Upload hdf5_getters.py

hdf5_getters.py

@@ -0,0 +1,476 @@
+"""
+Thierry Bertin-Mahieux (2010) Columbia University
+[email protected]
+
+
+This code contains a set of getters functions to access the fields
+from an HDF5 song file (regular file with one song or
+aggregate / summary file with many songs)
+
+This is part of the Million Song Dataset project from
+LabROSA (Columbia University) and The Echo Nest.
+
+
+Copyright 2010, Thierry Bertin-Mahieux
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+
+import tables
+
+
+def open_h5_file_read(h5filename):
+    """
+    Open an existing H5 in read mode.
+    Same function as in hdf5_utils, here so we avoid one import
+    """
+    return tables.open_file(h5filename, mode='r')
+
+
+def get_num_songs(h5):
+    """
+    Return the number of songs contained in this h5 file, i.e. the number of rows
+    for all basic informations like name, artist, ...
+    """
+    return h5.root.metadata.songs.nrows
+
+def get_artist_familiarity(h5,songidx=0):
+    """
+    Get artist familiarity from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_familiarity[songidx]
+
+def get_artist_hotttnesss(h5,songidx=0):
+    """
+    Get artist hotttnesss from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_hotttnesss[songidx]
+
+def get_artist_id(h5,songidx=0):
+    """
+    Get artist id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_id[songidx]
+
+def get_artist_mbid(h5,songidx=0):
+    """
+    Get artist musibrainz id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_mbid[songidx]
+
+def get_artist_playmeid(h5,songidx=0):
+    """
+    Get artist playme id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_playmeid[songidx]
+
+def get_artist_7digitalid(h5,songidx=0):
+    """
+    Get artist 7digital id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_7digitalid[songidx]
+
+def get_artist_latitude(h5,songidx=0):
+    """
+    Get artist latitude from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_latitude[songidx]
+
+def get_artist_longitude(h5,songidx=0):
+    """
+    Get artist longitude from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_longitude[songidx]
+
+def get_artist_location(h5,songidx=0):
+    """
+    Get artist location from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_location[songidx]
+
+def get_artist_name(h5,songidx=0):
+    """
+    Get artist name from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.artist_name[songidx]
+
+def get_release(h5,songidx=0):
+    """
+    Get release from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.release[songidx]
+
+def get_release_7digitalid(h5,songidx=0):
+    """
+    Get release 7digital id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.release_7digitalid[songidx]
+
+def get_song_id(h5,songidx=0):
+    """
+    Get song id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.song_id[songidx]
+
+def get_song_hotttnesss(h5,songidx=0):
+    """
+    Get song hotttnesss from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.song_hotttnesss[songidx]
+
+def get_title(h5,songidx=0):
+    """
+    Get title from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.title[songidx]
+
+def get_track_7digitalid(h5,songidx=0):
+    """
+    Get track 7digital id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.metadata.songs.cols.track_7digitalid[songidx]
+
+def get_similar_artists(h5,songidx=0):
+    """
+    Get similar artists array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.metadata.songs.nrows == songidx + 1:
+        return h5.root.metadata.similar_artists[h5.root.metadata.songs.cols.idx_similar_artists[songidx]:]
+    return h5.root.metadata.similar_artists[h5.root.metadata.songs.cols.idx_similar_artists[songidx]:
+                                            h5.root.metadata.songs.cols.idx_similar_artists[songidx+1]]
+
+def get_artist_terms(h5,songidx=0):
+    """
+    Get artist terms array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.metadata.songs.nrows == songidx + 1:
+        return h5.root.metadata.artist_terms[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:]
+    return h5.root.metadata.artist_terms[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:
+                                            h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]]
+
+def get_artist_terms_freq(h5,songidx=0):
+    """
+    Get artist terms array frequencies. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.metadata.songs.nrows == songidx + 1:
+        return h5.root.metadata.artist_terms_freq[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:]
+    return h5.root.metadata.artist_terms_freq[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:
+                                              h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]]
+
+def get_artist_terms_weight(h5,songidx=0):
+    """
+    Get artist terms array frequencies. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.metadata.songs.nrows == songidx + 1:
+        return h5.root.metadata.artist_terms_weight[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:]
+    return h5.root.metadata.artist_terms_weight[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:
+                                                h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]]
+
+def get_analysis_sample_rate(h5,songidx=0):
+    """
+    Get analysis sample rate from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.analysis_sample_rate[songidx]
+
+def get_audio_md5(h5,songidx=0):
+    """
+    Get audio MD5 from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.audio_md5[songidx]
+
+def get_danceability(h5,songidx=0):
+    """
+    Get danceability from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.danceability[songidx]
+
+def get_duration(h5,songidx=0):
+    """
+    Get duration from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.duration[songidx]
+
+def get_end_of_fade_in(h5,songidx=0):
+    """
+    Get end of fade in from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.end_of_fade_in[songidx]
+
+def get_energy(h5,songidx=0):
+    """
+    Get energy from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.energy[songidx]
+
+def get_key(h5,songidx=0):
+    """
+    Get key from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.key[songidx]
+
+def get_key_confidence(h5,songidx=0):
+    """
+    Get key confidence from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.key_confidence[songidx]
+
+def get_loudness(h5,songidx=0):
+    """
+    Get loudness from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.loudness[songidx]
+
+def get_mode(h5,songidx=0):
+    """
+    Get mode from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.mode[songidx]
+
+def get_mode_confidence(h5,songidx=0):
+    """
+    Get mode confidence from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.mode_confidence[songidx]
+
+def get_start_of_fade_out(h5,songidx=0):
+    """
+    Get start of fade out from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.start_of_fade_out[songidx]
+
+def get_tempo(h5,songidx=0):
+    """
+    Get tempo from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.tempo[songidx]
+
+def get_time_signature(h5,songidx=0):
+    """
+    Get signature from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.time_signature[songidx]
+
+def get_time_signature_confidence(h5,songidx=0):
+    """
+    Get signature confidence from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.time_signature_confidence[songidx]
+
+def get_track_id(h5,songidx=0):
+    """
+    Get track id from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.analysis.songs.cols.track_id[songidx]
+
+def get_segments_start(h5,songidx=0):
+    """
+    Get segments start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_start[h5.root.analysis.songs.cols.idx_segments_start[songidx]:]
+    return h5.root.analysis.segments_start[h5.root.analysis.songs.cols.idx_segments_start[songidx]:
+                                           h5.root.analysis.songs.cols.idx_segments_start[songidx+1]]
+    
+def get_segments_confidence(h5,songidx=0):
+    """
+    Get segments confidence array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_confidence[h5.root.analysis.songs.cols.idx_segments_confidence[songidx]:]
+    return h5.root.analysis.segments_confidence[h5.root.analysis.songs.cols.idx_segments_confidence[songidx]:
+                                                h5.root.analysis.songs.cols.idx_segments_confidence[songidx+1]]
+
+def get_segments_pitches(h5,songidx=0):
+    """
+    Get segments pitches array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_pitches[h5.root.analysis.songs.cols.idx_segments_pitches[songidx]:,:]
+    return h5.root.analysis.segments_pitches[h5.root.analysis.songs.cols.idx_segments_pitches[songidx]:
+                                             h5.root.analysis.songs.cols.idx_segments_pitches[songidx+1],:]
+
+def get_segments_timbre(h5,songidx=0):
+    """
+    Get segments timbre array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_timbre[h5.root.analysis.songs.cols.idx_segments_timbre[songidx]:,:]
+    return h5.root.analysis.segments_timbre[h5.root.analysis.songs.cols.idx_segments_timbre[songidx]:
+                                            h5.root.analysis.songs.cols.idx_segments_timbre[songidx+1],:]
+
+def get_segments_loudness_max(h5,songidx=0):
+    """
+    Get segments loudness max array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_loudness_max[h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx]:]
+    return h5.root.analysis.segments_loudness_max[h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx]:
+                                                  h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx+1]]
+
+def get_segments_loudness_max_time(h5,songidx=0):
+    """
+    Get segments loudness max time array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_loudness_max_time[h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx]:]
+    return h5.root.analysis.segments_loudness_max_time[h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx]:
+                                                       h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx+1]]
+
+def get_segments_loudness_start(h5,songidx=0):
+    """
+    Get segments loudness start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.segments_loudness_start[h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx]:]
+    return h5.root.analysis.segments_loudness_start[h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx]:
+                                                    h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx+1]]
+
+def get_sections_start(h5,songidx=0):
+    """
+    Get sections start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.sections_start[h5.root.analysis.songs.cols.idx_sections_start[songidx]:]
+    return h5.root.analysis.sections_start[h5.root.analysis.songs.cols.idx_sections_start[songidx]:
+                                           h5.root.analysis.songs.cols.idx_sections_start[songidx+1]]
+
+def get_sections_confidence(h5,songidx=0):
+    """
+    Get sections confidence array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.sections_confidence[h5.root.analysis.songs.cols.idx_sections_confidence[songidx]:]
+    return h5.root.analysis.sections_confidence[h5.root.analysis.songs.cols.idx_sections_confidence[songidx]:
+                                                h5.root.analysis.songs.cols.idx_sections_confidence[songidx+1]]
+
+def get_beats_start(h5,songidx=0):
+    """
+    Get beats start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.beats_start[h5.root.analysis.songs.cols.idx_beats_start[songidx]:]
+    return h5.root.analysis.beats_start[h5.root.analysis.songs.cols.idx_beats_start[songidx]:
+                                        h5.root.analysis.songs.cols.idx_beats_start[songidx+1]]
+
+def get_beats_confidence(h5,songidx=0):
+    """
+    Get beats confidence array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.beats_confidence[h5.root.analysis.songs.cols.idx_beats_confidence[songidx]:]
+    return h5.root.analysis.beats_confidence[h5.root.analysis.songs.cols.idx_beats_confidence[songidx]:
+                                             h5.root.analysis.songs.cols.idx_beats_confidence[songidx+1]]
+
+def get_bars_start(h5,songidx=0):
+    """
+    Get bars start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.bars_start[h5.root.analysis.songs.cols.idx_bars_start[songidx]:]
+    return h5.root.analysis.bars_start[h5.root.analysis.songs.cols.idx_bars_start[songidx]:
+                                       h5.root.analysis.songs.cols.idx_bars_start[songidx+1]]
+
+def get_bars_confidence(h5,songidx=0):
+    """
+    Get bars start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.bars_confidence[h5.root.analysis.songs.cols.idx_bars_confidence[songidx]:]
+    return h5.root.analysis.bars_confidence[h5.root.analysis.songs.cols.idx_bars_confidence[songidx]:
+                                            h5.root.analysis.songs.cols.idx_bars_confidence[songidx+1]]
+
+def get_tatums_start(h5,songidx=0):
+    """
+    Get tatums start array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.tatums_start[h5.root.analysis.songs.cols.idx_tatums_start[songidx]:]
+    return h5.root.analysis.tatums_start[h5.root.analysis.songs.cols.idx_tatums_start[songidx]:
+                                         h5.root.analysis.songs.cols.idx_tatums_start[songidx+1]]
+
+def get_tatums_confidence(h5,songidx=0):
+    """
+    Get tatums confidence array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.analysis.songs.nrows == songidx + 1:
+        return h5.root.analysis.tatums_confidence[h5.root.analysis.songs.cols.idx_tatums_confidence[songidx]:]
+    return h5.root.analysis.tatums_confidence[h5.root.analysis.songs.cols.idx_tatums_confidence[songidx]:
+                                              h5.root.analysis.songs.cols.idx_tatums_confidence[songidx+1]]
+
+def get_artist_mbtags(h5,songidx=0):
+    """
+    Get artist musicbrainz tag array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.musicbrainz.songs.nrows == songidx + 1:
+        return h5.root.musicbrainz.artist_mbtags[h5.root.musicbrainz.songs.cols.idx_artist_mbtags[songidx]:]
+    return h5.root.musicbrainz.artist_mbtags[h5.root.metadata.songs.cols.idx_artist_mbtags[songidx]:
+                                             h5.root.metadata.songs.cols.idx_artist_mbtags[songidx+1]]
+
+def get_artist_mbtags_count(h5,songidx=0):
+    """
+    Get artist musicbrainz tag count array. Takes care of the proper indexing if we are in aggregate
+    file. By default, return the array for the first song in the h5 file.
+    To get a regular numpy ndarray, cast the result to: numpy.array( )
+    """
+    if h5.root.musicbrainz.songs.nrows == songidx + 1:
+        return h5.root.musicbrainz.artist_mbtags_count[h5.root.musicbrainz.songs.cols.idx_artist_mbtags[songidx]:]
+    return h5.root.musicbrainz.artist_mbtags_count[h5.root.metadata.songs.cols.idx_artist_mbtags[songidx]:
+                                                   h5.root.metadata.songs.cols.idx_artist_mbtags[songidx+1]]
+
+def get_year(h5,songidx=0):
+    """
+    Get release year from a HDF5 song file, by default the first song in it
+    """
+    return h5.root.musicbrainz.songs.cols.year[songidx]