_1_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.broadcast_like_Dataset.broadcast_like.return._broadcast_helper_args_1_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2152, "end_line": 2174, "span_ids": ["Dataset.broadcast_like"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def broadcast_like(\n self, other: Union[\"Dataset\", \"DataArray\"], exclude: Iterable[Hashable] = None\n ) -> \"Dataset\":\n \"\"\"Broadcast this DataArray against another Dataset or DataArray.\n This is equivalent to xr.broadcast(other, self)[1]\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object against which to broadcast this array.\n exclude : iterable of hashable, optional\n Dimensions that must not be broadcasted\n\n \"\"\"\n if exclude is None:\n exclude = set()\n else:\n exclude = set(exclude)\n args = align(other, self, join=\"outer\", copy=False, exclude=exclude)\n\n dims_map, common_coords = _get_broadcast_dims_map_common_coords(args, exclude)\n\n return _broadcast_helper(args[1], exclude, dims_map, common_coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex_like_Dataset.reindex": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex_like_Dataset.reindex", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2176, "end_line": 2436, "span_ids": ["Dataset.reindex_like", "Dataset.reindex"], "tokens": 602}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reindex_like(\n self,\n other: Union[\"Dataset\", \"DataArray\"],\n method: str = None,\n tolerance: Number = None,\n copy: bool = True,\n fill_value: Any = dtypes.NA,\n ) -> \"Dataset\":\n \"\"\"Conform this object onto the indexes of another object, filling in\n missing values with ``fill_value``. The default fill value is NaN.\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object with an 'indexes' attribute giving a mapping from dimension\n names to pandas.Index objects, which provides coordinates upon\n which to index the variables in this dataset. The indexes on this\n other object need not be the same as the indexes on this\n dataset. Any mis-matched index values will be filled in with\n NaN, and any mis-matched dimension names will simply be ignored.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for filling index values from other not found in this\n dataset:\n\n * None (default): don't fill gaps\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations must\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n copy : bool, optional\n If ``copy=True``, data in the return value is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed\n with only slice operations, then the output may share memory with\n the input. In either case, a new xarray object is always returned.\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n reindexed : Dataset\n Another dataset, with this dataset's data but coordinates from the\n other object.\n\n See Also\n --------\n Dataset.reindex\n align\n \"\"\"\n indexers = alignment.reindex_like_indexers(self, other)\n return self.reindex(\n indexers=indexers,\n method=method,\n copy=copy,\n fill_value=fill_value,\n tolerance=tolerance,\n )\n\n def reindex(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance: Number = None,\n copy: bool = True,\n fill_value: Any = dtypes.NA,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex._Conform_this_object_on_Dataset.reindex._Conform_this_object_on": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex._Conform_this_object_on_Dataset.reindex._Conform_this_object_on", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2245, "end_line": 2417, "span_ids": ["Dataset.reindex"], "tokens": 2065}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reindex(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance: Number = None,\n copy: bool = True,\n fill_value: Any = dtypes.NA,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Conform this object onto a new set of indexes, filling in\n missing values with ``fill_value``. The default fill value is NaN.\n\n Parameters\n ----------\n indexers : dict. optional\n Dictionary with keys given by dimension names and values given by\n arrays of coordinates tick labels. Any mis-matched coordinate\n values will be filled in with NaN, and any mis-matched dimension\n names will simply be ignored.\n One of indexers or indexers_kwargs must be provided.\n method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional\n Method to use for filling index values in ``indexers`` not found in\n this dataset:\n\n * None (default): don't fill gaps\n * pad / ffill: propagate last valid index value forward\n * backfill / bfill: propagate next valid index value backward\n * nearest: use nearest valid index value\n tolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations must\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n copy : bool, optional\n If ``copy=True``, data in the return value is always copied. If\n ``copy=False`` and reindexing is unnecessary, or can be performed\n with only slice operations, then the output may share memory with\n the input. In either case, a new xarray object is always returned.\n fill_value : scalar, optional\n Value to use for newly missing values\n **indexers_kwarg : {dim: indexer, ...}, optional\n Keyword arguments in the same form as ``indexers``.\n One of indexers or indexers_kwargs must be provided.\n\n Returns\n -------\n reindexed : Dataset\n Another dataset, with this dataset's data but replaced coordinates.\n\n See Also\n --------\n Dataset.reindex_like\n align\n pandas.Index.get_indexer\n\n Examples\n --------\n\n Create a dataset with some fictional data.\n\n >>> import xarray as xr\n >>> import pandas as pd\n >>> x = xr.Dataset(\n ... {\n ... \"temperature\": (\"station\", 20 * np.random.rand(4)),\n ... \"pressure\": (\"station\", 500 * np.random.rand(4))\n ... },\n ... coords={\"station\": [\"boston\", \"nyc\", \"seattle\", \"denver\"]})\n >>> x\n \n Dimensions: (station: 4)\n Coordinates:\n * station (station) >> x.indexes\n station: Index(['boston', 'nyc', 'seattle', 'denver'], dtype='object', name='station')\n\n Create a new index and reindex the dataset. By default values in the new index that\n do not have corresponding records in the dataset are assigned `NaN`.\n\n >>> new_index = ['boston', 'austin', 'seattle', 'lincoln']\n >>> x.reindex({'station': new_index})\n \n Dimensions: (station: 4)\n Coordinates:\n * station (station) object 'boston' 'austin' 'seattle' 'lincoln'\n Data variables:\n temperature (station) float64 18.84 nan 19.22 nan\n pressure (station) float64 324.1 nan 122.8 nan\n\n We can fill in the missing values by passing a value to the keyword `fill_value`.\n\n >>> x.reindex({'station': new_index}, fill_value=0)\n \n Dimensions: (station: 4)\n Coordinates:\n * station (station) object 'boston' 'austin' 'seattle' 'lincoln'\n Data variables:\n temperature (station) float64 18.84 0.0 19.22 0.0\n pressure (station) float64 324.1 0.0 122.8 0.0\n\n Because the index is not monotonically increasing or decreasing, we cannot use arguments\n to the keyword method to fill the `NaN` values.\n\n >>> x.reindex({'station': new_index}, method='nearest')\n Traceback (most recent call last):\n ...\n raise ValueError('index must be monotonic increasing or decreasing')\n ValueError: index must be monotonic increasing or decreasing\n\n To further illustrate the filling functionality in reindex, we will create a\n dataset with a monotonically increasing index (for example, a sequence of dates).\n\n >>> x2 = xr.Dataset(\n ... {\n ... \"temperature\": (\"time\", [15.57, 12.77, np.nan, 0.3081, 16.59, 15.12]),\n ... \"pressure\": (\"time\", 500 * np.random.rand(6))\n ... },\n ... coords={\"time\": pd.date_range('01/01/2019', periods=6, freq='D')})\n >>> x2\n \n Dimensions: (time: 6)\n Coordinates:\n * time (time) datetime64[ns] 2019-01-01 2019-01-02 ... 2019-01-06\n Data variables:\n temperature (time) float64 15.57 12.77 nan 0.3081 16.59 15.12\n pressure (time) float64 103.4 122.7 452.0 444.0 399.2 486.0\n\n Suppose we decide to expand the dataset to cover a wider date range.\n\n >>> time_index2 = pd.date_range('12/29/2018', periods=10, freq='D')\n >>> x2.reindex({'time': time_index2})\n \n Dimensions: (time: 10)\n Coordinates:\n * time (time) datetime64[ns] 2018-12-29 2018-12-30 ... 2019-01-07\n Data variables:\n temperature (time) float64 nan nan nan 15.57 ... 0.3081 16.59 15.12 nan\n pressure (time) float64 nan nan nan 103.4 ... 444.0 399.2 486.0 nan\n\n The index entries that did not have a value in the original data frame (for example, `2018-12-29`)\n are by default filled with NaN. If desired, we can fill in the missing values using one of several options.\n\n For example, to back-propagate the last valid value to fill the `NaN` values,\n pass `bfill` as an argument to the `method` keyword.\n\n >>> x3 = x2.reindex({'time': time_index2}, method='bfill')\n >>> x3\n \n Dimensions: (time: 10)\n Coordinates:\n * time (time) datetime64[ns] 2018-12-29 2018-12-30 ... 2019-01-07\n Data variables:\n temperature (time) float64 15.57 15.57 15.57 15.57 ... 16.59 15.12 nan\n pressure (time) float64 103.4 103.4 103.4 103.4 ... 399.2 486.0 nan\n\n Please note that the `NaN` value present in the original dataset (at index value `2019-01-03`)\n will not be filled by any of the value propagation schemes.\n\n >>> x2.where(x2.temperature.isnull(), drop=True)\n \n Dimensions: (time: 1)\n Coordinates:\n * time (time) datetime64[ns] 2019-01-03\n Data variables:\n temperature (time) float64 nan\n pressure (time) float64 452.0\n >>> x3.where(x3.temperature.isnull(), drop=True)\n \n Dimensions: (time: 2)\n Coordinates:\n * time (time) datetime64[ns] 2019-01-03 2019-01-07\n Data variables:\n temperature (time) float64 nan nan\n pressure (time) float64 452.0 nan\n\n This is because filling while reindexing does not look at dataset values, but only compares\n the original and desired indexes. If you do want to fill in the `NaN` values present in the\n original dataset, use the :py:meth:`~Dataset.fillna()` method.\n\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex.indexers_Dataset.reindex.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reindex.indexers_Dataset.reindex.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2418, "end_line": 2436, "span_ids": ["Dataset.reindex"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reindex(\n self,\n indexers: Mapping[Hashable, Any] = None,\n method: str = None,\n tolerance: Number = None,\n copy: bool = True,\n fill_value: Any = dtypes.NA,\n **indexers_kwargs: Any,\n ) -> \"Dataset\":\n indexers = utils.either_dict_or_kwargs(indexers, indexers_kwargs, \"reindex\")\n\n bad_dims = [d for d in indexers if d not in self.dims]\n if bad_dims:\n raise ValueError(\"invalid reindex dimensions: %s\" % bad_dims)\n\n variables, indexes = alignment.reindex_variables(\n self.variables,\n self.sizes,\n self.indexes,\n indexers,\n method,\n tolerance,\n copy=copy,\n fill_value=fill_value,\n )\n coord_names = set(self._coord_names)\n coord_names.update(indexers)\n return self._replace_with_new_dims(variables, coord_names, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp_Dataset.interp.maybe_variable.try_.except_KeyError_.return.as_variable_k_range_obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp_Dataset.interp.maybe_variable.try_.except_KeyError_.return.as_variable_k_range_obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2438, "end_line": 2499, "span_ids": ["Dataset.interp"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def interp(\n self,\n coords: Mapping[Hashable, Any] = None,\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n **coords_kwargs: Any,\n ) -> \"Dataset\":\n \"\"\" Multidimensional interpolation of Dataset.\n\n Parameters\n ----------\n coords : dict, optional\n Mapping from dimension names to the new coordinates.\n New coordinate can be a scalar, array-like or DataArray.\n If DataArrays are passed as new coordates, their dimensions are\n used for the broadcasting.\n method: string, optional.\n {'linear', 'nearest'} for multidimensional array,\n {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}\n for 1-dimensional array. 'linear' is used by default.\n assume_sorted: boolean, optional\n If False, values of coordinates that are interpolated over can be\n in any order and they are sorted first. If True, interpolated\n coordinates are assumed to be an array of monotonically increasing\n values.\n kwargs: dictionary, optional\n Additional keyword passed to scipy's interpolator.\n **coords_kwarg : {dim: coordinate, ...}, optional\n The keyword arguments form of ``coords``.\n One of coords or coords_kwargs must be provided.\n\n Returns\n -------\n interpolated: xr.Dataset\n New dataset on the new coordinates.\n\n Notes\n -----\n scipy is required.\n\n See Also\n --------\n scipy.interpolate.interp1d\n scipy.interpolate.interpn\n \"\"\"\n from . import missing\n\n if kwargs is None:\n kwargs = {}\n\n coords = either_dict_or_kwargs(coords, coords_kwargs, \"interp\")\n indexers = dict(self._validate_interp_indexers(coords))\n\n obj = self if assume_sorted else self.sortby([k for k in coords])\n\n def maybe_variable(obj, k):\n # workaround to get variable for dimension without coordinate.\n try:\n return obj._variables[k]\n except KeyError:\n return as_variable((k, range(obj.dims[k])))\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp._validate_interp_indexer_Dataset.interp._validate_interp_indexer.return.x_new_x": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp._validate_interp_indexer_Dataset.interp._validate_interp_indexer.return.x_new_x", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2501, "end_line": 2515, "span_ids": ["Dataset.interp"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def interp(\n self,\n coords: Mapping[Hashable, Any] = None,\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n **coords_kwargs: Any,\n ) -> \"Dataset\":\n # ... other code\n\n def _validate_interp_indexer(x, new_x):\n # In the case of datetimes, the restrictions placed on indexers\n # used with interp are stronger than those which are placed on\n # isel, so we need an additional check after _validate_indexers.\n if _contains_datetime_like_objects(\n x\n ) and not _contains_datetime_like_objects(new_x):\n raise TypeError(\n \"When interpolating over a datetime-like \"\n \"coordinate, the coordinates to \"\n \"interpolate to must be either datetime \"\n \"strings or datetimes. \"\n \"Instead got\\n{}\".format(new_x)\n )\n return x, new_x\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp.variables_Dataset.interp.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp.variables_Dataset.interp.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2517, "end_line": 2552, "span_ids": ["Dataset.interp"], "tokens": 394}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def interp(\n self,\n coords: Mapping[Hashable, Any] = None,\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n **coords_kwargs: Any,\n ) -> \"Dataset\":\n # ... other code\n\n variables: Dict[Hashable, Variable] = {}\n for name, var in obj._variables.items():\n if name in indexers:\n continue\n\n if var.dtype.kind in \"uifc\":\n var_indexers = {\n k: _validate_interp_indexer(maybe_variable(obj, k), v)\n for k, v in indexers.items()\n if k in var.dims\n }\n variables[name] = missing.interp(var, var_indexers, method, **kwargs)\n elif all(d not in indexers for d in var.dims):\n # keep unrelated object array\n variables[name] = var\n\n coord_names = obj._coord_names & variables.keys()\n indexes = {k: v for k, v in obj.indexes.items() if k not in indexers}\n selected = self._replace_with_new_dims(\n variables.copy(), coord_names, indexes=indexes\n )\n\n # attach indexer as coordinate\n variables.update(indexers)\n for k, v in indexers.items():\n assert isinstance(v, Variable)\n if v.dims == (k,):\n indexes[k] = v.to_index()\n\n # Extract coordinates from indexers\n coord_vars, new_indexes = selected._get_indexers_coords_and_indexes(coords)\n variables.update(coord_vars)\n indexes.update(new_indexes)\n\n coord_names = obj._coord_names & variables.keys() | coord_vars.keys()\n return self._replace_with_new_dims(variables, coord_names, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp_like_Dataset.interp_like.return.ds_interp_numeric_coords_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interp_like_Dataset.interp_like.return.ds_interp_numeric_coords_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2554, "end_line": 2616, "span_ids": ["Dataset.interp_like"], "tokens": 494}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def interp_like(\n self,\n other: Union[\"Dataset\", \"DataArray\"],\n method: str = \"linear\",\n assume_sorted: bool = False,\n kwargs: Mapping[str, Any] = None,\n ) -> \"Dataset\":\n \"\"\"Interpolate this object onto the coordinates of another object,\n filling the out of range values with NaN.\n\n Parameters\n ----------\n other : Dataset or DataArray\n Object with an 'indexes' attribute giving a mapping from dimension\n names to an 1d array-like, which provides coordinates upon\n which to index the variables in this dataset.\n method: string, optional.\n {'linear', 'nearest'} for multidimensional array,\n {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}\n for 1-dimensional array. 'linear' is used by default.\n assume_sorted: boolean, optional\n If False, values of coordinates that are interpolated over can be\n in any order and they are sorted first. If True, interpolated\n coordinates are assumed to be an array of monotonically increasing\n values.\n kwargs: dictionary, optional\n Additional keyword passed to scipy's interpolator.\n\n Returns\n -------\n interpolated: xr.Dataset\n Another dataset by interpolating this dataset's data along the\n coordinates of the other object.\n\n Notes\n -----\n scipy is required.\n If the dataset has object-type coordinates, reindex is used for these\n coordinates instead of the interpolation.\n\n See Also\n --------\n Dataset.interp\n Dataset.reindex_like\n \"\"\"\n if kwargs is None:\n kwargs = {}\n coords = alignment.reindex_like_indexers(self, other)\n\n numeric_coords: Dict[Hashable, pd.Index] = {}\n object_coords: Dict[Hashable, pd.Index] = {}\n for k, v in coords.items():\n if v.dtype.kind in \"uifcMm\":\n numeric_coords[k] = v\n else:\n object_coords[k] = v\n\n ds = self\n if object_coords:\n # We do not support interpolation along object coordinate.\n # reindex instead.\n ds = self.reindex(object_coords)\n return ds.interp(numeric_coords, method, assume_sorted, kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._Helper_methods_for_rena_Dataset._rename_dims.return._name_dict_get_k_k_v_f": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._Helper_methods_for_rena_Dataset._rename_dims.return._name_dict_get_k_k_v_f", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2618, "end_line": 2634, "span_ids": ["Dataset._rename_vars", "Dataset._rename_dims", "Dataset.interp_like"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n # Helper methods for rename()\n def _rename_vars(self, name_dict, dims_dict):\n variables = {}\n coord_names = set()\n for k, v in self.variables.items():\n var = v.copy(deep=False)\n var.dims = tuple(dims_dict.get(dim, dim) for dim in v.dims)\n name = name_dict.get(k, k)\n if name in variables:\n raise ValueError(\"the new name %r conflicts\" % (name,))\n variables[name] = var\n if k in self._coord_names:\n coord_names.add(name)\n return variables, coord_names\n\n def _rename_dims(self, name_dict):\n return {name_dict.get(k, k): v for k, v in self.dims.items()}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._rename_indexes_Dataset._rename_all.return.variables_coord_names_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._rename_indexes_Dataset._rename_all.return.variables_coord_names_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2636, "end_line": 2662, "span_ids": ["Dataset._rename_indexes", "Dataset._rename_all"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _rename_indexes(self, name_dict, dims_set):\n if self._indexes is None:\n return None\n indexes = {}\n for k, v in self.indexes.items():\n new_name = name_dict.get(k, k)\n if new_name not in dims_set:\n continue\n if isinstance(v, pd.MultiIndex):\n new_names = [name_dict.get(k, k) for k in v.names]\n index = pd.MultiIndex(\n v.levels,\n v.labels,\n v.sortorder,\n names=new_names,\n verify_integrity=False,\n )\n else:\n index = pd.Index(v, name=new_name)\n indexes[new_name] = index\n return indexes\n\n def _rename_all(self, name_dict, dims_dict):\n variables, coord_names = self._rename_vars(name_dict, dims_dict)\n dims = self._rename_dims(dims_dict)\n indexes = self._rename_indexes(name_dict, dims.keys())\n return variables, coord_names, dims, indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_Dataset.rename.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_Dataset.rename.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2664, "end_line": 2705, "span_ids": ["Dataset.rename"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def rename(\n self,\n name_dict: Mapping[Hashable, Hashable] = None,\n inplace: bool = None,\n **names: Hashable,\n ) -> \"Dataset\":\n \"\"\"Returns a new object with renamed variables and dimensions.\n\n Parameters\n ----------\n name_dict : dict-like, optional\n Dictionary whose keys are current variable or dimension names and\n whose values are the desired names.\n **names, optional\n Keyword form of ``name_dict``.\n One of name_dict or names must be provided.\n\n Returns\n -------\n renamed : Dataset\n Dataset with renamed variables and dimensions.\n\n See Also\n --------\n Dataset.swap_dims\n Dataset.rename_vars\n Dataset.rename_dims\n DataArray.rename\n \"\"\"\n _check_inplace(inplace)\n name_dict = either_dict_or_kwargs(name_dict, names, \"rename\")\n for k in name_dict.keys():\n if k not in self and k not in self.dims:\n raise ValueError(\n \"cannot rename %r because it is not a \"\n \"variable or dimension in this dataset\" % k\n )\n\n variables, coord_names, dims, indexes = self._rename_all(\n name_dict=name_dict, dims_dict=name_dict\n )\n return self._replace(variables, coord_names, dims=dims, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_dims_Dataset.rename_dims.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_dims_Dataset.rename_dims.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2707, "end_line": 2744, "span_ids": ["Dataset.rename_dims"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def rename_dims(\n self, dims_dict: Mapping[Hashable, Hashable] = None, **dims: Hashable\n ) -> \"Dataset\":\n \"\"\"Returns a new object with renamed dimensions only.\n\n Parameters\n ----------\n dims_dict : dict-like, optional\n Dictionary whose keys are current dimension names and\n whose values are the desired names.\n **dims, optional\n Keyword form of ``dims_dict``.\n One of dims_dict or dims must be provided.\n\n Returns\n -------\n renamed : Dataset\n Dataset with renamed dimensions.\n\n See Also\n --------\n Dataset.swap_dims\n Dataset.rename\n Dataset.rename_vars\n DataArray.rename\n \"\"\"\n dims_dict = either_dict_or_kwargs(dims_dict, dims, \"rename_dims\")\n for k in dims_dict:\n if k not in self.dims:\n raise ValueError(\n \"cannot rename %r because it is not a \"\n \"dimension in this dataset\" % k\n )\n\n variables, coord_names, sizes, indexes = self._rename_all(\n name_dict={}, dims_dict=dims_dict\n )\n return self._replace(variables, coord_names, dims=sizes, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_vars_Dataset.rename_vars.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rename_vars_Dataset.rename_vars.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2746, "end_line": 2782, "span_ids": ["Dataset.rename_vars"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def rename_vars(\n self, name_dict: Mapping[Hashable, Hashable] = None, **names: Hashable\n ) -> \"Dataset\":\n \"\"\"Returns a new object with renamed variables including coordinates\n\n Parameters\n ----------\n name_dict : dict-like, optional\n Dictionary whose keys are current variable or coordinate names and\n whose values are the desired names.\n **names, optional\n Keyword form of ``name_dict``.\n One of name_dict or names must be provided.\n\n Returns\n -------\n renamed : Dataset\n Dataset with renamed variables including coordinates\n\n See Also\n --------\n Dataset.swap_dims\n Dataset.rename\n Dataset.rename_dims\n DataArray.rename\n \"\"\"\n name_dict = either_dict_or_kwargs(name_dict, names, \"rename_vars\")\n for k in name_dict:\n if k not in self:\n raise ValueError(\n \"cannot rename %r because it is not a \"\n \"variable or coordinate in this dataset\" % k\n )\n variables, coord_names, dims, indexes = self._rename_all(\n name_dict=name_dict, dims_dict={}\n )\n return self._replace(variables, coord_names, dims=dims, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.swap_dims_Dataset.swap_dims.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.swap_dims_Dataset.swap_dims.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2784, "end_line": 2865, "span_ids": ["Dataset.swap_dims"], "tokens": 686}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def swap_dims(\n self, dims_dict: Mapping[Hashable, Hashable], inplace: bool = None\n ) -> \"Dataset\":\n \"\"\"Returns a new object with swapped dimensions.\n\n Parameters\n ----------\n dims_dict : dict-like\n Dictionary whose keys are current dimension names and whose values\n are new names. Each value must already be a variable in the\n dataset.\n\n Returns\n -------\n swapped : Dataset\n Dataset with swapped dimensions.\n\n Examples\n --------\n >>> ds = xr.Dataset(data_vars={\"a\": (\"x\", [5, 7]), \"b\": (\"x\", [0.1, 2.4])},\n coords={\"x\": [\"a\", \"b\"], \"y\": (\"x\", [0, 1])})\n >>> ds\n \n Dimensions: (x: 2)\n Coordinates:\n * x (x) >> ds.swap_dims({\"x\": \"y\"})\n \n Dimensions: (y: 2)\n Coordinates:\n x (y) \"Dataset\":\n \"\"\"Return a new object with an additional axis (or axes) inserted at\n the corresponding position in the array shape. The new object is a\n view into the underlying array, not a copy.\n\n If dim is already a scalar coordinate, it will be promoted to a 1D\n coordinate consisting of a single value.\n\n Parameters\n ----------\n dim : hashable, sequence of hashable, mapping, or None\n Dimensions to include on the new variable. If provided as hashable\n or sequence of hashable, then dimensions are inserted with length\n 1. If provided as a mapping, then the keys are the new dimensions\n and the values are either integers (giving the length of the new\n dimensions) or array-like (giving the coordinates of the new\n dimensions).\n axis : integer, sequence of integers, or None\n Axis position(s) where new axis is to be inserted (position(s) on\n the result array). If a list (or tuple) of integers is passed,\n multiple axes are inserted. In this case, dim arguments should be\n same length list. If axis=None is passed, all the axes will be\n inserted to the start of the result array.\n **dim_kwargs : int or sequence/ndarray\n The keywords are arbitrary dimensions being inserted and the values\n are either the lengths of the new dims (if int is given), or their\n coordinates. Note, this is an alternative to passing a dict to the\n dim kwarg and will only be used if dim is None.\n\n Returns\n -------\n expanded : same type as caller\n This object, but with an additional dimension(s).\n \"\"\"\n if dim is None:\n pass\n elif isinstance(dim, Mapping):\n # We're later going to modify dim in place; don't tamper with\n # the input\n dim = dict(dim)\n elif isinstance(dim, int):\n raise TypeError(\n \"dim should be hashable or sequence of hashables or mapping\"\n )\n elif isinstance(dim, str) or not isinstance(dim, Sequence):\n dim = {dim: 1}\n elif isinstance(dim, Sequence):\n if len(dim) != len(set(dim)):\n raise ValueError(\"dims should not contain duplicate values.\")\n dim = {d: 1 for d in dim}\n\n dim = either_dict_or_kwargs(dim, dim_kwargs, \"expand_dims\")\n assert isinstance(dim, MutableMapping)\n\n if axis is None:\n axis = list(range(len(dim)))\n elif not isinstance(axis, Sequence):\n axis = [axis]\n\n if len(dim) != len(axis):\n raise ValueError(\"lengths of dim and axis should be identical.\")\n for d in dim:\n if d in self.dims:\n raise ValueError(\"Dimension {dim} already exists.\".format(dim=d))\n if d in self._variables and not utils.is_scalar(self._variables[d]):\n raise ValueError(\n \"{dim} already exists as coordinate or\"\n \" variable name.\".format(dim=d)\n )\n\n variables: Dict[Hashable, Variable] = {}\n coord_names = self._coord_names.copy()\n # If dim is a dict, then ensure that the values are either integers\n # or iterables.\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.expand_dims.for_k_v_in_dim_items__Dataset.expand_dims.return.self__replace_vars_and_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.expand_dims.for_k_v_in_dim_items__Dataset.expand_dims.return.self__replace_vars_and_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2946, "end_line": 2999, "span_ids": ["Dataset.expand_dims"], "tokens": 572}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def expand_dims(\n self,\n dim: Union[None, Hashable, Sequence[Hashable], Mapping[Hashable, Any]] = None,\n axis: Union[None, int, Sequence[int]] = None,\n **dim_kwargs: Any,\n ) -> \"Dataset\":\n # ... other code\n for k, v in dim.items():\n if hasattr(v, \"__iter__\"):\n # If the value for the new dimension is an iterable, then\n # save the coordinates to the variables dict, and set the\n # value within the dim dict to the length of the iterable\n # for later use.\n variables[k] = xr.IndexVariable((k,), v)\n coord_names.add(k)\n dim[k] = variables[k].size\n elif isinstance(v, int):\n pass # Do nothing if the dimensions value is just an int\n else:\n raise TypeError(\n \"The value of new dimension {k} must be \"\n \"an iterable or an int\".format(k=k)\n )\n\n for k, v in self._variables.items():\n if k not in dim:\n if k in coord_names: # Do not change coordinates\n variables[k] = v\n else:\n result_ndim = len(v.dims) + len(axis)\n for a in axis:\n if a < -result_ndim or result_ndim - 1 < a:\n raise IndexError(\n \"Axis {a} is out of bounds of the expanded\"\n \" dimension size {dim}.\".format(\n a=a, v=k, dim=result_ndim\n )\n )\n\n axis_pos = [a if a >= 0 else result_ndim + a for a in axis]\n if len(axis_pos) != len(set(axis_pos)):\n raise ValueError(\"axis should not contain duplicate\" \" values.\")\n # We need to sort them to make sure `axis` equals to the\n # axis positions of the result array.\n zip_axis_dim = sorted(zip(axis_pos, dim.items()))\n\n all_dims = list(zip(v.dims, v.shape))\n for d, c in zip_axis_dim:\n all_dims.insert(d, c)\n variables[k] = v.set_dims(dict(all_dims))\n else:\n # If dims includes a label of a non-dimension coordinate,\n # it will be promoted to a 1D coordinate with a single value.\n variables[k] = v.set_dims(k).to_index_variable()\n\n new_dims = self._dims.copy()\n new_dims.update(dim)\n\n return self._replace_vars_and_dims(\n variables, dims=new_dims, coord_names=coord_names\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.set_index_Dataset.set_index.return.self__replace_vars_and_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.set_index_Dataset.set_index.return.self__replace_vars_and_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3001, "end_line": 3066, "span_ids": ["Dataset.set_index"], "tokens": 611}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def set_index(\n self,\n indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]] = None,\n append: bool = False,\n inplace: bool = None,\n **indexes_kwargs: Union[Hashable, Sequence[Hashable]],\n ) -> \"Dataset\":\n \"\"\"Set Dataset (multi-)indexes using one or more existing coordinates\n or variables.\n\n Parameters\n ----------\n indexes : {dim: index, ...}\n Mapping from names matching dimensions and values given\n by (lists of) the names of existing coordinates or variables to set\n as new (multi-)index.\n append : bool, optional\n If True, append the supplied index(es) to the existing index(es).\n Otherwise replace the existing index(es) (default).\n **indexes_kwargs: optional\n The keyword arguments form of ``indexes``.\n One of indexes or indexes_kwargs must be provided.\n\n Returns\n -------\n obj : Dataset\n Another dataset, with this dataset's data but replaced coordinates.\n\n Examples\n --------\n >>> arr = xr.DataArray(data=np.ones((2, 3)),\n ... dims=['x', 'y'],\n ... coords={'x':\n ... range(2), 'y':\n ... range(3), 'a': ('x', [3, 4])\n ... })\n >>> ds = xr.Dataset({'v': arr})\n >>> ds\n \n Dimensions: (x: 2, y: 3)\n Coordinates:\n * x (x) int64 0 1\n * y (y) int64 0 1 2\n a (x) int64 3 4\n Data variables:\n v (x, y) float64 1.0 1.0 1.0 1.0 1.0 1.0\n >>> ds.set_index(x='a')\n \n Dimensions: (x: 2, y: 3)\n Coordinates:\n * x (x) int64 3 4\n * y (y) int64 0 1 2\n Data variables:\n v (x, y) float64 1.0 1.0 1.0 1.0 1.0 1.0\n\n See Also\n --------\n Dataset.reset_index\n Dataset.swap_dims\n \"\"\"\n _check_inplace(inplace)\n indexes = either_dict_or_kwargs(indexes, indexes_kwargs, \"set_index\")\n variables, coord_names = merge_indexes(\n indexes, self._variables, self._coord_names, append=append\n )\n return self._replace_vars_and_dims(variables, coord_names=coord_names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reset_index_Dataset.reset_index.return.self__replace_vars_and_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reset_index_Dataset.reset_index.return.self__replace_vars_and_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3068, "end_line": 3102, "span_ids": ["Dataset.reset_index"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reset_index(\n self,\n dims_or_levels: Union[Hashable, Sequence[Hashable]],\n drop: bool = False,\n inplace: bool = None,\n ) -> \"Dataset\":\n \"\"\"Reset the specified index(es) or multi-index level(s).\n\n Parameters\n ----------\n dims_or_levels : str or list\n Name(s) of the dimension(s) and/or multi-index level(s) that will\n be reset.\n drop : bool, optional\n If True, remove the specified indexes and/or multi-index levels\n instead of extracting them as new coordinates (default: False).\n\n Returns\n -------\n obj : Dataset\n Another dataset, with this dataset's data but replaced coordinates.\n\n See Also\n --------\n Dataset.set_index\n \"\"\"\n _check_inplace(inplace)\n variables, coord_names = split_indexes(\n dims_or_levels,\n self._variables,\n self._coord_names,\n cast(Mapping[Hashable, Hashable], self._level_coords),\n drop=drop,\n )\n return self._replace_vars_and_dims(variables, coord_names=coord_names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reorder_levels_Dataset.reorder_levels.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reorder_levels_Dataset.reorder_levels.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3104, "end_line": 3141, "span_ids": ["Dataset.reorder_levels"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reorder_levels(\n self,\n dim_order: Mapping[Hashable, Sequence[int]] = None,\n inplace: bool = None,\n **dim_order_kwargs: Sequence[int],\n ) -> \"Dataset\":\n \"\"\"Rearrange index levels using input order.\n\n Parameters\n ----------\n dim_order : optional\n Mapping from names matching dimensions and values given\n by lists representing new level orders. Every given dimension\n must have a multi-index.\n **dim_order_kwargs: optional\n The keyword arguments form of ``dim_order``.\n One of dim_order or dim_order_kwargs must be provided.\n\n Returns\n -------\n obj : Dataset\n Another dataset, with this dataset's data but replaced\n coordinates.\n \"\"\"\n _check_inplace(inplace)\n dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs, \"reorder_levels\")\n variables = self._variables.copy()\n indexes = dict(self.indexes)\n for dim, order in dim_order.items():\n coord = self._variables[dim]\n index = self.indexes[dim]\n if not isinstance(index, pd.MultiIndex):\n raise ValueError(\"coordinate %r has no MultiIndex\" % dim)\n new_index = index.reorder_levels(order)\n variables[dim] = IndexVariable(coord.dims, new_index)\n indexes[dim] = new_index\n\n return self._replace(variables, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._stack_once_Dataset._stack_once.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._stack_once_Dataset._stack_once.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3143, "end_line": 3169, "span_ids": ["Dataset._stack_once"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _stack_once(self, dims, new_dim):\n variables = {}\n for name, var in self.variables.items():\n if name not in dims:\n if any(d in var.dims for d in dims):\n add_dims = [d for d in dims if d not in var.dims]\n vdims = list(var.dims) + add_dims\n shape = [self.dims[d] for d in vdims]\n exp_var = var.set_dims(vdims, shape)\n stacked_var = exp_var.stack(**{new_dim: dims})\n variables[name] = stacked_var\n else:\n variables[name] = var.copy(deep=False)\n\n # consider dropping levels that are unused?\n levels = [self.get_index(dim) for dim in dims]\n idx = utils.multiindex_from_product_levels(levels, names=dims)\n variables[new_dim] = IndexVariable(new_dim, idx)\n\n coord_names = set(self._coord_names) - set(dims) | {new_dim}\n\n indexes = {k: v for k, v in self.indexes.items() if k not in dims}\n indexes[new_dim] = idx\n\n return self._replace_with_new_dims(\n variables, coord_names=coord_names, indexes=indexes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.stack_Dataset.stack.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.stack_Dataset.stack.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3171, "end_line": 3204, "span_ids": ["Dataset.stack"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def stack(\n self,\n dimensions: Mapping[Hashable, Sequence[Hashable]] = None,\n **dimensions_kwargs: Sequence[Hashable],\n ) -> \"Dataset\":\n \"\"\"\n Stack any number of existing dimensions into a single new dimension.\n\n New dimensions will be added at the end, and the corresponding\n coordinate variables will be combined into a MultiIndex.\n\n Parameters\n ----------\n dimensions : Mapping of the form new_name=(dim1, dim2, ...)\n Names of new dimensions, and the existing dimensions that they\n replace.\n **dimensions_kwargs:\n The keyword arguments form of ``dimensions``.\n One of dimensions or dimensions_kwargs must be provided.\n\n Returns\n -------\n stacked : Dataset\n Dataset with stacked data.\n\n See also\n --------\n Dataset.unstack\n \"\"\"\n dimensions = either_dict_or_kwargs(dimensions, dimensions_kwargs, \"stack\")\n result = self\n for new_dim, dims in dimensions.items():\n result = result._stack_once(dims, new_dim)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array_Dataset.to_stacked_array.for_variable_in_self_.if_not_dims_include_sampl.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array_Dataset.to_stacked_array.for_variable_in_self_.if_not_dims_include_sampl.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3206, "end_line": 3286, "span_ids": ["Dataset.to_stacked_array"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_stacked_array(\n self,\n new_dim: Hashable,\n sample_dims: Sequence[Hashable],\n variable_dim: str = \"variable\",\n name: Hashable = None,\n ) -> \"DataArray\":\n \"\"\"Combine variables of differing dimensionality into a DataArray\n without broadcasting.\n\n This method is similar to Dataset.to_array but does not broadcast the\n variables.\n\n Parameters\n ----------\n new_dim : Hashable\n Name of the new stacked coordinate\n sample_dims : Sequence[Hashable]\n Dimensions that **will not** be stacked. Each array in the dataset\n must share these dimensions. For machine learning applications,\n these define the dimensions over which samples are drawn.\n variable_dim : str, optional\n Name of the level in the stacked coordinate which corresponds to\n the variables.\n name : str, optional\n Name of the new data array.\n\n Returns\n -------\n stacked : DataArray\n DataArray with the specified dimensions and data variables\n stacked together. The stacked coordinate is named ``new_dim``\n and represented by a MultiIndex object with a level containing the\n data variable names. The name of this level is controlled using\n the ``variable_dim`` argument.\n\n See Also\n --------\n Dataset.to_array\n Dataset.stack\n DataArray.to_unstacked_dataset\n\n Examples\n --------\n >>> data = Dataset(\n ... data_vars={'a': (('x', 'y'), [[0, 1, 2], [3, 4, 5]]),\n ... 'b': ('x', [6, 7])},\n ... coords={'y': ['u', 'v', 'w']}\n ... )\n\n >>> data\n \n Dimensions: (x: 2, y: 3)\n Coordinates:\n * y (y) >> data.to_stacked_array(\"z\", sample_dims=['x'])\n \n array([[0, 1, 2, 6],\n [3, 4, 5, 7]])\n Coordinates:\n * z (z) MultiIndex\n - variable (z) object 'a' 'a' 'a' 'b'\n - y (z) object 'u' 'v' 'w' nan\n Dimensions without coordinates: x\n\n \"\"\"\n stacking_dims = tuple(dim for dim in self.dims if dim not in sample_dims)\n\n for variable in self:\n dims = self[variable].dims\n dims_include_sample_dims = set(sample_dims) <= set(dims)\n if not dims_include_sample_dims:\n raise ValueError(\n \"All variables in the dataset must contain the \"\n \"dimensions {}.\".format(dims)\n )\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array.ensure_stackable_Dataset.to_stacked_array.ensure_stackable.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array.ensure_stackable_Dataset.to_stacked_array.ensure_stackable.return._", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3288, "end_line": 3303, "span_ids": ["Dataset.to_stacked_array"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_stacked_array(\n self,\n new_dim: Hashable,\n sample_dims: Sequence[Hashable],\n variable_dim: str = \"variable\",\n name: Hashable = None,\n ) -> \"DataArray\":\n # ... other code\n\n def ensure_stackable(val):\n assign_coords = {variable_dim: val.name}\n for dim in stacking_dims:\n if dim not in val.dims:\n assign_coords[dim] = None\n\n expand_dims = set(stacking_dims).difference(set(val.dims))\n expand_dims.add(variable_dim)\n # must be list for .expand_dims\n expand_dims = list(expand_dims)\n\n return (\n val.assign_coords(**assign_coords)\n .expand_dims(expand_dims)\n .stack({new_dim: (variable_dim,) + stacking_dims})\n )\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array._concatenate_the_arrays_Dataset.to_stacked_array.return.data_array": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_stacked_array._concatenate_the_arrays_Dataset.to_stacked_array.return.data_array", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3305, "end_line": 3322, "span_ids": ["Dataset.to_stacked_array"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_stacked_array(\n self,\n new_dim: Hashable,\n sample_dims: Sequence[Hashable],\n variable_dim: str = \"variable\",\n name: Hashable = None,\n ) -> \"DataArray\":\n\n # concatenate the arrays\n stackable_vars = [ensure_stackable(self[key]) for key in self.data_vars]\n data_array = xr.concat(stackable_vars, dim=new_dim)\n\n # coerce the levels of the MultiIndex to have the same type as the\n # input dimensions. This code is messy, so it might be better to just\n # input a dummy value for the singleton dimension.\n idx = data_array.indexes[new_dim]\n levels = [idx.levels[0]] + [\n level.astype(self[level.name].dtype) for level in idx.levels[1:]\n ]\n new_idx = idx.set_levels(levels)\n data_array[new_dim] = IndexVariable(new_dim, new_idx)\n\n if name is not None:\n data_array.name = name\n\n return data_array", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._unstack_once_Dataset._unstack_once.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._unstack_once_Dataset._unstack_once.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3324, "end_line": 3357, "span_ids": ["Dataset._unstack_once"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _unstack_once(self, dim: Hashable) -> \"Dataset\":\n index = self.get_index(dim)\n index = index.remove_unused_levels()\n full_idx = pd.MultiIndex.from_product(index.levels, names=index.names)\n\n # take a shortcut in case the MultiIndex was not modified.\n if index.equals(full_idx):\n obj = self\n else:\n obj = self.reindex({dim: full_idx}, copy=False)\n\n new_dim_names = index.names\n new_dim_sizes = [lev.size for lev in index.levels]\n\n variables: Dict[Hashable, Variable] = {}\n indexes = {k: v for k, v in self.indexes.items() if k != dim}\n\n for name, var in obj.variables.items():\n if name != dim:\n if dim in var.dims:\n new_dims = dict(zip(new_dim_names, new_dim_sizes))\n variables[name] = var.unstack({dim: new_dims})\n else:\n variables[name] = var\n\n for name, lev in zip(new_dim_names, index.levels):\n variables[name] = IndexVariable(name, lev)\n indexes[name] = lev\n\n coord_names = set(self._coord_names) - {dim} | set(new_dim_names)\n\n return self._replace_with_new_dims(\n variables, coord_names=coord_names, indexes=indexes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.unstack_Dataset.unstack.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.unstack_Dataset.unstack.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3359, "end_line": 3409, "span_ids": ["Dataset.unstack"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def unstack(self, dim: Union[Hashable, Iterable[Hashable]] = None) -> \"Dataset\":\n \"\"\"\n Unstack existing dimensions corresponding to MultiIndexes into\n multiple new dimensions.\n\n New dimensions will be added at the end.\n\n Parameters\n ----------\n dim : Hashable or iterable of Hashable, optional\n Dimension(s) over which to unstack. By default unstacks all\n MultiIndexes.\n\n Returns\n -------\n unstacked : Dataset\n Dataset with unstacked data.\n\n See also\n --------\n Dataset.stack\n \"\"\"\n if dim is None:\n dims = [\n d for d in self.dims if isinstance(self.get_index(d), pd.MultiIndex)\n ]\n else:\n if isinstance(dim, str) or not isinstance(dim, Iterable):\n dims = [dim]\n else:\n dims = list(dim)\n\n missing_dims = [d for d in dims if d not in self.dims]\n if missing_dims:\n raise ValueError(\n \"Dataset does not contain the dimensions: %s\" % missing_dims\n )\n\n non_multi_dims = [\n d for d in dims if not isinstance(self.get_index(d), pd.MultiIndex)\n ]\n if non_multi_dims:\n raise ValueError(\n \"cannot unstack dimensions that do not \"\n \"have a MultiIndex: %s\" % non_multi_dims\n )\n\n result = self.copy(deep=False)\n for dim in dims:\n result = result._unstack_once(dim)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.update_Dataset.update.return.self__replace_inplace_Tru": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.update_Dataset.update.return.self__replace_inplace_Tru", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3411, "end_line": 3439, "span_ids": ["Dataset.update"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def update(self, other: \"CoercibleMapping\", inplace: bool = None) -> \"Dataset\":\n \"\"\"Update this dataset's variables with those from another dataset.\n\n Parameters\n ----------\n other : Dataset or castable to Dataset\n Variables with which to update this dataset. One of:\n\n - Dataset\n - mapping {var name: DataArray}\n - mapping {var name: Variable}\n - mapping {var name: (dimension name, array-like)}\n - mapping {var name: (tuple of dimension names, array-like)}\n\n\n Returns\n -------\n updated : Dataset\n Updated dataset.\n\n Raises\n ------\n ValueError\n If any dimensions would have inconsistent sizes in the updated\n dataset.\n \"\"\"\n _check_inplace(inplace)\n merge_result = dataset_update_method(self, other)\n return self._replace(inplace=True, **merge_result._asdict())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.merge_Dataset.merge.return.self__replace_merge_res": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.merge_Dataset.merge.return.self__replace_merge_res", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3441, "end_line": 3506, "span_ids": ["Dataset.merge"], "tokens": 553}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def merge(\n self,\n other: \"CoercibleMapping\",\n inplace: bool = None,\n overwrite_vars: Union[Hashable, Iterable[Hashable]] = frozenset(),\n compat: str = \"no_conflicts\",\n join: str = \"outer\",\n fill_value: Any = dtypes.NA,\n ) -> \"Dataset\":\n \"\"\"Merge the arrays of two datasets into a single dataset.\n\n This method generally does not allow for overriding data, with the\n exception of attributes, which are ignored on the second dataset.\n Variables with the same name are checked for conflicts via the equals\n or identical methods.\n\n Parameters\n ----------\n other : Dataset or castable to Dataset\n Dataset or variables to merge with this dataset.\n overwrite_vars : Hashable or iterable of Hashable, optional\n If provided, update variables of these name(s) without checking for\n conflicts in this dataset.\n compat : {'broadcast_equals', 'equals', 'identical',\n 'no_conflicts'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts:\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n Method for joining ``self`` and ``other`` along shared dimensions:\n\n - 'outer': use the union of the indexes\n - 'inner': use the intersection of the indexes\n - 'left': use indexes from ``self``\n - 'right': use indexes from ``other``\n - 'exact': error instead of aligning non-equal indexes\n fill_value: scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n merged : Dataset\n Merged dataset.\n\n Raises\n ------\n MergeError\n If any variables conflict (see ``compat``).\n \"\"\"\n _check_inplace(inplace)\n merge_result = dataset_merge_method(\n self,\n other,\n overwrite_vars=overwrite_vars,\n compat=compat,\n join=join,\n fill_value=fill_value,\n )\n return self._replace(**merge_result._asdict())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._assert_all_in_dataset_Dataset.drop_95._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._assert_all_in_dataset_Dataset.drop_95._", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3508, "end_line": 3532, "span_ids": ["Dataset.drop", "Dataset._assert_all_in_dataset", "Dataset.drop_95"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _assert_all_in_dataset(\n self, names: Iterable[Hashable], virtual_okay: bool = False\n ) -> None:\n bad_names = set(names) - set(self._variables)\n if virtual_okay:\n bad_names -= self.virtual_variables\n if bad_names:\n raise ValueError(\n \"One or more of the specified variables \"\n \"cannot be found in this dataset\"\n )\n\n # Drop variables\n @overload # noqa: F811\n def drop(\n self, labels: Union[Hashable, Iterable[Hashable]], *, errors: str = \"raise\"\n ) -> \"Dataset\":\n ...\n\n # Drop index labels along dimension\n @overload # noqa: F811\n def drop(\n self, labels: Any, dim: Hashable, *, errors: str = \"raise\" # array-like\n ) -> \"Dataset\":\n ...", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.drop_96_Dataset.drop_96.if_labels_kwargs_or_isins.else_.return.self__drop_labels_labels_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.drop_96_Dataset.drop_96.if_labels_kwargs_or_isins.else_.return.self__drop_labels_labels_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3534, "end_line": 3614, "span_ids": ["Dataset.drop_96"], "tokens": 737}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def drop( # noqa: F811\n self, labels=None, dim=None, *, errors=\"raise\", **labels_kwargs\n ):\n \"\"\"Drop variables or index labels from this dataset.\n\n Parameters\n ----------\n labels : hashable or iterable of hashables\n Name(s) of variables or index labels to drop.\n If dim is not None, labels can be any array-like.\n dim : None or hashable, optional\n Dimension along which to drop index labels. By default (if\n ``dim is None``), drops variables rather than index labels.\n errors: {'raise', 'ignore'}, optional\n If 'raise' (default), raises a ValueError error if\n any of the variable or index labels passed are not\n in the dataset. If 'ignore', any given labels that are in the\n dataset are dropped and no error is raised.\n **labels_kwargs : {dim: label, ...}, optional\n The keyword arguments form of ``dim`` and ``labels``.\n\n Returns\n -------\n dropped : Dataset\n\n Examples\n --------\n >>> data = np.random.randn(2, 3)\n >>> labels = ['a', 'b', 'c']\n >>> ds = xr.Dataset({'A': (['x', 'y'], data), 'y': labels})\n >>> ds.drop(y=['a', 'c'])\n \n Dimensions: (x: 2, y: 1)\n Coordinates:\n * y (y) >> ds.drop(y='b')\n \n Dimensions: (x: 2, y: 2)\n Coordinates:\n * y (y) \"Dataset\":\n if errors == \"raise\":\n self._assert_all_in_dataset(names)\n\n variables = {k: v for k, v in self._variables.items() if k not in names}\n coord_names = {k for k in self._coord_names if k in variables}\n indexes = {k: v for k, v in self.indexes.items() if k not in names}\n return self._replace_with_new_dims(\n variables, coord_names=coord_names, indexes=indexes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.drop_dims_Dataset.drop_dims.return.self__drop_vars_drop_vars": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.drop_dims_Dataset.drop_dims.return.self__drop_vars_drop_vars", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3640, "end_line": 3681, "span_ids": ["Dataset.drop_dims"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def drop_dims(\n self, drop_dims: Union[Hashable, Iterable[Hashable]], *, errors: str = \"raise\"\n ) -> \"Dataset\":\n \"\"\"Drop dimensions and associated variables from this dataset.\n\n Parameters\n ----------\n drop_dims : hashable or iterable of hashable\n Dimension or dimensions to drop.\n errors: {'raise', 'ignore'}, optional\n If 'raise' (default), raises a ValueError error if any of the\n dimensions passed are not in the dataset. If 'ignore', any given\n labels that are in the dataset are dropped and no error is raised.\n\n Returns\n -------\n obj : Dataset\n The dataset without the given dimensions (or any variables\n containing those dimensions)\n errors: {'raise', 'ignore'}, optional\n If 'raise' (default), raises a ValueError error if\n any of the dimensions passed are not\n in the dataset. If 'ignore', any given dimensions that are in the\n dataset are dropped and no error is raised.\n \"\"\"\n if errors not in [\"raise\", \"ignore\"]:\n raise ValueError('errors must be either \"raise\" or \"ignore\"')\n\n if isinstance(drop_dims, str) or not isinstance(drop_dims, Iterable):\n drop_dims = {drop_dims}\n else:\n drop_dims = set(drop_dims)\n\n if errors == \"raise\":\n missing_dims = drop_dims - set(self.dims)\n if missing_dims:\n raise ValueError(\n \"Dataset does not contain the dimensions: %s\" % missing_dims\n )\n\n drop_vars = {k for k, v in self._variables.items() if set(v.dims) & drop_dims}\n return self._drop_vars(drop_vars)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.transpose_Dataset.transpose.return.ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.transpose_Dataset.transpose.return.ds", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3683, "end_line": 3722, "span_ids": ["Dataset.transpose"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def transpose(self, *dims: Hashable) -> \"Dataset\":\n \"\"\"Return a new Dataset object with all array dimensions transposed.\n\n Although the order of dimensions on each array will change, the dataset\n dimensions themselves will remain in fixed (sorted) order.\n\n Parameters\n ----------\n *dims : Hashable, optional\n By default, reverse the dimensions on each array. Otherwise,\n reorder the dimensions to this order.\n\n Returns\n -------\n transposed : Dataset\n Each array in the dataset (including) coordinates will be\n transposed to the given order.\n\n Notes\n -----\n This operation returns a view of each array's data. It is\n lazy for dask-backed DataArrays but not for numpy-backed DataArrays\n -- the data will be fully loaded into memory.\n\n See Also\n --------\n numpy.transpose\n DataArray.transpose\n \"\"\"\n if dims:\n if set(dims) ^ set(self.dims):\n raise ValueError(\n \"arguments to transpose (%s) must be \"\n \"permuted dataset dimensions (%s)\" % (dims, tuple(self.dims))\n )\n ds = self.copy()\n for name, var in self._variables.items():\n var_dims = tuple(dim for dim in dims if dim in var.dims)\n ds._variables[name] = var.transpose(*var_dims)\n return ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.dropna_Dataset.dropna.return.self_isel_dim_mask_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.dropna_Dataset.dropna.return.self_isel_dim_mask_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3724, "end_line": 3783, "span_ids": ["Dataset.dropna"], "tokens": 469}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def dropna(\n self,\n dim: Hashable,\n how: str = \"any\",\n thresh: int = None,\n subset: Iterable[Hashable] = None,\n ):\n \"\"\"Returns a new dataset with dropped labels for missing values along\n the provided dimension.\n\n Parameters\n ----------\n dim : Hashable\n Dimension along which to drop missing values. Dropping along\n multiple dimensions simultaneously is not yet supported.\n how : {'any', 'all'}, optional\n * any : if any NA values are present, drop that label\n * all : if all values are NA, drop that label\n thresh : int, default None\n If supplied, require this many non-NA values.\n subset : iterable of hashable, optional\n Which variables to check for missing values. By default, all\n variables in the dataset are checked.\n\n Returns\n -------\n Dataset\n \"\"\"\n # TODO: consider supporting multiple dimensions? Or not, given that\n # there are some ugly edge cases, e.g., pandas's dropna differs\n # depending on the order of the supplied axes.\n\n if dim not in self.dims:\n raise ValueError(\"%s must be a single dataset dimension\" % dim)\n\n if subset is None:\n subset = iter(self.data_vars)\n\n count = np.zeros(self.dims[dim], dtype=np.int64)\n size = 0\n\n for k in subset:\n array = self._variables[k]\n if dim in array.dims:\n dims = [d for d in array.dims if d != dim]\n count += np.asarray(array.count(dims)) # type: ignore\n size += np.prod([self.dims[d] for d in dims])\n\n if thresh is not None:\n mask = count >= thresh\n elif how == \"any\":\n mask = count == size\n elif how == \"all\":\n mask = count > 0\n elif how is not None:\n raise ValueError(\"invalid how option: %s\" % how)\n else:\n raise TypeError(\"must specify how or thresh\")\n\n return self.isel({dim: mask})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.fillna_Dataset.fillna.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.fillna_Dataset.fillna.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3785, "end_line": 3865, "span_ids": ["Dataset.fillna"], "tokens": 897}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def fillna(self, value: Any) -> \"Dataset\":\n \"\"\"Fill missing values in this object.\n\n This operation follows the normal broadcasting and alignment rules that\n xarray uses for binary arithmetic, except the result is aligned to this\n object (``join='left'``) instead of aligned to the intersection of\n index coordinates (``join='inner'``).\n\n Parameters\n ----------\n value : scalar, ndarray, DataArray, dict or Dataset\n Used to fill all matching missing values in this dataset's data\n variables. Scalars, ndarrays or DataArrays arguments are used to\n fill all data with aligned coordinates (for DataArrays).\n Dictionaries or datasets match data variables and then align\n coordinates if necessary.\n\n Returns\n -------\n Dataset\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import xarray as xr\n >>> ds = xr.Dataset(\n ... {\n ... \"A\": (\"x\", [np.nan, 2, np.nan, 0]),\n ... \"B\": (\"x\", [3, 4, np.nan, 1]),\n ... \"C\": (\"x\", [np.nan, np.nan, np.nan, 5]),\n ... \"D\": (\"x\", [np.nan, 3, np.nan, 4])\n ... },\n ... coords={\"x\": [0, 1, 2, 3]})\n >>> ds\n \n Dimensions: (x: 4)\n Coordinates:\n * x (x) int64 0 1 2 3\n Data variables:\n A (x) float64 nan 2.0 nan 0.0\n B (x) float64 3.0 4.0 nan 1.0\n C (x) float64 nan nan nan 5.0\n D (x) float64 nan 3.0 nan 4.0\n\n Replace all `NaN` values with 0s.\n\n >>> ds.fillna(0)\n \n Dimensions: (x: 4)\n Coordinates:\n * x (x) int64 0 1 2 3\n Data variables:\n A (x) float64 0.0 2.0 0.0 0.0\n B (x) float64 3.0 4.0 0.0 1.0\n C (x) float64 0.0 0.0 0.0 5.0\n D (x) float64 0.0 3.0 0.0 4.0\n\n Replace all `NaN` elements in column \u2018A\u2019, \u2018B\u2019, \u2018C\u2019, and \u2018D\u2019, with 0, 1, 2, and 3 respectively.\n\n >>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}\n >>> ds.fillna(value=values)\n \n Dimensions: (x: 4)\n Coordinates:\n * x (x) int64 0 1 2 3\n Data variables:\n A (x) float64 0.0 2.0 0.0 0.0\n B (x) float64 3.0 4.0 1.0 1.0\n C (x) float64 2.0 2.0 2.0 5.0\n D (x) float64 3.0 3.0 3.0 4.0\n \"\"\"\n if utils.is_dict_like(value):\n value_keys = getattr(value, \"data_vars\", value).keys()\n if not set(value_keys) <= set(self.data_vars.keys()):\n raise ValueError(\n \"all variables in the argument to `fillna` \"\n \"must be contained in the original dataset\"\n )\n out = ops.fillna(self, value)\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interpolate_na_Dataset.interpolate_na.return.new": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.interpolate_na_Dataset.interpolate_na.return.new", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3867, "end_line": 3926, "span_ids": ["Dataset.interpolate_na"], "tokens": 505}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def interpolate_na(\n self,\n dim: Hashable = None,\n method: str = \"linear\",\n limit: int = None,\n use_coordinate: Union[bool, Hashable] = True,\n **kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Interpolate values according to different methods.\n\n Parameters\n ----------\n dim : Hashable\n Specifies the dimension along which to interpolate.\n method : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',\n 'polynomial', 'barycentric', 'krog', 'pchip',\n 'spline'}, optional\n String indicating which method to use for interpolation:\n\n - 'linear': linear interpolation (Default). Additional keyword\n arguments are passed to ``numpy.interp``\n - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',\n 'polynomial': are passed to ``scipy.interpolate.interp1d``. If\n method=='polynomial', the ``order`` keyword argument must also be\n provided.\n - 'barycentric', 'krog', 'pchip', 'spline': use their respective\n ``scipy.interpolate`` classes.\n use_coordinate : boolean or str, default True\n Specifies which index to use as the x values in the interpolation\n formulated as `y = f(x)`. If False, values are treated as if\n eqaully-spaced along `dim`. If True, the IndexVariable `dim` is\n used. If use_coordinate is a string, it specifies the name of a\n coordinate variariable to use as the index.\n limit : int, default None\n Maximum number of consecutive NaNs to fill. Must be greater than 0\n or None for no limit.\n kwargs : any\n parameters passed verbatim to the underlying interplation function\n\n Returns\n -------\n Dataset\n\n See also\n --------\n numpy.interp\n scipy.interpolate\n \"\"\"\n from .missing import interp_na, _apply_over_vars_with_dim\n\n new = _apply_over_vars_with_dim(\n interp_na,\n self,\n dim=dim,\n method=method,\n limit=limit,\n use_coordinate=use_coordinate,\n **kwargs,\n )\n return new", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.ffill_Dataset.ffill.return.new": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.ffill_Dataset.ffill.return.new", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3928, "end_line": 3951, "span_ids": ["Dataset.ffill"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def ffill(self, dim: Hashable, limit: int = None) -> \"Dataset\":\n \"\"\"Fill NaN values by propogating values forward\n\n *Requires bottleneck.*\n\n Parameters\n ----------\n dim : Hashable\n Specifies the dimension along which to propagate values when\n filling.\n limit : int, default None\n The maximum number of consecutive NaN values to forward fill. In\n other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. Must be greater\n than 0 or None for no limit.\n\n Returns\n -------\n Dataset\n \"\"\"\n from .missing import ffill, _apply_over_vars_with_dim\n\n new = _apply_over_vars_with_dim(ffill, self, dim=dim, limit=limit)\n return new", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.bfill_Dataset.bfill.return.new": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.bfill_Dataset.bfill.return.new", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3953, "end_line": 3976, "span_ids": ["Dataset.bfill"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def bfill(self, dim: Hashable, limit: int = None) -> \"Dataset\":\n \"\"\"Fill NaN values by propogating values backward\n\n *Requires bottleneck.*\n\n Parameters\n ----------\n dim : str\n Specifies the dimension along which to propagate values when\n filling.\n limit : int, default None\n The maximum number of consecutive NaN values to backward fill. In\n other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. Must be greater\n than 0 or None for no limit.\n\n Returns\n -------\n Dataset\n \"\"\"\n from .missing import bfill, _apply_over_vars_with_dim\n\n new = _apply_over_vars_with_dim(bfill, self, dim=dim, limit=limit)\n return new", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.combine_first_Dataset.combine_first.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.combine_first_Dataset.combine_first.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3978, "end_line": 3995, "span_ids": ["Dataset.combine_first"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def combine_first(self, other: \"Dataset\") -> \"Dataset\":\n \"\"\"Combine two Datasets, default to data_vars of self.\n\n The new coordinates follow the normal broadcasting and alignment rules\n of ``join='outer'``. Vacant cells in the expanded coordinates are\n filled with np.nan.\n\n Parameters\n ----------\n other : Dataset\n Used to fill all matching missing values in this array.\n\n Returns\n -------\n DataArray\n \"\"\"\n out = ops.fillna(self, other, join=\"outer\", dataset_join=\"outer\")\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reduce_Dataset.reduce.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.reduce_Dataset.reduce.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 3997, "end_line": 4088, "span_ids": ["Dataset.reduce"], "tokens": 784}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def reduce(\n self,\n func: Callable,\n dim: Union[Hashable, Iterable[Hashable]] = None,\n keep_attrs: bool = None,\n keepdims: bool = False,\n numeric_only: bool = False,\n allow_lazy: bool = False,\n **kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Reduce this dataset by applying `func` along some dimension(s).\n\n Parameters\n ----------\n func : callable\n Function which can be called in the form\n `f(x, axis=axis, **kwargs)` to return the result of reducing an\n np.ndarray over an integer valued axis.\n dim : str or sequence of str, optional\n Dimension(s) over which to apply `func`. By default `func` is\n applied over all dimensions.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n keepdims : bool, default False\n If True, the dimensions which are reduced are left in the result\n as dimensions of size one. Coordinates that use these dimensions\n are removed.\n numeric_only : bool, optional\n If True, only apply ``func`` to variables with a numeric dtype.\n **kwargs : Any\n Additional keyword arguments passed on to ``func``.\n\n Returns\n -------\n reduced : Dataset\n Dataset with this object's DataArrays replaced with new DataArrays\n of summarized data and the indicated dimension(s) removed.\n \"\"\"\n if dim is None or dim is ALL_DIMS:\n dims = set(self.dims)\n elif isinstance(dim, str) or not isinstance(dim, Iterable):\n dims = {dim}\n else:\n dims = set(dim)\n\n missing_dimensions = [d for d in dims if d not in self.dims]\n if missing_dimensions:\n raise ValueError(\n \"Dataset does not contain the dimensions: %s\" % missing_dimensions\n )\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n\n variables: Dict[Hashable, Variable] = {}\n for name, var in self._variables.items():\n reduce_dims = [d for d in var.dims if d in dims]\n if name in self.coords:\n if not reduce_dims:\n variables[name] = var\n else:\n if (\n not numeric_only\n or np.issubdtype(var.dtype, np.number)\n or (var.dtype == np.bool_)\n ):\n if len(reduce_dims) == 1:\n # unpack dimensions for the benefit of functions\n # like np.argmin which can't handle tuple arguments\n reduce_dims, = reduce_dims\n elif len(reduce_dims) == var.ndim:\n # prefer to aggregate over axis=None rather than\n # axis=(0, 1) if they will be equivalent, because\n # the former is often more efficient\n reduce_dims = None # type: ignore\n variables[name] = var.reduce(\n func,\n dim=reduce_dims,\n keep_attrs=keep_attrs,\n keepdims=keepdims,\n allow_lazy=allow_lazy,\n **kwargs,\n )\n\n coord_names = {k for k in self.coords if k in variables}\n indexes = {k: v for k, v in self.indexes.items() if k in variables}\n attrs = self.attrs if keep_attrs else None\n return self._replace_with_new_dims(\n variables, coord_names=coord_names, attrs=attrs, indexes=indexes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.apply_Dataset.apply.return.type_self_variables_att": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.apply_Dataset.apply.return.type_self_variables_att", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4090, "end_line": 4145, "span_ids": ["Dataset.apply"], "tokens": 562}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def apply(\n self,\n func: Callable,\n keep_attrs: bool = None,\n args: Iterable[Any] = (),\n **kwargs: Any,\n ) -> \"Dataset\":\n \"\"\"Apply a function over the data variables in this dataset.\n\n Parameters\n ----------\n func : callable\n Function which can be called in the form `func(x, *args, **kwargs)`\n to transform each DataArray `x` in this dataset into another\n DataArray.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False, the new object will\n be returned without attributes.\n args : tuple, optional\n Positional arguments passed on to `func`.\n **kwargs : Any\n Keyword arguments passed on to `func`.\n\n Returns\n -------\n applied : Dataset\n Resulting dataset from applying ``func`` over each data variable.\n\n Examples\n --------\n >>> da = xr.DataArray(np.random.randn(2, 3))\n >>> ds = xr.Dataset({'foo': da, 'bar': ('x', [-1, 2])})\n >>> ds\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Dimensions without coordinates: dim_0, dim_1, x\n Data variables:\n foo (dim_0, dim_1) float64 -0.3751 -1.951 -1.945 0.2948 0.711 -0.3948\n bar (x) int64 -1 2\n >>> ds.apply(np.fabs)\n \n Dimensions: (dim_0: 2, dim_1: 3, x: 2)\n Dimensions without coordinates: dim_0, dim_1, x\n Data variables:\n foo (dim_0, dim_1) float64 0.3751 1.951 1.945 0.2948 0.711 0.3948\n bar (x) float64 1.0 2.0\n \"\"\"\n variables = {\n k: maybe_wrap_array(v, func(v, *args, **kwargs))\n for k, v in self.data_vars.items()\n }\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self.attrs if keep_attrs else None\n return type(self)(variables, attrs=attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.assign_Dataset.assign.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.assign_Dataset.assign.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4147, "end_line": 4236, "span_ids": ["Dataset.assign"], "tokens": 937}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def assign(\n self, variables: Mapping[Hashable, Any] = None, **variables_kwargs: Hashable\n ) -> \"Dataset\":\n \"\"\"Assign new data variables to a Dataset, returning a new object\n with all the original variables in addition to the new ones.\n\n Parameters\n ----------\n variables : mapping, value pairs\n Mapping from variables names to the new values. If the new values\n are callable, they are computed on the Dataset and assigned to new\n data variables. If the values are not callable, (e.g. a DataArray,\n scalar, or array), they are simply assigned.\n **variables_kwargs:\n The keyword arguments form of ``variables``.\n One of variables or variables_kwargs must be provided.\n\n Returns\n -------\n ds : Dataset\n A new Dataset with the new variables in addition to all the\n existing variables.\n\n Notes\n -----\n Since ``kwargs`` is a dictionary, the order of your arguments may not\n be preserved, and so the order of the new variables is not well\n defined. Assigning multiple variables within the same ``assign`` is\n possible, but you cannot reference other variables created within the\n same ``assign`` call.\n\n See Also\n --------\n pandas.DataFrame.assign\n\n Examples\n --------\n >>> import numpy as np\n >>> import xarray as xr\n >>> x = xr.Dataset(\n ... {\n ... \"temperature_c\": ((\"lat\", \"lon\"), 20 * np.random.rand(4).reshape(2, 2)),\n ... \"precipitation\": ((\"lat\", \"lon\"), np.random.rand(4).reshape(2, 2)),\n ... },\n ... coords={\"lat\": [10, 20], \"lon\": [150, 160]},\n ... )\n >>> x\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lat (lat) int64 10 20\n * lon (lon) int64 150 160\n Data variables:\n temperature_c (lat, lon) float64 18.04 12.51 17.64 9.313\n precipitation (lat, lon) float64 0.4751 0.6827 0.3697 0.03524\n\n Where the value is a callable, evaluated on dataset:\n\n >>> x.assign(temperature_f = lambda x: x.temperature_c * 9 / 5 + 32)\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lat (lat) int64 10 20\n * lon (lon) int64 150 160\n Data variables:\n temperature_c (lat, lon) float64 18.04 12.51 17.64 9.313\n precipitation (lat, lon) float64 0.4751 0.6827 0.3697 0.03524\n temperature_f (lat, lon) float64 64.47 54.51 63.75 48.76\n\n Alternatively, the same behavior can be achieved by directly referencing an existing dataarray:\n\n >>> x.assign(temperature_f=x[\"temperature_c\"] * 9 / 5 + 32)\n \n Dimensions: (lat: 2, lon: 2)\n Coordinates:\n * lat (lat) int64 10 20\n * lon (lon) int64 150 160\n Data variables:\n temperature_c (lat, lon) float64 18.04 12.51 17.64 9.313\n precipitation (lat, lon) float64 0.4751 0.6827 0.3697 0.03524\n temperature_f (lat, lon) float64 64.47 54.51 63.75 48.76\n\n \"\"\"\n variables = either_dict_or_kwargs(variables, variables_kwargs, \"assign\")\n data = self.copy()\n # do all calculations first...\n results = data._calc_assign_results(variables)\n # ... and then assign\n data.update(results)\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_array_Dataset.to_array.return.DataArray_data_coords_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_array_Dataset.to_array.return.DataArray_data_coords_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4238, "end_line": 4267, "span_ids": ["Dataset.to_array"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_array(self, dim=\"variable\", name=None):\n \"\"\"Convert this dataset into an xarray.DataArray\n\n The data variables of this dataset will be broadcast against each other\n and stacked along the first axis of the new array. All coordinates of\n this dataset will remain coordinates.\n\n Parameters\n ----------\n dim : str, optional\n Name of the new dimension.\n name : str, optional\n Name of the new data array.\n\n Returns\n -------\n array : xarray.DataArray\n \"\"\"\n from .dataarray import DataArray\n\n data_vars = [self.variables[k] for k in self.data_vars]\n broadcast_vars = broadcast_variables(*data_vars)\n data = duck_array_ops.stack([b.data for b in broadcast_vars], axis=0)\n\n coords = dict(self.coords)\n coords[dim] = list(self.data_vars)\n\n dims = (dim,) + broadcast_vars[0].dims\n\n return DataArray(data, coords, dims, attrs=self.attrs, name=name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._to_dataframe_Dataset.to_dataframe.return.self__to_dataframe_self_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._to_dataframe_Dataset.to_dataframe.return.self__to_dataframe_self_d", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4269, "end_line": 4285, "span_ids": ["Dataset._to_dataframe", "Dataset.to_dataframe"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _to_dataframe(self, ordered_dims):\n columns = [k for k in self.variables if k not in self.dims]\n data = [\n self._variables[k].set_dims(ordered_dims).values.reshape(-1)\n for k in columns\n ]\n index = self.coords.to_index(ordered_dims)\n return pd.DataFrame(dict(zip(columns, data)), index=index)\n\n def to_dataframe(self):\n \"\"\"Convert this dataset into a pandas.DataFrame.\n\n Non-index variables in this dataset form the columns of the\n DataFrame. The DataFrame is be indexed by the Cartesian product of\n this dataset's indices.\n \"\"\"\n return self._to_dataframe(self.dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._set_sparse_data_from_dataframe_Dataset._set_sparse_data_from_dataframe.for_name_series_in_dataf.self_name_dims_data_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._set_sparse_data_from_dataframe_Dataset._set_sparse_data_from_dataframe.for_name_series_in_dataf.self_name_dims_data_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4287, "end_line": 4325, "span_ids": ["Dataset._set_sparse_data_from_dataframe"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _set_sparse_data_from_dataframe(\n self, dataframe: pd.DataFrame, dims: tuple, shape: Tuple[int, ...]\n ) -> None:\n from sparse import COO\n\n idx = dataframe.index\n if isinstance(idx, pd.MultiIndex):\n try:\n codes = idx.codes\n except AttributeError:\n # deprecated since pandas 0.24\n codes = idx.labels\n coords = np.stack([np.asarray(code) for code in codes], axis=0)\n is_sorted = idx.is_lexsorted\n else:\n coords = np.arange(idx.size).reshape(1, -1)\n is_sorted = True\n\n for name, series in dataframe.items():\n # Cast to a NumPy array first, in case the Series is a pandas\n # Extension array (which doesn't have a valid NumPy dtype)\n values = np.asarray(series)\n\n # In virtually all real use cases, the sparse array will now have\n # missing values and needs a fill_value. For consistency, don't\n # special case the rare exceptions (e.g., dtype=int without a\n # MultiIndex).\n dtype, fill_value = dtypes.maybe_promote(values.dtype)\n values = np.asarray(values, dtype=dtype)\n\n data = COO(\n coords,\n values,\n shape,\n has_duplicates=False,\n sorted=is_sorted,\n fill_value=fill_value,\n )\n self[name] = (dims, data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._set_numpy_data_from_dataframe_Dataset._set_numpy_data_from_dataframe.for_name_series_in_dataf.self_name_dims_data_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._set_numpy_data_from_dataframe_Dataset._set_numpy_data_from_dataframe.for_name_series_in_dataf.self_name_dims_data_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4327, "end_line": 4338, "span_ids": ["Dataset._set_numpy_data_from_dataframe"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _set_numpy_data_from_dataframe(\n self, dataframe: pd.DataFrame, dims: tuple, shape: Tuple[int, ...]\n ) -> None:\n idx = dataframe.index\n if isinstance(idx, pd.MultiIndex):\n # expand the DataFrame to include the product of all levels\n full_idx = pd.MultiIndex.from_product(idx.levels, names=idx.names)\n dataframe = dataframe.reindex(full_idx)\n\n for name, series in dataframe.items():\n data = np.asarray(series).reshape(shape)\n self[name] = (dims, data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.from_dataframe_Dataset.from_dataframe.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.from_dataframe_Dataset.from_dataframe.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4340, "end_line": 4397, "span_ids": ["Dataset.from_dataframe"], "tokens": 476}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @classmethod\n def from_dataframe(cls, dataframe: pd.DataFrame, sparse: bool = False) -> \"Dataset\":\n \"\"\"Convert a pandas.DataFrame into an xarray.Dataset\n\n Each column will be converted into an independent variable in the\n Dataset. If the dataframe's index is a MultiIndex, it will be expanded\n into a tensor product of one-dimensional indices (filling in missing\n values with NaN). This method will produce a Dataset very similar to\n that on which the 'to_dataframe' method was called, except with\n possibly redundant dimensions (since all dataset variables will have\n the same dimensionality)\n\n Parameters\n ----------\n dataframe : pandas.DataFrame\n DataFrame from which to copy data and indices.\n sparse : bool\n If true, create a sparse arrays instead of dense numpy arrays. This\n can potentially save a large amount of memory if the DataFrame has\n a MultiIndex. Requires the sparse package (sparse.pydata.org).\n\n Returns\n -------\n New Dataset.\n\n See also\n --------\n xarray.DataArray.from_series\n \"\"\"\n # TODO: Add an option to remove dimensions along which the variables\n # are constant, to enable consistent serialization to/from a dataframe,\n # even if some variables have different dimensionality.\n\n if not dataframe.columns.is_unique:\n raise ValueError(\"cannot convert DataFrame with non-unique columns\")\n\n idx = dataframe.index\n obj = cls()\n\n if isinstance(idx, pd.MultiIndex):\n dims = tuple(\n name if name is not None else \"level_%i\" % n\n for n, name in enumerate(idx.names)\n )\n for dim, lev in zip(dims, idx.levels):\n obj[dim] = (dim, lev)\n shape = tuple(lev.size for lev in idx.levels)\n else:\n index_name = idx.name if idx.name is not None else \"index\"\n dims = (index_name,)\n obj[index_name] = (dims, idx)\n shape = (idx.size,)\n\n if sparse:\n obj._set_sparse_data_from_dataframe(dataframe, dims, shape)\n else:\n obj._set_numpy_data_from_dataframe(dataframe, dims, shape)\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_dask_dataframe_Dataset.to_dask_dataframe.return.df": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_dask_dataframe_Dataset.to_dask_dataframe.return.df", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4399, "end_line": 4474, "span_ids": ["Dataset.to_dask_dataframe"], "tokens": 582}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_dask_dataframe(self, dim_order=None, set_index=False):\n \"\"\"\n Convert this dataset into a dask.dataframe.DataFrame.\n\n The dimensions, coordinates and data variables in this dataset form\n the columns of the DataFrame.\n\n Parameters\n ----------\n dim_order : list, optional\n Hierarchical dimension order for the resulting dataframe. All\n arrays are transposed to this order and then written out as flat\n vectors in contiguous order, so the last dimension in this list\n will be contiguous in the resulting DataFrame. This has a major\n influence on which operations are efficient on the resulting dask\n dataframe.\n\n If provided, must include all dimensions on this dataset. By\n default, dimensions are sorted alphabetically.\n set_index : bool, optional\n If set_index=True, the dask DataFrame is indexed by this dataset's\n coordinate. Since dask DataFrames to not support multi-indexes,\n set_index only works if the dataset only contains one dimension.\n\n Returns\n -------\n dask.dataframe.DataFrame\n \"\"\"\n\n import dask.array as da\n import dask.dataframe as dd\n\n if dim_order is None:\n dim_order = list(self.dims)\n elif set(dim_order) != set(self.dims):\n raise ValueError(\n \"dim_order {} does not match the set of dimensions on this \"\n \"Dataset: {}\".format(dim_order, list(self.dims))\n )\n\n ordered_dims = {k: self.dims[k] for k in dim_order}\n\n columns = list(ordered_dims)\n columns.extend(k for k in self.coords if k not in self.dims)\n columns.extend(self.data_vars)\n\n series_list = []\n for name in columns:\n try:\n var = self.variables[name]\n except KeyError:\n # dimension without a matching coordinate\n size = self.dims[name]\n data = da.arange(size, chunks=size, dtype=np.int64)\n var = Variable((name,), data)\n\n # IndexVariable objects have a dummy .chunk() method\n if isinstance(var, IndexVariable):\n var = var.to_base_variable()\n\n dask_array = var.set_dims(ordered_dims).chunk(self.chunks).data\n series = dd.from_array(dask_array.reshape(-1), columns=[name])\n series_list.append(series)\n\n df = dd.concat(series_list, axis=1)\n\n if set_index:\n if len(dim_order) == 1:\n (dim,) = dim_order\n df = df.set_index(dim)\n else:\n # triggers an error about multi-indexes, even if only one\n # dimension is passed\n df = df.set_index(dim_order)\n\n return df", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_dict_Dataset.to_dict.return.d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.to_dict_Dataset.to_dict.return.d", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4476, "end_line": 4505, "span_ids": ["Dataset.to_dict"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def to_dict(self, data=True):\n \"\"\"\n Convert this dataset to a dictionary following xarray naming\n conventions.\n\n Converts all variables and attributes to native Python objects\n Useful for coverting to json. To avoid datetime incompatibility\n use decode_times=False kwarg in xarrray.open_dataset.\n\n Parameters\n ----------\n data : bool, optional\n Whether to include the actual data in the dictionary. When set to\n False, returns just the schema.\n\n See also\n --------\n Dataset.from_dict\n \"\"\"\n d = {\n \"coords\": {},\n \"attrs\": decode_numpy_dict_values(self.attrs),\n \"dims\": dict(self.dims),\n \"data_vars\": {},\n }\n for k in self.coords:\n d[\"coords\"].update({k: self[k].variable.to_dict(data=data)})\n for k in self.data_vars:\n d[\"data_vars\"].update({k: self[k].variable.to_dict(data=data)})\n return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.from_dict_Dataset.from_dict.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.from_dict_Dataset.from_dict.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4507, "end_line": 4569, "span_ids": ["Dataset.from_dict"], "tokens": 476}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @classmethod\n def from_dict(cls, d):\n \"\"\"\n Convert a dictionary into an xarray.Dataset.\n\n Input dict can take several forms::\n\n d = {'t': {'dims': ('t'), 'data': t},\n 'a': {'dims': ('t'), 'data': x},\n 'b': {'dims': ('t'), 'data': y}}\n\n d = {'coords': {'t': {'dims': 't', 'data': t,\n 'attrs': {'units':'s'}}},\n 'attrs': {'title': 'air temperature'},\n 'dims': 't',\n 'data_vars': {'a': {'dims': 't', 'data': x, },\n 'b': {'dims': 't', 'data': y}}}\n\n where 't' is the name of the dimesion, 'a' and 'b' are names of data\n variables and t, x, and y are lists, numpy.arrays or pandas objects.\n\n Parameters\n ----------\n d : dict, with a minimum structure of {'var_0': {'dims': [..], \\\n 'data': [..]}, \\\n ...}\n\n Returns\n -------\n obj : xarray.Dataset\n\n See also\n --------\n Dataset.to_dict\n DataArray.from_dict\n \"\"\"\n\n if not {\"coords\", \"data_vars\"}.issubset(set(d)):\n variables = d.items()\n else:\n import itertools\n\n variables = itertools.chain(\n d.get(\"coords\", {}).items(), d.get(\"data_vars\", {}).items()\n )\n try:\n variable_dict = {\n k: (v[\"dims\"], v[\"data\"], v.get(\"attrs\")) for k, v in variables\n }\n except KeyError as e:\n raise ValueError(\n \"cannot convert dict without the key \"\n \"'{dims_data}'\".format(dims_data=str(e.args[0]))\n )\n obj = cls(variable_dict)\n\n # what if coords aren't dims?\n coords = set(d.get(\"coords\", {})) - set(d.get(\"dims\", {}))\n obj = obj.set_coords(coords)\n\n obj.attrs.update(d.get(\"attrs\", {}))\n\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._unary_op_Dataset._unary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._unary_op_Dataset._unary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4571, "end_line": 4584, "span_ids": ["Dataset._unary_op"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @staticmethod\n def _unary_op(f, keep_attrs=False):\n @functools.wraps(f)\n def func(self, *args, **kwargs):\n variables = {}\n for k, v in self._variables.items():\n if k in self._coord_names:\n variables[k] = v\n else:\n variables[k] = f(v, *args, **kwargs)\n attrs = self._attrs if keep_attrs else None\n return self._replace_with_new_dims(variables, attrs=attrs)\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._binary_op_Dataset._binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._binary_op_Dataset._binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4586, "end_line": 4601, "span_ids": ["Dataset._binary_op"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @staticmethod\n def _binary_op(f, reflexive=False, join=None):\n @functools.wraps(f)\n def func(self, other):\n from .dataarray import DataArray\n\n if isinstance(other, groupby.GroupBy):\n return NotImplemented\n align_type = OPTIONS[\"arithmetic_join\"] if join is None else join\n if isinstance(other, (DataArray, Dataset)):\n self, other = align(self, other, join=align_type, copy=False)\n g = f if not reflexive else lambda x, y: f(y, x)\n ds = self._calculate_binary_op(g, other, join=align_type)\n return ds\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._inplace_binary_op_Dataset._inplace_binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._inplace_binary_op_Dataset._inplace_binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4603, "end_line": 4629, "span_ids": ["Dataset._inplace_binary_op"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @staticmethod\n def _inplace_binary_op(f):\n @functools.wraps(f)\n def func(self, other):\n from .dataarray import DataArray\n\n if isinstance(other, groupby.GroupBy):\n raise TypeError(\n \"in-place operations between a Dataset and \"\n \"a grouped object are not permitted\"\n )\n # we don't actually modify arrays in-place with in-place Dataset\n # arithmetic -- this lets us automatically align things\n if isinstance(other, (DataArray, Dataset)):\n other = other.reindex_like(self, copy=False)\n g = ops.inplace_to_noninplace_op(f)\n ds = self._calculate_binary_op(g, other, inplace=True)\n self._replace_with_new_dims(\n ds._variables,\n ds._coord_names,\n attrs=ds._attrs,\n indexes=ds._indexes,\n inplace=True,\n )\n return self\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._calculate_binary_op_Dataset._calculate_binary_op.apply_over_both.return.dest_vars": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._calculate_binary_op_Dataset._calculate_binary_op.apply_over_both.return.dest_vars", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4631, "end_line": 4650, "span_ids": ["Dataset._calculate_binary_op"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _calculate_binary_op(self, f, other, join=\"inner\", inplace=False):\n def apply_over_both(lhs_data_vars, rhs_data_vars, lhs_vars, rhs_vars):\n if inplace and set(lhs_data_vars) != set(rhs_data_vars):\n raise ValueError(\n \"datasets must have the same data variables \"\n \"for in-place arithmetic operations: %s, %s\"\n % (list(lhs_data_vars), list(rhs_data_vars))\n )\n\n dest_vars = {}\n\n for k in lhs_data_vars:\n if k in rhs_data_vars:\n dest_vars[k] = f(lhs_vars[k], rhs_vars[k])\n elif join in [\"left\", \"outer\"]:\n dest_vars[k] = f(lhs_vars[k], np.nan)\n for k in rhs_data_vars:\n if k not in dest_vars and join in [\"right\", \"outer\"]:\n dest_vars[k] = f(rhs_vars[k], np.nan)\n return dest_vars\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._calculate_binary_op.if_utils_is_dict_like_oth_Dataset._copy_attrs_from.for_v_in_other_variables_.if_v_in_self_variables_.self.variables[v].attrs.other_variables_v_attrs": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._calculate_binary_op.if_utils_is_dict_like_oth_Dataset._copy_attrs_from.for_v_in_other_variables_.if_v_in_self_variables_.self.variables[v].attrs.other_variables_v_attrs", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4652, "end_line": 4678, "span_ids": ["Dataset._calculate_binary_op", "Dataset._copy_attrs_from"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _calculate_binary_op(self, f, other, join=\"inner\", inplace=False):\n # ... other code\n\n if utils.is_dict_like(other) and not isinstance(other, Dataset):\n # can't use our shortcut of doing the binary operation with\n # Variable objects, so apply over our data vars instead.\n new_data_vars = apply_over_both(\n self.data_vars, other, self.data_vars, other\n )\n return Dataset(new_data_vars)\n\n other_coords = getattr(other, \"coords\", None)\n ds = self.coords.merge(other_coords)\n\n if isinstance(other, Dataset):\n new_vars = apply_over_both(\n self.data_vars, other.data_vars, self.variables, other.variables\n )\n else:\n other_variable = getattr(other, \"variable\", other)\n new_vars = {k: f(self.variables[k], other_variable) for k in self.data_vars}\n ds._variables.update(new_vars)\n ds._dims = calculate_dimensions(ds._variables)\n return ds\n\n def _copy_attrs_from(self, other):\n self.attrs = other.attrs\n for v in other.variables:\n if v in self.variables:\n self.variables[v].attrs = other.variables[v].attrs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.diff_Dataset.diff.if_n_1_.else_.return.difference": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.diff_Dataset.diff.if_n_1_.else_.return.difference", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4680, "end_line": 4761, "span_ids": ["Dataset.diff"], "tokens": 597}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def diff(self, dim, n=1, label=\"upper\"):\n \"\"\"Calculate the n-th order discrete difference along given axis.\n\n Parameters\n ----------\n dim : str, optional\n Dimension over which to calculate the finite difference.\n n : int, optional\n The number of times values are differenced.\n label : str, optional\n The new coordinate in dimension ``dim`` will have the\n values of either the minuend's or subtrahend's coordinate\n for values 'upper' and 'lower', respectively. Other\n values are not supported.\n\n Returns\n -------\n difference : same type as caller\n The n-th order finite difference of this object.\n\n Examples\n --------\n >>> ds = xr.Dataset({'foo': ('x', [5, 5, 6, 6])})\n >>> ds.diff('x')\n \n Dimensions: (x: 3)\n Coordinates:\n * x (x) int64 1 2 3\n Data variables:\n foo (x) int64 0 1 0\n >>> ds.diff('x', 2)\n \n Dimensions: (x: 2)\n Coordinates:\n * x (x) int64 2 3\n Data variables:\n foo (x) int64 1 -1\n\n See Also\n --------\n Dataset.differentiate\n \"\"\"\n if n == 0:\n return self\n if n < 0:\n raise ValueError(\"order `n` must be non-negative but got {}\".format(n))\n\n # prepare slices\n kwargs_start = {dim: slice(None, -1)}\n kwargs_end = {dim: slice(1, None)}\n\n # prepare new coordinate\n if label == \"upper\":\n kwargs_new = kwargs_end\n elif label == \"lower\":\n kwargs_new = kwargs_start\n else:\n raise ValueError(\n \"The 'label' argument has to be either \" \"'upper' or 'lower'\"\n )\n\n variables = {}\n\n for name, var in self.variables.items():\n if dim in var.dims:\n if name in self.data_vars:\n variables[name] = var.isel(**kwargs_end) - var.isel(**kwargs_start)\n else:\n variables[name] = var.isel(**kwargs_new)\n else:\n variables[name] = var\n\n indexes = dict(self.indexes)\n if dim in indexes:\n indexes[dim] = indexes[dim][kwargs_new[dim]]\n\n difference = self._replace_with_new_dims(variables, indexes=indexes)\n\n if n > 1:\n return difference.diff(dim, n - 1)\n else:\n return difference", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.shift_Dataset.shift.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.shift_Dataset.shift.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4763, "end_line": 4816, "span_ids": ["Dataset.shift"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):\n \"\"\"Shift this dataset by an offset along one or more dimensions.\n\n Only data variables are moved; coordinates stay in place. This is\n consistent with the behavior of ``shift`` in pandas.\n\n Parameters\n ----------\n shifts : Mapping with the form of {dim: offset}\n Integer offset to shift along each of the given dimensions.\n Positive offsets shift to the right; negative offsets shift to the\n left.\n fill_value: scalar, optional\n Value to use for newly missing values\n **shifts_kwargs:\n The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwarg must be provided.\n\n Returns\n -------\n shifted : Dataset\n Dataset with the same coordinates and attributes but shifted data\n variables.\n\n See also\n --------\n roll\n\n Examples\n --------\n\n >>> ds = xr.Dataset({'foo': ('x', list('abcde'))})\n >>> ds.shift(x=2)\n \n Dimensions: (x: 5)\n Coordinates:\n * x (x) int64 0 1 2 3 4\n Data variables:\n foo (x) object nan nan 'a' 'b' 'c'\n \"\"\"\n shifts = either_dict_or_kwargs(shifts, shifts_kwargs, \"shift\")\n invalid = [k for k in shifts if k not in self.dims]\n if invalid:\n raise ValueError(\"dimensions %r do not exist\" % invalid)\n\n variables = {}\n for name, var in self.variables.items():\n if name in self.data_vars:\n var_shifts = {k: v for k, v in shifts.items() if k in var.dims}\n variables[name] = var.shift(fill_value=fill_value, shifts=var_shifts)\n else:\n variables[name] = var\n\n return self._replace(variables)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.roll_Dataset.roll.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.roll_Dataset.roll.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4818, "end_line": 4896, "span_ids": ["Dataset.roll"], "tokens": 608}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def roll(self, shifts=None, roll_coords=None, **shifts_kwargs):\n \"\"\"Roll this dataset by an offset along one or more dimensions.\n\n Unlike shift, roll may rotate all variables, including coordinates\n if specified. The direction of rotation is consistent with\n :py:func:`numpy.roll`.\n\n Parameters\n ----------\n\n shifts : dict, optional\n A dict with keys matching dimensions and values given\n by integers to rotate each of the given dimensions. Positive\n offsets roll to the right; negative offsets roll to the left.\n roll_coords : bool\n Indicates whether to roll the coordinates by the offset\n The current default of roll_coords (None, equivalent to True) is\n deprecated and will change to False in a future version.\n Explicitly pass roll_coords to silence the warning.\n **shifts_kwargs : {dim: offset, ...}, optional\n The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwargs must be provided.\n Returns\n -------\n rolled : Dataset\n Dataset with the same coordinates and attributes but rolled\n variables.\n\n See also\n --------\n shift\n\n Examples\n --------\n\n >>> ds = xr.Dataset({'foo': ('x', list('abcde'))})\n >>> ds.roll(x=2)\n \n Dimensions: (x: 5)\n Coordinates:\n * x (x) int64 3 4 0 1 2\n Data variables:\n foo (x) object 'd' 'e' 'a' 'b' 'c'\n \"\"\"\n shifts = either_dict_or_kwargs(shifts, shifts_kwargs, \"roll\")\n invalid = [k for k in shifts if k not in self.dims]\n if invalid:\n raise ValueError(\"dimensions %r do not exist\" % invalid)\n\n if roll_coords is None:\n warnings.warn(\n \"roll_coords will be set to False in the future.\"\n \" Explicitly set roll_coords to silence warning.\",\n FutureWarning,\n stacklevel=2,\n )\n roll_coords = True\n\n unrolled_vars = () if roll_coords else self.coords\n\n variables = {}\n for k, v in self.variables.items():\n if k not in unrolled_vars:\n variables[k] = v.roll(\n **{k: s for k, s in shifts.items() if k in v.dims}\n )\n else:\n variables[k] = v\n\n if roll_coords:\n indexes = {}\n for k, v in self.indexes.items():\n (dim,) = self.variables[k].dims\n if dim in shifts:\n indexes[k] = roll_index(v, shifts[dim])\n else:\n indexes = dict(self.indexes)\n\n return self._replace(variables, indexes=indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.sortby_Dataset.sortby.return.aligned_self_isel_indic": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.sortby_Dataset.sortby.return.aligned_self_isel_indic", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4898, "end_line": 4951, "span_ids": ["Dataset.sortby"], "tokens": 476}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def sortby(self, variables, ascending=True):\n \"\"\"\n Sort object by labels or values (along an axis).\n\n Sorts the dataset, either along specified dimensions,\n or according to values of 1-D dataarrays that share dimension\n with calling object.\n\n If the input variables are dataarrays, then the dataarrays are aligned\n (via left-join) to the calling object prior to sorting by cell values.\n NaNs are sorted to the end, following Numpy convention.\n\n If multiple sorts along the same dimension is\n given, numpy's lexsort is performed along that dimension:\n https://docs.scipy.org/doc/numpy/reference/generated/numpy.lexsort.html\n and the FIRST key in the sequence is used as the primary sort key,\n followed by the 2nd key, etc.\n\n Parameters\n ----------\n variables: str, DataArray, or list of either\n 1D DataArray objects or name(s) of 1D variable(s) in\n coords/data_vars whose values are used to sort the dataset.\n ascending: boolean, optional\n Whether to sort by ascending or descending order.\n\n Returns\n -------\n sorted: Dataset\n A new dataset where all the specified dims are sorted by dim\n labels.\n \"\"\"\n from .dataarray import DataArray\n\n if not isinstance(variables, list):\n variables = [variables]\n else:\n variables = variables\n variables = [v if isinstance(v, DataArray) else self[v] for v in variables]\n aligned_vars = align(self, *variables, join=\"left\")\n aligned_self = aligned_vars[0]\n aligned_other_vars = aligned_vars[1:]\n vars_by_dim = defaultdict(list)\n for data_array in aligned_other_vars:\n if data_array.ndim != 1:\n raise ValueError(\"Input DataArray is not 1-D.\")\n (key,) = data_array.dims\n vars_by_dim[key].append(data_array)\n\n indices = {}\n for key, arrays in vars_by_dim.items():\n order = np.lexsort(tuple(reversed(arrays)))\n indices[key] = order if ascending else order[::-1]\n return aligned_self.isel(**indices)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.quantile_Dataset.quantile.return.new": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.quantile_Dataset.quantile.return.new", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 4953, "end_line": 5052, "span_ids": ["Dataset.quantile"], "tokens": 869}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def quantile(\n self, q, dim=None, interpolation=\"linear\", numeric_only=False, keep_attrs=None\n ):\n \"\"\"Compute the qth quantile of the data along the specified dimension.\n\n Returns the qth quantiles(s) of the array elements for each variable\n in the Dataset.\n\n Parameters\n ----------\n q : float in range of [0,1] or array-like of floats\n Quantile to compute, which must be between 0 and 1 inclusive.\n dim : str or sequence of str, optional\n Dimension(s) over which to apply quantile.\n interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n This optional parameter specifies the interpolation method to\n use when the desired quantile lies between two data points\n ``i < j``:\n\n * linear: ``i + (j - i) * fraction``, where ``fraction`` is\n the fractional part of the index surrounded by ``i`` and\n ``j``.\n * lower: ``i``.\n * higher: ``j``.\n * nearest: ``i`` or ``j``, whichever is nearest.\n * midpoint: ``(i + j) / 2``.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n numeric_only : bool, optional\n If True, only apply ``func`` to variables with a numeric dtype.\n\n Returns\n -------\n quantiles : Dataset\n If `q` is a single quantile, then the result is a scalar for each\n variable in data_vars. If multiple percentiles are given, first\n axis of the result corresponds to the quantile and a quantile\n dimension is added to the return Dataset. The other dimensions are\n the dimensions that remain after the reduction of the array.\n\n See Also\n --------\n numpy.nanpercentile, pandas.Series.quantile, DataArray.quantile\n \"\"\"\n\n if isinstance(dim, str):\n dims = {dim}\n elif dim is None or dim is ALL_DIMS:\n dims = set(self.dims)\n else:\n dims = set(dim)\n\n _assert_empty(\n [d for d in dims if d not in self.dims],\n \"Dataset does not contain the dimensions: %s\",\n )\n\n q = np.asarray(q, dtype=np.float64)\n\n variables = {}\n for name, var in self.variables.items():\n reduce_dims = [d for d in var.dims if d in dims]\n if reduce_dims or not var.dims:\n if name not in self.coords:\n if (\n not numeric_only\n or np.issubdtype(var.dtype, np.number)\n or var.dtype == np.bool_\n ):\n if len(reduce_dims) == var.ndim:\n # prefer to aggregate over axis=None rather than\n # axis=(0, 1) if they will be equivalent, because\n # the former is often more efficient\n reduce_dims = None\n variables[name] = var.quantile(\n q,\n dim=reduce_dims,\n interpolation=interpolation,\n keep_attrs=keep_attrs,\n )\n\n else:\n variables[name] = var\n\n # construct the new dataset\n coord_names = {k for k in self.coords if k in variables}\n indexes = {k: v for k, v in self.indexes.items() if k in variables}\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self.attrs if keep_attrs else None\n new = self._replace_with_new_dims(\n variables, coord_names=coord_names, attrs=attrs, indexes=indexes\n )\n if \"quantile\" in new.dims:\n new.coords[\"quantile\"] = Variable(\"quantile\", q)\n else:\n new.coords[\"quantile\"] = q\n return new", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rank_Dataset.rank.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.rank_Dataset.rank.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5054, "end_line": 5097, "span_ids": ["Dataset.rank"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def rank(self, dim, pct=False, keep_attrs=None):\n \"\"\"Ranks the data.\n\n Equal values are assigned a rank that is the average of the ranks that\n would have been otherwise assigned to all of the values within\n that set.\n Ranks begin at 1, not 0. If pct is True, computes percentage ranks.\n\n NaNs in the input array are returned as NaNs.\n\n The `bottleneck` library is required.\n\n Parameters\n ----------\n dim : str\n Dimension over which to compute rank.\n pct : bool, optional\n If True, compute percentage ranks, otherwise compute integer ranks.\n keep_attrs : bool, optional\n If True, the dataset's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n\n Returns\n -------\n ranked : Dataset\n Variables that do not depend on `dim` are dropped.\n \"\"\"\n if dim not in self.dims:\n raise ValueError(\"Dataset does not contain the dimension: %s\" % dim)\n\n variables = {}\n for name, var in self.variables.items():\n if name in self.data_vars:\n if dim in var.dims:\n variables[name] = var.rank(dim, pct=pct)\n else:\n variables[name] = var\n\n coord_names = set(self.coords)\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self.attrs if keep_attrs else None\n return self._replace(variables, coord_names, attrs=attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.differentiate_Dataset.differentiate.return.self__replace_variables_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.differentiate_Dataset.differentiate.return.self__replace_variables_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5099, "end_line": 5156, "span_ids": ["Dataset.differentiate"], "tokens": 485}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def differentiate(self, coord, edge_order=1, datetime_unit=None):\n \"\"\" Differentiate with the second order accurate central\n differences.\n\n .. note::\n This feature is limited to simple cartesian geometry, i.e. coord\n must be one dimensional.\n\n Parameters\n ----------\n coord: str\n The coordinate to be used to compute the gradient.\n edge_order: 1 or 2. Default 1\n N-th order accurate differences at the boundaries.\n datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',\n 'us', 'ns', 'ps', 'fs', 'as'}\n Unit to compute gradient. Only valid for datetime coordinate.\n\n Returns\n -------\n differentiated: Dataset\n\n See also\n --------\n numpy.gradient: corresponding numpy function\n \"\"\"\n from .variable import Variable\n\n if coord not in self.variables and coord not in self.dims:\n raise ValueError(\"Coordinate {} does not exist.\".format(coord))\n\n coord_var = self[coord].variable\n if coord_var.ndim != 1:\n raise ValueError(\n \"Coordinate {} must be 1 dimensional but is {}\"\n \" dimensional\".format(coord, coord_var.ndim)\n )\n\n dim = coord_var.dims[0]\n if _contains_datetime_like_objects(coord_var):\n if coord_var.dtype.kind in \"mM\" and datetime_unit is None:\n datetime_unit, _ = np.datetime_data(coord_var.dtype)\n elif datetime_unit is None:\n datetime_unit = \"s\" # Default to seconds for cftime objects\n coord_var = coord_var._to_numeric(datetime_unit=datetime_unit)\n\n variables = {}\n for k, v in self.variables.items():\n if k in self.data_vars and dim in v.dims and k not in self.coords:\n if _contains_datetime_like_objects(v):\n v = v._to_numeric(datetime_unit=datetime_unit)\n grad = duck_array_ops.gradient(\n v.data, coord_var, edge_order=edge_order, axis=v.get_axis_num(dim)\n )\n variables[k] = Variable(v.dims, grad)\n else:\n variables[k] = v\n return self._replace(variables)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.integrate_Dataset.integrate.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.integrate_Dataset.integrate.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5158, "end_line": 5188, "span_ids": ["Dataset.integrate"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def integrate(self, coord, datetime_unit=None):\n \"\"\" integrate the array with the trapezoidal rule.\n\n .. note::\n This feature is limited to simple cartesian geometry, i.e. coord\n must be one dimensional.\n\n Parameters\n ----------\n dim: str, or a sequence of str\n Coordinate(s) used for the integration.\n datetime_unit\n Can be specify the unit if datetime coordinate is used. One of\n {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs',\n 'as'}\n\n Returns\n -------\n integrated: Dataset\n\n See also\n --------\n DataArray.integrate\n numpy.trapz: corresponding numpy function\n \"\"\"\n if not isinstance(coord, (list, tuple)):\n coord = (coord,)\n result = self\n for c in coord:\n result = result._integrate_one(c, datetime_unit=datetime_unit)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._integrate_one_Dataset._integrate_one.return.self__replace_with_new_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset._integrate_one_Dataset._integrate_one.return.self__replace_with_new_di", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5190, "end_line": 5233, "span_ids": ["Dataset._integrate_one"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def _integrate_one(self, coord, datetime_unit=None):\n from .variable import Variable\n\n if coord not in self.variables and coord not in self.dims:\n raise ValueError(\"Coordinate {} does not exist.\".format(coord))\n\n coord_var = self[coord].variable\n if coord_var.ndim != 1:\n raise ValueError(\n \"Coordinate {} must be 1 dimensional but is {}\"\n \" dimensional\".format(coord, coord_var.ndim)\n )\n\n dim = coord_var.dims[0]\n if _contains_datetime_like_objects(coord_var):\n if coord_var.dtype.kind in \"mM\" and datetime_unit is None:\n datetime_unit, _ = np.datetime_data(coord_var.dtype)\n elif datetime_unit is None:\n datetime_unit = \"s\" # Default to seconds for cftime objects\n coord_var = datetime_to_numeric(coord_var, datetime_unit=datetime_unit)\n\n variables = {}\n coord_names = set()\n for k, v in self.variables.items():\n if k in self.coords:\n if dim not in v.dims:\n variables[k] = v\n coord_names.add(k)\n else:\n if k in self.data_vars and dim in v.dims:\n if _contains_datetime_like_objects(v):\n v = datetime_to_numeric(v, datetime_unit=datetime_unit)\n integ = duck_array_ops.trapz(\n v.data, coord_var.data, axis=v.get_axis_num(dim)\n )\n v_dims = list(v.dims)\n v_dims.remove(dim)\n variables[k] = Variable(v_dims, integ)\n else:\n variables[k] = v\n indexes = {k: v for k, v in self.indexes.items() if k in variables}\n return self._replace_with_new_dims(\n variables, coord_names=coord_names, indexes=indexes\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.real_Dataset.plot.return._Dataset_PlotMethods_self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.real_Dataset.plot.return._Dataset_PlotMethods_self", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5235, "end_line": 5252, "span_ids": ["Dataset.plot", "Dataset.real", "Dataset.imag"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n @property\n def real(self):\n return self._unary_op(lambda x: x.real, keep_attrs=True)(self)\n\n @property\n def imag(self):\n return self._unary_op(lambda x: x.imag, keep_attrs=True)(self)\n\n @property\n def plot(self):\n \"\"\"\n Access plotting functions. Use it as a namespace to use\n xarray.plot functions as Dataset methods\n\n >>> ds.plot.scatter(...) # equivalent to xarray.plot.scatter(ds,...)\n\n \"\"\"\n return _Dataset_PlotMethods(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.filter_by_attrs_Dataset.filter_by_attrs.return.self_selection_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.filter_by_attrs_Dataset.filter_by_attrs.return.self_selection_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5254, "end_line": 5344, "span_ids": ["Dataset.filter_by_attrs"], "tokens": 1134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def filter_by_attrs(self, **kwargs):\n \"\"\"Returns a ``Dataset`` with variables that match specific conditions.\n\n Can pass in ``key=value`` or ``key=callable``. A Dataset is returned\n containing only the variables for which all the filter tests pass.\n These tests are either ``key=value`` for which the attribute ``key``\n has the exact value ``value`` or the callable passed into\n ``key=callable`` returns True. The callable will be passed a single\n value, either the value of the attribute ``key`` or ``None`` if the\n DataArray does not have an attribute with the name ``key``.\n\n Parameters\n ----------\n **kwargs : key=value\n key : str\n Attribute name.\n value : callable or obj\n If value is a callable, it should return a boolean in the form\n of bool = func(attr) where attr is da.attrs[key].\n Otherwise, value will be compared to the each\n DataArray's attrs[key].\n\n Returns\n -------\n new : Dataset\n New dataset with variables filtered by attribute.\n\n Examples\n --------\n >>> # Create an example dataset:\n >>> import numpy as np\n >>> import pandas as pd\n >>> import xarray as xr\n >>> temp = 15 + 8 * np.random.randn(2, 2, 3)\n >>> precip = 10 * np.random.rand(2, 2, 3)\n >>> lon = [[-99.83, -99.32], [-99.79, -99.23]]\n >>> lat = [[42.25, 42.21], [42.63, 42.59]]\n >>> dims = ['x', 'y', 'time']\n >>> temp_attr = dict(standard_name='air_potential_temperature')\n >>> precip_attr = dict(standard_name='convective_precipitation_flux')\n >>> ds = xr.Dataset({\n ... 'temperature': (dims, temp, temp_attr),\n ... 'precipitation': (dims, precip, precip_attr)},\n ... coords={\n ... 'lon': (['x', 'y'], lon),\n ... 'lat': (['x', 'y'], lat),\n ... 'time': pd.date_range('2014-09-06', periods=3),\n ... 'reference_time': pd.Timestamp('2014-09-05')})\n >>> # Get variables matching a specific standard_name.\n >>> ds.filter_by_attrs(standard_name='convective_precipitation_flux')\n \n Dimensions: (time: 3, x: 2, y: 2)\n Coordinates:\n * x (x) int64 0 1\n * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08\n lat (x, y) float64 42.25 42.21 42.63 42.59\n * y (y) int64 0 1\n reference_time datetime64[ns] 2014-09-05\n lon (x, y) float64 -99.83 -99.32 -99.79 -99.23\n Data variables:\n precipitation (x, y, time) float64 4.178 2.307 6.041 6.046 0.06648 ...\n >>> # Get all variables that have a standard_name attribute.\n >>> standard_name = lambda v: v is not None\n >>> ds.filter_by_attrs(standard_name=standard_name)\n \n Dimensions: (time: 3, x: 2, y: 2)\n Coordinates:\n lon (x, y) float64 -99.83 -99.32 -99.79 -99.23\n lat (x, y) float64 42.25 42.21 42.63 42.59\n * x (x) int64 0 1\n * y (y) int64 0 1\n * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08\n reference_time datetime64[ns] 2014-09-05\n Data variables:\n temperature (x, y, time) float64 25.86 20.82 6.954 23.13 10.25 11.68 ...\n precipitation (x, y, time) float64 5.702 0.9422 2.075 1.178 3.284 ...\n\n \"\"\"\n selection = []\n for var_name, variable in self.variables.items():\n has_value_flag = False\n for attr_name, pattern in kwargs.items():\n attr_value = variable.attrs.get(attr_name)\n if (callable(pattern) and pattern(attr_value)) or attr_value == pattern:\n has_value_flag = True\n else:\n has_value_flag = False\n break\n if has_value_flag is True:\n selection.append(var_name)\n return self[selection]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.unify_chunks_Dataset.unify_chunks.return.ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.unify_chunks_Dataset.unify_chunks.return.ds", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5346, "end_line": 5390, "span_ids": ["Dataset.unify_chunks"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def unify_chunks(self) -> \"Dataset\":\n \"\"\" Unify chunk size along all chunked dimensions of this Dataset.\n\n Returns\n -------\n\n Dataset with consistent chunk sizes for all dask-array variables\n\n See Also\n --------\n\n dask.array.core.unify_chunks\n \"\"\"\n\n try:\n self.chunks\n except ValueError: # \"inconsistent chunks\"\n pass\n else:\n # No variables with dask backend, or all chunks are already aligned\n return self.copy()\n\n # import dask is placed after the quick exit test above to allow\n # running this method if dask isn't installed and there are no chunks\n import dask.array\n\n ds = self.copy()\n\n dims_pos_map = {dim: index for index, dim in enumerate(ds.dims)}\n\n dask_array_names = []\n dask_unify_args = []\n for name, variable in ds.variables.items():\n if isinstance(variable.data, dask.array.Array):\n dims_tuple = [dims_pos_map[dim] for dim in variable.dims]\n dask_array_names.append(name)\n dask_unify_args.append(variable.data)\n dask_unify_args.append(dims_tuple)\n\n _, rechunked_arrays = dask.array.core.unify_chunks(*dask_unify_args)\n\n for name, new_array in zip(dask_array_names, rechunked_arrays):\n ds.variables[name]._data = new_array\n\n return ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.map_blocks_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dataset.py_Dataset.map_blocks_", "embedding": null, "metadata": {"file_path": "xarray/core/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 5392, "end_line": 5451, "span_ids": ["Dataset.map_blocks", "impl:10"], "tokens": 510}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(Mapping, ImplementsDatasetReduce, DataWithCoords):\n\n def map_blocks(\n self,\n func: \"Callable[..., T_DSorDA]\",\n args: Sequence[Any] = (),\n kwargs: Mapping[str, Any] = None,\n ) -> \"T_DSorDA\":\n \"\"\"\n Apply a function to each chunk of this Dataset. This method is experimental and\n its signature may change.\n\n Parameters\n ----------\n func: callable\n User-provided function that accepts a Dataset as its first parameter. The\n function will receive a subset of this Dataset, corresponding to one chunk\n along each chunked dimension. ``func`` will be executed as\n ``func(obj_subset, *args, **kwargs)``.\n\n The function will be first run on mocked-up data, that looks like this\n Dataset but has sizes 0, to determine properties of the returned object such\n as dtype, variable names, new dimensions and new indexes (if any).\n\n This function must return either a single DataArray or a single Dataset.\n\n This function cannot change size of existing dimensions, or add new chunked\n dimensions.\n args: Sequence\n Passed verbatim to func after unpacking, after the sliced DataArray. xarray\n objects, if any, will not be split by chunks. Passing dask collections is\n not allowed.\n kwargs: Mapping\n Passed verbatim to func after unpacking. xarray objects, if any, will not be\n split by chunks. Passing dask collections is not allowed.\n\n Returns\n -------\n A single DataArray or Dataset with dask backend, reassembled from the outputs of\n the function.\n\n Notes\n -----\n This method is designed for when one needs to manipulate a whole xarray object\n within each chunk. In the more common case where one can work on numpy arrays,\n it is recommended to use apply_ufunc.\n\n If none of the variables in this Dataset is backed by dask, calling this method\n is equivalent to calling ``func(self, *args, **kwargs)``.\n\n See Also\n --------\n dask.array.map_blocks, xarray.apply_ufunc, xarray.map_blocks,\n xarray.DataArray.map_blocks\n \"\"\"\n from .parallel import map_blocks\n\n return map_blocks(func, self, args, kwargs)\n\n\nops.inject_all_ops_and_reduce_methods(Dataset, array_only=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_functools_PROMOTE_TO_OBJECT._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_functools_PROMOTE_TO_OBJECT._", "embedding": null, "metadata": {"file_path": "xarray/core/dtypes.py", "file_name": "dtypes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 42, "span_ids": ["impl", "AlwaysLessThan.__eq__", "AlwaysLessThan.__lt__", "AlwaysGreaterThan.__gt__", "AlwaysGreaterThan.__eq__", "imports", "AlwaysGreaterThan", "impl:3", "AlwaysLessThan"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\n\nimport numpy as np\n\nfrom . import utils\n\n# Use as a sentinel value to indicate a dtype appropriate NA value.\nNA = utils.ReprObject(\"\")\n\n\n@functools.total_ordering\nclass AlwaysGreaterThan:\n def __gt__(self, other):\n return True\n\n def __eq__(self, other):\n return isinstance(other, type(self))\n\n\n@functools.total_ordering\nclass AlwaysLessThan:\n def __lt__(self, other):\n return True\n\n def __eq__(self, other):\n return isinstance(other, type(self))\n\n\n# Equivalence to np.inf (-np.inf) for object-type\nINF = AlwaysGreaterThan()\nNINF = AlwaysLessThan()\n\n\n# Pairs of types that, if both found, should be promoted to object dtype\n# instead of following NumPy's own type-promotion rules. These type promotion\n# rules match pandas instead. For reference, see the NumPy type hierarchy:\n# https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html\nPROMOTE_TO_OBJECT = [\n {np.number, np.character}, # numpy promotes to character\n {np.bool_, np.character}, # numpy promotes to character\n {np.bytes_, np.unicode_}, # numpy promotes to unicode\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_maybe_promote_maybe_promote.return.np_dtype_dtype_fill_val": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_maybe_promote_maybe_promote.return.np_dtype_dtype_fill_val", "embedding": null, "metadata": {"file_path": "xarray/core/dtypes.py", "file_name": "dtypes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 78, "span_ids": ["maybe_promote"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maybe_promote(dtype):\n \"\"\"Simpler equivalent of pandas.core.common._maybe_promote\n\n Parameters\n ----------\n dtype : np.dtype\n\n Returns\n -------\n dtype : Promoted dtype that can hold missing values.\n fill_value : Valid missing value for the promoted dtype.\n \"\"\"\n # N.B. these casting rules should match pandas\n if np.issubdtype(dtype, np.floating):\n fill_value = np.nan\n elif np.issubdtype(dtype, np.timedelta64):\n # See https://github.com/numpy/numpy/issues/10685\n # np.timedelta64 is a subclass of np.integer\n # Check np.timedelta64 before np.integer\n fill_value = np.timedelta64(\"NaT\")\n elif np.issubdtype(dtype, np.integer):\n if dtype.itemsize <= 2:\n dtype = np.float32\n else:\n dtype = np.float64\n fill_value = np.nan\n elif np.issubdtype(dtype, np.complexfloating):\n fill_value = np.nan + np.nan * 1j\n elif np.issubdtype(dtype, np.datetime64):\n fill_value = np.datetime64(\"NaT\")\n else:\n dtype = object\n fill_value = np.nan\n return np.dtype(dtype), fill_value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_NAT_TYPES_is_datetime_like.return.np_issubdtype_dtype_np_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_NAT_TYPES_is_datetime_like.return.np_issubdtype_dtype_np_d", "embedding": null, "metadata": {"file_path": "xarray/core/dtypes.py", "file_name": "dtypes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 142, "span_ids": ["get_neg_infinity", "get_fill_value", "impl:9", "is_datetime_like", "get_pos_infinity"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "NAT_TYPES = (np.datetime64(\"NaT\"), np.timedelta64(\"NaT\"))\n\n\ndef get_fill_value(dtype):\n \"\"\"Return an appropriate fill value for this dtype.\n\n Parameters\n ----------\n dtype : np.dtype\n\n Returns\n -------\n fill_value : Missing value corresponding to this dtype.\n \"\"\"\n _, fill_value = maybe_promote(dtype)\n return fill_value\n\n\ndef get_pos_infinity(dtype):\n \"\"\"Return an appropriate positive infinity for this dtype.\n\n Parameters\n ----------\n dtype : np.dtype\n\n Returns\n -------\n fill_value : positive infinity value corresponding to this dtype.\n \"\"\"\n if issubclass(dtype.type, (np.floating, np.integer)):\n return np.inf\n\n if issubclass(dtype.type, np.complexfloating):\n return np.inf + 1j * np.inf\n\n return INF\n\n\ndef get_neg_infinity(dtype):\n \"\"\"Return an appropriate positive infinity for this dtype.\n\n Parameters\n ----------\n dtype : np.dtype\n\n Returns\n -------\n fill_value : positive infinity value corresponding to this dtype.\n \"\"\"\n if issubclass(dtype.type, (np.floating, np.integer)):\n return -np.inf\n\n if issubclass(dtype.type, np.complexfloating):\n return -np.inf - 1j * np.inf\n\n return NINF\n\n\ndef is_datetime_like(dtype):\n \"\"\"Check if a dtype is a subclass of the numpy datetime types\n \"\"\"\n return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_result_type_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/dtypes.py_result_type_", "embedding": null, "metadata": {"file_path": "xarray/core/dtypes.py", "file_name": "dtypes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 145, "end_line": 170, "span_ids": ["result_type"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def result_type(*arrays_and_dtypes):\n \"\"\"Like np.result_type, but with type promotion rules matching pandas.\n\n Examples of changed behavior:\n number + string -> object (not string)\n bytes + unicode -> object (not unicode)\n\n Parameters\n ----------\n *arrays_and_dtypes : list of arrays and dtypes\n The dtype is extracted from both numpy and dask arrays.\n\n Returns\n -------\n numpy.dtype for the result.\n \"\"\"\n types = {np.result_type(t).type for t in arrays_and_dtypes}\n\n for left, right in PROMOTE_TO_OBJECT:\n if any(issubclass(t, left) for t in types) and any(\n issubclass(t, right) for t in types\n ):\n return np.dtype(object)\n\n return np.result_type(*arrays_and_dtypes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__Compatibility_module_d_try_.except_ImportError_._type_ignore": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__Compatibility_module_d_try_.except_ImportError_._type_ignore", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 21, "span_ids": ["impl", "imports:10", "impl:2", "docstring", "imports"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Compatibility module defining operations on duck numpy-arrays.\n\nCurrently, this means Dask or NumPy arrays. None of these functions should\naccept or return xarray objects.\n\"\"\"\nimport contextlib\nimport inspect\nimport warnings\nfrom functools import partial\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import dask_array_ops, dtypes, npcompat, nputils\nfrom .nputils import nanfirst, nanlast\nfrom .pycompat import dask_array_type\n\ntry:\n import dask.array as dask_array\nexcept ImportError:\n dask_array = None # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__dask_or_eager_func__dask_or_eager_func.return.f": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__dask_or_eager_func__dask_or_eager_func.return.f", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 24, "end_line": 54, "span_ids": ["_dask_or_eager_func"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dask_or_eager_func(\n name,\n eager_module=np,\n dask_module=dask_array,\n list_of_args=False,\n array_args=slice(1),\n requires_dask=None,\n):\n \"\"\"Create a function that dispatches to dask for dask array inputs.\"\"\"\n if dask_module is not None:\n\n def f(*args, **kwargs):\n if list_of_args:\n dispatch_args = args[0]\n else:\n dispatch_args = args[array_args]\n if any(isinstance(a, dask_array.Array) for a in dispatch_args):\n try:\n wrapped = getattr(dask_module, name)\n except AttributeError as e:\n raise AttributeError(\"%s: requires dask >=%s\" % (e, requires_dask))\n else:\n wrapped = getattr(eager_module, name)\n return wrapped(*args, **kwargs)\n\n else:\n\n def f(*args, **kwargs):\n return getattr(eager_module, name)(*args, **kwargs)\n\n return f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_fail_on_dask_array_input_pandas_isnull._dask_or_eager_func_isnu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_fail_on_dask_array_input_pandas_isnull._dask_or_eager_func_isnu", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 57, "end_line": 86, "span_ids": ["fail_on_dask_array_input", "impl:5"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def fail_on_dask_array_input(values, msg=None, func_name=None):\n if isinstance(values, dask_array_type):\n if msg is None:\n msg = \"%r is not yet a valid method on dask arrays\"\n if func_name is None:\n func_name = inspect.stack()[1][3]\n raise NotImplementedError(msg % func_name)\n\n\n# switch to use dask.array / __array_function__ version when dask supports it:\n# https://github.com/dask/dask/pull/4822\nmoveaxis = npcompat.moveaxis\n\naround = _dask_or_eager_func(\"around\")\nisclose = _dask_or_eager_func(\"isclose\")\n\n\nif hasattr(np, \"isnat\") and (\n dask_array is None or hasattr(dask_array_type, \"__array_ufunc__\")\n):\n # np.isnat is available since NumPy 1.13, so __array_ufunc__ is always\n # supported.\n isnat = np.isnat\nelse:\n isnat = _dask_or_eager_func(\"isnull\", eager_module=pd)\nisnan = _dask_or_eager_func(\"isnan\")\nzeros_like = _dask_or_eager_func(\"zeros_like\")\n\n\npandas_isnull = _dask_or_eager_func(\"isnull\", eager_module=pd)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_isnull_isnull.if_issubclass_scalar_type.else_.if_isinstance_data_np_n.else_.return.data_data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_isnull_isnull.if_issubclass_scalar_type.else_.if_isinstance_data_np_n.else_.return.data_data", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 89, "end_line": 112, "span_ids": ["isnull"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def isnull(data):\n data = asarray(data)\n scalar_type = data.dtype.type\n if issubclass(scalar_type, (np.datetime64, np.timedelta64)):\n # datetime types use NaT for null\n # note: must check timedelta64 before integers, because currently\n # timedelta64 inherits from np.integer\n return isnat(data)\n elif issubclass(scalar_type, np.inexact):\n # float types use NaN for null\n return isnan(data)\n elif issubclass(scalar_type, (np.bool_, np.integer, np.character, np.void)):\n # these types cannot represent missing values\n return zeros_like(data, dtype=bool)\n else:\n # at this point, array should have dtype=object\n if isinstance(data, (np.ndarray, dask_array_type)):\n return pandas_isnull(data)\n else:\n # Not reachable yet, but intended for use with other duck array\n # types. For full consistency with pandas, we should accept None as\n # a null value as well as NaN, but it isn't clear how to do this\n # with duck typing.\n return data != data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_notnull_gradient.return.np_gradient_x_coord_axi": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_notnull_gradient.return.np_gradient_x_coord_axi", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 115, "end_line": 138, "span_ids": ["impl:23", "gradient", "notnull"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def notnull(data):\n return ~isnull(data)\n\n\ntranspose = _dask_or_eager_func(\"transpose\")\n_where = _dask_or_eager_func(\"where\", array_args=slice(3))\nisin = _dask_or_eager_func(\"isin\", array_args=slice(2))\ntake = _dask_or_eager_func(\"take\")\nbroadcast_to = _dask_or_eager_func(\"broadcast_to\")\n\n_concatenate = _dask_or_eager_func(\"concatenate\", list_of_args=True)\n_stack = _dask_or_eager_func(\"stack\", list_of_args=True)\n\narray_all = _dask_or_eager_func(\"all\")\narray_any = _dask_or_eager_func(\"any\")\n\ntensordot = _dask_or_eager_func(\"tensordot\", array_args=slice(2))\neinsum = _dask_or_eager_func(\"einsum\", array_args=slice(1, None))\n\n\ndef gradient(x, coord, axis, edge_order):\n if isinstance(x, dask_array_type):\n return dask_array.gradient(x, coord, axis=axis, edge_order=edge_order)\n return np.gradient(x, coord, axis=axis, edge_order=edge_order)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_trapz_trapz.return.sum_integrand_axis_axis_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_trapz_trapz.return.sum_integrand_axis_axis_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 150, "span_ids": ["trapz"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def trapz(y, x, axis):\n if axis < 0:\n axis = y.ndim + axis\n x_sl1 = (slice(1, None),) + (None,) * (y.ndim - axis - 1)\n x_sl2 = (slice(None, -1),) + (None,) * (y.ndim - axis - 1)\n slice1 = (slice(None),) * axis + (slice(1, None),)\n slice2 = (slice(None),) * axis + (slice(None, -1),)\n dx = x[x_sl1] - x[x_sl2]\n integrand = dx * 0.5 * (y[tuple(slice1)] + y[tuple(slice2)])\n return sum(integrand, axis=axis, skipna=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_masked_invalid_as_shared_dtype.return._x_astype_out_type_copy_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_masked_invalid_as_shared_dtype.return._x_astype_out_type_copy_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 153, "end_line": 174, "span_ids": ["as_shared_dtype", "asarray", "impl:45"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "masked_invalid = _dask_or_eager_func(\n \"masked_invalid\", eager_module=np.ma, dask_module=getattr(dask_array, \"ma\", None)\n)\n\n\ndef asarray(data):\n return (\n data\n if (isinstance(data, dask_array_type) or hasattr(data, \"__array_function__\"))\n else np.asarray(data)\n )\n\n\ndef as_shared_dtype(scalars_or_arrays):\n \"\"\"Cast a arrays to a shared dtype using xarray's type promotion rules.\"\"\"\n arrays = [asarray(x) for x in scalars_or_arrays]\n # Pass arrays directly instead of dtypes to result_type so scalars\n # get handled properly.\n # Note that result_type() safely gets the dtype from dask arrays without\n # evaluating them.\n out_type = dtypes.result_type(*arrays)\n return [x.astype(out_type, copy=False) for x in arrays]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_allclose_or_equiv_allclose_or_equiv.return.bool_isclose_arr1_arr2_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_allclose_or_equiv_allclose_or_equiv.return.bool_isclose_arr1_arr2_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 177, "end_line": 184, "span_ids": ["allclose_or_equiv"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def allclose_or_equiv(arr1, arr2, rtol=1e-5, atol=1e-8):\n \"\"\"Like np.allclose, but also allows values to be NaN in both arrays\n \"\"\"\n arr1 = asarray(arr1)\n arr2 = asarray(arr2)\n if arr1.shape != arr2.shape:\n return False\n return bool(isclose(arr1, arr2, rtol=rtol, atol=atol, equal_nan=True).all())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_array_equiv_array_equiv.with_warnings_catch_warni.return.bool_flag_array_all_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_array_equiv_array_equiv.with_warnings_catch_warni.return.bool_flag_array_all_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 187, "end_line": 197, "span_ids": ["array_equiv"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def array_equiv(arr1, arr2):\n \"\"\"Like np.array_equal, but also allows values to be NaN in both arrays\n \"\"\"\n arr1 = asarray(arr1)\n arr2 = asarray(arr2)\n if arr1.shape != arr2.shape:\n return False\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"In the future, 'NAT == x'\")\n flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2))\n return bool(flag_array.all())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_array_notnull_equiv_array_notnull_equiv.with_warnings_catch_warni.return.bool_flag_array_all_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_array_notnull_equiv_array_notnull_equiv.with_warnings_catch_warni.return.bool_flag_array_all_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 200, "end_line": 211, "span_ids": ["array_notnull_equiv"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def array_notnull_equiv(arr1, arr2):\n \"\"\"Like np.array_equal, but also allows values to be NaN in either or both\n arrays\n \"\"\"\n arr1 = asarray(arr1)\n arr2 = asarray(arr2)\n if arr1.shape != arr2.shape:\n return False\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"In the future, 'NAT == x'\")\n flag_array = (arr1 == arr2) | isnull(arr1) | isnull(arr2)\n return bool(flag_array.all())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_count__ignore_warnings_if.if_condition_.else_.yield": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_count__ignore_warnings_if.if_condition_.else_.yield", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 252, "span_ids": ["where", "fillna", "count", "stack", "where_method", "_ignore_warnings_if", "concatenate"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def count(data, axis=None):\n \"\"\"Count the number of non-NA in this array along the given axis or axes\n \"\"\"\n return np.sum(np.logical_not(isnull(data)), axis=axis)\n\n\ndef where(condition, x, y):\n \"\"\"Three argument where() with better dtype promotion rules.\"\"\"\n return _where(condition, *as_shared_dtype([x, y]))\n\n\ndef where_method(data, cond, other=dtypes.NA):\n if other is dtypes.NA:\n other = dtypes.get_fill_value(data.dtype)\n return where(cond, data, other)\n\n\ndef fillna(data, other):\n return where(isnull(data), other, data)\n\n\ndef concatenate(arrays, axis=0):\n \"\"\"concatenate() with better dtype promotion rules.\"\"\"\n return _concatenate(as_shared_dtype(arrays), axis=axis)\n\n\ndef stack(arrays, axis=0):\n \"\"\"stack() with better dtype promotion rules.\"\"\"\n return _stack(as_shared_dtype(arrays), axis=axis)\n\n\n@contextlib.contextmanager\ndef _ignore_warnings_if(condition):\n if condition:\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n yield\n else:\n yield", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__create_nan_agg_method__create_nan_agg_method.return.f": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__create_nan_agg_method__create_nan_agg_method.return.f", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 292, "span_ids": ["_create_nan_agg_method"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_nan_agg_method(name, coerce_strings=False):\n from . import nanops\n\n def f(values, axis=None, skipna=None, **kwargs):\n if kwargs.pop(\"out\", None) is not None:\n raise TypeError(\"`out` is not valid for {}\".format(name))\n\n values = asarray(values)\n\n if coerce_strings and values.dtype.kind in \"SU\":\n values = values.astype(object)\n\n func = None\n if skipna or (skipna is None and values.dtype.kind in \"cfO\"):\n nanname = \"nan\" + name\n func = getattr(nanops, nanname)\n else:\n func = _dask_or_eager_func(name)\n\n try:\n return func(values, axis=axis, **kwargs)\n except AttributeError:\n if isinstance(values, dask_array_type):\n try: # dask/dask#3133 dask sometimes needs dtype argument\n # if func does not accept dtype, then raises TypeError\n return func(values, axis=axis, dtype=values.dtype, **kwargs)\n except (AttributeError, TypeError):\n msg = \"%s is not yet implemented on dask arrays\" % name\n else:\n msg = (\n \"%s is not available with skipna=False with the \"\n \"installed version of numpy; upgrade to numpy 1.12 \"\n \"or newer to use skipna=True or skipna=None\" % name\n )\n raise NotImplementedError(msg)\n\n f.__name__ = name\n return f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__Attributes_numeric_onl__mean._create_nan_agg_method_m": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__Attributes_numeric_onl__mean._create_nan_agg_method_m", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 295, "end_line": 319, "span_ids": ["_create_nan_agg_method", "impl:47"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Attributes `numeric_only`, `available_min_count` is used for docs.\n# See ops.inject_reduce_methods\nargmax = _create_nan_agg_method(\"argmax\", coerce_strings=True)\nargmin = _create_nan_agg_method(\"argmin\", coerce_strings=True)\nmax = _create_nan_agg_method(\"max\", coerce_strings=True)\nmin = _create_nan_agg_method(\"min\", coerce_strings=True)\nsum = _create_nan_agg_method(\"sum\")\nsum.numeric_only = True\nsum.available_min_count = True\nstd = _create_nan_agg_method(\"std\")\nstd.numeric_only = True\nvar = _create_nan_agg_method(\"var\")\nvar.numeric_only = True\nmedian = _create_nan_agg_method(\"median\")\nmedian.numeric_only = True\nprod = _create_nan_agg_method(\"prod\")\nprod.numeric_only = True\nsum.available_min_count = True\ncumprod_1d = _create_nan_agg_method(\"cumprod\")\ncumprod_1d.numeric_only = True\ncumsum_1d = _create_nan_agg_method(\"cumsum\")\ncumsum_1d.numeric_only = True\n\n\n_mean = _create_nan_agg_method(\"mean\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_datetime_to_numeric_datetime_to_numeric.return.array_astype_dtype_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_datetime_to_numeric_datetime_to_numeric.return.array_astype_dtype_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 322, "end_line": 357, "span_ids": ["datetime_to_numeric"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def datetime_to_numeric(array, offset=None, datetime_unit=None, dtype=float):\n \"\"\"Convert an array containing datetime-like data to an array of floats.\n\n Parameters\n ----------\n da : np.array\n Input data\n offset: Scalar with the same type of array or None\n If None, subtract minimum values to reduce round off error\n datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',\n 'us', 'ns', 'ps', 'fs', 'as'}\n dtype: target dtype\n\n Returns\n -------\n array\n \"\"\"\n # TODO: make this function dask-compatible?\n if offset is None:\n offset = array.min()\n array = array - offset\n\n if not hasattr(array, \"dtype\"): # scalar is converted to 0d-array\n array = np.array(array)\n\n if array.dtype.kind in \"O\":\n # possibly convert object array containing datetime.timedelta\n array = np.asarray(pd.Series(array.ravel())).reshape(array.shape)\n\n if datetime_unit:\n array = array / np.timedelta64(1, datetime_unit)\n\n # convert np.NaT to np.nan\n if array.dtype.kind in \"mM\":\n return np.where(isnull(array), np.nan, array.astype(dtype))\n return array.astype(dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__to_pytimedelta_mean.if_array_dtype_kind_in_M.else_.return._mean_array_axis_axis_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py__to_pytimedelta_mean.if_array_dtype_kind_in_M.else_.return._mean_array_axis_axis_s", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 360, "end_line": 393, "span_ids": ["mean", "_to_pytimedelta"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _to_pytimedelta(array, unit=\"us\"):\n index = pd.TimedeltaIndex(array.ravel(), unit=unit)\n return index.to_pytimedelta().reshape(array.shape)\n\n\ndef mean(array, axis=None, skipna=None, **kwargs):\n \"\"\"inhouse mean that can handle np.datetime64 or cftime.datetime\n dtypes\"\"\"\n from .common import _contains_cftime_datetimes\n\n array = asarray(array)\n if array.dtype.kind in \"Mm\":\n offset = min(array)\n # xarray always uses np.datetime64[ns] for np.datetime64 data\n dtype = \"timedelta64[ns]\"\n return (\n _mean(\n datetime_to_numeric(array, offset), axis=axis, skipna=skipna, **kwargs\n ).astype(dtype)\n + offset\n )\n elif _contains_cftime_datetimes(array):\n if isinstance(array, dask_array_type):\n raise NotImplementedError(\n \"Computing the mean of an array containing \"\n \"cftime.datetime objects is not yet implemented on \"\n \"dask arrays.\"\n )\n offset = min(array)\n timedeltas = datetime_to_numeric(array, offset, datetime_unit=\"us\")\n mean_timedeltas = _mean(timedeltas, axis=axis, skipna=skipna, **kwargs)\n return _to_pytimedelta(mean_timedeltas, unit=\"us\") + offset\n else:\n return _mean(array, axis=axis, skipna=skipna, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_mean.numeric_only_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/duck_array_ops.py_mean.numeric_only_", "embedding": null, "metadata": {"file_path": "xarray/core/duck_array_ops.py", "file_name": "duck_array_ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 396, "end_line": 457, "span_ids": ["last", "_nd_cum_func", "impl:89", "impl:91", "rolling_window", "first", "cumprod", "cumsum"], "tokens": 496}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "mean.numeric_only = True # type: ignore\n\n\ndef _nd_cum_func(cum_func, array, axis, **kwargs):\n array = asarray(array)\n if axis is None:\n axis = tuple(range(array.ndim))\n if isinstance(axis, int):\n axis = (axis,)\n\n out = array\n for ax in axis:\n out = cum_func(out, axis=ax, **kwargs)\n return out\n\n\ndef cumprod(array, axis=None, **kwargs):\n \"\"\"N-dimensional version of cumprod.\"\"\"\n return _nd_cum_func(cumprod_1d, array, axis, **kwargs)\n\n\ndef cumsum(array, axis=None, **kwargs):\n \"\"\"N-dimensional version of cumsum.\"\"\"\n return _nd_cum_func(cumsum_1d, array, axis, **kwargs)\n\n\n_fail_on_dask_array_input_skipna = partial(\n fail_on_dask_array_input,\n msg=\"%r with skipna=True is not yet implemented on dask arrays\",\n)\n\n\ndef first(values, axis, skipna=None):\n \"\"\"Return the first non-NA elements in this array along the given axis\n \"\"\"\n if (skipna or skipna is None) and values.dtype.kind not in \"iSU\":\n # only bother for dtypes that can hold NaN\n _fail_on_dask_array_input_skipna(values)\n return nanfirst(values, axis)\n return take(values, 0, axis=axis)\n\n\ndef last(values, axis, skipna=None):\n \"\"\"Return the last non-NA elements in this array along the given axis\n \"\"\"\n if (skipna or skipna is None) and values.dtype.kind not in \"iSU\":\n # only bother for dtypes that can hold NaN\n _fail_on_dask_array_input_skipna(values)\n return nanlast(values, axis)\n return take(values, -1, axis=axis)\n\n\ndef rolling_window(array, axis, window, center, fill_value):\n \"\"\"\n Make an ndarray with a rolling window of axis-th dimension.\n The rolling dimension will be placed at the last dimension.\n \"\"\"\n if isinstance(array, dask_array_type):\n return dask_array_ops.rolling_window(array, axis, window, center, fill_value)\n else: # np.ndarray\n return nputils.rolling_window(array, axis, window, center, fill_value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py_warnings__CachedAccessor.__get__.return.accessor_obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py_warnings__CachedAccessor.__get__.return.accessor_obj", "embedding": null, "metadata": {"file_path": "xarray/core/extensions.py", "file_name": "extensions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 39, "span_ids": ["_CachedAccessor.__get__", "imports", "_CachedAccessor", "AccessorRegistrationWarning"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\n\nfrom .dataarray import DataArray\nfrom .dataset import Dataset\n\n\nclass AccessorRegistrationWarning(Warning):\n \"\"\"Warning for conflicts in accessor registration.\"\"\"\n\n\nclass _CachedAccessor:\n \"\"\"Custom property-like object (descriptor) for caching accessors.\"\"\"\n\n def __init__(self, name, accessor):\n self._name = name\n self._accessor = accessor\n\n def __get__(self, obj, cls):\n if obj is None:\n # we're accessing the attribute of the class, i.e., Dataset.geo\n return self._accessor\n\n try:\n return obj._accessors[self._name]\n except TypeError:\n obj._accessors = {}\n except KeyError:\n pass\n\n try:\n accessor_obj = self._accessor(obj)\n except AttributeError:\n # __getattr__ on data object will swallow any AttributeErrors\n # raised when initializing the accessor, so we need to raise as\n # something else (GH933):\n raise RuntimeError(\"error initializing %r accessor.\" % self._name)\n\n obj._accessors[self._name] = accessor_obj\n return accessor_obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py__register_accessor_register_dataarray_accessor.return._register_accessor_name_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py__register_accessor_register_dataarray_accessor.return._register_accessor_name_", "embedding": null, "metadata": {"file_path": "xarray/core/extensions.py", "file_name": "extensions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 71, "span_ids": ["register_dataarray_accessor", "_register_accessor"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _register_accessor(name, cls):\n def decorator(accessor):\n if hasattr(cls, name):\n warnings.warn(\n \"registration of accessor %r under name %r for type %r is \"\n \"overriding a preexisting attribute with the same name.\"\n % (accessor, name, cls),\n AccessorRegistrationWarning,\n stacklevel=2,\n )\n setattr(cls, name, _CachedAccessor(name, accessor))\n return accessor\n\n return decorator\n\n\ndef register_dataarray_accessor(name):\n \"\"\"Register a custom accessor on xarray.DataArray objects.\n\n Parameters\n ----------\n name : str\n Name under which the accessor should be registered. A warning is issued\n if this name conflicts with a preexisting attribute.\n\n See also\n --------\n register_dataset_accessor\n \"\"\"\n return _register_accessor(name, DataArray)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py_register_dataset_accessor_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/extensions.py_register_dataset_accessor_", "embedding": null, "metadata": {"file_path": "xarray/core/extensions.py", "file_name": "extensions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 74, "end_line": 120, "span_ids": ["register_dataset_accessor"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def register_dataset_accessor(name):\n \"\"\"Register a custom property on xarray.Dataset objects.\n\n Parameters\n ----------\n name : str\n Name under which the accessor should be registered. A warning is issued\n if this name conflicts with a preexisting attribute.\n\n Examples\n --------\n\n In your library code::\n\n import xarray as xr\n\n @xr.register_dataset_accessor('geo')\n class GeoAccessor:\n def __init__(self, xarray_obj):\n self._obj = xarray_obj\n\n @property\n def center(self):\n # return the geographic center point of this dataset\n lon = self._obj.latitude\n lat = self._obj.longitude\n return (float(lon.mean()), float(lat.mean()))\n\n def plot(self):\n # plot this array's data on a map, e.g., using Cartopy\n pass\n\n Back in an interactive IPython session:\n\n >>> ds = xarray.Dataset({'longitude': np.linspace(0, 10),\n ... 'latitude': np.linspace(0, 20)})\n >>> ds.geo.center\n (5.0, 10.0)\n >>> ds.geo.plot()\n # plots data on a map\n\n See also\n --------\n register_dataarray_accessor\n \"\"\"\n return _register_accessor(name, Dataset)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__String_formatting_rout_wrap_indent.return.start_indent_join_x_for": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__String_formatting_rout_wrap_indent.return.start_indent_join_x_for", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 37, "span_ids": ["docstring", "pretty_print", "imports", "maybe_truncate", "wrap_indent"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"String formatting routines for __repr__.\n\"\"\"\nimport contextlib\nimport functools\nfrom datetime import datetime, timedelta\nfrom itertools import zip_longest\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.errors import OutOfBoundsDatetime\n\nfrom .duck_array_ops import array_equiv\nfrom .options import OPTIONS\nfrom .pycompat import dask_array_type, sparse_array_type\n\n\ndef pretty_print(x, numchars):\n \"\"\"Given an object `x`, call `str(x)` and format the returned string so\n that it is numchars long, padding with trailing spaces or truncating with\n ellipses as necessary\n \"\"\"\n s = maybe_truncate(x, numchars)\n return s + \" \" * max(numchars - len(s), 0)\n\n\ndef maybe_truncate(obj, maxlen=500):\n s = str(obj)\n if len(s) > maxlen:\n s = s[: (maxlen - 3)] + \"...\"\n return s\n\n\ndef wrap_indent(text, start=\"\", length=None):\n if length is None:\n length = len(start)\n indent = \"\\n\" + \" \" * length\n return start + indent.join(x for x in text.splitlines())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__get_indexer_at_least_n_items__get_indexer_at_least_n_items.return.indexer": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__get_indexer_at_least_n_items__get_indexer_at_least_n_items.return.indexer", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 50, "span_ids": ["_get_indexer_at_least_n_items"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_indexer_at_least_n_items(shape, n_desired, from_end):\n assert 0 < n_desired <= np.prod(shape)\n cum_items = np.cumprod(shape[::-1])\n n_steps = np.argmax(cum_items >= n_desired)\n stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps]))\n indexer = (\n ((-1 if from_end else 0),) * (len(shape) - 1 - n_steps)\n + ((slice(-stop, None) if from_end else slice(stop)),)\n + (slice(None),) * n_steps\n )\n return indexer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_first_n_items_first_n_items.return.np_asarray_array_flat_n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_first_n_items_first_n_items.return.np_asarray_array_flat_n", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 69, "span_ids": ["first_n_items"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def first_n_items(array, n_desired):\n \"\"\"Returns the first n_desired items of an array\"\"\"\n # Unfortunately, we can't just do array.flat[:n_desired] here because it\n # might not be a numpy.ndarray. Moreover, access to elements of the array\n # could be very expensive (e.g. if it's only available over DAP), so go out\n # of our way to get them in a single call to __getitem__ using only slices.\n if n_desired < 1:\n raise ValueError(\"must request at least one item\")\n\n if array.size == 0:\n # work around for https://github.com/numpy/numpy/issues/5195\n return []\n\n if n_desired < array.size:\n indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=False)\n array = array[indexer]\n return np.asarray(array).flat[:n_desired]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_last_n_items_last_n_items.return.np_asarray_array_flat_n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_last_n_items_last_n_items.return.np_asarray_array_flat_n", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 72, "end_line": 84, "span_ids": ["last_n_items"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def last_n_items(array, n_desired):\n \"\"\"Returns the last n_desired items of an array\"\"\"\n # Unfortunately, we can't just do array.flat[-n_desired:] here because it\n # might not be a numpy.ndarray. Moreover, access to elements of the array\n # could be very expensive (e.g. if it's only available over DAP), so go out\n # of our way to get them in a single call to __getitem__ using only slices.\n if (n_desired == 0) or (array.size == 0):\n return []\n\n if n_desired < array.size:\n indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=True)\n array = array[indexer]\n return np.asarray(array).flat[-n_desired:]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_last_item_format_timestamp.None_1.else_.if_time_str_00_00_00_.else_.return._T_format_date_str_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_last_item_format_timestamp.None_1.else_.if_time_str_00_00_00_.else_.return._T_format_date_str_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 87, "end_line": 114, "span_ids": ["format_timestamp", "last_item"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def last_item(array):\n \"\"\"Returns the last item of an array in a list or an empty list.\"\"\"\n if array.size == 0:\n # work around for https://github.com/numpy/numpy/issues/5195\n return []\n\n indexer = (slice(-1, None),) * array.ndim\n return np.ravel(np.asarray(array[indexer])).tolist()\n\n\ndef format_timestamp(t):\n \"\"\"Cast given object to a Timestamp and return a nicely formatted string\"\"\"\n # Timestamp is only valid for 1678 to 2262\n try:\n datetime_str = str(pd.Timestamp(t))\n except OutOfBoundsDatetime:\n datetime_str = str(t)\n\n try:\n date_str, time_str = datetime_str.split()\n except ValueError:\n # catch NaT and others that don't split nicely\n return datetime_str\n else:\n if time_str == \"00:00:00\":\n return date_str\n else:\n return \"{}T{}\".format(date_str, time_str)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_timedelta_format_timedelta.try_.else_.if_timedelta_format_d.else_.return.timedelta_str": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_timedelta_format_timedelta.try_.else_.if_timedelta_format_d.else_.return.timedelta_str", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 117, "end_line": 131, "span_ids": ["format_timedelta"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def format_timedelta(t, timedelta_format=None):\n \"\"\"Cast given object to a Timestamp and return a nicely formatted string\"\"\"\n timedelta_str = str(pd.Timedelta(t))\n try:\n days_str, time_str = timedelta_str.split(\" days \")\n except ValueError:\n # catch NaT and others that don't split nicely\n return timedelta_str\n else:\n if timedelta_format == \"date\":\n return days_str + \" days\"\n elif timedelta_format == \"time\":\n return time_str\n else:\n return timedelta_str", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_item_format_item.if_isinstance_x_np_time.else_.return.str_x_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_item_format_item.if_isinstance_x_np_time.else_.return.str_x_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 145, "span_ids": ["format_item"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def format_item(x, timedelta_format=None, quote_strings=True):\n \"\"\"Returns a succinct summary of an object as a string\"\"\"\n if isinstance(x, (np.datetime64, datetime)):\n return format_timestamp(x)\n if isinstance(x, (np.timedelta64, timedelta)):\n return format_timedelta(x, timedelta_format=timedelta_format)\n elif isinstance(x, (str, bytes)):\n return repr(x) if quote_strings else x\n elif isinstance(x, (float, np.float)):\n return \"{:.4}\".format(x)\n else:\n return str(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_items_format_items.return.formatted": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_items_format_items.return.formatted", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 163, "span_ids": ["format_items"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def format_items(x):\n \"\"\"Returns a succinct summaries of all items in a sequence as strings\"\"\"\n x = np.asarray(x)\n timedelta_format = \"datetime\"\n if np.issubdtype(x.dtype, np.timedelta64):\n x = np.asarray(x, dtype=\"timedelta64[ns]\")\n day_part = x[~pd.isnull(x)].astype(\"timedelta64[D]\").astype(\"timedelta64[ns]\")\n time_needed = x[~pd.isnull(x)] != day_part\n day_needed = day_part != np.timedelta64(0, \"ns\")\n if np.logical_not(day_needed).all():\n timedelta_format = \"time\"\n elif np.logical_not(time_needed).all():\n timedelta_format = \"date\"\n\n formatted = [format_item(xi, timedelta_format) for xi in x]\n return formatted", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_array_flat_format_array_flat.return.pprint_str": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_format_array_flat_format_array_flat.return.pprint_str", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 206, "span_ids": ["format_array_flat"], "tokens": 435}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def format_array_flat(array, max_width):\n \"\"\"Return a formatted string for as many items in the flattened version of\n array that will fit within max_width characters.\n \"\"\"\n # every item will take up at least two characters, but we always want to\n # print at least first and last items\n max_possibly_relevant = min(\n max(array.size, 1), max(int(np.ceil(max_width / 2.0)), 2)\n )\n relevant_front_items = format_items(\n first_n_items(array, (max_possibly_relevant + 1) // 2)\n )\n relevant_back_items = format_items(last_n_items(array, max_possibly_relevant // 2))\n # interleave relevant front and back items:\n # [a, b, c] and [y, z] -> [a, z, b, y, c]\n relevant_items = sum(\n zip_longest(relevant_front_items, reversed(relevant_back_items)), ()\n )[:max_possibly_relevant]\n\n cum_len = np.cumsum([len(s) + 1 for s in relevant_items]) - 1\n if (array.size > 2) and (\n (max_possibly_relevant < array.size) or (cum_len > max_width).any()\n ):\n padding = \" ... \"\n count = min(\n array.size, max(np.argmax(cum_len + len(padding) - 1 > max_width), 2)\n )\n else:\n count = array.size\n padding = \"\" if (count <= 1) else \" \"\n\n num_front = (count + 1) // 2\n num_back = count - num_front\n # note that num_back is 0 <--> array.size is 0 or 1\n # <--> relevant_back_items is []\n pprint_str = (\n \" \".join(relevant_front_items[:num_front])\n + padding\n + \" \".join(relevant_back_items[-num_back:])\n )\n return pprint_str", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__KNOWN_TYPE_REPRS_inline_dask_repr.return._dask_array_chunksize_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__KNOWN_TYPE_REPRS_inline_dask_repr.return._dask_array_chunksize_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 209, "end_line": 235, "span_ids": ["impl", "inline_dask_repr"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_KNOWN_TYPE_REPRS = {np.ndarray: \"np.ndarray\"}\nwith contextlib.suppress(ImportError):\n import sparse\n\n _KNOWN_TYPE_REPRS[sparse.COO] = \"sparse.COO\"\n\n\ndef inline_dask_repr(array):\n \"\"\"Similar to dask.array.DataArray.__repr__, but without\n redundant information that's already printed by the repr\n function of the xarray wrapper.\n \"\"\"\n assert isinstance(array, dask_array_type), array\n\n chunksize = tuple(c[0] for c in array.chunks)\n\n if hasattr(array, \"_meta\"):\n meta = array._meta\n if type(meta) in _KNOWN_TYPE_REPRS:\n meta_repr = _KNOWN_TYPE_REPRS[type(meta)]\n else:\n meta_repr = type(meta).__name__\n meta_string = \", meta={}\".format(meta_repr)\n else:\n meta_string = \"\"\n\n return \"dask.array\".format(chunksize, meta_string)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_inline_sparse_repr_inline_variable_array_repr.if_var__in_memory_.else_.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_inline_sparse_repr_inline_variable_array_repr.if_var__in_memory_.else_.return._", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 258, "span_ids": ["inline_sparse_repr", "inline_variable_array_repr"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inline_sparse_repr(array):\n \"\"\"Similar to sparse.COO.__repr__, but without the redundant shape/dtype.\"\"\"\n assert isinstance(array, sparse_array_type), array\n return \"<{}: nnz={:d}, fill_value={!s}>\".format(\n type(array).__name__, array.nnz, array.fill_value\n )\n\n\ndef inline_variable_array_repr(var, max_width):\n \"\"\"Build a one-line summary of a variable's data.\"\"\"\n if var._in_memory:\n return format_array_flat(var, max_width)\n elif isinstance(var._data, dask_array_type):\n return inline_dask_repr(var.data)\n elif isinstance(var._data, sparse_array_type):\n return inline_sparse_repr(var.data)\n elif hasattr(var._data, \"__array_function__\"):\n return maybe_truncate(repr(var._data).replace(\"\\n\", \" \"), max_width)\n else:\n # internal xarray array type\n return \"...\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_variable_summarize_variable.return.front_str_values_str": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_variable_summarize_variable.return.front_str_values_str", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 261, "end_line": 275, "span_ids": ["summarize_variable"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def summarize_variable(name, var, col_width, marker=\" \", max_width=None):\n \"\"\"Summarize a variable in one line, e.g., for the Dataset.__repr__.\"\"\"\n if max_width is None:\n max_width = OPTIONS[\"display_width\"]\n first_col = pretty_print(\" {} {} \".format(marker, name), col_width)\n if var.dims:\n dims_str = \"({}) \".format(\", \".join(map(str, var.dims)))\n else:\n dims_str = \"\"\n front_str = \"{}{}{} \".format(first_col, dims_str, var.dtype)\n\n values_width = max_width - len(front_str)\n values_str = inline_variable_array_repr(var, values_width)\n\n return front_str + values_str", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__summarize_coord_multiindex_summarize_datavar.return.summarize_variable_name_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__summarize_coord_multiindex_summarize_datavar.return.summarize_variable_name_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 295, "span_ids": ["_summarize_coord_multiindex", "_summarize_coord_levels", "summarize_datavar"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _summarize_coord_multiindex(coord, col_width, marker):\n first_col = pretty_print(\" {} {} \".format(marker, coord.name), col_width)\n return \"{}({}) MultiIndex\".format(first_col, str(coord.dims[0]))\n\n\ndef _summarize_coord_levels(coord, col_width, marker=\"-\"):\n return \"\\n\".join(\n [\n summarize_variable(\n lname, coord.get_level_variable(lname), col_width, marker=marker\n )\n for lname in coord.level_names\n ]\n )\n\n\ndef summarize_datavar(name, var, col_width):\n return summarize_variable(name, var.variable, col_width)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_coord_summarize_coord.return.summarize_variable_name_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_coord_summarize_coord.return.summarize_variable_name_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 298, "end_line": 310, "span_ids": ["summarize_coord"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def summarize_coord(name, var, col_width):\n is_index = name in var.dims\n marker = \"*\" if is_index else \" \"\n if is_index:\n coord = var.variable.to_index_variable()\n if coord.level_names is not None:\n return \"\\n\".join(\n [\n _summarize_coord_multiindex(coord, col_width, marker),\n _summarize_coord_levels(coord, col_width),\n ]\n )\n return summarize_variable(name, var.variable, col_width, marker)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_attr_summarize_attr.return.maybe_truncate_fo": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_summarize_attr_summarize_attr.return.maybe_truncate_fo", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 313, "end_line": 322, "span_ids": ["summarize_attr"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def summarize_attr(key, value, col_width=None):\n \"\"\"Summary for __repr__ - use ``X.attrs[key]`` for full value.\"\"\"\n # Indent key and add ':', then right-pad if col_width is not None\n k_str = \" {}:\".format(key)\n if col_width is not None:\n k_str = pretty_print(k_str, col_width)\n # Replace tabs and newlines, so we print on one line in known width\n v_str = str(value).replace(\"\\t\", \"\\\\t\").replace(\"\\n\", \"\\\\n\")\n # Finally, truncate to the desired display width\n return maybe_truncate(\"{} {}\".format(k_str, v_str), OPTIONS[\"display_width\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_EMPTY_REPR_set_numpy_options.try_.finally_.np_set_printoptions_ori": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_EMPTY_REPR_set_numpy_options.try_.finally_.np_set_printoptions_ori", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 325, "end_line": 408, "span_ids": ["impl:8", "_get_col_items", "unindexed_dims_repr", "coords_repr", "set_numpy_options", "impl:6", "_mapping_repr", "indexes_repr", "dim_summary", "_calculate_col_width"], "tokens": 568}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "EMPTY_REPR = \" *empty*\"\n\n\ndef _get_col_items(mapping):\n \"\"\"Get all column items to format, including both keys of `mapping`\n and MultiIndex levels if any.\n \"\"\"\n from .variable import IndexVariable\n\n col_items = []\n for k, v in mapping.items():\n col_items.append(k)\n var = getattr(v, \"variable\", v)\n if isinstance(var, IndexVariable):\n level_names = var.to_index_variable().level_names\n if level_names is not None:\n col_items += list(level_names)\n return col_items\n\n\ndef _calculate_col_width(col_items):\n max_name_length = max(len(str(s)) for s in col_items) if col_items else 0\n col_width = max(max_name_length, 7) + 6\n return col_width\n\n\ndef _mapping_repr(mapping, title, summarizer, col_width=None):\n if col_width is None:\n col_width = _calculate_col_width(mapping)\n summary = [\"{}:\".format(title)]\n if mapping:\n summary += [summarizer(k, v, col_width) for k, v in mapping.items()]\n else:\n summary += [EMPTY_REPR]\n return \"\\n\".join(summary)\n\n\ndata_vars_repr = functools.partial(\n _mapping_repr, title=\"Data variables\", summarizer=summarize_datavar\n)\n\n\nattrs_repr = functools.partial(\n _mapping_repr, title=\"Attributes\", summarizer=summarize_attr\n)\n\n\ndef coords_repr(coords, col_width=None):\n if col_width is None:\n col_width = _calculate_col_width(_get_col_items(coords))\n return _mapping_repr(\n coords, title=\"Coordinates\", summarizer=summarize_coord, col_width=col_width\n )\n\n\ndef indexes_repr(indexes):\n summary = []\n for k, v in indexes.items():\n summary.append(wrap_indent(repr(v), \"{}: \".format(k)))\n return \"\\n\".join(summary)\n\n\ndef dim_summary(obj):\n elements = [\"{}: {}\".format(k, v) for k, v in obj.sizes.items()]\n return \", \".join(elements)\n\n\ndef unindexed_dims_repr(dims, coords):\n unindexed_dims = [d for d in dims if d not in coords]\n if unindexed_dims:\n dims_str = \", \".join(\"{}\".format(d) for d in unindexed_dims)\n return \"Dimensions without coordinates: \" + dims_str\n else:\n return None\n\n\n@contextlib.contextmanager\ndef set_numpy_options(*args, **kwargs):\n original = np.get_printoptions()\n np.set_printoptions(*args, **kwargs)\n try:\n yield\n finally:\n np.set_printoptions(**original)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_short_numpy_repr_short_numpy_repr.with_set_numpy_options_.return.repr_array_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_short_numpy_repr_short_numpy_repr.with_set_numpy_options_.return.repr_array_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 411, "end_line": 425, "span_ids": ["short_numpy_repr"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def short_numpy_repr(array):\n array = np.asarray(array)\n\n # default to lower precision so a full (abbreviated) line can fit on\n # one line with the default display_width\n options = {\"precision\": 6, \"linewidth\": OPTIONS[\"display_width\"], \"threshold\": 200}\n if array.ndim < 3:\n edgeitems = 3\n elif array.ndim == 3:\n edgeitems = 2\n else:\n edgeitems = 1\n options[\"edgeitems\"] = edgeitems\n with set_numpy_options(**options):\n return repr(array)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_short_data_repr_short_data_repr.if_isinstance_array_np_n.else_.return._values_with_dtype_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_short_data_repr_short_data_repr.if_isinstance_array_np_n.else_.return._values_with_dtype_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 428, "end_line": 441, "span_ids": ["short_data_repr"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def short_data_repr(array):\n \"\"\"Format \"data\" for DataArray and Variable.\"\"\"\n internal_data = getattr(array, \"variable\", array)._data\n if isinstance(array, np.ndarray):\n return short_numpy_repr(array)\n elif hasattr(internal_data, \"__array_function__\") or isinstance(\n internal_data, dask_array_type\n ):\n return repr(array.data)\n elif array._in_memory or array.size < 1e5:\n return short_numpy_repr(array)\n else:\n # internal xarray array type\n return \"[{} values with dtype={}]\".format(array.size, array.dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_array_repr_array_repr.return._n_join_summary_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_array_repr_array_repr.return._n_join_summary_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 444, "end_line": 467, "span_ids": ["array_repr"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def array_repr(arr):\n # used for DataArray, Variable and IndexVariable\n if hasattr(arr, \"name\") and arr.name is not None:\n name_str = \"{!r} \".format(arr.name)\n else:\n name_str = \"\"\n\n summary = [\n \"\".format(type(arr).__name__, name_str, dim_summary(arr)),\n short_data_repr(arr),\n ]\n\n if hasattr(arr, \"coords\"):\n if arr.coords:\n summary.append(repr(arr.coords))\n\n unindexed_dims_str = unindexed_dims_repr(arr.dims, arr.coords)\n if unindexed_dims_str:\n summary.append(unindexed_dims_str)\n\n if arr.attrs:\n summary.append(attrs_repr(arr.attrs))\n\n return \"\\n\".join(summary)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_dataset_repr_diff_dim_summary.if_a_dims_b_dims_.else_.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_dataset_repr_diff_dim_summary.if_a_dims_b_dims_.else_.return._", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 470, "end_line": 499, "span_ids": ["dataset_repr", "diff_dim_summary"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dataset_repr(ds):\n summary = [\"\".format(type(ds).__name__)]\n\n col_width = _calculate_col_width(_get_col_items(ds.variables))\n\n dims_start = pretty_print(\"Dimensions:\", col_width)\n summary.append(\"{}({})\".format(dims_start, dim_summary(ds)))\n\n if ds.coords:\n summary.append(coords_repr(ds.coords, col_width=col_width))\n\n unindexed_dims_str = unindexed_dims_repr(ds.dims, ds.coords)\n if unindexed_dims_str:\n summary.append(unindexed_dims_str)\n\n summary.append(data_vars_repr(ds.data_vars, col_width=col_width))\n\n if ds.attrs:\n summary.append(attrs_repr(ds.attrs))\n\n return \"\\n\".join(summary)\n\n\ndef diff_dim_summary(a, b):\n if a.dims != b.dims:\n return \"Differing dimensions:\\n ({}) != ({})\".format(\n dim_summary(a), dim_summary(b)\n )\n else:\n return \"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__diff_mapping_repr__diff_mapping_repr.return._n_join_summary_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py__diff_mapping_repr__diff_mapping_repr.return._n_join_summary_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 502, "end_line": 555, "span_ids": ["_diff_mapping_repr"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _diff_mapping_repr(a_mapping, b_mapping, compat, title, summarizer, col_width=None):\n def extra_items_repr(extra_keys, mapping, ab_side):\n extra_repr = [summarizer(k, mapping[k], col_width) for k in extra_keys]\n if extra_repr:\n header = \"{} only on the {} object:\".format(title, ab_side)\n return [header] + extra_repr\n else:\n return []\n\n a_keys = set(a_mapping)\n b_keys = set(b_mapping)\n\n summary = []\n\n diff_items = []\n\n for k in a_keys & b_keys:\n try:\n # compare xarray variable\n compatible = getattr(a_mapping[k], compat)(b_mapping[k])\n is_variable = True\n except AttributeError:\n # compare attribute value\n compatible = a_mapping[k] == b_mapping[k]\n is_variable = False\n\n if not compatible:\n temp = [\n summarizer(k, vars[k], col_width) for vars in (a_mapping, b_mapping)\n ]\n\n if compat == \"identical\" and is_variable:\n attrs_summary = []\n\n for m in (a_mapping, b_mapping):\n attr_s = \"\\n\".join(\n [summarize_attr(ak, av) for ak, av in m[k].attrs.items()]\n )\n attrs_summary.append(attr_s)\n\n temp = [\n \"\\n\".join([var_s, attr_s]) if attr_s else var_s\n for var_s, attr_s in zip(temp, attrs_summary)\n ]\n\n diff_items += [ab_side + s[1:] for ab_side, s in zip((\"L\", \"R\"), temp)]\n\n if diff_items:\n summary += [\"Differing {}:\".format(title.lower())] + diff_items\n\n summary += extra_items_repr(a_keys - b_keys, a_mapping, \"left\")\n summary += extra_items_repr(b_keys - a_keys, b_mapping, \"right\")\n\n return \"\\n\".join(summary)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_coords_repr__compat_to_str.if_compat_equals_.else_.return.compat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_coords_repr__compat_to_str.if_compat_equals_.else_.return.compat", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 558, "end_line": 577, "span_ids": ["_compat_to_str", "impl:12"], "tokens": 104}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "diff_coords_repr = functools.partial(\n _diff_mapping_repr, title=\"Coordinates\", summarizer=summarize_coord\n)\n\n\ndiff_data_vars_repr = functools.partial(\n _diff_mapping_repr, title=\"Data variables\", summarizer=summarize_datavar\n)\n\n\ndiff_attrs_repr = functools.partial(\n _diff_mapping_repr, title=\"Attributes\", summarizer=summarize_attr\n)\n\n\ndef _compat_to_str(compat):\n if compat == \"equals\":\n return \"equal\"\n else:\n return compat", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_array_repr_diff_array_repr.return._n_join_summary_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_array_repr_diff_array_repr.return._n_join_summary_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 607, "span_ids": ["diff_array_repr"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def diff_array_repr(a, b, compat):\n # used for DataArray, Variable and IndexVariable\n summary = [\n \"Left and right {} objects are not {}\".format(\n type(a).__name__, _compat_to_str(compat)\n )\n ]\n\n summary.append(diff_dim_summary(a, b))\n\n if not array_equiv(a.data, b.data):\n temp = [wrap_indent(short_numpy_repr(obj), start=\" \") for obj in (a, b)]\n diff_data_repr = [\n ab_side + \"\\n\" + ab_data_repr\n for ab_side, ab_data_repr in zip((\"L\", \"R\"), temp)\n ]\n summary += [\"Differing values:\"] + diff_data_repr\n\n if hasattr(a, \"coords\"):\n col_width = _calculate_col_width(set(a.coords) | set(b.coords))\n summary.append(\n diff_coords_repr(a.coords, b.coords, compat, col_width=col_width)\n )\n\n if compat == \"identical\":\n summary.append(diff_attrs_repr(a.attrs, b.attrs, compat))\n\n return \"\\n\".join(summary)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_dataset_repr_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/formatting.py_diff_dataset_repr_", "embedding": null, "metadata": {"file_path": "xarray/core/formatting.py", "file_name": "formatting.py", "file_type": "text/x-python", "category": "implementation", "start_line": 610, "end_line": 631, "span_ids": ["diff_dataset_repr"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def diff_dataset_repr(a, b, compat):\n summary = [\n \"Left and right {} objects are not {}\".format(\n type(a).__name__, _compat_to_str(compat)\n )\n ]\n\n col_width = _calculate_col_width(\n set(_get_col_items(a.variables) + _get_col_items(b.variables))\n )\n\n summary.append(diff_dim_summary(a, b))\n summary.append(diff_coords_repr(a.coords, b.coords, compat, col_width=col_width))\n summary.append(\n diff_data_vars_repr(a.data_vars, b.data_vars, compat, col_width=col_width)\n )\n\n if compat == \"identical\":\n summary.append(diff_attrs_repr(a.attrs, b.attrs, compat))\n\n return \"\\n\".join(summary)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_datetime_IndexVariable": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_datetime_IndexVariable", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 22, "span_ids": ["imports"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import datetime\nimport functools\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import dtypes, duck_array_ops, nputils, ops\nfrom .arithmetic import SupportsArithmetic\nfrom .common import ALL_DIMS, ImplementsArrayReduce, ImplementsDatasetReduce\nfrom .concat import concat\nfrom .formatting import format_array_flat\nfrom .options import _get_keep_attrs\nfrom .pycompat import integer_types\nfrom .utils import (\n either_dict_or_kwargs,\n hashable,\n maybe_wrap_array,\n peek_at,\n safe_cast_to_index,\n)\nfrom .variable import IndexVariable, Variable, as_variable", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_unique_value_groups_unique_value_groups.return.values_groups": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_unique_value_groups_unique_value_groups.return.values_groups", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 25, "end_line": 49, "span_ids": ["unique_value_groups"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unique_value_groups(ar, sort=True):\n \"\"\"Group an array by its unique values.\n\n Parameters\n ----------\n ar : array-like\n Input array. This will be flattened if it is not already 1-D.\n sort : boolean, optional\n Whether or not to sort unique values.\n\n Returns\n -------\n values : np.ndarray\n Sorted, unique values as returned by `np.unique`.\n indices : list of lists of int\n Each element provides the integer indices in `ar` with values given by\n the corresponding value in `unique_values`.\n \"\"\"\n inverse, values = pd.factorize(ar, sort=sort)\n groups = [[] for _ in range(len(values))]\n for n, g in enumerate(inverse):\n if g >= 0:\n # pandas uses -1 to mark NaN, but doesn't include them in values\n groups[g].append(n)\n return values, groups", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__dummy_copy__dummy_copy.return.res": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__dummy_copy__dummy_copy.return.res", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 52, "end_line": 83, "span_ids": ["_dummy_copy"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dummy_copy(xarray_obj):\n from .dataset import Dataset\n from .dataarray import DataArray\n\n if isinstance(xarray_obj, Dataset):\n res = Dataset(\n {\n k: dtypes.get_fill_value(v.dtype)\n for k, v in xarray_obj.data_vars.items()\n },\n {\n k: dtypes.get_fill_value(v.dtype)\n for k, v in xarray_obj.coords.items()\n if k not in xarray_obj.dims\n },\n xarray_obj.attrs,\n )\n elif isinstance(xarray_obj, DataArray):\n res = DataArray(\n dtypes.get_fill_value(xarray_obj.dtype),\n {\n k: dtypes.get_fill_value(v.dtype)\n for k, v in xarray_obj.coords.items()\n if k not in xarray_obj.dims\n },\n dims=[],\n name=xarray_obj.name,\n attrs=xarray_obj.attrs,\n )\n else: # pragma: no cover\n raise AssertionError\n return res", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__is_one_or_none__consolidate_slices.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__is_one_or_none__consolidate_slices.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 86, "end_line": 109, "span_ids": ["_consolidate_slices", "_is_one_or_none"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _is_one_or_none(obj):\n return obj == 1 or obj is None\n\n\ndef _consolidate_slices(slices):\n \"\"\"Consolidate adjacent slices in a list of slices.\n \"\"\"\n result = []\n last_slice = slice(None)\n for slice_ in slices:\n if not isinstance(slice_, slice):\n raise ValueError(\"list element is not a slice: %r\" % slice_)\n if (\n result\n and last_slice.stop == slice_.start\n and _is_one_or_none(last_slice.step)\n and _is_one_or_none(slice_.step)\n ):\n last_slice = slice(last_slice.start, slice_.stop, slice_.step)\n result[-1] = last_slice\n else:\n result.append(slice_)\n last_slice = slice_\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__inverse_permutation_indices__inverse_permutation_indices.return.indices": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__inverse_permutation_indices__inverse_permutation_indices.return.indices", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 134, "span_ids": ["_inverse_permutation_indices"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _inverse_permutation_indices(positions):\n \"\"\"Like inverse_permutation, but also handles slices.\n\n Parameters\n ----------\n positions : list of np.ndarray or slice objects.\n If slice objects, all are assumed to be slices.\n\n Returns\n -------\n np.ndarray of indices or None, if no permutation is necessary.\n \"\"\"\n if not positions:\n return None\n\n if isinstance(positions[0], slice):\n positions = _consolidate_slices(positions)\n if positions == slice(None):\n return None\n positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions]\n\n indices = nputils.inverse_permutation(np.concatenate(positions))\n return indices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__DummyGroup__DummyGroup.__getitem__.return.self_values_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__DummyGroup__DummyGroup.__getitem__.return.self_values_key_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 137, "end_line": 169, "span_ids": ["_DummyGroup", "_DummyGroup.values", "_DummyGroup.shape", "_DummyGroup.__getitem__", "_DummyGroup.dims", "_DummyGroup.ndim"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _DummyGroup:\n \"\"\"Class for keeping track of grouped dimensions without coordinates.\n\n Should not be user visible.\n \"\"\"\n\n __slots__ = (\"name\", \"coords\", \"size\")\n\n def __init__(self, obj, name, coords):\n self.name = name\n self.coords = coords\n self.size = obj.sizes[name]\n\n @property\n def dims(self):\n return (self.name,)\n\n @property\n def ndim(self):\n return 1\n\n @property\n def values(self):\n return range(self.size)\n\n @property\n def shape(self):\n return (self.size,)\n\n def __getitem__(self, key):\n if isinstance(key, tuple):\n key = key[0]\n return self.values[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__ensure_1d__ensure_1d.return.group_obj_stacked_dim_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__ensure_1d__ensure_1d.return.group_obj_stacked_dim_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 172, "end_line": 186, "span_ids": ["_ensure_1d"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _ensure_1d(group, obj):\n if group.ndim != 1:\n # try to stack the dims of the group into a single dim\n orig_dims = group.dims\n stacked_dim = \"stacked_\" + \"_\".join(orig_dims)\n # these dimensions get created by the stack operation\n inserted_dims = [dim for dim in group.dims if dim not in group.coords]\n # the copy is necessary here, otherwise read only array raises error\n # in pandas: https://github.com/pydata/pandas/issues/12813\n group = group.stack(**{stacked_dim: orig_dims}).copy()\n obj = obj.stack(**{stacked_dim: orig_dims})\n else:\n stacked_dim = None\n inserted_dims = []\n return group, obj, stacked_dim, inserted_dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__unique_and_monotonic__apply_loffset.grouper.loffset.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py__unique_and_monotonic__apply_loffset.grouper.loffset.None", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 220, "span_ids": ["_unique_and_monotonic", "_apply_loffset"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _unique_and_monotonic(group):\n if isinstance(group, _DummyGroup):\n return True\n else:\n index = safe_cast_to_index(group)\n return index.is_unique and index.is_monotonic\n\n\ndef _apply_loffset(grouper, result):\n \"\"\"\n (copied from pandas)\n if loffset is set, offset the result index\n\n This is NOT an idempotent routine, it will be applied\n exactly once to the result.\n\n Parameters\n ----------\n result : Series or DataFrame\n the result of resample\n \"\"\"\n\n needs_offset = (\n isinstance(grouper.loffset, (pd.DateOffset, datetime.timedelta))\n and isinstance(result.index, pd.DatetimeIndex)\n and len(result.index) > 0\n )\n\n if needs_offset:\n result.index = result.index + grouper.loffset\n\n grouper.loffset = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy_GroupBy.__slots__._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy_GroupBy.__slots__._", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 223, "end_line": 252, "span_ids": ["GroupBy"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n \"\"\"A object that implements the split-apply-combine pattern.\n\n Modeled after `pandas.GroupBy`. The `GroupBy` object can be iterated over\n (unique_value, grouped_array) pairs, but the main way to interact with a\n groupby object are with the `apply` or `reduce` methods. You can also\n directly call numpy methods like `mean` or `std`.\n\n You should create a GroupBy object by using the `DataArray.groupby` or\n `Dataset.groupby` methods.\n\n See Also\n --------\n Dataset.groupby\n DataArray.groupby\n \"\"\"\n\n __slots__ = (\n \"_full_index\",\n \"_inserted_dims\",\n \"_group\",\n \"_group_dim\",\n \"_group_indices\",\n \"_groups\",\n \"_obj\",\n \"_restore_coord_dims\",\n \"_stacked_dim\",\n \"_unique_coord\",\n \"_dims\",\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.__init___GroupBy.__init__.self._dims.None": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.__init___GroupBy.__init__.self._dims.None", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 254, "end_line": 395, "span_ids": ["GroupBy"], "tokens": 1142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def __init__(\n self,\n obj,\n group,\n squeeze=False,\n grouper=None,\n bins=None,\n restore_coord_dims=None,\n cut_kwargs={},\n ):\n \"\"\"Create a GroupBy object\n\n Parameters\n ----------\n obj : Dataset or DataArray\n Object to group.\n group : DataArray\n Array with the group values.\n squeeze : boolean, optional\n If \"group\" is a coordinate of object, `squeeze` controls whether\n the subarrays have a dimension of length 1 along that coordinate or\n if the dimension is squeezed out.\n grouper : pd.Grouper, optional\n Used for grouping values along the `group` array.\n bins : array-like, optional\n If `bins` is specified, the groups will be discretized into the\n specified bins by `pandas.cut`.\n restore_coord_dims : bool, optional\n If True, also restore the dimension order of multi-dimensional\n coordinates.\n cut_kwargs : dict, optional\n Extra keyword arguments to pass to `pandas.cut`\n\n \"\"\"\n from .dataarray import DataArray\n\n if grouper is not None and bins is not None:\n raise TypeError(\"can't specify both `grouper` and `bins`\")\n\n if not isinstance(group, (DataArray, IndexVariable)):\n if not hashable(group):\n raise TypeError(\n \"`group` must be an xarray.DataArray or the \"\n \"name of an xarray variable or dimension\"\n )\n group = obj[group]\n if len(group) == 0:\n raise ValueError(\"{} must not be empty\".format(group.name))\n\n if group.name not in obj.coords and group.name in obj.dims:\n # DummyGroups should not appear on groupby results\n group = _DummyGroup(obj, group.name, group.coords)\n\n if getattr(group, \"name\", None) is None:\n raise ValueError(\"`group` must have a name\")\n\n group, obj, stacked_dim, inserted_dims = _ensure_1d(group, obj)\n group_dim, = group.dims\n\n expected_size = obj.sizes[group_dim]\n if group.size != expected_size:\n raise ValueError(\n \"the group variable's length does not \"\n \"match the length of this variable along its \"\n \"dimension\"\n )\n\n full_index = None\n\n if bins is not None:\n if np.isnan(bins).all():\n raise ValueError(\"All bin edges are NaN.\")\n binned = pd.cut(group.values, bins, **cut_kwargs)\n new_dim_name = group.name + \"_bins\"\n group = DataArray(binned, group.coords, name=new_dim_name)\n full_index = binned.categories\n\n if grouper is not None:\n index = safe_cast_to_index(group)\n if not index.is_monotonic:\n # TODO: sort instead of raising an error\n raise ValueError(\"index must be monotonic for resampling\")\n full_index, first_items = self._get_index_and_items(index, grouper)\n sbins = first_items.values.astype(np.int64)\n group_indices = [slice(i, j) for i, j in zip(sbins[:-1], sbins[1:])] + [\n slice(sbins[-1], None)\n ]\n unique_coord = IndexVariable(group.name, first_items.index)\n elif group.dims == (group.name,) and _unique_and_monotonic(group):\n # no need to factorize\n group_indices = np.arange(group.size)\n if not squeeze:\n # use slices to do views instead of fancy indexing\n # equivalent to: group_indices = group_indices.reshape(-1, 1)\n group_indices = [slice(i, i + 1) for i in group_indices]\n unique_coord = group\n else:\n # look through group to find the unique values\n unique_values, group_indices = unique_value_groups(\n safe_cast_to_index(group), sort=(bins is None)\n )\n unique_coord = IndexVariable(group.name, unique_values)\n\n if len(group_indices) == 0:\n if bins is not None:\n raise ValueError(\n \"None of the data falls within bins with edges %r\" % bins\n )\n else:\n raise ValueError(\n \"Failed to group data. Are you grouping by a variable that is all NaN?\"\n )\n\n if (\n isinstance(obj, DataArray)\n and restore_coord_dims is None\n and any(obj[c].ndim > 1 for c in obj.coords)\n ):\n warnings.warn(\n \"This DataArray contains multi-dimensional \"\n \"coordinates. In the future, the dimension order \"\n \"of these coordinates will be restored as well \"\n \"unless you specify restore_coord_dims=False.\",\n FutureWarning,\n stacklevel=2,\n )\n restore_coord_dims = False\n\n # specification for the groupby operation\n self._obj = obj\n self._group = group\n self._group_dim = group_dim\n self._group_indices = group_indices\n self._unique_coord = unique_coord\n self._stacked_dim = stacked_dim\n self._inserted_dims = inserted_dims\n self._full_index = full_index\n self._restore_coord_dims = restore_coord_dims\n\n # cached attributes\n self._groups = None\n self._dims = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.dims_GroupBy.__repr__.return._s_grouped_over_r_n_r": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.dims_GroupBy.__repr__.return._s_grouped_over_r_n_r", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 425, "span_ids": ["GroupBy.dims", "GroupBy.__len__", "GroupBy.groups", "GroupBy.__repr__", "GroupBy.__iter__"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n @property\n def dims(self):\n if self._dims is None:\n self._dims = self._obj.isel(\n **{self._group_dim: self._group_indices[0]}\n ).dims\n\n return self._dims\n\n @property\n def groups(self):\n # provided to mimic pandas.groupby\n if self._groups is None:\n self._groups = dict(zip(self._unique_coord.values, self._group_indices))\n return self._groups\n\n def __len__(self):\n return self._unique_coord.size\n\n def __iter__(self):\n return zip(self._unique_coord.values, self._iter_grouped())\n\n def __repr__(self):\n return \"%s, grouped over %r \\n%r groups with labels %s.\" % (\n self.__class__.__name__,\n self._unique_coord.name,\n self._unique_coord.size,\n \", \".join(format_array_flat(self._unique_coord, 30).split()),\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._get_index_and_items_GroupBy._get_index_and_items.return.full_index_first_items": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._get_index_and_items_GroupBy._get_index_and_items.return.full_index_first_items", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 427, "end_line": 439, "span_ids": ["GroupBy._get_index_and_items"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def _get_index_and_items(self, index, grouper):\n from .resample_cftime import CFTimeGrouper\n\n s = pd.Series(np.arange(index.size), index)\n if isinstance(grouper, CFTimeGrouper):\n first_items = grouper.first_items(index)\n else:\n first_items = s.groupby(grouper).first()\n _apply_loffset(grouper, first_items)\n full_index = first_items.index\n if first_items.isnull().any():\n first_items = first_items.dropna()\n return full_index, first_items", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._iter_grouped_GroupBy._binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._iter_grouped_GroupBy._binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 441, "end_line": 467, "span_ids": ["GroupBy._infer_concat_args", "GroupBy._binary_op", "GroupBy._iter_grouped"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def _iter_grouped(self):\n \"\"\"Iterate over each element in this group\"\"\"\n for indices in self._group_indices:\n yield self._obj.isel(**{self._group_dim: indices})\n\n def _infer_concat_args(self, applied_example):\n if self._group_dim in applied_example.dims:\n coord = self._group\n positions = self._group_indices\n else:\n coord = self._unique_coord\n positions = None\n dim, = coord.dims\n if isinstance(coord, _DummyGroup):\n coord = None\n return coord, dim, positions\n\n @staticmethod\n def _binary_op(f, reflexive=False, **ignored_kwargs):\n @functools.wraps(f)\n def func(self, other):\n g = f if not reflexive else lambda x, y: f(y, x)\n applied = self._yield_binary_applied(g, other)\n combined = self._combine(applied)\n return combined\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._yield_binary_applied_GroupBy._yield_binary_applied.for_group_value_obj_in_s.yield_result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._yield_binary_applied_GroupBy._yield_binary_applied.for_group_value_obj_in_s.yield_result", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 469, "end_line": 493, "span_ids": ["GroupBy._yield_binary_applied"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def _yield_binary_applied(self, func, other):\n dummy = None\n\n for group_value, obj in self:\n try:\n other_sel = other.sel(**{self._group.name: group_value})\n except AttributeError:\n raise TypeError(\n \"GroupBy objects only support binary ops \"\n \"when the other argument is a Dataset or \"\n \"DataArray\"\n )\n except (KeyError, ValueError):\n if self._group.name not in other.dims:\n raise ValueError(\n \"incompatible dimensions for a grouped \"\n \"binary operation: the group variable %r \"\n \"is not a dimension on the other argument\" % self._group.name\n )\n if dummy is None:\n dummy = _dummy_copy(other)\n other_sel = dummy\n\n result = func(obj, other_sel)\n yield result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._maybe_restore_empty_groups_GroupBy._maybe_unstack.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._maybe_restore_empty_groups_GroupBy._maybe_unstack.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 495, "end_line": 512, "span_ids": ["GroupBy._maybe_restore_empty_groups", "GroupBy._maybe_unstack"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def _maybe_restore_empty_groups(self, combined):\n \"\"\"Our index contained empty groups (e.g., from a resampling). If we\n reduced on that dimension, we want to restore the full index.\n \"\"\"\n if self._full_index is not None and self._group.name in combined.dims:\n indexers = {self._group.name: self._full_index}\n combined = combined.reindex(**indexers)\n return combined\n\n def _maybe_unstack(self, obj):\n \"\"\"This gets called if we are applying on an array with a\n multidimensional group.\"\"\"\n if self._stacked_dim is not None and self._stacked_dim in obj.dims:\n obj = obj.unstack(self._stacked_dim)\n for dim in self._inserted_dims:\n if dim in obj.coords:\n del obj.coords[dim]\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.fillna_GroupBy.fillna.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.fillna_GroupBy.fillna.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 514, "end_line": 537, "span_ids": ["GroupBy.fillna"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def fillna(self, value):\n \"\"\"Fill missing values in this object by group.\n\n This operation follows the normal broadcasting and alignment rules that\n xarray uses for binary arithmetic, except the result is aligned to this\n object (``join='left'``) instead of aligned to the intersection of\n index coordinates (``join='inner'``).\n\n Parameters\n ----------\n value : valid type for the grouped object's fillna method\n Used to fill all matching missing values by group.\n\n Returns\n -------\n same type as the grouped object\n\n See also\n --------\n Dataset.fillna\n DataArray.fillna\n \"\"\"\n out = ops.fillna(self, value)\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.where_GroupBy.where.return.ops_where_method_self_co": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy.where_GroupBy.where.return.ops_where_method_self_co", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 539, "end_line": 558, "span_ids": ["GroupBy.where"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def where(self, cond, other=dtypes.NA):\n \"\"\"Return elements from `self` or `other` depending on `cond`.\n\n Parameters\n ----------\n cond : DataArray or Dataset with boolean dtype\n Locations at which to preserve this objects values.\n other : scalar, DataArray or Dataset, optional\n Value to use for locations in this object where ``cond`` is False.\n By default, inserts missing values.\n\n Returns\n -------\n same type as the grouped object\n\n See also\n --------\n Dataset.where\n \"\"\"\n return ops.where_method(self, cond, other)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._first_or_last__maybe_reorder.if_order_is_None_.else_.return.xarray_obj_dim_order_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_GroupBy._first_or_last__maybe_reorder.if_order_is_None_.else_.return.xarray_obj_dim_order_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 560, "end_line": 599, "span_ids": ["GroupBy.last", "GroupBy.assign_coords", "GroupBy.first", "_maybe_reorder", "GroupBy._first_or_last"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GroupBy(SupportsArithmetic):\n\n def _first_or_last(self, op, skipna, keep_attrs):\n if isinstance(self._group_indices[0], integer_types):\n # NB. this is currently only used for reductions along an existing\n # dimension\n return self._obj\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=True)\n return self.reduce(\n op, self._group_dim, skipna=skipna, keep_attrs=keep_attrs, allow_lazy=True\n )\n\n def first(self, skipna=None, keep_attrs=None):\n \"\"\"Return the first element of each group along the group dimension\n \"\"\"\n return self._first_or_last(duck_array_ops.first, skipna, keep_attrs)\n\n def last(self, skipna=None, keep_attrs=None):\n \"\"\"Return the last element of each group along the group dimension\n \"\"\"\n return self._first_or_last(duck_array_ops.last, skipna, keep_attrs)\n\n def assign_coords(self, coords=None, **coords_kwargs):\n \"\"\"Assign coordinates by group.\n\n See also\n --------\n Dataset.assign_coords\n Dataset.swap_dims\n \"\"\"\n coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, \"assign_coords\")\n return self.apply(lambda ds: ds.assign_coords(**coords_kwargs))\n\n\ndef _maybe_reorder(xarray_obj, dim, positions):\n order = _inverse_permutation_indices(positions)\n\n if order is None:\n return xarray_obj\n else:\n return xarray_obj[{dim: order}]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy_DataArrayGroupBy._restore_dim_order.return.stacked_transpose_new_or": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy_DataArrayGroupBy._restore_dim_order.return.stacked_transpose_new_or", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 602, "end_line": 635, "span_ids": ["DataArrayGroupBy._concat_shortcut", "DataArrayGroupBy", "DataArrayGroupBy._restore_dim_order", "DataArrayGroupBy._iter_grouped_shortcut"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayGroupBy(GroupBy, ImplementsArrayReduce):\n \"\"\"GroupBy object specialized to grouping DataArray objects\n \"\"\"\n\n def _iter_grouped_shortcut(self):\n \"\"\"Fast version of `_iter_grouped` that yields Variables without\n metadata\n \"\"\"\n var = self._obj.variable\n for indices in self._group_indices:\n yield var[{self._group_dim: indices}]\n\n def _concat_shortcut(self, applied, dim, positions=None):\n # nb. don't worry too much about maintaining this method -- it does\n # speed things up, but it's not very interpretable and there are much\n # faster alternatives (e.g., doing the grouped aggregation in a\n # compiled language)\n stacked = Variable.concat(applied, dim, shortcut=True)\n reordered = _maybe_reorder(stacked, dim, positions)\n result = self._obj._replace_maybe_drop_dims(reordered)\n return result\n\n def _restore_dim_order(self, stacked):\n def lookup_order(dimension):\n if dimension == self._group.name:\n dimension, = self._group.dims\n if dimension in self._obj.dims:\n axis = self._obj.get_axis_num(dimension)\n else:\n axis = 1e6 # some arbitrarily high value\n return axis\n\n new_order = sorted(stacked.dims, key=lookup_order)\n return stacked.transpose(*new_order, transpose_coords=self._restore_coord_dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.apply_DataArrayGroupBy.apply.return.self__combine_applied_sh": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.apply_DataArrayGroupBy.apply.return.self__combine_applied_sh", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 637, "end_line": 682, "span_ids": ["DataArrayGroupBy.apply"], "tokens": 440}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayGroupBy(GroupBy, ImplementsArrayReduce):\n\n def apply(self, func, shortcut=False, args=(), **kwargs):\n \"\"\"Apply a function over each array in the group and concatenate them\n together into a new array.\n\n `func` is called like `func(ar, *args, **kwargs)` for each array `ar`\n in this group.\n\n Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n to stack together the array. The rule is:\n\n 1. If the dimension along which the group coordinate is defined is\n still in the first grouped array after applying `func`, then stack\n over this dimension.\n 2. Otherwise, stack over the new dimension given by name of this\n grouping (the argument to the `groupby` function).\n\n Parameters\n ----------\n func : function\n Callable to apply to each array.\n shortcut : bool, optional\n Whether or not to shortcut evaluation under the assumptions that:\n (1) The action of `func` does not depend on any of the array\n metadata (attributes or coordinates) but only on the data and\n dimensions.\n (2) The action of `func` creates arrays with homogeneous metadata,\n that is, with the same dimensions and attributes.\n If these conditions are satisfied `shortcut` provides significant\n speedup. This should be the case for many common groupby operations\n (e.g., applying numpy ufuncs).\n args : tuple, optional\n Positional arguments passed to `func`.\n **kwargs\n Used to call `func(ar, **kwargs)` for each array `ar`.\n\n Returns\n -------\n applied : DataArray or DataArray\n The result of splitting, applying and combining this array.\n \"\"\"\n if shortcut:\n grouped = self._iter_grouped_shortcut()\n else:\n grouped = self._iter_grouped()\n applied = (maybe_wrap_array(arr, func(arr, *args, **kwargs)) for arr in grouped)\n return self._combine(applied, shortcut=shortcut)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy._combine_DataArrayGroupBy._combine.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy._combine_DataArrayGroupBy._combine.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 684, "end_line": 704, "span_ids": ["DataArrayGroupBy._combine"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayGroupBy(GroupBy, ImplementsArrayReduce):\n\n def _combine(self, applied, restore_coord_dims=False, shortcut=False):\n \"\"\"Recombine the applied objects like the original.\"\"\"\n applied_example, applied = peek_at(applied)\n coord, dim, positions = self._infer_concat_args(applied_example)\n if shortcut:\n combined = self._concat_shortcut(applied, dim, positions)\n else:\n combined = concat(applied, dim)\n combined = _maybe_reorder(combined, dim, positions)\n\n if isinstance(combined, type(self._obj)):\n # only restore dimension order for arrays\n combined = self._restore_dim_order(combined)\n if coord is not None:\n if shortcut:\n combined._coords[coord.name] = as_variable(coord)\n else:\n combined.coords[coord.name] = coord\n combined = self._maybe_restore_empty_groups(combined)\n combined = self._maybe_unstack(combined)\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.quantile_DataArrayGroupBy.quantile.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.quantile_DataArrayGroupBy.quantile.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 706, "end_line": 758, "span_ids": ["DataArrayGroupBy.quantile"], "tokens": 464}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayGroupBy(GroupBy, ImplementsArrayReduce):\n\n def quantile(self, q, dim=None, interpolation=\"linear\", keep_attrs=None):\n \"\"\"Compute the qth quantile over each array in the groups and\n concatenate them together into a new array.\n\n Parameters\n ----------\n q : float in range of [0,1] (or sequence of floats)\n Quantile to compute, which must be between 0 and 1\n inclusive.\n dim : xarray.ALL_DIMS, str or sequence of str, optional\n Dimension(s) over which to apply quantile.\n Defaults to the grouped dimension.\n interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n This optional parameter specifies the interpolation method to\n use when the desired quantile lies between two data points\n ``i < j``:\n * linear: ``i + (j - i) * fraction``, where ``fraction`` is\n the fractional part of the index surrounded by ``i`` and\n ``j``.\n * lower: ``i``.\n * higher: ``j``.\n * nearest: ``i`` or ``j``, whichever is nearest.\n * midpoint: ``(i + j) / 2``.\n\n Returns\n -------\n quantiles : Variable\n If `q` is a single quantile, then the result\n is a scalar. If multiple percentiles are given, first axis of\n the result corresponds to the quantile and a quantile dimension\n is added to the return array. The other dimensions are the\n dimensions that remain after the reduction of the array.\n\n See Also\n --------\n numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile,\n DataArray.quantile\n \"\"\"\n if dim is None:\n dim = self._group_dim\n\n out = self.apply(\n self._obj.__class__.quantile,\n shortcut=False,\n q=q,\n dim=dim,\n interpolation=interpolation,\n keep_attrs=keep_attrs,\n )\n\n if np.asarray(q, dtype=np.float64).ndim == 0:\n out = out.drop(\"quantile\")\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.reduce_DataArrayGroupBy.reduce.return.self_apply_reduce_array_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DataArrayGroupBy.reduce_DataArrayGroupBy.reduce.return.self_apply_reduce_array_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 760, "end_line": 806, "span_ids": ["DataArrayGroupBy.reduce"], "tokens": 417}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayGroupBy(GroupBy, ImplementsArrayReduce):\n\n def reduce(\n self, func, dim=None, axis=None, keep_attrs=None, shortcut=True, **kwargs\n ):\n \"\"\"Reduce the items in this group by applying `func` along some\n dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, axis=axis, **kwargs)` to return the result of collapsing\n an np.ndarray over an integer valued axis.\n dim : xarray.ALL_DIMS, str or sequence of str, optional\n Dimension(s) over which to apply `func`.\n axis : int or sequence of int, optional\n Axis(es) over which to apply `func`. Only one of the 'dimension'\n and 'axis' arguments can be supplied. If neither are supplied, then\n `func` is calculated over all dimension for each group item.\n keep_attrs : bool, optional\n If True, the datasets's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : Array\n Array with summarized data and the indicated dimension(s)\n removed.\n \"\"\"\n if dim is None:\n dim = self._group_dim\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n\n if dim is not ALL_DIMS and dim not in self.dims:\n raise ValueError(\n \"cannot reduce over dimension %r. expected either xarray.ALL_DIMS to reduce over all dimensions or one or more of %r.\"\n % (dim, self.dims)\n )\n\n def reduce_array(ar):\n return ar.reduce(func, dim, axis, keep_attrs=keep_attrs, **kwargs)\n\n return self.apply(reduce_array, shortcut=shortcut)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_ops_inject_reduce_methods_DatasetGroupBy.apply.return.self__combine_applied_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_ops_inject_reduce_methods_DatasetGroupBy.apply.return.self__combine_applied_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 809, "end_line": 846, "span_ids": ["DatasetGroupBy", "impl", "DatasetGroupBy.apply"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "ops.inject_reduce_methods(DataArrayGroupBy)\nops.inject_binary_ops(DataArrayGroupBy)\n\n\nclass DatasetGroupBy(GroupBy, ImplementsDatasetReduce):\n def apply(self, func, args=(), shortcut=None, **kwargs):\n \"\"\"Apply a function over each Dataset in the group and concatenate them\n together into a new Dataset.\n\n `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`\n in this group.\n\n Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n to stack together the datasets. The rule is:\n\n 1. If the dimension along which the group coordinate is defined is\n still in the first grouped item after applying `func`, then stack\n over this dimension.\n 2. Otherwise, stack over the new dimension given by name of this\n grouping (the argument to the `groupby` function).\n\n Parameters\n ----------\n func : function\n Callable to apply to each sub-dataset.\n args : tuple, optional\n Positional arguments to pass to `func`.\n **kwargs\n Used to call `func(ds, **kwargs)` for each sub-dataset `ar`.\n\n Returns\n -------\n applied : Dataset or DataArray\n The result of splitting, applying and combining this dataset.\n \"\"\"\n # ignore shortcut if set (for now)\n applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())\n return self._combine(applied)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DatasetGroupBy._combine_DatasetGroupBy._combine.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DatasetGroupBy._combine_DatasetGroupBy._combine.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 848, "end_line": 858, "span_ids": ["DatasetGroupBy._combine"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetGroupBy(GroupBy, ImplementsDatasetReduce):\n\n def _combine(self, applied):\n \"\"\"Recombine the applied objects like the original.\"\"\"\n applied_example, applied = peek_at(applied)\n coord, dim, positions = self._infer_concat_args(applied_example)\n combined = concat(applied, dim)\n combined = _maybe_reorder(combined, dim, positions)\n if coord is not None:\n combined[coord.name] = coord\n combined = self._maybe_restore_empty_groups(combined)\n combined = self._maybe_unstack(combined)\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DatasetGroupBy.reduce_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/groupby.py_DatasetGroupBy.reduce_", "embedding": null, "metadata": {"file_path": "xarray/core/groupby.py", "file_name": "groupby.py", "file_type": "text/x-python", "category": "implementation", "start_line": 860, "end_line": 918, "span_ids": ["DatasetGroupBy.assign", "impl:3", "DatasetGroupBy.reduce"], "tokens": 455}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetGroupBy(GroupBy, ImplementsDatasetReduce):\n\n def reduce(self, func, dim=None, keep_attrs=None, **kwargs):\n \"\"\"Reduce the items in this group by applying `func` along some\n dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, axis=axis, **kwargs)` to return the result of collapsing\n an np.ndarray over an integer valued axis.\n dim : xarray.ALL_DIMS, str or sequence of str, optional\n Dimension(s) over which to apply `func`.\n axis : int or sequence of int, optional\n Axis(es) over which to apply `func`. Only one of the 'dimension'\n and 'axis' arguments can be supplied. If neither are supplied, then\n `func` is calculated over all dimension for each group item.\n keep_attrs : bool, optional\n If True, the datasets's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : Array\n Array with summarized data and the indicated dimension(s)\n removed.\n \"\"\"\n if dim is None:\n dim = self._group_dim\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n\n def reduce_dataset(ds):\n return ds.reduce(func, dim, keep_attrs, **kwargs)\n\n if dim is not ALL_DIMS and dim not in self.dims:\n raise ValueError(\n \"cannot reduce over dimension %r. expected either xarray.ALL_DIMS to reduce over all dimensions or one or more of %r.\"\n % (dim, self.dims)\n )\n\n return self.apply(reduce_dataset)\n\n def assign(self, **kwargs):\n \"\"\"Assign data variables by group.\n\n See also\n --------\n Dataset.assign\n \"\"\"\n return self.apply(lambda ds: ds.assign(**kwargs))\n\n\nops.inject_reduce_methods(DatasetGroupBy)\nops.inject_binary_ops(DatasetGroupBy)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_collections.abc_Indexes.__repr__.return.formatting_indexes_repr_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_collections.abc_Indexes.__repr__.return.formatting_indexes_repr_s", "embedding": null, "metadata": {"file_path": "xarray/core/indexes.py", "file_name": "indexes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 39, "span_ids": ["Indexes.__repr__", "Indexes.__contains__", "Indexes.__getitem__", "Indexes.__iter__", "imports", "Indexes", "Indexes.__len__"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import collections.abc\nfrom typing import Any, Dict, Hashable, Iterable, Mapping, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import formatting\nfrom .variable import Variable\n\n\nclass Indexes(collections.abc.Mapping):\n \"\"\"Immutable proxy for Dataset or DataArrary indexes.\"\"\"\n\n __slots__ = (\"_indexes\",)\n\n def __init__(self, indexes):\n \"\"\"Not for public consumption.\n\n Parameters\n ----------\n indexes : Dict[Any, pandas.Index]\n Indexes held by this object.\n \"\"\"\n self._indexes = indexes\n\n def __iter__(self):\n return iter(self._indexes)\n\n def __len__(self):\n return len(self._indexes)\n\n def __contains__(self, key):\n return key in self._indexes\n\n def __getitem__(self, key):\n return self._indexes[key]\n\n def __repr__(self):\n return formatting.indexes_repr(self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_default_indexes_default_indexes.return._key_coords_key_to_inde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_default_indexes_default_indexes.return._key_coords_key_to_inde", "embedding": null, "metadata": {"file_path": "xarray/core/indexes.py", "file_name": "indexes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 59, "span_ids": ["default_indexes"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def default_indexes(\n coords: Mapping[Any, Variable], dims: Iterable\n) -> Dict[Hashable, pd.Index]:\n \"\"\"Default indexes for a Dataset/DataArray.\n\n Parameters\n ----------\n coords : Mapping[Any, xarray.Variable]\n Coordinate variables from which to draw default indexes.\n dims : iterable\n Iterable of dimension names.\n\n Returns\n -------\n Mapping from indexing keys (levels/dimension names) to indexes used for\n indexing along that dimension.\n \"\"\"\n return {key: coords[key].to_index() for key in dims if key in coords}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_isel_variable_and_index_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexes.py_isel_variable_and_index_", "embedding": null, "metadata": {"file_path": "xarray/core/indexes.py", "file_name": "indexes.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 100, "span_ids": ["isel_variable_and_index", "roll_index"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def isel_variable_and_index(\n name: Hashable,\n variable: Variable,\n index: pd.Index,\n indexers: Mapping[Hashable, Union[int, slice, np.ndarray, Variable]],\n) -> Tuple[Variable, Optional[pd.Index]]:\n \"\"\"Index a Variable and pandas.Index together.\"\"\"\n if not indexers:\n # nothing to index\n return variable.copy(deep=False), index\n\n if len(variable.dims) > 1:\n raise NotImplementedError(\n \"indexing multi-dimensional variable with indexes is not \" \"supported yet\"\n )\n\n new_variable = variable.isel(indexers)\n\n if new_variable.dims != (name,):\n # can't preserve a index if result has new dimensions\n return new_variable, None\n\n # we need to compute the new index\n (dim,) = variable.dims\n indexer = indexers[dim]\n if isinstance(indexer, Variable):\n indexer = indexer.data\n new_index = index[indexer]\n return new_variable, new_index\n\n\ndef roll_index(index: pd.Index, count: int, axis: int = 0) -> pd.Index:\n \"\"\"Roll an pandas.Index.\"\"\"\n count %= index.shape[0]\n if count != 0:\n return index[-count:].append(index[:-count])\n else:\n return index[:]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_enum_expanded_indexer.return.tuple_new_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_enum_expanded_indexer.return.tuple_new_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 45, "span_ids": ["imports", "expanded_indexer"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import enum\nimport functools\nimport operator\nfrom collections import defaultdict\nfrom contextlib import suppress\nfrom datetime import timedelta\nfrom typing import Any, Callable, Sequence, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import duck_array_ops, nputils, utils\nfrom .npcompat import DTypeLike\nfrom .pycompat import dask_array_type, integer_types, sparse_array_type\nfrom .utils import is_dict_like, maybe_cast_to_coords_dtype\n\n\ndef expanded_indexer(key, ndim):\n \"\"\"Given a key for indexing an ndarray, return an equivalent key which is a\n tuple with length equal to the number of dimensions.\n\n The expansion is done by replacing all `Ellipsis` items with the right\n number of full slices and then padding the key with full slices so that it\n reaches the appropriate dimensionality.\n \"\"\"\n if not isinstance(key, tuple):\n # numpy treats non-tuple keys equivalent to tuples of length 1\n key = (key,)\n new_key = []\n # handling Ellipsis right is a little tricky, see:\n # http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing\n found_ellipsis = False\n for k in key:\n if k is Ellipsis:\n if not found_ellipsis:\n new_key.extend((ndim + 1 - len(key)) * [slice(None)])\n found_ellipsis = True\n else:\n new_key.append(slice(None))\n else:\n new_key.append(k)\n if len(new_key) > ndim:\n raise IndexError(\"too many indices\")\n new_key.extend((ndim - len(new_key)) * [slice(None)])\n return tuple(new_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__expand_slice__sanitize_slice_element.return.x": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__expand_slice__sanitize_slice_element.return.x", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 48, "end_line": 72, "span_ids": ["_expand_slice", "_sanitize_slice_element"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _expand_slice(slice_, size):\n return np.arange(*slice_.indices(size))\n\n\ndef _sanitize_slice_element(x):\n from .variable import Variable\n from .dataarray import DataArray\n\n if isinstance(x, (Variable, DataArray)):\n x = x.values\n\n if isinstance(x, np.ndarray):\n if x.ndim != 0:\n raise ValueError(\n \"cannot use non-scalar arrays in a slice for \"\n \"xarray indexing: {}\".format(x)\n )\n x = x[()]\n\n if isinstance(x, np.timedelta64):\n # pandas does not support indexing with np.timedelta64 yet:\n # https://github.com/pandas-dev/pandas/issues/20393\n x = pd.Timedelta(x)\n\n return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__asarray_tuplesafe_get_indexer_nd.return.indexer": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__asarray_tuplesafe_get_indexer_nd.return.indexer", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 106, "span_ids": ["get_indexer_nd", "_asarray_tuplesafe", "_is_nested_tuple"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _asarray_tuplesafe(values):\n \"\"\"\n Convert values into a numpy array of at most 1-dimension, while preserving\n tuples.\n\n Adapted from pandas.core.common._asarray_tuplesafe\n \"\"\"\n if isinstance(values, tuple):\n result = utils.to_0d_object_array(values)\n else:\n result = np.asarray(values)\n if result.ndim == 2:\n result = np.empty(len(values), dtype=object)\n result[:] = values\n\n return result\n\n\ndef _is_nested_tuple(possible_tuple):\n return isinstance(possible_tuple, tuple) and any(\n isinstance(value, (tuple, list, slice)) for value in possible_tuple\n )\n\n\ndef get_indexer_nd(index, labels, method=None, tolerance=None):\n \"\"\"Wrapper around :meth:`pandas.Index.get_indexer` supporting n-dimensional\n labels\n \"\"\"\n flat_labels = np.ravel(labels)\n flat_indexer = index.get_indexer(flat_labels, method=method, tolerance=tolerance)\n indexer = flat_indexer.reshape(labels.shape)\n return indexer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_convert_label_indexer_convert_label_indexer.return.indexer_new_index": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_convert_label_indexer_convert_label_indexer.return.indexer_new_index", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 109, "end_line": 194, "span_ids": ["convert_label_indexer"], "tokens": 759}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_label_indexer(index, label, index_name=\"\", method=None, tolerance=None):\n \"\"\"Given a pandas.Index and labels (e.g., from __getitem__) for one\n dimension, return an indexer suitable for indexing an ndarray along that\n dimension. If `index` is a pandas.MultiIndex and depending on `label`,\n return a new pandas.Index or pandas.MultiIndex (otherwise return None).\n \"\"\"\n new_index = None\n\n if isinstance(label, slice):\n if method is not None or tolerance is not None:\n raise NotImplementedError(\n \"cannot use ``method`` argument if any indexers are \" \"slice objects\"\n )\n indexer = index.slice_indexer(\n _sanitize_slice_element(label.start),\n _sanitize_slice_element(label.stop),\n _sanitize_slice_element(label.step),\n )\n if not isinstance(indexer, slice):\n # unlike pandas, in xarray we never want to silently convert a\n # slice indexer into an array indexer\n raise KeyError(\n \"cannot represent labeled-based slice indexer for \"\n \"dimension %r with a slice over integer positions; \"\n \"the index is unsorted or non-unique\" % index_name\n )\n\n elif is_dict_like(label):\n is_nested_vals = _is_nested_tuple(tuple(label.values()))\n if not isinstance(index, pd.MultiIndex):\n raise ValueError(\n \"cannot use a dict-like object for selection on \"\n \"a dimension that does not have a MultiIndex\"\n )\n elif len(label) == index.nlevels and not is_nested_vals:\n indexer = index.get_loc(tuple(label[k] for k in index.names))\n else:\n for k, v in label.items():\n # index should be an item (i.e. Hashable) not an array-like\n if isinstance(v, Sequence) and not isinstance(v, str):\n raise ValueError(\n \"Vectorized selection is not \"\n \"available along level variable: \" + k\n )\n indexer, new_index = index.get_loc_level(\n tuple(label.values()), level=tuple(label.keys())\n )\n\n # GH2619. Raise a KeyError if nothing is chosen\n if indexer.dtype.kind == \"b\" and indexer.sum() == 0:\n raise KeyError(\"{} not found\".format(label))\n\n elif isinstance(label, tuple) and isinstance(index, pd.MultiIndex):\n if _is_nested_tuple(label):\n indexer = index.get_locs(label)\n elif len(label) == index.nlevels:\n indexer = index.get_loc(label)\n else:\n indexer, new_index = index.get_loc_level(\n label, level=list(range(len(label)))\n )\n else:\n label = (\n label\n if getattr(label, \"ndim\", 1) > 1 # vectorized-indexing\n else _asarray_tuplesafe(label)\n )\n if label.ndim == 0:\n if isinstance(index, pd.MultiIndex):\n indexer, new_index = index.get_loc_level(label.item(), level=0)\n else:\n indexer = index.get_loc(\n label.item(), method=method, tolerance=tolerance\n )\n elif label.dtype.kind == \"b\":\n indexer = label\n else:\n if isinstance(index, pd.MultiIndex) and label.ndim > 1:\n raise ValueError(\n \"Vectorized selection is not available along \"\n \"MultiIndex variable: \" + index_name\n )\n indexer = get_indexer_nd(index, label, method, tolerance)\n if np.any(indexer < 0):\n raise KeyError(\"not all values found in index %r\" % index_name)\n return indexer, new_index", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_get_dim_indexers_get_dim_indexers.return.dim_indexers": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_get_dim_indexers_get_dim_indexers.return.dim_indexers", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 197, "end_line": 231, "span_ids": ["get_dim_indexers"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_dim_indexers(data_obj, indexers):\n \"\"\"Given a xarray data object and label based indexers, return a mapping\n of label indexers with only dimension names as keys.\n\n It groups multiple level indexers given on a multi-index dimension\n into a single, dictionary indexer for that dimension (Raise a ValueError\n if it is not possible).\n \"\"\"\n invalid = [\n k\n for k in indexers\n if k not in data_obj.dims and k not in data_obj._level_coords\n ]\n if invalid:\n raise ValueError(\"dimensions or multi-index levels %r do not exist\" % invalid)\n\n level_indexers = defaultdict(dict)\n dim_indexers = {}\n for key, label in indexers.items():\n dim, = data_obj[key].dims\n if key != dim:\n # assume here multi-index level indexer\n level_indexers[dim][key] = label\n else:\n dim_indexers[key] = label\n\n for dim, level_labels in level_indexers.items():\n if dim_indexers.get(dim, False):\n raise ValueError(\n \"cannot combine multi-index level indexers \"\n \"with an indexer for dimension %s\" % dim\n )\n dim_indexers[dim] = level_labels\n\n return dim_indexers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_remap_label_indexers_remap_label_indexers.return.pos_indexers_new_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_remap_label_indexers_remap_label_indexers.return.pos_indexers_new_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 234, "end_line": 266, "span_ids": ["remap_label_indexers"], "tokens": 297}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def remap_label_indexers(data_obj, indexers, method=None, tolerance=None):\n \"\"\"Given an xarray data object and label based indexers, return a mapping\n of equivalent location based indexers. Also return a mapping of updated\n pandas index objects (in case of multi-index level drop).\n \"\"\"\n if method is not None and not isinstance(method, str):\n raise TypeError(\"``method`` must be a string\")\n\n pos_indexers = {}\n new_indexes = {}\n\n dim_indexers = get_dim_indexers(data_obj, indexers)\n for dim, label in dim_indexers.items():\n try:\n index = data_obj.indexes[dim]\n except KeyError:\n # no index for this dimension: reuse the provided labels\n if method is not None or tolerance is not None:\n raise ValueError(\n \"cannot supply ``method`` or ``tolerance`` \"\n \"when the indexed dimension does not have \"\n \"an associated coordinate.\"\n )\n pos_indexers[dim] = label\n else:\n coords_dtype = data_obj.coords[dim].dtype\n label = maybe_cast_to_coords_dtype(label, coords_dtype)\n idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance)\n pos_indexers[dim] = idxr\n if new_idx is not None:\n new_indexes[dim] = new_idx\n\n return pos_indexers, new_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_slice_slice_slice_slice.return.slice_start_stop_step_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_slice_slice_slice_slice.return.slice_start_stop_step_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 288, "span_ids": ["slice_slice"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def slice_slice(old_slice, applied_slice, size):\n \"\"\"Given a slice and the size of the dimension to which it will be applied,\n index it with another slice to return a new slice equivalent to applying\n the slices sequentially\n \"\"\"\n step = (old_slice.step or 1) * (applied_slice.step or 1)\n\n # For now, use the hack of turning old_slice into an ndarray to reconstruct\n # the slice start and stop. This is not entirely ideal, but it is still\n # definitely better than leaving the indexer as an array.\n items = _expand_slice(old_slice, size)[applied_slice]\n if len(items) > 0:\n start = items[0]\n stop = items[-1] + int(np.sign(step))\n if stop < 0:\n stop = None\n else:\n start = 0\n stop = 0\n return slice(start, stop, step)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__index_indexer_1d__index_indexer_1d.return.indexer": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__index_indexer_1d__index_indexer_1d.return.indexer", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 291, "end_line": 303, "span_ids": ["_index_indexer_1d"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _index_indexer_1d(old_indexer, applied_indexer, size):\n assert isinstance(applied_indexer, integer_types + (slice, np.ndarray))\n if isinstance(applied_indexer, slice) and applied_indexer == slice(None):\n # shortcut for the usual case\n return old_indexer\n if isinstance(old_indexer, slice):\n if isinstance(applied_indexer, slice):\n indexer = slice_slice(old_indexer, applied_indexer, size)\n else:\n indexer = _expand_slice(old_indexer, size)[applied_indexer]\n else:\n indexer = old_indexer[applied_indexer]\n return indexer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_ExplicitIndexer_as_integer_slice.return.slice_start_stop_step_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_ExplicitIndexer_as_integer_slice.return.slice_start_stop_step_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 306, "end_line": 340, "span_ids": ["ExplicitIndexer.tuple", "ExplicitIndexer.__repr__", "ExplicitIndexer", "as_integer_slice", "as_integer_or_none"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ExplicitIndexer:\n \"\"\"Base class for explicit indexer objects.\n\n ExplicitIndexer objects wrap a tuple of values given by their ``tuple``\n property. These tuples should always have length equal to the number of\n dimensions on the indexed array.\n\n Do not instantiate BaseIndexer objects directly: instead, use one of the\n sub-classes BasicIndexer, OuterIndexer or VectorizedIndexer.\n \"\"\"\n\n __slots__ = (\"_key\",)\n\n def __init__(self, key):\n if type(self) is ExplicitIndexer:\n raise TypeError(\"cannot instantiate base ExplicitIndexer objects\")\n self._key = tuple(key)\n\n @property\n def tuple(self):\n return self._key\n\n def __repr__(self):\n return \"{}({})\".format(type(self).__name__, self.tuple)\n\n\ndef as_integer_or_none(value):\n return None if value is None else operator.index(value)\n\n\ndef as_integer_slice(value):\n start = as_integer_or_none(value.start)\n stop = as_integer_or_none(value.stop)\n step = as_integer_or_none(value.step)\n return slice(start, stop, step)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_BasicIndexer_BasicIndexer.__init__.super___init___new_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_BasicIndexer_BasicIndexer.__init__.super___init___new_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 343, "end_line": 371, "span_ids": ["BasicIndexer"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BasicIndexer(ExplicitIndexer):\n \"\"\"Tuple for basic indexing.\n\n All elements should be int or slice objects. Indexing follows NumPy's\n rules for basic indexing: each axis is independently sliced and axes\n indexed with an integer are dropped from the result.\n \"\"\"\n\n __slots__ = ()\n\n def __init__(self, key):\n if not isinstance(key, tuple):\n raise TypeError(\"key must be a tuple: {!r}\".format(key))\n\n new_key = []\n for k in key:\n if isinstance(k, integer_types):\n k = int(k)\n elif isinstance(k, slice):\n k = as_integer_slice(k)\n else:\n raise TypeError(\n \"unexpected indexer type for {}: {!r}\".format(\n type(self).__name__, k\n )\n )\n new_key.append(k)\n\n super().__init__(new_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_OuterIndexer_OuterIndexer.__init__.super___init___new_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_OuterIndexer_OuterIndexer.__init__.super___init___new_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 374, "end_line": 415, "span_ids": ["OuterIndexer"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OuterIndexer(ExplicitIndexer):\n \"\"\"Tuple for outer/orthogonal indexing.\n\n All elements should be int, slice or 1-dimensional np.ndarray objects with\n an integer dtype. Indexing is applied independently along each axis, and\n axes indexed with an integer are dropped from the result. This type of\n indexing works like MATLAB/Fortran.\n \"\"\"\n\n __slots__ = ()\n\n def __init__(self, key):\n if not isinstance(key, tuple):\n raise TypeError(\"key must be a tuple: {!r}\".format(key))\n\n new_key = []\n for k in key:\n if isinstance(k, integer_types):\n k = int(k)\n elif isinstance(k, slice):\n k = as_integer_slice(k)\n elif isinstance(k, np.ndarray):\n if not np.issubdtype(k.dtype, np.integer):\n raise TypeError(\n \"invalid indexer array, does not have \"\n \"integer dtype: {!r}\".format(k)\n )\n if k.ndim != 1:\n raise TypeError(\n \"invalid indexer array for {}, must have \"\n \"exactly 1 dimension: \".format(type(self).__name__, k)\n )\n k = np.asarray(k, dtype=np.int64)\n else:\n raise TypeError(\n \"unexpected indexer type for {}: {!r}\".format(\n type(self).__name__, k\n )\n )\n new_key.append(k)\n\n super().__init__(new_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_VectorizedIndexer_VectorizedIndexer.__init__.super___init___new_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_VectorizedIndexer_VectorizedIndexer.__init__.super___init___new_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 418, "end_line": 462, "span_ids": ["VectorizedIndexer"], "tokens": 342}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VectorizedIndexer(ExplicitIndexer):\n \"\"\"Tuple for vectorized indexing.\n\n All elements should be slice or N-dimensional np.ndarray objects with an\n integer dtype and the same number of dimensions. Indexing follows proposed\n rules for np.ndarray.vindex, which matches NumPy's advanced indexing rules\n (including broadcasting) except sliced axes are always moved to the end:\n https://github.com/numpy/numpy/pull/6256\n \"\"\"\n\n __slots__ = ()\n\n def __init__(self, key):\n if not isinstance(key, tuple):\n raise TypeError(\"key must be a tuple: {!r}\".format(key))\n\n new_key = []\n ndim = None\n for k in key:\n if isinstance(k, slice):\n k = as_integer_slice(k)\n elif isinstance(k, np.ndarray):\n if not np.issubdtype(k.dtype, np.integer):\n raise TypeError(\n \"invalid indexer array, does not have \"\n \"integer dtype: {!r}\".format(k)\n )\n if ndim is None:\n ndim = k.ndim\n elif ndim != k.ndim:\n ndims = [k.ndim for k in key if isinstance(k, np.ndarray)]\n raise ValueError(\n \"invalid indexer key: ndarray arguments \"\n \"have different numbers of dimensions: {}\".format(ndims)\n )\n k = np.asarray(k, dtype=np.int64)\n else:\n raise TypeError(\n \"unexpected indexer type for {}: {!r}\".format(\n type(self).__name__, k\n )\n )\n new_key.append(k)\n\n super().__init__(new_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_ExplicitlyIndexed_ImplicitToExplicitIndexingAdapter.__getitem__.if_isinstance_result_Exp.else_.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_ExplicitlyIndexed_ImplicitToExplicitIndexingAdapter.__getitem__.if_isinstance_result_Exp.else_.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 465, "end_line": 500, "span_ids": ["ImplicitToExplicitIndexingAdapter.__getitem__", "ExplicitlyIndexed", "ImplicitToExplicitIndexingAdapter", "ExplicitlyIndexedNDArrayMixin.__array__", "ImplicitToExplicitIndexingAdapter.__array__", "ExplicitlyIndexedNDArrayMixin"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ExplicitlyIndexed:\n \"\"\"Mixin to mark support for Indexer subclasses in indexing.\n \"\"\"\n\n __slots__ = ()\n\n\nclass ExplicitlyIndexedNDArrayMixin(utils.NDArrayMixin, ExplicitlyIndexed):\n __slots__ = ()\n\n def __array__(self, dtype=None):\n key = BasicIndexer((slice(None),) * self.ndim)\n return np.asarray(self[key], dtype=dtype)\n\n\nclass ImplicitToExplicitIndexingAdapter(utils.NDArrayMixin):\n \"\"\"Wrap an array, converting tuples into the indicated explicit indexer.\"\"\"\n\n __slots__ = (\"array\", \"indexer_cls\")\n\n def __init__(self, array, indexer_cls=BasicIndexer):\n self.array = as_indexable(array)\n self.indexer_cls = indexer_cls\n\n def __array__(self, dtype=None):\n return np.asarray(self.array, dtype=dtype)\n\n def __getitem__(self, key):\n key = expanded_indexer(key, self.ndim)\n result = self.array[self.indexer_cls(key)]\n if isinstance(result, ExplicitlyIndexed):\n return type(self)(result, self.indexer_cls)\n else:\n # Sometimes explicitly indexed arrays return NumPy arrays or\n # scalars.\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray_LazilyOuterIndexedArray.__init__.self.key.key": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray_LazilyOuterIndexedArray.__init__.self.key.key", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 503, "end_line": 528, "span_ids": ["LazilyOuterIndexedArray"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LazilyOuterIndexedArray(ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrap an array to make basic and outer indexing lazy.\n \"\"\"\n\n __slots__ = (\"array\", \"key\")\n\n def __init__(self, array, key=None):\n \"\"\"\n Parameters\n ----------\n array : array_like\n Array like object to index.\n key : ExplicitIndexer, optional\n Array indexer. If provided, it is assumed to already be in\n canonical expanded form.\n \"\"\"\n if isinstance(array, type(self)) and key is None:\n # unwrap\n key = array.key\n array = array.array\n\n if key is None:\n key = BasicIndexer((slice(None),) * array.ndim)\n\n self.array = as_indexable(array)\n self.key = key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray._updated_key_LazilyOuterIndexedArray._updated_key.return.OuterIndexer_full_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray._updated_key_LazilyOuterIndexedArray._updated_key.return.OuterIndexer_full_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 530, "end_line": 542, "span_ids": ["LazilyOuterIndexedArray._updated_key"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LazilyOuterIndexedArray(ExplicitlyIndexedNDArrayMixin):\n\n def _updated_key(self, new_key):\n iter_new_key = iter(expanded_indexer(new_key.tuple, self.ndim))\n full_key = []\n for size, k in zip(self.array.shape, self.key.tuple):\n if isinstance(k, integer_types):\n full_key.append(k)\n else:\n full_key.append(_index_indexer_1d(k, next(iter_new_key), size))\n full_key = tuple(full_key)\n\n if all(isinstance(k, integer_types + (slice,)) for k in full_key):\n return BasicIndexer(full_key)\n return OuterIndexer(full_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray.shape_LazilyOuterIndexedArray.__repr__.return._s_array_r_key_r_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyOuterIndexedArray.shape_LazilyOuterIndexedArray.__repr__.return._s_array_r_key_r_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 544, "end_line": 577, "span_ids": ["LazilyOuterIndexedArray.__repr__", "LazilyOuterIndexedArray.__array__", "LazilyOuterIndexedArray.shape", "LazilyOuterIndexedArray.__setitem__", "LazilyOuterIndexedArray.transpose", "LazilyOuterIndexedArray.__getitem__"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LazilyOuterIndexedArray(ExplicitlyIndexedNDArrayMixin):\n\n @property\n def shape(self):\n shape = []\n for size, k in zip(self.array.shape, self.key.tuple):\n if isinstance(k, slice):\n shape.append(len(range(*k.indices(size))))\n elif isinstance(k, np.ndarray):\n shape.append(k.size)\n return tuple(shape)\n\n def __array__(self, dtype=None):\n array = as_indexable(self.array)\n return np.asarray(array[self.key], dtype=None)\n\n def transpose(self, order):\n return LazilyVectorizedIndexedArray(self.array, self.key).transpose(order)\n\n def __getitem__(self, indexer):\n if isinstance(indexer, VectorizedIndexer):\n array = LazilyVectorizedIndexedArray(self.array, self.key)\n return array[indexer]\n return type(self)(self.array, self._updated_key(indexer))\n\n def __setitem__(self, key, value):\n if isinstance(key, VectorizedIndexer):\n raise NotImplementedError(\n \"Lazy item assignment with the vectorized indexer is not yet \"\n \"implemented. Load your data first by .load() or compute().\"\n )\n full_key = self._updated_key(key)\n self.array[full_key] = value\n\n def __repr__(self):\n return \"%s(array=%r, key=%r)\" % (type(self).__name__, self.array, self.key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyVectorizedIndexedArray_LazilyVectorizedIndexedArray.__repr__.return._s_array_r_key_r_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_LazilyVectorizedIndexedArray_LazilyVectorizedIndexedArray.__repr__.return._s_array_r_key_r_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 628, "span_ids": ["LazilyVectorizedIndexedArray", "LazilyVectorizedIndexedArray.shape", "LazilyVectorizedIndexedArray.transpose", "LazilyVectorizedIndexedArray._updated_key", "LazilyVectorizedIndexedArray.__setitem__", "LazilyVectorizedIndexedArray.__getitem__", "LazilyVectorizedIndexedArray.__repr__", "LazilyVectorizedIndexedArray.__array__"], "tokens": 414}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LazilyVectorizedIndexedArray(ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrap an array to make vectorized indexing lazy.\n \"\"\"\n\n __slots__ = (\"array\", \"key\")\n\n def __init__(self, array, key):\n \"\"\"\n Parameters\n ----------\n array : array_like\n Array like object to index.\n key : VectorizedIndexer\n \"\"\"\n if isinstance(key, (BasicIndexer, OuterIndexer)):\n self.key = _outer_to_vectorized_indexer(key, array.shape)\n else:\n self.key = _arrayize_vectorized_indexer(key, array.shape)\n self.array = as_indexable(array)\n\n @property\n def shape(self):\n return np.broadcast(*self.key.tuple).shape\n\n def __array__(self, dtype=None):\n return np.asarray(self.array[self.key], dtype=None)\n\n def _updated_key(self, new_key):\n return _combine_indexers(self.key, self.shape, new_key)\n\n def __getitem__(self, indexer):\n # If the indexed array becomes a scalar, return LazilyOuterIndexedArray\n if all(isinstance(ind, integer_types) for ind in indexer.tuple):\n key = BasicIndexer(tuple(k[indexer.tuple] for k in self.key.tuple))\n return LazilyOuterIndexedArray(self.array, key)\n return type(self)(self.array, self._updated_key(indexer))\n\n def transpose(self, order):\n key = VectorizedIndexer(tuple(k.transpose(order) for k in self.key.tuple))\n return type(self)(self.array, key)\n\n def __setitem__(self, key, value):\n raise NotImplementedError(\n \"Lazy item assignment with the vectorized indexer is not yet \"\n \"implemented. Load your data first by .load() or compute().\"\n )\n\n def __repr__(self):\n return \"%s(array=%r, key=%r)\" % (type(self).__name__, self.array, self.key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__wrap_numpy_scalars_CopyOnWriteArray.__setitem__.self_array_key_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__wrap_numpy_scalars_CopyOnWriteArray.__setitem__.self_array_key_value", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 631, "end_line": 662, "span_ids": ["CopyOnWriteArray.__setitem__", "CopyOnWriteArray", "CopyOnWriteArray.__getitem__", "_wrap_numpy_scalars", "CopyOnWriteArray._ensure_copied", "CopyOnWriteArray.__array__", "CopyOnWriteArray.transpose"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _wrap_numpy_scalars(array):\n \"\"\"Wrap NumPy scalars in 0d arrays.\"\"\"\n if np.isscalar(array):\n return np.array(array)\n else:\n return array\n\n\nclass CopyOnWriteArray(ExplicitlyIndexedNDArrayMixin):\n __slots__ = (\"array\", \"_copied\")\n\n def __init__(self, array):\n self.array = as_indexable(array)\n self._copied = False\n\n def _ensure_copied(self):\n if not self._copied:\n self.array = as_indexable(np.array(self.array))\n self._copied = True\n\n def __array__(self, dtype=None):\n return np.asarray(self.array, dtype=dtype)\n\n def __getitem__(self, key):\n return type(self)(_wrap_numpy_scalars(self.array[key]))\n\n def transpose(self, order):\n return self.array.transpose(order)\n\n def __setitem__(self, key, value):\n self._ensure_copied()\n self.array[key] = value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_MemoryCachedArray_MemoryCachedArray.__setitem__.self_array_key_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_MemoryCachedArray_MemoryCachedArray.__setitem__.self_array_key_value", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 665, "end_line": 686, "span_ids": ["MemoryCachedArray.__setitem__", "MemoryCachedArray._ensure_cached", "MemoryCachedArray.transpose", "MemoryCachedArray.__getitem__", "MemoryCachedArray.__array__", "MemoryCachedArray"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MemoryCachedArray(ExplicitlyIndexedNDArrayMixin):\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n self.array = _wrap_numpy_scalars(as_indexable(array))\n\n def _ensure_cached(self):\n if not isinstance(self.array, NumpyIndexingAdapter):\n self.array = NumpyIndexingAdapter(np.asarray(self.array))\n\n def __array__(self, dtype=None):\n self._ensure_cached()\n return np.asarray(self.array, dtype=dtype)\n\n def __getitem__(self, key):\n return type(self)(_wrap_numpy_scalars(self.array[key]))\n\n def transpose(self, order):\n return self.array.transpose(order)\n\n def __setitem__(self, key, value):\n self.array[key] = value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_as_indexable_as_indexable.raise_TypeError_Invalid_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_as_indexable_as_indexable.raise_TypeError_Invalid_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 689, "end_line": 706, "span_ids": ["as_indexable"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def as_indexable(array):\n \"\"\"\n This function always returns a ExplicitlyIndexed subclass,\n so that the vectorized indexing is always possible with the returned\n object.\n \"\"\"\n if isinstance(array, ExplicitlyIndexed):\n return array\n if isinstance(array, np.ndarray):\n return NumpyIndexingAdapter(array)\n if isinstance(array, pd.Index):\n return PandasIndexAdapter(array)\n if isinstance(array, dask_array_type):\n return DaskIndexingAdapter(array)\n if hasattr(array, \"__array_function__\"):\n return NdArrayLikeIndexingAdapter(array)\n\n raise TypeError(\"Invalid array type: {}\".format(type(array)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__outer_to_vectorized_indexer__outer_to_vectorized_indexer.return.VectorizedIndexer_tuple_n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__outer_to_vectorized_indexer__outer_to_vectorized_indexer.return.VectorizedIndexer_tuple_n", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 709, "end_line": 741, "span_ids": ["_outer_to_vectorized_indexer"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _outer_to_vectorized_indexer(key, shape):\n \"\"\"Convert an OuterIndexer into an vectorized indexer.\n\n Parameters\n ----------\n key : Outer/Basic Indexer\n An indexer to convert.\n shape : tuple\n Shape of the array subject to the indexing.\n\n Returns\n -------\n VectorizedIndexer\n Tuple suitable for use to index a NumPy array with vectorized indexing.\n Each element is an array: broadcasting them together gives the shape\n of the result.\n \"\"\"\n key = key.tuple\n\n n_dim = len([k for k in key if not isinstance(k, integer_types)])\n i_dim = 0\n new_key = []\n for k, size in zip(key, shape):\n if isinstance(k, integer_types):\n new_key.append(np.array(k).reshape((1,) * n_dim))\n else: # np.ndarray or slice\n if isinstance(k, slice):\n k = np.arange(*k.indices(size))\n assert k.dtype.kind in {\"i\", \"u\"}\n shape = [(1,) * i_dim + (k.size,) + (1,) * (n_dim - i_dim - 1)]\n new_key.append(k.reshape(*shape))\n i_dim += 1\n return VectorizedIndexer(tuple(new_key))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__outer_to_numpy_indexer__outer_to_numpy_indexer.if_len_k_for_k_in_key_tu.else_.return._outer_to_vectorized_inde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__outer_to_numpy_indexer__outer_to_numpy_indexer.if_len_k_for_k_in_key_tu.else_.return._outer_to_vectorized_inde", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 744, "end_line": 765, "span_ids": ["_outer_to_numpy_indexer"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _outer_to_numpy_indexer(key, shape):\n \"\"\"Convert an OuterIndexer into an indexer for NumPy.\n\n Parameters\n ----------\n key : Basic/OuterIndexer\n An indexer to convert.\n shape : tuple\n Shape of the array subject to the indexing.\n\n Returns\n -------\n tuple\n Tuple suitable for use to index a NumPy array.\n \"\"\"\n if len([k for k in key.tuple if not isinstance(k, slice)]) <= 1:\n # If there is only one vector and all others are slice,\n # it can be safely used in mixed basic/advanced indexing.\n # Boolean index should already be converted to integer array.\n return key.tuple\n else:\n return _outer_to_vectorized_indexer(key, shape).tuple", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__combine_indexers_IndexingSupport.VECTORIZED.3": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__combine_indexers_IndexingSupport.VECTORIZED.3", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 768, "end_line": 805, "span_ids": ["IndexingSupport", "_combine_indexers"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _combine_indexers(old_key, shape, new_key):\n \"\"\" Combine two indexers.\n\n Parameters\n ----------\n old_key: ExplicitIndexer\n The first indexer for the original array\n shape: tuple of ints\n Shape of the original array to be indexed by old_key\n new_key:\n The second indexer for indexing original[old_key]\n \"\"\"\n if not isinstance(old_key, VectorizedIndexer):\n old_key = _outer_to_vectorized_indexer(old_key, shape)\n if len(old_key.tuple) == 0:\n return new_key\n\n new_shape = np.broadcast(*old_key.tuple).shape\n if isinstance(new_key, VectorizedIndexer):\n new_key = _arrayize_vectorized_indexer(new_key, new_shape)\n else:\n new_key = _outer_to_vectorized_indexer(new_key, new_shape)\n\n return VectorizedIndexer(\n tuple(o[new_key.tuple] for o in np.broadcast_arrays(*old_key.tuple))\n )\n\n\n@enum.unique\nclass IndexingSupport(enum.Enum):\n # for backends that support only basic indexer\n BASIC = 0\n # for backends that support basic / outer indexer\n OUTER = 1\n # for backends that support outer indexer including at most 1 vector.\n OUTER_1VECTOR = 2\n # for backends that support full vectorized indexer.\n VECTORIZED = 3", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_explicit_indexing_adapter_explicit_indexing_adapter.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_explicit_indexing_adapter_explicit_indexing_adapter.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 808, "end_line": 840, "span_ids": ["explicit_indexing_adapter"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def explicit_indexing_adapter(\n key: ExplicitIndexer,\n shape: Tuple[int, ...],\n indexing_support: IndexingSupport,\n raw_indexing_method: Callable,\n) -> Any:\n \"\"\"Support explicit indexing by delegating to a raw indexing method.\n\n Outer and/or vectorized indexers are supported by indexing a second time\n with a NumPy array.\n\n Parameters\n ----------\n key : ExplicitIndexer\n Explicit indexing object.\n shape : Tuple[int, ...]\n Shape of the indexed array.\n indexing_support : IndexingSupport enum\n Form of indexing supported by raw_indexing_method.\n raw_indexing_method: callable\n Function (like ndarray.__getitem__) that when called with indexing key\n in the form of a tuple returns an indexed array.\n\n Returns\n -------\n Indexing result, in the form of a duck numpy-array.\n \"\"\"\n raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)\n result = raw_indexing_method(raw_key.tuple)\n if numpy_indices.tuple:\n # index the loaded np.ndarray\n result = NumpyIndexingAdapter(np.asarray(result))[numpy_indices]\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_decompose_indexer_decompose_indexer.raise_TypeError_unexpect": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_decompose_indexer_decompose_indexer.raise_TypeError_unexpect", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 843, "end_line": 850, "span_ids": ["decompose_indexer"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decompose_indexer(\n indexer: ExplicitIndexer, shape: Tuple[int, ...], indexing_support: IndexingSupport\n) -> Tuple[ExplicitIndexer, ExplicitIndexer]:\n if isinstance(indexer, VectorizedIndexer):\n return _decompose_vectorized_indexer(indexer, shape, indexing_support)\n if isinstance(indexer, (BasicIndexer, OuterIndexer)):\n return _decompose_outer_indexer(indexer, shape, indexing_support)\n raise TypeError(\"unexpected key type: {}\".format(indexer))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_slice__decompose_slice.if_step_0_.else_.return.slice_start_stop_step_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_slice__decompose_slice.if_step_0_.else_.return.slice_start_stop_step_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 853, "end_line": 866, "span_ids": ["_decompose_slice"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decompose_slice(key, size):\n \"\"\" convert a slice to successive two slices. The first slice always has\n a positive step.\n \"\"\"\n start, stop, step = key.indices(size)\n if step > 0:\n # If key already has a positive step, use it as is in the backend\n return key, slice(None)\n else:\n # determine stop precisely for step > 1 case\n # e.g. [98:2:-2] -> [98:3:-2]\n stop = start + int((stop - start - 1) / step) * step + 1\n start, stop = stop + 1, start + 1\n return slice(start, stop, -step), slice(None, None, -1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_vectorized_indexer__decompose_vectorized_indexer.return.backend_indexer1_np_inde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_vectorized_indexer__decompose_vectorized_indexer.return.backend_indexer1_np_inde", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 869, "end_line": 944, "span_ids": ["_decompose_vectorized_indexer"], "tokens": 756}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decompose_vectorized_indexer(\n indexer: VectorizedIndexer,\n shape: Tuple[int, ...],\n indexing_support: IndexingSupport,\n) -> Tuple[ExplicitIndexer, ExplicitIndexer]:\n \"\"\"\n Decompose vectorized indexer to the successive two indexers, where the\n first indexer will be used to index backend arrays, while the second one\n is used to index loaded on-memory np.ndarray.\n\n Parameters\n ----------\n indexer: VectorizedIndexer\n indexing_support: one of IndexerSupport entries\n\n Returns\n -------\n backend_indexer: OuterIndexer or BasicIndexer\n np_indexers: an ExplicitIndexer (VectorizedIndexer / BasicIndexer)\n\n Notes\n -----\n This function is used to realize the vectorized indexing for the backend\n arrays that only support basic or outer indexing.\n\n As an example, let us consider to index a few elements from a backend array\n with a vectorized indexer ([0, 3, 1], [2, 3, 2]).\n Even if the backend array only supports outer indexing, it is more\n efficient to load a subslice of the array than loading the entire array,\n\n >>> backend_indexer = OuterIndexer([0, 1, 3], [2, 3])\n >>> array = array[backend_indexer] # load subslice of the array\n >>> np_indexer = VectorizedIndexer([0, 2, 1], [0, 1, 0])\n >>> array[np_indexer] # vectorized indexing for on-memory np.ndarray.\n \"\"\"\n assert isinstance(indexer, VectorizedIndexer)\n\n if indexing_support is IndexingSupport.VECTORIZED:\n return indexer, BasicIndexer(())\n\n backend_indexer_elems = []\n np_indexer_elems = []\n # convert negative indices\n indexer_elems = [\n np.where(k < 0, k + s, k) if isinstance(k, np.ndarray) else k\n for k, s in zip(indexer.tuple, shape)\n ]\n\n for k, s in zip(indexer_elems, shape):\n if isinstance(k, slice):\n # If it is a slice, then we will slice it as-is\n # (but make its step positive) in the backend,\n # and then use all of it (slice(None)) for the in-memory portion.\n bk_slice, np_slice = _decompose_slice(k, s)\n backend_indexer_elems.append(bk_slice)\n np_indexer_elems.append(np_slice)\n else:\n # If it is a (multidimensional) np.ndarray, just pickup the used\n # keys without duplication and store them as a 1d-np.ndarray.\n oind, vind = np.unique(k, return_inverse=True)\n backend_indexer_elems.append(oind)\n np_indexer_elems.append(vind.reshape(*k.shape))\n\n backend_indexer = OuterIndexer(tuple(backend_indexer_elems))\n np_indexer = VectorizedIndexer(tuple(np_indexer_elems))\n\n if indexing_support is IndexingSupport.OUTER:\n return backend_indexer, np_indexer\n\n # If the backend does not support outer indexing,\n # backend_indexer (OuterIndexer) is also decomposed.\n backend_indexer1, np_indexer1 = _decompose_outer_indexer(\n backend_indexer, shape, indexing_support\n )\n np_indexer = _combine_indexers(np_indexer1, shape, np_indexer)\n return backend_indexer1, np_indexer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_outer_indexer__decompose_outer_indexer.indexer_elems.pos_indexer": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_outer_indexer__decompose_outer_indexer.indexer_elems.pos_indexer", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 947, "end_line": 997, "span_ids": ["_decompose_outer_indexer"], "tokens": 495}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decompose_outer_indexer(\n indexer: Union[BasicIndexer, OuterIndexer],\n shape: Tuple[int, ...],\n indexing_support: IndexingSupport,\n) -> Tuple[ExplicitIndexer, ExplicitIndexer]:\n \"\"\"\n Decompose outer indexer to the successive two indexers, where the\n first indexer will be used to index backend arrays, while the second one\n is used to index the loaded on-memory np.ndarray.\n\n Parameters\n ----------\n indexer: OuterIndexer or BasicIndexer\n indexing_support: One of the entries of IndexingSupport\n\n Returns\n -------\n backend_indexer: OuterIndexer or BasicIndexer\n np_indexers: an ExplicitIndexer (OuterIndexer / BasicIndexer)\n\n Notes\n -----\n This function is used to realize the vectorized indexing for the backend\n arrays that only support basic or outer indexing.\n\n As an example, let us consider to index a few elements from a backend array\n with a orthogonal indexer ([0, 3, 1], [2, 3, 2]).\n Even if the backend array only supports basic indexing, it is more\n efficient to load a subslice of the array than loading the entire array,\n\n >>> backend_indexer = BasicIndexer(slice(0, 3), slice(2, 3))\n >>> array = array[backend_indexer] # load subslice of the array\n >>> np_indexer = OuterIndexer([0, 2, 1], [0, 1, 0])\n >>> array[np_indexer] # outer indexing for on-memory np.ndarray.\n \"\"\"\n if indexing_support == IndexingSupport.VECTORIZED:\n return indexer, BasicIndexer(())\n assert isinstance(indexer, (OuterIndexer, BasicIndexer))\n\n backend_indexer = []\n np_indexer = []\n # make indexer positive\n pos_indexer = []\n for k, s in zip(indexer.tuple, shape):\n if isinstance(k, np.ndarray):\n pos_indexer.append(np.where(k < 0, k + s, k))\n elif isinstance(k, integer_types) and k < 0:\n pos_indexer.append(k + s)\n else:\n pos_indexer.append(k)\n indexer_elems = pos_indexer\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_outer_indexer.if_indexing_support_is_In__decompose_outer_indexer.return._BasicIndexer_tuple_backe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__decompose_outer_indexer.if_indexing_support_is_In__decompose_outer_indexer.return._BasicIndexer_tuple_backe", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 999, "end_line": 1066, "span_ids": ["_decompose_outer_indexer"], "tokens": 746}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _decompose_outer_indexer(\n indexer: Union[BasicIndexer, OuterIndexer],\n shape: Tuple[int, ...],\n indexing_support: IndexingSupport,\n) -> Tuple[ExplicitIndexer, ExplicitIndexer]:\n # ... other code\n\n if indexing_support is IndexingSupport.OUTER_1VECTOR:\n # some backends such as h5py supports only 1 vector in indexers\n # We choose the most efficient axis\n gains = [\n (np.max(k) - np.min(k) + 1.0) / len(np.unique(k))\n if isinstance(k, np.ndarray)\n else 0\n for k in indexer_elems\n ]\n array_index = np.argmax(np.array(gains)) if len(gains) > 0 else None\n\n for i, (k, s) in enumerate(zip(indexer_elems, shape)):\n if isinstance(k, np.ndarray) and i != array_index:\n # np.ndarray key is converted to slice that covers the entire\n # entries of this key.\n backend_indexer.append(slice(np.min(k), np.max(k) + 1))\n np_indexer.append(k - np.min(k))\n elif isinstance(k, np.ndarray):\n # Remove duplicates and sort them in the increasing order\n pkey, ekey = np.unique(k, return_inverse=True)\n backend_indexer.append(pkey)\n np_indexer.append(ekey)\n elif isinstance(k, integer_types):\n backend_indexer.append(k)\n else: # slice: convert positive step slice for backend\n bk_slice, np_slice = _decompose_slice(k, s)\n backend_indexer.append(bk_slice)\n np_indexer.append(np_slice)\n\n return (OuterIndexer(tuple(backend_indexer)), OuterIndexer(tuple(np_indexer)))\n\n if indexing_support == IndexingSupport.OUTER:\n for k, s in zip(indexer_elems, shape):\n if isinstance(k, slice):\n # slice: convert positive step slice for backend\n bk_slice, np_slice = _decompose_slice(k, s)\n backend_indexer.append(bk_slice)\n np_indexer.append(np_slice)\n elif isinstance(k, integer_types):\n backend_indexer.append(k)\n elif isinstance(k, np.ndarray) and (np.diff(k) >= 0).all():\n backend_indexer.append(k)\n np_indexer.append(slice(None))\n else:\n # Remove duplicates and sort them in the increasing order\n oind, vind = np.unique(k, return_inverse=True)\n backend_indexer.append(oind)\n np_indexer.append(vind.reshape(*k.shape))\n\n return (OuterIndexer(tuple(backend_indexer)), OuterIndexer(tuple(np_indexer)))\n\n # basic indexer\n assert indexing_support == IndexingSupport.BASIC\n\n for k, s in zip(indexer_elems, shape):\n if isinstance(k, np.ndarray):\n # np.ndarray key is converted to slice that covers the entire\n # entries of this key.\n backend_indexer.append(slice(np.min(k), np.max(k) + 1))\n np_indexer.append(k - np.min(k))\n elif isinstance(k, integer_types):\n backend_indexer.append(k)\n else: # slice: convert positive step slice for backend\n bk_slice, np_slice = _decompose_slice(k, s)\n backend_indexer.append(bk_slice)\n np_indexer.append(np_slice)\n\n return (BasicIndexer(tuple(backend_indexer)), OuterIndexer(tuple(np_indexer)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__arrayize_vectorized_indexer__arrayize_vectorized_indexer.return.VectorizedIndexer_tuple_n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__arrayize_vectorized_indexer__arrayize_vectorized_indexer.return.VectorizedIndexer_tuple_n", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1069, "end_line": 1086, "span_ids": ["_arrayize_vectorized_indexer"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _arrayize_vectorized_indexer(indexer, shape):\n \"\"\" Return an identical vindex but slices are replaced by arrays \"\"\"\n slices = [v for v in indexer.tuple if isinstance(v, slice)]\n if len(slices) == 0:\n return indexer\n\n arrays = [v for v in indexer.tuple if isinstance(v, np.ndarray)]\n n_dim = arrays[0].ndim if len(arrays) > 0 else 0\n i_dim = 0\n new_key = []\n for v, size in zip(indexer.tuple, shape):\n if isinstance(v, np.ndarray):\n new_key.append(np.reshape(v, v.shape + (1,) * len(slices)))\n else: # slice\n shape = (1,) * (n_dim + i_dim) + (-1,) + (1,) * (len(slices) - i_dim - 1)\n new_key.append(np.arange(*v.indices(size)).reshape(shape))\n i_dim += 1\n return VectorizedIndexer(tuple(new_key))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__dask_array_with_chunks_hint__logical_any.return.functools_reduce_operator": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__dask_array_with_chunks_hint__logical_any.return.functools_reduce_operator", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1089, "end_line": 1102, "span_ids": ["_logical_any", "_dask_array_with_chunks_hint"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dask_array_with_chunks_hint(array, chunks):\n \"\"\"Create a dask array using the chunks hint for dimensions of size > 1.\"\"\"\n import dask.array as da\n\n if len(chunks) < array.ndim:\n raise ValueError(\"not enough chunks in hint\")\n new_chunks = []\n for chunk, size in zip(chunks, array.shape):\n new_chunks.append(chunk if size > 1 else (1,))\n return da.from_array(array, new_chunks)\n\n\ndef _logical_any(args):\n return functools.reduce(operator.or_, args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__masked_result_drop_slice__masked_result_drop_slice.return.mask": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__masked_result_drop_slice__masked_result_drop_slice.return.mask", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1105, "end_line": 1125, "span_ids": ["_masked_result_drop_slice"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _masked_result_drop_slice(key, data=None):\n\n key = (k for k in key if not isinstance(k, slice))\n chunks_hint = getattr(data, \"chunks\", None)\n\n new_keys = []\n for k in key:\n if isinstance(k, np.ndarray):\n if isinstance(data, dask_array_type):\n new_keys.append(_dask_array_with_chunks_hint(k, chunks_hint))\n elif isinstance(data, sparse_array_type):\n import sparse\n\n new_keys.append(sparse.COO.from_numpy(k))\n else:\n new_keys.append(k)\n else:\n new_keys.append(k)\n\n mask = _logical_any(k == -1 for k in new_keys)\n return mask", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_create_mask_create_mask.return.mask": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_create_mask_create_mask.return.mask", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1128, "end_line": 1170, "span_ids": ["create_mask"], "tokens": 373}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_mask(indexer, shape, data=None):\n \"\"\"Create a mask for indexing with a fill-value.\n\n Parameters\n ----------\n indexer : ExplicitIndexer\n Indexer with -1 in integer or ndarray value to indicate locations in\n the result that should be masked.\n shape : tuple\n Shape of the array being indexed.\n data : optional\n Data for which mask is being created. If data is a dask arrays, its chunks\n are used as a hint for chunks on the resulting mask. If data is a sparse\n array, the returned mask is also a sparse array.\n\n Returns\n -------\n mask : bool, np.ndarray, SparseArray or dask.array.Array with dtype=bool\n Same type as data. Has the same shape as the indexing result.\n \"\"\"\n if isinstance(indexer, OuterIndexer):\n key = _outer_to_vectorized_indexer(indexer, shape).tuple\n assert not any(isinstance(k, slice) for k in key)\n mask = _masked_result_drop_slice(key, data)\n\n elif isinstance(indexer, VectorizedIndexer):\n key = indexer.tuple\n base_mask = _masked_result_drop_slice(key, data)\n slice_shape = tuple(\n np.arange(*k.indices(size)).size\n for k, size in zip(key, shape)\n if isinstance(k, slice)\n )\n expanded_mask = base_mask[(Ellipsis,) + (np.newaxis,) * len(slice_shape)]\n mask = duck_array_ops.broadcast_to(expanded_mask, base_mask.shape + slice_shape)\n\n elif isinstance(indexer, BasicIndexer):\n mask = any(k == -1 for k in indexer.tuple)\n\n else:\n raise TypeError(\"unexpected key type: {}\".format(type(indexer)))\n\n return mask", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__posify_mask_subindexer__posify_mask_subindexer.return.new_index": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py__posify_mask_subindexer__posify_mask_subindexer.return.new_index", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1173, "end_line": 1196, "span_ids": ["_posify_mask_subindexer"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _posify_mask_subindexer(index):\n \"\"\"Convert masked indices in a flat array to the nearest unmasked index.\n\n Parameters\n ----------\n index : np.ndarray\n One dimensional ndarray with dtype=int.\n\n Returns\n -------\n np.ndarray\n One dimensional ndarray with all values equal to -1 replaced by an\n adjacent non-masked element.\n \"\"\"\n masked = index == -1\n unmasked_locs = np.flatnonzero(~masked)\n if not unmasked_locs.size:\n # indexing unmasked_locs is invalid\n return np.zeros_like(index)\n masked_locs = np.flatnonzero(masked)\n prev_value = np.maximum(0, np.searchsorted(unmasked_locs, masked_locs) - 1)\n new_index = index.copy()\n new_index[masked_locs] = index[unmasked_locs[prev_value]]\n return new_index", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_posify_mask_indexer_posify_mask_indexer.return.type_indexer_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_posify_mask_indexer_posify_mask_indexer.return.type_indexer_key_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1199, "end_line": 1222, "span_ids": ["posify_mask_indexer"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def posify_mask_indexer(indexer):\n \"\"\"Convert masked values (-1) in an indexer to nearest unmasked values.\n\n This routine is useful for dask, where it can be much faster to index\n adjacent points than arbitrary points from the end of an array.\n\n Parameters\n ----------\n indexer : ExplicitIndexer\n Input indexer.\n\n Returns\n -------\n ExplicitIndexer\n Same type of input, with all values in ndarray keys equal to -1\n replaced by an adjacent non-masked element.\n \"\"\"\n key = tuple(\n _posify_mask_subindexer(k.ravel()).reshape(k.shape)\n if isinstance(k, np.ndarray)\n else k\n for k in indexer.tuple\n )\n return type(indexer)(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_NumpyIndexingAdapter_NumpyIndexingAdapter._indexing_array_and_key.return.array_key": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_NumpyIndexingAdapter_NumpyIndexingAdapter._indexing_array_and_key.return.array_key", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1225, "end_line": 1255, "span_ids": ["NumpyIndexingAdapter._indexing_array_and_key", "NumpyIndexingAdapter"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyIndexingAdapter(ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrap a NumPy array to use explicit indexing.\"\"\"\n\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n # In NumpyIndexingAdapter we only allow to store bare np.ndarray\n if not isinstance(array, np.ndarray):\n raise TypeError(\n \"NumpyIndexingAdapter only wraps np.ndarray. \"\n \"Trying to wrap {}\".format(type(array))\n )\n self.array = array\n\n def _indexing_array_and_key(self, key):\n if isinstance(key, OuterIndexer):\n array = self.array\n key = _outer_to_numpy_indexer(key, self.array.shape)\n elif isinstance(key, VectorizedIndexer):\n array = nputils.NumpyVIndexAdapter(self.array)\n key = key.tuple\n elif isinstance(key, BasicIndexer):\n array = self.array\n # We want 0d slices rather than scalars. This is achieved by\n # appending an ellipsis (see\n # https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#detailed-notes).\n key = key.tuple + (Ellipsis,)\n else:\n raise TypeError(\"unexpected key type: {}\".format(type(key)))\n\n return array, key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_NumpyIndexingAdapter.transpose_NdArrayLikeIndexingAdapter.__init__.self.array.array": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_NumpyIndexingAdapter.transpose_NdArrayLikeIndexingAdapter.__init__.self.array.array", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1257, "end_line": 1288, "span_ids": ["NdArrayLikeIndexingAdapter", "NumpyIndexingAdapter.__setitem__", "NumpyIndexingAdapter.__getitem__", "NumpyIndexingAdapter.transpose"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyIndexingAdapter(ExplicitlyIndexedNDArrayMixin):\n\n def transpose(self, order):\n return self.array.transpose(order)\n\n def __getitem__(self, key):\n array, key = self._indexing_array_and_key(key)\n return array[key]\n\n def __setitem__(self, key, value):\n array, key = self._indexing_array_and_key(key)\n try:\n array[key] = value\n except ValueError:\n # More informative exception if read-only view\n if not array.flags.writeable and not array.flags.owndata:\n raise ValueError(\n \"Assignment destination is a view. \"\n \"Do you want to .copy() array first?\"\n )\n else:\n raise\n\n\nclass NdArrayLikeIndexingAdapter(NumpyIndexingAdapter):\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n if not hasattr(array, \"__array_function__\"):\n raise TypeError(\n \"NdArrayLikeIndexingAdapter must wrap an object that \"\n \"implements the __array_function__ protocol\"\n )\n self.array = array", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_DaskIndexingAdapter_DaskIndexingAdapter.transpose.return.self_array_transpose_orde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_DaskIndexingAdapter_DaskIndexingAdapter.transpose.return.self_array_transpose_orde", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1291, "end_line": 1330, "span_ids": ["DaskIndexingAdapter.__getitem__", "DaskIndexingAdapter.transpose", "DaskIndexingAdapter.__setitem__", "DaskIndexingAdapter"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DaskIndexingAdapter(ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrap a dask array to support explicit indexing.\"\"\"\n\n __slots__ = (\"array\",)\n\n def __init__(self, array):\n \"\"\" This adapter is created in Variable.__getitem__ in\n Variable._broadcast_indexes.\n \"\"\"\n self.array = array\n\n def __getitem__(self, key):\n if isinstance(key, BasicIndexer):\n return self.array[key.tuple]\n elif isinstance(key, VectorizedIndexer):\n return self.array.vindex[key.tuple]\n else:\n assert isinstance(key, OuterIndexer)\n key = key.tuple\n try:\n return self.array[key]\n except NotImplementedError:\n # manual orthogonal indexing.\n # TODO: port this upstream into dask in a saner way.\n value = self.array\n for axis, subkey in reversed(list(enumerate(key))):\n value = value[(slice(None),) * axis + (subkey,)]\n return value\n\n def __setitem__(self, key, value):\n raise TypeError(\n \"this variable's data is stored in a dask array, \"\n \"which does not support item assignment. To \"\n \"assign to this variable, you must first load it \"\n \"into memory explicitly using the .load() \"\n \"method or accessing its .values attribute.\"\n )\n\n def transpose(self, order):\n return self.array.transpose(order)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter_PandasIndexAdapter.shape.return._len_self_array_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter_PandasIndexAdapter.shape.return._len_self_array_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1333, "end_line": 1371, "span_ids": ["PandasIndexAdapter.dtype", "PandasIndexAdapter", "PandasIndexAdapter.__array__", "PandasIndexAdapter.shape"], "tokens": 286}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PandasIndexAdapter(ExplicitlyIndexedNDArrayMixin):\n \"\"\"Wrap a pandas.Index to preserve dtypes and handle explicit indexing.\n \"\"\"\n\n __slots__ = (\"array\", \"_dtype\")\n\n def __init__(self, array: Any, dtype: DTypeLike = None):\n self.array = utils.safe_cast_to_index(array)\n if dtype is None:\n if isinstance(array, pd.PeriodIndex):\n dtype = np.dtype(\"O\")\n elif hasattr(array, \"categories\"):\n # category isn't a real numpy dtype\n dtype = array.categories.dtype\n elif not utils.is_valid_numpy_dtype(array.dtype):\n dtype = np.dtype(\"O\")\n else:\n dtype = array.dtype\n else:\n dtype = np.dtype(dtype)\n self._dtype = dtype\n\n @property\n def dtype(self) -> np.dtype:\n return self._dtype\n\n def __array__(self, dtype: DTypeLike = None) -> np.ndarray:\n if dtype is None:\n dtype = self.dtype\n array = self.array\n if isinstance(array, pd.PeriodIndex):\n with suppress(AttributeError):\n # this might not be public API\n array = array.astype(\"object\")\n return np.asarray(array.values, dtype=dtype)\n\n @property\n def shape(self) -> Tuple[int]:\n return (len(self.array),)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter.__getitem___PandasIndexAdapter.__getitem__.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter.__getitem___PandasIndexAdapter.__getitem__.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1373, "end_line": 1410, "span_ids": ["PandasIndexAdapter.__getitem__"], "tokens": 381}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PandasIndexAdapter(ExplicitlyIndexedNDArrayMixin):\n\n def __getitem__(\n self, indexer\n ) -> Union[NumpyIndexingAdapter, np.ndarray, np.datetime64, np.timedelta64]:\n key = indexer.tuple\n if isinstance(key, tuple) and len(key) == 1:\n # unpack key so it can index a pandas.Index object (pandas.Index\n # objects don't like tuples)\n key, = key\n\n if getattr(key, \"ndim\", 0) > 1: # Return np-array if multidimensional\n return NumpyIndexingAdapter(self.array.values)[indexer]\n\n result = self.array[key]\n\n if isinstance(result, pd.Index):\n result = PandasIndexAdapter(result, dtype=self.dtype)\n else:\n # result is a scalar\n if result is pd.NaT:\n # work around the impossibility of casting NaT with asarray\n # note: it probably would be better in general to return\n # pd.Timestamp rather np.than datetime64 but this is easier\n # (for now)\n result = np.datetime64(\"NaT\", \"ns\")\n elif isinstance(result, timedelta):\n result = np.timedelta64(getattr(result, \"value\", result), \"ns\")\n elif isinstance(result, pd.Timestamp):\n # Work around for GH: pydata/xarray#1932 and numpy/numpy#10668\n # numpy fails to convert pd.Timestamp to np.datetime64[ns]\n result = np.asarray(result.to_datetime64())\n elif self.dtype != object:\n result = np.asarray(result, dtype=self.dtype)\n\n # as for numpy.ndarray indexing, we always want the result to be\n # a NumPy array.\n result = utils.to_0d_array(result)\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter.transpose_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/indexing.py_PandasIndexAdapter.transpose_", "embedding": null, "metadata": {"file_path": "xarray/core/indexing.py", "file_name": "indexing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1412, "end_line": 1428, "span_ids": ["PandasIndexAdapter.copy", "PandasIndexAdapter.transpose", "PandasIndexAdapter.__repr__"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PandasIndexAdapter(ExplicitlyIndexedNDArrayMixin):\n\n def transpose(self, order) -> pd.Index:\n return self.array # self.array should be always one-dimensional\n\n def __repr__(self) -> str:\n return \"%s(array=%r, dtype=%r)\" % (type(self).__name__, self.array, self.dtype)\n\n def copy(self, deep: bool = True) -> \"PandasIndexAdapter\":\n # Not the same as just writing `self.array.copy(deep=deep)`, as\n # shallow copies of the underlying numpy.ndarrays become deep ones\n # upon pickling\n # >>> len(pickle.dumps((self.array, self.array)))\n # 4000281\n # >>> len(pickle.dumps((self.array, self.array.copy(deep=False))))\n # 8000341\n array = self.array.copy(deep=True) if deep else self.array\n return PandasIndexAdapter(array, self._dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_from_typing_import__MergeError._TODO_move_this_to_an_x": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_from_typing_import__MergeError._TODO_move_this_to_an_x", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 77, "span_ids": ["impl", "broadcast_dimension_size", "MergeError", "impl:2", "imports", "imports:7"], "tokens": 485}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import (\n TYPE_CHECKING,\n AbstractSet,\n Any,\n Dict,\n Hashable,\n Iterable,\n List,\n Mapping,\n NamedTuple,\n Optional,\n Sequence,\n Set,\n Tuple,\n Union,\n)\n\nimport pandas as pd\n\nfrom . import dtypes, pdcompat\nfrom .alignment import deep_align\nfrom .utils import Frozen, dict_equiv\nfrom .variable import Variable, as_variable, assert_unique_multiindex_level_names\n\nif TYPE_CHECKING:\n from .coordinates import Coordinates\n from .dataarray import DataArray\n from .dataset import Dataset\n\n DimsLike = Union[Hashable, Sequence[Hashable]]\n ArrayLike = Any\n VariableLike = Union[\n ArrayLike,\n Tuple[DimsLike, ArrayLike],\n Tuple[DimsLike, ArrayLike, Mapping],\n Tuple[DimsLike, ArrayLike, Mapping, Mapping],\n ]\n XarrayValue = Union[DataArray, Variable, VariableLike]\n DatasetLike = Union[Dataset, Mapping[Hashable, XarrayValue]]\n CoercibleValue = Union[XarrayValue, pd.Series, pd.DataFrame]\n CoercibleMapping = Union[Dataset, Mapping[Hashable, CoercibleValue]]\n\n\nPANDAS_TYPES = (pd.Series, pd.DataFrame, pdcompat.Panel)\n\n_VALID_COMPAT = Frozen(\n {\n \"identical\": 0,\n \"equals\": 1,\n \"broadcast_equals\": 2,\n \"minimal\": 3,\n \"no_conflicts\": 4,\n \"override\": 5,\n }\n)\n\n\ndef broadcast_dimension_size(variables: List[Variable],) -> Dict[Hashable, int]:\n \"\"\"Extract dimension sizes from a dictionary of variables.\n\n Raises ValueError if any dimensions have different sizes.\n \"\"\"\n dims: Dict[Hashable, int] = {}\n for var in variables:\n for dim, size in zip(var.dims, var.shape):\n if dim in dims and size != dims[dim]:\n raise ValueError(\"index %r not aligned\" % dim)\n dims[dim] = size\n return dims\n\n\nclass MergeError(ValueError):\n \"\"\"Error class for merge failures due to incompatible arguments.\n \"\"\"\n\n # inherits from ValueError for backward compatibility\n # TODO: move this to an xarray.exceptions module?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_unique_variable_MergeElement.Tuple_Variable_Optional_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_unique_variable_MergeElement.Tuple_Variable_Optional_", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 150, "span_ids": ["unique_variable", "impl:20", "_assert_compat_valid"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unique_variable(\n name: Hashable,\n variables: List[Variable],\n compat: str = \"broadcast_equals\",\n equals: bool = None,\n) -> Variable:\n \"\"\"Return the unique variable from a list of variables or raise MergeError.\n\n Parameters\n ----------\n name : hashable\n Name for this variable.\n variables : list of xarray.Variable\n List of Variable objects, all of which go by the same name in different\n inputs.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional\n Type of equality check to use.\n equals: None or bool,\n corresponding to result of compat test\n\n Returns\n -------\n Variable to use in the result.\n\n Raises\n ------\n MergeError: if any of the variables are not equal.\n \"\"\"\n out = variables[0]\n\n if len(variables) == 1 or compat == \"override\":\n return out\n\n combine_method = None\n\n if compat == \"minimal\":\n compat = \"broadcast_equals\"\n\n if compat == \"broadcast_equals\":\n dim_lengths = broadcast_dimension_size(variables)\n out = out.set_dims(dim_lengths)\n\n if compat == \"no_conflicts\":\n combine_method = \"fillna\"\n\n if equals is None:\n out = out.compute()\n for var in variables[1:]:\n equals = getattr(out, compat)(var)\n if not equals:\n break\n\n if not equals:\n raise MergeError(\n \"conflicting values for variable {!r} on objects to be combined. \"\n \"You can skip this check by specifying compat='override'.\".format(name)\n )\n\n if combine_method:\n for var in variables[1:]:\n out = getattr(out, combine_method)(var)\n\n return out\n\n\ndef _assert_compat_valid(compat):\n if compat not in _VALID_COMPAT:\n raise ValueError(\"compat=%r invalid: must be %s\" % (compat, set(_VALID_COMPAT)))\n\n\nMergeElement = Tuple[Variable, Optional[pd.Index]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_collected_merge_collected.return.merged_vars_merged_index": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_collected_merge_collected.return.merged_vars_merged_index", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 153, "end_line": 224, "span_ids": ["merge_collected"], "tokens": 551}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_collected(\n grouped: Dict[Hashable, List[MergeElement]],\n prioritized: Mapping[Hashable, MergeElement] = None,\n compat: str = \"minimal\",\n) -> Tuple[Dict[Hashable, Variable], Dict[Hashable, pd.Index]]:\n \"\"\"Merge dicts of variables, while resolving conflicts appropriately.\n\n Parameters\n ----------\n\n Type of equality check to use when checking for conflicts.\n\n Returns\n -------\n Dict with keys taken by the union of keys on list_of_mappings,\n and Variable values corresponding to those that should be found on the\n merged result.\n \"\"\"\n if prioritized is None:\n prioritized = {}\n\n _assert_compat_valid(compat)\n\n merged_vars: Dict[Hashable, Variable] = {}\n merged_indexes: Dict[Hashable, pd.Index] = {}\n\n for name, elements_list in grouped.items():\n if name in prioritized:\n variable, index = prioritized[name]\n merged_vars[name] = variable\n if index is not None:\n merged_indexes[name] = index\n else:\n indexed_elements = [\n (variable, index)\n for variable, index in elements_list\n if index is not None\n ]\n\n if indexed_elements:\n # TODO(shoyer): consider adjusting this logic. Are we really\n # OK throwing away variable without an index in favor of\n # indexed variables, without even checking if values match?\n variable, index = indexed_elements[0]\n for _, other_index in indexed_elements[1:]:\n if not index.equals(other_index):\n raise MergeError(\n \"conflicting values for index %r on objects to be \"\n \"combined:\\nfirst value: %r\\nsecond value: %r\"\n % (name, index, other_index)\n )\n if compat == \"identical\":\n for other_variable, _ in indexed_elements[1:]:\n if not dict_equiv(variable.attrs, other_variable.attrs):\n raise MergeError(\n \"conflicting attribute values on combined \"\n \"variable %r:\\nfirst value: %r\\nsecond value: %r\"\n % (name, variable.attrs, other_variable.attrs)\n )\n merged_vars[name] = variable\n merged_indexes[name] = index\n else:\n variables = [variable for variable, _ in elements_list]\n try:\n merged_vars[name] = unique_variable(name, variables, compat)\n except MergeError:\n if compat != \"minimal\":\n # we need more than \"minimal\" compatibility (for which\n # we drop conflicting coordinates)\n raise\n\n return merged_vars, merged_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_collect_variables_and_indexes_collect_variables_and_indexes.return.grouped": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_collect_variables_and_indexes_collect_variables_and_indexes.return.grouped", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 227, "end_line": 274, "span_ids": ["collect_variables_and_indexes"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def collect_variables_and_indexes(\n list_of_mappings: \"List[DatasetLike]\",\n) -> Dict[Hashable, List[MergeElement]]:\n \"\"\"Collect variables and indexes from list of mappings of xarray objects.\n\n Mappings must either be Dataset objects, or have values of one of the\n following types:\n - an xarray.Variable\n - a tuple `(dims, data[, attrs[, encoding]])` that can be converted in\n an xarray.Variable\n - or an xarray.DataArray\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n\n grouped: Dict[Hashable, List[Tuple[Variable, pd.Index]]] = {}\n\n def append(name, variable, index):\n values = grouped.setdefault(name, [])\n values.append((variable, index))\n\n def append_all(variables, indexes):\n for name, variable in variables.items():\n append(name, variable, indexes.get(name))\n\n for mapping in list_of_mappings:\n if isinstance(mapping, Dataset):\n append_all(mapping.variables, mapping.indexes)\n continue\n\n for name, variable in mapping.items():\n if isinstance(variable, DataArray):\n coords = variable._coords.copy() # use private API for speed\n indexes = dict(variable.indexes)\n # explicitly overwritten variables should take precedence\n coords.pop(name, None)\n indexes.pop(name, None)\n append_all(coords, indexes)\n\n variable = as_variable(variable, name=name)\n if variable.dims == (name,):\n variable = variable.to_index_variable()\n index = variable.to_index()\n else:\n index = None\n append(name, variable, index)\n\n return grouped", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_collect_from_coordinates_collect_from_coordinates.return.grouped": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_collect_from_coordinates_collect_from_coordinates.return.grouped", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 277, "end_line": 289, "span_ids": ["collect_from_coordinates"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def collect_from_coordinates(\n list_of_coords: \"List[Coordinates]\"\n) -> Dict[Hashable, List[MergeElement]]:\n \"\"\"Collect variables and indexes to be merged from Coordinate objects.\"\"\"\n grouped: Dict[Hashable, List[Tuple[Variable, pd.Index]]] = {}\n\n for coords in list_of_coords:\n variables = coords.variables\n indexes = coords.indexes\n for name, variable in variables.items():\n value = grouped.setdefault(name, [])\n value.append((variable, indexes.get(name)))\n return grouped", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_coordinates_without_align_merge_coordinates_without_align.return.merge_collected_filtered_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_coordinates_without_align_merge_coordinates_without_align.return.merge_collected_filtered_", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 292, "end_line": 317, "span_ids": ["merge_coordinates_without_align"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_coordinates_without_align(\n objects: \"List[Coordinates]\",\n prioritized: Mapping[Hashable, MergeElement] = None,\n exclude_dims: AbstractSet = frozenset(),\n) -> Tuple[Dict[Hashable, Variable], Dict[Hashable, pd.Index]]:\n \"\"\"Merge variables/indexes from coordinates without automatic alignments.\n\n This function is used for merging coordinate from pre-existing xarray\n objects.\n \"\"\"\n collected = collect_from_coordinates(objects)\n\n if exclude_dims:\n filtered: Dict[Hashable, List[MergeElement]] = {}\n for name, elements in collected.items():\n new_elements = [\n (variable, index)\n for variable, index in elements\n if exclude_dims.isdisjoint(variable.dims)\n ]\n if new_elements:\n filtered[name] = new_elements\n else:\n filtered = collected\n\n return merge_collected(filtered, prioritized)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_determine_coords_determine_coords.return.coord_names_noncoord_nam": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_determine_coords_determine_coords.return.coord_names_noncoord_nam", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 320, "end_line": 355, "span_ids": ["determine_coords"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def determine_coords(\n list_of_mappings: Iterable[\"DatasetLike\"]\n) -> Tuple[Set[Hashable], Set[Hashable]]:\n \"\"\"Given a list of dicts with xarray object values, identify coordinates.\n\n Parameters\n ----------\n list_of_mappings : list of dict or Dataset objects\n Of the same form as the arguments to expand_variable_dicts.\n\n Returns\n -------\n coord_names : set of variable names\n noncoord_names : set of variable names\n All variable found in the input should appear in either the set of\n coordinate or non-coordinate names.\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n\n coord_names: Set[Hashable] = set()\n noncoord_names: Set[Hashable] = set()\n\n for mapping in list_of_mappings:\n if isinstance(mapping, Dataset):\n coord_names.update(mapping.coords)\n noncoord_names.update(mapping.data_vars)\n else:\n for name, var in mapping.items():\n if isinstance(var, DataArray):\n coords = set(var._coords) # use private API for speed\n # explicitly overwritten variables should take precedence\n coords.discard(name)\n coord_names.update(coords)\n\n return coord_names, noncoord_names", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_coerce_pandas_values_coerce_pandas_values.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_coerce_pandas_values_coerce_pandas_values.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 358, "end_line": 388, "span_ids": ["coerce_pandas_values"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def coerce_pandas_values(objects: Iterable[\"CoercibleMapping\"]) -> List[\"DatasetLike\"]:\n \"\"\"Convert pandas values found in a list of labeled objects.\n\n Parameters\n ----------\n objects : list of Dataset or mappings\n The mappings may contain any sort of objects coercible to\n xarray.Variables as keys, including pandas objects.\n\n Returns\n -------\n List of Dataset or dictionary objects. Any inputs or values in the inputs\n that were pandas objects have been converted into native xarray objects.\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n\n out = []\n for obj in objects:\n if isinstance(obj, Dataset):\n variables: \"DatasetLike\" = obj\n else:\n variables = {}\n if isinstance(obj, PANDAS_TYPES):\n obj = dict(obj.iteritems())\n for k, v in obj.items():\n if isinstance(v, PANDAS_TYPES):\n v = DataArray(v)\n variables[k] = v\n out.append(variables)\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py__get_priority_vars_and_indexes__get_priority_vars_and_indexes.return.grouped": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py__get_priority_vars_and_indexes__get_priority_vars_and_indexes.return.grouped", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 391, "end_line": 421, "span_ids": ["_get_priority_vars_and_indexes"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_priority_vars_and_indexes(\n objects: List[\"DatasetLike\"], priority_arg: Optional[int], compat: str = \"equals\"\n) -> Dict[Hashable, MergeElement]:\n \"\"\"Extract the priority variable from a list of mappings.\n\n We need this method because in some cases the priority argument itself\n might have conflicting values (e.g., if it is a dict with two DataArray\n values with conflicting coordinate values).\n\n Parameters\n ----------\n objects : list of dictionaries of variables\n Dictionaries in which to find the priority variables.\n priority_arg : int or None\n Integer object whose variable should take priority.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional\n Compatibility checks to use when merging variables.\n\n Returns\n -------\n A dictionary of variables and associated indexes (if any) to prioritize.\n \"\"\"\n if priority_arg is None:\n return {}\n\n collected = collect_variables_and_indexes([objects[priority_arg]])\n variables, indexes = merge_collected(collected, compat=compat)\n grouped: Dict[Hashable, MergeElement] = {}\n for name, variable in variables.items():\n grouped[name] = (variable, indexes.get(name))\n return grouped", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_coords_merge_coords.return.variables_out_indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_coords_merge_coords.return.variables_out_indexes", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 424, "end_line": 447, "span_ids": ["merge_coords"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_coords(\n objects: Iterable[\"CoercibleMapping\"],\n compat: str = \"minimal\",\n join: str = \"outer\",\n priority_arg: Optional[int] = None,\n indexes: Optional[Mapping[Hashable, pd.Index]] = None,\n fill_value: object = dtypes.NA,\n) -> Tuple[Dict[Hashable, Variable], Dict[Hashable, pd.Index]]:\n \"\"\"Merge coordinate variables.\n\n See merge_core below for argument descriptions. This works similarly to\n merge_core, except everything we don't worry about whether variables are\n coordinates or not.\n \"\"\"\n _assert_compat_valid(compat)\n coerced = coerce_pandas_values(objects)\n aligned = deep_align(\n coerced, join=join, copy=False, indexes=indexes, fill_value=fill_value\n )\n collected = collect_variables_and_indexes(aligned)\n prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat)\n variables, out_indexes = merge_collected(collected, prioritized, compat=compat)\n assert_unique_multiindex_level_names(variables)\n return variables, out_indexes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_data_and_coords__extract_indexes_from_coords.for_name_variable_in_coo.if_variable_dims_name.yield_name_variable_to_i": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_data_and_coords__extract_indexes_from_coords.for_name_variable_in_coo.if_variable_dims_name.yield_name_variable_to_i", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 450, "end_line": 465, "span_ids": ["merge_data_and_coords", "_extract_indexes_from_coords"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_data_and_coords(data, coords, compat=\"broadcast_equals\", join=\"outer\"):\n \"\"\"Used in Dataset.__init__.\"\"\"\n objects = [data, coords]\n explicit_coords = coords.keys()\n indexes = dict(_extract_indexes_from_coords(coords))\n return merge_core(\n objects, compat, join, explicit_coords=explicit_coords, indexes=indexes\n )\n\n\ndef _extract_indexes_from_coords(coords):\n \"\"\"Yields the name & index of valid indexes from a mapping of coords\"\"\"\n for name, variable in coords.items():\n variable = as_variable(variable, name=name)\n if variable.dims == (name,):\n yield name, variable.to_index()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_assert_valid_explicit_coords__MergeResult.indexes": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_assert_valid_explicit_coords__MergeResult.indexes", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 468, "end_line": 487, "span_ids": ["_MergeResult", "assert_valid_explicit_coords"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_valid_explicit_coords(variables, dims, explicit_coords):\n \"\"\"Validate explicit coordinate names/dims.\n\n Raise a MergeError if an explicit coord shares a name with a dimension\n but is comprised of arbitrary dimensions.\n \"\"\"\n for coord_name in explicit_coords:\n if coord_name in dims and variables[coord_name].dims != (coord_name,):\n raise MergeError(\n \"coordinate %s shares a name with a dataset dimension, but is \"\n \"not a 1D variable along that dimension. This is disallowed \"\n \"by the xarray data model.\" % coord_name\n )\n\n\nclass _MergeResult(NamedTuple):\n variables: Dict[Hashable, Variable]\n coord_names: Set[Hashable]\n dims: Dict[Hashable, int]\n indexes: Dict[Hashable, pd.Index]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_core_merge": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge_core_merge", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 490, "end_line": 784, "span_ids": ["merge_core", "merge"], "tokens": 634}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge_core(\n objects: Iterable[\"CoercibleMapping\"],\n compat: str = \"broadcast_equals\",\n join: str = \"outer\",\n priority_arg: Optional[int] = None,\n explicit_coords: Optional[Sequence] = None,\n indexes: Optional[Mapping[Hashable, pd.Index]] = None,\n fill_value: object = dtypes.NA,\n) -> _MergeResult:\n \"\"\"Core logic for merging labeled objects.\n\n This is not public API.\n\n Parameters\n ----------\n objects : list of mappings\n All values must be convertable to labeled arrays.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional\n Compatibility checks to use when merging variables.\n join : {'outer', 'inner', 'left', 'right'}, optional\n How to combine objects with different indexes.\n priority_arg : integer, optional\n Optional argument in `objects` that takes precedence over the others.\n explicit_coords : set, optional\n An explicit list of variables from `objects` that are coordinates.\n indexes : dict, optional\n Dictionary with values given by pandas.Index objects.\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n variables : dict\n Dictionary of Variable objects.\n coord_names : set\n Set of coordinate names.\n dims : dict\n Dictionary mapping from dimension names to sizes.\n\n Raises\n ------\n MergeError if the merge cannot be done successfully.\n \"\"\"\n from .dataset import calculate_dimensions\n\n _assert_compat_valid(compat)\n\n coerced = coerce_pandas_values(objects)\n aligned = deep_align(\n coerced, join=join, copy=False, indexes=indexes, fill_value=fill_value\n )\n collected = collect_variables_and_indexes(aligned)\n\n prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat)\n variables, out_indexes = merge_collected(collected, prioritized, compat=compat)\n assert_unique_multiindex_level_names(variables)\n\n dims = calculate_dimensions(variables)\n\n coord_names, noncoord_names = determine_coords(coerced)\n if explicit_coords is not None:\n assert_valid_explicit_coords(variables, dims, explicit_coords)\n coord_names.update(explicit_coords)\n for dim, size in dims.items():\n if dim in variables:\n coord_names.add(dim)\n ambiguous_coords = coord_names.intersection(noncoord_names)\n if ambiguous_coords:\n raise MergeError(\n \"unable to determine if these variables should be \"\n \"coordinates or not in the merged result: %s\" % ambiguous_coords\n )\n\n return _MergeResult(variables, coord_names, dims, out_indexes)\n\n\ndef merge(\n objects: Iterable[Union[\"DataArray\", \"CoercibleMapping\"]],\n compat: str = \"no_conflicts\",\n join: str = \"outer\",\n fill_value: object = dtypes.NA,\n) -> \"Dataset\":\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge._Merge_any_number_of_xa_merge._Merge_any_number_of_xa": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge._Merge_any_number_of_xa_merge._Merge_any_number_of_xa", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 572, "end_line": 767, "span_ids": ["merge"], "tokens": 2583}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge(\n objects: Iterable[Union[\"DataArray\", \"CoercibleMapping\"]],\n compat: str = \"no_conflicts\",\n join: str = \"outer\",\n fill_value: object = dtypes.NA,\n) -> \"Dataset\":\n \"\"\"Merge any number of xarray objects into a single Dataset as variables.\n\n Parameters\n ----------\n objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]]\n Merge together all variables from these objects. If any of them are\n DataArray objects, they must have a name.\n compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts', 'override'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts:\n\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n - 'override': skip comparing and pick variable from first dataset\n join : {'outer', 'inner', 'left', 'right', 'exact'}, optional\n String indicating how to combine differing indexes in objects.\n\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': instead of aligning, raise `ValueError` when indexes to be\n aligned are not equal\n - 'override': if indexes are of same size, rewrite indexes to be\n those of the first object with that dimension. Indexes for the same\n dimension must have the same size in all objects.\n fill_value : scalar, optional\n Value to use for newly missing values\n\n Returns\n -------\n Dataset\n Dataset with combined variables from each object.\n\n Examples\n --------\n >>> import xarray as xr\n >>> x = xr.DataArray(\n ... [[1.0, 2.0], [3.0, 5.0]],\n ... dims=(\"lat\", \"lon\"),\n ... coords={\"lat\": [35.0, 40.0], \"lon\": [100.0, 120.0]},\n ... name=\"var1\",\n ... )\n >>> y = xr.DataArray(\n ... [[5.0, 6.0], [7.0, 8.0]],\n ... dims=(\"lat\", \"lon\"),\n ... coords={\"lat\": [35.0, 42.0], \"lon\": [100.0, 150.0]},\n ... name=\"var2\",\n ... )\n >>> z = xr.DataArray(\n ... [[0.0, 3.0], [4.0, 9.0]],\n ... dims=(\"time\", \"lon\"),\n ... coords={\"time\": [30.0, 60.0], \"lon\": [100.0, 150.0]},\n ... name=\"var3\",\n ... )\n\n >>> x\n \n array([[1., 2.],\n [3., 5.]])\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n\n >>> y\n \n array([[5., 6.],\n [7., 8.]])\n Coordinates:\n * lat (lat) float64 35.0 42.0\n * lon (lon) float64 100.0 150.0\n\n >>> z\n \n array([[0., 3.],\n [4., 9.]])\n Coordinates:\n * time (time) float64 30.0 60.0\n * lon (lon) float64 100.0 150.0\n\n >>> xr.merge([x, y, z])\n \n Dimensions: (lat: 3, lon: 3, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0 150.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 nan 3.0 5.0 nan nan nan nan\n var2 (lat, lon) float64 5.0 nan 6.0 nan nan nan 7.0 nan 8.0\n var3 (time, lon) float64 0.0 nan 3.0 4.0 nan 9.0\n\n >>> xr.merge([x, y, z], compat='identical')\n \n Dimensions: (lat: 3, lon: 3, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0 150.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 nan 3.0 5.0 nan nan nan nan\n var2 (lat, lon) float64 5.0 nan 6.0 nan nan nan 7.0 nan 8.0\n var3 (time, lon) float64 0.0 nan 3.0 4.0 nan 9.0\n\n >>> xr.merge([x, y, z], compat='equals')\n \n Dimensions: (lat: 3, lon: 3, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0 150.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 nan 3.0 5.0 nan nan nan nan\n var2 (lat, lon) float64 5.0 nan 6.0 nan nan nan 7.0 nan 8.0\n var3 (time, lon) float64 0.0 nan 3.0 4.0 nan 9.0\n\n >>> xr.merge([x, y, z], compat='equals', fill_value=-999.)\n \n Dimensions: (lat: 3, lon: 3, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0 150.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 -999.0 3.0 ... -999.0 -999.0 -999.0\n var2 (lat, lon) float64 5.0 -999.0 6.0 -999.0 ... -999.0 7.0 -999.0 8.0\n var3 (time, lon) float64 0.0 -999.0 3.0 4.0 -999.0 9.0\n\n >>> xr.merge([x, y, z], join='override')\n \n Dimensions: (lat: 2, lon: 2, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0\n * lon (lon) float64 100.0 120.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 3.0 5.0\n var2 (lat, lon) float64 5.0 6.0 7.0 8.0\n var3 (time, lon) float64 0.0 3.0 4.0 9.0\n\n >>> xr.merge([x, y, z], join='inner')\n \n Dimensions: (lat: 1, lon: 1, time: 2)\n Coordinates:\n * lat (lat) float64 35.0\n * lon (lon) float64 100.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0\n var2 (lat, lon) float64 5.0\n var3 (time, lon) float64 0.0 4.0\n\n >>> xr.merge([x, y, z], compat='identical', join='inner')\n \n Dimensions: (lat: 1, lon: 1, time: 2)\n Coordinates:\n * lat (lat) float64 35.0\n * lon (lon) float64 100.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0\n var2 (lat, lon) float64 5.0\n var3 (time, lon) float64 0.0 4.0\n\n >>> xr.merge([x, y, z], compat='broadcast_equals', join='outer')\n \n Dimensions: (lat: 3, lon: 3, time: 2)\n Coordinates:\n * lat (lat) float64 35.0 40.0 42.0\n * lon (lon) float64 100.0 120.0 150.0\n * time (time) float64 30.0 60.0\n Data variables:\n var1 (lat, lon) float64 1.0 2.0 nan 3.0 5.0 nan nan nan nan\n var2 (lat, lon) float64 5.0 nan 6.0 nan nan nan 7.0 nan 8.0\n var3 (time, lon) float64 0.0 nan 3.0 4.0 nan 9.0\n\n >>> xr.merge([x, y, z], join='exact')\n Traceback (most recent call last):\n ...\n ValueError: indexes along dimension 'lat' are not equal\n\n Raises\n ------\n xarray.MergeError\n If any variables with the same name have conflicting values.\n\n See also\n --------\n concat\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge.DataArray_merge.return.merged": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_merge.DataArray_merge.return.merged", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 768, "end_line": 784, "span_ids": ["merge"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def merge(\n objects: Iterable[Union[\"DataArray\", \"CoercibleMapping\"]],\n compat: str = \"no_conflicts\",\n join: str = \"outer\",\n fill_value: object = dtypes.NA,\n) -> \"Dataset\":\n from .dataarray import DataArray\n from .dataset import Dataset\n\n dict_like_objects = list()\n for obj in objects:\n if not isinstance(obj, (DataArray, Dataset, dict)):\n raise TypeError(\n \"objects must be an iterable containing only \"\n \"Dataset(s), DataArray(s), and dictionaries.\"\n )\n\n obj = obj.to_dataset() if isinstance(obj, DataArray) else obj\n dict_like_objects.append(obj)\n\n merge_result = merge_core(dict_like_objects, compat, join, fill_value=fill_value)\n merged = Dataset._construct_direct(**merge_result._asdict())\n return merged", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_dataset_merge_method_dataset_merge_method.return.merge_core_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_dataset_merge_method_dataset_merge_method.return.merge_core_", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 787, "end_line": 825, "span_ids": ["dataset_merge_method"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dataset_merge_method(\n dataset: \"Dataset\",\n other: \"CoercibleMapping\",\n overwrite_vars: Union[Hashable, Iterable[Hashable]],\n compat: str,\n join: str,\n fill_value: Any,\n) -> _MergeResult:\n \"\"\"Guts of the Dataset.merge method.\n \"\"\"\n # we are locked into supporting overwrite_vars for the Dataset.merge\n # method due for backwards compatibility\n # TODO: consider deprecating it?\n\n if isinstance(overwrite_vars, Iterable) and not isinstance(overwrite_vars, str):\n overwrite_vars = set(overwrite_vars)\n else:\n overwrite_vars = {overwrite_vars}\n\n if not overwrite_vars:\n objs = [dataset, other]\n priority_arg = None\n elif overwrite_vars == set(other):\n objs = [dataset, other]\n priority_arg = 1\n else:\n other_overwrite: Dict[Hashable, CoercibleValue] = {}\n other_no_overwrite: Dict[Hashable, CoercibleValue] = {}\n for k, v in other.items():\n if k in overwrite_vars:\n other_overwrite[k] = v\n else:\n other_no_overwrite[k] = v\n objs = [dataset, other_no_overwrite, other_overwrite]\n priority_arg = 2\n\n return merge_core(\n objs, compat, join, priority_arg=priority_arg, fill_value=fill_value\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_dataset_update_method_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/merge.py_dataset_update_method_", "embedding": null, "metadata": {"file_path": "xarray/core/merge.py", "file_name": "merge.py", "file_type": "text/x-python", "category": "implementation", "start_line": 828, "end_line": 854, "span_ids": ["dataset_update_method"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dataset_update_method(\n dataset: \"Dataset\", other: \"CoercibleMapping\"\n) -> _MergeResult:\n \"\"\"Guts of the Dataset.update method.\n\n This drops a duplicated coordinates from `other` if `other` is not an\n `xarray.Dataset`, e.g., if it's a dict with DataArray values (GH2068,\n GH2180).\n \"\"\"\n from .dataarray import DataArray\n from .dataset import Dataset\n\n if not isinstance(other, Dataset):\n other = dict(other)\n for key, value in other.items():\n if isinstance(value, DataArray):\n # drop conflicting coordinates\n coord_names = [\n c\n for c in value.coords\n if c not in value.dims and c in dataset.coords\n ]\n if coord_names:\n other[key] = value.drop(coord_names)\n\n return merge_core([dataset, other], priority_arg=1, indexes=dataset.indexes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_warnings_BaseInterpolator.__repr__.return._type_method_method_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_warnings_BaseInterpolator.__repr__.return._type_method_method_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 31, "span_ids": ["BaseInterpolator.__repr__", "imports", "BaseInterpolator.__call__", "BaseInterpolator"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom functools import partial\nfrom typing import Any, Callable, Dict, Sequence\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import utils\nfrom .common import _contains_datetime_like_objects\nfrom .computation import apply_ufunc\nfrom .duck_array_ops import dask_array_type\nfrom .utils import OrderedSet, is_scalar\nfrom .variable import Variable, broadcast_variables\n\n\nclass BaseInterpolator:\n \"\"\"Generic interpolator class for normalizing interpolation methods\n \"\"\"\n\n cons_kwargs: Dict[str, Any]\n call_kwargs: Dict[str, Any]\n f: Callable\n method: str\n\n def __call__(self, x):\n return self.f(x, **self.call_kwargs)\n\n def __repr__(self):\n return \"{type}: method={method}\".format(\n type=self.__class__.__name__, method=self.method\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_NumpyInterpolator_NumpyInterpolator.__call__.return.self_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_NumpyInterpolator_NumpyInterpolator.__call__.return.self_f_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 34, "end_line": 75, "span_ids": ["NumpyInterpolator.__call__", "NumpyInterpolator"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyInterpolator(BaseInterpolator):\n \"\"\"One-dimensional linear interpolation.\n\n See Also\n --------\n numpy.interp\n \"\"\"\n\n def __init__(self, xi, yi, method=\"linear\", fill_value=None, period=None):\n\n if method != \"linear\":\n raise ValueError(\"only method `linear` is valid for the NumpyInterpolator\")\n\n self.method = method\n self.f = np.interp\n self.cons_kwargs = {}\n self.call_kwargs = {\"period\": period}\n\n self._xi = xi\n self._yi = yi\n\n if fill_value is None:\n self._left = np.nan\n self._right = np.nan\n elif isinstance(fill_value, Sequence) and len(fill_value) == 2:\n self._left = fill_value[0]\n self._right = fill_value[1]\n elif is_scalar(fill_value):\n self._left = fill_value\n self._right = fill_value\n else:\n raise ValueError(\"%s is not a valid fill_value\" % fill_value)\n\n def __call__(self, x):\n return self.f(\n x,\n self._xi,\n self._yi,\n left=self._left,\n right=self._right,\n **self.call_kwargs\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_ScipyInterpolator_ScipyInterpolator.__init__.self.f.interp1d_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_ScipyInterpolator_ScipyInterpolator.__init__.self.f.interp1d_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 130, "span_ids": ["ScipyInterpolator"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScipyInterpolator(BaseInterpolator):\n \"\"\"Interpolate a 1-D function using Scipy interp1d\n\n See Also\n --------\n scipy.interpolate.interp1d\n \"\"\"\n\n def __init__(\n self,\n xi,\n yi,\n method=None,\n fill_value=None,\n assume_sorted=True,\n copy=False,\n bounds_error=False,\n order=None,\n **kwargs\n ):\n from scipy.interpolate import interp1d\n\n if method is None:\n raise ValueError(\n \"method is a required argument, please supply a \"\n \"valid scipy.inter1d method (kind)\"\n )\n\n if method == \"polynomial\":\n if order is None:\n raise ValueError(\"order is required when method=polynomial\")\n method = order\n\n self.method = method\n\n self.cons_kwargs = kwargs\n self.call_kwargs = {}\n\n if fill_value is None and method == \"linear\":\n fill_value = np.nan, np.nan\n elif fill_value is None:\n fill_value = np.nan\n\n self.f = interp1d(\n xi,\n yi,\n kind=self.method,\n fill_value=fill_value,\n bounds_error=False,\n assume_sorted=assume_sorted,\n copy=copy,\n **self.cons_kwargs\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_SplineInterpolator__apply_over_vars_with_dim.return.ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_SplineInterpolator__apply_over_vars_with_dim.return.ds", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 133, "end_line": 178, "span_ids": ["SplineInterpolator", "_apply_over_vars_with_dim"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplineInterpolator(BaseInterpolator):\n \"\"\"One-dimensional smoothing spline fit to a given set of data points.\n\n See Also\n --------\n scipy.interpolate.UnivariateSpline\n \"\"\"\n\n def __init__(\n self,\n xi,\n yi,\n method=\"spline\",\n fill_value=None,\n order=3,\n nu=0,\n ext=None,\n **kwargs\n ):\n from scipy.interpolate import UnivariateSpline\n\n if method != \"spline\":\n raise ValueError(\"only method `spline` is valid for the SplineInterpolator\")\n\n self.method = method\n self.cons_kwargs = kwargs\n self.call_kwargs = {\"nu\": nu, \"ext\": ext}\n\n if fill_value is not None:\n raise ValueError(\"SplineInterpolator does not support fill_value\")\n\n self.f = UnivariateSpline(xi, yi, k=order, **self.cons_kwargs)\n\n\ndef _apply_over_vars_with_dim(func, self, dim=None, **kwargs):\n \"\"\"Wrapper for datasets\n \"\"\"\n ds = type(self)(coords=self.coords, attrs=self.attrs)\n\n for name, var in self.data_vars.items():\n if dim in var.dims:\n ds[name] = func(var, dim=dim, **kwargs)\n else:\n ds[name] = var\n\n return ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_get_clean_interp_index_get_clean_interp_index.return.index": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_get_clean_interp_index_get_clean_interp_index.return.index", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 181, "end_line": 219, "span_ids": ["get_clean_interp_index"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_clean_interp_index(arr, dim, use_coordinate=True):\n \"\"\"get index to use for x values in interpolation.\n\n If use_coordinate is True, the coordinate that shares the name of the\n dimension along which interpolation is being performed will be used as the\n x values.\n\n If use_coordinate is False, the x values are set as an equally spaced\n sequence.\n \"\"\"\n if use_coordinate:\n if use_coordinate is True:\n index = arr.get_index(dim)\n else:\n index = arr.coords[use_coordinate]\n if index.ndim != 1:\n raise ValueError(\n \"Coordinates used for interpolation must be 1D, \"\n \"%s is %dD.\" % (use_coordinate, index.ndim)\n )\n\n # raise if index cannot be cast to a float (e.g. MultiIndex)\n try:\n index = index.values.astype(np.float64)\n except (TypeError, ValueError):\n # pandas raises a TypeError\n # xarray/nuppy raise a ValueError\n raise TypeError(\n \"Index must be castable to float64 to support\"\n \"interpolation, got: %s\" % type(index)\n )\n # check index sorting now so we can skip it later\n if not (np.diff(index) > 0).all():\n raise ValueError(\"Index must be monotonicly increasing\")\n else:\n axis = arr.get_axis_num(dim)\n index = np.arange(arr.shape[axis], dtype=np.float64)\n\n return index", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_na_interp_na.return.arr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_na_interp_na.return.arr", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 222, "end_line": 256, "span_ids": ["interp_na"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def interp_na(\n self, dim=None, use_coordinate=True, method=\"linear\", limit=None, **kwargs\n):\n \"\"\"Interpolate values according to different methods.\n \"\"\"\n if dim is None:\n raise NotImplementedError(\"dim is a required argument\")\n\n if limit is not None:\n valids = _get_valid_fill_mask(self, dim, limit)\n\n # method\n index = get_clean_interp_index(self, dim, use_coordinate=use_coordinate)\n interp_class, kwargs = _get_interpolator(method, **kwargs)\n interpolator = partial(func_interpolate_na, interp_class, **kwargs)\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"overflow\", RuntimeWarning)\n warnings.filterwarnings(\"ignore\", \"invalid value\", RuntimeWarning)\n arr = apply_ufunc(\n interpolator,\n index,\n self,\n input_core_dims=[[dim], [dim]],\n output_core_dims=[[dim]],\n output_dtypes=[self.dtype],\n dask=\"parallelized\",\n vectorize=True,\n keep_attrs=True,\n ).transpose(*self.dims)\n\n if limit is not None:\n arr = arr.where(valids)\n\n return arr", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_func_interpolate_na_func_interpolate_na.return.out": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_func_interpolate_na_func_interpolate_na.return.out", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 259, "end_line": 275, "span_ids": ["func_interpolate_na"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def func_interpolate_na(interpolator, x, y, **kwargs):\n \"\"\"helper function to apply interpolation along 1 dimension\"\"\"\n # it would be nice if this wasn't necessary, works around:\n # \"ValueError: assignment destination is read-only\" in assignment below\n out = y.copy()\n\n nans = pd.isnull(y)\n nonans = ~nans\n\n # fast track for no-nans and all-nans cases\n n_nans = nans.sum()\n if n_nans == 0 or n_nans == len(y):\n return y\n\n f = interpolator(x[nonans], y[nonans], **kwargs)\n out[nans] = f(x[nans])\n return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__bfill_bfill.return.apply_ufunc_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__bfill_bfill.return.apply_ufunc_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 324, "span_ids": ["ffill", "_bfill", "bfill"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _bfill(arr, n=None, axis=-1):\n \"\"\"inverse of ffill\"\"\"\n import bottleneck as bn\n\n arr = np.flip(arr, axis=axis)\n\n # fill\n arr = bn.push(arr, axis=axis, n=n)\n\n # reverse back to original\n return np.flip(arr, axis=axis)\n\n\ndef ffill(arr, dim=None, limit=None):\n \"\"\"forward fill missing values\"\"\"\n import bottleneck as bn\n\n axis = arr.get_axis_num(dim)\n\n # work around for bottleneck 178\n _limit = limit if limit is not None else arr.shape[axis]\n\n return apply_ufunc(\n bn.push,\n arr,\n dask=\"parallelized\",\n keep_attrs=True,\n output_dtypes=[arr.dtype],\n kwargs=dict(n=_limit, axis=axis),\n ).transpose(*arr.dims)\n\n\ndef bfill(arr, dim=None, limit=None):\n \"\"\"backfill missing values\"\"\"\n axis = arr.get_axis_num(dim)\n\n # work around for bottleneck 178\n _limit = limit if limit is not None else arr.shape[axis]\n\n return apply_ufunc(\n _bfill,\n arr,\n dask=\"parallelized\",\n keep_attrs=True,\n output_dtypes=[arr.dtype],\n kwargs=dict(n=_limit, axis=axis),\n ).transpose(*arr.dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_interpolator__get_interpolator.return.interp_class_kwargs": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_interpolator__get_interpolator.return.interp_class_kwargs", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 327, "end_line": 392, "span_ids": ["_get_interpolator"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_interpolator(method, vectorizeable_only=False, **kwargs):\n \"\"\"helper function to select the appropriate interpolator class\n\n returns interpolator class and keyword arguments for the class\n \"\"\"\n interp1d_methods = [\n \"linear\",\n \"nearest\",\n \"zero\",\n \"slinear\",\n \"quadratic\",\n \"cubic\",\n \"polynomial\",\n ]\n valid_methods = interp1d_methods + [\n \"barycentric\",\n \"krog\",\n \"pchip\",\n \"spline\",\n \"akima\",\n ]\n\n has_scipy = True\n try:\n from scipy import interpolate\n except ImportError:\n has_scipy = False\n\n # prioritize scipy.interpolate\n if (\n method == \"linear\"\n and not kwargs.get(\"fill_value\", None) == \"extrapolate\"\n and not vectorizeable_only\n ):\n kwargs.update(method=method)\n interp_class = NumpyInterpolator\n\n elif method in valid_methods:\n if not has_scipy:\n raise ImportError(\"Interpolation with method `%s` requires scipy\" % method)\n\n if method in interp1d_methods:\n kwargs.update(method=method)\n interp_class = ScipyInterpolator\n elif vectorizeable_only:\n raise ValueError(\n \"{} is not a vectorizeable interpolator. \"\n \"Available methods are {}\".format(method, interp1d_methods)\n )\n elif method == \"barycentric\":\n interp_class = interpolate.BarycentricInterpolator\n elif method == \"krog\":\n interp_class = interpolate.KroghInterpolator\n elif method == \"pchip\":\n interp_class = interpolate.PchipInterpolator\n elif method == \"spline\":\n kwargs.update(method=method)\n interp_class = SplineInterpolator\n elif method == \"akima\":\n interp_class = interpolate.Akima1DInterpolator\n else:\n raise ValueError(\"%s is not a valid scipy interpolator\" % method)\n else:\n raise ValueError(\"%s is not a valid interpolator\" % method)\n\n return interp_class, kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_interpolator_nd__get_interpolator_nd.return.interp_class_kwargs": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_interpolator_nd__get_interpolator_nd.return.interp_class_kwargs", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 395, "end_line": 416, "span_ids": ["_get_interpolator_nd"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_interpolator_nd(method, **kwargs):\n \"\"\"helper function to select the appropriate interpolator class\n\n returns interpolator class and keyword arguments for the class\n \"\"\"\n valid_methods = [\"linear\", \"nearest\"]\n\n try:\n from scipy import interpolate\n except ImportError:\n raise ImportError(\"Interpolation with method `%s` requires scipy\" % method)\n\n if method in valid_methods:\n kwargs.update(method=method)\n interp_class = interpolate.interpn\n else:\n raise ValueError(\n \"%s is not a valid interpolator for interpolating \"\n \"over multiple dimensions.\" % method\n )\n\n return interp_class, kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_valid_fill_mask__assert_single_chunk.for_axis_in_axes_.if_len_var_chunks_axis_.raise_NotImplementedError": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__get_valid_fill_mask__assert_single_chunk.for_axis_in_axes_.if_len_var_chunks_axis_.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 419, "end_line": 439, "span_ids": ["_get_valid_fill_mask", "_assert_single_chunk"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_valid_fill_mask(arr, dim, limit):\n \"\"\"helper function to determine values that can be filled when limit is not\n None\"\"\"\n kw = {dim: limit + 1}\n # we explicitly use construct method to avoid copy.\n new_dim = utils.get_temp_dimname(arr.dims, \"_window\")\n return (\n arr.isnull()\n .rolling(min_periods=1, **kw)\n .construct(new_dim, fill_value=False)\n .sum(new_dim, skipna=False)\n ) <= limit\n\n\ndef _assert_single_chunk(var, axes):\n for axis in axes:\n if len(var.chunks[axis]) > 1 or var.chunks[axis][0] < var.shape[axis]:\n raise NotImplementedError(\n \"Chunking along the dimension to be interpolated \"\n \"({}) is not yet supported.\".format(axis)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__localize__localize.return.var_isel_indexes_inde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__localize__localize.return.var_isel_indexes_inde", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 442, "end_line": 454, "span_ids": ["_localize"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _localize(var, indexes_coords):\n \"\"\" Speed up for linear and nearest neighbor method.\n Only consider a subspace that is needed for the interpolation\n \"\"\"\n indexes = {}\n for dim, [x, new_x] in indexes_coords.items():\n index = x.to_index()\n imin = index.get_loc(np.min(new_x.values), method=\"nearest\")\n imax = index.get_loc(np.max(new_x.values), method=\"nearest\")\n\n indexes[dim] = slice(max(imin - 2, 0), imax + 2)\n indexes_coords[dim] = (x[indexes[dim]], new_x)\n return var.isel(**indexes), indexes_coords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__floatize_x__floatize_x.return.x_new_x": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__floatize_x__floatize_x.return.x_new_x", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 457, "end_line": 474, "span_ids": ["_floatize_x"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _floatize_x(x, new_x):\n \"\"\" Make x and new_x float.\n This is particulary useful for datetime dtype.\n x, new_x: tuple of np.ndarray\n \"\"\"\n x = list(x)\n new_x = list(new_x)\n for i in range(len(x)):\n if _contains_datetime_like_objects(x[i]):\n # Scipy casts coordinates to np.float64, which is not accurate\n # enough for datetime64 (uses 64bit integer).\n # We assume that the most of the bits are used to represent the\n # offset (min(x)) and the variation (x - min(x)) can be\n # represented by float.\n xmin = x[i].values.min()\n x[i] = x[i]._to_numeric(offset=xmin, dtype=np.float64)\n new_x[i] = new_x[i]._to_numeric(offset=xmin, dtype=np.float64)\n return x, new_x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_interp.return.result_transpose_tuple_o": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_interp.return.result_transpose_tuple_o", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 477, "end_line": 535, "span_ids": ["interp"], "tokens": 423}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def interp(var, indexes_coords, method, **kwargs):\n \"\"\" Make an interpolation of Variable\n\n Parameters\n ----------\n var: Variable\n index_coords:\n Mapping from dimension name to a pair of original and new coordinates.\n Original coordinates should be sorted in strictly ascending order.\n Note that all the coordinates should be Variable objects.\n method: string\n One of {'linear', 'nearest', 'zero', 'slinear', 'quadratic',\n 'cubic'}. For multidimensional interpolation, only\n {'linear', 'nearest'} can be used.\n **kwargs:\n keyword arguments to be passed to scipy.interpolate\n\n Returns\n -------\n Interpolated Variable\n\n See Also\n --------\n DataArray.interp\n Dataset.interp\n \"\"\"\n if not indexes_coords:\n return var.copy()\n\n # simple speed up for the local interpolation\n if method in [\"linear\", \"nearest\"]:\n var, indexes_coords = _localize(var, indexes_coords)\n\n # default behavior\n kwargs[\"bounds_error\"] = kwargs.get(\"bounds_error\", False)\n\n # target dimensions\n dims = list(indexes_coords)\n x, new_x = zip(*[indexes_coords[d] for d in dims])\n destination = broadcast_variables(*new_x)\n\n # transpose to make the interpolated axis to the last position\n broadcast_dims = [d for d in var.dims if d not in dims]\n original_dims = broadcast_dims + dims\n new_dims = broadcast_dims + list(destination[0].dims)\n interped = interp_func(\n var.transpose(*original_dims).data, x, destination, method, kwargs\n )\n\n result = Variable(new_dims, interped, attrs=var.attrs)\n\n # dimension of the output array\n out_dims = OrderedSet()\n for d in var.dims:\n if d in dims:\n out_dims.update(indexes_coords[d][1].dims)\n else:\n out_dims.add(d)\n return result.transpose(*tuple(out_dims))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_func_interp_func.return._interpnd_var_x_new_x_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py_interp_func_interp_func.return._interpnd_var_x_new_x_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 538, "end_line": 599, "span_ids": ["interp_func"], "tokens": 438}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def interp_func(var, x, new_x, method, kwargs):\n \"\"\"\n multi-dimensional interpolation for array-like. Interpolated axes should be\n located in the last position.\n\n Parameters\n ----------\n var: np.ndarray or dask.array.Array\n Array to be interpolated. The final dimension is interpolated.\n x: a list of 1d array.\n Original coordinates. Should not contain NaN.\n new_x: a list of 1d array\n New coordinates. Should not contain NaN.\n method: string\n {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'} for\n 1-dimensional itnterpolation.\n {'linear', 'nearest'} for multidimensional interpolation\n **kwargs:\n Optional keyword arguments to be passed to scipy.interpolator\n\n Returns\n -------\n interpolated: array\n Interpolated array\n\n Note\n ----\n This requiers scipy installed.\n\n See Also\n --------\n scipy.interpolate.interp1d\n \"\"\"\n if not x:\n return var.copy()\n\n if len(x) == 1:\n func, kwargs = _get_interpolator(method, vectorizeable_only=True, **kwargs)\n else:\n func, kwargs = _get_interpolator_nd(method, **kwargs)\n\n if isinstance(var, dask_array_type):\n import dask.array as da\n\n _assert_single_chunk(var, range(var.ndim - len(x), var.ndim))\n chunks = var.chunks[: -len(x)] + new_x[0].shape\n drop_axis = range(var.ndim - len(x), var.ndim)\n new_axis = range(var.ndim - len(x), var.ndim - len(x) + new_x[0].ndim)\n return da.map_blocks(\n _interpnd,\n var,\n x,\n new_x,\n func,\n kwargs,\n dtype=var.dtype,\n chunks=chunks,\n new_axis=new_axis,\n drop_axis=drop_axis,\n )\n\n return _interpnd(var, x, new_x, func, kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__interp1d__interp1d.return.rslt": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__interp1d__interp1d.return.rslt", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 602, "end_line": 610, "span_ids": ["_interp1d"], "tokens": 110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _interp1d(var, x, new_x, func, kwargs):\n # x, new_x are tuples of size 1.\n x, new_x = x[0], new_x[0]\n rslt = func(x, var, assume_sorted=True, **kwargs)(np.ravel(new_x))\n if new_x.ndim > 1:\n return rslt.reshape(var.shape[:-1] + new_x.shape)\n if new_x.ndim == 0:\n return rslt[..., -1]\n return rslt", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__interpnd_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/missing.py__interpnd_", "embedding": null, "metadata": {"file_path": "xarray/core/missing.py", "file_name": "missing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 613, "end_line": 627, "span_ids": ["_interpnd"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _interpnd(var, x, new_x, func, kwargs):\n x, new_x = _floatize_x(x, new_x)\n\n if len(x) == 1:\n return _interp1d(var, x, new_x, func, kwargs)\n\n # move the interpolation axes to the start position\n var = var.transpose(range(-len(x), var.ndim - len(x)))\n # stack new_x to 1 vector, with reshape\n xi = np.stack([x1.values.ravel() for x1 in new_x], axis=-1)\n rslt = func(x, var, xi, **kwargs)\n # move back the interpolation axes to the last position\n rslt = rslt.transpose(range(-rslt.ndim + 1, 1))\n return rslt.reshape(rslt.shape[:-1] + new_x[0].shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_np__replace_nan.return.where_method_val_mask_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_np__replace_nan.return.where_method_val_mask_a", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 19, "span_ids": ["imports:5", "impl", "_replace_nan", "impl:2", "imports"], "tokens": 124}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom . import dtypes, nputils, utils\nfrom .duck_array_ops import _dask_or_eager_func, count, fillna, isnull, where_method\nfrom .pycompat import dask_array_type\n\ntry:\n import dask.array as dask_array\nexcept ImportError:\n dask_array = None\n\n\ndef _replace_nan(a, val):\n \"\"\"\n replace nan in a by val, and returns the replaced array and the nan\n position\n \"\"\"\n mask = isnull(a)\n return where_method(val, mask, a), mask", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__maybe_null_out__maybe_null_out.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__maybe_null_out__maybe_null_out.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 22, "end_line": 43, "span_ids": ["_maybe_null_out"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _maybe_null_out(result, axis, mask, min_count=1):\n \"\"\"\n xarray version of pandas.core.nanops._maybe_null_out\n \"\"\"\n if hasattr(axis, \"__len__\"): # if tuple or list\n raise ValueError(\n \"min_count is not available for reduction \" \"with more than one dimensions.\"\n )\n\n if axis is not None and getattr(result, \"ndim\", False):\n null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0\n if null_mask.any():\n dtype, fill_value = dtypes.maybe_promote(result.dtype)\n result = result.astype(dtype)\n result[null_mask] = fill_value\n\n elif getattr(result, \"dtype\", None) not in dtypes.NAT_TYPES:\n null_mask = mask.size - mask.sum()\n if null_mask < min_count:\n result = np.nan\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nan_argminmax_object__nan_argminmax_object.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nan_argminmax_object__nan_argminmax_object.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 58, "span_ids": ["_nan_argminmax_object"], "tokens": 124}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _nan_argminmax_object(func, fill_value, value, axis=None, **kwargs):\n \"\"\" In house nanargmin, nanargmax for object arrays. Always return integer\n type\n \"\"\"\n valid_count = count(value, axis=axis)\n value = fillna(value, fill_value)\n data = _dask_or_eager_func(func)(value, axis=axis, **kwargs)\n\n # TODO This will evaluate dask arrays and might be costly.\n if (valid_count == 0).any():\n raise ValueError(\"All-NaN slice encountered\")\n\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nan_minmax_object__nan_minmax_object.return.where_method_data_valid_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nan_minmax_object__nan_minmax_object.return.where_method_data_valid_", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 61, "end_line": 71, "span_ids": ["_nan_minmax_object"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _nan_minmax_object(func, fill_value, value, axis=None, **kwargs):\n \"\"\" In house nanmin and nanmax for object array \"\"\"\n valid_count = count(value, axis=axis)\n filled_value = fillna(value, fill_value)\n data = getattr(np, func)(filled_value, axis=axis, **kwargs)\n if not hasattr(data, \"dtype\"): # scalar case\n data = fill_value if valid_count == 0 else data\n # we've computed a single min, max value of type object.\n # don't let np.array turn a tuple back into an array\n return utils.to_0d_object_array(data)\n return where_method(data, valid_count != 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_nanmin_nansum.if_min_count_is_not_None_.else_.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_nanmin_nansum.if_min_count_is_not_None_.else_.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 74, "end_line": 114, "span_ids": ["nanmin", "nanargmax", "nanmax", "nanargmin", "nansum"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def nanmin(a, axis=None, out=None):\n if a.dtype.kind == \"O\":\n return _nan_minmax_object(\"min\", dtypes.get_pos_infinity(a.dtype), a, axis)\n\n module = dask_array if isinstance(a, dask_array_type) else nputils\n return module.nanmin(a, axis=axis)\n\n\ndef nanmax(a, axis=None, out=None):\n if a.dtype.kind == \"O\":\n return _nan_minmax_object(\"max\", dtypes.get_neg_infinity(a.dtype), a, axis)\n\n module = dask_array if isinstance(a, dask_array_type) else nputils\n return module.nanmax(a, axis=axis)\n\n\ndef nanargmin(a, axis=None):\n if a.dtype.kind == \"O\":\n fill_value = dtypes.get_pos_infinity(a.dtype)\n return _nan_argminmax_object(\"argmin\", fill_value, a, axis=axis)\n\n module = dask_array if isinstance(a, dask_array_type) else nputils\n return module.nanargmin(a, axis=axis)\n\n\ndef nanargmax(a, axis=None):\n if a.dtype.kind == \"O\":\n fill_value = dtypes.get_neg_infinity(a.dtype)\n return _nan_argminmax_object(\"argmax\", fill_value, a, axis=axis)\n\n module = dask_array if isinstance(a, dask_array_type) else nputils\n return module.nanargmax(a, axis=axis)\n\n\ndef nansum(a, axis=None, dtype=None, out=None, min_count=None):\n a, mask = _replace_nan(a, 0)\n result = _dask_or_eager_func(\"sum\")(a, axis=axis, dtype=dtype)\n if min_count is not None:\n return _maybe_null_out(result, axis, mask, min_count)\n else:\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nanmean_ddof_object__nanmean_ddof_object.return.where_method_data_valid_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py__nanmean_ddof_object__nanmean_ddof_object.return.where_method_data_valid_", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 117, "end_line": 130, "span_ids": ["_nanmean_ddof_object"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _nanmean_ddof_object(ddof, value, axis=None, dtype=None, **kwargs):\n \"\"\" In house nanmean. ddof argument will be used in _nanvar method \"\"\"\n from .duck_array_ops import count, fillna, _dask_or_eager_func, where_method\n\n valid_count = count(value, axis=axis)\n value = fillna(value, 0)\n # As dtype inference is impossible for object dtype, we assume float\n # https://github.com/dask/dask/issues/3162\n if dtype is None and value.dtype.kind == \"O\":\n dtype = value.dtype if value.dtype.kind in [\"cf\"] else float\n\n data = _dask_or_eager_func(\"sum\")(value, axis=axis, dtype=dtype, **kwargs)\n data = data / (valid_count - ddof)\n return where_method(data, valid_count != 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_nanmean_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nanops.py_nanmean_", "embedding": null, "metadata": {"file_path": "xarray/core/nanops.py", "file_name": "nanops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 133, "end_line": 189, "span_ids": ["nanvar", "nanstd", "nanprod", "nancumsum", "_nanvar_object", "nanmedian", "nanmean", "nancumprod"], "tokens": 554}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def nanmean(a, axis=None, dtype=None, out=None):\n if a.dtype.kind == \"O\":\n return _nanmean_ddof_object(0, a, axis=axis, dtype=dtype)\n\n if isinstance(a, dask_array_type):\n return dask_array.nanmean(a, axis=axis, dtype=dtype)\n\n return np.nanmean(a, axis=axis, dtype=dtype)\n\n\ndef nanmedian(a, axis=None, out=None):\n return _dask_or_eager_func(\"nanmedian\", eager_module=nputils)(a, axis=axis)\n\n\ndef _nanvar_object(value, axis=None, ddof=0, keepdims=False, **kwargs):\n value_mean = _nanmean_ddof_object(\n ddof=0, value=value, axis=axis, keepdims=True, **kwargs\n )\n squared = (value.astype(value_mean.dtype) - value_mean) ** 2\n return _nanmean_ddof_object(ddof, squared, axis=axis, keepdims=keepdims, **kwargs)\n\n\ndef nanvar(a, axis=None, dtype=None, out=None, ddof=0):\n if a.dtype.kind == \"O\":\n return _nanvar_object(a, axis=axis, dtype=dtype, ddof=ddof)\n\n return _dask_or_eager_func(\"nanvar\", eager_module=nputils)(\n a, axis=axis, dtype=dtype, ddof=ddof\n )\n\n\ndef nanstd(a, axis=None, dtype=None, out=None, ddof=0):\n return _dask_or_eager_func(\"nanstd\", eager_module=nputils)(\n a, axis=axis, dtype=dtype, ddof=ddof\n )\n\n\ndef nanprod(a, axis=None, dtype=None, out=None, min_count=None):\n a, mask = _replace_nan(a, 1)\n result = _dask_or_eager_func(\"nanprod\")(a, axis=axis, dtype=dtype, out=out)\n if min_count is not None:\n return _maybe_null_out(result, axis, mask, min_count)\n else:\n return result\n\n\ndef nancumsum(a, axis=None, dtype=None, out=None):\n return _dask_or_eager_func(\"nancumsum\", eager_module=nputils)(\n a, axis=axis, dtype=dtype\n )\n\n\ndef nancumprod(a, axis=None, dtype=None, out=None):\n return _dask_or_eager_func(\"nancumprod\", eager_module=nputils)(\n a, axis=axis, dtype=dtype\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py_builtins__validate_axis.return.axis": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py_builtins__validate_axis.return.axis", "embedding": null, "metadata": {"file_path": "xarray/core/npcompat.py", "file_name": "npcompat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 50, "span_ids": ["_validate_axis", "imports"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import builtins\nimport operator\nfrom typing import Union\n\nimport numpy as np\n\n\n# Vendored from NumPy 1.12; we need a version that support duck typing, even\n# on dask arrays with __array_function__ enabled.\ndef _validate_axis(axis, ndim, argname):\n try:\n axis = [operator.index(axis)]\n except TypeError:\n axis = list(axis)\n axis = [a + ndim if a < 0 else a for a in axis]\n if not builtins.all(0 <= a < ndim for a in axis):\n raise ValueError(\"invalid axis for this array in `%s` argument\" % argname)\n if len(set(axis)) != len(axis):\n raise ValueError(\"repeated axis in `%s` argument\" % argname)\n return axis", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py_moveaxis_moveaxis.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py_moveaxis_moveaxis.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/npcompat.py", "file_name": "npcompat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 75, "span_ids": ["moveaxis"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def moveaxis(a, source, destination):\n try:\n # allow duck-array types if they define transpose\n transpose = a.transpose\n except AttributeError:\n a = np.asarray(a)\n transpose = a.transpose\n\n source = _validate_axis(source, a.ndim, \"source\")\n destination = _validate_axis(destination, a.ndim, \"destination\")\n if len(source) != len(destination):\n raise ValueError(\n \"`source` and `destination` arguments must have \"\n \"the same number of elements\"\n )\n\n order = [n for n in range(a.ndim) if n not in source]\n\n for dest, src in sorted(zip(destination, source)):\n order.insert(dest, src)\n\n result = transpose(order)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py__Type_annotations_stubs__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/npcompat.py__Type_annotations_stubs__", "embedding": null, "metadata": {"file_path": "xarray/core/npcompat.py", "file_name": "npcompat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 97, "span_ids": ["impl", "_is_nep18_active.A", "moveaxis", "impl:3", "_is_nep18_active", "_is_nep18_active.A.__array_function__"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Type annotations stubs. See also / to be replaced by:\n# https://github.com/numpy/numpy/issues/7370\n# https://github.com/numpy/numpy-stubs/\nDTypeLike = Union[np.dtype, str]\n\n\n# from dask/array/utils.py\ndef _is_nep18_active():\n class A:\n def __array_function__(self, *args, **kwargs):\n return True\n\n try:\n return np.concatenate([A()])\n except ValueError:\n return False\n\n\nIS_NEP18_ACTIVE = _is_nep18_active()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_warnings_nanlast.return._select_along_axis_values": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_warnings_nanlast.return._select_along_axis_values", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 41, "span_ids": ["_validate_axis", "impl", "impl:2", "_select_along_axis", "nanfirst", "imports:4", "imports", "nanlast"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\n\nimport numpy as np\nimport pandas as pd\n\ntry:\n import bottleneck as bn\n\n _USE_BOTTLENECK = True\nexcept ImportError:\n # use numpy methods instead\n bn = np\n _USE_BOTTLENECK = False\n\n\ndef _validate_axis(data, axis):\n ndim = data.ndim\n if not -ndim <= axis < ndim:\n raise IndexError(\"axis %r out of bounds [-%r, %r)\" % (axis, ndim, ndim))\n if axis < 0:\n axis += ndim\n return axis\n\n\ndef _select_along_axis(values, idx, axis):\n other_ind = np.ix_(*[np.arange(s) for s in idx.shape])\n sl = other_ind[:axis] + (idx,) + other_ind[axis:]\n return values[sl]\n\n\ndef nanfirst(values, axis):\n axis = _validate_axis(values, axis)\n idx_first = np.argmax(~pd.isnull(values), axis=axis)\n return _select_along_axis(values, idx_first, axis)\n\n\ndef nanlast(values, axis):\n axis = _validate_axis(values, axis)\n rev = (slice(None),) * axis + (slice(None, None, -1),)\n idx_last = -1 - np.argmax(~pd.isnull(values)[rev], axis=axis)\n return _select_along_axis(values, idx_last, axis)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_inverse_permutation_inverse_permutation.return.inverse_permutation": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_inverse_permutation_inverse_permutation.return.inverse_permutation", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 61, "span_ids": ["inverse_permutation"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inverse_permutation(indices):\n \"\"\"Return indices for an inverse permutation.\n\n Parameters\n ----------\n indices : 1D np.ndarray with dtype=int\n Integer positions to assign elements to.\n\n Returns\n -------\n inverse_permutation : 1D np.ndarray with dtype=int\n Integer indices to take from the original array to create the\n permutation.\n \"\"\"\n # use intp instead of int64 because of windows :(\n inverse_permutation = np.empty(len(indices), dtype=np.intp)\n inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp)\n return inverse_permutation", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__ensure_bool_is_ndarray__ensure_bool_is_ndarray.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__ensure_bool_is_ndarray__ensure_bool_is_ndarray.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 75, "span_ids": ["_ensure_bool_is_ndarray"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _ensure_bool_is_ndarray(result, *args):\n # numpy will sometimes return a scalar value from binary comparisons if it\n # can't handle the comparison instead of broadcasting, e.g.,\n # In [10]: 1 == np.array(['a', 'b'])\n # Out[10]: False\n # This function ensures that the result is the appropriate shape in these\n # cases\n if isinstance(result, bool):\n shape = np.broadcast(*args).shape\n constructor = np.ones if result else np.zeros\n result = constructor(shape, dtype=bool)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_array_eq__is_contiguous.return.True": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_array_eq__is_contiguous.return.True", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 97, "span_ids": ["array_eq", "array_ne", "_is_contiguous"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def array_eq(self, other):\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", r\"elementwise comparison failed\")\n return _ensure_bool_is_ndarray(self == other, self, other)\n\n\ndef array_ne(self, other):\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", r\"elementwise comparison failed\")\n return _ensure_bool_is_ndarray(self != other, self, other)\n\n\ndef _is_contiguous(positions):\n \"\"\"Given a non-empty list, does it consist of contiguous integers?\"\"\"\n previous = positions[0]\n for current in positions[1:]:\n if current != previous + 1:\n return False\n previous = current\n return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__advanced_indexer_subspaces__advanced_indexer_subspaces.return.mixed_positions_vindex_p": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__advanced_indexer_subspaces__advanced_indexer_subspaces.return.mixed_positions_vindex_p", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 120, "span_ids": ["_advanced_indexer_subspaces"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _advanced_indexer_subspaces(key):\n \"\"\"Indices of the advanced indexes subspaces for mixed indexing and vindex.\n \"\"\"\n if not isinstance(key, tuple):\n key = (key,)\n advanced_index_positions = [\n i for i, k in enumerate(key) if not isinstance(k, slice)\n ]\n\n if not advanced_index_positions or not _is_contiguous(advanced_index_positions):\n # Nothing to reorder: dimensions on the indexing result are already\n # ordered like vindex. See NumPy's rule for \"Combining advanced and\n # basic indexing\":\n # https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing\n return (), ()\n\n non_slices = [k for k in key if not isinstance(k, slice)]\n ndim = len(np.broadcast(*non_slices).shape)\n mixed_positions = advanced_index_positions[0] + np.arange(ndim)\n vindex_positions = np.arange(ndim)\n return mixed_positions, vindex_positions", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_NumpyVIndexAdapter_NumpyVIndexAdapter.__setitem__.self__array_key_np_mov": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_NumpyVIndexAdapter_NumpyVIndexAdapter.__setitem__.self__array_key_np_mov", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 140, "span_ids": ["NumpyVIndexAdapter.__getitem__", "NumpyVIndexAdapter", "NumpyVIndexAdapter.__setitem__"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyVIndexAdapter:\n \"\"\"Object that implements indexing like vindex on a np.ndarray.\n\n This is a pure Python implementation of (some of) the logic in this NumPy\n proposal: https://github.com/numpy/numpy/pull/6256\n \"\"\"\n\n def __init__(self, array):\n self._array = array\n\n def __getitem__(self, key):\n mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)\n return np.moveaxis(self._array[key], mixed_positions, vindex_positions)\n\n def __setitem__(self, key, value):\n \"\"\"Value must have dimensionality matching the key.\"\"\"\n mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)\n self._array[key] = np.moveaxis(value, vindex_positions, mixed_positions)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_rolling_window_rolling_window.return._rolling_window_a_window": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_rolling_window_rolling_window.return._rolling_window_a_window", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 143, "end_line": 153, "span_ids": ["rolling_window"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def rolling_window(a, axis, window, center, fill_value):\n \"\"\" rolling window with padding. \"\"\"\n pads = [(0, 0) for s in a.shape]\n if center:\n start = int(window / 2) # 10 -> 5, 9 -> 4\n end = window - 1 - start\n pads[axis] = (start, end)\n else:\n pads[axis] = (window - 1, 0)\n a = np.pad(a, pads, mode=\"constant\", constant_values=fill_value)\n return _rolling_window(a, window, axis)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__rolling_window__rolling_window.return.np_swapaxes_rolling_2_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__rolling_window__rolling_window.return.np_swapaxes_rolling_2_", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 202, "span_ids": ["_rolling_window"], "tokens": 430}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _rolling_window(a, window, axis=-1):\n \"\"\"\n Make an ndarray with a rolling window along axis.\n\n Parameters\n ----------\n a : array_like\n Array to add rolling window to\n axis: int\n axis position along which rolling window will be applied.\n window : int\n Size of rolling window\n\n Returns\n -------\n Array that is a view of the original array with a added dimension\n of size w.\n\n Examples\n --------\n >>> x=np.arange(10).reshape((2,5))\n >>> np.rolling_window(x, 3, axis=-1)\n array([[[0, 1, 2], [1, 2, 3], [2, 3, 4]],\n [[5, 6, 7], [6, 7, 8], [7, 8, 9]]])\n\n Calculate rolling mean of last dimension:\n >>> np.mean(np.rolling_window(x, 3, axis=-1), -1)\n array([[ 1., 2., 3.],\n [ 6., 7., 8.]])\n\n This function is taken from https://github.com/numpy/numpy/pull/31\n but slightly modified to accept axis option.\n \"\"\"\n axis = _validate_axis(a, axis)\n a = np.swapaxes(a, axis, -1)\n\n if window < 1:\n raise ValueError(\"`window` must be at least 1. Given : {}\".format(window))\n if window > a.shape[-1]:\n raise ValueError(\"`window` is too long. Given : {}\".format(window))\n\n shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)\n strides = a.strides + (a.strides[-1],)\n rolling = np.lib.stride_tricks.as_strided(\n a, shape=shape, strides=strides, writeable=False\n )\n return np.swapaxes(rolling, -2, axis)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__create_bottleneck_method__create_bottleneck_method.return.f": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py__create_bottleneck_method__create_bottleneck_method.return.f", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 205, "end_line": 228, "span_ids": ["_create_bottleneck_method"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_bottleneck_method(name, npmodule=np):\n def f(values, axis=None, **kwargs):\n dtype = kwargs.get(\"dtype\", None)\n bn_func = getattr(bn, name, None)\n\n if (\n _USE_BOTTLENECK\n and isinstance(values, np.ndarray)\n and bn_func is not None\n and not isinstance(axis, tuple)\n and values.dtype.kind in \"uifc\"\n and values.dtype.isnative\n and (dtype is None or np.dtype(dtype) == values.dtype)\n ):\n # bottleneck does not take care dtype, min_count\n kwargs.pop(\"dtype\", None)\n result = bn_func(values, axis=axis, **kwargs)\n else:\n result = getattr(npmodule, name)(values, axis=axis, **kwargs)\n\n return result\n\n f.__name__ = name\n return f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_nanmin_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/nputils.py_nanmin_", "embedding": null, "metadata": {"file_path": "xarray/core/nputils.py", "file_name": "nputils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 231, "end_line": 242, "span_ids": ["impl:9"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "nanmin = _create_bottleneck_method(\"nanmin\")\nnanmax = _create_bottleneck_method(\"nanmax\")\nnanmean = _create_bottleneck_method(\"nanmean\")\nnanmedian = _create_bottleneck_method(\"nanmedian\")\nnanvar = _create_bottleneck_method(\"nanvar\")\nnanstd = _create_bottleneck_method(\"nanstd\")\nnanprod = _create_bottleneck_method(\"nanprod\")\nnancumsum = _create_bottleneck_method(\"nancumsum\")\nnancumprod = _create_bottleneck_method(\"nancumprod\")\nnanargmin = _create_bottleneck_method(\"nanargmin\")\nnanargmax = _create_bottleneck_method(\"nanargmax\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__Define_core_operations__CUM_DOCSTRING_TEMPLATE._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__Define_core_operations__CUM_DOCSTRING_TEMPLATE._", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 88, "span_ids": ["imports:5", "impl", "impl:2", "docstring", "imports"], "tokens": 593}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Define core operations for xarray objects.\n\nTODO(shoyer): rewrite this module, making use of xarray.core.computation,\nNumPy's __array_ufunc__ and mixin classes instead of the unintuitive \"inject\"\nfunctions.\n\"\"\"\n\nimport operator\n\nimport numpy as np\n\nfrom . import dtypes, duck_array_ops\nfrom .nputils import array_eq, array_ne\n\ntry:\n import bottleneck as bn\n\n has_bottleneck = True\nexcept ImportError:\n # use numpy methods instead\n bn = np\n has_bottleneck = False\n\n\nUNARY_OPS = [\"neg\", \"pos\", \"abs\", \"invert\"]\nCMP_BINARY_OPS = [\"lt\", \"le\", \"ge\", \"gt\"]\nNUM_BINARY_OPS = [\n \"add\",\n \"sub\",\n \"mul\",\n \"truediv\",\n \"floordiv\",\n \"mod\",\n \"pow\",\n \"and\",\n \"xor\",\n \"or\",\n]\n\n# methods which pass on the numpy return value unchanged\n# be careful not to list methods that we would want to wrap later\nNUMPY_SAME_METHODS = [\"item\", \"searchsorted\"]\n# methods which don't modify the data shape, so the result should still be\n# wrapped in an Variable/DataArray\nNUMPY_UNARY_METHODS = [\"astype\", \"argsort\", \"clip\", \"conj\", \"conjugate\"]\nPANDAS_UNARY_FUNCTIONS = [\"isnull\", \"notnull\"]\n# methods which remove an axis\nREDUCE_METHODS = [\"all\", \"any\"]\nNAN_REDUCE_METHODS = [\n \"argmax\",\n \"argmin\",\n \"max\",\n \"min\",\n \"mean\",\n \"prod\",\n \"sum\",\n \"std\",\n \"var\",\n \"median\",\n]\nNAN_CUM_METHODS = [\"cumsum\", \"cumprod\"]\n# TODO: wrap take, dot, sort\n\n\n_CUM_DOCSTRING_TEMPLATE = \"\"\"\\\nApply `{name}` along some dimension of {cls}.\n\nParameters\n----------\n{extra_args}\nskipna : bool, optional\n If True, skip missing values (as marked by NaN). By default, only\n skips missing values for float dtypes; other dtypes either do not\n have a sentinel missing value (int) or skipna=True has not been\n implemented (object, datetime64 or timedelta64).\nkeep_attrs : bool, optional\n If True, the attributes (`attrs`) will be copied from the original\n object to the new one. If False (default), the new object will be\n returned without attributes.\n**kwargs : dict\n Additional keyword arguments passed on to `{name}`.\n\nReturns\n-------\ncumvalue : {cls}\n New {cls} object with `{name}` applied to its data along the\n indicated dimension.\n\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__REDUCE_DOCSTRING_TEMPLATE__COARSEN_REDUCE_DOCSTRING_TEMPLATE._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__REDUCE_DOCSTRING_TEMPLATE__COARSEN_REDUCE_DOCSTRING_TEMPLATE._", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 90, "end_line": 134, "span_ids": ["impl:2", "impl:31"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_REDUCE_DOCSTRING_TEMPLATE = \"\"\"\\\nReduce this {cls}'s data by applying `{name}` along some dimension(s).\n\nParameters\n----------\n{extra_args}\nskipna : bool, optional\n If True, skip missing values (as marked by NaN). By default, only\n skips missing values for float dtypes; other dtypes either do not\n have a sentinel missing value (int) or skipna=True has not been\n implemented (object, datetime64 or timedelta64).{min_count_docs}\nkeep_attrs : bool, optional\n If True, the attributes (`attrs`) will be copied from the original\n object to the new one. If False (default), the new object will be\n returned without attributes.\n**kwargs : dict\n Additional keyword arguments passed on to the appropriate array\n function for calculating `{name}` on this object's data.\n\nReturns\n-------\nreduced : {cls}\n New {cls} object with `{name}` applied to its data and the\n indicated dimension(s) removed.\n\"\"\"\n\n_MINCOUNT_DOCSTRING = \"\"\"\nmin_count : int, default None\n The required number of valid values to perform the operation.\n If fewer than min_count non-NA values are present the result will\n be NA. New in version 0.10.8: Added with the default being None.\"\"\"\n\n_COARSEN_REDUCE_DOCSTRING_TEMPLATE = \"\"\"\\\nCoarsen this object by applying `{name}` along its dimensions.\n\nParameters\n----------\n**kwargs : dict\n Additional keyword arguments passed on to `{name}`.\n\nReturns\n-------\nreduced : DataArray or Dataset\n New object with `{name}` applied along its coasen dimnensions.\n\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_fillna_fillna.return.apply_ufunc_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_fillna_fillna.return.apply_ufunc_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 137, "end_line": 171, "span_ids": ["fillna"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def fillna(data, other, join=\"left\", dataset_join=\"left\"):\n \"\"\"Fill missing values in this object with data from the other object.\n Follows normal broadcasting and alignment rules.\n\n Parameters\n ----------\n join : {'outer', 'inner', 'left', 'right'}, optional\n Method for joining the indexes of the passed objects along each\n dimension\n - 'outer': use the union of object indexes\n - 'inner': use the intersection of object indexes\n - 'left': use indexes from the first object with each dimension\n - 'right': use indexes from the last object with each dimension\n - 'exact': raise `ValueError` instead of aligning when indexes to be\n aligned are not equal\n dataset_join : {'outer', 'inner', 'left', 'right'}, optional\n Method for joining variables of Dataset objects with mismatched\n data variables.\n - 'outer': take variables from both Dataset objects\n - 'inner': take only overlapped variables\n - 'left': take only variables from the first object\n - 'right': take only variables from the last object\n \"\"\"\n from .computation import apply_ufunc\n\n return apply_ufunc(\n duck_array_ops.fillna,\n data,\n other,\n join=join,\n dask=\"allowed\",\n dataset_join=dataset_join,\n dataset_fill_value=np.nan,\n keep_attrs=True,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_where_method_where_method.return.apply_ufunc_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_where_method_where_method.return.apply_ufunc_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 174, "end_line": 202, "span_ids": ["where_method"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def where_method(self, cond, other=dtypes.NA):\n \"\"\"Return elements from `self` or `other` depending on `cond`.\n\n Parameters\n ----------\n cond : DataArray or Dataset with boolean dtype\n Locations at which to preserve this objects values.\n other : scalar, DataArray or Dataset, optional\n Value to use for locations in this object where ``cond`` is False.\n By default, inserts missing values.\n\n Returns\n -------\n Same type as caller.\n \"\"\"\n from .computation import apply_ufunc\n\n # alignment for three arguments is complicated, so don't support it yet\n join = \"inner\" if other is dtypes.NA else \"exact\"\n return apply_ufunc(\n duck_array_ops.where_method,\n self,\n cond,\n other,\n join=join,\n dataset_join=join,\n dask=\"allowed\",\n keep_attrs=True,\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__call_possibly_missing_method__method_wrapper.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__call_possibly_missing_method__method_wrapper.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 205, "end_line": 232, "span_ids": ["_values_method_wrapper", "_method_wrapper", "_call_possibly_missing_method"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _call_possibly_missing_method(arg, name, args, kwargs):\n try:\n method = getattr(arg, name)\n except AttributeError:\n duck_array_ops.fail_on_dask_array_input(arg, func_name=name)\n if hasattr(arg, \"data\"):\n duck_array_ops.fail_on_dask_array_input(arg.data, func_name=name)\n raise\n else:\n return method(*args, **kwargs)\n\n\ndef _values_method_wrapper(name):\n def func(self, *args, **kwargs):\n return _call_possibly_missing_method(self.data, name, args, kwargs)\n\n func.__name__ = name\n func.__doc__ = getattr(np.ndarray, name).__doc__\n return func\n\n\ndef _method_wrapper(name):\n def func(self, *args, **kwargs):\n return _call_possibly_missing_method(self, name, args, kwargs)\n\n func.__name__ = name\n func.__doc__ = getattr(np.ndarray, name).__doc__\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__func_slash_method_wrapper__func_slash_method_wrapper.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py__func_slash_method_wrapper__func_slash_method_wrapper.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 235, "end_line": 250, "span_ids": ["_func_slash_method_wrapper"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _func_slash_method_wrapper(f, name=None):\n # try to wrap a method, but if not found use the function\n # this is useful when patching in a function as both a DataArray and\n # Dataset method\n if name is None:\n name = f.__name__\n\n def func(self, *args, **kwargs):\n try:\n return getattr(self, name)(*args, **kwargs)\n except AttributeError:\n return f(self, *args, **kwargs)\n\n func.__name__ = name\n func.__doc__ = f.__doc__\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_reduce_methods_inject_reduce_methods.for_name_f_include_skip.setattr_cls_name_func_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_reduce_methods_inject_reduce_methods.for_name_f_include_skip.setattr_cls_name_func_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 253, "end_line": 275, "span_ids": ["inject_reduce_methods"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inject_reduce_methods(cls):\n methods = (\n [\n (name, getattr(duck_array_ops, \"array_%s\" % name), False)\n for name in REDUCE_METHODS\n ]\n + [(name, getattr(duck_array_ops, name), True) for name in NAN_REDUCE_METHODS]\n + [(\"count\", duck_array_ops.count, False)]\n )\n for name, f, include_skipna in methods:\n numeric_only = getattr(f, \"numeric_only\", False)\n available_min_count = getattr(f, \"available_min_count\", False)\n min_count_docs = _MINCOUNT_DOCSTRING if available_min_count else \"\"\n\n func = cls._reduce_method(f, include_skipna, numeric_only)\n func.__name__ = name\n func.__doc__ = _REDUCE_DOCSTRING_TEMPLATE.format(\n name=name,\n cls=cls.__name__,\n extra_args=cls._reduce_extra_args_docstring.format(name=name),\n min_count_docs=min_count_docs,\n )\n setattr(cls, name, func)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_cum_methods_inplace_to_noninplace_op.return.NON_INPLACE_OP_f_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_cum_methods_inplace_to_noninplace_op.return.NON_INPLACE_OP_f_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 304, "span_ids": ["op_str", "inject_cum_methods", "impl:35", "get_op", "inplace_to_noninplace_op"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inject_cum_methods(cls):\n methods = [(name, getattr(duck_array_ops, name), True) for name in NAN_CUM_METHODS]\n for name, f, include_skipna in methods:\n numeric_only = getattr(f, \"numeric_only\", False)\n func = cls._reduce_method(f, include_skipna, numeric_only)\n func.__name__ = name\n func.__doc__ = _CUM_DOCSTRING_TEMPLATE.format(\n name=name,\n cls=cls.__name__,\n extra_args=cls._cum_extra_args_docstring.format(name=name),\n )\n setattr(cls, name, func)\n\n\ndef op_str(name):\n return \"__%s__\" % name\n\n\ndef get_op(name):\n return getattr(operator, op_str(name))\n\n\nNON_INPLACE_OP = {get_op(\"i\" + name): get_op(name) for name in NUM_BINARY_OPS}\n\n\ndef inplace_to_noninplace_op(f):\n return NON_INPLACE_OP[f]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_binary_ops_inject_binary_ops.for_name_in_NUM_BINARY_OP.if_inplace_.setattr_cls_op_str_i_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_binary_ops_inject_binary_ops.for_name_in_NUM_BINARY_OP.if_inplace_.setattr_cls_op_str_i_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 318, "span_ids": ["inject_binary_ops"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inject_binary_ops(cls, inplace=False):\n for name in CMP_BINARY_OPS + NUM_BINARY_OPS:\n setattr(cls, op_str(name), cls._binary_op(get_op(name)))\n\n for name, f in [(\"eq\", array_eq), (\"ne\", array_ne)]:\n setattr(cls, op_str(name), cls._binary_op(f))\n\n for name in NUM_BINARY_OPS:\n # only numeric operations have in-place and reflexive variants\n setattr(cls, op_str(\"r\" + name), cls._binary_op(get_op(name), reflexive=True))\n if inplace:\n setattr(cls, op_str(\"i\" + name), cls._inplace_binary_op(get_op(\"i\" + name)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_all_ops_and_reduce_methods_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/ops.py_inject_all_ops_and_reduce_methods_", "embedding": null, "metadata": {"file_path": "xarray/core/ops.py", "file_name": "ops.py", "file_type": "text/x-python", "category": "implementation", "start_line": 321, "end_line": 360, "span_ids": ["inject_coarsen_methods", "inject_all_ops_and_reduce_methods"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def inject_all_ops_and_reduce_methods(cls, priority=50, array_only=True):\n # prioritize our operations over those of numpy.ndarray (priority=1)\n # and numpy.matrix (priority=10)\n cls.__array_priority__ = priority\n\n # patch in standard special operations\n for name in UNARY_OPS:\n setattr(cls, op_str(name), cls._unary_op(get_op(name)))\n inject_binary_ops(cls, inplace=True)\n\n # patch in numpy/pandas methods\n for name in NUMPY_UNARY_METHODS:\n setattr(cls, name, cls._unary_op(_method_wrapper(name)))\n\n for name in PANDAS_UNARY_FUNCTIONS:\n f = _func_slash_method_wrapper(getattr(duck_array_ops, name), name=name)\n setattr(cls, name, cls._unary_op(f))\n\n f = _func_slash_method_wrapper(duck_array_ops.around, name=\"round\")\n setattr(cls, \"round\", cls._unary_op(f))\n\n if array_only:\n # these methods don't return arrays of the same shape as the input, so\n # don't try to patch these in for Dataset objects\n for name in NUMPY_SAME_METHODS:\n setattr(cls, name, _values_method_wrapper(name))\n\n inject_reduce_methods(cls)\n inject_cum_methods(cls)\n\n\ndef inject_coarsen_methods(cls):\n # standard numpy reduce methods\n methods = [(name, getattr(duck_array_ops, name)) for name in NAN_REDUCE_METHODS]\n for name, f in methods:\n func = cls._reduce_method(f)\n func.__name__ = name\n func.__doc__ = _COARSEN_REDUCE_DOCSTRING_TEMPLATE.format(name=func.__name__)\n setattr(cls, name, func)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/options.py_warnings__get_keep_attrs.if_global_choice_defa.else_.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/options.py_warnings__get_keep_attrs.if_global_choice_defa.else_.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/core/options.py", "file_name": "options.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 71, "span_ids": ["impl", "_get_keep_attrs", "impl:23", "_warn_on_setting_enable_cftimeindex", "imports", "impl:21", "_set_file_cache_maxsize", "_positive_integer"], "tokens": 487}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\n\nDISPLAY_WIDTH = \"display_width\"\nARITHMETIC_JOIN = \"arithmetic_join\"\nENABLE_CFTIMEINDEX = \"enable_cftimeindex\"\nFILE_CACHE_MAXSIZE = \"file_cache_maxsize\"\nWARN_FOR_UNCLOSED_FILES = \"warn_for_unclosed_files\"\nCMAP_SEQUENTIAL = \"cmap_sequential\"\nCMAP_DIVERGENT = \"cmap_divergent\"\nKEEP_ATTRS = \"keep_attrs\"\n\n\nOPTIONS = {\n DISPLAY_WIDTH: 80,\n ARITHMETIC_JOIN: \"inner\",\n ENABLE_CFTIMEINDEX: True,\n FILE_CACHE_MAXSIZE: 128,\n WARN_FOR_UNCLOSED_FILES: False,\n CMAP_SEQUENTIAL: \"viridis\",\n CMAP_DIVERGENT: \"RdBu_r\",\n KEEP_ATTRS: \"default\",\n}\n\n_JOIN_OPTIONS = frozenset([\"inner\", \"outer\", \"left\", \"right\", \"exact\"])\n\n\ndef _positive_integer(value):\n return isinstance(value, int) and value > 0\n\n\n_VALIDATORS = {\n DISPLAY_WIDTH: _positive_integer,\n ARITHMETIC_JOIN: _JOIN_OPTIONS.__contains__,\n ENABLE_CFTIMEINDEX: lambda value: isinstance(value, bool),\n FILE_CACHE_MAXSIZE: _positive_integer,\n WARN_FOR_UNCLOSED_FILES: lambda value: isinstance(value, bool),\n KEEP_ATTRS: lambda choice: choice in [True, False, \"default\"],\n}\n\n\ndef _set_file_cache_maxsize(value):\n from ..backends.file_manager import FILE_CACHE\n\n FILE_CACHE.maxsize = value\n\n\ndef _warn_on_setting_enable_cftimeindex(enable_cftimeindex):\n warnings.warn(\n \"The enable_cftimeindex option is now a no-op \"\n \"and will be removed in a future version of xarray.\",\n FutureWarning,\n )\n\n\n_SETTERS = {\n FILE_CACHE_MAXSIZE: _set_file_cache_maxsize,\n ENABLE_CFTIMEINDEX: _warn_on_setting_enable_cftimeindex,\n}\n\n\ndef _get_keep_attrs(default):\n global_choice = OPTIONS[\"keep_attrs\"]\n\n if global_choice == \"default\":\n return default\n elif global_choice in [True, False]:\n return global_choice\n else:\n raise ValueError(\n \"The global option keep_attrs must be one of\" \" True, False or 'default'.\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/options.py_set_options_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/options.py_set_options_", "embedding": null, "metadata": {"file_path": "xarray/core/options.py", "file_name": "options.py", "file_type": "text/x-python", "category": "implementation", "start_line": 74, "end_line": 143, "span_ids": ["set_options.__exit__", "set_options", "set_options.__enter__", "set_options._apply_update"], "tokens": 671}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class set_options:\n \"\"\"Set options for xarray in a controlled context.\n\n Currently supported options:\n\n - ``display_width``: maximum display width for ``repr`` on xarray objects.\n Default: ``80``.\n - ``arithmetic_join``: DataArray/Dataset alignment in binary operations.\n Default: ``'inner'``.\n - ``file_cache_maxsize``: maximum number of open files to hold in xarray's\n global least-recently-usage cached. This should be smaller than your\n system's per-process file descriptor limit, e.g., ``ulimit -n`` on Linux.\n Default: 128.\n - ``warn_for_unclosed_files``: whether or not to issue a warning when\n unclosed files are deallocated (default False). This is mostly useful\n for debugging.\n - ``cmap_sequential``: colormap to use for nondivergent data plots.\n Default: ``viridis``. If string, must be matplotlib built-in colormap.\n Can also be a Colormap object (e.g. mpl.cm.magma)\n - ``cmap_divergent``: colormap to use for divergent data plots.\n Default: ``RdBu_r``. If string, must be matplotlib built-in colormap.\n Can also be a Colormap object (e.g. mpl.cm.magma)\n - ``keep_attrs``: rule for whether to keep attributes on xarray\n Datasets/dataarrays after operations. Either ``True`` to always keep\n attrs, ``False`` to always discard them, or ``'default'`` to use original\n logic that attrs should only be kept in unambiguous circumstances.\n Default: ``'default'``.\n\n You can use ``set_options`` either as a context manager:\n\n >>> ds = xr.Dataset({'x': np.arange(1000)})\n >>> with xr.set_options(display_width=40):\n ... print(ds)\n \n Dimensions: (x: 1000)\n Coordinates:\n * x (x) int64 0 1 2 3 4 5 6 ...\n Data variables:\n *empty*\n\n Or to set global options:\n\n >>> xr.set_options(display_width=80)\n \"\"\"\n\n def __init__(self, **kwargs):\n self.old = {}\n for k, v in kwargs.items():\n if k not in OPTIONS:\n raise ValueError(\n \"argument name %r is not in the set of valid options %r\"\n % (k, set(OPTIONS))\n )\n if k in _VALIDATORS and not _VALIDATORS[k](v):\n raise ValueError(\"option %r given an invalid value: %r\" % (k, v))\n self.old[k] = OPTIONS[k]\n self._apply_update(kwargs)\n\n def _apply_update(self, options_dict):\n for k, v in options_dict.items():\n if k in _SETTERS:\n _SETTERS[k](v)\n OPTIONS.update(options_dict)\n\n def __enter__(self):\n return\n\n def __exit__(self, type, value, traceback):\n self._apply_update(self.old)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_try__dataset_to_dataarray.return.next_iter_obj_data_vars_v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_try__dataset_to_dataarray.return.next_iter_obj_data_vars_v", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 41, "span_ids": ["impl", "imports:5", "impl:2", "imports", "dataset_to_dataarray", "impl:4"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "try:\n import dask\n import dask.array\n from dask.highlevelgraph import HighLevelGraph\n from .dask_array_compat import meta_from_array\n\nexcept ImportError:\n pass\n\nimport itertools\nimport operator\nfrom typing import (\n Any,\n Callable,\n Dict,\n Hashable,\n Mapping,\n Sequence,\n Tuple,\n TypeVar,\n Union,\n)\n\nimport numpy as np\n\nfrom .dataarray import DataArray\nfrom .dataset import Dataset\n\nT_DSorDA = TypeVar(\"T_DSorDA\", DataArray, Dataset)\n\n\ndef dataset_to_dataarray(obj: Dataset) -> DataArray:\n if not isinstance(obj, Dataset):\n raise TypeError(\"Expected Dataset, got %s\" % type(obj))\n\n if len(obj.data_vars) > 1:\n raise TypeError(\n \"Trying to convert Dataset with more than one data variable to DataArray\"\n )\n\n return next(iter(obj.data_vars.values()))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_make_meta_make_meta.return.meta": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_make_meta_make_meta.return.meta", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 67, "span_ids": ["make_meta"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_meta(obj):\n \"\"\"If obj is a DataArray or Dataset, return a new object of the same type and with\n the same variables and dtypes, but where all variables have size 0 and numpy\n backend.\n If obj is neither a DataArray nor Dataset, return it unaltered.\n \"\"\"\n if isinstance(obj, DataArray):\n obj_array = obj\n obj = obj._to_temp_dataset()\n elif isinstance(obj, Dataset):\n obj_array = None\n else:\n return obj\n\n meta = Dataset()\n for name, variable in obj.variables.items():\n meta_obj = meta_from_array(variable.data, ndim=variable.ndim)\n meta[name] = (variable.dims, meta_obj, variable.attrs)\n meta.attrs = obj.attrs\n meta = meta.set_coords(obj.coords)\n\n if obj_array is not None:\n return obj_array._from_temp_dataset(meta)\n return meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_infer_template_make_dict.return._k_v_data_for_k_v_in_x_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_infer_template_make_dict.return._k_v_data_for_k_v_in_x_", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 100, "span_ids": ["infer_template", "make_dict"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def infer_template(\n func: Callable[..., T_DSorDA], obj: Union[DataArray, Dataset], *args, **kwargs\n) -> T_DSorDA:\n \"\"\"Infer return object by running the function on meta objects.\n \"\"\"\n meta_args = [make_meta(arg) for arg in (obj,) + args]\n\n try:\n template = func(*meta_args, **kwargs)\n except Exception as e:\n raise Exception(\n \"Cannot infer object returned from running user provided function.\"\n ) from e\n\n if not isinstance(template, (Dataset, DataArray)):\n raise TypeError(\n \"Function must return an xarray DataArray or Dataset. Instead it returned \"\n f\"{type(template)}\"\n )\n\n return template\n\n\ndef make_dict(x: Union[DataArray, Dataset]) -> Dict[Hashable, Any]:\n \"\"\"Map variable name to numpy(-like) data\n (Dataset.to_dict() is too complicated).\n \"\"\"\n if isinstance(x, DataArray):\n x = x._to_temp_dataset()\n\n return {k: v.data for k, v in x.variables.items()}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks_map_blocks.if_not_dask_is_dask_colle.return.func_obj_args_kwargs": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks_map_blocks.if_not_dask_is_dask_colle.return.func_obj_args_kwargs", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 103, "end_line": 188, "span_ids": ["map_blocks"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_blocks(\n func: Callable[..., T_DSorDA],\n obj: Union[DataArray, Dataset],\n args: Sequence[Any] = (),\n kwargs: Mapping[str, Any] = None,\n) -> T_DSorDA:\n \"\"\"Apply a function to each chunk of a DataArray or Dataset. This function is\n experimental and its signature may change.\n\n Parameters\n ----------\n func: callable\n User-provided function that accepts a DataArray or Dataset as its first\n parameter. The function will receive a subset of 'obj' (see below),\n corresponding to one chunk along each chunked dimension. ``func`` will be\n executed as ``func(obj_subset, *args, **kwargs)``.\n\n The function will be first run on mocked-up data, that looks like 'obj' but\n has sizes 0, to determine properties of the returned object such as dtype,\n variable names, new dimensions and new indexes (if any).\n\n This function must return either a single DataArray or a single Dataset.\n\n This function cannot change size of existing dimensions, or add new chunked\n dimensions.\n obj: DataArray, Dataset\n Passed to the function as its first argument, one dask chunk at a time.\n args: Sequence\n Passed verbatim to func after unpacking, after the sliced obj. xarray objects,\n if any, will not be split by chunks. Passing dask collections is not allowed.\n kwargs: Mapping\n Passed verbatim to func after unpacking. xarray objects, if any, will not be\n split by chunks. Passing dask collections is not allowed.\n\n Returns\n -------\n A single DataArray or Dataset with dask backend, reassembled from the outputs of the\n function.\n\n Notes\n -----\n This function is designed for when one needs to manipulate a whole xarray object\n within each chunk. In the more common case where one can work on numpy arrays, it is\n recommended to use apply_ufunc.\n\n If none of the variables in obj is backed by dask, calling this function is\n equivalent to calling ``func(obj, *args, **kwargs)``.\n\n See Also\n --------\n dask.array.map_blocks, xarray.apply_ufunc, xarray.Dataset.map_blocks,\n xarray.DataArray.map_blocks\n \"\"\"\n\n def _wrapper(func, obj, to_array, args, kwargs):\n if to_array:\n obj = dataset_to_dataarray(obj)\n\n result = func(obj, *args, **kwargs)\n\n for name, index in result.indexes.items():\n if name in obj.indexes:\n if len(index) != len(obj.indexes[name]):\n raise ValueError(\n \"Length of the %r dimension has changed. This is not allowed.\"\n % name\n )\n\n return make_dict(result)\n\n if not isinstance(args, Sequence):\n raise TypeError(\"args must be a sequence (for example, a list or tuple).\")\n if kwargs is None:\n kwargs = {}\n elif not isinstance(kwargs, Mapping):\n raise TypeError(\"kwargs must be a mapping (for example, a dict)\")\n\n for value in list(args) + list(kwargs.values()):\n if dask.is_dask_collection(value):\n raise TypeError(\n \"Cannot pass dask collections in args or kwargs yet. Please compute or \"\n \"load values before passing to map_blocks.\"\n )\n\n if not dask.is_dask_collection(obj):\n return func(obj, *args, **kwargs)\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks.if_isinstance_obj_DataAr_map_blocks._iterate_over_all_possib": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks.if_isinstance_obj_DataAr_map_blocks._iterate_over_all_possib", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 237, "span_ids": ["map_blocks"], "tokens": 447}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_blocks(\n func: Callable[..., T_DSorDA],\n obj: Union[DataArray, Dataset],\n args: Sequence[Any] = (),\n kwargs: Mapping[str, Any] = None,\n) -> T_DSorDA:\n # ... other code\n\n if isinstance(obj, DataArray):\n # only using _to_temp_dataset would break\n # func = lambda x: x.to_dataset()\n # since that relies on preserving name.\n if obj.name is None:\n dataset = obj._to_temp_dataset()\n else:\n dataset = obj.to_dataset()\n input_is_array = True\n else:\n dataset = obj\n input_is_array = False\n\n input_chunks = dataset.chunks\n\n template: Union[DataArray, Dataset] = infer_template(func, obj, *args, **kwargs)\n if isinstance(template, DataArray):\n result_is_array = True\n template_name = template.name\n template = template._to_temp_dataset()\n elif isinstance(template, Dataset):\n result_is_array = False\n else:\n raise TypeError(\n f\"func output must be DataArray or Dataset; got {type(template)}\"\n )\n\n template_indexes = set(template.indexes)\n dataset_indexes = set(dataset.indexes)\n preserved_indexes = template_indexes & dataset_indexes\n new_indexes = template_indexes - dataset_indexes\n indexes = {dim: dataset.indexes[dim] for dim in preserved_indexes}\n indexes.update({k: template.indexes[k] for k in new_indexes})\n\n graph: Dict[Any, Any] = {}\n gname = \"%s-%s\" % (\n dask.utils.funcname(func),\n dask.base.tokenize(dataset, args, kwargs),\n )\n\n # map dims to list of chunk indexes\n ichunk = {dim: range(len(chunks_v)) for dim, chunks_v in input_chunks.items()}\n # mapping from chunk index to slice bounds\n chunk_index_bounds = {\n dim: np.cumsum((0,) + chunks_v) for dim, chunks_v in input_chunks.items()\n }\n\n # iterate over all possible chunk combinations\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks.for_v_in_itertools_produc_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/parallel.py_map_blocks.for_v_in_itertools_produc_", "embedding": null, "metadata": {"file_path": "xarray/core/parallel.py", "file_name": "parallel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 340, "span_ids": ["map_blocks"], "tokens": 843}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_blocks(\n func: Callable[..., T_DSorDA],\n obj: Union[DataArray, Dataset],\n args: Sequence[Any] = (),\n kwargs: Mapping[str, Any] = None,\n) -> T_DSorDA:\n # ... other code\n for v in itertools.product(*ichunk.values()):\n chunk_index_dict = dict(zip(dataset.dims, v))\n\n # this will become [[name1, variable1],\n # [name2, variable2],\n # ...]\n # which is passed to dict and then to Dataset\n data_vars = []\n coords = []\n\n for name, variable in dataset.variables.items():\n # make a task that creates tuple of (dims, chunk)\n if dask.is_dask_collection(variable.data):\n # recursively index into dask_keys nested list to get chunk\n chunk = variable.__dask_keys__()\n for dim in variable.dims:\n chunk = chunk[chunk_index_dict[dim]]\n\n chunk_variable_task = (\"%s-%s\" % (gname, chunk[0]),) + v\n graph[chunk_variable_task] = (\n tuple,\n [variable.dims, chunk, variable.attrs],\n )\n else:\n # non-dask array with possibly chunked dimensions\n # index into variable appropriately\n subsetter = {}\n for dim in variable.dims:\n if dim in chunk_index_dict:\n which_chunk = chunk_index_dict[dim]\n subsetter[dim] = slice(\n chunk_index_bounds[dim][which_chunk],\n chunk_index_bounds[dim][which_chunk + 1],\n )\n\n subset = variable.isel(subsetter)\n chunk_variable_task = (\n \"%s-%s\" % (gname, dask.base.tokenize(subset)),\n ) + v\n graph[chunk_variable_task] = (\n tuple,\n [subset.dims, subset, subset.attrs],\n )\n\n # this task creates dict mapping variable name to above tuple\n if name in dataset._coord_names:\n coords.append([name, chunk_variable_task])\n else:\n data_vars.append([name, chunk_variable_task])\n\n from_wrapper = (gname,) + v\n graph[from_wrapper] = (\n _wrapper,\n func,\n (Dataset, (dict, data_vars), (dict, coords), dataset.attrs),\n input_is_array,\n args,\n kwargs,\n )\n\n # mapping from variable name to dask graph key\n var_key_map: Dict[Hashable, str] = {}\n for name, variable in template.variables.items():\n if name in indexes:\n continue\n gname_l = \"%s-%s\" % (gname, name)\n var_key_map[name] = gname_l\n\n key: Tuple[Any, ...] = (gname_l,)\n for dim in variable.dims:\n if dim in chunk_index_dict:\n key += (chunk_index_dict[dim],)\n else:\n # unchunked dimensions in the input have one chunk in the result\n key += (0,)\n\n graph[key] = (operator.getitem, from_wrapper, name)\n\n graph = HighLevelGraph.from_collections(gname, graph, dependencies=[dataset])\n\n result = Dataset(coords=indexes, attrs=template.attrs)\n for name, gname_l in var_key_map.items():\n dims = template[name].dims\n var_chunks = []\n for dim in dims:\n if dim in input_chunks:\n var_chunks.append(input_chunks[dim])\n elif dim in indexes:\n var_chunks.append((len(indexes[dim]),))\n\n data = dask.array.Array(\n graph, name=gname_l, chunks=var_chunks, dtype=template[name].dtype\n )\n result[name] = (dims, data, template[name].attrs)\n\n result = result.set_coords(template._coord_names)\n\n if result_is_array:\n da = dataset_to_dataarray(result)\n da.name = template_name\n return da # type: ignore\n return result # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/pdcompat.py__The_remove_unused_level_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/pdcompat.py__The_remove_unused_level_", "embedding": null, "metadata": {"file_path": "xarray/core/pdcompat.py", "file_name": "pdcompat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 59, "span_ids": ["impl", "if_LooseVersion_pd___vers.else_.Panel", "if_LooseVersion_pd___vers.else_.Panel:docstring", "count_not_none", "docstring", "if_LooseVersion_pd___vers.else_.Panel:2", "imports"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# The remove_unused_levels defined here was copied based on the source code\n# defined in pandas.core.indexes.muli.py\n\nfrom distutils.version import LooseVersion\n\nimport pandas as pd\n\n# allow ourselves to type checks for Panel even after it's removed\nif LooseVersion(pd.__version__) < \"0.25.0\":\n Panel = pd.Panel\nelse:\n\n class Panel: # type: ignore\n pass\n\n\ndef count_not_none(*args) -> int:\n \"\"\"Compute the number of non-None arguments.\n\n Copied from pandas.core.common.count_not_none (not part of the public API)\n \"\"\"\n return sum([arg is not None for arg in args])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/pycompat.py__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/pycompat.py__", "embedding": null, "metadata": {"file_path": "xarray/core/pycompat.py", "file_name": "pycompat.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 20, "span_ids": ["impl", "imports", "impl:10", "imports:2", "imports:3", "impl:4"], "tokens": 98}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\ninteger_types = (int, np.integer)\n\ntry:\n # solely for isinstance checks\n import dask.array\n\n dask_array_type = (dask.array.Array,)\nexcept ImportError: # pragma: no cover\n dask_array_type = ()\n\ntry:\n # solely for isinstance checks\n import sparse\n\n sparse_array_type = (sparse.SparseArray,)\nexcept ImportError: # pragma: no cover\n sparse_array_type = ()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_ops_Resample._upsample.if_method_asfreq_.else_.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_ops_Resample._upsample.if_method_asfreq_.else_.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 67, "span_ids": ["Resample._upsample", "impl", "Resample", "imports"], "tokens": 452}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from . import ops\nfrom .groupby import DataArrayGroupBy, DatasetGroupBy\n\nRESAMPLE_DIM = \"__resample_dim__\"\n\n\nclass Resample:\n \"\"\"An object that extends the `GroupBy` object with additional logic\n for handling specialized re-sampling operations.\n\n You should create a `Resample` object by using the `DataArray.resample` or\n `Dataset.resample` methods. The dimension along re-sampling\n\n See Also\n --------\n DataArray.resample\n Dataset.resample\n\n \"\"\"\n\n def _upsample(self, method, *args, **kwargs):\n \"\"\"Dispatch function to call appropriate up-sampling methods on\n data.\n\n This method should not be called directly; instead, use one of the\n wrapper functions supplied by `Resample`.\n\n Parameters\n ----------\n method : str {'asfreq', 'pad', 'ffill', 'backfill', 'bfill', 'nearest',\n 'interpolate'}\n Method to use for up-sampling\n\n See Also\n --------\n Resample.asfreq\n Resample.pad\n Resample.backfill\n Resample.interpolate\n\n \"\"\"\n\n upsampled_index = self._full_index\n\n # Drop non-dimension coordinates along the resampled dimension\n for k, v in self._obj.coords.items():\n if k == self._dim:\n continue\n if self._dim in v.dims:\n self._obj = self._obj.drop(k)\n\n if method == \"asfreq\":\n return self.mean(self._dim)\n\n elif method in [\"pad\", \"ffill\", \"backfill\", \"bfill\", \"nearest\"]:\n kwargs = kwargs.copy()\n kwargs.update(**{self._dim: upsampled_index})\n return self._obj.reindex(method=method, *args, **kwargs)\n\n elif method == \"interpolate\":\n return self._interpolate(*args, **kwargs)\n\n else:\n raise ValueError(\n 'Specified method was \"{}\" but must be one of'\n '\"asfreq\", \"ffill\", \"bfill\", or \"interpolate\"'.format(method)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.asfreq_Resample.pad.return.self__upsample_pad_tol": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.asfreq_Resample.pad.return.self__upsample_pad_tol", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 88, "span_ids": ["Resample.asfreq", "Resample.pad"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample:\n\n def asfreq(self):\n \"\"\"Return values of original object at the new up-sampling frequency;\n essentially a re-index with new times set to NaN.\n \"\"\"\n return self._upsample(\"asfreq\")\n\n def pad(self, tolerance=None):\n \"\"\"Forward fill new values at up-sampled frequency.\n\n Parameters\n ----------\n tolerance : optional\n Maximum distance between original and new labels to limit\n the up-sampling method.\n Up-sampled data with indices that satisfy the equation\n ``abs(index[indexer] - target) <= tolerance`` are filled by\n new values. Data with indices that are outside the given\n tolerance are filled with ``NaN`` s\n \"\"\"\n return self._upsample(\"pad\", tolerance=tolerance)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.ffill_Resample.backfill.return.self__upsample_backfill_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.ffill_Resample.backfill.return.self__upsample_backfill_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 90, "end_line": 105, "span_ids": ["Resample:3", "Resample.backfill"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample:\n\n ffill = pad\n\n def backfill(self, tolerance=None):\n \"\"\"Backward fill new values at up-sampled frequency.\n\n Parameters\n ----------\n tolerance : optional\n Maximum distance between original and new labels to limit\n the up-sampling method.\n Up-sampled data with indices that satisfy the equation\n ``abs(index[indexer] - target) <= tolerance`` are filled by\n new values. Data with indices that are outside the given\n tolerance are filled with ``NaN`` s\n \"\"\"\n return self._upsample(\"backfill\", tolerance=tolerance)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.bfill_Resample.nearest.return.self__upsample_nearest_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.bfill_Resample.nearest.return.self__upsample_nearest_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 123, "span_ids": ["Resample.nearest", "Resample:5"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample:\n\n bfill = backfill\n\n def nearest(self, tolerance=None):\n \"\"\"Take new values from nearest original coordinate to up-sampled\n frequency coordinates.\n\n Parameters\n ----------\n tolerance : optional\n Maximum distance between original and new labels to limit\n the up-sampling method.\n Up-sampled data with indices that satisfy the equation\n ``abs(index[indexer] - target) <= tolerance`` are filled by\n new values. Data with indices that are outside the given\n tolerance are filled with ``NaN`` s\n \"\"\"\n return self._upsample(\"nearest\", tolerance=tolerance)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.interpolate_Resample._interpolate.return.dummy_interp_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_Resample.interpolate_Resample._interpolate.return.dummy_interp_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 125, "end_line": 155, "span_ids": ["Resample.interpolate", "Resample._interpolate"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample:\n\n def interpolate(self, kind=\"linear\"):\n \"\"\"Interpolate up-sampled data using the original data\n as knots.\n\n Parameters\n ----------\n kind : str {'linear', 'nearest', 'zero', 'slinear',\n 'quadratic', 'cubic'}\n Interpolation scheme to use\n\n See Also\n --------\n scipy.interpolate.interp1d\n\n \"\"\"\n return self._interpolate(kind=kind)\n\n def _interpolate(self, kind=\"linear\"):\n \"\"\"Apply scipy.interpolate.interp1d along resampling dimension.\"\"\"\n # drop any existing non-dimension coordinates along the resampling\n # dimension\n dummy = self._obj.copy()\n for k, v in self._obj.coords.items():\n if k != self._dim and self._dim in v.dims:\n dummy = dummy.drop(k)\n return dummy.interp(\n assume_sorted=True,\n method=kind,\n kwargs={\"bounds_error\": False},\n **{self._dim: self._full_index}\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DataArrayResample_DataArrayResample.__init__.super___init___args_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DataArrayResample_DataArrayResample.__init__.super___init___args_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 158, "end_line": 174, "span_ids": ["DataArrayResample"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayResample(DataArrayGroupBy, Resample):\n \"\"\"DataArrayGroupBy object specialized to time resampling operations over a\n specified dimension\n \"\"\"\n\n def __init__(self, *args, dim=None, resample_dim=None, **kwargs):\n\n if dim == resample_dim:\n raise ValueError(\n \"Proxy resampling dimension ('{}') \"\n \"cannot have the same name as actual dimension \"\n \"('{}')! \".format(resample_dim, dim)\n )\n self._dim = dim\n self._resample_dim = resample_dim\n\n super().__init__(*args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DataArrayResample.apply_DataArrayResample.apply.return.combined": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DataArrayResample.apply_DataArrayResample.apply.return.combined", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 226, "span_ids": ["DataArrayResample.apply"], "tokens": 484}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayResample(DataArrayGroupBy, Resample):\n\n def apply(self, func, shortcut=False, args=(), **kwargs):\n \"\"\"Apply a function over each array in the group and concatenate them\n together into a new array.\n\n `func` is called like `func(ar, *args, **kwargs)` for each array `ar`\n in this group.\n\n Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n to stack together the array. The rule is:\n 1. If the dimension along which the group coordinate is defined is\n still in the first grouped array after applying `func`, then stack\n over this dimension.\n 2. Otherwise, stack over the new dimension given by name of this\n grouping (the argument to the `groupby` function).\n\n Parameters\n ----------\n func : function\n Callable to apply to each array.\n shortcut : bool, optional\n Whether or not to shortcut evaluation under the assumptions that:\n (1) The action of `func` does not depend on any of the array\n metadata (attributes or coordinates) but only on the data and\n dimensions.\n (2) The action of `func` creates arrays with homogeneous metadata,\n that is, with the same dimensions and attributes.\n If these conditions are satisfied `shortcut` provides significant\n speedup. This should be the case for many common groupby operations\n (e.g., applying numpy ufuncs).\n args : tuple, optional\n Positional arguments passed on to `func`.\n **kwargs\n Used to call `func(ar, **kwargs)` for each array `ar`.\n\n Returns\n -------\n applied : DataArray or DataArray\n The result of splitting, applying and combining this array.\n \"\"\"\n combined = super().apply(func, shortcut=shortcut, args=args, **kwargs)\n\n # If the aggregation function didn't drop the original resampling\n # dimension, then we need to do so before we can rename the proxy\n # dimension we used.\n if self._dim in combined.coords:\n combined = combined.drop(self._dim)\n\n if self._resample_dim in combined.dims:\n combined = combined.rename({self._resample_dim: self._dim})\n\n return combined", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_ops_inject_reduce_methods_DatasetResample.__init__.super___init___args_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_ops_inject_reduce_methods_DatasetResample.__init__.super___init___args_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 229, "end_line": 248, "span_ids": ["DatasetResample", "impl:3"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "ops.inject_reduce_methods(DataArrayResample)\nops.inject_binary_ops(DataArrayResample)\n\n\nclass DatasetResample(DatasetGroupBy, Resample):\n \"\"\"DatasetGroupBy object specialized to resampling a specified dimension\n \"\"\"\n\n def __init__(self, *args, dim=None, resample_dim=None, **kwargs):\n\n if dim == resample_dim:\n raise ValueError(\n \"Proxy resampling dimension ('{}') \"\n \"cannot have the same name as actual dimension \"\n \"('{}')! \".format(resample_dim, dim)\n )\n self._dim = dim\n self._resample_dim = resample_dim\n\n super().__init__(*args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DatasetResample.apply_DatasetResample.apply.return.combined_rename_self__re": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DatasetResample.apply_DatasetResample.apply.return.combined_rename_self__re", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 250, "end_line": 283, "span_ids": ["DatasetResample.apply"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetResample(DatasetGroupBy, Resample):\n\n def apply(self, func, args=(), shortcut=None, **kwargs):\n \"\"\"Apply a function over each Dataset in the groups generated for\n resampling and concatenate them together into a new Dataset.\n\n `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`\n in this group.\n\n Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n to stack together the datasets. The rule is:\n 1. If the dimension along which the group coordinate is defined is\n still in the first grouped item after applying `func`, then stack\n over this dimension.\n 2. Otherwise, stack over the new dimension given by name of this\n grouping (the argument to the `groupby` function).\n\n Parameters\n ----------\n func : function\n Callable to apply to each sub-dataset.\n args : tuple, optional\n Positional arguments passed on to `func`.\n **kwargs\n Used to call `func(ds, **kwargs)` for each sub-dataset `ar`.\n\n Returns\n -------\n applied : Dataset or DataArray\n The result of splitting, applying and combining this dataset.\n \"\"\"\n # ignore shortcut if set (for now)\n applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())\n combined = self._combine(applied)\n\n return combined.rename({self._resample_dim: self._dim})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DatasetResample.reduce_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample.py_DatasetResample.reduce_", "embedding": null, "metadata": {"file_path": "xarray/core/resample.py", "file_name": "resample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 285, "end_line": 315, "span_ids": ["DatasetResample.reduce", "impl:5"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetResample(DatasetGroupBy, Resample):\n\n def reduce(self, func, dim=None, keep_attrs=None, **kwargs):\n \"\"\"Reduce the items in this group by applying `func` along the\n pre-defined resampling dimension.\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, axis=axis, **kwargs)` to return the result of collapsing\n an np.ndarray over an integer valued axis.\n dim : str or sequence of str, optional\n Dimension(s) over which to apply `func`.\n keep_attrs : bool, optional\n If True, the datasets's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : Array\n Array with summarized data and the indicated dimension(s)\n removed.\n \"\"\"\n return super().reduce(func, dim, keep_attrs, **kwargs)\n\n\nops.inject_reduce_methods(DatasetResample)\nops.inject_binary_ops(DatasetResample)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__Resampling_for_CFTimeI_CFTimeIndex": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__Resampling_for_CFTimeI_CFTimeIndex", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 54, "span_ids": ["docstring", "imports"], "tokens": 110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Resampling for CFTimeIndex. Does not support non-integer freq.\"\"\"\n# The mechanisms for resampling CFTimeIndex was copied and adapted from\n# the source code defined in pandas.core.resample\n#\n\nimport datetime\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..coding.cftime_offsets import (\n CFTIME_TICKS,\n Day,\n MonthEnd,\n QuarterEnd,\n YearEnd,\n cftime_range,\n normalize_date,\n to_offset,\n)\nfrom ..coding.cftimeindex import CFTimeIndex", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_CFTimeGrouper_CFTimeGrouper.__init__.if_isinstance_self_freq_.else_.if_self_label_is_None_.self.label._left_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_CFTimeGrouper_CFTimeGrouper.__init__.if_isinstance_self_freq_.else_.if_self_label_is_None_.self.label._left_", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 57, "end_line": 78, "span_ids": ["CFTimeGrouper"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeGrouper:\n \"\"\"This is a simple container for the grouping parameters that implements a\n single method, the only one required for resampling in xarray. It cannot\n be used in a call to groupby like a pandas.Grouper object can.\"\"\"\n\n def __init__(self, freq, closed=None, label=None, base=0, loffset=None):\n self.freq = to_offset(freq)\n self.closed = closed\n self.label = label\n self.base = base\n self.loffset = loffset\n\n if isinstance(self.freq, (MonthEnd, QuarterEnd, YearEnd)):\n if self.closed is None:\n self.closed = \"right\"\n if self.label is None:\n self.label = \"right\"\n else:\n if self.closed is None:\n self.closed = \"left\"\n if self.label is None:\n self.label = \"left\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_CFTimeGrouper.first_items_CFTimeGrouper.first_items.return.first_items_where_non_dup": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_CFTimeGrouper.first_items_CFTimeGrouper.first_items.return.first_items_where_non_dup", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 110, "span_ids": ["CFTimeGrouper.first_items"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFTimeGrouper:\n\n def first_items(self, index):\n \"\"\"Meant to reproduce the results of the following\n\n grouper = pandas.Grouper(...)\n first_items = pd.Series(np.arange(len(index)),\n index).groupby(grouper).first()\n\n with index being a CFTimeIndex instead of a DatetimeIndex.\n \"\"\"\n\n datetime_bins, labels = _get_time_bins(\n index, self.freq, self.closed, self.label, self.base\n )\n if self.loffset is not None:\n if isinstance(self.loffset, datetime.timedelta):\n labels = labels + self.loffset\n else:\n labels = labels + to_offset(self.loffset)\n\n # check binner fits data\n if index[0] < datetime_bins[0]:\n raise ValueError(\"Value falls before first bin\")\n if index[-1] > datetime_bins[-1]:\n raise ValueError(\"Value falls after last bin\")\n\n integer_bins = np.searchsorted(index, datetime_bins, side=self.closed)[:-1]\n first_items = pd.Series(integer_bins, labels)\n\n # Mask duplicate values with NaNs, preserving the last values\n non_duplicate = ~first_items.duplicated(\"last\")\n return first_items.where(non_duplicate)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__get_time_bins__get_time_bins.return.datetime_bins_labels": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__get_time_bins__get_time_bins.return.datetime_bins_labels", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 113, "end_line": 175, "span_ids": ["_get_time_bins"], "tokens": 564}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_time_bins(index, freq, closed, label, base):\n \"\"\"Obtain the bins and their respective labels for resampling operations.\n\n Parameters\n ----------\n index : CFTimeIndex\n Index object to be resampled (e.g., CFTimeIndex named 'time').\n freq : xarray.coding.cftime_offsets.BaseCFTimeOffset\n The offset object representing target conversion a.k.a. resampling\n frequency (e.g., 'MS', '2D', 'H', or '3T' with\n coding.cftime_offsets.to_offset() applied to it).\n closed : 'left' or 'right', optional\n Which side of bin interval is closed.\n The default is 'left' for all frequency offsets except for 'M' and 'A',\n which have a default of 'right'.\n label : 'left' or 'right', optional\n Which bin edge label to label bucket with.\n The default is 'left' for all frequency offsets except for 'M' and 'A',\n which have a default of 'right'.\n base : int, optional\n For frequencies that evenly subdivide 1 day, the \"origin\" of the\n aggregated intervals. For example, for '5min' frequency, base could\n range from 0 through 4. Defaults to 0.\n\n Returns\n -------\n datetime_bins : CFTimeIndex\n Defines the edge of resampling bins by which original index values will\n be grouped into.\n labels : CFTimeIndex\n Define what the user actually sees the bins labeled as.\n \"\"\"\n\n if not isinstance(index, CFTimeIndex):\n raise TypeError(\n \"index must be a CFTimeIndex, but got \"\n \"an instance of %r\" % type(index).__name__\n )\n if len(index) == 0:\n datetime_bins = labels = CFTimeIndex(data=[], name=index.name)\n return datetime_bins, labels\n\n first, last = _get_range_edges(\n index.min(), index.max(), freq, closed=closed, base=base\n )\n datetime_bins = labels = cftime_range(\n freq=freq, start=first, end=last, name=index.name\n )\n\n datetime_bins, labels = _adjust_bin_edges(\n datetime_bins, freq, closed, index, labels\n )\n\n if label == \"right\":\n labels = labels[1:]\n else:\n labels = labels[:-1]\n\n # TODO: when CFTimeIndex supports missing values, if the reference index\n # contains missing values, insert the appropriate NaN value at the\n # beginning of the datetime_bins and labels indexes.\n\n return datetime_bins, labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__adjust_bin_edges__adjust_bin_edges.return.datetime_bins_labels": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__adjust_bin_edges__adjust_bin_edges.return.datetime_bins_labels", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 223, "span_ids": ["_adjust_bin_edges"], "tokens": 552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _adjust_bin_edges(datetime_bins, offset, closed, index, labels):\n \"\"\"This is required for determining the bin edges resampling with\n daily frequencies greater than one day, month end, and year end\n frequencies.\n\n Consider the following example. Let's say you want to downsample the\n time series with the following coordinates to month end frequency:\n\n CFTimeIndex([2000-01-01 12:00:00, 2000-01-31 12:00:00,\n 2000-02-01 12:00:00], dtype='object')\n\n Without this adjustment, _get_time_bins with month-end frequency will\n return the following index for the bin edges (default closed='right' and\n label='right' in this case):\n\n CFTimeIndex([1999-12-31 00:00:00, 2000-01-31 00:00:00,\n 2000-02-29 00:00:00], dtype='object')\n\n If 2000-01-31 is used as a bound for a bin, the value on\n 2000-01-31T12:00:00 (at noon on January 31st), will not be included in the\n month of January. To account for this, pandas adds a day minus one worth\n of microseconds to the bin edges generated by cftime range, so that we do\n bin the value at noon on January 31st in the January bin. This results in\n an index with bin edges like the following:\n\n CFTimeIndex([1999-12-31 23:59:59, 2000-01-31 23:59:59,\n 2000-02-29 23:59:59], dtype='object')\n\n The labels are still:\n\n CFTimeIndex([2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object')\n\n This is also required for daily frequencies longer than one day and\n year-end frequencies.\n \"\"\"\n is_super_daily = isinstance(offset, (MonthEnd, QuarterEnd, YearEnd)) or (\n isinstance(offset, Day) and offset.n > 1\n )\n if is_super_daily:\n if closed == \"right\":\n datetime_bins = datetime_bins + datetime.timedelta(days=1, microseconds=-1)\n if datetime_bins[-2] > index.max():\n datetime_bins = datetime_bins[:-1]\n labels = labels[:-1]\n\n return datetime_bins, labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__get_range_edges__get_range_edges.return.first_last": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__get_range_edges__get_range_edges.return.first_last", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 226, "end_line": 271, "span_ids": ["_get_range_edges"], "tokens": 401}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_range_edges(first, last, offset, closed=\"left\", base=0):\n \"\"\" Get the correct starting and ending datetimes for the resampled\n CFTimeIndex range.\n\n Parameters\n ----------\n first : cftime.datetime\n Uncorrected starting datetime object for resampled CFTimeIndex range.\n Usually the min of the original CFTimeIndex.\n last : cftime.datetime\n Uncorrected ending datetime object for resampled CFTimeIndex range.\n Usually the max of the original CFTimeIndex.\n offset : xarray.coding.cftime_offsets.BaseCFTimeOffset\n The offset object representing target conversion a.k.a. resampling\n frequency. Contains information on offset type (e.g. Day or 'D') and\n offset magnitude (e.g., n = 3).\n closed : 'left' or 'right', optional\n Which side of bin interval is closed. Defaults to 'left'.\n base : int, optional\n For frequencies that evenly subdivide 1 day, the \"origin\" of the\n aggregated intervals. For example, for '5min' frequency, base could\n range from 0 through 4. Defaults to 0.\n\n Returns\n -------\n first : cftime.datetime\n Corrected starting datetime object for resampled CFTimeIndex range.\n last : cftime.datetime\n Corrected ending datetime object for resampled CFTimeIndex range.\n \"\"\"\n if isinstance(offset, CFTIME_TICKS):\n first, last = _adjust_dates_anchored(\n first, last, offset, closed=closed, base=base\n )\n return first, last\n else:\n first = normalize_date(first)\n last = normalize_date(last)\n\n if closed == \"left\":\n first = offset.rollback(first)\n else:\n first = first - offset\n\n last = last + offset\n return first, last", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__adjust_dates_anchored__adjust_dates_anchored.return.fresult_lresult": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py__adjust_dates_anchored__adjust_dates_anchored.return.fresult_lresult", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 274, "end_line": 333, "span_ids": ["_adjust_dates_anchored"], "tokens": 571}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _adjust_dates_anchored(first, last, offset, closed=\"right\", base=0):\n \"\"\" First and last offsets should be calculated from the start day to fix\n an error cause by resampling across multiple days when a one day period is\n not a multiple of the frequency.\n See https://github.com/pandas-dev/pandas/issues/8683\n\n Parameters\n ----------\n first : cftime.datetime\n A datetime object representing the start of a CFTimeIndex range.\n last : cftime.datetime\n A datetime object representing the end of a CFTimeIndex range.\n offset : xarray.coding.cftime_offsets.BaseCFTimeOffset\n The offset object representing target conversion a.k.a. resampling\n frequency. Contains information on offset type (e.g. Day or 'D') and\n offset magnitude (e.g., n = 3).\n closed : 'left' or 'right', optional\n Which side of bin interval is closed. Defaults to 'right'.\n base : int, optional\n For frequencies that evenly subdivide 1 day, the \"origin\" of the\n aggregated intervals. For example, for '5min' frequency, base could\n range from 0 through 4. Defaults to 0.\n\n Returns\n -------\n fresult : cftime.datetime\n A datetime object representing the start of a date range that has been\n adjusted to fix resampling errors.\n lresult : cftime.datetime\n A datetime object representing the end of a date range that has been\n adjusted to fix resampling errors.\n \"\"\"\n\n base = base % offset.n\n start_day = normalize_date(first)\n base_td = type(offset)(n=base).as_timedelta()\n start_day += base_td\n foffset = exact_cftime_datetime_difference(start_day, first) % offset.as_timedelta()\n loffset = exact_cftime_datetime_difference(start_day, last) % offset.as_timedelta()\n if closed == \"right\":\n if foffset.total_seconds() > 0:\n fresult = first - foffset\n else:\n fresult = first - offset.as_timedelta()\n\n if loffset.total_seconds() > 0:\n lresult = last + (offset.as_timedelta() - loffset)\n else:\n lresult = last\n else:\n if foffset.total_seconds() > 0:\n fresult = first - foffset\n else:\n fresult = first\n\n if loffset.total_seconds() > 0:\n lresult = last + (offset.as_timedelta() - loffset)\n else:\n lresult = last + offset.as_timedelta()\n return fresult, lresult", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_exact_cftime_datetime_difference_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/resample_cftime.py_exact_cftime_datetime_difference_", "embedding": null, "metadata": {"file_path": "xarray/core/resample_cftime.py", "file_name": "resample_cftime.py", "file_type": "text/x-python", "category": "implementation", "start_line": 336, "end_line": 374, "span_ids": ["exact_cftime_datetime_difference"], "tokens": 311}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def exact_cftime_datetime_difference(a, b):\n \"\"\"Exact computation of b - a\n\n Assumes:\n\n a = a_0 + a_m\n b = b_0 + b_m\n\n Here a_0, and b_0 represent the input dates rounded\n down to the nearest second, and a_m, and b_m represent\n the remaining microseconds associated with date a and\n date b.\n\n We can then express the value of b - a as:\n\n b - a = (b_0 + b_m) - (a_0 + a_m) = b_0 - a_0 + b_m - a_m\n\n By construction, we know that b_0 - a_0 must be a round number\n of seconds. Therefore we can take the result of b_0 - a_0 using\n ordinary cftime.datetime arithmetic and round to the nearest\n second. b_m - a_m is the remainder, in microseconds, and we\n can simply add this to the rounded timedelta.\n\n Parameters\n ----------\n a : cftime.datetime\n Input datetime\n b : cftime.datetime\n Input datetime\n\n Returns\n -------\n datetime.timedelta\n \"\"\"\n seconds = b.replace(microsecond=0) - a.replace(microsecond=0)\n seconds = int(round(seconds.total_seconds()))\n microseconds = b.microsecond - a.microsecond\n return datetime.timedelta(seconds=seconds, microseconds=microseconds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_functools__ROLLING_REDUCE_DOCSTRING_TEMPLATE._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_functools__ROLLING_REDUCE_DOCSTRING_TEMPLATE._", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 29, "span_ids": ["impl:2", "impl", "imports", "imports:7"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\n\nimport numpy as np\n\nfrom . import dtypes, duck_array_ops, utils\nfrom .dask_array_ops import dask_rolling_wrapper\nfrom .ops import inject_coarsen_methods\nfrom .pycompat import dask_array_type\n\ntry:\n import bottleneck\nexcept ImportError:\n # use numpy methods instead\n bottleneck = None\n\n\n_ROLLING_REDUCE_DOCSTRING_TEMPLATE = \"\"\"\\\nReduce this object's data windows by applying `{name}` along its dimension.\n\nParameters\n----------\n**kwargs : dict\n Additional keyword arguments passed on to `{name}`.\n\nReturns\n-------\nreduced : same type as caller\n New object with `{name}` applied along its rolling dimnension.\n\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_Rolling_Rolling.count.__doc__._ROLLING_REDUCE_DOCSTRING": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_Rolling_Rolling.count.__doc__._ROLLING_REDUCE_DOCSTRING", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 138, "span_ids": ["Rolling._min_periods", "Rolling.__len__", "Rolling.count", "Rolling:27", "Rolling._reduce_method", "Rolling:7", "Rolling.__repr__", "Rolling"], "tokens": 758}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rolling:\n \"\"\"A object that implements the moving window pattern.\n\n See Also\n --------\n Dataset.groupby\n DataArray.groupby\n Dataset.rolling\n DataArray.rolling\n \"\"\"\n\n __slots__ = (\"obj\", \"window\", \"min_periods\", \"center\", \"dim\")\n _attributes = (\"window\", \"min_periods\", \"center\", \"dim\")\n\n def __init__(self, obj, windows, min_periods=None, center=False):\n \"\"\"\n Moving window object.\n\n Parameters\n ----------\n obj : Dataset or DataArray\n Object to window.\n windows : A mapping from a dimension name to window size\n dim : str\n Name of the dimension to create the rolling iterator\n along (e.g., `time`).\n window : int\n Size of the moving window.\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA). The default, None, is equivalent to\n setting min_periods equal to the size of the window.\n center : boolean, default False\n Set the labels at the center of the window.\n\n Returns\n -------\n rolling : type of input argument\n \"\"\"\n if len(windows) != 1:\n raise ValueError(\"exactly one dim/window should be provided\")\n\n dim, window = next(iter(windows.items()))\n\n if window <= 0:\n raise ValueError(\"window must be > 0\")\n\n self.obj = obj\n\n # attributes\n self.window = window\n if min_periods is not None and min_periods <= 0:\n raise ValueError(\"min_periods must be greater than zero or None\")\n self.min_periods = min_periods\n\n self.center = center\n self.dim = dim\n\n @property\n def _min_periods(self):\n return self.min_periods if self.min_periods is not None else self.window\n\n def __repr__(self):\n \"\"\"provide a nice str repr of our rolling object\"\"\"\n\n attrs = [\n \"{k}->{v}\".format(k=k, v=getattr(self, k))\n for k in self._attributes\n if getattr(self, k, None) is not None\n ]\n return \"{klass} [{attrs}]\".format(\n klass=self.__class__.__name__, attrs=\",\".join(attrs)\n )\n\n def __len__(self):\n return self.obj.sizes[self.dim]\n\n def _reduce_method(name):\n array_agg_func = getattr(duck_array_ops, name)\n bottleneck_move_func = getattr(bottleneck, \"move_\" + name, None)\n\n def method(self, **kwargs):\n return self._numpy_or_bottleneck_reduce(\n array_agg_func, bottleneck_move_func, **kwargs\n )\n\n method.__name__ = name\n method.__doc__ = _ROLLING_REDUCE_DOCSTRING_TEMPLATE.format(name=name)\n return method\n\n argmax = _reduce_method(\"argmax\")\n argmin = _reduce_method(\"argmin\")\n max = _reduce_method(\"max\")\n min = _reduce_method(\"min\")\n mean = _reduce_method(\"mean\")\n prod = _reduce_method(\"prod\")\n sum = _reduce_method(\"sum\")\n std = _reduce_method(\"std\")\n var = _reduce_method(\"var\")\n median = _reduce_method(\"median\")\n\n def count(self):\n rolling_count = self._counts()\n enough_periods = rolling_count >= self._min_periods\n return rolling_count.where(enough_periods)\n\n count.__doc__ = _ROLLING_REDUCE_DOCSTRING_TEMPLATE.format(name=\"count\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling_DataArrayRolling.__iter__.for_label_start_stop_.yield_label_window_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling_DataArrayRolling.__iter__.for_label_start_stop_.yield_label_window_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 192, "span_ids": ["DataArrayRolling.__iter__", "DataArrayRolling"], "tokens": 383}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n __slots__ = (\"window_labels\",)\n\n def __init__(self, obj, windows, min_periods=None, center=False):\n \"\"\"\n Moving window object for DataArray.\n You should use DataArray.rolling() method to construct this object\n instead of the class constructor.\n\n Parameters\n ----------\n obj : DataArray\n Object to window.\n windows : A mapping from a dimension name to window size\n dim : str\n Name of the dimension to create the rolling iterator\n along (e.g., `time`).\n window : int\n Size of the moving window.\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA). The default, None, is equivalent to\n setting min_periods equal to the size of the window.\n center : boolean, default False\n Set the labels at the center of the window.\n\n Returns\n -------\n rolling : type of input argument\n\n See Also\n --------\n DataArray.rolling\n DataArray.groupby\n Dataset.rolling\n Dataset.groupby\n \"\"\"\n super().__init__(obj, windows, min_periods=min_periods, center=center)\n\n self.window_labels = self.obj[self.dim]\n\n def __iter__(self):\n stops = np.arange(1, len(self.window_labels) + 1)\n starts = stops - int(self.window)\n starts[: int(self.window)] = 0\n for (label, start, stop) in zip(self.window_labels, starts, stops):\n window = self.obj.isel(**{self.dim: slice(start, stop)})\n\n counts = window.count(dim=self.dim)\n window = window.where(counts >= self._min_periods)\n\n yield (label, window)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling.construct_DataArrayRolling.construct.return.result_isel_self_dim_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling.construct_DataArrayRolling.construct.return.result_isel_self_dim_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 194, "end_line": 240, "span_ids": ["DataArrayRolling.construct"], "tokens": 529}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n\n def construct(self, window_dim, stride=1, fill_value=dtypes.NA):\n \"\"\"\n Convert this rolling object to xr.DataArray,\n where the window dimension is stacked as a new dimension\n\n Parameters\n ----------\n window_dim: str\n New name of the window dimension.\n stride: integer, optional\n Size of stride for the rolling window.\n fill_value: optional. Default dtypes.NA\n Filling value to match the dimension size.\n\n Returns\n -------\n DataArray that is a view of the original array. The returned array is\n not writeable.\n\n Examples\n --------\n >>> da = DataArray(np.arange(8).reshape(2, 4), dims=('a', 'b'))\n >>>\n >>> rolling = da.rolling(b=3)\n >>> rolling.construct('window_dim')\n \n array([[[np.nan, np.nan, 0], [np.nan, 0, 1], [0, 1, 2], [1, 2, 3]],\n [[np.nan, np.nan, 4], [np.nan, 4, 5], [4, 5, 6], [5, 6, 7]]])\n Dimensions without coordinates: a, b, window_dim\n >>>\n >>> rolling = da.rolling(b=3, center=True)\n >>> rolling.construct('window_dim')\n \n array([[[np.nan, 0, 1], [0, 1, 2], [1, 2, 3], [2, 3, np.nan]],\n [[np.nan, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, np.nan]]])\n Dimensions without coordinates: a, b, window_dim\n \"\"\"\n\n from .dataarray import DataArray\n\n window = self.obj.variable.rolling_window(\n self.dim, self.window, window_dim, self.center, fill_value=fill_value\n )\n result = DataArray(\n window, dims=self.obj.dims + (window_dim,), coords=self.obj.coords\n )\n return result.isel(**{self.dim: slice(None, None, stride)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling.reduce_DataArrayRolling.reduce.return.result_where_counts_se": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling.reduce_DataArrayRolling.reduce.return.result_where_counts_se", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 242, "end_line": 289, "span_ids": ["DataArrayRolling.reduce"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n\n def reduce(self, func, **kwargs):\n \"\"\"Reduce the items in this group by applying `func` along some\n dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, **kwargs)` to return the result of collapsing an\n np.ndarray over an the rolling dimension.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : DataArray\n Array with summarized data.\n\n Examples\n --------\n >>> da = DataArray(np.arange(8).reshape(2, 4), dims=('a', 'b'))\n >>>\n >>> rolling = da.rolling(b=3)\n >>> rolling.construct('window_dim')\n \n array([[[np.nan, np.nan, 0], [np.nan, 0, 1], [0, 1, 2], [1, 2, 3]],\n [[np.nan, np.nan, 4], [np.nan, 4, 5], [4, 5, 6], [5, 6, 7]]])\n Dimensions without coordinates: a, b, window_dim\n >>>\n >>> rolling.reduce(np.sum)\n \n array([[nan, nan, 3., 6.],\n [nan, nan, 15., 18.]])\n Dimensions without coordinates: a, b\n >>>\n >>> rolling = da.rolling(b=3, min_periods=1)\n >>> rolling.reduce(np.nansum)\n \n array([[ 0., 1., 3., 6.],\n [ 4., 9., 15., 18.]])\n \"\"\"\n rolling_dim = utils.get_temp_dimname(self.obj.dims, \"_rolling_dim\")\n windows = self.construct(rolling_dim)\n result = windows.reduce(func, dim=rolling_dim, **kwargs)\n\n # Find valid windows based on count.\n counts = self._counts()\n return result.where(counts >= self._min_periods)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._counts_DataArrayRolling._counts.return.counts": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._counts_DataArrayRolling._counts.return.counts", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 291, "end_line": 305, "span_ids": ["DataArrayRolling._counts"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n\n def _counts(self):\n \"\"\" Number of non-nan entries in each rolling window. \"\"\"\n\n rolling_dim = utils.get_temp_dimname(self.obj.dims, \"_rolling_dim\")\n # We use False as the fill_value instead of np.nan, since boolean\n # array is faster to be reduced than object array.\n # The use of skipna==False is also faster since it does not need to\n # copy the strided array.\n counts = (\n self.obj.notnull()\n .rolling(center=self.center, **{self.dim: self.window})\n .construct(rolling_dim, fill_value=False)\n .sum(dim=rolling_dim, skipna=False)\n )\n return counts", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._bottleneck_reduce_DataArrayRolling._bottleneck_reduce.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._bottleneck_reduce_DataArrayRolling._bottleneck_reduce.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 348, "span_ids": ["DataArrayRolling._bottleneck_reduce"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n\n def _bottleneck_reduce(self, func, **kwargs):\n from .dataarray import DataArray\n\n # bottleneck doesn't allow min_count to be 0, although it should\n # work the same as if min_count = 1\n if self.min_periods is not None and self.min_periods == 0:\n min_count = 1\n else:\n min_count = self.min_periods\n\n axis = self.obj.get_axis_num(self.dim)\n\n padded = self.obj.variable\n if self.center:\n if isinstance(padded.data, dask_array_type):\n # Workaround to make the padded chunk size is larger than\n # self.window-1\n shift = -(self.window + 1) // 2\n offset = (self.window - 1) // 2\n valid = (slice(None),) * axis + (\n slice(offset, offset + self.obj.shape[axis]),\n )\n else:\n shift = (-self.window // 2) + 1\n valid = (slice(None),) * axis + (slice(-shift, None),)\n padded = padded.pad_with_fill_value({self.dim: (0, -shift)})\n\n if isinstance(padded.data, dask_array_type):\n raise AssertionError(\"should not be reachable\")\n values = dask_rolling_wrapper(\n func, padded.data, window=self.window, min_count=min_count, axis=axis\n )\n else:\n values = func(\n padded.data, window=self.window, min_count=min_count, axis=axis\n )\n\n if self.center:\n values = values[valid]\n result = DataArray(values, self.obj.coords)\n\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._numpy_or_bottleneck_reduce_DataArrayRolling._numpy_or_bottleneck_reduce.if_bottleneck_move_func_i.else_.return.self_reduce_array_agg_fun": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayRolling._numpy_or_bottleneck_reduce_DataArrayRolling._numpy_or_bottleneck_reduce.if_bottleneck_move_func_i.else_.return.self_reduce_array_agg_fun", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 350, "end_line": 361, "span_ids": ["DataArrayRolling._numpy_or_bottleneck_reduce"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayRolling(Rolling):\n\n def _numpy_or_bottleneck_reduce(\n self, array_agg_func, bottleneck_move_func, **kwargs\n ):\n if bottleneck_move_func is not None and not isinstance(\n self.obj.data, dask_array_type\n ):\n # TODO: renable bottleneck with dask after the issues\n # underlying https://github.com/pydata/xarray/issues/2940 are\n # fixed.\n return self._bottleneck_reduce(bottleneck_move_func, **kwargs)\n else:\n return self.reduce(array_agg_func, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling_DatasetRolling._dataset_implementation.return.Dataset_reduced_coords_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling_DatasetRolling._dataset_implementation.return.Dataset_reduced_coords_s", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 364, "end_line": 420, "span_ids": ["DatasetRolling", "DatasetRolling._dataset_implementation"], "tokens": 427}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetRolling(Rolling):\n __slots__ = (\"rollings\",)\n\n def __init__(self, obj, windows, min_periods=None, center=False):\n \"\"\"\n Moving window object for Dataset.\n You should use Dataset.rolling() method to construct this object\n instead of the class constructor.\n\n Parameters\n ----------\n obj : Dataset\n Object to window.\n windows : A mapping from a dimension name to window size\n dim : str\n Name of the dimension to create the rolling iterator\n along (e.g., `time`).\n window : int\n Size of the moving window.\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA). The default, None, is equivalent to\n setting min_periods equal to the size of the window.\n center : boolean, default False\n Set the labels at the center of the window.\n\n Returns\n -------\n rolling : type of input argument\n\n See Also\n --------\n Dataset.rolling\n DataArray.rolling\n Dataset.groupby\n DataArray.groupby\n \"\"\"\n super().__init__(obj, windows, min_periods, center)\n if self.dim not in self.obj.dims:\n raise KeyError(self.dim)\n # Keep each Rolling object as a dictionary\n self.rollings = {}\n for key, da in self.obj.data_vars.items():\n # keeps rollings only for the dataset depending on slf.dim\n if self.dim in da.dims:\n self.rollings[key] = DataArrayRolling(da, windows, min_periods, center)\n\n def _dataset_implementation(self, func, **kwargs):\n from .dataset import Dataset\n\n reduced = {}\n for key, da in self.obj.data_vars.items():\n if self.dim in da.dims:\n reduced[key] = func(self.rollings[key], **kwargs)\n else:\n reduced[key] = self.obj[key]\n return Dataset(reduced, coords=self.obj.coords)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling.reduce_DatasetRolling._numpy_or_bottleneck_reduce.return.self__dataset_implementat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling.reduce_DatasetRolling._numpy_or_bottleneck_reduce.return.self__dataset_implementat", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 422, "end_line": 457, "span_ids": ["DatasetRolling._counts", "DatasetRolling.reduce", "DatasetRolling._numpy_or_bottleneck_reduce"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetRolling(Rolling):\n\n def reduce(self, func, **kwargs):\n \"\"\"Reduce the items in this group by applying `func` along some\n dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, **kwargs)` to return the result of collapsing an\n np.ndarray over an the rolling dimension.\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : DataArray\n Array with summarized data.\n \"\"\"\n return self._dataset_implementation(\n functools.partial(DataArrayRolling.reduce, func=func), **kwargs\n )\n\n def _counts(self):\n return self._dataset_implementation(DataArrayRolling._counts)\n\n def _numpy_or_bottleneck_reduce(\n self, array_agg_func, bottleneck_move_func, **kwargs\n ):\n return self._dataset_implementation(\n functools.partial(\n DataArrayRolling._numpy_or_bottleneck_reduce,\n array_agg_func=array_agg_func,\n bottleneck_move_func=bottleneck_move_func,\n ),\n **kwargs\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling.construct_DatasetRolling.construct.return.Dataset_dataset_coords_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetRolling.construct_DatasetRolling.construct.return.Dataset_dataset_coords_s", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 459, "end_line": 490, "span_ids": ["DatasetRolling.construct"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetRolling(Rolling):\n\n def construct(self, window_dim, stride=1, fill_value=dtypes.NA):\n \"\"\"\n Convert this rolling object to xr.Dataset,\n where the window dimension is stacked as a new dimension\n\n Parameters\n ----------\n window_dim: str\n New name of the window dimension.\n stride: integer, optional\n size of stride for the rolling window.\n fill_value: optional. Default dtypes.NA\n Filling value to match the dimension size.\n\n Returns\n -------\n Dataset with variables converted from rolling object.\n \"\"\"\n\n from .dataset import Dataset\n\n dataset = {}\n for key, da in self.obj.data_vars.items():\n if self.dim in da.dims:\n dataset[key] = self.rollings[key].construct(\n window_dim, fill_value=fill_value\n )\n else:\n dataset[key] = da\n return Dataset(dataset, coords=self.obj.coords).isel(\n **{self.dim: slice(None, None, stride)}\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_Coarsen_Coarsen.__repr__.return._klass_attrs_forma": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_Coarsen_Coarsen.__repr__.return._klass_attrs_forma", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 493, "end_line": 552, "span_ids": ["Coarsen.__repr__", "Coarsen"], "tokens": 455}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Coarsen:\n \"\"\"A object that implements the coarsen.\n\n See Also\n --------\n Dataset.coarsen\n DataArray.coarsen\n \"\"\"\n\n __slots__ = (\"obj\", \"boundary\", \"coord_func\", \"windows\", \"side\", \"trim_excess\")\n _attributes = (\"windows\", \"side\", \"trim_excess\")\n\n def __init__(self, obj, windows, boundary, side, coord_func):\n \"\"\"\n Moving window object.\n\n Parameters\n ----------\n obj : Dataset or DataArray\n Object to window.\n windows : A mapping from a dimension name to window size\n dim : str\n Name of the dimension to create the rolling iterator\n along (e.g., `time`).\n window : int\n Size of the moving window.\n boundary : 'exact' | 'trim' | 'pad'\n If 'exact', a ValueError will be raised if dimension size is not a\n multiple of window size. If 'trim', the excess indexes are trimed.\n If 'pad', NA will be padded.\n side : 'left' or 'right' or mapping from dimension to 'left' or 'right'\n coord_func: mapping from coordinate name to func.\n\n Returns\n -------\n coarsen\n \"\"\"\n self.obj = obj\n self.windows = windows\n self.side = side\n self.boundary = boundary\n\n if not utils.is_dict_like(coord_func):\n coord_func = {d: coord_func for d in self.obj.dims}\n for c in self.obj.coords:\n if c not in coord_func:\n coord_func[c] = duck_array_ops.mean\n self.coord_func = coord_func\n\n def __repr__(self):\n \"\"\"provide a nice str repr of our coarsen object\"\"\"\n\n attrs = [\n \"{k}->{v}\".format(k=k, v=getattr(self, k))\n for k in self._attributes\n if getattr(self, k, None) is not None\n ]\n return \"{klass} [{attrs}]\".format(\n klass=self.__class__.__name__, attrs=\",\".join(attrs)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayCoarsen_DataArrayCoarsen._reduce_method.return.wrapped_func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DataArrayCoarsen_DataArrayCoarsen._reduce_method.return.wrapped_func", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 555, "end_line": 584, "span_ids": ["DataArrayCoarsen", "DataArrayCoarsen._reduce_method"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataArrayCoarsen(Coarsen):\n __slots__ = ()\n\n @classmethod\n def _reduce_method(cls, func):\n \"\"\"\n Return a wrapped function for injecting numpy methods.\n see ops.inject_coarsen_methods\n \"\"\"\n\n def wrapped_func(self, **kwargs):\n from .dataarray import DataArray\n\n reduced = self.obj.variable.coarsen(\n self.windows, func, self.boundary, self.side\n )\n coords = {}\n for c, v in self.obj.coords.items():\n if c == self.obj.name:\n coords[c] = reduced\n else:\n if any(d in self.windows for d in v.dims):\n coords[c] = v.variable.coarsen(\n self.windows, self.coord_func[c], self.boundary, self.side\n )\n else:\n coords[c] = v\n return DataArray(reduced, dims=self.obj.dims, coords=coords)\n\n return wrapped_func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetCoarsen_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling.py_DatasetCoarsen_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling.py", "file_name": "rolling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 587, "end_line": 621, "span_ids": ["DatasetCoarsen", "DatasetCoarsen._reduce_method", "impl:7"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetCoarsen(Coarsen):\n __slots__ = ()\n\n @classmethod\n def _reduce_method(cls, func):\n \"\"\"\n Return a wrapped function for injecting numpy methods.\n see ops.inject_coarsen_methods\n \"\"\"\n\n def wrapped_func(self, **kwargs):\n from .dataset import Dataset\n\n reduced = {}\n for key, da in self.obj.data_vars.items():\n reduced[key] = da.variable.coarsen(\n self.windows, func, self.boundary, self.side\n )\n\n coords = {}\n for c, v in self.obj.coords.items():\n if any(d in self.windows for d in v.dims):\n coords[c] = v.variable.coarsen(\n self.windows, self.coord_func[c], self.boundary, self.side\n )\n else:\n coords[c] = v.variable\n return Dataset(reduced, coords=coords)\n\n return wrapped_func\n\n\ninject_coarsen_methods(DataArrayCoarsen)\ninject_coarsen_methods(DatasetCoarsen)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_np_move_exp_nanmean.if_axis_.else_.return.numbagg_move_exp_nanmean_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_np_move_exp_nanmean.if_axis_.else_.return.numbagg_move_exp_nanmean_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling_exp.py", "file_name": "rolling_exp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 23, "span_ids": ["move_exp_nanmean", "_get_alpha", "imports"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nfrom .pdcompat import count_not_none\nfrom .pycompat import dask_array_type\n\n\ndef _get_alpha(com=None, span=None, halflife=None, alpha=None):\n # pandas defines in terms of com (converting to alpha in the algo)\n # so use its function to get a com and then convert to alpha\n\n com = _get_center_of_mass(com, span, halflife, alpha)\n return 1 / (1 + com)\n\n\ndef move_exp_nanmean(array, *, axis, alpha):\n if isinstance(array, dask_array_type):\n raise TypeError(\"rolling_exp is not currently support for dask arrays\")\n import numbagg\n\n if axis == ():\n return array.astype(np.float64)\n else:\n return numbagg.move_exp_nanmean(array, axis=axis, alpha=alpha)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py__get_center_of_mass__get_center_of_mass.return.float_comass_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py__get_center_of_mass__get_center_of_mass.return.float_comass_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling_exp.py", "file_name": "rolling_exp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 26, "end_line": 56, "span_ids": ["_get_center_of_mass"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_center_of_mass(comass, span, halflife, alpha):\n valid_count = count_not_none(comass, span, halflife, alpha)\n if valid_count > 1:\n raise ValueError(\"comass, span, halflife, and alpha \" \"are mutually exclusive\")\n\n # Convert to center of mass; domain checks ensure 0 < alpha <= 1\n if comass is not None:\n if comass < 0:\n raise ValueError(\"comass must satisfy: comass >= 0\")\n elif span is not None:\n if span < 1:\n raise ValueError(\"span must satisfy: span >= 1\")\n comass = (span - 1) / 2.0\n elif halflife is not None:\n if halflife <= 0:\n raise ValueError(\"halflife must satisfy: halflife > 0\")\n decay = 1 - np.exp(np.log(0.5) / halflife)\n comass = 1 / decay - 1\n elif alpha is not None:\n if alpha <= 0 or alpha > 1:\n raise ValueError(\"alpha must satisfy: 0 < alpha <= 1\")\n comass = (1.0 - alpha) / alpha\n else:\n raise ValueError(\"Must pass one of comass, span, halflife, or alpha\")\n\n return float(comass)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_RollingExp_RollingExp.__init__.self.alpha._get_alpha_window_type": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_RollingExp_RollingExp.__init__.self.alpha._get_alpha_window_type", "embedding": null, "metadata": {"file_path": "xarray/core/rolling_exp.py", "file_name": "rolling_exp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 89, "span_ids": ["RollingExp"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RollingExp:\n \"\"\"\n Exponentially-weighted moving window object.\n Similar to EWM in pandas\n\n Parameters\n ----------\n obj : Dataset or DataArray\n Object to window.\n windows : A single mapping from a single dimension name to window value\n dim : str\n Name of the dimension to create the rolling exponential window\n along (e.g., `time`).\n window : int\n Size of the moving window. The type of this is specified in\n `window_type`\n window_type : str, one of ['span', 'com', 'halflife', 'alpha'], default 'span'\n The format of the previously supplied window. Each is a simple\n numerical transformation of the others. Described in detail:\n https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.ewm.html\n\n Returns\n -------\n RollingExp : type of input argument\n \"\"\"\n\n def __init__(self, obj, windows, window_type=\"span\"):\n self.obj = obj\n dim, window = next(iter(windows.items()))\n self.dim = dim\n self.alpha = _get_alpha(**{window_type: window})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_RollingExp.mean_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/rolling_exp.py_RollingExp.mean_", "embedding": null, "metadata": {"file_path": "xarray/core/rolling_exp.py", "file_name": "rolling_exp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 105, "span_ids": ["RollingExp.mean"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RollingExp:\n\n def mean(self):\n \"\"\"\n Exponentially weighted moving average\n\n Examples\n --------\n >>> da = xr.DataArray([1,1,2,2,2], dims='x')\n >>> da.rolling_exp(x=2, window_type='span').mean()\n \n array([1. , 1. , 1.692308, 1.9 , 1.966942])\n Dimensions without coordinates: x\n \"\"\"\n\n return self.obj.reduce(move_exp_nanmean, dim=self.dim, alpha=self.alpha)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py__Internal_utilties_not_maybe_cast_to_coords_dtype.return.label": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py__Internal_utilties_not_maybe_cast_to_coords_dtype.return.label", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 81, "span_ids": ["alias_warning", "impl", "docstring", "_check_inplace", "alias_message", "imports", "alias", "maybe_cast_to_coords_dtype", "_maybe_cast_to_cftimeindex"], "tokens": 459}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Internal utilties; not for external use\n\"\"\"\nimport contextlib\nimport functools\nimport itertools\nimport os.path\nimport re\nimport warnings\nfrom typing import (\n AbstractSet,\n Any,\n Callable,\n Container,\n Dict,\n Hashable,\n Iterable,\n Iterator,\n Mapping,\n MutableMapping,\n MutableSet,\n Optional,\n Sequence,\n Tuple,\n TypeVar,\n cast,\n)\n\nimport numpy as np\nimport pandas as pd\n\nK = TypeVar(\"K\")\nV = TypeVar(\"V\")\nT = TypeVar(\"T\")\n\n\ndef _check_inplace(inplace: Optional[bool]) -> None:\n if inplace is not None:\n raise TypeError(\n \"The `inplace` argument has been removed from xarray. \"\n \"You can achieve an identical effect with python's standard assignment.\"\n )\n\n\ndef alias_message(old_name: str, new_name: str) -> str:\n return \"%s has been deprecated. Use %s instead.\" % (old_name, new_name)\n\n\ndef alias_warning(old_name: str, new_name: str, stacklevel: int = 3) -> None:\n warnings.warn(\n alias_message(old_name, new_name), FutureWarning, stacklevel=stacklevel\n )\n\n\ndef alias(obj: Callable[..., T], old_name: str) -> Callable[..., T]:\n assert isinstance(old_name, str)\n\n @functools.wraps(obj)\n def wrapper(*args, **kwargs):\n alias_warning(old_name, obj.__name__)\n return obj(*args, **kwargs)\n\n wrapper.__doc__ = alias_message(old_name, obj.__name__)\n return wrapper\n\n\ndef _maybe_cast_to_cftimeindex(index: pd.Index) -> pd.Index:\n from ..coding.cftimeindex import CFTimeIndex\n\n if len(index) > 0 and index.dtype == \"O\":\n try:\n return CFTimeIndex(index)\n except (ImportError, TypeError):\n return index\n else:\n return index\n\n\ndef maybe_cast_to_coords_dtype(label, coords_dtype):\n if coords_dtype.kind == \"f\" and not isinstance(label, slice):\n label = np.asarray(label, dtype=coords_dtype)\n return label", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_safe_cast_to_index_safe_cast_to_index.return._maybe_cast_to_cftimeinde": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_safe_cast_to_index_safe_cast_to_index.return._maybe_cast_to_cftimeinde", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 102, "span_ids": ["safe_cast_to_index"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def safe_cast_to_index(array: Any) -> pd.Index:\n \"\"\"Given an array, safely cast it to a pandas.Index.\n\n If it is already a pandas.Index, return it unchanged.\n\n Unlike pandas.Index, if the array has dtype=object or dtype=timedelta64,\n this function will not attempt to do automatic type conversion but will\n always return an index with dtype=object.\n \"\"\"\n if isinstance(array, pd.Index):\n index = array\n elif hasattr(array, \"to_index\"):\n index = array.to_index()\n else:\n kwargs = {}\n if hasattr(array, \"dtype\") and array.dtype.kind == \"O\":\n kwargs[\"dtype\"] = object\n index = pd.Index(np.asarray(array), **kwargs)\n return _maybe_cast_to_cftimeindex(index)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_multiindex_from_product_levels_multiindex_from_product_levels.return.pd_MultiIndex_levels_lab": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_multiindex_from_product_levels_multiindex_from_product_levels.return.pd_MultiIndex_levels_lab", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 129, "span_ids": ["multiindex_from_product_levels"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def multiindex_from_product_levels(\n levels: Sequence[pd.Index], names: Sequence[str] = None\n) -> pd.MultiIndex:\n \"\"\"Creating a MultiIndex from a product without refactorizing levels.\n\n Keeping levels the same gives back the original labels when we unstack.\n\n Parameters\n ----------\n levels : sequence of pd.Index\n Values for each MultiIndex level.\n names : optional sequence of objects\n Names for each level.\n\n Returns\n -------\n pandas.MultiIndex\n \"\"\"\n if any(not isinstance(lev, pd.Index) for lev in levels):\n raise TypeError(\"levels must be a list of pd.Index objects\")\n\n split_labels, levels = zip(*[lev.factorize() for lev in levels])\n labels_mesh = np.meshgrid(*split_labels, indexing=\"ij\")\n labels = [x.ravel() for x in labels_mesh]\n return pd.MultiIndex(levels, labels, sortorder=0, names=names)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_maybe_wrap_array_equivalent.if_isinstance_first_np_n.else_.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_maybe_wrap_array_equivalent.if_isinstance_first_np_n.else_.return._", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 162, "span_ids": ["equivalent", "maybe_wrap_array"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maybe_wrap_array(original, new_array):\n \"\"\"Wrap a transformed array with __array_wrap__ is it can be done safely.\n\n This lets us treat arbitrary functions that take and return ndarray objects\n like ufuncs, as long as they return an array with the same shape.\n \"\"\"\n # in case func lost array's metadata\n if isinstance(new_array, np.ndarray) and new_array.shape == original.shape:\n return original.__array_wrap__(new_array)\n else:\n return new_array\n\n\ndef equivalent(first: T, second: T) -> bool:\n \"\"\"Compare two objects for equivalence (identity or equality), using\n array_equiv if either object is an ndarray. If both objects are lists,\n equivalent is sequentially called on all the elements.\n \"\"\"\n # TODO: refactor to avoid circular import\n from . import duck_array_ops\n\n if isinstance(first, np.ndarray) or isinstance(second, np.ndarray):\n return duck_array_ops.array_equiv(first, second)\n elif isinstance(first, list) or isinstance(second, list):\n return list_equiv(first, second)\n else:\n return (\n (first is second)\n or (first == second)\n or (pd.isnull(first) and pd.isnull(second))\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_list_equiv_peek_at.return.peek_itertools_chain_pe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_list_equiv_peek_at.return.peek_itertools_chain_pe", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 165, "end_line": 181, "span_ids": ["list_equiv", "peek_at"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def list_equiv(first, second):\n equiv = True\n if len(first) != len(second):\n return False\n else:\n for f, s in zip(first, second):\n equiv = equiv and equivalent(f, s)\n return equiv\n\n\ndef peek_at(iterable: Iterable[T]) -> Tuple[T, Iterator[T]]:\n \"\"\"Returns the first value from iterable, as well as a new iterator with\n the same content as the original iterable\n \"\"\"\n gen = iter(iterable)\n peek = next(gen)\n return peek, itertools.chain([peek], gen)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_update_safety_check_update_safety_check.for_k_v_in_second_dict_i.if_k_in_first_dict_and_no.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_update_safety_check_update_safety_check.for_k_v_in_second_dict_i.if_k_in_first_dict_and_no.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 184, "end_line": 209, "span_ids": ["update_safety_check"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def update_safety_check(\n first_dict: MutableMapping[K, V],\n second_dict: Mapping[K, V],\n compat: Callable[[V, V], bool] = equivalent,\n) -> None:\n \"\"\"Check the safety of updating one dictionary with another.\n\n Raises ValueError if dictionaries have non-compatible values for any key,\n where compatibility is determined by identity (they are the same item) or\n the `compat` function.\n\n Parameters\n ----------\n first_dict, second_dict : dict-like\n All items in the second dictionary are checked against for conflicts\n against items in the first dictionary.\n compat : function, optional\n Binary operator to determine if two values are compatible. By default,\n checks for equivalence.\n \"\"\"\n for k, v in second_dict.items():\n if k in first_dict and not compat(v, first_dict[k]):\n raise ValueError(\n \"unsafe to merge dictionaries without \"\n \"overriding values; conflicting key %r\" % k\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_remove_incompatible_items_is_list_like.return.isinstance_value_list_o": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_remove_incompatible_items_is_list_like.return.isinstance_value_list_o", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 212, "end_line": 244, "span_ids": ["remove_incompatible_items", "is_list_like", "is_dict_like", "is_full_slice"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def remove_incompatible_items(\n first_dict: MutableMapping[K, V],\n second_dict: Mapping[K, V],\n compat: Callable[[V, V], bool] = equivalent,\n) -> None:\n \"\"\"Remove incompatible items from the first dictionary in-place.\n\n Items are retained if their keys are found in both dictionaries and the\n values are compatible.\n\n Parameters\n ----------\n first_dict, second_dict : dict-like\n Mappings to merge.\n compat : function, optional\n Binary operator to determine if two values are compatible. By default,\n checks for equivalence.\n \"\"\"\n for k in list(first_dict):\n if k not in second_dict or not compat(first_dict[k], second_dict[k]):\n del first_dict[k]\n\n\ndef is_dict_like(value: Any) -> bool:\n return hasattr(value, \"keys\") and hasattr(value, \"__getitem__\")\n\n\ndef is_full_slice(value: Any) -> bool:\n return isinstance(value, slice) and value == slice(None)\n\n\ndef is_list_like(value: Any) -> bool:\n return isinstance(value, list) or isinstance(value, tuple)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_either_dict_or_kwargs_either_dict_or_kwargs.if_pos_kwargs_is_not_None.else_.return.cast_Mapping_Hashable_T_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_either_dict_or_kwargs_either_dict_or_kwargs.if_pos_kwargs_is_not_None.else_.return.cast_Mapping_Hashable_T_", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 247, "end_line": 266, "span_ids": ["either_dict_or_kwargs"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def either_dict_or_kwargs(\n pos_kwargs: Optional[Mapping[Hashable, T]],\n kw_kwargs: Mapping[str, T],\n func_name: str,\n) -> Mapping[Hashable, T]:\n if pos_kwargs is not None:\n if not is_dict_like(pos_kwargs):\n raise ValueError(\n \"the first argument to .%s must be a dictionary\" % func_name\n )\n if kw_kwargs:\n raise ValueError(\n \"cannot specify both keyword and positional \"\n \"arguments to .%s\" % func_name\n )\n return pos_kwargs\n else:\n # Need an explicit cast to appease mypy due to invariance; see\n # https://github.com/python/mypy/issues/6228\n return cast(Mapping[Hashable, T], kw_kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_scalar_is_scalar.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_scalar_is_scalar.return._", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 285, "span_ids": ["is_scalar"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_scalar(value: Any, include_0d: bool = True) -> bool:\n \"\"\"Whether to treat a value as a scalar.\n\n Any non-iterable, string, or 0-D array\n \"\"\"\n from .variable import NON_NUMPY_SUPPORTED_ARRAY_TYPES\n\n if include_0d:\n include_0d = getattr(value, \"ndim\", None) == 0\n return (\n include_0d\n or isinstance(value, (str, bytes))\n or not (\n isinstance(value, (Iterable,) + NON_NUMPY_SUPPORTED_ARRAY_TYPES)\n or hasattr(value, \"__array_function__\")\n )\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_valid_numpy_dtype_to_0d_array.if_np_isscalar_value_or_.else_.return.to_0d_object_array_value_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_valid_numpy_dtype_to_0d_array.if_np_isscalar_value_or_.else_.return.to_0d_object_array_value_", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 288, "end_line": 311, "span_ids": ["is_valid_numpy_dtype", "to_0d_object_array", "to_0d_array"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_valid_numpy_dtype(dtype: Any) -> bool:\n try:\n np.dtype(dtype)\n except (TypeError, ValueError):\n return False\n else:\n return True\n\n\ndef to_0d_object_array(value: Any) -> np.ndarray:\n \"\"\"Given a value, wrap it in a 0-D numpy.ndarray with dtype=object.\n \"\"\"\n result = np.empty((), dtype=object)\n result[()] = value\n return result\n\n\ndef to_0d_array(value: Any) -> np.ndarray:\n \"\"\"Given a value, wrap it in a 0-D numpy.ndarray.\n \"\"\"\n if np.isscalar(value) or (isinstance(value, np.ndarray) and value.ndim == 0):\n return np.array(value)\n else:\n return to_0d_object_array(value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_dict_equiv_dict_equiv.return.True": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_dict_equiv_dict_equiv.return.True", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 314, "end_line": 341, "span_ids": ["dict_equiv"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dict_equiv(\n first: Mapping[K, V],\n second: Mapping[K, V],\n compat: Callable[[V, V], bool] = equivalent,\n) -> bool:\n \"\"\"Test equivalence of two dict-like objects. If any of the values are\n numpy arrays, compare them correctly.\n\n Parameters\n ----------\n first, second : dict-like\n Dictionaries to compare for equality\n compat : function, optional\n Binary operator to determine if two values are compatible. By default,\n checks for equivalence.\n\n Returns\n -------\n equals : bool\n True if the dictionaries are equal\n \"\"\"\n for k in first:\n if k not in second or not compat(first[k], second[k]):\n return False\n for k in second:\n if k not in first:\n return False\n return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_ordered_dict_intersection_ordered_dict_intersection.return.new_dict": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_ordered_dict_intersection_ordered_dict_intersection.return.new_dict", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 344, "end_line": 369, "span_ids": ["ordered_dict_intersection"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ordered_dict_intersection(\n first_dict: Mapping[K, V],\n second_dict: Mapping[K, V],\n compat: Callable[[V, V], bool] = equivalent,\n) -> MutableMapping[K, V]:\n \"\"\"Return the intersection of two dictionaries as a new dictionary.\n\n Items are retained if their keys are found in both dictionaries and the\n values are compatible.\n\n Parameters\n ----------\n first_dict, second_dict : dict-like\n Mappings to merge.\n compat : function, optional\n Binary operator to determine if two values are compatible. By default,\n checks for equivalence.\n\n Returns\n -------\n intersection : dict\n Intersection of the contents.\n \"\"\"\n new_dict = dict(first_dict)\n remove_incompatible_items(new_dict, second_dict, compat)\n return new_dict", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_Frozen_FrozenDict.return.Frozen_dict_args_kwar": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_Frozen_FrozenDict.return.Frozen_dict_args_kwar", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 372, "end_line": 400, "span_ids": ["Frozen.__iter__", "Frozen", "Frozen.__getitem__", "Frozen.__len__", "Frozen.__repr__", "Frozen.__contains__", "FrozenDict"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Frozen(Mapping[K, V]):\n \"\"\"Wrapper around an object implementing the mapping interface to make it\n immutable. If you really want to modify the mapping, the mutable version is\n saved under the `mapping` attribute.\n \"\"\"\n\n __slots__ = (\"mapping\",)\n\n def __init__(self, mapping: Mapping[K, V]):\n self.mapping = mapping\n\n def __getitem__(self, key: K) -> V:\n return self.mapping[key]\n\n def __iter__(self) -> Iterator[K]:\n return iter(self.mapping)\n\n def __len__(self) -> int:\n return len(self.mapping)\n\n def __contains__(self, key: object) -> bool:\n return key in self.mapping\n\n def __repr__(self) -> str:\n return \"%s(%r)\" % (type(self).__name__, self.mapping)\n\n\ndef FrozenDict(*args, **kwargs) -> Frozen:\n return Frozen(dict(*args, **kwargs))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_SortedKeysDict_SortedKeysDict.__repr__.return._s_r_type_self___": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_SortedKeysDict_SortedKeysDict.__repr__.return._s_r_type_self___", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 403, "end_line": 433, "span_ids": ["SortedKeysDict", "SortedKeysDict.__delitem__", "SortedKeysDict.__repr__", "SortedKeysDict.__getitem__", "SortedKeysDict.__contains__", "SortedKeysDict.__setitem__", "SortedKeysDict.__len__", "SortedKeysDict.__iter__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SortedKeysDict(MutableMapping[K, V]):\n \"\"\"An wrapper for dictionary-like objects that always iterates over its\n items in sorted order by key but is otherwise equivalent to the underlying\n mapping.\n \"\"\"\n\n __slots__ = (\"mapping\",)\n\n def __init__(self, mapping: MutableMapping[K, V] = None):\n self.mapping = {} if mapping is None else mapping\n\n def __getitem__(self, key: K) -> V:\n return self.mapping[key]\n\n def __setitem__(self, key: K, value: V) -> None:\n self.mapping[key] = value\n\n def __delitem__(self, key: K) -> None:\n del self.mapping[key]\n\n def __iter__(self) -> Iterator[K]:\n return iter(sorted(self.mapping))\n\n def __len__(self) -> int:\n return len(self.mapping)\n\n def __contains__(self, key: object) -> bool:\n return key in self.mapping\n\n def __repr__(self) -> str:\n return \"%s(%r)\" % (type(self).__name__, self.mapping)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_OrderedSet_OrderedSet.__repr__.return._s_r_type_self___": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_OrderedSet_OrderedSet.__repr__.return._s_r_type_self___", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 436, "end_line": 479, "span_ids": ["OrderedSet.update", "OrderedSet.add", "OrderedSet.__repr__", "OrderedSet.__contains__", "OrderedSet.__len__", "OrderedSet", "OrderedSet.discard", "OrderedSet.__iter__"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OrderedSet(MutableSet[T]):\n \"\"\"A simple ordered set.\n\n The API matches the builtin set, but it preserves insertion order of elements, like\n a dict. Note that, unlike in an OrderedDict, equality tests are not order-sensitive.\n \"\"\"\n\n _d: Dict[T, None]\n\n __slots__ = (\"_d\",)\n\n def __init__(self, values: AbstractSet[T] = None):\n self._d = {}\n if values is not None:\n # Disable type checking - both mypy and PyCharm believe that\n # we're altering the type of self in place (see signature of\n # MutableSet.__ior__)\n self |= values # type: ignore\n\n # Required methods for MutableSet\n\n def __contains__(self, value: object) -> bool:\n return value in self._d\n\n def __iter__(self) -> Iterator[T]:\n return iter(self._d)\n\n def __len__(self) -> int:\n return len(self._d)\n\n def add(self, value: T) -> None:\n self._d[value] = None\n\n def discard(self, value: T) -> None:\n del self._d[value]\n\n # Additional methods\n\n def update(self, values: AbstractSet[T]) -> None:\n # See comment on __init__ re. type checking\n self |= values # type: ignore\n\n def __repr__(self) -> str:\n return \"%s(%r)\" % (type(self).__name__, list(self))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_NdimSizeLenMixin_NdimSizeLenMixin.__len__.try_.except_IndexError_.raise_TypeError_len_of": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_NdimSizeLenMixin_NdimSizeLenMixin.__len__.try_.except_IndexError_.raise_TypeError_len_of", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 482, "end_line": 502, "span_ids": ["NdimSizeLenMixin.ndim", "NdimSizeLenMixin", "NdimSizeLenMixin.__len__", "NdimSizeLenMixin.size"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NdimSizeLenMixin:\n \"\"\"Mixin class that extends a class that defines a ``shape`` property to\n one that also defines ``ndim``, ``size`` and ``__len__``.\n \"\"\"\n\n __slots__ = ()\n\n @property\n def ndim(self: Any) -> int:\n return len(self.shape)\n\n @property\n def size(self: Any) -> int:\n # cast to int so that shape = () gives size = 1\n return int(np.prod(self.shape))\n\n def __len__(self: Any) -> int:\n try:\n return self.shape[0]\n except IndexError:\n raise TypeError(\"len() of unsized object\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_NDArrayMixin_NDArrayMixin.__repr__.return._s_array_r_type_se": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_NDArrayMixin_NDArrayMixin.__repr__.return._s_array_r_type_se", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 505, "end_line": 527, "span_ids": ["NDArrayMixin", "NDArrayMixin.shape", "NDArrayMixin.dtype", "NDArrayMixin.__getitem__", "NDArrayMixin.__repr__"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NDArrayMixin(NdimSizeLenMixin):\n \"\"\"Mixin class for making wrappers of N-dimensional arrays that conform to\n the ndarray interface required for the data argument to Variable objects.\n\n A subclass should set the `array` property and override one or more of\n `dtype`, `shape` and `__getitem__`.\n \"\"\"\n\n __slots__ = ()\n\n @property\n def dtype(self: Any) -> np.dtype:\n return self.array.dtype\n\n @property\n def shape(self: Any) -> Tuple[int]:\n return self.array.shape\n\n def __getitem__(self: Any, key):\n return self.array[key]\n\n def __repr__(self: Any) -> str:\n return \"%s(array=%r)\" % (type(self).__name__, self.array)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_ReprObject_ReprObject.__hash__.return.hash_ReprObject_self__v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_ReprObject_ReprObject.__hash__.return.hash_ReprObject_self__v", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 530, "end_line": 548, "span_ids": ["ReprObject.__repr__", "ReprObject", "ReprObject.__hash__", "ReprObject.__eq__"], "tokens": 124}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ReprObject:\n \"\"\"Object that prints as the given value, for use with sentinel values.\n \"\"\"\n\n __slots__ = (\"_value\",)\n\n def __init__(self, value: str):\n self._value = value\n\n def __repr__(self) -> str:\n return self._value\n\n def __eq__(self, other) -> bool:\n if isinstance(other, ReprObject):\n return self._value == other._value\n return False\n\n def __hash__(self) -> int:\n return hash((ReprObject, self._value))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_close_on_error_is_grib_path.return.ext_in_grib_grb_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_close_on_error_is_grib_path.return.ext_in_grib_grb_", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 551, "end_line": 569, "span_ids": ["is_grib_path", "is_remote_uri", "close_on_error"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@contextlib.contextmanager\ndef close_on_error(f):\n \"\"\"Context manager to ensure that a file opened by xarray is closed if an\n exception is raised before the user sees the file object.\n \"\"\"\n try:\n yield\n except Exception:\n f.close()\n raise\n\n\ndef is_remote_uri(path: str) -> bool:\n return bool(re.search(r\"^https?\\://\", path))\n\n\ndef is_grib_path(path: str) -> bool:\n _, ext = os.path.splitext(path)\n return ext in [\".grib\", \".grb\", \".grib2\", \".grb2\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_uniform_spaced_is_uniform_spaced.return.bool_np_isclose_diffs_min": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_is_uniform_spaced_is_uniform_spaced.return.bool_np_isclose_diffs_min", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 572, "end_line": 584, "span_ids": ["is_uniform_spaced"], "tokens": 105}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_uniform_spaced(arr, **kwargs) -> bool:\n \"\"\"Return True if values of an array are uniformly spaced and sorted.\n\n >>> is_uniform_spaced(range(5))\n True\n >>> is_uniform_spaced([-4, 0, 100])\n False\n\n kwargs are additional arguments to ``np.isclose``\n \"\"\"\n arr = np.array(arr, dtype=float)\n diffs = np.diff(arr)\n return bool(np.isclose(diffs.min(), diffs.max(), **kwargs))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_hashable_ensure_us_time_resolution.return.val": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_hashable_ensure_us_time_resolution.return.val", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 587, "end_line": 621, "span_ids": ["hashable", "not_implemented", "decode_numpy_dict_values", "ensure_us_time_resolution"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def hashable(v: Any) -> bool:\n \"\"\"Determine whether `v` can be hashed.\n \"\"\"\n try:\n hash(v)\n except TypeError:\n return False\n return True\n\n\ndef not_implemented(*args, **kwargs):\n return NotImplemented\n\n\ndef decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]:\n \"\"\"Convert attribute values from numpy objects to native Python objects,\n for use in to_dict\n \"\"\"\n attrs = dict(attrs)\n for k, v in attrs.items():\n if isinstance(v, np.ndarray):\n attrs[k] = v.tolist()\n elif isinstance(v, np.generic):\n attrs[k] = v.item()\n return attrs\n\n\ndef ensure_us_time_resolution(val):\n \"\"\"Convert val out of numpy time, for use in to_dict.\n Needed because of numpy bug GH#7619\"\"\"\n if np.issubdtype(val.dtype, np.datetime64):\n val = val.astype(\"datetime64[us]\")\n elif np.issubdtype(val.dtype, np.timedelta64):\n val = val.astype(\"timedelta64[us]\")\n return val", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_HiddenKeyDict_HiddenKeyDict.__len__.return.len_self__data_num_hid": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_HiddenKeyDict_HiddenKeyDict.__len__.return.len_self__data_num_hid", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 624, "end_line": 660, "span_ids": ["HiddenKeyDict.__setitem__", "HiddenKeyDict.__delitem__", "HiddenKeyDict.__len__", "HiddenKeyDict._raise_if_hidden", "HiddenKeyDict.__iter__", "HiddenKeyDict", "HiddenKeyDict.__getitem__"], "tokens": 307}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HiddenKeyDict(MutableMapping[K, V]):\n \"\"\"Acts like a normal dictionary, but hides certain keys.\n \"\"\"\n\n __slots__ = (\"_data\", \"_hidden_keys\")\n\n # ``__init__`` method required to create instance from class.\n\n def __init__(self, data: MutableMapping[K, V], hidden_keys: Iterable[K]):\n self._data = data\n self._hidden_keys = frozenset(hidden_keys)\n\n def _raise_if_hidden(self, key: K) -> None:\n if key in self._hidden_keys:\n raise KeyError(\"Key `%r` is hidden.\" % key)\n\n # The next five methods are requirements of the ABC.\n def __setitem__(self, key: K, value: V) -> None:\n self._raise_if_hidden(key)\n self._data[key] = value\n\n def __getitem__(self, key: K) -> V:\n self._raise_if_hidden(key)\n return self._data[key]\n\n def __delitem__(self, key: K) -> None:\n self._raise_if_hidden(key)\n del self._data[key]\n\n def __iter__(self) -> Iterator[K]:\n for k in self._data:\n if k not in self._hidden_keys:\n yield k\n\n def __len__(self) -> int:\n num_hidden = len(self._hidden_keys & self._data.keys())\n return len(self._data) - num_hidden", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_get_temp_dimname_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/utils.py_get_temp_dimname_", "embedding": null, "metadata": {"file_path": "xarray/core/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 663, "end_line": 679, "span_ids": ["get_temp_dimname"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_temp_dimname(dims: Container[Hashable], new_dim: Hashable) -> Hashable:\n \"\"\" Get an new dimension name based on new_dim, that is not used in dims.\n If the same name exists, we add an underscore(s) in the head.\n\n Example1:\n dims: ['a', 'b', 'c']\n new_dim: ['_rolling']\n -> ['_rolling']\n Example2:\n dims: ['a', 'b', 'c', '_rolling']\n new_dim: ['_rolling']\n -> ['__rolling']\n \"\"\"\n while new_dim in dims:\n new_dim = \"_\" + str(new_dim)\n return new_dim", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_functools_MissingDimensionsError._TODO_move_this_to_an_x": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_functools_MissingDimensionsError._TODO_move_this_to_an_x", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 62, "span_ids": ["impl", "MissingDimensionsError", "impl:2", "imports", "imports:16"], "tokens": 382}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\nimport itertools\nfrom collections import defaultdict\nfrom datetime import timedelta\nfrom distutils.version import LooseVersion\nfrom typing import Any, Dict, Hashable, Mapping, TypeVar, Union\n\nimport numpy as np\nimport pandas as pd\n\nimport xarray as xr # only for Dataset and DataArray\n\nfrom . import arithmetic, common, dtypes, duck_array_ops, indexing, nputils, ops, utils\nfrom .indexing import (\n BasicIndexer,\n OuterIndexer,\n PandasIndexAdapter,\n VectorizedIndexer,\n as_indexable,\n)\nfrom .npcompat import IS_NEP18_ACTIVE\nfrom .options import _get_keep_attrs\nfrom .pycompat import dask_array_type, integer_types\nfrom .utils import (\n OrderedSet,\n decode_numpy_dict_values,\n either_dict_or_kwargs,\n ensure_us_time_resolution,\n)\n\ntry:\n import dask.array as da\nexcept ImportError:\n pass\n\n\nNON_NUMPY_SUPPORTED_ARRAY_TYPES = (\n indexing.ExplicitlyIndexed,\n pd.Index,\n) + dask_array_type\n# https://github.com/python/mypy/issues/224\nBASIC_INDEXING_TYPES = integer_types + (slice,) # type: ignore\n\nVariableType = TypeVar(\"VariableType\", bound=\"Variable\")\n\"\"\"Type annotation to be used when methods of Variable return self or a copy of self.\nWhen called from an instance of a subclass, e.g. IndexVariable, mypy identifies the\noutput as an instance of the subclass.\n\nUsage::\n\n class Variable:\n def f(self: VariableType, ...) -> VariableType:\n ...\n\"\"\"\n\n\nclass MissingDimensionsError(ValueError):\n \"\"\"Error class used when we can't safely guess a dimension name.\n \"\"\"\n\n # inherits from ValueError for backward compatibility\n # TODO: move this to an xarray.exceptions module?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_as_variable_as_variable.return.obj": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_as_variable_as_variable.return.obj", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 143, "span_ids": ["as_variable"], "tokens": 633}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def as_variable(obj, name=None) -> \"Union[Variable, IndexVariable]\":\n \"\"\"Convert an object into a Variable.\n\n Parameters\n ----------\n obj : object\n Object to convert into a Variable.\n\n - If the object is already a Variable, return a shallow copy.\n - Otherwise, if the object has 'dims' and 'data' attributes, convert\n it into a new Variable.\n - If all else fails, attempt to convert the object into a Variable by\n unpacking it into the arguments for creating a new Variable.\n name : str, optional\n If provided:\n\n - `obj` can be a 1D array, which is assumed to label coordinate values\n along a dimension of this given name.\n - Variables with name matching one of their dimensions are converted\n into `IndexVariable` objects.\n\n Returns\n -------\n var : Variable\n The newly created variable.\n\n \"\"\"\n from .dataarray import DataArray\n\n # TODO: consider extending this method to automatically handle Iris and\n if isinstance(obj, DataArray):\n # extract the primary Variable from DataArrays\n obj = obj.variable\n\n if isinstance(obj, Variable):\n obj = obj.copy(deep=False)\n elif isinstance(obj, tuple):\n try:\n obj = Variable(*obj)\n except (TypeError, ValueError) as error:\n # use .format() instead of % because it handles tuples consistently\n raise error.__class__(\n \"Could not convert tuple of form \"\n \"(dims, data[, attrs, encoding]): \"\n \"{} to Variable.\".format(obj)\n )\n elif utils.is_scalar(obj):\n obj = Variable([], obj)\n elif isinstance(obj, (pd.Index, IndexVariable)) and obj.name is not None:\n obj = Variable(obj.name, obj)\n elif isinstance(obj, (set, dict)):\n raise TypeError(\"variable %r has invalid type %r\" % (name, type(obj)))\n elif name is not None:\n data = as_compatible_data(obj)\n if data.ndim != 1:\n raise MissingDimensionsError(\n \"cannot set variable %r with %r-dimensional data \"\n \"without explicit dimension names. Pass a tuple of \"\n \"(dims, data) instead.\" % (name, data.ndim)\n )\n obj = Variable(name, data, fastpath=True)\n else:\n raise TypeError(\n \"unable to convert object into a variable without an \"\n \"explicit list of dimensions: %r\" % obj\n )\n\n if name is not None and name in obj.dims:\n # convert the Variable into an Index\n if obj.ndim != 1:\n raise MissingDimensionsError(\n \"%r has more than 1-dimension and the same name as one of its \"\n \"dimensions %r. xarray disallows such variables because they \"\n \"conflict with the coordinates used to label \"\n \"dimensions.\" % (name, obj.dims)\n )\n obj = obj.to_index_variable()\n\n return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__maybe_wrap_data__possibly_convert_objects.return.np_asarray_pd_Series_valu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__maybe_wrap_data__possibly_convert_objects.return.np_asarray_pd_Series_valu", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 146, "end_line": 163, "span_ids": ["_possibly_convert_objects", "_maybe_wrap_data"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _maybe_wrap_data(data):\n \"\"\"\n Put pandas.Index and numpy.ndarray arguments in adapter objects to ensure\n they can be indexed properly.\n\n NumpyArrayAdapter, PandasIndexAdapter and LazilyOuterIndexedArray should\n all pass through unmodified.\n \"\"\"\n if isinstance(data, pd.Index):\n return PandasIndexAdapter(data)\n return data\n\n\ndef _possibly_convert_objects(values):\n \"\"\"Convert arrays of datetime.datetime and datetime.timedelta objects into\n datetime64 and timedelta64, according to the pandas convention.\n \"\"\"\n return np.asarray(pd.Series(values.ravel())).reshape(values.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_as_compatible_data_as_compatible_data.return._maybe_wrap_data_data_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_as_compatible_data_as_compatible_data.return._maybe_wrap_data_data_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 233, "span_ids": ["as_compatible_data"], "tokens": 551}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def as_compatible_data(data, fastpath=False):\n \"\"\"Prepare and wrap data to put in a Variable.\n\n - If data does not have the necessary attributes, convert it to ndarray.\n - If data has dtype=datetime64, ensure that it has ns precision. If it's a\n pandas.Timestamp, convert it to datetime64.\n - If data is already a pandas or xarray object (other than an Index), just\n use the values.\n\n Finally, wrap it up with an adapter if necessary.\n \"\"\"\n if fastpath and getattr(data, \"ndim\", 0) > 0:\n # can't use fastpath (yet) for scalars\n return _maybe_wrap_data(data)\n\n if isinstance(data, Variable):\n return data.data\n\n if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):\n return _maybe_wrap_data(data)\n\n if isinstance(data, tuple):\n data = utils.to_0d_object_array(data)\n\n if isinstance(data, pd.Timestamp):\n # TODO: convert, handle datetime objects, too\n data = np.datetime64(data.value, \"ns\")\n\n if isinstance(data, timedelta):\n data = np.timedelta64(getattr(data, \"value\", data), \"ns\")\n\n # we don't want nested self-described arrays\n data = getattr(data, \"values\", data)\n\n if isinstance(data, np.ma.MaskedArray):\n mask = np.ma.getmaskarray(data)\n if mask.any():\n dtype, fill_value = dtypes.maybe_promote(data.dtype)\n data = np.asarray(data, dtype=dtype)\n data[mask] = fill_value\n else:\n data = np.asarray(data)\n\n if not isinstance(data, np.ndarray):\n if hasattr(data, \"__array_function__\"):\n if IS_NEP18_ACTIVE:\n return data\n else:\n raise TypeError(\n \"Got an NumPy-like array type providing the \"\n \"__array_function__ protocol but NEP18 is not enabled. \"\n \"Check that numpy >= v1.16 and that the environment \"\n 'variable \"NUMPY_EXPERIMENTAL_ARRAY_FUNCTION\" is set to '\n '\"1\"'\n )\n\n # validate whether the data is valid data types\n data = np.asarray(data)\n\n if isinstance(data, np.ndarray):\n if data.dtype.kind == \"O\":\n data = _possibly_convert_objects(data)\n elif data.dtype.kind == \"M\":\n data = np.asarray(data, \"datetime64[ns]\")\n elif data.dtype.kind == \"m\":\n data = np.asarray(data, \"timedelta64[ns]\")\n\n return _maybe_wrap_data(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__as_array_or_item__as_array_or_item.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__as_array_or_item__as_array_or_item.return.data", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 256, "span_ids": ["_as_array_or_item"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _as_array_or_item(data):\n \"\"\"Return the given values as a numpy array, or as an individual item if\n it's a 0d datetime64 or timedelta64 array.\n\n Importantly, this function does not copy data if it is already an ndarray -\n otherwise, it will not be possible to update Variable values in place.\n\n This function mostly exists because 0-dimensional ndarrays with\n dtype=datetime64 are broken :(\n https://github.com/numpy/numpy/issues/4337\n https://github.com/numpy/numpy/issues/7619\n\n TODO: remove this (replace with np.asarray) once these issues are fixed\n \"\"\"\n data = np.asarray(data)\n if data.ndim == 0:\n if data.dtype.kind == \"M\":\n data = np.datetime64(data, \"ns\")\n elif data.dtype.kind == \"m\":\n data = np.timedelta64(data, \"ns\")\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable_Variable.data_5.self._data.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable_Variable.data_5.self._data.data", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 259, "end_line": 346, "span_ids": ["Variable.data_5", "Variable._in_memory", "Variable.shape", "Variable.data", "Variable", "Variable.dtype", "Variable.nbytes"], "tokens": 756}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n \"\"\"A netcdf-like variable consisting of dimensions, data and attributes\n which describe a single Array. A single Variable object is not fully\n described outside the context of its parent Dataset (if you want such a\n fully described object, use a DataArray instead).\n\n The main functional difference between Variables and numpy arrays is that\n numerical operations on Variables implement array broadcasting by dimension\n name. For example, adding an Variable with dimensions `('time',)` to\n another Variable with dimensions `('space',)` results in a new Variable\n with dimensions `('time', 'space')`. Furthermore, numpy reduce operations\n like ``mean`` or ``sum`` are overwritten to take a \"dimension\" argument\n instead of an \"axis\".\n\n Variables are light-weight objects used as the building block for datasets.\n They are more primitive objects, so operations with them provide marginally\n higher performance than using DataArrays. However, manipulating data in the\n form of a Dataset or DataArray should almost always be preferred, because\n they can use more complete metadata in context of coordinate labels.\n \"\"\"\n\n __slots__ = (\"_dims\", \"_data\", \"_attrs\", \"_encoding\")\n\n def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False):\n \"\"\"\n Parameters\n ----------\n dims : str or sequence of str\n Name(s) of the the data dimension(s). Must be either a string (only\n for 1D data) or a sequence of strings with length equal to the\n number of dimensions.\n data : array_like\n Data array which supports numpy-like data access.\n attrs : dict_like or None, optional\n Attributes to assign to the new variable. If None (default), an\n empty attribute dictionary is initialized.\n encoding : dict_like or None, optional\n Dictionary specifying how to encode this array's data into a\n serialized format like netCDF4. Currently used keys (for netCDF)\n include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'.\n Well-behaved code to serialize a Variable should ignore\n unrecognized encoding items.\n \"\"\"\n self._data = as_compatible_data(data, fastpath=fastpath)\n self._dims = self._parse_dimensions(dims)\n self._attrs = None\n self._encoding = None\n if attrs is not None:\n self.attrs = attrs\n if encoding is not None:\n self.encoding = encoding\n\n @property\n def dtype(self):\n return self._data.dtype\n\n @property\n def shape(self):\n return self._data.shape\n\n @property\n def nbytes(self):\n return self.size * self.dtype.itemsize\n\n @property\n def _in_memory(self):\n return isinstance(self._data, (np.ndarray, np.number, PandasIndexAdapter)) or (\n isinstance(self._data, indexing.MemoryCachedArray)\n and isinstance(self._data.array, indexing.NumpyIndexingAdapter)\n )\n\n @property\n def data(self):\n if hasattr(self._data, \"__array_function__\") or isinstance(\n self._data, dask_array_type\n ):\n return self._data\n else:\n return self.values\n\n @data.setter\n def data(self, data):\n data = as_compatible_data(data)\n if data.shape != self.shape:\n raise ValueError(\"replacement data must match the Variable's shape\")\n self._data = data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.load_Variable.load.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.load_Variable.load.return.self", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 348, "end_line": 369, "span_ids": ["Variable.load"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def load(self, **kwargs):\n \"\"\"Manually trigger loading of this variable's data from disk or a\n remote source into memory and return this variable.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n if isinstance(self._data, dask_array_type):\n self._data = as_compatible_data(self._data.compute(**kwargs))\n elif not hasattr(self._data, \"__array_function__\"):\n self._data = np.asarray(self._data)\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.compute_Variable.compute.return.new_load_kwargs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.compute_Variable.compute.return.new_load_kwargs_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 371, "end_line": 390, "span_ids": ["Variable.compute"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def compute(self, **kwargs):\n \"\"\"Manually trigger loading of this variable's data from disk or a\n remote source into memory and return a new variable. The original is\n left unaltered.\n\n Normally, it should not be necessary to call this method in user code,\n because all xarray functions should either work on deferred data or\n load data automatically.\n\n Parameters\n ----------\n **kwargs : dict\n Additional keyword arguments passed on to ``dask.array.compute``.\n\n See Also\n --------\n dask.array.compute\n \"\"\"\n new = self.copy(deep=False)\n return new.load(**kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__dask_graph___Variable._item_key_to_tuple.if_utils_is_dict_like_key.else_.return.key": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__dask_graph___Variable._item_key_to_tuple.if_utils_is_dict_like_key.else_.return.key", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 392, "end_line": 497, "span_ids": ["Variable.__dask_optimize__", "Variable.values", "Variable.__dask_layers__", "Variable:5", "Variable.__dask_graph__", "Variable._parse_dimensions", "Variable.to_index_variable", "Variable:7", "Variable.to_index", "Variable.__dask_scheduler__", "Variable._item_key_to_tuple", "Variable.to_dict", "Variable.to_base_variable", "Variable.dims_23", "Variable.__dask_postcompute__", "Variable.__dask_keys__", "Variable._dask_finalize", "Variable.__dask_postpersist__", "Variable.dims", "Variable.values_17"], "tokens": 809}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def __dask_graph__(self):\n if isinstance(self._data, dask_array_type):\n return self._data.__dask_graph__()\n else:\n return None\n\n def __dask_keys__(self):\n return self._data.__dask_keys__()\n\n def __dask_layers__(self):\n return self._data.__dask_layers__()\n\n @property\n def __dask_optimize__(self):\n return self._data.__dask_optimize__\n\n @property\n def __dask_scheduler__(self):\n return self._data.__dask_scheduler__\n\n def __dask_postcompute__(self):\n array_func, array_args = self._data.__dask_postcompute__()\n return (\n self._dask_finalize,\n (array_func, array_args, self._dims, self._attrs, self._encoding),\n )\n\n def __dask_postpersist__(self):\n array_func, array_args = self._data.__dask_postpersist__()\n return (\n self._dask_finalize,\n (array_func, array_args, self._dims, self._attrs, self._encoding),\n )\n\n @staticmethod\n def _dask_finalize(results, array_func, array_args, dims, attrs, encoding):\n if isinstance(results, dict): # persist case\n name = array_args[0]\n results = {k: v for k, v in results.items() if k[0] == name}\n data = array_func(results, *array_args)\n return Variable(dims, data, attrs=attrs, encoding=encoding)\n\n @property\n def values(self):\n \"\"\"The variable's data as a numpy.ndarray\"\"\"\n return _as_array_or_item(self._data)\n\n @values.setter\n def values(self, values):\n self.data = values\n\n def to_base_variable(self):\n \"\"\"Return this variable as a base xarray.Variable\"\"\"\n return Variable(\n self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True\n )\n\n to_variable = utils.alias(to_base_variable, \"to_variable\")\n\n def to_index_variable(self):\n \"\"\"Return this variable as an xarray.IndexVariable\"\"\"\n return IndexVariable(\n self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True\n )\n\n to_coord = utils.alias(to_index_variable, \"to_coord\")\n\n def to_index(self):\n \"\"\"Convert this variable to a pandas.Index\"\"\"\n return self.to_index_variable().to_index()\n\n def to_dict(self, data=True):\n \"\"\"Dictionary representation of variable.\"\"\"\n item = {\"dims\": self.dims, \"attrs\": decode_numpy_dict_values(self.attrs)}\n if data:\n item[\"data\"] = ensure_us_time_resolution(self.values).tolist()\n else:\n item.update({\"dtype\": str(self.dtype), \"shape\": self.shape})\n return item\n\n @property\n def dims(self):\n \"\"\"Tuple of dimension names with which this variable is associated.\n \"\"\"\n return self._dims\n\n @dims.setter\n def dims(self, value):\n self._dims = self._parse_dimensions(value)\n\n def _parse_dimensions(self, dims):\n if isinstance(dims, str):\n dims = (dims,)\n dims = tuple(dims)\n if len(dims) != self.ndim:\n raise ValueError(\n \"dimensions %s must have the same length as the \"\n \"number of data dimensions, ndim=%s\" % (dims, self.ndim)\n )\n return dims\n\n def _item_key_to_tuple(self, key):\n if utils.is_dict_like(key):\n return tuple(key.get(dim, slice(None)) for dim in self.dims)\n else:\n return key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_Variable._broadcast_indexes.return.self__broadcast_indexes_v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_Variable._broadcast_indexes.return.self__broadcast_indexes_v", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 499, "end_line": 555, "span_ids": ["Variable._broadcast_indexes"], "tokens": 530}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _broadcast_indexes(self, key):\n \"\"\"Prepare an indexing key for an indexing operation.\n\n Parameters\n -----------\n key: int, slice, array, dict or tuple of integer, slices and arrays\n Any valid input for indexing.\n\n Returns\n -------\n dims: tuple\n Dimension of the resultant variable.\n indexers: IndexingTuple subclass\n Tuple of integer, array-like, or slices to use when indexing\n self._data. The type of this argument indicates the type of\n indexing to perform, either basic, outer or vectorized.\n new_order : Optional[Sequence[int]]\n Optional reordering to do on the result of indexing. If not None,\n the first len(new_order) indexing should be moved to these\n positions.\n \"\"\"\n key = self._item_key_to_tuple(key) # key is a tuple\n # key is a tuple of full size\n key = indexing.expanded_indexer(key, self.ndim)\n # Convert a scalar Variable to an integer\n key = tuple(\n k.data.item() if isinstance(k, Variable) and k.ndim == 0 else k for k in key\n )\n # Convert a 0d-array to an integer\n key = tuple(\n k.item() if isinstance(k, np.ndarray) and k.ndim == 0 else k for k in key\n )\n\n if all(isinstance(k, BASIC_INDEXING_TYPES) for k in key):\n return self._broadcast_indexes_basic(key)\n\n self._validate_indexers(key)\n # Detect it can be mapped as an outer indexer\n # If all key is unlabeled, or\n # key can be mapped as an OuterIndexer.\n if all(not isinstance(k, Variable) for k in key):\n return self._broadcast_indexes_outer(key)\n\n # If all key is 1-dimensional and there are no duplicate labels,\n # key can be mapped as an OuterIndexer.\n dims = []\n for k, d in zip(key, self.dims):\n if isinstance(k, Variable):\n if len(k.dims) > 1:\n return self._broadcast_indexes_vectorized(key)\n dims.append(k.dims[0])\n elif not isinstance(k, integer_types):\n dims.append(d)\n if len(set(dims)) == len(dims):\n return self._broadcast_indexes_outer(key)\n\n return self._broadcast_indexes_vectorized(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_basic_Variable._validate_indexers.for_dim_k_in_zip_self_di.if_isinstance_k_BASIC_IN.else_.if_k_dtype_kind_b_.if_getattr_k_dims_di.raise_IndexError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_basic_Variable._validate_indexers.for_dim_k_in_zip_self_di.if_isinstance_k_BASIC_IN.else_.if_k_dtype_kind_b_.if_getattr_k_dims_di.raise_IndexError_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 557, "end_line": 594, "span_ids": ["Variable._broadcast_indexes_basic", "Variable._validate_indexers"], "tokens": 334}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _broadcast_indexes_basic(self, key):\n dims = tuple(\n dim for k, dim in zip(key, self.dims) if not isinstance(k, integer_types)\n )\n return dims, BasicIndexer(key), None\n\n def _validate_indexers(self, key):\n \"\"\" Make sanity checks \"\"\"\n for dim, k in zip(self.dims, key):\n if isinstance(k, BASIC_INDEXING_TYPES):\n pass\n else:\n if not isinstance(k, Variable):\n k = np.asarray(k)\n if k.ndim > 1:\n raise IndexError(\n \"Unlabeled multi-dimensional array cannot be \"\n \"used for indexing: {}\".format(k)\n )\n if k.dtype.kind == \"b\":\n if self.shape[self.get_axis_num(dim)] != len(k):\n raise IndexError(\n \"Boolean array size {:d} is used to index array \"\n \"with shape {:s}.\".format(len(k), str(self.shape))\n )\n if k.ndim > 1:\n raise IndexError(\n \"{}-dimensional boolean indexing is \"\n \"not supported. \".format(k.ndim)\n )\n if getattr(k, \"dims\", (dim,)) != (dim,):\n raise IndexError(\n \"Boolean indexer should be unlabeled or on the \"\n \"same dimension to the indexed array. Indexer is \"\n \"on {:s} but the target dimension is {:s}.\".format(\n str(k.dims), dim\n )\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_outer_Variable._nonzero.return.tuple_Variable_dim_nz_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_outer_Variable._nonzero.return.tuple_Variable_dim_nz_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 596, "end_line": 620, "span_ids": ["Variable._broadcast_indexes_outer", "Variable._nonzero"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _broadcast_indexes_outer(self, key):\n dims = tuple(\n k.dims[0] if isinstance(k, Variable) else dim\n for k, dim in zip(key, self.dims)\n if not isinstance(k, integer_types)\n )\n\n new_key = []\n for k in key:\n if isinstance(k, Variable):\n k = k.data\n if not isinstance(k, BASIC_INDEXING_TYPES):\n k = np.asarray(k)\n if k.dtype.kind == \"b\":\n (k,) = np.nonzero(k)\n new_key.append(k)\n\n return dims, OuterIndexer(tuple(new_key)), None\n\n def _nonzero(self):\n \"\"\" Equivalent numpy's nonzero but returns a tuple of Varibles. \"\"\"\n # TODO we should replace dask's native nonzero\n # after https://github.com/dask/dask/issues/1076 is implemented.\n nonzeros = np.nonzero(self.data)\n return tuple(Variable((dim), nz) for nz, dim in zip(nonzeros, self.dims))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_vectorized_Variable._broadcast_indexes_vectorized.return.out_dims_VectorizedIndex": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._broadcast_indexes_vectorized_Variable._broadcast_indexes_vectorized.return.out_dims_VectorizedIndex", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 622, "end_line": 676, "span_ids": ["Variable._broadcast_indexes_vectorized"], "tokens": 439}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _broadcast_indexes_vectorized(self, key):\n variables = []\n out_dims_set = OrderedSet()\n for dim, value in zip(self.dims, key):\n if isinstance(value, slice):\n out_dims_set.add(dim)\n else:\n variable = (\n value\n if isinstance(value, Variable)\n else as_variable(value, name=dim)\n )\n if variable.dtype.kind == \"b\": # boolean indexing case\n (variable,) = variable._nonzero()\n\n variables.append(variable)\n out_dims_set.update(variable.dims)\n\n variable_dims = set()\n for variable in variables:\n variable_dims.update(variable.dims)\n\n slices = []\n for i, (dim, value) in enumerate(zip(self.dims, key)):\n if isinstance(value, slice):\n if dim in variable_dims:\n # We only convert slice objects to variables if they share\n # a dimension with at least one other variable. Otherwise,\n # we can equivalently leave them as slices aknd transpose\n # the result. This is significantly faster/more efficient\n # for most array backends.\n values = np.arange(*value.indices(self.sizes[dim]))\n variables.insert(i - len(slices), Variable((dim,), values))\n else:\n slices.append((i, value))\n\n try:\n variables = _broadcast_compat_variables(*variables)\n except ValueError:\n raise IndexError(\"Dimensions of indexers mismatch: {}\".format(key))\n\n out_key = [variable.data for variable in variables]\n out_dims = tuple(out_dims_set)\n slice_positions = set()\n for i, value in slices:\n out_key.insert(i, value)\n new_position = out_dims.index(self.dims[i])\n slice_positions.add(new_position)\n\n if slice_positions:\n new_order = [i for i in range(len(out_dims)) if i not in slice_positions]\n else:\n new_order = None\n\n return out_dims, VectorizedIndexer(tuple(out_key)), new_order", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__getitem___Variable._finalize_indexing_result.return.type_self_dims_data_se": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__getitem___Variable._finalize_indexing_result.return.type_self_dims_data_se", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 678, "end_line": 700, "span_ids": ["Variable.__getitem__", "Variable._finalize_indexing_result"], "tokens": 291}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def __getitem__(self: VariableType, key) -> VariableType:\n \"\"\"Return a new Variable object whose contents are consistent with\n getting the provided key from the underlying data.\n\n NB. __getitem__ and __setitem__ implement xarray-style indexing,\n where if keys are unlabeled arrays, we index the array orthogonally\n with them. If keys are labeled array (such as Variables), they are\n broadcasted with our usual scheme and then the array is indexed with\n the broadcasted key, like numpy's fancy indexing.\n\n If you really want to do indexing like `x[x > 0]`, manipulate the numpy\n array `x.values` directly.\n \"\"\"\n dims, indexer, new_order = self._broadcast_indexes(key)\n data = as_indexable(self._data)[indexer]\n if new_order:\n data = duck_array_ops.moveaxis(data, range(len(new_order)), new_order)\n return self._finalize_indexing_result(dims, data)\n\n def _finalize_indexing_result(self: VariableType, dims, data) -> VariableType:\n \"\"\"Used by IndexVariable to return IndexVariable objects when possible.\n \"\"\"\n return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._getitem_with_mask_Variable._getitem_with_mask.return.self__finalize_indexing_r": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._getitem_with_mask_Variable._getitem_with_mask.return.self__finalize_indexing_r", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 702, "end_line": 737, "span_ids": ["Variable._getitem_with_mask"], "tokens": 392}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _getitem_with_mask(self, key, fill_value=dtypes.NA):\n \"\"\"Index this Variable with -1 remapped to fill_value.\"\"\"\n # TODO(shoyer): expose this method in public API somewhere (isel?) and\n # use it for reindex.\n # TODO(shoyer): add a sanity check that all other integers are\n # non-negative\n # TODO(shoyer): add an optimization, remapping -1 to an adjacent value\n # that is actually indexed rather than mapping it to the last value\n # along each axis.\n\n if fill_value is dtypes.NA:\n fill_value = dtypes.get_fill_value(self.dtype)\n\n dims, indexer, new_order = self._broadcast_indexes(key)\n\n if self.size:\n if isinstance(self._data, dask_array_type):\n # dask's indexing is faster this way; also vindex does not\n # support negative indices yet:\n # https://github.com/dask/dask/pull/2967\n actual_indexer = indexing.posify_mask_indexer(indexer)\n else:\n actual_indexer = indexer\n\n data = as_indexable(self._data)[actual_indexer]\n mask = indexing.create_mask(indexer, self.shape, data)\n data = duck_array_ops.where(mask, fill_value, data)\n else:\n # array cannot be indexed along dimensions of size 0, so just\n # build the mask directly instead.\n mask = indexing.create_mask(indexer, self.shape)\n data = np.broadcast_to(fill_value, getattr(mask, \"shape\", ()))\n\n if new_order:\n data = duck_array_ops.moveaxis(data, range(len(new_order)), new_order)\n return self._finalize_indexing_result(dims, data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__setitem___Variable.__setitem__.indexable_index_tuple_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__setitem___Variable.__setitem__.indexable_index_tuple_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 739, "end_line": 768, "span_ids": ["Variable.__setitem__"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def __setitem__(self, key, value):\n \"\"\"__setitem__ is overloaded to access the underlying numpy values with\n orthogonal indexing.\n\n See __getitem__ for more details.\n \"\"\"\n dims, index_tuple, new_order = self._broadcast_indexes(key)\n\n if not isinstance(value, Variable):\n value = as_compatible_data(value)\n if value.ndim > len(dims):\n raise ValueError(\n \"shape mismatch: value array of shape %s could not be \"\n \"broadcast to indexing result with %s dimensions\"\n % (value.shape, len(dims))\n )\n if value.ndim == 0:\n value = Variable((), value)\n else:\n value = Variable(dims[-value.ndim :], value)\n # broadcast to become assignable\n value = value.set_dims(dims).data\n\n if new_order:\n value = duck_array_ops.asarray(value)\n value = value[(len(dims) - value.ndim) * (np.newaxis,) + (Ellipsis,)]\n value = duck_array_ops.moveaxis(value, new_order, range(len(new_order)))\n\n indexable = as_indexable(self._data)\n indexable[index_tuple] = value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.attrs_Variable.encoding_39.try_.except_ValueError_.raise_ValueError_encodin": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.attrs_Variable.encoding_39.try_.except_ValueError_.raise_ValueError_encodin", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 770, "end_line": 795, "span_ids": ["Variable.attrs", "Variable.encoding", "Variable.encoding_39", "Variable.attrs_37"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n @property\n def attrs(self) -> Dict[Hashable, Any]:\n \"\"\"Dictionary of local attributes on this variable.\n \"\"\"\n if self._attrs is None:\n self._attrs = {}\n return self._attrs\n\n @attrs.setter\n def attrs(self, value: Mapping[Hashable, Any]) -> None:\n self._attrs = dict(value)\n\n @property\n def encoding(self):\n \"\"\"Dictionary of encodings on this variable.\n \"\"\"\n if self._encoding is None:\n self._encoding = {}\n return self._encoding\n\n @encoding.setter\n def encoding(self, value):\n try:\n self._encoding = dict(value)\n except ValueError:\n raise ValueError(\"encoding must be castable to a dictionary\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.copy_Variable.copy.return.type_self_self_dims_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.copy_Variable.copy.return.type_self_self_dims_dat", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 797, "end_line": 881, "span_ids": ["Variable.copy"], "tokens": 660}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def copy(self, deep=True, data=None):\n \"\"\"Returns a copy of this object.\n\n If `deep=True`, the data array is loaded into memory and copied onto\n the new object. Dimensions, attributes and encodings are always copied.\n\n Use `data` to create a new object with the same structure as\n original but entirely new data.\n\n Parameters\n ----------\n deep : bool, optional\n Whether the data array is loaded into memory and copied onto\n the new object. Default is True.\n data : array_like, optional\n Data to use in the new object. Must have same shape as original.\n When `data` is used, `deep` is ignored.\n\n Returns\n -------\n object : Variable\n New object with dimensions, attributes, encodings, and optionally\n data copied from original.\n\n Examples\n --------\n\n Shallow copy versus deep copy\n\n >>> var = xr.Variable(data=[1, 2, 3], dims='x')\n >>> var.copy()\n \n array([1, 2, 3])\n >>> var_0 = var.copy(deep=False)\n >>> var_0[0] = 7\n >>> var_0\n \n array([7, 2, 3])\n >>> var\n \n array([7, 2, 3])\n\n Changing the data using the ``data`` argument maintains the\n structure of the original object, but with the new data. Original\n object is unaffected.\n\n >>> var.copy(data=[0.1, 0.2, 0.3])\n \n array([ 0.1, 0.2, 0.3])\n >>> var\n \n array([7, 2, 3])\n\n See Also\n --------\n pandas.DataFrame.copy\n \"\"\"\n if data is None:\n data = self._data\n\n if isinstance(data, indexing.MemoryCachedArray):\n # don't share caching between copies\n data = indexing.MemoryCachedArray(data.array)\n\n if deep:\n if hasattr(data, \"__array_function__\") or isinstance(\n data, dask_array_type\n ):\n data = data.copy()\n elif not isinstance(data, PandasIndexAdapter):\n # pandas.Index is immutable\n data = np.array(data)\n else:\n data = as_compatible_data(data)\n if self.shape != data.shape:\n raise ValueError(\n \"Data shape {} must match shape of object {}\".format(\n data.shape, self.shape\n )\n )\n\n # note:\n # dims is already an immutable tuple\n # attributes and encoding will be copied when the new Array is created\n return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__copy___Variable._array_counter.itertools_count_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.__copy___Variable._array_counter.itertools_count_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 883, "end_line": 902, "span_ids": ["Variable.chunks", "Variable.__copy__", "Variable:11", "Variable:9", "Variable.__deepcopy__"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def __copy__(self):\n return self.copy(deep=False)\n\n def __deepcopy__(self, memo=None):\n # memo does nothing but is required for compatibility with\n # copy.deepcopy\n return self.copy(deep=True)\n\n # mutable objects should not be hashable\n # https://github.com/python/mypy/issues/4266\n __hash__ = None # type: ignore\n\n @property\n def chunks(self):\n \"\"\"Block dimensions for this array's data or None if it's not a dask\n array.\n \"\"\"\n return getattr(self._data, \"chunks\", None)\n\n _array_counter = itertools.count()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.chunk_Variable.chunk.return.type_self_self_dims_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.chunk_Variable.chunk.return.type_self_self_dims_dat", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 904, "end_line": 970, "span_ids": ["Variable.chunk"], "tokens": 651}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def chunk(self, chunks=None, name=None, lock=False):\n \"\"\"Coerce this array's data into a dask arrays with the given chunks.\n\n If this variable is a non-dask array, it will be converted to dask\n array. If it's a dask array, it will be rechunked to the given chunk\n sizes.\n\n If neither chunks is not provided for one or more dimensions, chunk\n sizes along that dimension will not be updated; non-dask arrays will be\n converted into dask arrays with a single block.\n\n Parameters\n ----------\n chunks : int, tuple or dict, optional\n Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or\n ``{'x': 5, 'y': 5}``.\n name : str, optional\n Used to generate the name for this array in the internal dask\n graph. Does not need not be unique.\n lock : optional\n Passed on to :py:func:`dask.array.from_array`, if the array is not\n already as dask array.\n\n Returns\n -------\n chunked : xarray.Variable\n \"\"\"\n import dask\n import dask.array as da\n\n if utils.is_dict_like(chunks):\n chunks = {self.get_axis_num(dim): chunk for dim, chunk in chunks.items()}\n\n if chunks is None:\n chunks = self.chunks or self.shape\n\n data = self._data\n if isinstance(data, da.Array):\n data = data.rechunk(chunks)\n else:\n if isinstance(data, indexing.ExplicitlyIndexed):\n # Unambiguously handle array storage backends (like NetCDF4 and h5py)\n # that can't handle general array indexing. For example, in netCDF4 you\n # can do \"outer\" indexing along two dimensions independent, which works\n # differently from how NumPy handles it.\n # da.from_array works by using lazy indexing with a tuple of slices.\n # Using OuterIndexer is a pragmatic choice: dask does not yet handle\n # different indexing types in an explicit way:\n # https://github.com/dask/dask/issues/2883\n data = indexing.ImplicitToExplicitIndexingAdapter(\n data, indexing.OuterIndexer\n )\n if LooseVersion(dask.__version__) < \"2.0.0\":\n kwargs = {}\n else:\n # All of our lazily loaded backend array classes should use NumPy\n # array operations.\n kwargs = {\"meta\": np.ndarray}\n else:\n kwargs = {}\n\n if utils.is_dict_like(chunks):\n chunks = tuple(chunks.get(n, s) for n, s in enumerate(self.shape))\n\n data = da.from_array(data, chunks, name=name, lock=lock, **kwargs)\n\n return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.isel_Variable.isel.return.self_key_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.isel_Variable.isel.return.self_key_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 972, "end_line": 1000, "span_ids": ["Variable.isel"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def isel(\n self: VariableType,\n indexers: Mapping[Hashable, Any] = None,\n **indexers_kwargs: Any\n ) -> VariableType:\n \"\"\"Return a new array indexed along the specified dimension(s).\n\n Parameters\n ----------\n **indexers : {dim: indexer, ...}\n Keyword arguments with names matching dimensions and values given\n by integers, slice objects or arrays.\n\n Returns\n -------\n obj : Array object\n A new Array with the selected data and dimensions. In general,\n the new variable's data will be a view of this variable's data,\n unless numpy fancy indexing was triggered by using an array\n indexer, in which case the data will be a copy.\n \"\"\"\n indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \"isel\")\n\n invalid = indexers.keys() - set(self.dims)\n if invalid:\n raise ValueError(\"dimensions %r do not exist\" % invalid)\n\n key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)\n return self[key]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.squeeze_Variable.squeeze.return.self_isel_d_0_for_d_in_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.squeeze_Variable.squeeze.return.self_isel_d_0_for_d_in_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1002, "end_line": 1023, "span_ids": ["Variable.squeeze"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def squeeze(self, dim=None):\n \"\"\"Return a new object with squeezed data.\n\n Parameters\n ----------\n dim : None or str or tuple of str, optional\n Selects a subset of the length one dimensions. If a dimension is\n selected with length greater than one, an error is raised. If\n None, all length one dimensions are squeezed.\n\n Returns\n -------\n squeezed : same type as caller\n This object, but with with all or a subset of the dimensions of\n length 1 removed.\n\n See Also\n --------\n numpy.squeeze\n \"\"\"\n dims = common.get_squeeze_dims(self, dim)\n return self.isel({d: 0 for d in dims})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._shift_one_dim_Variable._shift_one_dim.return.type_self_self_dims_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._shift_one_dim_Variable._shift_one_dim.return.type_self_self_dims_dat", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1025, "end_line": 1067, "span_ids": ["Variable._shift_one_dim"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _shift_one_dim(self, dim, count, fill_value=dtypes.NA):\n axis = self.get_axis_num(dim)\n\n if count > 0:\n keep = slice(None, -count)\n elif count < 0:\n keep = slice(-count, None)\n else:\n keep = slice(None)\n\n trimmed_data = self[(slice(None),) * axis + (keep,)].data\n\n if fill_value is dtypes.NA:\n dtype, fill_value = dtypes.maybe_promote(self.dtype)\n else:\n dtype = self.dtype\n\n shape = list(self.shape)\n shape[axis] = min(abs(count), shape[axis])\n\n if isinstance(trimmed_data, dask_array_type):\n chunks = list(trimmed_data.chunks)\n chunks[axis] = (shape[axis],)\n full = functools.partial(da.full, chunks=chunks)\n else:\n full = np.full\n\n filler = full(shape, fill_value, dtype=dtype)\n\n if count > 0:\n arrays = [filler, trimmed_data]\n else:\n arrays = [trimmed_data, filler]\n\n data = duck_array_ops.concatenate(arrays, axis)\n\n if isinstance(data, dask_array_type):\n # chunked data should come out with the same chunks; this makes\n # it feasible to combine shifted and unshifted data\n # TODO: remove this once dask.array automatically aligns chunks\n data = data.rechunk(self.data.chunks)\n\n return type(self)(self.dims, data, self._attrs, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.shift_Variable.shift.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.shift_Variable.shift.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1069, "end_line": 1094, "span_ids": ["Variable.shift"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):\n \"\"\"\n Return a new Variable with shifted data.\n\n Parameters\n ----------\n shifts : mapping of the form {dim: offset}\n Integer offset to shift along each of the given dimensions.\n Positive offsets shift to the right; negative offsets shift to the\n left.\n fill_value: scalar, optional\n Value to use for newly missing values\n **shifts_kwargs:\n The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwarg must be provided.\n\n Returns\n -------\n shifted : Variable\n Variable with the same dimensions and attributes but shifted data.\n \"\"\"\n shifts = either_dict_or_kwargs(shifts, shifts_kwargs, \"shift\")\n result = self\n for dim, count in shifts.items():\n result = result._shift_one_dim(dim, count, fill_value=fill_value)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.pad_with_fill_value_Variable.pad_with_fill_value.return.type_self_self_dims_arr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.pad_with_fill_value_Variable.pad_with_fill_value.return.type_self_self_dims_arr", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1096, "end_line": 1156, "span_ids": ["Variable.pad_with_fill_value"], "tokens": 496}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def pad_with_fill_value(\n self, pad_widths=None, fill_value=dtypes.NA, **pad_widths_kwargs\n ):\n \"\"\"\n Return a new Variable with paddings.\n\n Parameters\n ----------\n pad_width: Mapping of the form {dim: (before, after)}\n Number of values padded to the edges of each dimension.\n **pad_widths_kwargs:\n Keyword argument for pad_widths\n \"\"\"\n pad_widths = either_dict_or_kwargs(pad_widths, pad_widths_kwargs, \"pad\")\n\n if fill_value is dtypes.NA:\n dtype, fill_value = dtypes.maybe_promote(self.dtype)\n else:\n dtype = self.dtype\n\n if isinstance(self.data, dask_array_type):\n array = self.data\n\n # Dask does not yet support pad. We manually implement it.\n # https://github.com/dask/dask/issues/1926\n for d, pad in pad_widths.items():\n axis = self.get_axis_num(d)\n before_shape = list(array.shape)\n before_shape[axis] = pad[0]\n before_chunks = list(array.chunks)\n before_chunks[axis] = (pad[0],)\n after_shape = list(array.shape)\n after_shape[axis] = pad[1]\n after_chunks = list(array.chunks)\n after_chunks[axis] = (pad[1],)\n\n arrays = []\n if pad[0] > 0:\n arrays.append(\n da.full(\n before_shape, fill_value, dtype=dtype, chunks=before_chunks\n )\n )\n arrays.append(array)\n if pad[1] > 0:\n arrays.append(\n da.full(\n after_shape, fill_value, dtype=dtype, chunks=after_chunks\n )\n )\n if len(arrays) > 1:\n array = da.concatenate(arrays, axis=axis)\n else:\n pads = [(0, 0) if d not in pad_widths else pad_widths[d] for d in self.dims]\n array = np.pad(\n self.data.astype(dtype, copy=False),\n pads,\n mode=\"constant\",\n constant_values=fill_value,\n )\n return type(self)(self.dims, array)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._roll_one_dim_Variable._roll_one_dim.return.type_self_self_dims_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._roll_one_dim_Variable._roll_one_dim.return.type_self_self_dims_dat", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1158, "end_line": 1177, "span_ids": ["Variable._roll_one_dim"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _roll_one_dim(self, dim, count):\n axis = self.get_axis_num(dim)\n\n count %= self.shape[axis]\n if count != 0:\n indices = [slice(-count, None), slice(None, -count)]\n else:\n indices = [slice(None)]\n\n arrays = [self[(slice(None),) * axis + (idx,)].data for idx in indices]\n\n data = duck_array_ops.concatenate(arrays, axis)\n\n if isinstance(data, dask_array_type):\n # chunked data should come out with the same chunks; this makes\n # it feasible to combine shifted and unshifted data\n # TODO: remove this once dask.array automatically aligns chunks\n data = data.rechunk(self.data.chunks)\n\n return type(self)(self.dims, data, self._attrs, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.roll_Variable.roll.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.roll_Variable.roll.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1179, "end_line": 1203, "span_ids": ["Variable.roll"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def roll(self, shifts=None, **shifts_kwargs):\n \"\"\"\n Return a new Variable with rolld data.\n\n Parameters\n ----------\n shifts : mapping of the form {dim: offset}\n Integer offset to roll along each of the given dimensions.\n Positive offsets roll to the right; negative offsets roll to the\n left.\n **shifts_kwargs:\n The keyword arguments form of ``shifts``.\n One of shifts or shifts_kwarg must be provided.\n\n Returns\n -------\n shifted : Variable\n Variable with the same dimensions and attributes but rolled data.\n \"\"\"\n shifts = either_dict_or_kwargs(shifts, shifts_kwargs, \"roll\")\n\n result = self\n for dim, count in shifts.items():\n result = result._roll_one_dim(dim, count)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.transpose_Variable.T.return.self_transpose_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.transpose_Variable.T.return.self_transpose_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1205, "end_line": 1240, "span_ids": ["Variable.T", "Variable.transpose"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def transpose(self, *dims) -> \"Variable\":\n \"\"\"Return a new Variable object with transposed dimensions.\n\n Parameters\n ----------\n *dims : str, optional\n By default, reverse the dimensions. Otherwise, reorder the\n dimensions to this order.\n\n Returns\n -------\n transposed : Variable\n The returned object has transposed data and dimensions with the\n same attributes as the original.\n\n Notes\n -----\n This operation returns a view of this variable's data. It is\n lazy for dask-backed Variables but not for numpy-backed Variables.\n\n See Also\n --------\n numpy.transpose\n \"\"\"\n if len(dims) == 0:\n dims = self.dims[::-1]\n axes = self.get_axis_num(dims)\n if len(dims) < 2: # no need to transpose if only one dimension\n return self.copy(deep=False)\n\n data = as_indexable(self._data).transpose(axes)\n return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)\n\n @property\n def T(self) -> \"Variable\":\n return self.transpose()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.set_dims_Variable.set_dims.return.expanded_var_transpose_d": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.set_dims_Variable.set_dims.return.expanded_var_transpose_d", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1242, "end_line": 1289, "span_ids": ["Variable.set_dims"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def set_dims(self, dims, shape=None):\n \"\"\"Return a new variable with given set of dimensions.\n This method might be used to attach new dimension(s) to variable.\n\n When possible, this operation does not copy this variable's data.\n\n Parameters\n ----------\n dims : str or sequence of str or dict\n Dimensions to include on the new variable. If a dict, values are\n used to provide the sizes of new dimensions; otherwise, new\n dimensions are inserted with length 1.\n\n Returns\n -------\n Variable\n \"\"\"\n if isinstance(dims, str):\n dims = [dims]\n\n if shape is None and utils.is_dict_like(dims):\n shape = dims.values()\n\n missing_dims = set(self.dims) - set(dims)\n if missing_dims:\n raise ValueError(\n \"new dimensions %r must be a superset of \"\n \"existing dimensions %r\" % (dims, self.dims)\n )\n\n self_dims = set(self.dims)\n expanded_dims = tuple(d for d in dims if d not in self_dims) + self.dims\n\n if self.dims == expanded_dims:\n # don't use broadcast_to unless necessary so the result remains\n # writeable if possible\n expanded_data = self.data\n elif shape is not None:\n dims_map = dict(zip(dims, shape))\n tmp_shape = tuple(dims_map[d] for d in expanded_dims)\n expanded_data = duck_array_ops.broadcast_to(self.data, tmp_shape)\n else:\n expanded_data = self.data[(None,) * (len(expanded_dims) - self.ndim)]\n\n expanded_var = Variable(\n expanded_dims, expanded_data, self._attrs, self._encoding, fastpath=True\n )\n return expanded_var.transpose(*dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._stack_once_Variable._stack_once.return.Variable_new_dims_new_da": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._stack_once_Variable._stack_once.return.Variable_new_dims_new_da", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1291, "end_line": 1313, "span_ids": ["Variable._stack_once"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _stack_once(self, dims, new_dim):\n if not set(dims) <= set(self.dims):\n raise ValueError(\"invalid existing dimensions: %s\" % dims)\n\n if new_dim in self.dims:\n raise ValueError(\n \"cannot create a new dimension with the same \"\n \"name as an existing dimension\"\n )\n\n if len(dims) == 0:\n # don't stack\n return self.copy(deep=False)\n\n other_dims = [d for d in self.dims if d not in dims]\n dim_order = other_dims + list(dims)\n reordered = self.transpose(*dim_order)\n\n new_shape = reordered.shape[: len(other_dims)] + (-1,)\n new_data = reordered.data.reshape(new_shape)\n new_dims = reordered.dims[: len(other_dims)] + (new_dim,)\n\n return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.stack_Variable.stack.return.result": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.stack_Variable.stack.return.result", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1315, "end_line": 1344, "span_ids": ["Variable.stack"], "tokens": 223}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def stack(self, dimensions=None, **dimensions_kwargs):\n \"\"\"\n Stack any number of existing dimensions into a single new dimension.\n\n New dimensions will be added at the end, and the order of the data\n along each new dimension will be in contiguous (C) order.\n\n Parameters\n ----------\n dimensions : Mapping of form new_name=(dim1, dim2, ...)\n Names of new dimensions, and the existing dimensions that they\n replace.\n **dimensions_kwargs:\n The keyword arguments form of ``dimensions``.\n One of dimensions or dimensions_kwargs must be provided.\n\n Returns\n -------\n stacked : Variable\n Variable with the same attributes but stacked data.\n\n See also\n --------\n Variable.unstack\n \"\"\"\n dimensions = either_dict_or_kwargs(dimensions, dimensions_kwargs, \"stack\")\n result = self\n for new_dim, dims in dimensions.items():\n result = result._stack_once(dims, new_dim)\n return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._unstack_once_Variable._unstack_once.return.Variable_new_dims_new_da": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._unstack_once_Variable._unstack_once.return.Variable_new_dims_new_da", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1346, "end_line": 1373, "span_ids": ["Variable._unstack_once"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _unstack_once(self, dims, old_dim):\n new_dim_names = tuple(dims.keys())\n new_dim_sizes = tuple(dims.values())\n\n if old_dim not in self.dims:\n raise ValueError(\"invalid existing dimension: %s\" % old_dim)\n\n if set(new_dim_names).intersection(self.dims):\n raise ValueError(\n \"cannot create a new dimension with the same \"\n \"name as an existing dimension\"\n )\n\n if np.prod(new_dim_sizes) != self.sizes[old_dim]:\n raise ValueError(\n \"the product of the new dimension sizes must \"\n \"equal the size of the old dimension\"\n )\n\n other_dims = [d for d in self.dims if d != old_dim]\n dim_order = other_dims + [old_dim]\n reordered = self.transpose(*dim_order)\n\n new_shape = reordered.shape[: len(other_dims)] + new_dim_sizes\n new_data = reordered.data.reshape(new_shape)\n new_dims = reordered.dims[: len(other_dims)] + new_dim_names\n\n return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.unstack_Variable.where.return.ops_where_method_self_co": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.unstack_Variable.where.return.ops_where_method_self_co", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1375, "end_line": 1410, "span_ids": ["Variable.where", "Variable.unstack", "Variable.fillna"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def unstack(self, dimensions=None, **dimensions_kwargs):\n \"\"\"\n Unstack an existing dimension into multiple new dimensions.\n\n New dimensions will be added at the end, and the order of the data\n along each new dimension will be in contiguous (C) order.\n\n Parameters\n ----------\n dimensions : mapping of the form old_dim={dim1: size1, ...}\n Names of existing dimensions, and the new dimensions and sizes\n that they map to.\n **dimensions_kwargs:\n The keyword arguments form of ``dimensions``.\n One of dimensions or dimensions_kwargs must be provided.\n\n Returns\n -------\n unstacked : Variable\n Variable with the same attributes but unstacked data.\n\n See also\n --------\n Variable.stack\n \"\"\"\n dimensions = either_dict_or_kwargs(dimensions, dimensions_kwargs, \"unstack\")\n result = self\n for old_dim, dims in dimensions.items():\n result = result._unstack_once(dims, old_dim)\n return result\n\n def fillna(self, value):\n return ops.fillna(self, value)\n\n def where(self, cond, other=dtypes.NA):\n return ops.where_method(self, cond, other)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.reduce_Variable.reduce.return.Variable_dims_data_attr": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.reduce_Variable.reduce.return.Variable_dims_data_attr", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1412, "end_line": 1493, "span_ids": ["Variable.reduce"], "tokens": 667}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def reduce(\n self,\n func,\n dim=None,\n axis=None,\n keep_attrs=None,\n keepdims=False,\n allow_lazy=False,\n **kwargs\n ):\n \"\"\"Reduce this array by applying `func` along some dimension(s).\n\n Parameters\n ----------\n func : function\n Function which can be called in the form\n `func(x, axis=axis, **kwargs)` to return the result of reducing an\n np.ndarray over an integer valued axis.\n dim : str or sequence of str, optional\n Dimension(s) over which to apply `func`.\n axis : int or sequence of int, optional\n Axis(es) over which to apply `func`. Only one of the 'dim'\n and 'axis' arguments can be supplied. If neither are supplied, then\n the reduction is calculated over the flattened array (by calling\n `func(x)` without an axis argument).\n keep_attrs : bool, optional\n If True, the variable's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n keepdims : bool, default False\n If True, the dimensions which are reduced are left in the result\n as dimensions of size one\n **kwargs : dict\n Additional keyword arguments passed on to `func`.\n\n Returns\n -------\n reduced : Array\n Array with summarized data and the indicated dimension(s)\n removed.\n \"\"\"\n if dim is common.ALL_DIMS:\n dim = None\n if dim is not None and axis is not None:\n raise ValueError(\"cannot supply both 'axis' and 'dim' arguments\")\n\n if dim is not None:\n axis = self.get_axis_num(dim)\n input_data = self.data if allow_lazy else self.values\n if axis is not None:\n data = func(input_data, axis=axis, **kwargs)\n else:\n data = func(input_data, **kwargs)\n\n if getattr(data, \"shape\", ()) == self.shape:\n dims = self.dims\n else:\n removed_axes = (\n range(self.ndim) if axis is None else np.atleast_1d(axis) % self.ndim\n )\n if keepdims:\n # Insert np.newaxis for removed dims\n slices = tuple(\n np.newaxis if i in removed_axes else slice(None, None)\n for i in range(self.ndim)\n )\n if getattr(data, \"shape\", None) is None:\n # Reduce has produced a scalar value, not an array-like\n data = np.asanyarray(data)[slices]\n else:\n data = data[slices]\n dims = self.dims\n else:\n dims = [\n adim for n, adim in enumerate(self.dims) if n not in removed_axes\n ]\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self._attrs if keep_attrs else None\n\n return Variable(dims, data, attrs=attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.concat_Variable.concat.return.cls_dims_data_attrs_en": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.concat_Variable.concat.return.cls_dims_data_attrs_en", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1495, "end_line": 1558, "span_ids": ["Variable.concat"], "tokens": 562}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n @classmethod\n def concat(cls, variables, dim=\"concat_dim\", positions=None, shortcut=False):\n \"\"\"Concatenate variables along a new or existing dimension.\n\n Parameters\n ----------\n variables : iterable of Array\n Arrays to stack together. Each variable is expected to have\n matching dimensions and shape except for along the stacked\n dimension.\n dim : str or DataArray, optional\n Name of the dimension to stack along. This can either be a new\n dimension name, in which case it is added along axis=0, or an\n existing dimension name, in which case the location of the\n dimension is unchanged. Where to insert the new dimension is\n determined by the first variable.\n positions : None or list of integer arrays, optional\n List of integer arrays which specifies the integer positions to\n which to assign each dataset along the concatenated dimension.\n If not supplied, objects are concatenated in the provided order.\n shortcut : bool, optional\n This option is used internally to speed-up groupby operations.\n If `shortcut` is True, some checks of internal consistency between\n arrays to concatenate are skipped.\n\n Returns\n -------\n stacked : Variable\n Concatenated Variable formed by stacking all the supplied variables\n along the given dimension.\n \"\"\"\n if not isinstance(dim, str):\n dim, = dim.dims\n\n # can't do this lazily: we need to loop through variables at least\n # twice\n variables = list(variables)\n first_var = variables[0]\n\n arrays = [v.data for v in variables]\n\n if dim in first_var.dims:\n axis = first_var.get_axis_num(dim)\n dims = first_var.dims\n data = duck_array_ops.concatenate(arrays, axis=axis)\n if positions is not None:\n # TODO: deprecate this option -- we don't need it for groupby\n # any more.\n indices = nputils.inverse_permutation(np.concatenate(positions))\n data = duck_array_ops.take(data, indices, axis=axis)\n else:\n axis = 0\n dims = (dim,) + first_var.dims\n data = duck_array_ops.stack(arrays, axis=axis)\n\n attrs = dict(first_var.attrs)\n encoding = dict(first_var.encoding)\n if not shortcut:\n for var in variables:\n if var.dims != first_var.dims:\n raise ValueError(\"inconsistent dimensions\")\n utils.remove_incompatible_items(attrs, var.attrs)\n\n return cls(dims, data, attrs, encoding)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.equals_Variable.equals.try_.except_TypeError_Attrib.return.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.equals_Variable.equals.try_.except_TypeError_Attrib.return.False", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1560, "end_line": 1576, "span_ids": ["Variable.equals"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def equals(self, other, equiv=duck_array_ops.array_equiv):\n \"\"\"True if two Variables have the same dimensions and values;\n otherwise False.\n\n Variables can still be equal (like pandas objects) if they have NaN\n values in the same locations.\n\n This method is necessary because `v1 == v2` for Variables\n does element-wise comparisons (like numpy.ndarrays).\n \"\"\"\n other = getattr(other, \"variable\", other)\n try:\n return self.dims == other.dims and (\n self._data is other._data or equiv(self.data, other.data)\n )\n except (TypeError, AttributeError):\n return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.broadcast_equals_Variable.no_conflicts.return.self_broadcast_equals_oth": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.broadcast_equals_Variable.no_conflicts.return.self_broadcast_equals_oth", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1578, "end_line": 1606, "span_ids": ["Variable.identical", "Variable.no_conflicts", "Variable.broadcast_equals"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def broadcast_equals(self, other, equiv=duck_array_ops.array_equiv):\n \"\"\"True if two Variables have the values after being broadcast against\n each other; otherwise False.\n\n Variables can still be equal (like pandas objects) if they have NaN\n values in the same locations.\n \"\"\"\n try:\n self, other = broadcast_variables(self, other)\n except (ValueError, AttributeError):\n return False\n return self.equals(other, equiv=equiv)\n\n def identical(self, other):\n \"\"\"Like equals, but also checks attributes.\n \"\"\"\n try:\n return utils.dict_equiv(self.attrs, other.attrs) and self.equals(other)\n except (TypeError, AttributeError):\n return False\n\n def no_conflicts(self, other):\n \"\"\"True if the intersection of two Variable's non-null data is\n equal; otherwise false.\n\n Variables can thus still be equal if there are locations where either,\n or both, contain NaN values.\n \"\"\"\n return self.broadcast_equals(other, equiv=duck_array_ops.array_notnull_equiv)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.quantile_Variable.quantile.return.Variable_new_dims_qs_at": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.quantile_Variable.quantile.return.Variable_new_dims_qs_at", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1608, "end_line": 1683, "span_ids": ["Variable.quantile"], "tokens": 666}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def quantile(self, q, dim=None, interpolation=\"linear\", keep_attrs=None):\n \"\"\"Compute the qth quantile of the data along the specified dimension.\n\n Returns the qth quantiles(s) of the array elements.\n\n Parameters\n ----------\n q : float in range of [0,1] (or sequence of floats)\n Quantile to compute, which must be between 0 and 1\n inclusive.\n dim : str or sequence of str, optional\n Dimension(s) over which to apply quantile.\n interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n This optional parameter specifies the interpolation method to\n use when the desired quantile lies between two data points\n ``i < j``:\n * linear: ``i + (j - i) * fraction``, where ``fraction`` is\n the fractional part of the index surrounded by ``i`` and\n ``j``.\n * lower: ``i``.\n * higher: ``j``.\n * nearest: ``i`` or ``j``, whichever is nearest.\n * midpoint: ``(i + j) / 2``.\n keep_attrs : bool, optional\n If True, the variable's attributes (`attrs`) will be copied from\n the original object to the new one. If False (default), the new\n object will be returned without attributes.\n\n Returns\n -------\n quantiles : Variable\n If `q` is a single quantile, then the result\n is a scalar. If multiple percentiles are given, first axis of\n the result corresponds to the quantile and a quantile dimension\n is added to the return array. The other dimensions are the\n dimensions that remain after the reduction of the array.\n\n See Also\n --------\n numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile,\n DataArray.quantile\n \"\"\"\n if isinstance(self.data, dask_array_type):\n raise TypeError(\n \"quantile does not work for arrays stored as dask \"\n \"arrays. Load the data via .compute() or .load() \"\n \"prior to calling this method.\"\n )\n\n q = np.asarray(q, dtype=np.float64)\n\n new_dims = list(self.dims)\n if dim is not None:\n axis = self.get_axis_num(dim)\n if utils.is_scalar(dim):\n new_dims.remove(dim)\n else:\n for d in dim:\n new_dims.remove(d)\n else:\n axis = None\n new_dims = []\n\n # Only add the quantile dimension if q is array-like\n if q.ndim != 0:\n new_dims = [\"quantile\"] + new_dims\n\n qs = np.nanpercentile(\n self.data, q * 100.0, axis=axis, interpolation=interpolation\n )\n\n if keep_attrs is None:\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self._attrs if keep_attrs else None\n\n return Variable(new_dims, qs, attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.rank_Variable.rank.return.Variable_self_dims_ranke": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.rank_Variable.rank.return.Variable_self_dims_ranke", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1685, "end_line": 1732, "span_ids": ["Variable.rank"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def rank(self, dim, pct=False):\n \"\"\"Ranks the data.\n\n Equal values are assigned a rank that is the average of the ranks that\n would have been otherwise assigned to all of the values within that\n set. Ranks begin at 1, not 0. If `pct`, computes percentage ranks.\n\n NaNs in the input array are returned as NaNs.\n\n The `bottleneck` library is required.\n\n Parameters\n ----------\n dim : str\n Dimension over which to compute rank.\n pct : bool, optional\n If True, compute percentage ranks, otherwise compute integer ranks.\n\n Returns\n -------\n ranked : Variable\n\n See Also\n --------\n Dataset.rank, DataArray.rank\n \"\"\"\n import bottleneck as bn\n\n data = self.data\n\n if isinstance(data, dask_array_type):\n raise TypeError(\n \"rank does not work for arrays stored as dask \"\n \"arrays. Load the data via .compute() or .load() \"\n \"prior to calling this method.\"\n )\n elif not isinstance(data, np.ndarray):\n raise TypeError(\n \"rank is not implemented for {} objects.\".format(type(data))\n )\n\n axis = self.get_axis_num(dim)\n func = bn.nanrankdata if self.dtype.kind == \"f\" else bn.rankdata\n ranked = func(data, axis=axis)\n if pct:\n count = np.sum(~np.isnan(data), axis=axis, keepdims=True)\n ranked /= count\n return Variable(self.dims, ranked)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.rolling_window_Variable.rolling_window.return.Variable_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.rolling_window_Variable.rolling_window.return.Variable_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1734, "end_line": 1791, "span_ids": ["Variable.rolling_window"], "tokens": 586}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def rolling_window(\n self, dim, window, window_dim, center=False, fill_value=dtypes.NA\n ):\n \"\"\"\n Make a rolling_window along dim and add a new_dim to the last place.\n\n Parameters\n ----------\n dim: str\n Dimension over which to compute rolling_window\n window: int\n Window size of the rolling\n window_dim: str\n New name of the window dimension.\n center: boolean. default False.\n If True, pad fill_value for both ends. Otherwise, pad in the head\n of the axis.\n fill_value:\n value to be filled.\n\n Returns\n -------\n Variable that is a view of the original array with a added dimension of\n size w.\n The return dim: self.dims + (window_dim, )\n The return shape: self.shape + (window, )\n\n Examples\n --------\n >>> v=Variable(('a', 'b'), np.arange(8).reshape((2,4)))\n >>> v.rolling_window(x, 'b', 3, 'window_dim')\n \n array([[[nan, nan, 0], [nan, 0, 1], [0, 1, 2], [1, 2, 3]],\n [[nan, nan, 4], [nan, 4, 5], [4, 5, 6], [5, 6, 7]]])\n\n >>> v.rolling_window(x, 'b', 3, 'window_dim', center=True)\n \n array([[[nan, 0, 1], [0, 1, 2], [1, 2, 3], [2, 3, nan]],\n [[nan, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, nan]]])\n \"\"\"\n if fill_value is dtypes.NA: # np.nan is passed\n dtype, fill_value = dtypes.maybe_promote(self.dtype)\n array = self.astype(dtype, copy=False).data\n else:\n dtype = self.dtype\n array = self.data\n\n new_dims = self.dims + (window_dim,)\n return Variable(\n new_dims,\n duck_array_ops.rolling_window(\n array,\n axis=self.get_axis_num(dim),\n window=window,\n center=center,\n fill_value=fill_value,\n ),\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.coarsen_Variable.coarsen.return.type_self_self_dims_fun": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.coarsen_Variable.coarsen.return.type_self_self_dims_fun", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1793, "end_line": 1807, "span_ids": ["Variable.coarsen"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def coarsen(self, windows, func, boundary=\"exact\", side=\"left\"):\n \"\"\"\n Apply\n \"\"\"\n windows = {k: v for k, v in windows.items() if k in self.dims}\n if not windows:\n return self.copy()\n\n reshaped, axes = self._coarsen_reshape(windows, boundary, side)\n if isinstance(func, str):\n name = func\n func = getattr(duck_array_ops, name, None)\n if func is None:\n raise NameError(\"{} is not a valid method.\".format(name))\n return type(self)(self.dims, func(reshaped, axis=axes), self._attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._coarsen_reshape_Variable._coarsen_reshape.return.variable_data_reshape_sha": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._coarsen_reshape_Variable._coarsen_reshape.return.variable_data_reshape_sha", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1809, "end_line": 1872, "span_ids": ["Variable._coarsen_reshape"], "tokens": 540}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n def _coarsen_reshape(self, windows, boundary, side):\n \"\"\"\n Construct a reshaped-array for corsen\n \"\"\"\n if not utils.is_dict_like(boundary):\n boundary = {d: boundary for d in windows.keys()}\n\n if not utils.is_dict_like(side):\n side = {d: side for d in windows.keys()}\n\n # remove unrelated dimensions\n boundary = {k: v for k, v in boundary.items() if k in windows}\n side = {k: v for k, v in side.items() if k in windows}\n\n for d, window in windows.items():\n if window <= 0:\n raise ValueError(\"window must be > 0. Given {}\".format(window))\n\n variable = self\n for d, window in windows.items():\n # trim or pad the object\n size = variable.shape[self._get_axis_num(d)]\n n = int(size / window)\n if boundary[d] == \"exact\":\n if n * window != size:\n raise ValueError(\n \"Could not coarsen a dimension of size {} with \"\n \"window {}\".format(size, window)\n )\n elif boundary[d] == \"trim\":\n if side[d] == \"left\":\n variable = variable.isel({d: slice(0, window * n)})\n else:\n excess = size - window * n\n variable = variable.isel({d: slice(excess, None)})\n elif boundary[d] == \"pad\": # pad\n pad = window * n - size\n if pad < 0:\n pad += window\n if side[d] == \"left\":\n pad_widths = {d: (0, pad)}\n else:\n pad_widths = {d: (pad, 0)}\n variable = variable.pad_with_fill_value(pad_widths)\n else:\n raise TypeError(\n \"{} is invalid for boundary. Valid option is 'exact', \"\n \"'trim' and 'pad'\".format(boundary[d])\n )\n\n shape = []\n axes = []\n axis_count = 0\n for i, d in enumerate(variable.dims):\n if d in windows:\n size = variable.shape[i]\n shape.append(int(size / windows[d]))\n shape.append(windows[d])\n axis_count += 1\n axes.append(i + axis_count)\n else:\n shape.append(variable.shape[i])\n\n return variable.data.reshape(shape), tuple(axes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.real_Variable._unary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable.real_Variable._unary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1874, "end_line": 1892, "span_ids": ["Variable.real", "Variable._unary_op", "Variable.imag", "Variable.__array_wrap__"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n @property\n def real(self):\n return type(self)(self.dims, self.data.real, self._attrs)\n\n @property\n def imag(self):\n return type(self)(self.dims, self.data.imag, self._attrs)\n\n def __array_wrap__(self, obj, context=None):\n return Variable(self.dims, obj)\n\n @staticmethod\n def _unary_op(f):\n @functools.wraps(f)\n def func(self, *args, **kwargs):\n with np.errstate(all=\"ignore\"):\n return self.__array_wrap__(f(self.data, *args, **kwargs))\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._binary_op_Variable._binary_op.return.func": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._binary_op_Variable._binary_op.return.func", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1894, "end_line": 1912, "span_ids": ["Variable._binary_op"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n @staticmethod\n def _binary_op(f, reflexive=False, **ignored_kwargs):\n @functools.wraps(f)\n def func(self, other):\n if isinstance(other, (xr.DataArray, xr.Dataset)):\n return NotImplemented\n self_data, other_data, dims = _broadcast_compat_data(self, other)\n keep_attrs = _get_keep_attrs(default=False)\n attrs = self._attrs if keep_attrs else None\n with np.errstate(all=\"ignore\"):\n new_data = (\n f(self_data, other_data)\n if not reflexive\n else f(other_data, self_data)\n )\n result = Variable(dims, new_data, attrs=attrs)\n return result\n\n return func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._inplace_binary_op_ops_inject_all_ops_and_re": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_Variable._inplace_binary_op_ops_inject_all_ops_and_re", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1914, "end_line": 1939, "span_ids": ["Variable._inplace_binary_op", "Variable._to_numeric", "impl:10"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Variable(\n common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin\n):\n\n @staticmethod\n def _inplace_binary_op(f):\n @functools.wraps(f)\n def func(self, other):\n if isinstance(other, xr.Dataset):\n raise TypeError(\"cannot add a Dataset to a Variable in-place\")\n self_data, other_data, dims = _broadcast_compat_data(self, other)\n if dims != self.dims:\n raise ValueError(\"dimensions cannot change for in-place \" \"operations\")\n with np.errstate(all=\"ignore\"):\n self.values = f(self_data, other_data)\n return self\n\n return func\n\n def _to_numeric(self, offset=None, datetime_unit=None, dtype=float):\n \"\"\" A (private) method to convert datetime array to numeric dtype\n See duck_array_ops.datetime_to_numeric\n \"\"\"\n numeric_array = duck_array_ops.datetime_to_numeric(\n self.data, offset, datetime_unit, dtype\n )\n return type(self)(self.dims, numeric_array, self._attrs)\n\n\nops.inject_all_ops_and_reduce_methods(Variable)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable_IndexVariable.__setitem__.raise_TypeError_s_value": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable_IndexVariable.__setitem__.raise_TypeError_s_value", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1942, "end_line": 1987, "span_ids": ["IndexVariable.data", "IndexVariable.load", "IndexVariable.chunk", "IndexVariable", "IndexVariable._finalize_indexing_result", "IndexVariable.__setitem__"], "tokens": 429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n \"\"\"Wrapper for accommodating a pandas.Index in an xarray.Variable.\n\n IndexVariable preserve loaded values in the form of a pandas.Index instead\n of a NumPy array. Hence, their values are immutable and must always be one-\n dimensional.\n\n They also have a name property, which is the name of their sole dimension\n unless another name is given.\n \"\"\"\n\n __slots__ = ()\n\n def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False):\n super().__init__(dims, data, attrs, encoding, fastpath)\n if self.ndim != 1:\n raise ValueError(\"%s objects must be 1-dimensional\" % type(self).__name__)\n\n # Unlike in Variable, always eagerly load values into memory\n if not isinstance(self._data, PandasIndexAdapter):\n self._data = PandasIndexAdapter(self._data)\n\n def load(self):\n # data is already loaded into memory for IndexVariable\n return self\n\n # https://github.com/python/mypy/issues/1465\n @Variable.data.setter # type: ignore\n def data(self, data):\n Variable.data.fset(self, data)\n if not isinstance(self._data, PandasIndexAdapter):\n self._data = PandasIndexAdapter(self._data)\n\n def chunk(self, chunks=None, name=None, lock=False):\n # Dummy - do not chunk. This method is invoked e.g. by Dataset.chunk()\n return self.copy(deep=False)\n\n def _finalize_indexing_result(self, dims, data):\n if getattr(data, \"ndim\", 0) != 1:\n # returns Variable rather than IndexVariable if multi-dimensional\n return Variable(dims, data, self._attrs, self._encoding)\n else:\n return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)\n\n def __setitem__(self, key, value):\n raise TypeError(\"%s values cannot be modified\" % type(self).__name__)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.concat_IndexVariable.concat.return.cls_first_var_dims_data_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.concat_IndexVariable.concat.return.cls_first_var_dims_data_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1989, "end_line": 2026, "span_ids": ["IndexVariable.concat"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n\n @classmethod\n def concat(cls, variables, dim=\"concat_dim\", positions=None, shortcut=False):\n \"\"\"Specialized version of Variable.concat for IndexVariable objects.\n\n This exists because we want to avoid converting Index objects to NumPy\n arrays, if possible.\n \"\"\"\n if not isinstance(dim, str):\n dim, = dim.dims\n\n variables = list(variables)\n first_var = variables[0]\n\n if any(not isinstance(v, cls) for v in variables):\n raise TypeError(\n \"IndexVariable.concat requires that all input \"\n \"variables be IndexVariable objects\"\n )\n\n indexes = [v._data.array for v in variables]\n\n if not indexes:\n data = []\n else:\n data = indexes[0].append(indexes[1:])\n\n if positions is not None:\n indices = nputils.inverse_permutation(np.concatenate(positions))\n data = data.take(indices)\n\n attrs = dict(first_var.attrs)\n if not shortcut:\n for var in variables:\n if var.dims != first_var.dims:\n raise ValueError(\"inconsistent dimensions\")\n utils.remove_incompatible_items(attrs, var.attrs)\n\n return cls(first_var.dims, data, attrs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.copy_IndexVariable.copy.return.type_self_self_dims_dat": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.copy_IndexVariable.copy.return.type_self_self_dims_dat", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2028, "end_line": 2062, "span_ids": ["IndexVariable.copy"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n\n def copy(self, deep=True, data=None):\n \"\"\"Returns a copy of this object.\n\n `deep` is ignored since data is stored in the form of\n pandas.Index, which is already immutable. Dimensions, attributes\n and encodings are always copied.\n\n Use `data` to create a new object with the same structure as\n original but entirely new data.\n\n Parameters\n ----------\n deep : bool, optional\n Deep is ignored when data is given. Whether the data array is\n loaded into memory and copied onto the new object. Default is True.\n data : array_like, optional\n Data to use in the new object. Must have same shape as original.\n\n Returns\n -------\n object : Variable\n New object with dimensions, attributes, encodings, and optionally\n data copied from original.\n \"\"\"\n if data is None:\n data = self._data.copy(deep=deep)\n else:\n data = as_compatible_data(data)\n if self.shape != data.shape:\n raise ValueError(\n \"Data shape {} must match shape of object {}\".format(\n data.shape, self.shape\n )\n )\n return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.equals_IndexVariable.to_coord.utils_alias_to_index_vari": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.equals_IndexVariable.to_coord.utils_alias_to_index_vari", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2064, "end_line": 2083, "span_ids": ["IndexVariable.to_index_variable", "IndexVariable:5", "IndexVariable._data_equals", "IndexVariable.equals"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n\n def equals(self, other, equiv=None):\n # if equiv is specified, super up\n if equiv is not None:\n return super().equals(other, equiv)\n\n # otherwise use the native index equals, rather than looking at _data\n other = getattr(other, \"variable\", other)\n try:\n return self.dims == other.dims and self._data_equals(other)\n except (TypeError, AttributeError):\n return False\n\n def _data_equals(self, other):\n return self.to_index().equals(other.to_index())\n\n def to_index_variable(self):\n \"\"\"Return this variable as an xarray.IndexVariable\"\"\"\n return self\n\n to_coord = utils.alias(to_index_variable, \"to_coord\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.to_index_IndexVariable.to_index.return.index": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.to_index_IndexVariable.to_index.return.index", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2085, "end_line": 2101, "span_ids": ["IndexVariable.to_index"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n\n def to_index(self):\n \"\"\"Convert this variable to a pandas.Index\"\"\"\n # n.b. creating a new pandas.Index from an old pandas.Index is\n # basically free as pandas.Index objects are immutable\n assert self.ndim == 1\n index = self._data.array\n if isinstance(index, pd.MultiIndex):\n # set default names for multi-index unnamed levels so that\n # we can safely rename dimension / coordinate later\n valid_level_names = [\n name or \"{}_level_{}\".format(self.dims[0], i)\n for i, name in enumerate(index.names)\n ]\n index = index.set_names(valid_level_names)\n else:\n index = index.set_names(self.name)\n return index", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.level_names_IndexVariable.name_14.raise_AttributeError_can": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_IndexVariable.level_names_IndexVariable.name_14.raise_AttributeError_can", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2103, "end_line": 2127, "span_ids": ["IndexVariable.name", "IndexVariable.name_14", "IndexVariable.get_level_variable", "IndexVariable.level_names"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IndexVariable(Variable):\n\n @property\n def level_names(self):\n \"\"\"Return MultiIndex level names or None if this IndexVariable has no\n MultiIndex.\n \"\"\"\n index = self.to_index()\n if isinstance(index, pd.MultiIndex):\n return index.names\n else:\n return None\n\n def get_level_variable(self, level):\n \"\"\"Return a new IndexVariable from a given MultiIndex level.\"\"\"\n if self.level_names is None:\n raise ValueError(\"IndexVariable %r has no MultiIndex\" % self.name)\n index = self.to_index()\n return type(self)(self.dims, index.get_level_values(level))\n\n @property\n def name(self):\n return self.dims[0]\n\n @name.setter\n def name(self, value):\n raise AttributeError(\"cannot modify name of IndexVariable in-place\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__for_backwards_compatibi__unified_dims.return.all_dims": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__for_backwards_compatibi__unified_dims.return.all_dims", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2130, "end_line": 2153, "span_ids": ["IndexVariable.name_14", "_unified_dims", "impl:11"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# for backwards compatibility\nCoordinate = utils.alias(IndexVariable, \"Coordinate\")\n\n\ndef _unified_dims(variables):\n # validate dimensions\n all_dims = {}\n for var in variables:\n var_dims = var.dims\n if len(set(var_dims)) < len(var_dims):\n raise ValueError(\n \"broadcasting cannot handle duplicate \"\n \"dimensions: %r\" % list(var_dims)\n )\n for d, s in zip(var_dims, var.shape):\n if d not in all_dims:\n all_dims[d] = s\n elif all_dims[d] != s:\n raise ValueError(\n \"operands cannot be broadcast together \"\n \"with mismatched lengths for dimension %r: %s\"\n % (d, (all_dims[d], s))\n )\n return all_dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__broadcast_compat_variables_broadcast_variables.return.tuple_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__broadcast_compat_variables_broadcast_variables.return.tuple_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2156, "end_line": 2180, "span_ids": ["broadcast_variables", "_broadcast_compat_variables"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _broadcast_compat_variables(*variables):\n \"\"\"Create broadcast compatible variables, with the same dimensions.\n\n Unlike the result of broadcast_variables(), some variables may have\n dimensions of size 1 instead of the the size of the broadcast dimension.\n \"\"\"\n dims = tuple(_unified_dims(variables))\n return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)\n\n\ndef broadcast_variables(*variables):\n \"\"\"Given any number of variables, return variables with matching dimensions\n and broadcast data.\n\n The data on the returned variables will be a view of the data on the\n corresponding original arrays, but dimensions will be reordered and\n inserted so that both broadcast arrays have the same dimensions. The new\n dimensions are sorted in order of appearance in the first variable's\n dimensions followed by the second variable's dimensions.\n \"\"\"\n dims_map = _unified_dims(variables)\n dims_tuple = tuple(dims_map)\n return tuple(\n var.set_dims(dims_map) if var.dims != dims_tuple else var for var in variables\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__broadcast_compat_data__broadcast_compat_data.return.self_data_other_data_di": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py__broadcast_compat_data__broadcast_compat_data.return.self_data_other_data_di", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2183, "end_line": 2195, "span_ids": ["_broadcast_compat_data"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _broadcast_compat_data(self, other):\n if all(hasattr(other, attr) for attr in [\"dims\", \"data\", \"shape\", \"encoding\"]):\n # `other` satisfies the necessary Variable API for broadcast_variables\n new_self, new_other = _broadcast_compat_variables(self, other)\n self_data = new_self.data\n other_data = new_other.data\n dims = new_self.dims\n else:\n # rely on numpy broadcasting rules\n self_data = self.data\n other_data = other\n dims = self.dims\n return self_data, other_data, dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_concat_concat.if_all_isinstance_v_Inde.else_.return.Variable_concat_variables": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_concat_concat.if_all_isinstance_v_Inde.else_.return.Variable_concat_variables", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2198, "end_line": 2232, "span_ids": ["concat"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def concat(variables, dim=\"concat_dim\", positions=None, shortcut=False):\n \"\"\"Concatenate variables along a new or existing dimension.\n\n Parameters\n ----------\n variables : iterable of Array\n Arrays to stack together. Each variable is expected to have\n matching dimensions and shape except for along the stacked\n dimension.\n dim : str or DataArray, optional\n Name of the dimension to stack along. This can either be a new\n dimension name, in which case it is added along axis=0, or an\n existing dimension name, in which case the location of the\n dimension is unchanged. Where to insert the new dimension is\n determined by the first variable.\n positions : None or list of integer arrays, optional\n List of integer arrays which specifies the integer positions to which\n to assign each dataset along the concatenated dimension. If not\n supplied, objects are concatenated in the provided order.\n shortcut : bool, optional\n This option is used internally to speed-up groupby operations.\n If `shortcut` is True, some checks of internal consistency between\n arrays to concatenate are skipped.\n\n Returns\n -------\n stacked : Variable\n Concatenated Variable formed by stacking all the supplied variables\n along the given dimension.\n \"\"\"\n variables = list(variables)\n if all(isinstance(v, IndexVariable) for v in variables):\n return IndexVariable.concat(variables, dim, positions, shortcut)\n else:\n return Variable.concat(variables, dim, positions, shortcut)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_assert_unique_multiindex_level_names_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/core/variable.py_assert_unique_multiindex_level_names_", "embedding": null, "metadata": {"file_path": "xarray/core/variable.py", "file_name": "variable.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2235, "end_line": 2269, "span_ids": ["assert_unique_multiindex_level_names"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_unique_multiindex_level_names(variables):\n \"\"\"Check for uniqueness of MultiIndex level names in all given\n variables.\n\n Not public API. Used for checking consistency of DataArray and Dataset\n objects.\n \"\"\"\n level_names = defaultdict(list)\n all_level_names = set()\n for var_name, var in variables.items():\n if isinstance(var._data, PandasIndexAdapter):\n idx_level_names = var.to_index_variable().level_names\n if idx_level_names is not None:\n for n in idx_level_names:\n level_names[n].append(\"%r (%s)\" % (n, var_name))\n if idx_level_names:\n all_level_names.update(idx_level_names)\n\n for k, v in level_names.items():\n if k in variables:\n v.append(\"(%s)\" % k)\n\n duplicate_names = [v for v in level_names.values() if len(v) > 1]\n if duplicate_names:\n conflict_str = \"\\n\".join([\", \".join(v) for v in duplicate_names])\n raise ValueError(\"conflicting MultiIndex level name(s):\\n%s\" % conflict_str)\n # Check confliction between level names and dimensions GH:2299\n for k, v in variables.items():\n for d in v.dims:\n if d in all_level_names:\n raise ValueError(\n \"conflicting level / dimension names. {} \"\n \"already exists as a level name.\".format(d)\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/__init__.py__": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/__init__.py__", "embedding": null, "metadata": {"file_path": "xarray/plot/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 15, "span_ids": ["impl", "imports"], "tokens": 83}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .facetgrid import FacetGrid\nfrom .plot import contour, contourf, hist, imshow, line, pcolormesh, plot, step\n\n__all__ = [\n \"plot\",\n \"line\",\n \"step\",\n \"contour\",\n \"contourf\",\n \"hist\",\n \"imshow\",\n \"pcolormesh\",\n \"FacetGrid\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_functools__infer_meta_data.return._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_functools__infer_meta_data.return._", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 76, "span_ids": ["_infer_meta_data", "impl", "imports"], "tokens": 518}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..core.alignment import broadcast\nfrom .facetgrid import _easy_facetgrid\nfrom .utils import (\n _add_colorbar,\n _is_numeric,\n _process_cmap_cbar_kwargs,\n get_axis,\n label_from_attrs,\n)\n\n# copied from seaborn\n_MARKERSIZE_RANGE = np.array([18.0, 72.0])\n\n\ndef _infer_meta_data(ds, x, y, hue, hue_style, add_guide):\n dvars = set(ds.variables.keys())\n error_msg = \" must be one of ({0:s})\".format(\", \".join(dvars))\n\n if x not in dvars:\n raise ValueError(\"x\" + error_msg)\n\n if y not in dvars:\n raise ValueError(\"y\" + error_msg)\n\n if hue is not None and hue not in dvars:\n raise ValueError(\"hue\" + error_msg)\n\n if hue:\n hue_is_numeric = _is_numeric(ds[hue].values)\n\n if hue_style is None:\n hue_style = \"continuous\" if hue_is_numeric else \"discrete\"\n\n if not hue_is_numeric and (hue_style == \"continuous\"):\n raise ValueError(\n \"Cannot create a colorbar for a non numeric\" \" coordinate: \" + hue\n )\n\n if add_guide is None or add_guide is True:\n add_colorbar = True if hue_style == \"continuous\" else False\n add_legend = True if hue_style == \"discrete\" else False\n else:\n add_colorbar = False\n add_legend = False\n else:\n if add_guide is True:\n raise ValueError(\"Cannot set add_guide when hue is None.\")\n add_legend = False\n add_colorbar = False\n\n if hue_style is not None and hue_style not in [\"discrete\", \"continuous\"]:\n raise ValueError(\n \"hue_style must be either None, 'discrete' \" \"or 'continuous'.\"\n )\n\n if hue:\n hue_label = label_from_attrs(ds[hue])\n hue = ds[hue]\n else:\n hue_label = None\n hue = None\n\n return {\n \"add_colorbar\": add_colorbar,\n \"add_legend\": add_legend,\n \"hue_label\": hue_label,\n \"hue_style\": hue_style,\n \"xlabel\": label_from_attrs(ds[x]),\n \"ylabel\": label_from_attrs(ds[y]),\n \"hue\": hue,\n }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__infer_scatter_data__infer_scatter_data.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__infer_scatter_data__infer_scatter_data.return.data", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 79, "end_line": 107, "span_ids": ["_infer_scatter_data"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_scatter_data(ds, x, y, hue, markersize, size_norm, size_mapping=None):\n\n broadcast_keys = [\"x\", \"y\"]\n to_broadcast = [ds[x], ds[y]]\n if hue:\n to_broadcast.append(ds[hue])\n broadcast_keys.append(\"hue\")\n if markersize:\n to_broadcast.append(ds[markersize])\n broadcast_keys.append(\"size\")\n\n broadcasted = dict(zip(broadcast_keys, broadcast(*to_broadcast)))\n\n data = {\"x\": broadcasted[\"x\"], \"y\": broadcasted[\"y\"], \"hue\": None, \"sizes\": None}\n\n if hue:\n data[\"hue\"] = broadcasted[\"hue\"]\n\n if markersize:\n size = broadcasted[\"size\"]\n\n if size_mapping is None:\n size_mapping = _parse_size(size, size_norm)\n\n data[\"sizes\"] = size.copy(\n data=np.reshape(size_mapping.loc[size.values.ravel()].values, size.shape)\n )\n\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_None_1__Dataset_PlotMethods.__call__.raise_ValueError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_None_1__Dataset_PlotMethods.__call__.raise_ValueError_", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 164, "span_ids": ["_Dataset_PlotMethods.__call__", "_parse_size", "_infer_scatter_data", "_Dataset_PlotMethods"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# copied from seaborn\ndef _parse_size(data, norm):\n\n import matplotlib as mpl\n\n if data is None:\n return None\n\n data = data.values.flatten()\n\n if not _is_numeric(data):\n levels = np.unique(data)\n numbers = np.arange(1, 1 + len(levels))[::-1]\n else:\n levels = numbers = np.sort(np.unique(data))\n\n min_width, max_width = _MARKERSIZE_RANGE\n # width_range = min_width, max_width\n\n if norm is None:\n norm = mpl.colors.Normalize()\n elif isinstance(norm, tuple):\n norm = mpl.colors.Normalize(*norm)\n elif not isinstance(norm, mpl.colors.Normalize):\n err = \"``size_norm`` must be None, tuple, \" \"or Normalize object.\"\n raise ValueError(err)\n\n norm.clip = True\n if not norm.scaled():\n norm(np.asarray(numbers))\n # limits = norm.vmin, norm.vmax\n\n scl = norm(numbers)\n widths = np.asarray(min_width + scl * (max_width - min_width))\n if scl.mask.any():\n widths[scl.mask] = 0\n sizes = dict(zip(levels, widths))\n\n return pd.Series(sizes)\n\n\nclass _Dataset_PlotMethods(object):\n \"\"\"\n Enables use of xarray.plot functions as attributes on a Dataset.\n For example, Dataset.plot.scatter\n \"\"\"\n\n def __init__(self, dataset):\n self._ds = dataset\n\n def __call__(self, *args, **kwargs):\n raise ValueError(\n \"Dataset.plot cannot be called directly. Use \"\n \"an explicit plot method, e.g. ds.plot.scatter(...)\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot__dsplot.commondoc._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot__dsplot.commondoc._", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 167, "end_line": 243, "span_ids": ["_dsplot"], "tokens": 931}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dsplot(plotfunc):\n commondoc = \"\"\"\n Parameters\n ----------\n\n ds : Dataset\n x, y : string\n Variable names for x, y axis.\n hue: str, optional\n Variable by which to color scattered points\n hue_style: str, optional\n Can be either 'discrete' (legend) or 'continuous' (color bar).\n markersize: str, optional (scatter only)\n Variably by which to vary size of scattered points\n size_norm: optional\n Either None or 'Norm' instance to normalize the 'markersize' variable.\n add_guide: bool, optional\n Add a guide that depends on hue_style\n - for \"discrete\", build a legend.\n This is the default for non-numeric `hue` variables.\n - for \"continuous\", build a colorbar\n row : string, optional\n If passed, make row faceted plots on this dimension name\n col : string, optional\n If passed, make column faceted plots on this dimension name\n col_wrap : integer, optional\n Use together with ``col`` to wrap faceted plots\n ax : matplotlib axes, optional\n If None, uses the current axis. Not applicable when using facets.\n subplot_kws : dict, optional\n Dictionary of keyword arguments for matplotlib subplots. Only applies\n to FacetGrid plotting.\n aspect : scalar, optional\n Aspect ratio of plot, so that ``aspect * size`` gives the width in\n inches. Only used if a ``size`` is provided.\n size : scalar, optional\n If provided, create a new figure for the plot with the given size.\n Height (in inches) of each plot. See also: ``aspect``.\n norm : ``matplotlib.colors.Normalize`` instance, optional\n If the ``norm`` has vmin or vmax specified, the corresponding kwarg\n must be None.\n vmin, vmax : floats, optional\n Values to anchor the colormap, otherwise they are inferred from the\n data and other keyword arguments. When a diverging dataset is inferred,\n setting one of these values will fix the other by symmetry around\n ``center``. Setting both values prevents use of a diverging colormap.\n If discrete levels are provided as an explicit list, both of these\n values are ignored.\n cmap : matplotlib colormap name or object, optional\n The mapping from data values to color space. If not provided, this\n will be either be ``viridis`` (if the function infers a sequential\n dataset) or ``RdBu_r`` (if the function infers a diverging dataset).\n When `Seaborn` is installed, ``cmap`` may also be a `seaborn`\n color palette. If ``cmap`` is seaborn color palette and the plot type\n is not ``contour`` or ``contourf``, ``levels`` must also be specified.\n colors : discrete colors to plot, optional\n A single color or a list of colors. If the plot type is not ``contour``\n or ``contourf``, the ``levels`` argument is required.\n center : float, optional\n The value at which to center the colormap. Passing this value implies\n use of a diverging colormap. Setting it to ``False`` prevents use of a\n diverging colormap.\n robust : bool, optional\n If True and ``vmin`` or ``vmax`` are absent, the colormap range is\n computed with 2nd and 98th percentiles instead of the extreme values.\n extend : {'neither', 'both', 'min', 'max'}, optional\n How to draw arrows extending the colorbar beyond its limits. If not\n provided, extend is inferred from vmin, vmax and the data limits.\n levels : int or list-like object, optional\n Split the colormap (cmap) into discrete color intervals. If an integer\n is provided, \"nice\" levels are chosen based on the data range: this can\n imply that the final number of levels is not exactly the expected one.\n Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to\n setting ``levels=np.linspace(vmin, vmax, N)``.\n **kwargs : optional\n Additional keyword arguments to matplotlib\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot._Build_on_the_original_d__dsplot.newplotfunc.return.primitive": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot._Build_on_the_original_d__dsplot.newplotfunc.return.primitive", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 245, "end_line": 351, "span_ids": ["_dsplot"], "tokens": 770}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dsplot(plotfunc):\n\n # Build on the original docstring\n plotfunc.__doc__ = \"%s\\n%s\" % (plotfunc.__doc__, commondoc)\n\n @functools.wraps(plotfunc)\n def newplotfunc(\n ds,\n x=None,\n y=None,\n hue=None,\n hue_style=None,\n col=None,\n row=None,\n ax=None,\n figsize=None,\n size=None,\n col_wrap=None,\n sharex=True,\n sharey=True,\n aspect=None,\n subplot_kws=None,\n add_guide=None,\n cbar_kwargs=None,\n cbar_ax=None,\n vmin=None,\n vmax=None,\n norm=None,\n infer_intervals=None,\n center=None,\n levels=None,\n robust=None,\n colors=None,\n extend=None,\n cmap=None,\n **kwargs\n ):\n\n _is_facetgrid = kwargs.pop(\"_is_facetgrid\", False)\n if _is_facetgrid: # facetgrid call\n meta_data = kwargs.pop(\"meta_data\")\n else:\n meta_data = _infer_meta_data(ds, x, y, hue, hue_style, add_guide)\n\n hue_style = meta_data[\"hue_style\"]\n\n # handle facetgrids first\n if col or row:\n allargs = locals().copy()\n allargs[\"plotfunc\"] = globals()[plotfunc.__name__]\n allargs[\"data\"] = ds\n # TODO dcherian: why do I need to remove kwargs?\n for arg in [\"meta_data\", \"kwargs\", \"ds\"]:\n del allargs[arg]\n\n return _easy_facetgrid(kind=\"dataset\", **allargs, **kwargs)\n\n figsize = kwargs.pop(\"figsize\", None)\n ax = get_axis(figsize, size, aspect, ax)\n\n if hue_style == \"continuous\" and hue is not None:\n if _is_facetgrid:\n cbar_kwargs = meta_data[\"cbar_kwargs\"]\n cmap_params = meta_data[\"cmap_params\"]\n else:\n cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(\n plotfunc, ds[hue].values, **locals()\n )\n\n # subset that can be passed to scatter, hist2d\n cmap_params_subset = dict(\n (vv, cmap_params[vv]) for vv in [\"vmin\", \"vmax\", \"norm\", \"cmap\"]\n )\n\n else:\n cmap_params_subset = {}\n\n primitive = plotfunc(\n ds=ds,\n x=x,\n y=y,\n hue=hue,\n hue_style=hue_style,\n ax=ax,\n cmap_params=cmap_params_subset,\n **kwargs\n )\n\n if _is_facetgrid: # if this was called from Facetgrid.map_dataset,\n return primitive # finish here. Else, make labels\n\n if meta_data.get(\"xlabel\", None):\n ax.set_xlabel(meta_data.get(\"xlabel\"))\n if meta_data.get(\"ylabel\", None):\n ax.set_ylabel(meta_data.get(\"ylabel\"))\n\n if meta_data[\"add_legend\"]:\n ax.legend(\n handles=primitive,\n labels=list(meta_data[\"hue\"].values),\n title=meta_data.get(\"hue_label\", None),\n )\n if meta_data[\"add_colorbar\"]:\n cbar_kwargs = {} if cbar_kwargs is None else cbar_kwargs\n if \"label\" not in cbar_kwargs:\n cbar_kwargs[\"label\"] = meta_data.get(\"hue_label\", None)\n _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params)\n\n return primitive\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot.plotmethod__dsplot.return.newplotfunc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py__dsplot.plotmethod__dsplot.return.newplotfunc", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 353, "end_line": 401, "span_ids": ["_dsplot"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _dsplot(plotfunc):\n # ... other code\n\n @functools.wraps(newplotfunc)\n def plotmethod(\n _PlotMethods_obj,\n x=None,\n y=None,\n hue=None,\n hue_style=None,\n col=None,\n row=None,\n ax=None,\n figsize=None,\n col_wrap=None,\n sharex=True,\n sharey=True,\n aspect=None,\n size=None,\n subplot_kws=None,\n add_guide=None,\n cbar_kwargs=None,\n cbar_ax=None,\n vmin=None,\n vmax=None,\n norm=None,\n infer_intervals=None,\n center=None,\n levels=None,\n robust=None,\n colors=None,\n extend=None,\n cmap=None,\n **kwargs\n ):\n \"\"\"\n The method should have the same signature as the function.\n\n This just makes the method work on Plotmethods objects,\n and passes all the other arguments straight through.\n \"\"\"\n allargs = locals()\n allargs[\"ds\"] = _PlotMethods_obj._ds\n allargs.update(kwargs)\n for arg in [\"_PlotMethods_obj\", \"newplotfunc\", \"kwargs\"]:\n del allargs[arg]\n return newplotfunc(**allargs)\n\n # Add to class _PlotMethods\n setattr(_Dataset_PlotMethods, plotmethod.__name__, plotmethod)\n\n return newplotfunc", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_scatter_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/dataset_plot.py_scatter_", "embedding": null, "metadata": {"file_path": "xarray/plot/dataset_plot.py", "file_name": "dataset_plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 404, "end_line": 454, "span_ids": ["scatter"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_dsplot\ndef scatter(ds, x, y, ax, **kwargs):\n \"\"\"\n Scatter Dataset data variables against each other.\n \"\"\"\n\n if \"add_colorbar\" in kwargs or \"add_legend\" in kwargs:\n raise ValueError(\n \"Dataset.plot.scatter does not accept \"\n \"'add_colorbar' or 'add_legend'. \"\n \"Use 'add_guide' instead.\"\n )\n\n cmap_params = kwargs.pop(\"cmap_params\")\n hue = kwargs.pop(\"hue\")\n hue_style = kwargs.pop(\"hue_style\")\n markersize = kwargs.pop(\"markersize\", None)\n size_norm = kwargs.pop(\"size_norm\", None)\n size_mapping = kwargs.pop(\"size_mapping\", None) # set by facetgrid\n\n # need to infer size_mapping with full dataset\n data = _infer_scatter_data(ds, x, y, hue, markersize, size_norm, size_mapping)\n\n if hue_style == \"discrete\":\n primitive = []\n for label in np.unique(data[\"hue\"].values):\n mask = data[\"hue\"] == label\n if data[\"sizes\"] is not None:\n kwargs.update(s=data[\"sizes\"].where(mask, drop=True).values.flatten())\n\n primitive.append(\n ax.scatter(\n data[\"x\"].where(mask, drop=True).values.flatten(),\n data[\"y\"].where(mask, drop=True).values.flatten(),\n label=label,\n **kwargs\n )\n )\n\n elif hue is None or hue_style == \"continuous\":\n if data[\"sizes\"] is not None:\n kwargs.update(s=data[\"sizes\"].values.ravel())\n if data[\"hue\"] is not None:\n kwargs.update(c=data[\"hue\"].values.ravel())\n\n primitive = ax.scatter(\n data[\"x\"].values.ravel(), data[\"y\"].values.ravel(), **cmap_params, **kwargs\n )\n\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_functools__nicetitle.return.title": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_functools__nicetitle.return.title", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 32, "span_ids": ["impl", "_nicetitle", "imports"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import functools\nimport itertools\nimport warnings\n\nimport numpy as np\n\nfrom ..core.formatting import format_item\nfrom .utils import (\n _infer_xy_labels,\n _process_cmap_cbar_kwargs,\n import_matplotlib_pyplot,\n label_from_attrs,\n)\n\n# Overrides axes.labelsize, xtick.major.size, ytick.major.size\n# from mpl.rcParams\n_FONTSIZE = \"small\"\n# For major ticks on x, y axes\n_NTICKS = 5\n\n\ndef _nicetitle(coord, value, maxchar, template):\n \"\"\"\n Put coord, value in template and truncate at maxchar\n \"\"\"\n prettyvalue = format_item(value, quote_strings=False)\n title = template.format(coord=coord, value=prettyvalue)\n\n if len(title) > maxchar:\n title = title[: (maxchar - 3)] + \"...\"\n\n return title", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid_FacetGrid._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid_FacetGrid._", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 35, "end_line": 70, "span_ids": ["FacetGrid"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n \"\"\"\n Initialize the matplotlib figure and FacetGrid object.\n\n The :class:`FacetGrid` is an object that links a xarray DataArray to\n a matplotlib figure with a particular structure.\n\n In particular, :class:`FacetGrid` is used to draw plots with multiple\n Axes where each Axes shows the same relationship conditioned on\n different levels of some dimension. It's possible to condition on up to\n two variables by assigning variables to the rows and columns of the\n grid.\n\n The general approach to plotting here is called \"small multiples\",\n where the same kind of plot is repeated multiple times, and the\n specific use of small multiples to display the same relationship\n conditioned on one ore more other variables is often called a \"trellis\n plot\".\n\n The basic workflow is to initialize the :class:`FacetGrid` object with\n the DataArray and the variable names that are used to structure the grid.\n Then plotting functions can be applied to each subset by calling\n :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`.\n\n Attributes\n ----------\n axes : numpy object array\n Contains axes in corresponding position, as returned from\n plt.subplots\n fig : matplotlib.Figure\n The figure containing all the axes\n name_dicts : numpy object array\n Contains dictionaries mapping coordinate names to values. None is\n used as a sentinel value for axes which should remain empty, ie.\n sometimes the bottom right grid\n \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.__init___FacetGrid.__init__.self._finalized.False": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.__init___FacetGrid.__init__.self._finalized.False", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 72, "end_line": 207, "span_ids": ["FacetGrid"], "tokens": 1034}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def __init__(\n self,\n data,\n col=None,\n row=None,\n col_wrap=None,\n sharex=True,\n sharey=True,\n figsize=None,\n aspect=1,\n size=3,\n subplot_kws=None,\n ):\n \"\"\"\n Parameters\n ----------\n data : DataArray\n xarray DataArray to be plotted\n row, col : strings\n Dimesion names that define subsets of the data, which will be drawn\n on separate facets in the grid.\n col_wrap : int, optional\n \"Wrap\" the column variable at this width, so that the column facets\n sharex : bool, optional\n If true, the facets will share x axes\n sharey : bool, optional\n If true, the facets will share y axes\n figsize : tuple, optional\n A tuple (width, height) of the figure in inches.\n If set, overrides ``size`` and ``aspect``.\n aspect : scalar, optional\n Aspect ratio of each facet, so that ``aspect * size`` gives the\n width of each facet in inches\n size : scalar, optional\n Height (in inches) of each facet. See also: ``aspect``\n subplot_kws : dict, optional\n Dictionary of keyword arguments for matplotlib subplots\n\n \"\"\"\n\n plt = import_matplotlib_pyplot()\n\n # Handle corner case of nonunique coordinates\n rep_col = col is not None and not data[col].to_index().is_unique\n rep_row = row is not None and not data[row].to_index().is_unique\n if rep_col or rep_row:\n raise ValueError(\n \"Coordinates used for faceting cannot \"\n \"contain repeated (nonunique) values.\"\n )\n\n # single_group is the grouping variable, if there is exactly one\n if col and row:\n single_group = False\n nrow = len(data[row])\n ncol = len(data[col])\n nfacet = nrow * ncol\n if col_wrap is not None:\n warnings.warn(\"Ignoring col_wrap since both col and row \" \"were passed\")\n elif row and not col:\n single_group = row\n elif not row and col:\n single_group = col\n else:\n raise ValueError(\"Pass a coordinate name as an argument for row or col\")\n\n # Compute grid shape\n if single_group:\n nfacet = len(data[single_group])\n if col:\n # idea - could add heuristic for nice shapes like 3x4\n ncol = nfacet\n if row:\n ncol = 1\n if col_wrap is not None:\n # Overrides previous settings\n ncol = col_wrap\n nrow = int(np.ceil(nfacet / ncol))\n\n # Set the subplot kwargs\n subplot_kws = {} if subplot_kws is None else subplot_kws\n\n if figsize is None:\n # Calculate the base figure size with extra horizontal space for a\n # colorbar\n cbar_space = 1\n figsize = (ncol * size * aspect + cbar_space, nrow * size)\n\n fig, axes = plt.subplots(\n nrow,\n ncol,\n sharex=sharex,\n sharey=sharey,\n squeeze=False,\n figsize=figsize,\n subplot_kw=subplot_kws,\n )\n\n # Set up the lists of names for the row and column facet variables\n col_names = list(data[col].values) if col else []\n row_names = list(data[row].values) if row else []\n\n if single_group:\n full = [{single_group: x} for x in data[single_group].values]\n empty = [None for x in range(nrow * ncol - len(full))]\n name_dicts = full + empty\n else:\n rowcols = itertools.product(row_names, col_names)\n name_dicts = [{row: r, col: c} for r, c in rowcols]\n\n name_dicts = np.array(name_dicts).reshape(nrow, ncol)\n\n # Set up the class attributes\n # ---------------------------\n\n # First the public API\n self.data = data\n self.name_dicts = name_dicts\n self.fig = fig\n self.axes = axes\n self.row_names = row_names\n self.col_names = col_names\n self.figlegend = None\n\n # Next the private variables\n self._single_group = single_group\n self._nrow = nrow\n self._row_var = row\n self._ncol = ncol\n self._col_var = col\n self._col_wrap = col_wrap\n self._x_var = None\n self._y_var = None\n self._cmap_extend = None\n self._mappables = []\n self._finalized = False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid._left_axes_FacetGrid.map_dataarray.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid._left_axes_FacetGrid.map_dataarray.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 209, "end_line": 278, "span_ids": ["FacetGrid.map_dataarray", "FacetGrid._left_axes", "FacetGrid._bottom_axes"], "tokens": 488}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n @property\n def _left_axes(self):\n return self.axes[:, 0]\n\n @property\n def _bottom_axes(self):\n return self.axes[-1, :]\n\n def map_dataarray(self, func, x, y, **kwargs):\n \"\"\"\n Apply a plotting function to a 2d facet's subset of the data.\n\n This is more convenient and less general than ``FacetGrid.map``\n\n Parameters\n ----------\n func : callable\n A plotting function with the same signature as a 2d xarray\n plotting method such as `xarray.plot.imshow`\n x, y : string\n Names of the coordinates to plot on x, y axes\n kwargs :\n additional keyword arguments to func\n\n Returns\n -------\n self : FacetGrid object\n\n \"\"\"\n\n if kwargs.get(\"cbar_ax\", None) is not None:\n raise ValueError(\"cbar_ax not supported by FacetGrid.\")\n\n cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(\n func, self.data.values, **kwargs\n )\n\n self._cmap_extend = cmap_params.get(\"extend\")\n\n # Order is important\n func_kwargs = {\n k: v\n for k, v in kwargs.items()\n if k not in {\"cmap\", \"colors\", \"cbar_kwargs\", \"levels\"}\n }\n func_kwargs.update(cmap_params)\n func_kwargs.update({\"add_colorbar\": False, \"add_labels\": False})\n\n # Get x, y labels for the first subplot\n x, y = _infer_xy_labels(\n darray=self.data.loc[self.name_dicts.flat[0]],\n x=x,\n y=y,\n imshow=func.__name__ == \"imshow\",\n rgb=kwargs.get(\"rgb\", None),\n )\n\n for d, ax in zip(self.name_dicts.flat, self.axes.flat):\n # None is the sentinel value\n if d is not None:\n subset = self.data.loc[d]\n mappable = func(subset, x=x, y=y, ax=ax, **func_kwargs)\n self._mappables.append(mappable)\n\n self._finalize_grid(x, y)\n\n if kwargs.get(\"add_colorbar\", True):\n self.add_colorbar(**cbar_kwargs)\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_dataarray_line_FacetGrid.map_dataarray_line.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_dataarray_line_FacetGrid.map_dataarray_line.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 280, "end_line": 312, "span_ids": ["FacetGrid.map_dataarray_line"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def map_dataarray_line(\n self, func, x, y, hue, add_legend=True, _labels=None, **kwargs\n ):\n from .plot import _infer_line_data\n\n for d, ax in zip(self.name_dicts.flat, self.axes.flat):\n # None is the sentinel value\n if d is not None:\n subset = self.data.loc[d]\n mappable = func(\n subset,\n x=x,\n y=y,\n ax=ax,\n hue=hue,\n add_legend=False,\n _labels=False,\n **kwargs\n )\n self._mappables.append(mappable)\n\n _, _, hueplt, xlabel, ylabel, huelabel = _infer_line_data(\n darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, hue=hue\n )\n\n self._hue_var = hueplt\n self._hue_label = huelabel\n self._finalize_grid(xlabel, ylabel)\n\n if add_legend and hueplt is not None and huelabel is not None:\n self.add_legend()\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_dataset_FacetGrid.map_dataset.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_dataset_FacetGrid.map_dataset.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 314, "end_line": 358, "span_ids": ["FacetGrid.map_dataset"], "tokens": 420}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def map_dataset(\n self, func, x=None, y=None, hue=None, hue_style=None, add_guide=None, **kwargs\n ):\n from .dataset_plot import _infer_meta_data, _parse_size\n\n kwargs[\"add_guide\"] = False\n kwargs[\"_is_facetgrid\"] = True\n\n if kwargs.get(\"markersize\", None):\n kwargs[\"size_mapping\"] = _parse_size(\n self.data[kwargs[\"markersize\"]], kwargs.pop(\"size_norm\", None)\n )\n\n meta_data = _infer_meta_data(self.data, x, y, hue, hue_style, add_guide)\n kwargs[\"meta_data\"] = meta_data\n\n if hue and meta_data[\"hue_style\"] == \"continuous\":\n cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(\n func, self.data[hue].values, **kwargs\n )\n kwargs[\"meta_data\"][\"cmap_params\"] = cmap_params\n kwargs[\"meta_data\"][\"cbar_kwargs\"] = cbar_kwargs\n\n for d, ax in zip(self.name_dicts.flat, self.axes.flat):\n # None is the sentinel value\n if d is not None:\n subset = self.data.loc[d]\n maybe_mappable = func(\n ds=subset, x=x, y=y, hue=hue, hue_style=hue_style, ax=ax, **kwargs\n )\n # TODO: this is needed to get legends to work.\n # but maybe_mappable is a list in that case :/\n self._mappables.append(maybe_mappable)\n\n self._finalize_grid(meta_data[\"xlabel\"], meta_data[\"ylabel\"])\n\n if hue:\n self._hue_label = meta_data.pop(\"hue_label\", None)\n if meta_data[\"add_legend\"]:\n self._hue_var = meta_data[\"hue\"]\n self.add_legend()\n elif meta_data[\"add_colorbar\"]:\n self.add_colorbar(label=self._hue_label, **cbar_kwargs)\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid._finalize_grid_FacetGrid.add_legend.self_fig_subplots_adjust_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid._finalize_grid_FacetGrid.add_legend.self_fig_subplots_adjust_", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 360, "end_line": 402, "span_ids": ["FacetGrid._finalize_grid", "FacetGrid.add_legend"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def _finalize_grid(self, *axlabels):\n \"\"\"Finalize the annotations and layout.\"\"\"\n if not self._finalized:\n self.set_axis_labels(*axlabels)\n self.set_titles()\n self.fig.tight_layout()\n\n for ax, namedict in zip(self.axes.flat, self.name_dicts.flat):\n if namedict is None:\n ax.set_visible(False)\n\n self._finalized = True\n\n def add_legend(self, **kwargs):\n figlegend = self.fig.legend(\n handles=self._mappables[-1],\n labels=list(self._hue_var.values),\n title=self._hue_label,\n loc=\"center right\",\n **kwargs\n )\n\n self.figlegend = figlegend\n # Draw the plot to set the bounding boxes correctly\n self.fig.draw(self.fig.canvas.get_renderer())\n\n # Calculate and set the new width of the figure so the legend fits\n legend_width = figlegend.get_window_extent().width / self.fig.dpi\n figure_width = self.fig.get_figwidth()\n self.fig.set_figwidth(figure_width + legend_width)\n\n # Draw the plot again to get the new transformations\n self.fig.draw(self.fig.canvas.get_renderer())\n\n # Now calculate how much space we need on the right side\n legend_width = figlegend.get_window_extent().width / self.fig.dpi\n space_needed = legend_width / (figure_width + legend_width) + 0.02\n # margin = .01\n # _space_needed = margin + space_needed\n right = 1 - space_needed\n\n # Place the subplot axes to give space for the legend\n self.fig.subplots_adjust(right=right)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.add_colorbar_FacetGrid.set_axis_labels.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.add_colorbar_FacetGrid.set_axis_labels.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 404, "end_line": 433, "span_ids": ["FacetGrid.set_axis_labels", "FacetGrid.add_colorbar"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def add_colorbar(self, **kwargs):\n \"\"\"Draw a colorbar\n \"\"\"\n kwargs = kwargs.copy()\n if self._cmap_extend is not None:\n kwargs.setdefault(\"extend\", self._cmap_extend)\n if \"label\" not in kwargs:\n kwargs.setdefault(\"label\", label_from_attrs(self.data))\n self.cbar = self.fig.colorbar(\n self._mappables[-1], ax=list(self.axes.flat), **kwargs\n )\n return self\n\n def set_axis_labels(self, x_var=None, y_var=None):\n \"\"\"Set axis labels on the left column and bottom row of the grid.\"\"\"\n if x_var is not None:\n if x_var in self.data.coords:\n self._x_var = x_var\n self.set_xlabels(label_from_attrs(self.data[x_var]))\n else:\n # x_var is a string\n self.set_xlabels(x_var)\n\n if y_var is not None:\n if y_var in self.data.coords:\n self._y_var = y_var\n self.set_ylabels(label_from_attrs(self.data[y_var]))\n else:\n self.set_ylabels(y_var)\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_xlabels_FacetGrid.set_ylabels.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_xlabels_FacetGrid.set_ylabels.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 435, "end_line": 449, "span_ids": ["FacetGrid.set_xlabels", "FacetGrid.set_ylabels"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def set_xlabels(self, label=None, **kwargs):\n \"\"\"Label the x axis on the bottom row of the grid.\"\"\"\n if label is None:\n label = label_from_attrs(self.data[self._x_var])\n for ax in self._bottom_axes:\n ax.set_xlabel(label, **kwargs)\n return self\n\n def set_ylabels(self, label=None, **kwargs):\n \"\"\"Label the y axis on the left column of the grid.\"\"\"\n if label is None:\n label = label_from_attrs(self.data[self._y_var])\n for ax in self._left_axes:\n ax.set_ylabel(label, **kwargs)\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_titles_FacetGrid.set_titles.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_titles_FacetGrid.set_titles.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 451, "end_line": 502, "span_ids": ["FacetGrid.set_titles"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def set_titles(self, template=\"{coord} = {value}\", maxchar=30, size=None, **kwargs):\n \"\"\"\n Draw titles either above each facet or on the grid margins.\n\n Parameters\n ----------\n template : string\n Template for plot titles containing {coord} and {value}\n maxchar : int\n Truncate titles at maxchar\n kwargs : keyword args\n additional arguments to matplotlib.text\n\n Returns\n -------\n self: FacetGrid object\n\n \"\"\"\n import matplotlib as mpl\n\n if size is None:\n size = mpl.rcParams[\"axes.labelsize\"]\n\n nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template)\n\n if self._single_group:\n for d, ax in zip(self.name_dicts.flat, self.axes.flat):\n # Only label the ones with data\n if d is not None:\n coord, value = list(d.items()).pop()\n title = nicetitle(coord, value, maxchar=maxchar)\n ax.set_title(title, size=size, **kwargs)\n else:\n # The row titles on the right edge of the grid\n for ax, row_name in zip(self.axes[:, -1], self.row_names):\n title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar)\n ax.annotate(\n title,\n xy=(1.02, 0.5),\n xycoords=\"axes fraction\",\n rotation=270,\n ha=\"left\",\n va=\"center\",\n **kwargs\n )\n\n # The column titles on the top row\n for ax, col_name in zip(self.axes[0, :], self.col_names):\n title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar)\n ax.set_title(title, size=size, **kwargs)\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_ticks_FacetGrid.set_ticks.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.set_ticks_FacetGrid.set_ticks.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 504, "end_line": 534, "span_ids": ["FacetGrid.set_ticks"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE):\n \"\"\"\n Set and control tick behavior\n\n Parameters\n ----------\n max_xticks, max_yticks : int, optional\n Maximum number of labeled ticks to plot on x, y axes\n fontsize : string or int\n Font size as used by matplotlib text\n\n Returns\n -------\n self : FacetGrid object\n\n \"\"\"\n from matplotlib.ticker import MaxNLocator\n\n # Both are necessary\n x_major_locator = MaxNLocator(nbins=max_xticks)\n y_major_locator = MaxNLocator(nbins=max_yticks)\n\n for ax in self.axes.flat:\n ax.xaxis.set_major_locator(x_major_locator)\n ax.yaxis.set_major_locator(y_major_locator)\n for tick in itertools.chain(\n ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks()\n ):\n tick.label1.set_fontsize(fontsize)\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_FacetGrid.map.return.self": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py_FacetGrid.map_FacetGrid.map.return.self", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 536, "end_line": 574, "span_ids": ["FacetGrid.map"], "tokens": 349}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FacetGrid:\n\n def map(self, func, *args, **kwargs):\n \"\"\"\n Apply a plotting function to each facet's subset of the data.\n\n Parameters\n ----------\n func : callable\n A plotting function that takes data and keyword arguments. It\n must plot to the currently active matplotlib Axes and take a\n `color` keyword argument. If faceting on the `hue` dimension,\n it must also take a `label` keyword argument.\n args : strings\n Column names in self.data that identify variables with data to\n plot. The data for each variable is passed to `func` in the\n order the variables are specified in the call.\n kwargs : keyword arguments\n All keyword arguments are passed to the plotting function.\n\n Returns\n -------\n self : FacetGrid object\n\n \"\"\"\n plt = import_matplotlib_pyplot()\n\n for ax, namedict in zip(self.axes.flat, self.name_dicts.flat):\n if namedict is not None:\n data = self.data.loc[namedict]\n plt.sca(ax)\n innerargs = [data[a].values for a in args]\n maybe_mappable = func(*innerargs, **kwargs)\n # TODO: better way to verify that an artist is mappable?\n # https://stackoverflow.com/questions/33023036/is-it-possible-to-detect-if-a-matplotlib-artist-is-a-mappable-suitable-for-use-w#33023522\n if maybe_mappable and hasattr(maybe_mappable, \"autoscale_None\"):\n self._mappables.append(maybe_mappable)\n\n self._finalize_grid(*args[:2])\n\n return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py__easy_facetgrid_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/facetgrid.py__easy_facetgrid_", "embedding": null, "metadata": {"file_path": "xarray/plot/facetgrid.py", "file_name": "facetgrid.py", "file_type": "text/x-python", "category": "implementation", "start_line": 577, "end_line": 630, "span_ids": ["_easy_facetgrid"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _easy_facetgrid(\n data,\n plotfunc,\n kind,\n x=None,\n y=None,\n row=None,\n col=None,\n col_wrap=None,\n sharex=True,\n sharey=True,\n aspect=None,\n size=None,\n subplot_kws=None,\n ax=None,\n figsize=None,\n **kwargs\n):\n \"\"\"\n Convenience method to call xarray.plot.FacetGrid from 2d plotting methods\n\n kwargs are the arguments to 2d plotting method\n \"\"\"\n if ax is not None:\n raise ValueError(\"Can't use axes when making faceted plots.\")\n if aspect is None:\n aspect = 1\n if size is None:\n size = 3\n elif figsize is not None:\n raise ValueError(\"cannot provide both `figsize` and `size` arguments\")\n\n g = FacetGrid(\n data=data,\n col=col,\n row=row,\n col_wrap=col_wrap,\n sharex=sharex,\n sharey=sharey,\n figsize=figsize,\n aspect=aspect,\n size=size,\n subplot_kws=subplot_kws,\n )\n\n if kind == \"line\":\n return g.map_dataarray_line(plotfunc, x, y, **kwargs)\n\n if kind == \"dataarray\":\n return g.map_dataarray(plotfunc, x, y, **kwargs)\n\n if kind == \"dataset\":\n return g.map_dataset(plotfunc, x, y, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py____add_colorbar": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py____add_colorbar", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 30, "span_ids": ["docstring", "imports"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"\nUse this module directly:\n import xarray.plot as xplt\n\nOr use the methods on a DataArray or Dataset:\n DataArray.plot._____\n Dataset.plot._____\n\"\"\"\nimport functools\n\nimport numpy as np\nimport pandas as pd\n\nfrom .facetgrid import _easy_facetgrid\nfrom .utils import (\n _add_colorbar,\n _ensure_plottable,\n _infer_interval_breaks,\n _infer_xy_labels,\n _interval_to_double_bound_points,\n _interval_to_mid_points,\n _process_cmap_cbar_kwargs,\n _rescale_imshow_rgb,\n _resolve_intervals_2dplot,\n _update_axes,\n _valid_other_type,\n get_axis,\n import_matplotlib_pyplot,\n label_from_attrs,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__infer_line_data__infer_line_data.return.xplt_yplt_hueplt_xlabe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__infer_line_data__infer_line_data.return.xplt_yplt_hueplt_xlabe", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 115, "span_ids": ["_infer_line_data"], "tokens": 755}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_line_data(darray, x, y, hue):\n error_msg = \"must be either None or one of ({:s})\".format(\n \", \".join([repr(dd) for dd in darray.dims])\n )\n ndims = len(darray.dims)\n\n if x is not None and x not in darray.dims and x not in darray.coords:\n raise ValueError(\"x \" + error_msg)\n\n if y is not None and y not in darray.dims and y not in darray.coords:\n raise ValueError(\"y \" + error_msg)\n\n if x is not None and y is not None:\n raise ValueError(\"You cannot specify both x and y kwargs\" \"for line plots.\")\n\n if ndims == 1:\n huename = None\n hueplt = None\n huelabel = \"\"\n\n if x is not None:\n xplt = darray[x]\n yplt = darray\n\n elif y is not None:\n xplt = darray\n yplt = darray[y]\n\n else: # Both x & y are None\n dim = darray.dims[0]\n xplt = darray[dim]\n yplt = darray\n\n else:\n if x is None and y is None and hue is None:\n raise ValueError(\"For 2D inputs, please\" \"specify either hue, x or y.\")\n\n if y is None:\n xname, huename = _infer_xy_labels(darray=darray, x=x, y=hue)\n xplt = darray[xname]\n if xplt.ndim > 1:\n if huename in darray.dims:\n otherindex = 1 if darray.dims.index(huename) == 0 else 0\n otherdim = darray.dims[otherindex]\n yplt = darray.transpose(otherdim, huename, transpose_coords=False)\n xplt = xplt.transpose(otherdim, huename, transpose_coords=False)\n else:\n raise ValueError(\n \"For 2D inputs, hue must be a dimension\"\n \" i.e. one of \" + repr(darray.dims)\n )\n\n else:\n xdim, = darray[xname].dims\n huedim, = darray[huename].dims\n yplt = darray.transpose(xdim, huedim)\n\n else:\n yname, huename = _infer_xy_labels(darray=darray, x=y, y=hue)\n yplt = darray[yname]\n if yplt.ndim > 1:\n if huename in darray.dims:\n otherindex = 1 if darray.dims.index(huename) == 0 else 0\n otherdim = darray.dims[otherindex]\n xplt = darray.transpose(otherdim, huename, transpose_coords=False)\n else:\n raise ValueError(\n \"For 2D inputs, hue must be a dimension\"\n \" i.e. one of \" + repr(darray.dims)\n )\n\n else:\n ydim, = darray[yname].dims\n huedim, = darray[huename].dims\n xplt = darray.transpose(ydim, huedim)\n\n huelabel = label_from_attrs(darray[huename])\n hueplt = darray[huename]\n\n xlabel = label_from_attrs(xplt)\n ylabel = label_from_attrs(yplt)\n\n return xplt, yplt, hueplt, xlabel, ylabel, huelabel", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_plot_plot.return.plotfunc_darray_kwargs": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_plot_plot.return.plotfunc_darray_kwargs", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 202, "span_ids": ["plot"], "tokens": 585}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot(\n darray,\n row=None,\n col=None,\n col_wrap=None,\n ax=None,\n hue=None,\n rtol=0.01,\n subplot_kws=None,\n **kwargs\n):\n \"\"\"\n Default plot of DataArray using matplotlib.pyplot.\n\n Calls xarray plotting function based on the dimensions of\n darray.squeeze()\n\n =============== ===========================\n Dimensions Plotting function\n --------------- ---------------------------\n 1 :py:func:`xarray.plot.line`\n 2 :py:func:`xarray.plot.pcolormesh`\n Anything else :py:func:`xarray.plot.hist`\n =============== ===========================\n\n Parameters\n ----------\n darray : DataArray\n row : string, optional\n If passed, make row faceted plots on this dimension name\n col : string, optional\n If passed, make column faceted plots on this dimension name\n hue : string, optional\n If passed, make faceted line plots with hue on this dimension name\n col_wrap : integer, optional\n Use together with ``col`` to wrap faceted plots\n ax : matplotlib axes, optional\n If None, uses the current axis. Not applicable when using facets.\n rtol : number, optional\n Relative tolerance used to determine if the indexes\n are uniformly spaced. Usually a small positive number.\n subplot_kws : dict, optional\n Dictionary of keyword arguments for matplotlib subplots. Only applies\n to FacetGrid plotting.\n **kwargs : optional\n Additional keyword arguments to matplotlib\n\n \"\"\"\n darray = darray.squeeze().compute()\n\n plot_dims = set(darray.dims)\n plot_dims.discard(row)\n plot_dims.discard(col)\n plot_dims.discard(hue)\n\n ndims = len(plot_dims)\n\n error_msg = (\n \"Only 1d and 2d plots are supported for facets in xarray. \"\n \"See the package `Seaborn` for more options.\"\n )\n\n if ndims in [1, 2]:\n if row or col:\n kwargs[\"row\"] = row\n kwargs[\"col\"] = col\n kwargs[\"col_wrap\"] = col_wrap\n kwargs[\"subplot_kws\"] = subplot_kws\n if ndims == 1:\n plotfunc = line\n kwargs[\"hue\"] = hue\n elif ndims == 2:\n if hue:\n plotfunc = line\n kwargs[\"hue\"] = hue\n else:\n plotfunc = pcolormesh\n else:\n if row or col or hue:\n raise ValueError(error_msg)\n plotfunc = hist\n\n kwargs[\"ax\"] = ax\n\n return plotfunc(darray, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__This_function_signature_line.ax.get_axis_figsize_size_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__This_function_signature_line.ax.get_axis_figsize_size_a", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 205, "end_line": 296, "span_ids": ["line", "plot"], "tokens": 771}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# This function signature should not change so that it can use\n# matplotlib format strings\ndef line(\n darray,\n *args,\n row=None,\n col=None,\n figsize=None,\n aspect=None,\n size=None,\n ax=None,\n hue=None,\n x=None,\n y=None,\n xincrease=None,\n yincrease=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n add_legend=True,\n _labels=True,\n **kwargs\n):\n \"\"\"\n Line plot of DataArray index against values\n\n Wraps :func:`matplotlib:matplotlib.pyplot.plot`\n\n Parameters\n ----------\n darray : DataArray\n Must be 1 dimensional\n figsize : tuple, optional\n A tuple (width, height) of the figure in inches.\n Mutually exclusive with ``size`` and ``ax``.\n aspect : scalar, optional\n Aspect ratio of plot, so that ``aspect * size`` gives the width in\n inches. Only used if a ``size`` is provided.\n size : scalar, optional\n If provided, create a new figure for the plot with the given size.\n Height (in inches) of each plot. See also: ``aspect``.\n ax : matplotlib axes object, optional\n Axis on which to plot this figure. By default, use the current axis.\n Mutually exclusive with ``size`` and ``figsize``.\n hue : string, optional\n Dimension or coordinate for which you want multiple lines plotted.\n If plotting against a 2D coordinate, ``hue`` must be a dimension.\n x, y : string, optional\n Dimensions or coordinates for x, y axis.\n Only one of these may be specified.\n The other coordinate plots values from the DataArray on which this\n plot method is called.\n xscale, yscale : 'linear', 'symlog', 'log', 'logit', optional\n Specifies scaling for the x- and y-axes respectively\n xticks, yticks : Specify tick locations for x- and y-axes\n xlim, ylim : Specify x- and y-axes limits\n xincrease : None, True, or False, optional\n Should the values on the x axes be increasing from left to right?\n if None, use the default for the matplotlib function.\n yincrease : None, True, or False, optional\n Should the values on the y axes be increasing from top to bottom?\n if None, use the default for the matplotlib function.\n add_legend : boolean, optional\n Add legend with y axis coordinates (2D inputs only).\n *args, **kwargs : optional\n Additional arguments to matplotlib.pyplot.plot\n \"\"\"\n # Handle facetgrids first\n if row or col:\n allargs = locals().copy()\n allargs.update(allargs.pop(\"kwargs\"))\n allargs.pop(\"darray\")\n return _easy_facetgrid(darray, line, kind=\"line\", **allargs)\n\n ndims = len(darray.dims)\n if ndims > 2:\n raise ValueError(\n \"Line plots are for 1- or 2-dimensional DataArrays. \"\n \"Passed DataArray has {ndims} \"\n \"dimensions\".format(ndims=ndims)\n )\n\n # The allargs dict passed to _easy_facetgrid above contains args\n if args is ():\n args = kwargs.pop(\"args\", ())\n else:\n assert \"args\" not in kwargs\n\n ax = get_axis(figsize, size, aspect, ax)\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_line.xplt_yplt_hueplt_xlabe_line.return.primitive": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_line.xplt_yplt_hueplt_xlabe_line.return.primitive", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 297, "end_line": 350, "span_ids": ["line"], "tokens": 592}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def line(\n darray,\n *args,\n row=None,\n col=None,\n figsize=None,\n aspect=None,\n size=None,\n ax=None,\n hue=None,\n x=None,\n y=None,\n xincrease=None,\n yincrease=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n add_legend=True,\n _labels=True,\n **kwargs\n):\n # ... other code\n xplt, yplt, hueplt, xlabel, ylabel, hue_label = _infer_line_data(darray, x, y, hue)\n\n # Remove pd.Intervals if contained in xplt.values.\n if _valid_other_type(xplt.values, [pd.Interval]):\n # Is it a step plot? (see matplotlib.Axes.step)\n if kwargs.get(\"linestyle\", \"\").startswith(\"steps-\"):\n xplt_val, yplt_val = _interval_to_double_bound_points(\n xplt.values, yplt.values\n )\n # Remove steps-* to be sure that matplotlib is not confused\n kwargs[\"linestyle\"] = (\n kwargs[\"linestyle\"]\n .replace(\"steps-pre\", \"\")\n .replace(\"steps-post\", \"\")\n .replace(\"steps-mid\", \"\")\n )\n if kwargs[\"linestyle\"] == \"\":\n del kwargs[\"linestyle\"]\n else:\n xplt_val = _interval_to_mid_points(xplt.values)\n yplt_val = yplt.values\n xlabel += \"_center\"\n else:\n xplt_val = xplt.values\n yplt_val = yplt.values\n\n _ensure_plottable(xplt_val, yplt_val)\n\n primitive = ax.plot(xplt_val, yplt_val, *args, **kwargs)\n\n if _labels:\n if xlabel is not None:\n ax.set_xlabel(xlabel)\n\n if ylabel is not None:\n ax.set_ylabel(ylabel)\n\n ax.set_title(darray._title_for_slice())\n\n if darray.ndim == 2 and add_legend:\n ax.legend(handles=primitive, labels=list(hueplt.values), title=hue_label)\n\n # Rotate dates on xlabels\n # Do this without calling autofmt_xdate so that x-axes ticks\n # on other subplots (if any) are not deleted.\n # https://stackoverflow.com/questions/17430105/autofmt-xdate-deletes-x-axis-labels-of-all-subplots\n if np.issubdtype(xplt.dtype, np.datetime64):\n for xlabels in ax.get_xticklabels():\n xlabels.set_rotation(30)\n xlabels.set_ha(\"right\")\n\n _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim)\n\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_step_step.return.line_darray_args_lines": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_step_step.return.line_darray_args_lines", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 353, "end_line": 390, "span_ids": ["step"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def step(darray, *args, where=\"pre\", linestyle=None, ls=None, **kwargs):\n \"\"\"\n Step plot of DataArray index against values\n\n Similar to :func:`matplotlib:matplotlib.pyplot.step`\n\n Parameters\n ----------\n where : {'pre', 'post', 'mid'}, optional, default 'pre'\n Define where the steps should be placed:\n - 'pre': The y value is continued constantly to the left from\n every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the\n value ``y[i]``.\n - 'post': The y value is continued constantly to the right from\n every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the\n value ``y[i]``.\n - 'mid': Steps occur half-way between the *x* positions.\n Note that this parameter is ignored if the x coordinate consists of\n :py:func:`pandas.Interval` values, e.g. as a result of\n :py:func:`xarray.Dataset.groupby_bins`. In this case, the actual\n boundaries of the interval are used.\n\n *args, **kwargs : optional\n Additional arguments following :py:func:`xarray.plot.line`\n \"\"\"\n if where not in {\"pre\", \"post\", \"mid\"}:\n raise ValueError(\"'where' argument to step must be \" \"'pre', 'post' or 'mid'\")\n\n if ls is not None:\n if linestyle is None:\n linestyle = ls\n else:\n raise TypeError(\"ls and linestyle are mutually exclusive\")\n if linestyle is None:\n linestyle = \"\"\n linestyle = \"steps-\" + where + linestyle\n\n return line(darray, *args, linestyle=linestyle, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_hist_hist.return.primitive": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_hist_hist.return.primitive", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 393, "end_line": 448, "span_ids": ["hist"], "tokens": 381}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def hist(\n darray,\n figsize=None,\n size=None,\n aspect=None,\n ax=None,\n xincrease=None,\n yincrease=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n **kwargs\n):\n \"\"\"\n Histogram of DataArray\n\n Wraps :func:`matplotlib:matplotlib.pyplot.hist`\n\n Plots N dimensional arrays by first flattening the array.\n\n Parameters\n ----------\n darray : DataArray\n Can be any dimension\n figsize : tuple, optional\n A tuple (width, height) of the figure in inches.\n Mutually exclusive with ``size`` and ``ax``.\n aspect : scalar, optional\n Aspect ratio of plot, so that ``aspect * size`` gives the width in\n inches. Only used if a ``size`` is provided.\n size : scalar, optional\n If provided, create a new figure for the plot with the given size.\n Height (in inches) of each plot. See also: ``aspect``.\n ax : matplotlib axes object, optional\n Axis on which to plot this figure. By default, use the current axis.\n Mutually exclusive with ``size`` and ``figsize``.\n **kwargs : optional\n Additional keyword arguments to matplotlib.pyplot.hist\n\n \"\"\"\n ax = get_axis(figsize, size, aspect, ax)\n\n no_nan = np.ravel(darray.values)\n no_nan = no_nan[pd.notnull(no_nan)]\n\n primitive = ax.hist(no_nan, **kwargs)\n\n ax.set_title(\"Histogram\")\n ax.set_xlabel(label_from_attrs(darray))\n\n _update_axes(ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim)\n\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__MUST_run_before_any_2d___PlotMethods.step.return.step_self__da_args_k": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__MUST_run_before_any_2d___PlotMethods.step.return.step_self__da_args_k", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 451, "end_line": 477, "span_ids": ["_PlotMethods.hist", "_PlotMethods", "_PlotMethods.__call__", "_PlotMethods.line", "_PlotMethods.step", "hist"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# MUST run before any 2d plotting functions are defined since\n# _plot2d decorator adds them as methods here.\nclass _PlotMethods:\n \"\"\"\n Enables use of xarray.plot functions as attributes on a DataArray.\n For example, DataArray.plot.imshow\n \"\"\"\n\n __slots__ = (\"_da\",)\n\n def __init__(self, darray):\n self._da = darray\n\n def __call__(self, **kwargs):\n return plot(self._da, **kwargs)\n\n @functools.wraps(hist)\n def hist(self, ax=None, **kwargs):\n return hist(self._da, ax=ax, **kwargs)\n\n @functools.wraps(line)\n def line(self, *args, **kwargs):\n return line(self._da, *args, **kwargs)\n\n @functools.wraps(step)\n def step(self, *args, **kwargs):\n return step(self._da, *args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d__plot2d.commondoc._": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d__plot2d.commondoc._", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 480, "end_line": 584, "span_ids": ["_plot2d"], "tokens": 1237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _plot2d(plotfunc):\n \"\"\"\n Decorator for common 2d plotting logic\n\n Also adds the 2d plot method to class _PlotMethods\n \"\"\"\n commondoc = \"\"\"\n Parameters\n ----------\n darray : DataArray\n Must be 2 dimensional, unless creating faceted plots\n x : string, optional\n Coordinate for x axis. If None use darray.dims[1]\n y : string, optional\n Coordinate for y axis. If None use darray.dims[0]\n figsize : tuple, optional\n A tuple (width, height) of the figure in inches.\n Mutually exclusive with ``size`` and ``ax``.\n aspect : scalar, optional\n Aspect ratio of plot, so that ``aspect * size`` gives the width in\n inches. Only used if a ``size`` is provided.\n size : scalar, optional\n If provided, create a new figure for the plot with the given size.\n Height (in inches) of each plot. See also: ``aspect``.\n ax : matplotlib axes object, optional\n Axis on which to plot this figure. By default, use the current axis.\n Mutually exclusive with ``size`` and ``figsize``.\n row : string, optional\n If passed, make row faceted plots on this dimension name\n col : string, optional\n If passed, make column faceted plots on this dimension name\n col_wrap : integer, optional\n Use together with ``col`` to wrap faceted plots\n xscale, yscale : 'linear', 'symlog', 'log', 'logit', optional\n Specifies scaling for the x- and y-axes respectively\n xticks, yticks : Specify tick locations for x- and y-axes\n xlim, ylim : Specify x- and y-axes limits\n xincrease : None, True, or False, optional\n Should the values on the x axes be increasing from left to right?\n if None, use the default for the matplotlib function.\n yincrease : None, True, or False, optional\n Should the values on the y axes be increasing from top to bottom?\n if None, use the default for the matplotlib function.\n add_colorbar : Boolean, optional\n Adds colorbar to axis\n add_labels : Boolean, optional\n Use xarray metadata to label axes\n norm : ``matplotlib.colors.Normalize`` instance, optional\n If the ``norm`` has vmin or vmax specified, the corresponding kwarg\n must be None.\n vmin, vmax : floats, optional\n Values to anchor the colormap, otherwise they are inferred from the\n data and other keyword arguments. When a diverging dataset is inferred,\n setting one of these values will fix the other by symmetry around\n ``center``. Setting both values prevents use of a diverging colormap.\n If discrete levels are provided as an explicit list, both of these\n values are ignored.\n cmap : matplotlib colormap name or object, optional\n The mapping from data values to color space. If not provided, this\n will be either be ``viridis`` (if the function infers a sequential\n dataset) or ``RdBu_r`` (if the function infers a diverging dataset).\n When `Seaborn` is installed, ``cmap`` may also be a `seaborn`\n color palette. If ``cmap`` is seaborn color palette and the plot type\n is not ``contour`` or ``contourf``, ``levels`` must also be specified.\n colors : discrete colors to plot, optional\n A single color or a list of colors. If the plot type is not ``contour``\n or ``contourf``, the ``levels`` argument is required.\n center : float, optional\n The value at which to center the colormap. Passing this value implies\n use of a diverging colormap. Setting it to ``False`` prevents use of a\n diverging colormap.\n robust : bool, optional\n If True and ``vmin`` or ``vmax`` are absent, the colormap range is\n computed with 2nd and 98th percentiles instead of the extreme values.\n extend : {'neither', 'both', 'min', 'max'}, optional\n How to draw arrows extending the colorbar beyond its limits. If not\n provided, extend is inferred from vmin, vmax and the data limits.\n levels : int or list-like object, optional\n Split the colormap (cmap) into discrete color intervals. If an integer\n is provided, \"nice\" levels are chosen based on the data range: this can\n imply that the final number of levels is not exactly the expected one.\n Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to\n setting ``levels=np.linspace(vmin, vmax, N)``.\n infer_intervals : bool, optional\n Only applies to pcolormesh. If True, the coordinate intervals are\n passed to pcolormesh. If False, the original coordinates are used\n (this can be useful for certain map projections). The default is to\n always infer intervals, unless the mesh is irregular and plotted on\n a map projection.\n subplot_kws : dict, optional\n Dictionary of keyword arguments for matplotlib subplots. Only applies\n to FacetGrid plotting.\n cbar_ax : matplotlib Axes, optional\n Axes in which to draw the colorbar.\n cbar_kwargs : dict, optional\n Dictionary of keyword arguments to pass to the colorbar.\n **kwargs : optional\n Additional arguments to wrapped matplotlib function\n\n Returns\n -------\n artist :\n The same type of primitive artist that the wrapped matplotlib\n function returns\n \"\"\"\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d._Build_on_the_original_d__plot2d.newplotfunc._xlab_may_be_the_name_of": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d._Build_on_the_original_d__plot2d.newplotfunc._xlab_may_be_the_name_of", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 586, "end_line": 681, "span_ids": ["_plot2d"], "tokens": 779}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _plot2d(plotfunc):\n\n # Build on the original docstring\n plotfunc.__doc__ = \"%s\\n%s\" % (plotfunc.__doc__, commondoc)\n\n @functools.wraps(plotfunc)\n def newplotfunc(\n darray,\n x=None,\n y=None,\n figsize=None,\n size=None,\n aspect=None,\n ax=None,\n row=None,\n col=None,\n col_wrap=None,\n xincrease=True,\n yincrease=True,\n add_colorbar=None,\n add_labels=True,\n vmin=None,\n vmax=None,\n cmap=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n infer_intervals=None,\n colors=None,\n subplot_kws=None,\n cbar_ax=None,\n cbar_kwargs=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n norm=None,\n **kwargs\n ):\n # All 2d plots in xarray share this function signature.\n # Method signature below should be consistent.\n\n # Decide on a default for the colorbar before facetgrids\n if add_colorbar is None:\n add_colorbar = plotfunc.__name__ != \"contour\"\n imshow_rgb = plotfunc.__name__ == \"imshow\" and darray.ndim == (\n 3 + (row is not None) + (col is not None)\n )\n if imshow_rgb:\n # Don't add a colorbar when showing an image with explicit colors\n add_colorbar = False\n # Matplotlib does not support normalising RGB data, so do it here.\n # See eg. https://github.com/matplotlib/matplotlib/pull/10220\n if robust or vmax is not None or vmin is not None:\n darray = _rescale_imshow_rgb(darray, vmin, vmax, robust)\n vmin, vmax, robust = None, None, False\n\n # Handle facetgrids first\n if row or col:\n allargs = locals().copy()\n del allargs[\"darray\"]\n del allargs[\"imshow_rgb\"]\n allargs.update(allargs.pop(\"kwargs\"))\n # Need the decorated plotting function\n allargs[\"plotfunc\"] = globals()[plotfunc.__name__]\n return _easy_facetgrid(darray, kind=\"dataarray\", **allargs)\n\n plt = import_matplotlib_pyplot()\n\n rgb = kwargs.pop(\"rgb\", None)\n if rgb is not None and plotfunc.__name__ != \"imshow\":\n raise ValueError('The \"rgb\" keyword is only valid for imshow()')\n elif rgb is not None and not imshow_rgb:\n raise ValueError(\n 'The \"rgb\" keyword is only valid for imshow()'\n \"with a three-dimensional array (per facet)\"\n )\n\n xlab, ylab = _infer_xy_labels(\n darray=darray, x=x, y=y, imshow=imshow_rgb, rgb=rgb\n )\n\n # better to pass the ndarrays directly to plotting functions\n xval = darray[xlab].values\n yval = darray[ylab].values\n\n # check if we need to broadcast one dimension\n if xval.ndim < yval.ndim:\n xval = np.broadcast_to(xval, yval.shape)\n\n if yval.ndim < xval.ndim:\n yval = np.broadcast_to(yval, xval.shape)\n\n # May need to transpose for correct x, y labels\n # xlab may be the name of a coord, we have to check for dim names\n # ... other code\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.newplotfunc.None_6__plot2d.newplotfunc.if_add_labels_.ax_set_title_darray__titl": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.newplotfunc.None_6__plot2d.newplotfunc.if_add_labels_.ax_set_title_darray__titl", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 682, "end_line": 744, "span_ids": ["_plot2d"], "tokens": 803}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _plot2d(plotfunc):\n\n @functools.wraps(plotfunc)\n def newplotfunc(\n darray,\n x=None,\n y=None,\n figsize=None,\n size=None,\n aspect=None,\n ax=None,\n row=None,\n col=None,\n col_wrap=None,\n xincrease=True,\n yincrease=True,\n add_colorbar=None,\n add_labels=True,\n vmin=None,\n vmax=None,\n cmap=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n infer_intervals=None,\n colors=None,\n subplot_kws=None,\n cbar_ax=None,\n cbar_kwargs=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n norm=None,\n **kwargs\n ):\n # ... other code\n if imshow_rgb:\n # For RGB[A] images, matplotlib requires the color dimension\n # to be last. In Xarray the order should be unimportant, so\n # we transpose to (y, x, color) to make this work.\n yx_dims = (ylab, xlab)\n dims = yx_dims + tuple(d for d in darray.dims if d not in yx_dims)\n if dims != darray.dims:\n darray = darray.transpose(*dims, transpose_coords=True)\n elif darray[xlab].dims[-1] == darray.dims[0]:\n darray = darray.transpose(transpose_coords=True)\n\n # Pass the data as a masked ndarray too\n zval = darray.to_masked_array(copy=False)\n\n # Replace pd.Intervals if contained in xval or yval.\n xplt, xlab_extra = _resolve_intervals_2dplot(xval, plotfunc.__name__)\n yplt, ylab_extra = _resolve_intervals_2dplot(yval, plotfunc.__name__)\n\n _ensure_plottable(xplt, yplt)\n\n cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(\n plotfunc, zval.data, **locals()\n )\n\n if \"contour\" in plotfunc.__name__:\n # extend is a keyword argument only for contour and contourf, but\n # passing it to the colorbar is sufficient for imshow and\n # pcolormesh\n kwargs[\"extend\"] = cmap_params[\"extend\"]\n kwargs[\"levels\"] = cmap_params[\"levels\"]\n # if colors == a single color, matplotlib draws dashed negative\n # contours. we lose this feature if we pass cmap and not colors\n if isinstance(colors, str):\n cmap_params[\"cmap\"] = None\n kwargs[\"colors\"] = colors\n\n if \"pcolormesh\" == plotfunc.__name__:\n kwargs[\"infer_intervals\"] = infer_intervals\n\n if \"imshow\" == plotfunc.__name__ and isinstance(aspect, str):\n # forbid usage of mpl strings\n raise ValueError(\n \"plt.imshow's `aspect` kwarg is not available \" \"in xarray\"\n )\n\n ax = get_axis(figsize, size, aspect, ax)\n primitive = plotfunc(\n xplt,\n yplt,\n zval,\n ax=ax,\n cmap=cmap_params[\"cmap\"],\n vmin=cmap_params[\"vmin\"],\n vmax=cmap_params[\"vmax\"],\n norm=cmap_params[\"norm\"],\n **kwargs\n )\n\n # Label the plot with metadata\n if add_labels:\n ax.set_xlabel(label_from_attrs(darray[xlab], xlab_extra))\n ax.set_ylabel(label_from_attrs(darray[ylab], ylab_extra))\n ax.set_title(darray._title_for_slice())\n # ... other code\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.newplotfunc.if_add_colorbar___plot2d._For_use_as_DataArray_pl": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.newplotfunc.if_add_colorbar___plot2d._For_use_as_DataArray_pl", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 746, "end_line": 775, "span_ids": ["_plot2d"], "tokens": 480}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _plot2d(plotfunc):\n\n @functools.wraps(plotfunc)\n def newplotfunc(\n darray,\n x=None,\n y=None,\n figsize=None,\n size=None,\n aspect=None,\n ax=None,\n row=None,\n col=None,\n col_wrap=None,\n xincrease=True,\n yincrease=True,\n add_colorbar=None,\n add_labels=True,\n vmin=None,\n vmax=None,\n cmap=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n infer_intervals=None,\n colors=None,\n subplot_kws=None,\n cbar_ax=None,\n cbar_kwargs=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n norm=None,\n **kwargs\n ):\n # ... other code\n\n if add_colorbar:\n if add_labels and \"label\" not in cbar_kwargs:\n cbar_kwargs[\"label\"] = label_from_attrs(darray)\n cbar = _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params)\n elif cbar_ax is not None or cbar_kwargs:\n # inform the user about keywords which aren't used\n raise ValueError(\n \"cbar_ax and cbar_kwargs can't be used with \" \"add_colorbar=False.\"\n )\n\n # origin kwarg overrides yincrease\n if \"origin\" in kwargs:\n yincrease = None\n\n _update_axes(\n ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim\n )\n\n # Rotate dates on xlabels\n # Do this without calling autofmt_xdate so that x-axes ticks\n # on other subplots (if any) are not deleted.\n # https://stackoverflow.com/questions/17430105/autofmt-xdate-deletes-x-axis-labels-of-all-subplots\n if np.issubdtype(xplt.dtype, np.datetime64):\n for xlabels in ax.get_xticklabels():\n xlabels.set_rotation(30)\n xlabels.set_ha(\"right\")\n\n return primitive\n\n # For use as DataArray.plot.plotmethod\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.plotmethod__plot2d.return.newplotfunc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py__plot2d.plotmethod__plot2d.return.newplotfunc", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 776, "end_line": 829, "span_ids": ["_plot2d"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _plot2d(plotfunc):\n # ... other code\n @functools.wraps(newplotfunc)\n def plotmethod(\n _PlotMethods_obj,\n x=None,\n y=None,\n figsize=None,\n size=None,\n aspect=None,\n ax=None,\n row=None,\n col=None,\n col_wrap=None,\n xincrease=True,\n yincrease=True,\n add_colorbar=None,\n add_labels=True,\n vmin=None,\n vmax=None,\n cmap=None,\n colors=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n infer_intervals=None,\n subplot_kws=None,\n cbar_ax=None,\n cbar_kwargs=None,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n norm=None,\n **kwargs\n ):\n \"\"\"\n The method should have the same signature as the function.\n\n This just makes the method work on Plotmethods objects,\n and passes all the other arguments straight through.\n \"\"\"\n allargs = locals()\n allargs[\"darray\"] = _PlotMethods_obj._da\n allargs.update(kwargs)\n for arg in [\"_PlotMethods_obj\", \"newplotfunc\", \"kwargs\"]:\n del allargs[arg]\n return newplotfunc(**allargs)\n\n # Add to class _PlotMethods\n setattr(_PlotMethods, plotmethod.__name__, plotmethod)\n\n return newplotfunc", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_imshow_imshow.return.primitive": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_imshow_imshow.return.primitive", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 832, "end_line": 904, "span_ids": ["imshow"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_plot2d\ndef imshow(x, y, z, ax, **kwargs):\n \"\"\"\n Image plot of 2d DataArray using matplotlib.pyplot\n\n Wraps :func:`matplotlib:matplotlib.pyplot.imshow`\n\n While other plot methods require the DataArray to be strictly\n two-dimensional, ``imshow`` also accepts a 3D array where some\n dimension can be interpreted as RGB or RGBA color channels and\n allows this dimension to be specified via the kwarg ``rgb=``.\n\n Unlike matplotlib, Xarray can apply ``vmin`` and ``vmax`` to RGB or RGBA\n data, by applying a single scaling factor and offset to all bands.\n Passing ``robust=True`` infers ``vmin`` and ``vmax``\n :ref:`in the usual way `.\n\n .. note::\n This function needs uniformly spaced coordinates to\n properly label the axes. Call DataArray.plot() to check.\n\n The pixels are centered on the coordinates values. Ie, if the coordinate\n value is 3.2 then the pixels for those coordinates will be centered on 3.2.\n \"\"\"\n\n if x.ndim != 1 or y.ndim != 1:\n raise ValueError(\n \"imshow requires 1D coordinates, try using \" \"pcolormesh or contour(f)\"\n )\n\n # Centering the pixels- Assumes uniform spacing\n try:\n xstep = (x[1] - x[0]) / 2.0\n except IndexError:\n # Arbitrary default value, similar to matplotlib behaviour\n xstep = 0.1\n try:\n ystep = (y[1] - y[0]) / 2.0\n except IndexError:\n ystep = 0.1\n left, right = x[0] - xstep, x[-1] + xstep\n bottom, top = y[-1] + ystep, y[0] - ystep\n\n defaults = {\"origin\": \"upper\", \"interpolation\": \"nearest\"}\n\n if not hasattr(ax, \"projection\"):\n # not for cartopy geoaxes\n defaults[\"aspect\"] = \"auto\"\n\n # Allow user to override these defaults\n defaults.update(kwargs)\n\n if defaults[\"origin\"] == \"upper\":\n defaults[\"extent\"] = [left, right, bottom, top]\n else:\n defaults[\"extent\"] = [left, right, top, bottom]\n\n if z.ndim == 3:\n # matplotlib imshow uses black for missing data, but Xarray makes\n # missing data transparent. We therefore add an alpha channel if\n # there isn't one, and set it to transparent where data is masked.\n if z.shape[-1] == 3:\n alpha = np.ma.ones(z.shape[:2] + (1,), dtype=z.dtype)\n if np.issubdtype(z.dtype, np.integer):\n alpha *= 255\n z = np.ma.concatenate((z, alpha), axis=2)\n else:\n z = z.copy()\n z[np.any(z.mask, axis=-1), -1] = 0\n\n primitive = ax.imshow(z, **defaults)\n\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_contour_contourf.return.primitive": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_contour_contourf.return.primitive", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 907, "end_line": 926, "span_ids": ["contourf", "contour"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_plot2d\ndef contour(x, y, z, ax, **kwargs):\n \"\"\"\n Contour plot of 2d DataArray\n\n Wraps :func:`matplotlib:matplotlib.pyplot.contour`\n \"\"\"\n primitive = ax.contour(x, y, z, **kwargs)\n return primitive\n\n\n@_plot2d\ndef contourf(x, y, z, ax, **kwargs):\n \"\"\"\n Filled contour plot of 2d DataArray\n\n Wraps :func:`matplotlib:matplotlib.pyplot.contourf`\n \"\"\"\n primitive = ax.contourf(x, y, z, **kwargs)\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_pcolormesh_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/plot.py_pcolormesh_", "embedding": null, "metadata": {"file_path": "xarray/plot/plot.py", "file_name": "plot.py", "file_type": "text/x-python", "category": "implementation", "start_line": 929, "end_line": 977, "span_ids": ["pcolormesh"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_plot2d\ndef pcolormesh(x, y, z, ax, infer_intervals=None, **kwargs):\n \"\"\"\n Pseudocolor plot of 2d DataArray\n\n Wraps :func:`matplotlib:matplotlib.pyplot.pcolormesh`\n \"\"\"\n\n # decide on a default for infer_intervals (GH781)\n x = np.asarray(x)\n if infer_intervals is None:\n if hasattr(ax, \"projection\"):\n if len(x.shape) == 1:\n infer_intervals = True\n else:\n infer_intervals = False\n else:\n infer_intervals = True\n\n if infer_intervals and (\n (np.shape(x)[0] == np.shape(z)[1])\n or ((x.ndim > 1) and (np.shape(x)[1] == np.shape(z)[1]))\n ):\n if len(x.shape) == 1:\n x = _infer_interval_breaks(x, check_monotonic=True)\n else:\n # we have to infer the intervals on both axes\n x = _infer_interval_breaks(x, axis=1)\n x = _infer_interval_breaks(x, axis=0)\n\n if infer_intervals and (np.shape(y)[0] == np.shape(z)[0]):\n if len(y.shape) == 1:\n y = _infer_interval_breaks(y, check_monotonic=True)\n else:\n # we have to infer the intervals on both axes\n y = _infer_interval_breaks(y, axis=1)\n y = _infer_interval_breaks(y, axis=0)\n\n primitive = ax.pcolormesh(x, y, z, **kwargs)\n\n # by default, pcolormesh picks \"round\" values for bounds\n # this results in ugly looking plots with lots of surrounding whitespace\n if not hasattr(ax, \"projection\") and x.ndim == 1 and y.ndim == 1:\n # not a cartopy geoaxis\n ax.set_xlim(x[0], x[-1])\n ax.set_ylim(y[0], y[-1])\n\n return primitive", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_itertools__determine_extend.return.extend": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_itertools__determine_extend.return.extend", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 74, "span_ids": ["import_seaborn", "impl", "impl:2", "docstring", "_determine_extend", "imports:11", "import_matplotlib_pyplot", "impl:9", "register_pandas_datetime_converter_if_needed", "imports"], "tokens": 399}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\nimport textwrap\nimport warnings\nfrom datetime import datetime\nfrom inspect import getfullargspec\nfrom typing import Any, Iterable, Mapping, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..core.options import OPTIONS\nfrom ..core.utils import is_scalar\n\ntry:\n import nc_time_axis # noqa: F401\n\n nc_time_axis_available = True\nexcept ImportError:\n nc_time_axis_available = False\n\nROBUST_PERCENTILE = 2.0\n\n\ndef import_seaborn():\n \"\"\"import seaborn and handle deprecation of apionly module\"\"\"\n with warnings.catch_warnings(record=True) as w:\n warnings.simplefilter(\"always\")\n try:\n import seaborn.apionly as sns\n\n if (\n w\n and issubclass(w[-1].category, UserWarning)\n and (\"seaborn.apionly module\" in str(w[-1].message))\n ):\n raise ImportError\n except ImportError:\n import seaborn as sns\n finally:\n warnings.resetwarnings()\n return sns\n\n\n_registered = False\n\n\ndef register_pandas_datetime_converter_if_needed():\n # based on https://github.com/pandas-dev/pandas/pull/17710\n global _registered\n if not _registered:\n pd.plotting.register_matplotlib_converters()\n _registered = True\n\n\ndef import_matplotlib_pyplot():\n \"\"\"Import pyplot as register appropriate converters.\"\"\"\n register_pandas_datetime_converter_if_needed()\n import matplotlib.pyplot as plt\n\n return plt\n\n\ndef _determine_extend(calc_data, vmin, vmax):\n extend_min = calc_data.min() < vmin\n extend_max = calc_data.max() > vmax\n if extend_min and extend_max:\n extend = \"both\"\n elif extend_min:\n extend = \"min\"\n elif extend_max:\n extend = \"max\"\n else:\n extend = \"neither\"\n return extend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__build_discrete_cmap__build_discrete_cmap.return.new_cmap_cnorm": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__build_discrete_cmap__build_discrete_cmap.return.new_cmap_cnorm", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 101, "span_ids": ["_build_discrete_cmap"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _build_discrete_cmap(cmap, levels, extend, filled):\n \"\"\"\n Build a discrete colormap and normalization of the data.\n \"\"\"\n import matplotlib as mpl\n\n if not filled:\n # non-filled contour plots\n extend = \"max\"\n\n if extend == \"both\":\n ext_n = 2\n elif extend in [\"min\", \"max\"]:\n ext_n = 1\n else:\n ext_n = 0\n\n n_colors = len(levels) + ext_n - 1\n pal = _color_palette(cmap, n_colors)\n\n new_cmap, cnorm = mpl.colors.from_levels_and_colors(levels, pal, extend=extend)\n # copy the old cmap name, for easier testing\n new_cmap.name = getattr(cmap, \"name\", cmap)\n\n return new_cmap, cnorm", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__color_palette__color_palette.return.pal": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__color_palette__color_palette.return.pal", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 133, "span_ids": ["_color_palette"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _color_palette(cmap, n_colors):\n import matplotlib.pyplot as plt\n from matplotlib.colors import ListedColormap\n\n colors_i = np.linspace(0, 1.0, n_colors)\n if isinstance(cmap, (list, tuple)):\n # we have a list of colors\n cmap = ListedColormap(cmap, N=n_colors)\n pal = cmap(colors_i)\n elif isinstance(cmap, str):\n # we have some sort of named palette\n try:\n # is this a matplotlib cmap?\n cmap = plt.get_cmap(cmap)\n pal = cmap(colors_i)\n except ValueError:\n # ValueError happens when mpl doesn't like a colormap, try seaborn\n try:\n from seaborn.apionly import color_palette\n\n pal = color_palette(cmap, n_colors=n_colors)\n except (ValueError, ImportError):\n # or maybe we just got a single color as a string\n cmap = ListedColormap([cmap], N=n_colors)\n pal = cmap(colors_i)\n else:\n # cmap better be a LinearSegmentedColormap (e.g. viridis)\n pal = cmap(colors_i)\n\n return pal", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py___determine_cmap_params___determine_cmap_params._Handle_discrete_levels": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py___determine_cmap_params___determine_cmap_params._Handle_discrete_levels", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 136, "end_line": 261, "span_ids": ["_color_palette", "_determine_cmap_params"], "tokens": 861}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# _determine_cmap_params is adapted from Seaborn:\n# https://github.com/mwaskom/seaborn/blob/v0.6/seaborn/matrix.py#L158\n\n\ndef _determine_cmap_params(\n plot_data,\n vmin=None,\n vmax=None,\n cmap=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n filled=True,\n norm=None,\n):\n \"\"\"\n Use some heuristics to set good defaults for colorbar and range.\n\n Parameters\n ==========\n plot_data: Numpy array\n Doesn't handle xarray objects\n\n Returns\n =======\n cmap_params : dict\n Use depends on the type of the plotting function\n \"\"\"\n import matplotlib as mpl\n\n calc_data = np.ravel(plot_data[np.isfinite(plot_data)])\n\n # Handle all-NaN input data gracefully\n if calc_data.size == 0:\n # Arbitrary default for when all values are NaN\n calc_data = np.array(0.0)\n\n # Setting center=False prevents a divergent cmap\n possibly_divergent = center is not False\n\n # Set center to 0 so math below makes sense but remember its state\n center_is_none = False\n if center is None:\n center = 0\n center_is_none = True\n\n # Setting both vmin and vmax prevents a divergent cmap\n if (vmin is not None) and (vmax is not None):\n possibly_divergent = False\n\n # Setting vmin or vmax implies linspaced levels\n user_minmax = (vmin is not None) or (vmax is not None)\n\n # vlim might be computed below\n vlim = None\n\n # save state; needed later\n vmin_was_none = vmin is None\n vmax_was_none = vmax is None\n\n if vmin is None:\n if robust:\n vmin = np.percentile(calc_data, ROBUST_PERCENTILE)\n else:\n vmin = calc_data.min()\n elif possibly_divergent:\n vlim = abs(vmin - center)\n\n if vmax is None:\n if robust:\n vmax = np.percentile(calc_data, 100 - ROBUST_PERCENTILE)\n else:\n vmax = calc_data.max()\n elif possibly_divergent:\n vlim = abs(vmax - center)\n\n if possibly_divergent:\n # kwargs not specific about divergent or not: infer defaults from data\n divergent = ((vmin < 0) and (vmax > 0)) or not center_is_none\n else:\n divergent = False\n\n # A divergent map should be symmetric around the center value\n if divergent:\n if vlim is None:\n vlim = max(abs(vmin - center), abs(vmax - center))\n vmin, vmax = -vlim, vlim\n\n # Now add in the centering value and set the limits\n vmin += center\n vmax += center\n\n # now check norm and harmonize with vmin, vmax\n if norm is not None:\n if norm.vmin is None:\n norm.vmin = vmin\n else:\n if not vmin_was_none and vmin != norm.vmin:\n raise ValueError(\n \"Cannot supply vmin and a norm\" + \" with a different vmin.\"\n )\n vmin = norm.vmin\n\n if norm.vmax is None:\n norm.vmax = vmax\n else:\n if not vmax_was_none and vmax != norm.vmax:\n raise ValueError(\n \"Cannot supply vmax and a norm\" + \" with a different vmax.\"\n )\n vmax = norm.vmax\n\n # if BoundaryNorm, then set levels\n if isinstance(norm, mpl.colors.BoundaryNorm):\n levels = norm.boundaries\n\n # Choose default colormaps if not provided\n if cmap is None:\n if divergent:\n cmap = OPTIONS[\"cmap_divergent\"]\n else:\n cmap = OPTIONS[\"cmap_sequential\"]\n\n # Handle discrete levels\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__determine_cmap_params.if_levels_is_not_None_and__determine_cmap_params.return.dict_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__determine_cmap_params.if_levels_is_not_None_and__determine_cmap_params.return.dict_", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 262, "end_line": 283, "span_ids": ["_determine_cmap_params"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _determine_cmap_params(\n plot_data,\n vmin=None,\n vmax=None,\n cmap=None,\n center=None,\n robust=False,\n extend=None,\n levels=None,\n filled=True,\n norm=None,\n):\n # ... other code\n if levels is not None and norm is None:\n if is_scalar(levels):\n if user_minmax:\n levels = np.linspace(vmin, vmax, levels)\n elif levels == 1:\n levels = np.asarray([(vmin + vmax) / 2])\n else:\n # N in MaxNLocator refers to bins, not ticks\n ticker = mpl.ticker.MaxNLocator(levels - 1)\n levels = ticker.tick_values(vmin, vmax)\n vmin, vmax = levels[0], levels[-1]\n\n if extend is None:\n extend = _determine_extend(calc_data, vmin, vmax)\n\n if levels is not None or isinstance(norm, mpl.colors.BoundaryNorm):\n cmap, newnorm = _build_discrete_cmap(cmap, levels, extend, filled)\n norm = newnorm if norm is None else norm\n\n return dict(\n vmin=vmin, vmax=vmax, cmap=cmap, extend=extend, levels=levels, norm=norm\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_xy_labels_3d__infer_xy_labels_3d.return._infer_xy_labels_darray_i": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_xy_labels_3d__infer_xy_labels_3d.return._infer_xy_labels_darray_i", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 286, "end_line": 341, "span_ids": ["_infer_xy_labels_3d"], "tokens": 589}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_xy_labels_3d(darray, x, y, rgb):\n \"\"\"\n Determine x and y labels for showing RGB images.\n\n Attempts to infer which dimension is RGB/RGBA by size and order of dims.\n\n \"\"\"\n assert rgb is None or rgb != x\n assert rgb is None or rgb != y\n # Start by detecting and reporting invalid combinations of arguments\n assert darray.ndim == 3\n not_none = [a for a in (x, y, rgb) if a is not None]\n if len(set(not_none)) < len(not_none):\n raise ValueError(\n \"Dimension names must be None or unique strings, but imshow was \"\n \"passed x=%r, y=%r, and rgb=%r.\" % (x, y, rgb)\n )\n for label in not_none:\n if label not in darray.dims:\n raise ValueError(\"%r is not a dimension\" % (label,))\n\n # Then calculate rgb dimension if certain and check validity\n could_be_color = [\n label\n for label in darray.dims\n if darray[label].size in (3, 4) and label not in (x, y)\n ]\n if rgb is None and not could_be_color:\n raise ValueError(\n \"A 3-dimensional array was passed to imshow(), but there is no \"\n \"dimension that could be color. At least one dimension must be \"\n \"of size 3 (RGB) or 4 (RGBA), and not given as x or y.\"\n )\n if rgb is None and len(could_be_color) == 1:\n rgb = could_be_color[0]\n if rgb is not None and darray[rgb].size not in (3, 4):\n raise ValueError(\n \"Cannot interpret dim %r of size %s as RGB or RGBA.\"\n % (rgb, darray[rgb].size)\n )\n\n # If rgb dimension is still unknown, there must be two or three dimensions\n # in could_be_color. We therefore warn, and use a heuristic to break ties.\n if rgb is None:\n assert len(could_be_color) in (2, 3)\n rgb = could_be_color[-1]\n warnings.warn(\n \"Several dimensions of this array could be colors. Xarray \"\n \"will use the last possible dimension (%r) to match \"\n \"matplotlib.pyplot.imshow. You can pass names of x, y, \"\n \"and/or rgb dimensions to override this guess.\" % rgb\n )\n assert rgb is not None\n\n # Finally, we pick out the red slice and delegate to the 2D version:\n return _infer_xy_labels(darray.isel(**{rgb: 0}), x, y)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_xy_labels__infer_xy_labels.return.x_y": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_xy_labels__infer_xy_labels.return.x_y", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 344, "end_line": 368, "span_ids": ["_infer_xy_labels"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_xy_labels(darray, x, y, imshow=False, rgb=None):\n \"\"\"\n Determine x and y labels. For use in _plot2d\n\n darray must be a 2 dimensional data array, or 3d for imshow only.\n \"\"\"\n assert x is None or x != y\n if imshow and darray.ndim == 3:\n return _infer_xy_labels_3d(darray, x, y, rgb)\n\n if x is None and y is None:\n if darray.ndim != 2:\n raise ValueError(\"DataArray must be 2d\")\n y, x = darray.dims\n elif x is None:\n if y not in darray.dims and y not in darray.coords:\n raise ValueError(\"y must be a dimension name if x is not supplied\")\n x = darray.dims[0] if y == darray.dims[1] else darray.dims[1]\n elif y is None:\n if x not in darray.dims and x not in darray.coords:\n raise ValueError(\"x must be a dimension name if y is not supplied\")\n y = darray.dims[0] if x == darray.dims[1] else darray.dims[1]\n elif any(k not in darray.coords and k not in darray.dims for k in (x, y)):\n raise ValueError(\"x and y must be coordinate variables\")\n return x, y", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_get_axis_get_axis.return.ax": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_get_axis_get_axis.return.ax", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 371, "end_line": 395, "span_ids": ["get_axis"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_axis(figsize, size, aspect, ax):\n import matplotlib as mpl\n import matplotlib.pyplot as plt\n\n if figsize is not None:\n if ax is not None:\n raise ValueError(\"cannot provide both `figsize` and \" \"`ax` arguments\")\n if size is not None:\n raise ValueError(\"cannot provide both `figsize` and \" \"`size` arguments\")\n _, ax = plt.subplots(figsize=figsize)\n elif size is not None:\n if ax is not None:\n raise ValueError(\"cannot provide both `size` and `ax` arguments\")\n if aspect is None:\n width, height = mpl.rcParams[\"figure.figsize\"]\n aspect = width / height\n figsize = (size * aspect, size)\n _, ax = plt.subplots(figsize=figsize)\n elif aspect is not None:\n raise ValueError(\"cannot provide `aspect` argument without `size`\")\n\n if ax is None:\n ax = plt.gca()\n\n return ax", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_label_from_attrs_label_from_attrs.return._n_join_textwrap_wrap_n": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py_label_from_attrs_label_from_attrs.return._n_join_textwrap_wrap_n", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 398, "end_line": 416, "span_ids": ["label_from_attrs"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def label_from_attrs(da, extra=\"\"):\n \"\"\" Makes informative labels if variable metadata (attrs) follows\n CF conventions. \"\"\"\n\n if da.attrs.get(\"long_name\"):\n name = da.attrs[\"long_name\"]\n elif da.attrs.get(\"standard_name\"):\n name = da.attrs[\"standard_name\"]\n elif da.name is not None:\n name = da.name\n else:\n name = \"\"\n\n if da.attrs.get(\"units\"):\n units = \" [{}]\".format(da.attrs[\"units\"])\n else:\n units = \"\"\n\n return \"\\n\".join(textwrap.wrap(name + extra + units, 30))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__interval_to_mid_points__interval_to_bound_points.return.array_boundaries": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__interval_to_mid_points__interval_to_bound_points.return.array_boundaries", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 419, "end_line": 437, "span_ids": ["_interval_to_bound_points", "_interval_to_mid_points"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _interval_to_mid_points(array):\n \"\"\"\n Helper function which returns an array\n with the Intervals' mid points.\n \"\"\"\n\n return np.array([x.mid for x in array])\n\n\ndef _interval_to_bound_points(array):\n \"\"\"\n Helper function which returns an array\n with the Intervals' boundaries.\n \"\"\"\n\n array_boundaries = np.array([x.left for x in array])\n array_boundaries = np.concatenate((array_boundaries, np.array([array[-1].right])))\n\n return array_boundaries", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__interval_to_double_bound_points__interval_to_double_bound_points.return.xarray_yarray": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__interval_to_double_bound_points__interval_to_double_bound_points.return.xarray_yarray", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 440, "end_line": 453, "span_ids": ["_interval_to_double_bound_points"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _interval_to_double_bound_points(xarray, yarray):\n \"\"\"\n Helper function to deal with a xarray consisting of pd.Intervals. Each\n interval is replaced with both boundaries. I.e. the length of xarray\n doubles. yarray is modified so it matches the new shape of xarray.\n \"\"\"\n\n xarray1 = np.array([x.left for x in xarray])\n xarray2 = np.array([x.right for x in xarray])\n\n xarray = list(itertools.chain.from_iterable(zip(xarray1, xarray2)))\n yarray = list(itertools.chain.from_iterable(zip(yarray, yarray)))\n\n return xarray, yarray", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__resolve_intervals_2dplot__resolve_intervals_2dplot.return.val_label_extra": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__resolve_intervals_2dplot__resolve_intervals_2dplot.return.val_label_extra", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 456, "end_line": 470, "span_ids": ["_resolve_intervals_2dplot"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _resolve_intervals_2dplot(val, func_name):\n \"\"\"\n Helper function to replace the values of a coordinate array containing\n pd.Interval with their mid-points or - for pcolormesh - boundaries which\n increases length by 1.\n \"\"\"\n label_extra = \"\"\n if _valid_other_type(val, [pd.Interval]):\n if func_name == \"pcolormesh\":\n val = _interval_to_bound_points(val)\n else:\n val = _interval_to_mid_points(val)\n label_extra = \"_center\"\n\n return val, label_extra", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__valid_other_type__valid_numpy_subdtype.return.any_np_issubdtype_x_dtype": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__valid_other_type__valid_numpy_subdtype.return.any_np_issubdtype_x_dtype", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 473, "end_line": 489, "span_ids": ["_valid_numpy_subdtype", "_valid_other_type"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _valid_other_type(x, types):\n \"\"\"\n Do all elements of x have a type from types?\n \"\"\"\n return all(any(isinstance(el, t) for t in types) for el in np.ravel(x))\n\n\ndef _valid_numpy_subdtype(x, numpy_types):\n \"\"\"\n Is any dtype from numpy_types superior to the dtype of x?\n \"\"\"\n # If any of the types given in numpy_types is understood as numpy.generic,\n # all possible x will be considered valid. This is probably unwanted.\n for t in numpy_types:\n assert not np.issubdtype(np.generic, t)\n\n return any(np.issubdtype(x.dtype, t) for t in numpy_types)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__ensure_plottable__ensure_plottable.for_x_in_args_.if_.raise_ImportError_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__ensure_plottable__ensure_plottable.for_x_in_args_.if_.raise_ImportError_", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 492, "end_line": 527, "span_ids": ["_ensure_plottable"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _ensure_plottable(*args):\n \"\"\"\n Raise exception if there is anything in args that can't be plotted on an\n axis by matplotlib.\n \"\"\"\n numpy_types = [np.floating, np.integer, np.timedelta64, np.datetime64]\n other_types = [datetime]\n try:\n import cftime\n\n cftime_datetime = [cftime.datetime]\n except ImportError:\n cftime_datetime = []\n other_types = other_types + cftime_datetime\n for x in args:\n if not (\n _valid_numpy_subdtype(np.array(x), numpy_types)\n or _valid_other_type(np.array(x), other_types)\n ):\n raise TypeError(\n \"Plotting requires coordinates to be numeric \"\n \"or dates of type np.datetime64, \"\n \"datetime.datetime, cftime.datetime or \"\n \"pd.Interval.\"\n )\n if (\n _valid_other_type(np.array(x), cftime_datetime)\n and not nc_time_axis_available\n ):\n raise ImportError(\n \"Plotting of arrays of cftime.datetime \"\n \"objects or arrays indexed by \"\n \"cftime.datetime objects requires the \"\n \"optional `nc-time-axis` (v1.2.0 or later) \"\n \"package.\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__is_numeric__add_colorbar.return.cbar": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__is_numeric__add_colorbar.return.cbar", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 530, "end_line": 545, "span_ids": ["_add_colorbar", "_is_numeric"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _is_numeric(arr):\n numpy_types = [np.floating, np.integer]\n return _valid_numpy_subdtype(arr, numpy_types)\n\n\ndef _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params):\n plt = import_matplotlib_pyplot()\n cbar_kwargs.setdefault(\"extend\", cmap_params[\"extend\"])\n if cbar_ax is None:\n cbar_kwargs.setdefault(\"ax\", ax)\n else:\n cbar_kwargs.setdefault(\"cax\", cbar_ax)\n\n cbar = plt.colorbar(primitive, **cbar_kwargs)\n\n return cbar", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__rescale_imshow_rgb__rescale_imshow_rgb.with_warnings_catch_warni.return.minimum_maximum_darray_0": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__rescale_imshow_rgb__rescale_imshow_rgb.with_warnings_catch_warni.return.minimum_maximum_darray_0", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 548, "end_line": 584, "span_ids": ["_rescale_imshow_rgb"], "tokens": 458}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _rescale_imshow_rgb(darray, vmin, vmax, robust):\n assert robust or vmin is not None or vmax is not None\n # TODO: remove when min numpy version is bumped to 1.13\n # There's a cyclic dependency via DataArray, so we can't import from\n # xarray.ufuncs in global scope.\n from xarray.ufuncs import maximum, minimum\n\n # Calculate vmin and vmax automatically for `robust=True`\n if robust:\n if vmax is None:\n vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE)\n if vmin is None:\n vmin = np.nanpercentile(darray, ROBUST_PERCENTILE)\n # If not robust and one bound is None, calculate the default other bound\n # and check that an interval between them exists.\n elif vmax is None:\n vmax = 255 if np.issubdtype(darray.dtype, np.integer) else 1\n if vmax < vmin:\n raise ValueError(\n \"vmin=%r is less than the default vmax (%r) - you must supply \"\n \"a vmax > vmin in this case.\" % (vmin, vmax)\n )\n elif vmin is None:\n vmin = 0\n if vmin > vmax:\n raise ValueError(\n \"vmax=%r is less than the default vmin (0) - you must supply \"\n \"a vmin < vmax in this case.\" % vmax\n )\n # Scale interval [vmin .. vmax] to [0 .. 1], with darray as 64-bit float\n # to avoid precision loss, integer over/underflow, etc with extreme inputs.\n # After scaling, downcast to 32-bit float. This substantially reduces\n # memory usage after we hand `darray` off to matplotlib.\n darray = ((darray.astype(\"f8\") - vmin) / (vmax - vmin)).astype(\"f4\")\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"xarray.ufuncs\", PendingDeprecationWarning)\n return minimum(maximum(darray, 0), 1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__update_axes__update_axes.if_ylim_is_not_None_.ax_set_ylim_ylim_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__update_axes__update_axes.if_ylim_is_not_None_.ax_set_ylim_ylim_", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 587, "end_line": 633, "span_ids": ["_update_axes"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _update_axes(\n ax,\n xincrease,\n yincrease,\n xscale=None,\n yscale=None,\n xticks=None,\n yticks=None,\n xlim=None,\n ylim=None,\n):\n \"\"\"\n Update axes with provided parameters\n \"\"\"\n if xincrease is None:\n pass\n elif xincrease and ax.xaxis_inverted():\n ax.invert_xaxis()\n elif not xincrease and not ax.xaxis_inverted():\n ax.invert_xaxis()\n\n if yincrease is None:\n pass\n elif yincrease and ax.yaxis_inverted():\n ax.invert_yaxis()\n elif not yincrease and not ax.yaxis_inverted():\n ax.invert_yaxis()\n\n # The default xscale, yscale needs to be None.\n # If we set a scale it resets the axes formatters,\n # This means that set_xscale('linear') on a datetime axis\n # will remove the date labels. So only set the scale when explicitly\n # asked to. https://github.com/matplotlib/matplotlib/issues/8740\n if xscale is not None:\n ax.set_xscale(xscale)\n if yscale is not None:\n ax.set_yscale(yscale)\n\n if xticks is not None:\n ax.set_xticks(xticks)\n if yticks is not None:\n ax.set_yticks(yticks)\n\n if xlim is not None:\n ax.set_xlim(xlim)\n if ylim is not None:\n ax.set_ylim(ylim)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__is_monotonic__is_monotonic.if_coord_shape_axis_3_.else_.return.np_all_delta_pos_or_np_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__is_monotonic__is_monotonic.if_coord_shape_axis_3_.else_.return.np_all_delta_pos_or_np_a", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 636, "end_line": 655, "span_ids": ["_is_monotonic"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _is_monotonic(coord, axis=0):\n \"\"\"\n >>> _is_monotonic(np.array([0, 1, 2]))\n True\n >>> _is_monotonic(np.array([2, 1, 0]))\n True\n >>> _is_monotonic(np.array([0, 2, 1]))\n False\n \"\"\"\n if coord.shape[axis] < 3:\n return True\n else:\n n = coord.shape[axis]\n delta_pos = coord.take(np.arange(1, n), axis=axis) >= coord.take(\n np.arange(0, n - 1), axis=axis\n )\n delta_neg = coord.take(np.arange(1, n), axis=axis) <= coord.take(\n np.arange(0, n - 1), axis=axis\n )\n return np.all(delta_pos) or np.all(delta_neg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_interval_breaks__infer_interval_breaks.return.np_concatenate_first_co": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__infer_interval_breaks__infer_interval_breaks.return.np_concatenate_first_co", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 658, "end_line": 686, "span_ids": ["_infer_interval_breaks"], "tokens": 384}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _infer_interval_breaks(coord, axis=0, check_monotonic=False):\n \"\"\"\n >>> _infer_interval_breaks(np.arange(5))\n array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])\n >>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)\n array([[-0.5, 0.5, 1.5],\n [ 2.5, 3.5, 4.5]])\n \"\"\"\n coord = np.asarray(coord)\n\n if check_monotonic and not _is_monotonic(coord, axis=axis):\n raise ValueError(\n \"The input coordinate is not sorted in increasing \"\n \"order along axis %d. This can lead to unexpected \"\n \"results. Consider calling the `sortby` method on \"\n \"the input DataArray. To plot data with categorical \"\n \"axes, consider using the `heatmap` function from \"\n \"the `seaborn` statistical plotting library.\" % axis\n )\n\n deltas = 0.5 * np.diff(coord, axis=axis)\n if deltas.size == 0:\n deltas = np.array(0.0)\n first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis)\n last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis)\n trim_last = tuple(\n slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)\n )\n return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__process_cmap_cbar_kwargs_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/plot/utils.py__process_cmap_cbar_kwargs_", "embedding": null, "metadata": {"file_path": "xarray/plot/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 689, "end_line": 745, "span_ids": ["_process_cmap_cbar_kwargs"], "tokens": 394}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _process_cmap_cbar_kwargs(\n func,\n data,\n cmap=None,\n colors=None,\n cbar_kwargs: Union[Iterable[Tuple[str, Any]], Mapping[str, Any]] = None,\n levels=None,\n **kwargs\n):\n \"\"\"\n Parameters\n ==========\n func : plotting function\n data : ndarray,\n Data values\n\n Returns\n =======\n cmap_params\n\n cbar_kwargs\n \"\"\"\n cbar_kwargs = {} if cbar_kwargs is None else dict(cbar_kwargs)\n\n if \"contour\" in func.__name__ and levels is None:\n levels = 7 # this is the matplotlib default\n\n # colors is mutually exclusive with cmap\n if cmap and colors:\n raise ValueError(\"Can't specify both cmap and colors.\")\n\n # colors is only valid when levels is supplied or the plot is of type\n # contour or contourf\n if colors and ((\"contour\" not in func.__name__) and (levels is None)):\n raise ValueError(\"Can only specify colors with contour or levels\")\n\n # we should not be getting a list of colors in cmap anymore\n # is there a better way to do this test?\n if isinstance(cmap, (list, tuple)):\n raise ValueError(\n \"Specifying a list of colors in cmap is deprecated. \"\n \"Use colors keyword instead.\"\n )\n\n cmap_kwargs = {\n \"plot_data\": data,\n \"levels\": levels,\n \"cmap\": colors if colors else cmap,\n \"filled\": func.__name__ != \"contour\",\n }\n\n cmap_args = getfullargspec(_determine_cmap_params).args\n cmap_kwargs.update((a, kwargs[a]) for a in cmap_args if a in kwargs)\n cmap_params = _determine_cmap_params(**cmap_kwargs)\n\n return cmap_params, cbar_kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__Testing_functions_expo__decode_string_data.return.data": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__Testing_functions_expo__decode_string_data.return.data", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 17, "span_ids": ["docstring", "imports", "_decode_string_data"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "\"\"\"Testing functions exposed to the user API\"\"\"\nfrom typing import Hashable, Set, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom xarray.core import duck_array_ops, formatting\nfrom xarray.core.dataarray import DataArray\nfrom xarray.core.dataset import Dataset\nfrom xarray.core.indexes import default_indexes\nfrom xarray.core.variable import IndexVariable, Variable\n\n\ndef _decode_string_data(data):\n if data.dtype.kind == \"S\":\n return np.core.defchararray.decode(data, \"utf-8\", \"replace\")\n return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__data_allclose_or_equiv__data_allclose_or_equiv.if_any_arr_dtype_kind_in_.else_.return.duck_array_ops_allclose_o": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__data_allclose_or_equiv__data_allclose_or_equiv.if_any_arr_dtype_kind_in_.else_.return.duck_array_ops_allclose_o", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 20, "end_line": 28, "span_ids": ["_data_allclose_or_equiv"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _data_allclose_or_equiv(arr1, arr2, rtol=1e-05, atol=1e-08, decode_bytes=True):\n if any(arr.dtype.kind == \"S\" for arr in [arr1, arr2]) and decode_bytes:\n arr1 = _decode_string_data(arr1)\n arr2 = _decode_string_data(arr2)\n exact_dtypes = [\"M\", \"m\", \"O\", \"S\", \"U\"]\n if any(arr.dtype.kind in exact_dtypes for arr in [arr1, arr2]):\n return duck_array_ops.array_equiv(arr1, arr2)\n else:\n return duck_array_ops.allclose_or_equiv(arr1, arr2, rtol=rtol, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_equal_assert_equal.if_isinstance_a_Variabl.else_.raise_TypeError_not_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_equal_assert_equal.if_isinstance_a_Variabl.else_.raise_TypeError_not_s", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 59, "span_ids": ["assert_equal"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_equal(a, b):\n \"\"\"Like :py:func:`numpy.testing.assert_array_equal`, but for xarray\n objects.\n\n Raises an AssertionError if two objects are not equal. This will match\n data values, dimensions and coordinates, but not names or attributes\n (except for Dataset objects for which the variable names must match).\n Arrays with NaN in the same location are considered equal.\n\n Parameters\n ----------\n a : xarray.Dataset, xarray.DataArray or xarray.Variable\n The first object to compare.\n b : xarray.Dataset, xarray.DataArray or xarray.Variable\n The second object to compare.\n\n See also\n --------\n assert_identical, assert_allclose, Dataset.equals, DataArray.equals,\n numpy.testing.assert_array_equal\n \"\"\"\n __tracebackhide__ = True\n assert type(a) == type(b)\n if isinstance(a, (Variable, DataArray)):\n assert a.equals(b), formatting.diff_array_repr(a, b, \"equals\")\n elif isinstance(a, Dataset):\n assert a.equals(b), formatting.diff_dataset_repr(a, b, \"equals\")\n else:\n raise TypeError(\"{} not supported by assertion comparison\".format(type(a)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_identical_assert_identical.if_isinstance_a_Variable.else_.raise_TypeError_not_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_identical_assert_identical.if_isinstance_a_Variable.else_.raise_TypeError_not_s", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 89, "span_ids": ["assert_identical"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_identical(a, b):\n \"\"\"Like :py:func:`xarray.testing.assert_equal`, but also matches the\n objects' names and attributes.\n\n Raises an AssertionError if two objects are not identical.\n\n Parameters\n ----------\n a : xarray.Dataset, xarray.DataArray or xarray.Variable\n The first object to compare.\n b : xarray.Dataset, xarray.DataArray or xarray.Variable\n The second object to compare.\n\n See also\n --------\n assert_equal, assert_allclose, Dataset.equals, DataArray.equals\n \"\"\"\n __tracebackhide__ = True\n assert type(a) == type(b)\n if isinstance(a, Variable):\n assert a.identical(b), formatting.diff_array_repr(a, b, \"identical\")\n elif isinstance(a, DataArray):\n assert a.name == b.name\n assert a.identical(b), formatting.diff_array_repr(a, b, \"identical\")\n elif isinstance(a, (Dataset, Variable)):\n assert a.identical(b), formatting.diff_dataset_repr(a, b, \"identical\")\n else:\n raise TypeError(\"{} not supported by assertion comparison\".format(type(a)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_allclose_assert_allclose.if_isinstance_a_Variable.else_.raise_TypeError_not_s": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_allclose_assert_allclose.if_isinstance_a_Variable.else_.raise_TypeError_not_s", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 92, "end_line": 141, "span_ids": ["assert_allclose"], "tokens": 492}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_allclose(a, b, rtol=1e-05, atol=1e-08, decode_bytes=True):\n \"\"\"Like :py:func:`numpy.testing.assert_allclose`, but for xarray objects.\n\n Raises an AssertionError if two objects are not equal up to desired\n tolerance.\n\n Parameters\n ----------\n a : xarray.Dataset, xarray.DataArray or xarray.Variable\n The first object to compare.\n b : xarray.Dataset, xarray.DataArray or xarray.Variable\n The second object to compare.\n rtol : float, optional\n Relative tolerance.\n atol : float, optional\n Absolute tolerance.\n decode_bytes : bool, optional\n Whether byte dtypes should be decoded to strings as UTF-8 or not.\n This is useful for testing serialization methods on Python 3 that\n return saved strings as bytes.\n\n See also\n --------\n assert_identical, assert_equal, numpy.testing.assert_allclose\n \"\"\"\n __tracebackhide__ = True\n assert type(a) == type(b)\n kwargs = dict(rtol=rtol, atol=atol, decode_bytes=decode_bytes)\n if isinstance(a, Variable):\n assert a.dims == b.dims\n allclose = _data_allclose_or_equiv(a.values, b.values, **kwargs)\n assert allclose, \"{}\\n{}\".format(a.values, b.values)\n elif isinstance(a, DataArray):\n assert_allclose(a.variable, b.variable, **kwargs)\n assert set(a.coords) == set(b.coords)\n for v in a.coords.variables:\n # can't recurse with this function as coord is sometimes a\n # DataArray, so call into _data_allclose_or_equiv directly\n allclose = _data_allclose_or_equiv(\n a.coords[v].values, b.coords[v].values, **kwargs\n )\n assert allclose, \"{}\\n{}\".format(a.coords[v].values, b.coords[v].values)\n elif isinstance(a, Dataset):\n assert set(a.data_vars) == set(b.data_vars)\n assert set(a.coords) == set(b.coords)\n for k in list(a.variables) + list(a.coords):\n assert_allclose(a[k], b[k], **kwargs)\n\n else:\n raise TypeError(\"{} not supported by assertion comparison\".format(type(a)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_chunks_equal_assert_chunks_equal.assert_left_chunks_rig": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py_assert_chunks_equal_assert_chunks_equal.assert_left_chunks_rig", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 161, "span_ids": ["assert_chunks_equal"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_chunks_equal(a, b):\n \"\"\"\n Assert that chunksizes along chunked dimensions are equal.\n\n Parameters\n ----------\n a : xarray.Dataset or xarray.DataArray\n The first object to compare.\n b : xarray.Dataset or xarray.DataArray\n The second object to compare.\n \"\"\"\n\n if isinstance(a, DataArray) != isinstance(b, DataArray):\n raise TypeError(\"a and b have mismatched types\")\n\n left = a.unify_chunks()\n right = b.unify_chunks()\n assert left.chunks == right.chunks", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_indexes_invariants_checks__assert_indexes_invariants_checks.assert_all_v_equals_defau": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_indexes_invariants_checks__assert_indexes_invariants_checks.assert_all_v_equals_defau", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 164, "end_line": 179, "span_ids": ["_assert_indexes_invariants_checks"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_indexes_invariants_checks(indexes, possible_coord_variables, dims):\n assert isinstance(indexes, dict), indexes\n assert all(isinstance(v, pd.Index) for v in indexes.values()), {\n k: type(v) for k, v in indexes.items()\n }\n\n index_vars = {\n k for k, v in possible_coord_variables.items() if isinstance(v, IndexVariable)\n }\n assert indexes.keys() <= index_vars, (set(indexes), index_vars)\n\n # Note: when we support non-default indexes, these checks should be opt-in\n # only!\n defaults = default_indexes(possible_coord_variables, dims)\n assert indexes.keys() == defaults.keys(), (set(indexes), set(defaults))\n assert all(v.equals(defaults[k]) for k, v in indexes.items()), (indexes, defaults)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_variable_invariants__assert_variable_invariants.assert_isinstance_var__at": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_variable_invariants__assert_variable_invariants.assert_isinstance_var__at", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 182, "end_line": 195, "span_ids": ["_assert_variable_invariants"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_variable_invariants(var: Variable, name: Hashable = None):\n if name is None:\n name_or_empty: tuple = ()\n else:\n name_or_empty = (name,)\n assert isinstance(var._dims, tuple), name_or_empty + (var._dims,)\n assert len(var._dims) == len(var._data.shape), name_or_empty + (\n var._dims,\n var._data.shape,\n )\n assert isinstance(var._encoding, (type(None), dict)), name_or_empty + (\n var._encoding,\n )\n assert isinstance(var._attrs, (type(None), dict)), name_or_empty + (var._attrs,)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_dataarray_invariants__assert_dataarray_invariants.if_da__indexes_is_not_Non._assert_indexes_invariant": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_dataarray_invariants__assert_dataarray_invariants.if_da__indexes_is_not_Non._assert_indexes_invariant", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 198, "end_line": 215, "span_ids": ["_assert_dataarray_invariants"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_dataarray_invariants(da: DataArray):\n assert isinstance(da._variable, Variable), da._variable\n _assert_variable_invariants(da._variable)\n\n assert isinstance(da._coords, dict), da._coords\n assert all(isinstance(v, Variable) for v in da._coords.values()), da._coords\n assert all(set(v.dims) <= set(da.dims) for v in da._coords.values()), (\n da.dims,\n {k: v.dims for k, v in da._coords.items()},\n )\n assert all(\n isinstance(v, IndexVariable) for (k, v) in da._coords.items() if v.dims == (k,)\n ), {k: type(v) for k, v in da._coords.items()}\n for k, v in da._coords.items():\n _assert_variable_invariants(v, k)\n\n if da._indexes is not None:\n _assert_indexes_invariants_checks(da._indexes, da._coords, da.dims)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_dataset_invariants__assert_dataset_invariants.assert_isinstance_ds__att": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_dataset_invariants__assert_dataset_invariants.assert_isinstance_ds__att", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 218, "end_line": 252, "span_ids": ["_assert_dataset_invariants"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_dataset_invariants(ds: Dataset):\n assert isinstance(ds._variables, dict), type(ds._variables)\n assert all(isinstance(v, Variable) for v in ds._variables.values()), ds._variables\n for k, v in ds._variables.items():\n _assert_variable_invariants(v, k)\n\n assert isinstance(ds._coord_names, set), ds._coord_names\n assert ds._coord_names <= ds._variables.keys(), (\n ds._coord_names,\n set(ds._variables),\n )\n\n assert type(ds._dims) is dict, ds._dims\n assert all(isinstance(v, int) for v in ds._dims.values()), ds._dims\n var_dims: Set[Hashable] = set()\n for v in ds._variables.values():\n var_dims.update(v.dims)\n assert ds._dims.keys() == var_dims, (set(ds._dims), var_dims)\n assert all(\n ds._dims[k] == v.sizes[k] for v in ds._variables.values() for k in v.sizes\n ), (ds._dims, {k: v.sizes for k, v in ds._variables.items()})\n assert all(\n isinstance(v, IndexVariable)\n for (k, v) in ds._variables.items()\n if v.dims == (k,)\n ), {k: type(v) for k, v in ds._variables.items() if v.dims == (k,)}\n assert all(v.dims == (k,) for (k, v) in ds._variables.items() if k in ds._dims), {\n k: v.dims for k, v in ds._variables.items() if k in ds._dims\n }\n\n if ds._indexes is not None:\n _assert_indexes_invariants_checks(ds._indexes, ds._variables, ds._dims)\n\n assert isinstance(ds._encoding, (type(None), dict))\n assert isinstance(ds._attrs, (type(None), dict))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_internal_invariants_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/testing.py__assert_internal_invariants_", "embedding": null, "metadata": {"file_path": "xarray/testing.py", "file_name": "testing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 274, "span_ids": ["_assert_internal_invariants"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _assert_internal_invariants(xarray_obj: Union[DataArray, Dataset, Variable],):\n \"\"\"Validate that an xarray object satisfies its own internal invariants.\n\n This exists for the benefit of xarray's own test suite, but may be useful\n in external projects if they (ill-advisedly) create objects using xarray's\n private APIs.\n \"\"\"\n if isinstance(xarray_obj, Variable):\n _assert_variable_invariants(xarray_obj)\n elif isinstance(xarray_obj, DataArray):\n _assert_dataarray_invariants(xarray_obj)\n elif isinstance(xarray_obj, Dataset):\n _assert_dataset_invariants(xarray_obj)\n else:\n raise TypeError(\n \"{} is not a supported type for xarray invariant checks\".format(\n type(xarray_obj)\n )\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/__init__.py_importlib_requires_cftime_or_netCDF4.pytest_mark_skipif_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/__init__.py_importlib_requires_cftime_or_netCDF4.pytest_mark_skipif_", "embedding": null, "metadata": {"file_path": "xarray/tests/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "test", "start_line": 1, "end_line": 84, "span_ids": ["LooseVersion", "impl", "impl:2", "impl:7", "docstring", "_importorskip", "imports", "imports:18"], "tokens": 789}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import importlib\nimport platform\nimport re\nimport warnings\nfrom contextlib import contextmanager\nfrom distutils import version\nfrom unittest import mock # noqa: F401\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_equal # noqa: F401\nfrom pandas.testing import assert_frame_equal # noqa: F401\n\nimport xarray.testing\nfrom xarray.core import utils\nfrom xarray.core.duck_array_ops import allclose_or_equiv # noqa: F401\nfrom xarray.core.indexing import ExplicitlyIndexed\nfrom xarray.core.options import set_options\nfrom xarray.plot.utils import import_seaborn\n\n# import mpl and change the backend before other mpl imports\ntry:\n import matplotlib as mpl\n\n # Order of imports is important here.\n # Using a different backend makes Travis CI work\n mpl.use(\"Agg\")\nexcept ImportError:\n pass\n\n\narm_xfail = pytest.mark.xfail(\n platform.machine() == \"aarch64\" or \"arm\" in platform.machine(),\n reason=\"expected failure on ARM\",\n)\n\n\ndef _importorskip(modname, minversion=None):\n try:\n mod = importlib.import_module(modname)\n has = True\n if minversion is not None:\n if LooseVersion(mod.__version__) < LooseVersion(minversion):\n raise ImportError(\"Minimum version not satisfied\")\n except ImportError:\n has = False\n func = pytest.mark.skipif(not has, reason=\"requires {}\".format(modname))\n return has, func\n\n\ndef LooseVersion(vstring):\n # Our development version is something like '0.10.9+aac7bfc'\n # This function just ignored the git commit id.\n vstring = vstring.split(\"+\")[0]\n return version.LooseVersion(vstring)\n\n\nhas_matplotlib, requires_matplotlib = _importorskip(\"matplotlib\")\nhas_scipy, requires_scipy = _importorskip(\"scipy\")\nhas_pydap, requires_pydap = _importorskip(\"pydap.client\")\nhas_netCDF4, requires_netCDF4 = _importorskip(\"netCDF4\")\nhas_h5netcdf, requires_h5netcdf = _importorskip(\"h5netcdf\")\nhas_pynio, requires_pynio = _importorskip(\"Nio\")\nhas_pseudonetcdf, requires_pseudonetcdf = _importorskip(\"PseudoNetCDF\")\nhas_cftime, requires_cftime = _importorskip(\"cftime\")\nhas_dask, requires_dask = _importorskip(\"dask\")\nhas_bottleneck, requires_bottleneck = _importorskip(\"bottleneck\")\nhas_nc_time_axis, requires_nc_time_axis = _importorskip(\"nc_time_axis\")\nhas_rasterio, requires_rasterio = _importorskip(\"rasterio\")\nhas_zarr, requires_zarr = _importorskip(\"zarr\")\nhas_iris, requires_iris = _importorskip(\"iris\")\nhas_cfgrib, requires_cfgrib = _importorskip(\"cfgrib\")\nhas_numbagg, requires_numbagg = _importorskip(\"numbagg\")\nhas_sparse, requires_sparse = _importorskip(\"sparse\")\n\n# some special cases\nhas_scipy_or_netCDF4 = has_scipy or has_netCDF4\nrequires_scipy_or_netCDF4 = pytest.mark.skipif(\n not has_scipy_or_netCDF4, reason=\"requires scipy or netCDF4\"\n)\nhas_cftime_or_netCDF4 = has_cftime or has_netCDF4\nrequires_cftime_or_netCDF4 = pytest.mark.skipif(\n not has_cftime_or_netCDF4, reason=\"requires cftime or netCDF4\"\n)\ntry:\n# ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/__init__.py_None_1_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/__init__.py_None_1_", "embedding": null, "metadata": {"file_path": "xarray/tests/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 176, "span_ids": ["InaccessibleArray", "assert_allclose", "ReturnItem.__getitem__", "assert_identical", "impl:7", "IndexerMaker.__getitem__", "source_ndarray", "InaccessibleArray.__getitem__", "UnexpectedDataAccess", "assert_equal", "IndexerMaker", "ReturnItem", "raises_regex"], "tokens": 551}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": " # noqa: F401\ntry:\n import_seaborn()\n has_seaborn = True\nexcept ImportError:\n has_seaborn = False\nrequires_seaborn = pytest.mark.skipif(not has_seaborn, reason=\"requires seaborn\")\n\n# change some global options for tests\nset_options(warn_for_unclosed_files=True)\n\nif has_dask:\n import dask\n\n dask.config.set(scheduler=\"single-threaded\")\n\nflaky = pytest.mark.flaky\nnetwork = pytest.mark.network\n\n\n@contextmanager\ndef raises_regex(error, pattern):\n __tracebackhide__ = True\n with pytest.raises(error) as excinfo:\n yield\n message = str(excinfo.value)\n if not re.search(pattern, message):\n raise AssertionError(\n \"exception %r did not match pattern %r\" % (excinfo.value, pattern)\n )\n\n\nclass UnexpectedDataAccess(Exception):\n pass\n\n\nclass InaccessibleArray(utils.NDArrayMixin, ExplicitlyIndexed):\n def __init__(self, array):\n self.array = array\n\n def __getitem__(self, key):\n raise UnexpectedDataAccess(\"Tried accessing data\")\n\n\nclass ReturnItem:\n def __getitem__(self, key):\n return key\n\n\nclass IndexerMaker:\n def __init__(self, indexer_cls):\n self._indexer_cls = indexer_cls\n\n def __getitem__(self, key):\n if not isinstance(key, tuple):\n key = (key,)\n return self._indexer_cls(key)\n\n\ndef source_ndarray(array):\n \"\"\"Given an ndarray, return the base object which holds its memory, or the\n object itself.\n \"\"\"\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", \"DatetimeIndex.base\")\n warnings.filterwarnings(\"ignore\", \"TimedeltaIndex.base\")\n base = getattr(array, \"base\", np.asarray(array).base)\n if base is None:\n base = array\n return base\n\n\n# Internal versions of xarray's test functions that validate additional\n# invariants\n\n\ndef assert_equal(a, b):\n xarray.testing.assert_equal(a, b)\n xarray.testing._assert_internal_invariants(a)\n xarray.testing._assert_internal_invariants(b)\n\n\ndef assert_identical(a, b):\n xarray.testing.assert_identical(a, b)\n xarray.testing._assert_internal_invariants(a)\n xarray.testing._assert_internal_invariants(b)\n\n\ndef assert_allclose(a, b, **kwargs):\n xarray.testing.assert_allclose(a, b, **kwargs)\n xarray.testing._assert_internal_invariants(a)\n xarray.testing._assert_internal_invariants(b)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_np_TestDatetimeAccessor.setup.self.times_data.xr_DataArray_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_np_TestDatetimeAccessor.setup.self.times_data.xr_DataArray_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 1, "end_line": 40, "span_ids": ["TestDatetimeAccessor.setup", "TestDatetimeAccessor", "imports"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\nimport pandas as pd\nimport pytest\n\nimport xarray as xr\n\nfrom . import (\n assert_array_equal,\n assert_equal,\n has_cftime,\n has_cftime_or_netCDF4,\n has_dask,\n raises_regex,\n requires_dask,\n)\n\n\nclass TestDatetimeAccessor:\n @pytest.fixture(autouse=True)\n def setup(self):\n nt = 100\n data = np.random.rand(10, 10, nt)\n lons = np.linspace(0, 11, 10)\n lats = np.linspace(0, 20, 10)\n self.times = pd.date_range(start=\"2000/01/01\", freq=\"H\", periods=nt)\n\n self.data = xr.DataArray(\n data,\n coords=[lons, lats, self.times],\n dims=[\"lon\", \"lat\", \"time\"],\n name=\"data\",\n )\n\n self.times_arr = np.random.choice(self.times, size=(10, 10, nt))\n self.times_data = xr.DataArray(\n self.times_arr,\n coords=[lons, lats, self.times],\n dims=[\"lon\", \"lat\", \"time\"],\n name=\"data\",\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_field_access_TestDatetimeAccessor.test_field_access.assert_equal_hours_self_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_field_access_TestDatetimeAccessor.test_field_access.assert_equal_hours_self_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 59, "span_ids": ["TestDatetimeAccessor.test_field_access"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatetimeAccessor:\n\n def test_field_access(self):\n years = xr.DataArray(\n self.times.year, name=\"year\", coords=[self.times], dims=[\"time\"]\n )\n months = xr.DataArray(\n self.times.month, name=\"month\", coords=[self.times], dims=[\"time\"]\n )\n days = xr.DataArray(\n self.times.day, name=\"day\", coords=[self.times], dims=[\"time\"]\n )\n hours = xr.DataArray(\n self.times.hour, name=\"hour\", coords=[self.times], dims=[\"time\"]\n )\n\n assert_equal(years, self.data.time.dt.year)\n assert_equal(months, self.data.time.dt.month)\n assert_equal(days, self.data.time.dt.day)\n assert_equal(hours, self.data.time.dt.hour)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_strftime_TestDatetimeAccessor.test_not_datetime_type.with_raises_regex_TypeErr.nontime_data_time_dt": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_strftime_TestDatetimeAccessor.test_not_datetime_type.with_raises_regex_TypeErr.nontime_data_time_dt", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 71, "span_ids": ["TestDatetimeAccessor.test_not_datetime_type", "TestDatetimeAccessor.test_strftime"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatetimeAccessor:\n\n def test_strftime(self):\n assert (\n \"2000-01-01 01:00:00\" == self.data.time.dt.strftime(\"%Y-%m-%d %H:%M:%S\")[1]\n )\n\n def test_not_datetime_type(self):\n nontime_data = self.data.copy()\n int_data = np.arange(len(self.data.time)).astype(\"int8\")\n nontime_data[\"time\"].values = int_data\n with raises_regex(TypeError, \"dt\"):\n nontime_data.time.dt", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_dask_field_access_TestDatetimeAccessor.test_dask_field_access.assert_equal_strftime_da": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_dask_field_access_TestDatetimeAccessor.test_dask_field_access.assert_equal_strftime_da", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 73, "end_line": 122, "span_ids": ["TestDatetimeAccessor.test_dask_field_access"], "tokens": 563}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatetimeAccessor:\n\n @requires_dask\n def test_dask_field_access(self):\n import dask.array as da\n\n years = self.times_data.dt.year\n months = self.times_data.dt.month\n hours = self.times_data.dt.hour\n days = self.times_data.dt.day\n floor = self.times_data.dt.floor(\"D\")\n ceil = self.times_data.dt.ceil(\"D\")\n round = self.times_data.dt.round(\"D\")\n strftime = self.times_data.dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n dask_times_arr = da.from_array(self.times_arr, chunks=(5, 5, 50))\n dask_times_2d = xr.DataArray(\n dask_times_arr, coords=self.data.coords, dims=self.data.dims, name=\"data\"\n )\n dask_year = dask_times_2d.dt.year\n dask_month = dask_times_2d.dt.month\n dask_day = dask_times_2d.dt.day\n dask_hour = dask_times_2d.dt.hour\n dask_floor = dask_times_2d.dt.floor(\"D\")\n dask_ceil = dask_times_2d.dt.ceil(\"D\")\n dask_round = dask_times_2d.dt.round(\"D\")\n dask_strftime = dask_times_2d.dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Test that the data isn't eagerly evaluated\n assert isinstance(dask_year.data, da.Array)\n assert isinstance(dask_month.data, da.Array)\n assert isinstance(dask_day.data, da.Array)\n assert isinstance(dask_hour.data, da.Array)\n assert isinstance(dask_strftime.data, da.Array)\n\n # Double check that outcome chunksize is unchanged\n dask_chunks = dask_times_2d.chunks\n assert dask_year.data.chunks == dask_chunks\n assert dask_month.data.chunks == dask_chunks\n assert dask_day.data.chunks == dask_chunks\n assert dask_hour.data.chunks == dask_chunks\n assert dask_strftime.data.chunks == dask_chunks\n\n # Check the actual output from the accessors\n assert_equal(years, dask_year.compute())\n assert_equal(months, dask_month.compute())\n assert_equal(days, dask_day.compute())\n assert_equal(hours, dask_hour.compute())\n assert_equal(floor, dask_floor.compute())\n assert_equal(ceil, dask_ceil.compute())\n assert_equal(round, dask_round.compute())\n assert_equal(strftime, dask_strftime.compute())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_seasons_TestDatetimeAccessor.test_seasons.assert_array_equal_season": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_seasons_TestDatetimeAccessor.test_seasons.assert_array_equal_season", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 124, "end_line": 143, "span_ids": ["TestDatetimeAccessor.test_seasons"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatetimeAccessor:\n\n def test_seasons(self):\n dates = pd.date_range(start=\"2000/01/01\", freq=\"M\", periods=12)\n dates = xr.DataArray(dates)\n seasons = [\n \"DJF\",\n \"DJF\",\n \"MAM\",\n \"MAM\",\n \"MAM\",\n \"JJA\",\n \"JJA\",\n \"JJA\",\n \"SON\",\n \"SON\",\n \"SON\",\n \"DJF\",\n ]\n seasons = xr.DataArray(seasons)\n\n assert_array_equal(seasons.values, dates.dt.season.values)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_rounders_TestDatetimeAccessor.test_rounders.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_TestDatetimeAccessor.test_rounders_TestDatetimeAccessor.test_rounders.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 145, "end_line": 150, "span_ids": ["TestDatetimeAccessor.test_rounders"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatetimeAccessor:\n\n def test_rounders(self):\n dates = pd.date_range(\"2014-01-01\", \"2014-05-01\", freq=\"H\")\n xdates = xr.DataArray(np.arange(len(dates)), dims=[\"time\"], coords=[dates])\n assert_array_equal(dates.floor(\"D\").values, xdates.time.dt.floor(\"D\").values)\n assert_array_equal(dates.ceil(\"D\").values, xdates.time.dt.ceil(\"D\").values)\n assert_array_equal(dates.round(\"D\").values, xdates.time.dt.round(\"D\").values)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py__CFTIME_CALENDARS_times_3d.return.xr_DataArray_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py__CFTIME_CALENDARS_times_3d.return.xr_DataArray_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 153, "end_line": 199, "span_ids": ["impl", "times", "data", "calendar", "times_3d"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_CFTIME_CALENDARS = [\n \"365_day\",\n \"360_day\",\n \"julian\",\n \"all_leap\",\n \"366_day\",\n \"gregorian\",\n \"proleptic_gregorian\",\n]\n_NT = 100\n\n\n@pytest.fixture(params=_CFTIME_CALENDARS)\ndef calendar(request):\n return request.param\n\n\n@pytest.fixture()\ndef times(calendar):\n import cftime\n\n return cftime.num2date(\n np.arange(_NT),\n units=\"hours since 2000-01-01\",\n calendar=calendar,\n only_use_cftime_datetimes=True,\n )\n\n\n@pytest.fixture()\ndef data(times):\n data = np.random.rand(10, 10, _NT)\n lons = np.linspace(0, 11, 10)\n lats = np.linspace(0, 20, 10)\n return xr.DataArray(\n data, coords=[lons, lats, times], dims=[\"lon\", \"lat\", \"time\"], name=\"data\"\n )\n\n\n@pytest.fixture()\ndef times_3d(times):\n lons = np.linspace(0, 11, 10)\n lats = np.linspace(0, 20, 10)\n times_arr = np.random.choice(times, size=(10, 10, _NT))\n return xr.DataArray(\n times_arr, coords=[lons, lats, times], dims=[\"lon\", \"lat\", \"time\"], name=\"data\"\n )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_field_access_test_field_access.assert_equal_result_expe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_field_access_test_field_access.assert_equal_result_expe", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 202, "end_line": 217, "span_ids": ["test_field_access"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.mark.skipif(not has_cftime, reason=\"cftime not installed\")\n@pytest.mark.parametrize(\n \"field\", [\"year\", \"month\", \"day\", \"hour\", \"dayofyear\", \"dayofweek\"]\n)\ndef test_field_access(data, field):\n if field == \"dayofyear\" or field == \"dayofweek\":\n pytest.importorskip(\"cftime\", minversion=\"1.0.2.1\")\n result = getattr(data.time.dt, field)\n expected = xr.DataArray(\n getattr(xr.coding.cftimeindex.CFTimeIndex(data.time.values), field),\n name=field,\n coords=data.time.coords,\n dims=data.time.dims,\n )\n\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_cftime_strftime_access_test_cftime_strftime_access.assert_equal_result_expe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_cftime_strftime_access_test_cftime_strftime_access.assert_equal_result_expe", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 220, "end_line": 232, "span_ids": ["test_cftime_strftime_access"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.mark.skipif(not has_cftime, reason=\"cftime not installed\")\ndef test_cftime_strftime_access(data):\n \"\"\" compare cftime formatting against datetime formatting \"\"\"\n date_format = \"%Y%m%d%H\"\n result = data.time.dt.strftime(date_format)\n datetime_array = xr.DataArray(\n xr.coding.cftimeindex.CFTimeIndex(data.time.values).to_datetimeindex(),\n name=\"stftime\",\n coords=data.time.coords,\n dims=data.time.dims,\n )\n expected = datetime_array.dt.strftime(date_format)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_dask_field_access_1d_test_dask_field_access_1d.assert_equal_result_compu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_dask_field_access_1d_test_dask_field_access_1d.assert_equal_result_compu", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 235, "end_line": 254, "span_ids": ["test_dask_field_access_1d"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.mark.skipif(not has_dask, reason=\"dask not installed\")\n@pytest.mark.skipif(not has_cftime, reason=\"cftime not installed\")\n@pytest.mark.parametrize(\n \"field\", [\"year\", \"month\", \"day\", \"hour\", \"dayofyear\", \"dayofweek\"]\n)\ndef test_dask_field_access_1d(data, field):\n import dask.array as da\n\n if field == \"dayofyear\" or field == \"dayofweek\":\n pytest.importorskip(\"cftime\", minversion=\"1.0.2.1\")\n expected = xr.DataArray(\n getattr(xr.coding.cftimeindex.CFTimeIndex(data.time.values), field),\n name=field,\n dims=[\"time\"],\n )\n times = xr.DataArray(data.time.values, dims=[\"time\"]).chunk({\"time\": 50})\n result = getattr(times.dt, field)\n assert isinstance(result.data, da.Array)\n assert result.chunks == times.chunks\n assert_equal(result.compute(), expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_dask_field_access_test_dask_field_access.assert_equal_result_compu": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_test_dask_field_access_test_dask_field_access.assert_equal_result_compu", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 257, "end_line": 279, "span_ids": ["test_dask_field_access"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.mark.skipif(not has_dask, reason=\"dask not installed\")\n@pytest.mark.skipif(not has_cftime, reason=\"cftime not installed\")\n@pytest.mark.parametrize(\n \"field\", [\"year\", \"month\", \"day\", \"hour\", \"dayofyear\", \"dayofweek\"]\n)\ndef test_dask_field_access(times_3d, data, field):\n import dask.array as da\n\n if field == \"dayofyear\" or field == \"dayofweek\":\n pytest.importorskip(\"cftime\", minversion=\"1.0.2.1\")\n expected = xr.DataArray(\n getattr(\n xr.coding.cftimeindex.CFTimeIndex(times_3d.values.ravel()), field\n ).reshape(times_3d.shape),\n name=field,\n coords=times_3d.coords,\n dims=times_3d.dims,\n )\n times_3d = times_3d.chunk({\"lon\": 5, \"lat\": 5, \"time\": 50})\n result = getattr(times_3d.dt, field)\n assert isinstance(result.data, da.Array)\n assert result.chunks == times_3d.chunks\n assert_equal(result.compute(), expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_cftime_date_type_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_dt.py_cftime_date_type_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_dt.py", "file_name": "test_accessor_dt.py", "file_type": "text/x-python", "category": "test", "start_line": 282, "end_line": 322, "span_ids": ["test_seasons", "cftime_date_type", "test_dt_accessor_error_netCDF4"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@pytest.fixture()\ndef cftime_date_type(calendar):\n from .test_coding_times import _all_cftime_date_types\n\n return _all_cftime_date_types()[calendar]\n\n\n@pytest.mark.skipif(not has_cftime, reason=\"cftime not installed\")\ndef test_seasons(cftime_date_type):\n dates = np.array([cftime_date_type(2000, month, 15) for month in range(1, 13)])\n dates = xr.DataArray(dates)\n seasons = [\n \"DJF\",\n \"DJF\",\n \"MAM\",\n \"MAM\",\n \"MAM\",\n \"JJA\",\n \"JJA\",\n \"JJA\",\n \"SON\",\n \"SON\",\n \"SON\",\n \"DJF\",\n ]\n seasons = xr.DataArray(seasons)\n\n assert_array_equal(seasons.values, dates.dt.season.values)\n\n\n@pytest.mark.skipif(not has_cftime_or_netCDF4, reason=\"cftime or netCDF4 not installed\")\ndef test_dt_accessor_error_netCDF4(cftime_date_type):\n da = xr.DataArray(\n [cftime_date_type(1, 1, 1), cftime_date_type(2, 1, 1)], dims=[\"time\"]\n )\n if not has_cftime:\n with pytest.raises(TypeError):\n da.dt.month\n else:\n da.dt.month", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py__Tests_for_the_str_acc_test_count.assert_equal_result_expe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py__Tests_for_the_str_acc_test_count.assert_equal_result_expe", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 1, "end_line": 71, "span_ids": ["test_dask", "dtype", "docstring", "test_count", "imports"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Tests for the `str` accessor are derived from the original\n# pandas string accessor tests.\n\nimport re\n\nimport numpy as np\nimport pytest\n\nimport xarray as xr\n\nfrom . import assert_equal, requires_dask\n\n\n@pytest.fixture(params=[np.str_, np.bytes_])\ndef dtype(request):\n return request.param\n\n\n@requires_dask\ndef test_dask():\n import dask.array as da\n\n arr = da.from_array([\"a\", \"b\", \"c\"], chunks=-1)\n xarr = xr.DataArray(arr)\n\n result = xarr.str.len().compute()\n expected = xr.DataArray([1, 1, 1])\n assert_equal(result, expected)\n\n\ndef test_count(dtype):\n values = xr.DataArray([\"foo\", \"foofoo\", \"foooofooofommmfoo\"]).astype(dtype)\n result = values.str.count(\"f[o]+\")\n expected = xr.DataArray([1, 2, 4])\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_contains_test_starts_ends_with.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_contains_test_starts_ends_with.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 93, "span_ids": ["test_starts_ends_with", "test_contains"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_contains(dtype):\n values = xr.DataArray([\"Foo\", \"xYz\", \"fOOomMm__fOo\", \"MMM_\"]).astype(dtype)\n # case insensitive using regex\n result = values.str.contains(\"FOO|mmm\", case=False)\n expected = xr.DataArray([True, False, True, True])\n assert_equal(result, expected)\n # case insensitive without regex\n result = values.str.contains(\"foo\", regex=False, case=False)\n expected = xr.DataArray([True, False, True, False])\n assert_equal(result, expected)\n\n\ndef test_starts_ends_with(dtype):\n values = xr.DataArray([\"om\", \"foo_nom\", \"nom\", \"bar_foo\", \"foo\"]).astype(dtype)\n result = values.str.startswith(\"foo\")\n expected = xr.DataArray([False, True, False, False, True])\n assert_equal(result, expected)\n result = values.str.endswith(\"foo\")\n expected = xr.DataArray([False, False, False, True, True])\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_case_test_case.assert_equal_da_str_upper": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_case_test_case.assert_equal_da_str_upper", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 96, "end_line": 107, "span_ids": ["test_case"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_case(dtype):\n da = xr.DataArray([\"SOme word\"]).astype(dtype)\n capitalized = xr.DataArray([\"Some word\"]).astype(dtype)\n lowered = xr.DataArray([\"some word\"]).astype(dtype)\n swapped = xr.DataArray([\"soME WORD\"]).astype(dtype)\n titled = xr.DataArray([\"Some Word\"]).astype(dtype)\n uppered = xr.DataArray([\"SOME WORD\"]).astype(dtype)\n assert_equal(da.str.capitalize(), capitalized)\n assert_equal(da.str.lower(), lowered)\n assert_equal(da.str.swapcase(), swapped)\n assert_equal(da.str.title(), titled)\n assert_equal(da.str.upper(), uppered)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_test_replace.None_4": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_test_replace.None_4", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 110, "end_line": 139, "span_ids": ["test_replace"], "tokens": 341}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_replace(dtype):\n values = xr.DataArray([\"fooBAD__barBAD\"]).astype(dtype)\n result = values.str.replace(\"BAD[_]*\", \"\")\n expected = xr.DataArray([\"foobar\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.replace(\"BAD[_]*\", \"\", n=1)\n expected = xr.DataArray([\"foobarBAD\"]).astype(dtype)\n assert_equal(result, expected)\n\n s = xr.DataArray([\"A\", \"B\", \"C\", \"Aaba\", \"Baca\", \"\", \"CABA\", \"dog\", \"cat\"]).astype(\n dtype\n )\n result = s.str.replace(\"A\", \"YYY\")\n expected = xr.DataArray(\n [\"YYY\", \"B\", \"C\", \"YYYaba\", \"Baca\", \"\", \"CYYYBYYY\", \"dog\", \"cat\"]\n ).astype(dtype)\n assert_equal(result, expected)\n\n result = s.str.replace(\"A\", \"YYY\", case=False)\n expected = xr.DataArray(\n [\"YYY\", \"B\", \"C\", \"YYYYYYbYYY\", \"BYYYcYYY\", \"\", \"CYYYBYYY\", \"dog\", \"cYYYt\"]\n ).astype(dtype)\n assert_equal(result, expected)\n\n result = s.str.replace(\"^.a|dog\", \"XX-XX \", case=False)\n expected = xr.DataArray(\n [\"A\", \"B\", \"C\", \"XX-XX ba\", \"XX-XX ca\", \"\", \"XX-XX BA\", \"XX-XX \", \"XX-XX t\"]\n ).astype(dtype)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_callable_test_replace_unicode.assert_equal_result_expe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_callable_test_replace_unicode.assert_equal_result_expe", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 142, "end_line": 164, "span_ids": ["test_replace_callable", "test_replace_unicode"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_replace_callable():\n values = xr.DataArray([\"fooBAD__barBAD\"])\n # test with callable\n repl = lambda m: m.group(0).swapcase()\n result = values.str.replace(\"[a-z][A-Z]{2}\", repl, n=2)\n exp = xr.DataArray([\"foObaD__baRbaD\"])\n assert_equal(result, exp)\n # test regex named groups\n values = xr.DataArray([\"Foo Bar Baz\"])\n pat = r\"(?P\\w+) (?P\\w+) (?P\\w+)\"\n repl = lambda m: m.group(\"middle\").swapcase()\n result = values.str.replace(pat, repl)\n exp = xr.DataArray([\"bAR\"])\n assert_equal(result, exp)\n\n\ndef test_replace_unicode():\n # flags + unicode\n values = xr.DataArray([b\"abcd,\\xc3\\xa0\".decode(\"utf-8\")])\n expected = xr.DataArray([b\"abcd, \\xc3\\xa0\".decode(\"utf-8\")])\n pat = re.compile(r\"(?<=\\w),(?=\\w)\", flags=re.UNICODE)\n result = values.str.replace(pat, \", \")\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_compiled_regex_test_replace_compiled_regex.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_compiled_regex_test_replace_compiled_regex.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 167, "end_line": 199, "span_ids": ["test_replace_compiled_regex"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_replace_compiled_regex(dtype):\n values = xr.DataArray([\"fooBAD__barBAD\"]).astype(dtype)\n # test with compiled regex\n pat = re.compile(dtype(\"BAD[_]*\"))\n result = values.str.replace(pat, \"\")\n expected = xr.DataArray([\"foobar\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.replace(pat, \"\", n=1)\n expected = xr.DataArray([\"foobarBAD\"]).astype(dtype)\n assert_equal(result, expected)\n\n # case and flags provided to str.replace will have no effect\n # and will produce warnings\n values = xr.DataArray([\"fooBAD__barBAD__bad\"]).astype(dtype)\n pat = re.compile(dtype(\"BAD[_]*\"))\n\n with pytest.raises(ValueError, match=\"case and flags cannot be\"):\n result = values.str.replace(pat, \"\", flags=re.IGNORECASE)\n\n with pytest.raises(ValueError, match=\"case and flags cannot be\"):\n result = values.str.replace(pat, \"\", case=False)\n\n with pytest.raises(ValueError, match=\"case and flags cannot be\"):\n result = values.str.replace(pat, \"\", case=True)\n\n # test with callable\n values = xr.DataArray([\"fooBAD__barBAD\"]).astype(dtype)\n repl = lambda m: m.group(0).swapcase()\n pat = re.compile(dtype(\"[a-z][A-Z]{2}\"))\n result = values.str.replace(pat, repl, n=2)\n expected = xr.DataArray([\"foObaD__baRbaD\"]).astype(dtype)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_literal_test_replace_literal.None_1.values_str_replace_compil": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_replace_literal_test_replace_literal.None_1.values_str_replace_compil", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 202, "end_line": 224, "span_ids": ["test_replace_literal"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_replace_literal(dtype):\n # GH16808 literal replace (regex=False vs regex=True)\n values = xr.DataArray([\"f.o\", \"foo\"]).astype(dtype)\n expected = xr.DataArray([\"bao\", \"bao\"]).astype(dtype)\n result = values.str.replace(\"f.\", \"ba\")\n assert_equal(result, expected)\n\n expected = xr.DataArray([\"bao\", \"foo\"]).astype(dtype)\n result = values.str.replace(\"f.\", \"ba\", regex=False)\n assert_equal(result, expected)\n\n # Cannot do a literal replace if given a callable repl or compiled\n # pattern\n callable_repl = lambda m: m.group(0).swapcase()\n compiled_pat = re.compile(\"[a-z][A-Z]{2}\")\n\n msg = \"Cannot use a callable replacement when regex=False\"\n with pytest.raises(ValueError, match=msg):\n values.str.replace(\"abc\", callable_repl, regex=False)\n\n msg = \"Cannot use a compiled regex as replacement pattern with regex=False\"\n with pytest.raises(ValueError, match=msg):\n values.str.replace(compiled_pat, \"\", regex=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_repeat_test_match.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_repeat_test_match.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 227, "end_line": 244, "span_ids": ["test_repeat", "test_match"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_repeat(dtype):\n values = xr.DataArray([\"a\", \"b\", \"c\", \"d\"]).astype(dtype)\n result = values.str.repeat(3)\n expected = xr.DataArray([\"aaa\", \"bbb\", \"ccc\", \"ddd\"]).astype(dtype)\n assert_equal(result, expected)\n\n\ndef test_match(dtype):\n # New match behavior introduced in 0.13\n values = xr.DataArray([\"fooBAD__barBAD\", \"foo\"]).astype(dtype)\n result = values.str.match(\".*(BAD[_]+).*(BAD)\")\n expected = xr.DataArray([True, False])\n assert_equal(result, expected)\n\n values = xr.DataArray([\"fooBAD__barBAD\", \"foo\"]).astype(dtype)\n result = values.str.match(\".*BAD[_]+.*BAD\")\n expected = xr.DataArray([True, False])\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_empty_str_methods_test_empty_str_methods.None_35": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_empty_str_methods_test_empty_str_methods.None_35", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 247, "end_line": 290, "span_ids": ["test_empty_str_methods"], "tokens": 504}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_empty_str_methods():\n empty = xr.DataArray(np.empty(shape=(0,), dtype=\"U\"))\n empty_str = empty\n empty_int = xr.DataArray(np.empty(shape=(0,), dtype=int))\n empty_bool = xr.DataArray(np.empty(shape=(0,), dtype=bool))\n empty_bytes = xr.DataArray(np.empty(shape=(0,), dtype=\"S\"))\n\n assert_equal(empty_str, empty.str.title())\n assert_equal(empty_int, empty.str.count(\"a\"))\n assert_equal(empty_bool, empty.str.contains(\"a\"))\n assert_equal(empty_bool, empty.str.startswith(\"a\"))\n assert_equal(empty_bool, empty.str.endswith(\"a\"))\n assert_equal(empty_str, empty.str.lower())\n assert_equal(empty_str, empty.str.upper())\n assert_equal(empty_str, empty.str.replace(\"a\", \"b\"))\n assert_equal(empty_str, empty.str.repeat(3))\n assert_equal(empty_bool, empty.str.match(\"^a\"))\n assert_equal(empty_int, empty.str.len())\n assert_equal(empty_int, empty.str.find(\"a\"))\n assert_equal(empty_int, empty.str.rfind(\"a\"))\n assert_equal(empty_str, empty.str.pad(42))\n assert_equal(empty_str, empty.str.center(42))\n assert_equal(empty_str, empty.str.slice(stop=1))\n assert_equal(empty_str, empty.str.slice(step=1))\n assert_equal(empty_str, empty.str.strip())\n assert_equal(empty_str, empty.str.lstrip())\n assert_equal(empty_str, empty.str.rstrip())\n assert_equal(empty_str, empty.str.wrap(42))\n assert_equal(empty_str, empty.str.get(0))\n assert_equal(empty_str, empty_bytes.str.decode(\"ascii\"))\n assert_equal(empty_bytes, empty.str.encode(\"ascii\"))\n assert_equal(empty_str, empty.str.isalnum())\n assert_equal(empty_str, empty.str.isalpha())\n assert_equal(empty_str, empty.str.isdigit())\n assert_equal(empty_str, empty.str.isspace())\n assert_equal(empty_str, empty.str.islower())\n assert_equal(empty_str, empty.str.isupper())\n assert_equal(empty_str, empty.str.istitle())\n assert_equal(empty_str, empty.str.isnumeric())\n assert_equal(empty_str, empty.str.isdecimal())\n assert_equal(empty_str, empty.str.capitalize())\n assert_equal(empty_str, empty.str.swapcase())\n table = str.maketrans(\"a\", \"b\")\n assert_equal(empty_str, empty.str.translate(table))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_ismethods_test_ismethods.None_6": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_ismethods_test_ismethods.None_6", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 293, "end_line": 310, "span_ids": ["test_ismethods"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_ismethods(dtype):\n values = [\"A\", \"b\", \"Xy\", \"4\", \"3A\", \"\", \"TT\", \"55\", \"-\", \" \"]\n str_s = xr.DataArray(values).astype(dtype)\n alnum_e = [True, True, True, True, True, False, True, True, False, False]\n alpha_e = [True, True, True, False, False, False, True, False, False, False]\n digit_e = [False, False, False, True, False, False, False, True, False, False]\n space_e = [False, False, False, False, False, False, False, False, False, True]\n lower_e = [False, True, False, False, False, False, False, False, False, False]\n upper_e = [True, False, False, False, True, False, True, False, False, False]\n title_e = [True, False, True, False, True, False, False, False, False, False]\n\n assert_equal(str_s.str.isalnum(), xr.DataArray(alnum_e))\n assert_equal(str_s.str.isalpha(), xr.DataArray(alpha_e))\n assert_equal(str_s.str.isdigit(), xr.DataArray(digit_e))\n assert_equal(str_s.str.isspace(), xr.DataArray(space_e))\n assert_equal(str_s.str.islower(), xr.DataArray(lower_e))\n assert_equal(str_s.str.isupper(), xr.DataArray(upper_e))\n assert_equal(str_s.str.istitle(), xr.DataArray(title_e))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_isnumeric_test_len.assert_equal_result_expe": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_isnumeric_test_len.assert_equal_result_expe", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 313, "end_line": 330, "span_ids": ["test_len", "test_isnumeric"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_isnumeric():\n # 0x00bc: \u00bc VULGAR FRACTION ONE QUARTER\n # 0x2605: \u2605 not number\n # 0x1378: \u1378 ETHIOPIC NUMBER SEVENTY\n # 0xFF13: \uff13 Em 3\n values = [\"A\", \"3\", \"\u00bc\", \"\u2605\", \"\u1378\", \"\uff13\", \"four\"]\n s = xr.DataArray(values)\n numeric_e = [False, True, True, False, True, True, False]\n decimal_e = [False, True, False, False, False, True, False]\n assert_equal(s.str.isnumeric(), xr.DataArray(numeric_e))\n assert_equal(s.str.isdecimal(), xr.DataArray(decimal_e))\n\n\ndef test_len(dtype):\n values = [\"foo\", \"fooo\", \"fooooo\", \"fooooooo\"]\n result = xr.DataArray(values).astype(dtype).str.len()\n expected = xr.DataArray([len(x) for x in values])\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_find_test_find.assert_equal_result_xp_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_find_test_find.assert_equal_result_xp_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 333, "end_line": 364, "span_ids": ["test_find"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_find(dtype):\n values = xr.DataArray([\"ABCDEFG\", \"BCDEFEF\", \"DEFGHIJEF\", \"EFGHEF\", \"XXX\"])\n values = values.astype(dtype)\n result = values.str.find(\"EF\")\n assert_equal(result, xr.DataArray([4, 3, 1, 0, -1]))\n expected = xr.DataArray([v.find(dtype(\"EF\")) for v in values.values])\n assert_equal(result, expected)\n\n result = values.str.rfind(\"EF\")\n assert_equal(result, xr.DataArray([4, 5, 7, 4, -1]))\n expected = xr.DataArray([v.rfind(dtype(\"EF\")) for v in values.values])\n assert_equal(result, expected)\n\n result = values.str.find(\"EF\", 3)\n assert_equal(result, xr.DataArray([4, 3, 7, 4, -1]))\n expected = xr.DataArray([v.find(dtype(\"EF\"), 3) for v in values.values])\n assert_equal(result, expected)\n\n result = values.str.rfind(\"EF\", 3)\n assert_equal(result, xr.DataArray([4, 5, 7, 4, -1]))\n expected = xr.DataArray([v.rfind(dtype(\"EF\"), 3) for v in values.values])\n assert_equal(result, expected)\n\n result = values.str.find(\"EF\", 3, 6)\n assert_equal(result, xr.DataArray([4, 3, -1, 4, -1]))\n expected = xr.DataArray([v.find(dtype(\"EF\"), 3, 6) for v in values.values])\n assert_equal(result, expected)\n\n result = values.str.rfind(\"EF\", 3, 6)\n assert_equal(result, xr.DataArray([4, 3, -1, 4, -1]))\n xp = xr.DataArray([v.rfind(dtype(\"EF\"), 3, 6) for v in values.values])\n assert_equal(result, xp)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_index_test_index.with_pytest_raises_ValueE.result.s_str_index_DE_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_index_test_index.with_pytest_raises_ValueE.result.s_str_index_DE_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 367, "end_line": 389, "span_ids": ["test_index"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_index(dtype):\n s = xr.DataArray([\"ABCDEFG\", \"BCDEFEF\", \"DEFGHIJEF\", \"EFGHEF\"]).astype(dtype)\n\n result = s.str.index(\"EF\")\n assert_equal(result, xr.DataArray([4, 3, 1, 0]))\n\n result = s.str.rindex(\"EF\")\n assert_equal(result, xr.DataArray([4, 5, 7, 4]))\n\n result = s.str.index(\"EF\", 3)\n assert_equal(result, xr.DataArray([4, 3, 7, 4]))\n\n result = s.str.rindex(\"EF\", 3)\n assert_equal(result, xr.DataArray([4, 5, 7, 4]))\n\n result = s.str.index(\"E\", 4, 8)\n assert_equal(result, xr.DataArray([4, 5, 7, 4]))\n\n result = s.str.rindex(\"E\", 0, 5)\n assert_equal(result, xr.DataArray([4, 3, 1, 4]))\n\n with pytest.raises(ValueError):\n result = s.str.index(\"DE\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_pad_test_pad.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_pad_test_pad.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 392, "end_line": 405, "span_ids": ["test_pad"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_pad(dtype):\n values = xr.DataArray([\"a\", \"b\", \"c\", \"eeeee\"]).astype(dtype)\n\n result = values.str.pad(5, side=\"left\")\n expected = xr.DataArray([\" a\", \" b\", \" c\", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.pad(5, side=\"right\")\n expected = xr.DataArray([\"a \", \"b \", \"c \", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.pad(5, side=\"both\")\n expected = xr.DataArray([\" a \", \" b \", \" c \", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_pad_fillchar_test_pad_fillchar.with_pytest_raises_TypeEr.result.values_str_pad_5_fillcha": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_pad_fillchar_test_pad_fillchar.with_pytest_raises_TypeEr.result.values_str_pad_5_fillcha", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 408, "end_line": 425, "span_ids": ["test_pad_fillchar"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_pad_fillchar(dtype):\n values = xr.DataArray([\"a\", \"b\", \"c\", \"eeeee\"]).astype(dtype)\n\n result = values.str.pad(5, side=\"left\", fillchar=\"X\")\n expected = xr.DataArray([\"XXXXa\", \"XXXXb\", \"XXXXc\", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.pad(5, side=\"right\", fillchar=\"X\")\n expected = xr.DataArray([\"aXXXX\", \"bXXXX\", \"cXXXX\", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.pad(5, side=\"both\", fillchar=\"X\")\n expected = xr.DataArray([\"XXaXX\", \"XXbXX\", \"XXcXX\", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n msg = \"fillchar must be a character, not str\"\n with pytest.raises(TypeError, match=msg):\n result = values.str.pad(5, fillchar=\"XY\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_translate_test_center_ljust_rjust.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_translate_test_center_ljust_rjust.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 428, "end_line": 449, "span_ids": ["test_center_ljust_rjust", "test_translate"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_translate():\n values = xr.DataArray([\"abcdefg\", \"abcc\", \"cdddfg\", \"cdefggg\"])\n table = str.maketrans(\"abc\", \"cde\")\n result = values.str.translate(table)\n expected = xr.DataArray([\"cdedefg\", \"cdee\", \"edddfg\", \"edefggg\"])\n assert_equal(result, expected)\n\n\ndef test_center_ljust_rjust(dtype):\n values = xr.DataArray([\"a\", \"b\", \"c\", \"eeeee\"]).astype(dtype)\n\n result = values.str.center(5)\n expected = xr.DataArray([\" a \", \" b \", \" c \", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.ljust(5)\n expected = xr.DataArray([\"a \", \"b \", \"c \", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.rjust(5)\n expected = xr.DataArray([\" a\", \" b\", \" c\", \"eeeee\"]).astype(dtype)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_center_ljust_rjust_fillchar_test_center_ljust_rjust_fillchar.None_2.values_str_rjust_5_fillc": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_center_ljust_rjust_fillchar_test_center_ljust_rjust_fillchar.None_2.values_str_rjust_5_fillc", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 452, "end_line": 478, "span_ids": ["test_center_ljust_rjust_fillchar"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_center_ljust_rjust_fillchar(dtype):\n values = xr.DataArray([\"a\", \"bb\", \"cccc\", \"ddddd\", \"eeeeee\"]).astype(dtype)\n result = values.str.center(5, fillchar=\"X\")\n expected = xr.DataArray([\"XXaXX\", \"XXbbX\", \"Xcccc\", \"ddddd\", \"eeeeee\"])\n assert_equal(result, expected.astype(dtype))\n\n result = values.str.ljust(5, fillchar=\"X\")\n expected = xr.DataArray([\"aXXXX\", \"bbXXX\", \"ccccX\", \"ddddd\", \"eeeeee\"])\n assert_equal(result, expected.astype(dtype))\n\n result = values.str.rjust(5, fillchar=\"X\")\n expected = xr.DataArray([\"XXXXa\", \"XXXbb\", \"Xcccc\", \"ddddd\", \"eeeeee\"])\n assert_equal(result, expected.astype(dtype))\n\n # If fillchar is not a charatter, normal str raises TypeError\n # 'aaa'.ljust(5, 'XY')\n # TypeError: must be char, not str\n template = \"fillchar must be a character, not {dtype}\"\n\n with pytest.raises(TypeError, match=template.format(dtype=\"str\")):\n values.str.center(5, fillchar=\"XY\")\n\n with pytest.raises(TypeError, match=template.format(dtype=\"str\")):\n values.str.ljust(5, fillchar=\"XY\")\n\n with pytest.raises(TypeError, match=template.format(dtype=\"str\")):\n values.str.rjust(5, fillchar=\"XY\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_zfill_test_zfill.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_zfill_test_zfill.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 481, "end_line": 490, "span_ids": ["test_zfill"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_zfill(dtype):\n values = xr.DataArray([\"1\", \"22\", \"aaa\", \"333\", \"45678\"]).astype(dtype)\n\n result = values.str.zfill(5)\n expected = xr.DataArray([\"00001\", \"00022\", \"00aaa\", \"00333\", \"45678\"])\n assert_equal(result, expected.astype(dtype))\n\n result = values.str.zfill(3)\n expected = xr.DataArray([\"001\", \"022\", \"aaa\", \"333\", \"45678\"])\n assert_equal(result, expected.astype(dtype))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_slice_test_slice.for_start_stop_step_in_.try_.except_IndexError_.raise": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_slice_test_slice.for_start_stop_step_in_.try_.except_IndexError_.raise", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 493, "end_line": 507, "span_ids": ["test_slice"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_slice(dtype):\n arr = xr.DataArray([\"aafootwo\", \"aabartwo\", \"aabazqux\"]).astype(dtype)\n\n result = arr.str.slice(2, 5)\n exp = xr.DataArray([\"foo\", \"bar\", \"baz\"]).astype(dtype)\n assert_equal(result, exp)\n\n for start, stop, step in [(0, 3, -1), (None, None, -1), (3, 10, 2), (3, 0, -1)]:\n try:\n result = arr.str[start:stop:step]\n expected = xr.DataArray([s[start:stop:step] for s in arr.values])\n assert_equal(result, expected.astype(dtype))\n except IndexError:\n print(\"failed on %s:%s:%s\" % (start, stop, step))\n raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_slice_replace_test_slice_replace.None_7": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_slice_replace_test_slice_replace.None_7", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 510, "end_line": 544, "span_ids": ["test_slice_replace"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_slice_replace(dtype):\n da = lambda x: xr.DataArray(x).astype(dtype)\n values = da([\"short\", \"a bit longer\", \"evenlongerthanthat\", \"\"])\n\n expected = da([\"shrt\", \"a it longer\", \"evnlongerthanthat\", \"\"])\n result = values.str.slice_replace(2, 3)\n assert_equal(result, expected)\n\n expected = da([\"shzrt\", \"a zit longer\", \"evznlongerthanthat\", \"z\"])\n result = values.str.slice_replace(2, 3, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"shzort\", \"a zbit longer\", \"evzenlongerthanthat\", \"z\"])\n result = values.str.slice_replace(2, 2, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"shzort\", \"a zbit longer\", \"evzenlongerthanthat\", \"z\"])\n result = values.str.slice_replace(2, 1, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"shorz\", \"a bit longez\", \"evenlongerthanthaz\", \"z\"])\n result = values.str.slice_replace(-1, None, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"zrt\", \"zer\", \"zat\", \"z\"])\n result = values.str.slice_replace(None, -2, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"shortz\", \"a bit znger\", \"evenlozerthanthat\", \"z\"])\n result = values.str.slice_replace(6, 8, \"z\")\n assert_equal(result, expected)\n\n expected = da([\"zrt\", \"a zit longer\", \"evenlongzerthanthat\", \"z\"])\n result = values.str.slice_replace(-10, 3, \"z\")\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_strip_lstrip_rstrip_test_strip_lstrip_rstrip.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_strip_lstrip_rstrip_test_strip_lstrip_rstrip.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 547, "end_line": 560, "span_ids": ["test_strip_lstrip_rstrip"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_strip_lstrip_rstrip(dtype):\n values = xr.DataArray([\" aa \", \" bb \\n\", \"cc \"]).astype(dtype)\n\n result = values.str.strip()\n expected = xr.DataArray([\"aa\", \"bb\", \"cc\"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.lstrip()\n expected = xr.DataArray([\"aa \", \"bb \\n\", \"cc \"]).astype(dtype)\n assert_equal(result, expected)\n\n result = values.str.rstrip()\n expected = xr.DataArray([\" aa\", \" bb\", \"cc\"]).astype(dtype)\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_strip_lstrip_rstrip_args_test_strip_lstrip_rstrip_args.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_strip_lstrip_rstrip_args_test_strip_lstrip_rstrip_args.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 563, "end_line": 576, "span_ids": ["test_strip_lstrip_rstrip_args"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_strip_lstrip_rstrip_args(dtype):\n values = xr.DataArray([\"xxABCxx\", \"xx BNSD\", \"LDFJH xx\"]).astype(dtype)\n\n rs = values.str.strip(\"x\")\n xp = xr.DataArray([\"ABC\", \" BNSD\", \"LDFJH \"]).astype(dtype)\n assert_equal(rs, xp)\n\n rs = values.str.lstrip(\"x\")\n xp = xr.DataArray([\"ABCxx\", \" BNSD\", \"LDFJH xx\"]).astype(dtype)\n assert_equal(rs, xp)\n\n rs = values.str.rstrip(\"x\")\n xp = xr.DataArray([\"xxABC\", \"xx BNSD\", \"LDFJH \"]).astype(dtype)\n assert_equal(rs, xp)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_wrap_test_wrap.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_wrap_test_wrap.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 579, "end_line": 621, "span_ids": ["test_wrap"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_wrap():\n # test values are: two words less than width, two words equal to width,\n # two words greater than width, one word less than width, one word\n # equal to width, one word greater than width, multiple tokens with\n # trailing whitespace equal to width\n values = xr.DataArray(\n [\n \"hello world\",\n \"hello world!\",\n \"hello world!!\",\n \"abcdefabcde\",\n \"abcdefabcdef\",\n \"abcdefabcdefa\",\n \"ab ab ab ab \",\n \"ab ab ab ab a\",\n \"\\t\",\n ]\n )\n\n # expected values\n xp = xr.DataArray(\n [\n \"hello world\",\n \"hello world!\",\n \"hello\\nworld!!\",\n \"abcdefabcde\",\n \"abcdefabcdef\",\n \"abcdefabcdef\\na\",\n \"ab ab ab ab\",\n \"ab ab ab ab\\na\",\n \"\",\n ]\n )\n\n rs = values.str.wrap(12, break_long_words=True)\n assert_equal(rs, xp)\n\n # test with pre and post whitespace (non-unicode), NaN, and non-ascii\n # Unicode\n values = xr.DataArray([\" pre \", \"\\xac\\u20ac\\U00008000 abadcafe\"])\n xp = xr.DataArray([\" pre\", \"\\xac\\u20ac\\U00008000 ab\\nadcafe\"])\n rs = values.str.wrap(6)\n assert_equal(rs, xp)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_get_test_encode_decode.assert_equal_data_decode": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_get_test_encode_decode.assert_equal_data_decode", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 624, "end_line": 649, "span_ids": ["test_encode_decode", "test_get"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_get(dtype):\n values = xr.DataArray([\"a_b_c\", \"c_d_e\", \"f_g_h\"]).astype(dtype)\n\n result = values.str[2]\n expected = xr.DataArray([\"b\", \"d\", \"g\"]).astype(dtype)\n assert_equal(result, expected)\n\n # bounds testing\n values = xr.DataArray([\"1_2_3_4_5\", \"6_7_8_9_10\", \"11_12\"]).astype(dtype)\n\n # positive index\n result = values.str[5]\n expected = xr.DataArray([\"_\", \"_\", \"\"]).astype(dtype)\n assert_equal(result, expected)\n\n # negative index\n result = values.str[-6]\n expected = xr.DataArray([\"_\", \"8\", \"\"]).astype(dtype)\n assert_equal(result, expected)\n\n\ndef test_encode_decode():\n data = xr.DataArray([\"a\", \"b\", \"a\\xe4\"])\n encoded = data.str.encode(\"utf-8\")\n decoded = encoded.str.decode(\"utf-8\")\n assert_equal(data, decoded)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_encode_decode_errors_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_accessor_str.py_test_encode_decode_errors_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_accessor_str.py", "file_name": "test_accessor_str.py", "file_type": "text/x-python", "category": "test", "start_line": 652, "end_line": 680, "span_ids": ["test_encode_decode_errors"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_encode_decode_errors():\n encodeBase = xr.DataArray([\"a\", \"b\", \"a\\x9d\"])\n\n msg = (\n r\"'charmap' codec can't encode character '\\\\x9d' in position 1:\"\n \" character maps to \"\n )\n with pytest.raises(UnicodeEncodeError, match=msg):\n encodeBase.str.encode(\"cp1252\")\n\n f = lambda x: x.encode(\"cp1252\", \"ignore\")\n result = encodeBase.str.encode(\"cp1252\", \"ignore\")\n expected = xr.DataArray([f(x) for x in encodeBase.values.tolist()])\n assert_equal(result, expected)\n\n decodeBase = xr.DataArray([b\"a\", b\"b\", b\"a\\x9d\"])\n\n msg = (\n \"'charmap' codec can't decode byte 0x9d in position 1:\"\n \" character maps to \"\n )\n with pytest.raises(UnicodeDecodeError, match=msg):\n decodeBase.str.decode(\"cp1252\")\n\n f = lambda x: x.decode(\"cp1252\", \"ignore\")\n result = decodeBase.str.decode(\"cp1252\", \"ignore\")\n expected = xr.DataArray([f(x) for x in decodeBase.values.tolist()])\n assert_equal(result, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_contextlib_create_encoded_masked_and_scaled_data.return.Dataset_x_t_np_in": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_contextlib_create_encoded_masked_and_scaled_data.return.Dataset_x_t_np_in", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 1, "end_line": 112, "span_ids": ["open_example_mfdataset", "create_masked_and_scaled_data", "impl", "impl:2", "create_encoded_masked_and_scaled_data", "open_example_dataset", "imports:31", "imports", "imports:32", "impl:5"], "tokens": 684}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import contextlib\nimport itertools\nimport math\nimport os.path\nimport pickle\nimport shutil\nimport sys\nimport tempfile\nimport warnings\nfrom contextlib import ExitStack\nfrom io import BytesIO\nfrom pathlib import Path\nfrom typing import Optional\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.errors import OutOfBoundsDatetime\n\nimport xarray as xr\nfrom xarray import (\n DataArray,\n Dataset,\n backends,\n load_dataarray,\n load_dataset,\n open_dataarray,\n open_dataset,\n open_mfdataset,\n save_mfdataset,\n)\nfrom xarray.backends.common import robust_getitem\nfrom xarray.backends.netCDF4_ import _extract_nc4_variable_encoding\nfrom xarray.backends.pydap_ import PydapDataStore\nfrom xarray.coding.variables import SerializationWarning\nfrom xarray.core import indexing\nfrom xarray.core.options import set_options\nfrom xarray.core.pycompat import dask_array_type\nfrom xarray.tests import mock\n\nfrom . import (\n arm_xfail,\n assert_allclose,\n assert_array_equal,\n assert_equal,\n assert_identical,\n has_dask,\n has_netCDF4,\n has_scipy,\n network,\n raises_regex,\n requires_cfgrib,\n requires_cftime,\n requires_dask,\n requires_h5netcdf,\n requires_netCDF4,\n requires_pseudonetcdf,\n requires_pydap,\n requires_pynio,\n requires_rasterio,\n requires_scipy,\n requires_scipy_or_netCDF4,\n requires_zarr,\n)\nfrom .test_coding_times import (\n _ALL_CALENDARS,\n _NON_STANDARD_CALENDARS,\n _STANDARD_CALENDARS,\n)\nfrom .test_dataset import create_append_test_data, create_test_data\n\ntry:\n import netCDF4 as nc4\nexcept ImportError:\n pass\n\ntry:\n import dask.array as da\nexcept ImportError:\n pass\n\nON_WINDOWS = sys.platform == \"win32\"\n\n\ndef open_example_dataset(name, *args, **kwargs):\n return open_dataset(\n os.path.join(os.path.dirname(__file__), \"data\", name), *args, **kwargs\n )\n\n\ndef open_example_mfdataset(names, *args, **kwargs):\n return open_mfdataset(\n [os.path.join(os.path.dirname(__file__), \"data\", name) for name in names],\n *args,\n **kwargs\n )\n\n\ndef create_masked_and_scaled_data():\n x = np.array([np.nan, np.nan, 10, 10.1, 10.2], dtype=np.float32)\n encoding = {\n \"_FillValue\": -1,\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n \"dtype\": \"i2\",\n }\n return Dataset({\"x\": (\"t\", x, {}, encoding)})\n\n\ndef create_encoded_masked_and_scaled_data():\n attributes = {\"_FillValue\": -1, \"add_offset\": 10, \"scale_factor\": np.float32(0.1)}\n return Dataset({\"x\": (\"t\", np.int16([-1, -1, 0, 1, 2]), attributes)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_unsigned_masked_scaled_data_create_unsigned_masked_scaled_data.return.Dataset_x_t_x_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_unsigned_masked_scaled_data_create_unsigned_masked_scaled_data.return.Dataset_x_t_x_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 115, "end_line": 124, "span_ids": ["create_unsigned_masked_scaled_data"], "tokens": 105}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_unsigned_masked_scaled_data():\n encoding = {\n \"_FillValue\": 255,\n \"_Unsigned\": \"true\",\n \"dtype\": \"i1\",\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=np.float32)\n return Dataset({\"x\": (\"t\", x, {}, encoding)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_encoded_unsigned_masked_scaled_data_create_encoded_unsigned_masked_scaled_data.return.Dataset_x_t_sb_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_encoded_unsigned_masked_scaled_data_create_encoded_unsigned_masked_scaled_data.return.Dataset_x_t_sb_a", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 127, "end_line": 138, "span_ids": ["create_encoded_unsigned_masked_scaled_data"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_encoded_unsigned_masked_scaled_data():\n # These are values as written to the file: the _FillValue will\n # be represented in the signed form.\n attributes = {\n \"_FillValue\": -1,\n \"_Unsigned\": \"true\",\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n # Create unsigned data corresponding to [0, 1, 127, 128, 255] unsigned\n sb = np.asarray([0, 1, 127, -128, -1], dtype=\"i1\")\n return Dataset({\"x\": (\"t\", sb, attributes)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_bad_unsigned_masked_scaled_data_create_bad_unsigned_masked_scaled_data.return.Dataset_x_t_x_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_bad_unsigned_masked_scaled_data_create_bad_unsigned_masked_scaled_data.return.Dataset_x_t_x_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 141, "end_line": 150, "span_ids": ["create_bad_unsigned_masked_scaled_data"], "tokens": 105}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_bad_unsigned_masked_scaled_data():\n encoding = {\n \"_FillValue\": 255,\n \"_Unsigned\": True,\n \"dtype\": \"i1\",\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=np.float32)\n return Dataset({\"x\": (\"t\", x, {}, encoding)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_bad_encoded_unsigned_masked_scaled_data_create_bad_encoded_unsigned_masked_scaled_data.return.Dataset_x_t_sb_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_bad_encoded_unsigned_masked_scaled_data_create_bad_encoded_unsigned_masked_scaled_data.return.Dataset_x_t_sb_a", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 153, "end_line": 164, "span_ids": ["create_bad_encoded_unsigned_masked_scaled_data"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_bad_encoded_unsigned_masked_scaled_data():\n # These are values as written to the file: the _FillValue will\n # be represented in the signed form.\n attributes = {\n \"_FillValue\": -1,\n \"_Unsigned\": True,\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned\n sb = np.asarray([0, 1, 127, -128, -1], dtype=\"i1\")\n return Dataset({\"x\": (\"t\", sb, attributes)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_signed_masked_scaled_data_create_encoded_signed_masked_scaled_data.return.Dataset_x_t_sb_a": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_signed_masked_scaled_data_create_encoded_signed_masked_scaled_data.return.Dataset_x_t_sb_a", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 167, "end_line": 190, "span_ids": ["create_encoded_signed_masked_scaled_data", "create_signed_masked_scaled_data"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_signed_masked_scaled_data():\n encoding = {\n \"_FillValue\": -127,\n \"_Unsigned\": \"false\",\n \"dtype\": \"i1\",\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n x = np.array([-1.0, 10.1, 22.7, np.nan], dtype=np.float32)\n return Dataset({\"x\": (\"t\", x, {}, encoding)})\n\n\ndef create_encoded_signed_masked_scaled_data():\n # These are values as written to the file: the _FillValue will\n # be represented in the signed form.\n attributes = {\n \"_FillValue\": -127,\n \"_Unsigned\": \"false\",\n \"add_offset\": 10,\n \"scale_factor\": np.float32(0.1),\n }\n # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned\n sb = np.asarray([-110, 1, 127, -127], dtype=\"i1\")\n return Dataset({\"x\": (\"t\", sb, attributes)})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_boolean_data_TestCommon.test_robust_getitem.assert_actual_0": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_create_boolean_data_TestCommon.test_robust_getitem.assert_actual_0", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 193, "end_line": 220, "span_ids": ["TestCommon.test_robust_getitem.UnreliableArray", "TestCommon.test_robust_getitem", "TestCommon.test_robust_getitem.UnreliableArrayFailure", "create_boolean_data", "TestCommon", "TestCommon.test_robust_getitem.UnreliableArrayFailure:2", "TestCommon.test_robust_getitem.UnreliableArray.__init__"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_boolean_data():\n attributes = {\"units\": \"-\"}\n return Dataset({\"x\": (\"t\", [True, False, False, True], attributes)})\n\n\nclass TestCommon:\n def test_robust_getitem(self):\n class UnreliableArrayFailure(Exception):\n pass\n\n class UnreliableArray:\n def __init__(self, array, failures=1):\n self.array = array\n self.failures = failures\n\n def __getitem__(self, key):\n if self.failures > 0:\n self.failures -= 1\n raise UnreliableArrayFailure\n return self.array[key]\n\n array = UnreliableArray([0])\n with pytest.raises(UnreliableArrayFailure):\n array[0]\n assert array[0] == 0\n\n actual = robust_getitem(array, 0, catch=UnreliableArrayFailure, initial_delay=0)\n assert actual == 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_NetCDF3Only_DatasetIOBase.roundtrip.with_create_tmp_file_allo.with_self_open_path_op.yield_ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_NetCDF3Only_DatasetIOBase.roundtrip.with_create_tmp_file_allo.with_self_open_path_op.yield_ds", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 223, "end_line": 245, "span_ids": ["DatasetIOBase", "NetCDF3Only", "DatasetIOBase.roundtrip", "DatasetIOBase.create_store"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetCDF3Only:\n pass\n\n\nclass DatasetIOBase:\n engine: Optional[str] = None\n file_format: Optional[str] = None\n\n def create_store(self):\n raise NotImplementedError()\n\n @contextlib.contextmanager\n def roundtrip(\n self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False\n ):\n if save_kwargs is None:\n save_kwargs = {}\n if open_kwargs is None:\n open_kwargs = {}\n with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path:\n self.save(data, path, **save_kwargs)\n with self.open(path, **open_kwargs) as ds:\n yield ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.roundtrip_append_DatasetIOBase.roundtrip_append.with_create_tmp_file_allo.with_self_open_path_op.yield_ds": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.roundtrip_append_DatasetIOBase.roundtrip_append.with_create_tmp_file_allo.with_self_open_path_op.yield_ds", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 247, "end_line": 260, "span_ids": ["DatasetIOBase.roundtrip_append"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n @contextlib.contextmanager\n def roundtrip_append(\n self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False\n ):\n if save_kwargs is None:\n save_kwargs = {}\n if open_kwargs is None:\n open_kwargs = {}\n with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path:\n for i, key in enumerate(data.variables):\n mode = \"a\" if i > 0 else \"w\"\n self.save(data[[key]], path, mode=mode, **save_kwargs)\n with self.open(path, **open_kwargs) as ds:\n yield ds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase._The_save_open_methods_m_DatasetIOBase.test_write_store.with_self_create_store_.with_xr_decode_cf_store_.assert_allclose_expected_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase._The_save_open_methods_m_DatasetIOBase.test_write_store.with_self_create_store_.with_xr_decode_cf_store_.assert_allclose_expected_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 262, "end_line": 288, "span_ids": ["DatasetIOBase.test_zero_dimensional_variable", "DatasetIOBase.roundtrip_append", "DatasetIOBase.save", "DatasetIOBase.open", "DatasetIOBase.test_write_store"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n # The save/open methods may be overwritten below\n def save(self, dataset, path, **kwargs):\n return dataset.to_netcdf(\n path, engine=self.engine, format=self.file_format, **kwargs\n )\n\n @contextlib.contextmanager\n def open(self, path, **kwargs):\n with open_dataset(path, engine=self.engine, **kwargs) as ds:\n yield ds\n\n def test_zero_dimensional_variable(self):\n expected = create_test_data()\n expected[\"float_var\"] = ([], 1.0e9, {\"units\": \"units of awesome\"})\n expected[\"bytes_var\"] = ([], b\"foobar\")\n expected[\"string_var\"] = ([], \"foobar\")\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n\n def test_write_store(self):\n expected = create_test_data()\n with self.create_store() as store:\n expected.dump_to_store(store)\n # we need to cf decode the store because it has time and\n # non-dimension coordinates\n with xr.decode_cf(store) as actual:\n assert_allclose(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.check_dtypes_roundtripped_DatasetIOBase.test_roundtrip_test_data.with_self_roundtrip_expec.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.check_dtypes_roundtripped_DatasetIOBase.test_roundtrip_test_data.with_self_roundtrip_expec.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 290, "end_line": 308, "span_ids": ["DatasetIOBase.check_dtypes_roundtripped", "DatasetIOBase.test_roundtrip_test_data"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def check_dtypes_roundtripped(self, expected, actual):\n for k in expected.variables:\n expected_dtype = expected.variables[k].dtype\n if isinstance(self, NetCDF3Only) and expected_dtype == \"int64\":\n # downcast\n expected_dtype = np.dtype(\"int32\")\n actual_dtype = actual.variables[k].dtype\n # TODO: check expected behavior for string dtypes more carefully\n string_kinds = {\"O\", \"S\", \"U\"}\n assert expected_dtype == actual_dtype or (\n expected_dtype.kind in string_kinds\n and actual_dtype.kind in string_kinds\n )\n\n def test_roundtrip_test_data(self):\n expected = create_test_data()\n with self.roundtrip(expected) as actual:\n self.check_dtypes_roundtripped(expected, actual)\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_load_DatasetIOBase.test_load.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_load_DatasetIOBase.test_load.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 310, "end_line": 341, "span_ids": ["DatasetIOBase.test_load"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_load(self):\n expected = create_test_data()\n\n @contextlib.contextmanager\n def assert_loads(vars=None):\n if vars is None:\n vars = expected\n with self.roundtrip(expected) as actual:\n for k, v in actual.variables.items():\n # IndexVariables are eagerly loaded into memory\n assert v._in_memory == (k in actual.dims)\n yield actual\n for k, v in actual.variables.items():\n if k in vars:\n assert v._in_memory\n assert_identical(expected, actual)\n\n with pytest.raises(AssertionError):\n # make sure the contextmanager works!\n with assert_loads() as ds:\n pass\n\n with assert_loads() as ds:\n ds.load()\n\n with assert_loads([\"var1\", \"dim1\", \"dim2\"]) as ds:\n ds[\"var1\"].load()\n\n # verify we can read data even after closing the file\n with self.roundtrip(expected) as ds:\n actual = ds.load()\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dataset_compute_DatasetIOBase.test_dataset_compute.with_self_roundtrip_expec.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dataset_compute_DatasetIOBase.test_dataset_compute.with_self_roundtrip_expec.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 343, "end_line": 360, "span_ids": ["DatasetIOBase.test_dataset_compute"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_dataset_compute(self):\n expected = create_test_data()\n\n with self.roundtrip(expected) as actual:\n # Test Dataset.compute()\n for k, v in actual.variables.items():\n # IndexVariables are eagerly cached\n assert v._in_memory == (k in actual.dims)\n\n computed = actual.compute()\n\n for k, v in actual.variables.items():\n assert v._in_memory == (k in actual.dims)\n for v in computed.variables.values():\n assert v._in_memory\n\n assert_identical(expected, actual)\n assert_identical(expected, computed)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_pickle_DatasetIOBase.test_pickle.with_self_roundtrip_expec.with_pickle_loads_raw_pic.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_pickle_DatasetIOBase.test_pickle.with_self_roundtrip_expec.with_pickle_loads_raw_pic.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 362, "end_line": 371, "span_ids": ["DatasetIOBase.test_pickle"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_pickle(self):\n if not has_dask:\n pytest.xfail(\"pickling requires dask for SerializableLock\")\n expected = Dataset({\"foo\": (\"x\", [42])})\n with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped:\n with roundtripped:\n # Windows doesn't like reopening an already open file\n raw_pickle = pickle.dumps(roundtripped)\n with pickle.loads(raw_pickle) as unpickled_ds:\n assert_identical(expected, unpickled_ds)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_pickle_dataarray_DatasetIOBase.test_pickle_dataarray.with_self_roundtrip_expec.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_pickle_dataarray_DatasetIOBase.test_pickle_dataarray.with_self_roundtrip_expec.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 373, "end_line": 384, "span_ids": ["DatasetIOBase.test_pickle_dataarray"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n @pytest.mark.filterwarnings(\"ignore:deallocating CachingFileManager\")\n def test_pickle_dataarray(self):\n if not has_dask:\n pytest.xfail(\"pickling requires dask for SerializableLock\")\n expected = Dataset({\"foo\": (\"x\", [42])})\n with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped:\n with roundtripped:\n raw_pickle = pickle.dumps(roundtripped[\"foo\"])\n # TODO: figure out how to explicitly close the file for the\n # unpickled DataArray?\n unpickled = pickle.loads(raw_pickle)\n assert_identical(expected[\"foo\"], unpickled)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dataset_caching_DatasetIOBase.test_roundtrip_None_variable.with_self_roundtrip_expec.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dataset_caching_DatasetIOBase.test_roundtrip_None_variable.with_self_roundtrip_expec.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 386, "end_line": 403, "span_ids": ["DatasetIOBase.test_roundtrip_None_variable", "DatasetIOBase.test_dataset_caching"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_dataset_caching(self):\n expected = Dataset({\"foo\": (\"x\", [5, 6, 7])})\n with self.roundtrip(expected) as actual:\n assert isinstance(actual.foo.variable._data, indexing.MemoryCachedArray)\n assert not actual.foo.variable._in_memory\n actual.foo.values # cache\n assert actual.foo.variable._in_memory\n\n with self.roundtrip(expected, open_kwargs={\"cache\": False}) as actual:\n assert isinstance(actual.foo.variable._data, indexing.CopyOnWriteArray)\n assert not actual.foo.variable._in_memory\n actual.foo.values # no caching\n assert not actual.foo.variable._in_memory\n\n def test_roundtrip_None_variable(self):\n expected = Dataset({None: ((\"x\", \"y\"), [[0, 1], [2, 3]])})\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_object_dtype_DatasetIOBase.test_roundtrip_object_dtype.with_self_roundtrip_origi.try_.except_AssertionError_.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_object_dtype_DatasetIOBase.test_roundtrip_object_dtype.with_self_roundtrip_origi.try_.except_AssertionError_.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 405, "end_line": 438, "span_ids": ["DatasetIOBase.test_roundtrip_object_dtype"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_roundtrip_object_dtype(self):\n floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object)\n floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object)\n bytes_ = np.array([b\"ab\", b\"cdef\", b\"g\"], dtype=object)\n bytes_nans = np.array([b\"ab\", b\"cdef\", np.nan], dtype=object)\n strings = np.array([\"ab\", \"cdef\", \"g\"], dtype=object)\n strings_nans = np.array([\"ab\", \"cdef\", np.nan], dtype=object)\n all_nans = np.array([np.nan, np.nan], dtype=object)\n original = Dataset(\n {\n \"floats\": (\"a\", floats),\n \"floats_nans\": (\"a\", floats_nans),\n \"bytes\": (\"b\", bytes_),\n \"bytes_nans\": (\"b\", bytes_nans),\n \"strings\": (\"b\", strings),\n \"strings_nans\": (\"b\", strings_nans),\n \"all_nans\": (\"c\", all_nans),\n \"nan\": ([], np.nan),\n }\n )\n expected = original.copy(deep=True)\n with self.roundtrip(original) as actual:\n try:\n assert_identical(expected, actual)\n except AssertionError:\n # Most stores use '' for nans in strings, but some don't.\n # First try the ideal case (where the store returns exactly)\n # the original Dataset), then try a more realistic case.\n # This currently includes all netCDF files when encoding is not\n # explicitly set.\n # https://github.com/pydata/xarray/issues/1647\n expected[\"bytes_nans\"][-1] = b\"\"\n expected[\"strings_nans\"][-1] = \"\"\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_string_data_DatasetIOBase.test_roundtrip_string_encoded_characters.None_1.assert_actual_x_encodi": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_string_data_DatasetIOBase.test_roundtrip_string_encoded_characters.None_1.assert_actual_x_encodi", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 440, "end_line": 455, "span_ids": ["DatasetIOBase.test_roundtrip_string_data", "DatasetIOBase.test_roundtrip_string_encoded_characters"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_roundtrip_string_data(self):\n expected = Dataset({\"x\": (\"t\", [\"ab\", \"cdef\"])})\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n\n def test_roundtrip_string_encoded_characters(self):\n expected = Dataset({\"x\": (\"t\", [\"ab\", \"cdef\"])})\n expected[\"x\"].encoding[\"dtype\"] = \"S1\"\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n assert actual[\"x\"].encoding[\"_Encoding\"] == \"utf-8\"\n\n expected[\"x\"].encoding[\"_Encoding\"] = \"ascii\"\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n assert actual[\"x\"].encoding[\"_Encoding\"] == \"ascii\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_numpy_datetime_data_DatasetIOBase.test_roundtrip_numpy_datetime_data.with_self_roundtrip_expec.assert_actual_t0_encoding": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_numpy_datetime_data_DatasetIOBase.test_roundtrip_numpy_datetime_data.with_self_roundtrip_expec.assert_actual_t0_encoding", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 457, "end_line": 464, "span_ids": ["DatasetIOBase.test_roundtrip_numpy_datetime_data"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n @arm_xfail\n def test_roundtrip_numpy_datetime_data(self):\n times = pd.to_datetime([\"2000-01-01\", \"2000-01-02\", \"NaT\"])\n expected = Dataset({\"t\": (\"t\", times), \"t0\": times[0]})\n kwargs = {\"encoding\": {\"t0\": {\"units\": \"days since 1950-01-01\"}}}\n with self.roundtrip(expected, save_kwargs=kwargs) as actual:\n assert_identical(expected, actual)\n assert actual.t0.encoding[\"units\"] == \"days since 1950-01-01\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_cftime_datetime_data_DatasetIOBase.test_roundtrip_cftime_datetime_data.for_date_type_in_date_typ.with_warnings_catch_warni.with_self_roundtrip_expec.None_5": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_cftime_datetime_data_DatasetIOBase.test_roundtrip_cftime_datetime_data.for_date_type_in_date_typ.with_warnings_catch_warni.with_self_roundtrip_expec.None_5", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 466, "end_line": 495, "span_ids": ["DatasetIOBase.test_roundtrip_cftime_datetime_data"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n @requires_cftime\n def test_roundtrip_cftime_datetime_data(self):\n from .test_coding_times import _all_cftime_date_types\n\n date_types = _all_cftime_date_types()\n for date_type in date_types.values():\n times = [date_type(1, 1, 1), date_type(1, 1, 2)]\n expected = Dataset({\"t\": (\"t\", times), \"t0\": times[0]})\n kwargs = {\"encoding\": {\"t0\": {\"units\": \"days since 0001-01-01\"}}}\n expected_decoded_t = np.array(times)\n expected_decoded_t0 = np.array([date_type(1, 1, 1)])\n expected_calendar = times[0].calendar\n\n with warnings.catch_warnings():\n if expected_calendar in {\"proleptic_gregorian\", \"gregorian\"}:\n warnings.filterwarnings(\"ignore\", \"Unable to decode time axis\")\n\n with self.roundtrip(expected, save_kwargs=kwargs) as actual:\n abs_diff = abs(actual.t.values - expected_decoded_t)\n assert (abs_diff <= np.timedelta64(1, \"s\")).all()\n assert (\n actual.t.encoding[\"units\"]\n == \"days since 0001-01-01 00:00:00.000000\"\n )\n assert actual.t.encoding[\"calendar\"] == expected_calendar\n\n abs_diff = abs(actual.t0.values - expected_decoded_t0)\n assert (abs_diff <= np.timedelta64(1, \"s\")).all()\n assert actual.t0.encoding[\"units\"] == \"days since 0001-01-01\"\n assert actual.t.encoding[\"calendar\"] == expected_calendar", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_timedelta_data_DatasetIOBase.test_roundtrip_boolean_dtype.with_self_roundtrip_origi.assert_actual_x_dtype_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_roundtrip_timedelta_data_DatasetIOBase.test_roundtrip_boolean_dtype.with_self_roundtrip_origi.assert_actual_x_dtype_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 497, "end_line": 542, "span_ids": ["DatasetIOBase.test_roundtrip_global_coordinates", "DatasetIOBase.test_roundtrip_float64_data", "DatasetIOBase.test_roundtrip_coordinates", "DatasetIOBase.test_roundtrip_example_1_netcdf", "DatasetIOBase.test_roundtrip_boolean_dtype", "DatasetIOBase.test_roundtrip_timedelta_data", "DatasetIOBase.test_roundtrip_coordinates_with_space"], "tokens": 493}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_roundtrip_timedelta_data(self):\n time_deltas = pd.to_timedelta([\"1h\", \"2h\", \"NaT\"])\n expected = Dataset({\"td\": (\"td\", time_deltas), \"td0\": time_deltas[0]})\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n\n def test_roundtrip_float64_data(self):\n expected = Dataset({\"x\": (\"y\", np.array([1.0, 2.0, np.pi], dtype=\"float64\"))})\n with self.roundtrip(expected) as actual:\n assert_identical(expected, actual)\n\n def test_roundtrip_example_1_netcdf(self):\n with open_example_dataset(\"example_1.nc\") as expected:\n with self.roundtrip(expected) as actual:\n # we allow the attributes to differ since that\n # will depend on the encoding used. For example,\n # without CF encoding 'actual' will end up with\n # a dtype attribute.\n assert_equal(expected, actual)\n\n def test_roundtrip_coordinates(self):\n original = Dataset(\n {\"foo\": (\"x\", [0, 1])}, {\"x\": [2, 3], \"y\": (\"a\", [42]), \"z\": (\"x\", [4, 5])}\n )\n\n with self.roundtrip(original) as actual:\n assert_identical(original, actual)\n\n def test_roundtrip_global_coordinates(self):\n original = Dataset({\"x\": [2, 3], \"y\": (\"a\", [42]), \"z\": (\"x\", [4, 5])})\n with self.roundtrip(original) as actual:\n assert_identical(original, actual)\n\n def test_roundtrip_coordinates_with_space(self):\n original = Dataset(coords={\"x\": 0, \"y z\": 1})\n expected = Dataset({\"y z\": 1}, {\"x\": 0})\n with pytest.warns(xr.SerializationWarning):\n with self.roundtrip(original) as actual:\n assert_identical(expected, actual)\n\n def test_roundtrip_boolean_dtype(self):\n original = create_boolean_data()\n assert original[\"x\"].dtype == \"bool\"\n with self.roundtrip(original) as actual:\n assert_identical(original, actual)\n assert actual[\"x\"].dtype == \"bool\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_orthogonal_indexing_DatasetIOBase.test_orthogonal_indexing.with_self_roundtrip_in_me.None_1": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_orthogonal_indexing_DatasetIOBase.test_orthogonal_indexing.with_self_roundtrip_in_me.None_1", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 544, "end_line": 556, "span_ids": ["DatasetIOBase.test_orthogonal_indexing"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_orthogonal_indexing(self):\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n indexers = {\"dim1\": [1, 2, 0], \"dim2\": [3, 2, 0, 3], \"dim3\": np.arange(5)}\n expected = in_memory.isel(**indexers)\n actual = on_disk.isel(**indexers)\n # make sure the array is not yet loaded into memory\n assert not actual[\"var1\"].variable._in_memory\n assert_identical(expected, actual)\n # do it twice, to make sure we're switched from orthogonal -> numpy\n # when we cached the values\n actual = on_disk.isel(**indexers)\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_vectorized_indexing_DatasetIOBase.test_vectorized_indexing.None_5": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_vectorized_indexing_DatasetIOBase.test_vectorized_indexing.None_5", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 558, "end_line": 638, "span_ids": ["DatasetIOBase.test_vectorized_indexing"], "tokens": 778}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_vectorized_indexing(self):\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n indexers = {\n \"dim1\": DataArray([0, 2, 0], dims=\"a\"),\n \"dim2\": DataArray([0, 2, 3], dims=\"a\"),\n }\n expected = in_memory.isel(**indexers)\n actual = on_disk.isel(**indexers)\n # make sure the array is not yet loaded into memory\n assert not actual[\"var1\"].variable._in_memory\n assert_identical(expected, actual.load())\n # do it twice, to make sure we're switched from\n # vectorized -> numpy when we cached the values\n actual = on_disk.isel(**indexers)\n assert_identical(expected, actual)\n\n def multiple_indexing(indexers):\n # make sure a sequence of lazy indexings certainly works.\n with self.roundtrip(in_memory) as on_disk:\n actual = on_disk[\"var3\"]\n expected = in_memory[\"var3\"]\n for ind in indexers:\n actual = actual.isel(**ind)\n expected = expected.isel(**ind)\n # make sure the array is not yet loaded into memory\n assert not actual.variable._in_memory\n assert_identical(expected, actual.load())\n\n # two-staged vectorized-indexing\n indexers = [\n {\n \"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"]),\n \"dim3\": DataArray([[0, 4], [1, 3], [2, 2]], dims=[\"a\", \"b\"]),\n },\n {\"a\": DataArray([0, 1], dims=[\"c\"]), \"b\": DataArray([0, 1], dims=[\"c\"])},\n ]\n multiple_indexing(indexers)\n\n # vectorized-slice mixed\n indexers = [\n {\n \"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"]),\n \"dim3\": slice(None, 10),\n }\n ]\n multiple_indexing(indexers)\n\n # vectorized-integer mixed\n indexers = [\n {\"dim3\": 0},\n {\"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"])},\n {\"a\": slice(None, None, 2)},\n ]\n multiple_indexing(indexers)\n\n # vectorized-integer mixed\n indexers = [\n {\"dim3\": 0},\n {\"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"])},\n {\"a\": 1, \"b\": 0},\n ]\n multiple_indexing(indexers)\n\n # with negative step slice.\n indexers = [\n {\n \"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"]),\n \"dim3\": slice(-1, 1, -1),\n }\n ]\n multiple_indexing(indexers)\n\n # with negative step slice.\n indexers = [\n {\n \"dim1\": DataArray([[0, 7], [2, 6], [3, 5]], dims=[\"a\", \"b\"]),\n \"dim3\": slice(-1, 1, -2),\n }\n ]\n multiple_indexing(indexers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_isel_dataarray_DatasetIOBase.validate_array_type.for_k_v_in_ds_variables_.find_and_validate_array_v": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_isel_dataarray_DatasetIOBase.validate_array_type.for_k_v_in_ds_variables_.find_and_validate_array_v", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 640, "end_line": 668, "span_ids": ["DatasetIOBase.validate_array_type", "DatasetIOBase.test_isel_dataarray"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_isel_dataarray(self):\n # Make sure isel works lazily. GH:issue:1688\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n expected = in_memory.isel(dim2=in_memory[\"dim2\"] < 3)\n actual = on_disk.isel(dim2=on_disk[\"dim2\"] < 3)\n assert_identical(expected, actual)\n\n def validate_array_type(self, ds):\n # Make sure that only NumpyIndexingAdapter stores a bare np.ndarray.\n def find_and_validate_array(obj):\n # recursively called function. obj: array or array wrapper.\n if hasattr(obj, \"array\"):\n if isinstance(obj.array, indexing.ExplicitlyIndexed):\n find_and_validate_array(obj.array)\n else:\n if isinstance(obj.array, np.ndarray):\n assert isinstance(obj, indexing.NumpyIndexingAdapter)\n elif isinstance(obj.array, dask_array_type):\n assert isinstance(obj, indexing.DaskIndexingAdapter)\n elif isinstance(obj.array, pd.Index):\n assert isinstance(obj, indexing.PandasIndexAdapter)\n else:\n raise TypeError(\n \"{} is wrapped by {}\".format(type(obj.array), type(obj))\n )\n\n for k, v in ds.variables.items():\n find_and_validate_array(v._data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_array_type_after_indexing_DatasetIOBase.test_array_type_after_indexing.with_self_roundtrip_in_me.None_4": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_array_type_after_indexing_DatasetIOBase.test_array_type_after_indexing.with_self_roundtrip_in_me.None_4", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 670, "end_line": 683, "span_ids": ["DatasetIOBase.test_array_type_after_indexing"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_array_type_after_indexing(self):\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n self.validate_array_type(on_disk)\n indexers = {\"dim1\": [1, 2, 0], \"dim2\": [3, 2, 0, 3], \"dim3\": np.arange(5)}\n expected = in_memory.isel(**indexers)\n actual = on_disk.isel(**indexers)\n assert_identical(expected, actual)\n self.validate_array_type(actual)\n # do it twice, to make sure we're switched from orthogonal -> numpy\n # when we cached the values\n actual = on_disk.isel(**indexers)\n assert_identical(expected, actual)\n self.validate_array_type(actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dropna_DatasetIOBase.test_ondisk_after_print.with_self_roundtrip_in_me.assert_not_on_disk_var1_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_DatasetIOBase.test_dropna_DatasetIOBase.test_ondisk_after_print.with_self_roundtrip_in_me.assert_not_on_disk_var1_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 685, "end_line": 708, "span_ids": ["DatasetIOBase.test_ondisk_after_print", "DatasetIOBase.test_dropna"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetIOBase:\n\n def test_dropna(self):\n # regression test for GH:issue:1694\n a = np.random.randn(4, 3)\n a[1, 1] = np.NaN\n in_memory = xr.Dataset(\n {\"a\": ((\"y\", \"x\"), a)}, coords={\"y\": np.arange(4), \"x\": np.arange(3)}\n )\n\n assert_identical(\n in_memory.dropna(dim=\"x\"), in_memory.isel(x=slice(None, None, 2))\n )\n\n with self.roundtrip(in_memory) as on_disk:\n self.validate_array_type(on_disk)\n expected = in_memory.dropna(dim=\"x\")\n actual = on_disk.dropna(dim=\"x\")\n assert_identical(expected, actual)\n\n def test_ondisk_after_print(self):\n \"\"\" Make sure print does not load file into memory \"\"\"\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n repr(on_disk)\n assert not on_disk[\"var1\"]._in_memory", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase_CFEncodedBase.test_roundtrip_bytes_with_fill_value.None_1.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase_CFEncodedBase.test_roundtrip_bytes_with_fill_value.None_1.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 711, "end_line": 722, "span_ids": ["CFEncodedBase", "CFEncodedBase.test_roundtrip_bytes_with_fill_value"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFEncodedBase(DatasetIOBase):\n def test_roundtrip_bytes_with_fill_value(self):\n values = np.array([b\"ab\", b\"cdef\", np.nan], dtype=object)\n encoding = {\"_FillValue\": b\"X\", \"dtype\": \"S1\"}\n original = Dataset({\"x\": (\"t\", values, {}, encoding)})\n expected = original.copy(deep=True)\n with self.roundtrip(original) as actual:\n assert_identical(expected, actual)\n\n original = Dataset({\"x\": (\"t\", values, {}, {\"_FillValue\": b\"\"})})\n with self.roundtrip(original) as actual:\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_string_with_fill_value_nchar_CFEncodedBase.test_roundtrip_string_with_fill_value_nchar.with_pytest_raises_NotImp.with_self_roundtrip_origi.assert_identical_expected": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_string_with_fill_value_nchar_CFEncodedBase.test_roundtrip_string_with_fill_value_nchar.with_pytest_raises_NotImp.with_self_roundtrip_origi.assert_identical_expected", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 724, "end_line": 733, "span_ids": ["CFEncodedBase.test_roundtrip_string_with_fill_value_nchar"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFEncodedBase(DatasetIOBase):\n\n def test_roundtrip_string_with_fill_value_nchar(self):\n values = np.array([\"ab\", \"cdef\", np.nan], dtype=object)\n expected = Dataset({\"x\": (\"t\", values)})\n\n encoding = {\"dtype\": \"S1\", \"_FillValue\": b\"X\"}\n original = Dataset({\"x\": (\"t\", values, {}, encoding)})\n # Not supported yet.\n with pytest.raises(NotImplementedError):\n with self.roundtrip(original) as actual:\n assert_identical(expected, actual)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_mask_and_scale_CFEncodedBase.test_roundtrip_mask_and_scale.None_3.assert_allclose_decoded_": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_mask_and_scale_CFEncodedBase.test_roundtrip_mask_and_scale.None_3.assert_allclose_decoded_", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 735, "end_line": 782, "span_ids": ["CFEncodedBase.test_roundtrip_mask_and_scale"], "tokens": 401}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFEncodedBase(DatasetIOBase):\n\n @pytest.mark.parametrize(\n \"decoded_fn, encoded_fn\",\n [\n (\n create_unsigned_masked_scaled_data,\n create_encoded_unsigned_masked_scaled_data,\n ),\n pytest.param(\n create_bad_unsigned_masked_scaled_data,\n create_bad_encoded_unsigned_masked_scaled_data,\n marks=pytest.mark.xfail(reason=\"Bad _Unsigned attribute.\"),\n ),\n (\n create_signed_masked_scaled_data,\n create_encoded_signed_masked_scaled_data,\n ),\n (create_masked_and_scaled_data, create_encoded_masked_and_scaled_data),\n ],\n )\n def test_roundtrip_mask_and_scale(self, decoded_fn, encoded_fn):\n decoded = decoded_fn()\n encoded = encoded_fn()\n\n with self.roundtrip(decoded) as actual:\n for k in decoded.variables:\n assert decoded.variables[k].dtype == actual.variables[k].dtype\n assert_allclose(decoded, actual, decode_bytes=False)\n\n with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual:\n # TODO: this assumes that all roundtrips will first\n # encode. Is that something we want to test for?\n for k in encoded.variables:\n assert encoded.variables[k].dtype == actual.variables[k].dtype\n assert_allclose(encoded, actual, decode_bytes=False)\n\n with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual:\n for k in encoded.variables:\n assert encoded.variables[k].dtype == actual.variables[k].dtype\n assert_allclose(encoded, actual, decode_bytes=False)\n\n # make sure roundtrip encoding didn't change the\n # original dataset.\n assert_allclose(encoded, encoded_fn(), decode_bytes=False)\n\n with self.roundtrip(encoded) as actual:\n for k in decoded.variables:\n assert decoded.variables[k].dtype == actual.variables[k].dtype\n assert_allclose(decoded, actual, decode_bytes=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_coordinates_encoding_CFEncodedBase.test_coordinates_encoding.None_3.with_open_dataset_tmp_fil.None_2": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_coordinates_encoding_CFEncodedBase.test_coordinates_encoding.None_3.with_open_dataset_tmp_fil.None_2", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 784, "end_line": 811, "span_ids": ["CFEncodedBase.test_coordinates_encoding"], "tokens": 311}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFEncodedBase(DatasetIOBase):\n\n def test_coordinates_encoding(self):\n def equals_latlon(obj):\n return obj == \"lat lon\" or obj == \"lon lat\"\n\n original = Dataset(\n {\"temp\": (\"x\", [0, 1]), \"precip\": (\"x\", [0, -1])},\n {\"lat\": (\"x\", [2, 3]), \"lon\": (\"x\", [4, 5])},\n )\n with self.roundtrip(original) as actual:\n assert_identical(actual, original)\n with create_tmp_file() as tmp_file:\n original.to_netcdf(tmp_file)\n with open_dataset(tmp_file, decode_coords=False) as ds:\n assert equals_latlon(ds[\"temp\"].attrs[\"coordinates\"])\n assert equals_latlon(ds[\"precip\"].attrs[\"coordinates\"])\n assert \"coordinates\" not in ds.attrs\n assert \"coordinates\" not in ds[\"lat\"].attrs\n assert \"coordinates\" not in ds[\"lon\"].attrs\n\n modified = original.drop([\"temp\", \"precip\"])\n with self.roundtrip(modified) as actual:\n assert_identical(actual, modified)\n with create_tmp_file() as tmp_file:\n modified.to_netcdf(tmp_file)\n with open_dataset(tmp_file, decode_coords=False) as ds:\n assert equals_latlon(ds.attrs[\"coordinates\"])\n assert \"coordinates\" not in ds[\"lat\"].attrs\n assert \"coordinates\" not in ds[\"lon\"].attrs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_endian_CFEncodedBase.test_invalid_dataarray_names_raise.for_name_e_in_zip_0_4.with_raises_regex_e_.with_self_roundtrip_ds_.pass": {"__data__": {"id_": "/tmp/repos/swe-bench_pydata__xarray/xarray/tests/test_backends.py_CFEncodedBase.test_roundtrip_endian_CFEncodedBase.test_invalid_dataarray_names_raise.for_name_e_in_zip_0_4.with_raises_regex_e_.with_self_roundtrip_ds_.pass", "embedding": null, "metadata": {"file_path": "xarray/tests/test_backends.py", "file_name": "test_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 813, "end_line": 845, "span_ids": ["CFEncodedBase.test_invalid_dataarray_names_raise", "CFEncodedBase.test_roundtrip_endian"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CFEncodedBase(DatasetIOBase):\n\n def test_roundtrip_endian(self):\n ds = Dataset(\n {\n \"x\": np.arange(3, 10, dtype=\">i2\"),\n \"y\": np.arange(3, 20, dtype=\"