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lambda scientist
commited on
Commit
•
07cafd6
1
Parent(s):
a2e6ff7
update
Browse files- .gitignore +167 -0
- pages/1_HE_Staining_Analysis.py +10 -10
- pages/2_SDH_Staining_Analysis.py +8 -8
- pages/3_Breast_Muscle_Analysis.py +10 -10
- pages/4_ATP_Staining_Analysis.py +8 -8
.gitignore
ADDED
@@ -0,0 +1,167 @@
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# Byte-compiled / optimized / DLL files
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+
__pycache__/
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+
*.py[cod]
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+
*$py.class
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# C extensions
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*.so
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+
# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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+
downloads/
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+
eggs/
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+
.eggs/
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+
lib/
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+
lib64/
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+
parts/
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+
sdist/
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+
var/
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+
wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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+
MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
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+
*.manifest
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+
*.spec
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+
# Installer logs
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36 |
+
pip-log.txt
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pip-delete-this-directory.txt
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+
# Unit test / coverage reports
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+
htmlcov/
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.tox/
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+
.nox/
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+
.coverage
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+
.coverage.*
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.cache
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+
nosetests.xml
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+
coverage.xml
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+
*.cover
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+
*.py,cover
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+
.hypothesis/
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.pytest_cache/
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+
cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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+
.ipynb_checkpoints
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+
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# IPython
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+
profile_default/
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ipython_config.py
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# pyenv
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+
# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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+
# .python-version
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# pipenv
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+
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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+
#Pipfile.lock
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+
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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+
celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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+
.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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+
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# Custom Corentin
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+
*.html
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MyoQuant
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+
EHRoes
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*.h5
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log
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pid
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*.npy
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*.tiff
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*.npz
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*.jpg
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*.csv
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*.png
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*.tif
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pages/1_HE_Staining_Analysis.py
CHANGED
@@ -32,37 +32,37 @@ st.set_page_config(
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use_GPU = is_gpu_availiable()
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35 |
-
@st.
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36 |
def st_load_cellpose():
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return load_cellpose()
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-
@st.
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def st_load_stardist():
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return load_stardist()
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44 |
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-
@st.
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46 |
def st_run_cellpose(image_ndarray, _model):
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47 |
return run_cellpose(image_ndarray, _model)
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-
@st.
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def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh):
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return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh)
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-
@st.
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def st_df_from_cellpose_mask(mask):
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return df_from_cellpose_mask(mask)
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-
@st.
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def st_df_from_stardist_mask(mask):
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return df_from_stardist_mask(mask)
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-
@st.
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66 |
def st_predict_all_cells(
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image_ndarray, df_cellpose, mask_stardist, internalised_threshold
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):
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@@ -71,12 +71,12 @@ def st_predict_all_cells(
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)
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-
@st.
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def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist):
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return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist)
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-
@st.
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def st_single_cell_analysis(
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81 |
single_cell_img,
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single_cell_mask,
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@@ -99,7 +99,7 @@ def st_single_cell_analysis(
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)
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-
@st.
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def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat):
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return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat)
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105 |
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32 |
use_GPU = is_gpu_availiable()
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33 |
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+
@st.cache_resource
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36 |
def st_load_cellpose():
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37 |
return load_cellpose()
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+
@st.cache_resource
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def st_load_stardist():
|
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return load_stardist()
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+
@st.cache_data
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def st_run_cellpose(image_ndarray, _model):
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return run_cellpose(image_ndarray, _model)
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+
@st.cache_data
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def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh):
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return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh)
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+
@st.cache_data
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def st_df_from_cellpose_mask(mask):
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return df_from_cellpose_mask(mask)
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+
@st.cache_data
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def st_df_from_stardist_mask(mask):
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return df_from_stardist_mask(mask)
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+
@st.cache_data
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66 |
def st_predict_all_cells(
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image_ndarray, df_cellpose, mask_stardist, internalised_threshold
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):
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)
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+
@st.cache_data
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def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist):
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return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist)
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+
@st.cache_data
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def st_single_cell_analysis(
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single_cell_img,
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single_cell_mask,
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)
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+
@st.cache_data
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def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat):
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return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat)
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pages/2_SDH_Staining_Analysis.py
CHANGED
@@ -34,42 +34,42 @@ st.set_page_config(
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use_GPU = is_gpu_availiable()
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-
@st.
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def st_load_sdh_model(model_path):
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return load_sdh_model(model_path)
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@st.
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def st_load_cellpose():
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return load_cellpose()
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-
@st.
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def st_run_cellpose(image_ndarray, _model):
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return run_cellpose(image_ndarray, _model)
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50 |
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51 |
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-
@st.
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def st_df_from_cellpose_mask(mask):
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return df_from_cellpose_mask(mask)
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-
@st.
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def st_predict_all_cells(image_ndarray, cellpose_df, _model_SDH):
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return predict_all_cells(image_ndarray, cellpose_df, _model_SDH)
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-
@st.
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def st_extract_single_image(image_ndarray, cellpose_df, index):
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return extract_single_image(image_ndarray, cellpose_df, index)
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-
@st.
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def st_predict_single_cell(image_ndarray, _model_SDH):
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return predict_single_cell(image_ndarray, _model_SDH)
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-
@st.
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def st_paint_full_image(image_sdh, df_cellpose, class_predicted_all):
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return paint_full_image(image_sdh, df_cellpose, class_predicted_all)
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75 |
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use_GPU = is_gpu_availiable()
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+
@st.cache_resource
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def st_load_sdh_model(model_path):
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return load_sdh_model(model_path)
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40 |
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41 |
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+
@st.cache_resource
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43 |
def st_load_cellpose():
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44 |
return load_cellpose()
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+
@st.cache_data
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48 |
def st_run_cellpose(image_ndarray, _model):
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49 |
return run_cellpose(image_ndarray, _model)
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50 |
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51 |
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52 |
+
@st.cache_data
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53 |
def st_df_from_cellpose_mask(mask):
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54 |
return df_from_cellpose_mask(mask)
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55 |
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56 |
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+
@st.cache_data
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58 |
def st_predict_all_cells(image_ndarray, cellpose_df, _model_SDH):
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59 |
return predict_all_cells(image_ndarray, cellpose_df, _model_SDH)
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60 |
|
61 |
|
62 |
+
@st.cache_data
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63 |
def st_extract_single_image(image_ndarray, cellpose_df, index):
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64 |
return extract_single_image(image_ndarray, cellpose_df, index)
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65 |
|
66 |
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+
@st.cache_data
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68 |
def st_predict_single_cell(image_ndarray, _model_SDH):
|
69 |
return predict_single_cell(image_ndarray, _model_SDH)
|
70 |
|
71 |
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+
@st.cache_data
|
73 |
def st_paint_full_image(image_sdh, df_cellpose, class_predicted_all):
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74 |
return paint_full_image(image_sdh, df_cellpose, class_predicted_all)
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75 |
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pages/3_Breast_Muscle_Analysis.py
CHANGED
@@ -35,37 +35,37 @@ st.set_page_config(
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35 |
use_GPU = is_gpu_availiable()
|
36 |
|
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38 |
-
@st.
|
39 |
def st_load_cellpose():
|
40 |
return load_cellpose()
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41 |
|
42 |
|
43 |
-
@st.
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44 |
def st_load_stardist(fluo=False):
|
45 |
return load_stardist(fluo)
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46 |
|
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|
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-
@st.
|
49 |
def st_run_cellpose(image_ndarray, _model):
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50 |
return run_cellpose(image_ndarray, _model)
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51 |
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52 |
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53 |
-
@st.
|
54 |
def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh):
|
55 |
return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh)
|
56 |
|
57 |
|
58 |
-
@st.
|
59 |
def st_df_from_cellpose_mask(mask):
|
60 |
return df_from_cellpose_mask(mask)
|
61 |
|
62 |
|
63 |
-
@st.
|
64 |
def st_df_from_stardist_mask(mask):
|
65 |
return df_from_stardist_mask(mask)
|
66 |
|
67 |
|
68 |
-
@st.
|
69 |
def st_predict_all_cells(
|
70 |
image_ndarray, df_cellpose, mask_stardist, internalised_threshold
|
71 |
):
|
@@ -74,12 +74,12 @@ def st_predict_all_cells(
|
|
74 |
)
|
75 |
|
76 |
|
77 |
-
@st.
|
78 |
def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist):
|
79 |
return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist)
|
80 |
|
81 |
|
82 |
-
@st.
|
83 |
def st_single_cell_analysis(
|
84 |
single_cell_img,
|
85 |
single_cell_mask,
|
@@ -102,7 +102,7 @@ def st_single_cell_analysis(
|
|
102 |
)
|
103 |
|
104 |
|
105 |
-
@st.
|
106 |
def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat):
|
107 |
return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat)
|
108 |
|
|
|
35 |
use_GPU = is_gpu_availiable()
|
36 |
|
37 |
|
38 |
+
@st.cache_resource
|
39 |
def st_load_cellpose():
|
40 |
return load_cellpose()
|
41 |
|
42 |
|
43 |
+
@st.cache_resource
|
44 |
def st_load_stardist(fluo=False):
|
45 |
return load_stardist(fluo)
|
46 |
|
47 |
|
48 |
+
@st.cache_data
|
49 |
def st_run_cellpose(image_ndarray, _model):
|
50 |
return run_cellpose(image_ndarray, _model)
|
51 |
|
52 |
|
53 |
+
@st.cache_data
|
54 |
def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh):
|
55 |
return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh)
|
56 |
|
57 |
|
58 |
+
@st.cache_data
|
59 |
def st_df_from_cellpose_mask(mask):
|
60 |
return df_from_cellpose_mask(mask)
|
61 |
|
62 |
|
63 |
+
@st.cache_data
|
64 |
def st_df_from_stardist_mask(mask):
|
65 |
return df_from_stardist_mask(mask)
|
66 |
|
67 |
|
68 |
+
@st.cache_data
|
69 |
def st_predict_all_cells(
|
70 |
image_ndarray, df_cellpose, mask_stardist, internalised_threshold
|
71 |
):
|
|
|
74 |
)
|
75 |
|
76 |
|
77 |
+
@st.cache_data
|
78 |
def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist):
|
79 |
return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist)
|
80 |
|
81 |
|
82 |
+
@st.cache_data
|
83 |
def st_single_cell_analysis(
|
84 |
single_cell_img,
|
85 |
single_cell_mask,
|
|
|
102 |
)
|
103 |
|
104 |
|
105 |
+
@st.cache_data
|
106 |
def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat):
|
107 |
return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat)
|
108 |
|
pages/4_ATP_Staining_Analysis.py
CHANGED
@@ -34,42 +34,42 @@ st.set_page_config(
|
|
34 |
)
|
35 |
|
36 |
|
37 |
-
@st.
|
38 |
def st_load_cellpose():
|
39 |
return load_cellpose()
|
40 |
|
41 |
|
42 |
-
@st.
|
43 |
def st_run_cellpose(image_atp, _model):
|
44 |
return run_cellpose(image_atp, _model)
|
45 |
|
46 |
|
47 |
-
@st.
|
48 |
def st_df_from_cellpose_mask(mask):
|
49 |
return df_from_cellpose_mask(mask)
|
50 |
|
51 |
|
52 |
-
@st.
|
53 |
def st_get_all_intensity(image_atp, df_cellpose):
|
54 |
return get_all_intensity(image_atp, df_cellpose)
|
55 |
|
56 |
|
57 |
-
@st.
|
58 |
def st_estimate_threshold(intensity_list):
|
59 |
return estimate_threshold(intensity_list)
|
60 |
|
61 |
|
62 |
-
@st.
|
63 |
def st_plot_density(all_cell_median_intensity, intensity_threshold):
|
64 |
return plot_density(all_cell_median_intensity, intensity_threshold)
|
65 |
|
66 |
|
67 |
-
@st.
|
68 |
def st_predict_all_cells(image_atp, cellpose_df, intensity_threshold):
|
69 |
return predict_all_cells(image_atp, cellpose_df, intensity_threshold)
|
70 |
|
71 |
|
72 |
-
@st.
|
73 |
def st_paint_full_image(image_atp, df_cellpose, class_predicted_all):
|
74 |
return paint_full_image(image_atp, df_cellpose, class_predicted_all)
|
75 |
|
|
|
34 |
)
|
35 |
|
36 |
|
37 |
+
@st.cache_resource
|
38 |
def st_load_cellpose():
|
39 |
return load_cellpose()
|
40 |
|
41 |
|
42 |
+
@st.cache_data
|
43 |
def st_run_cellpose(image_atp, _model):
|
44 |
return run_cellpose(image_atp, _model)
|
45 |
|
46 |
|
47 |
+
@st.cache_data
|
48 |
def st_df_from_cellpose_mask(mask):
|
49 |
return df_from_cellpose_mask(mask)
|
50 |
|
51 |
|
52 |
+
@st.cache_data
|
53 |
def st_get_all_intensity(image_atp, df_cellpose):
|
54 |
return get_all_intensity(image_atp, df_cellpose)
|
55 |
|
56 |
|
57 |
+
@st.cache_data
|
58 |
def st_estimate_threshold(intensity_list):
|
59 |
return estimate_threshold(intensity_list)
|
60 |
|
61 |
|
62 |
+
@st.cache_data
|
63 |
def st_plot_density(all_cell_median_intensity, intensity_threshold):
|
64 |
return plot_density(all_cell_median_intensity, intensity_threshold)
|
65 |
|
66 |
|
67 |
+
@st.cache_data
|
68 |
def st_predict_all_cells(image_atp, cellpose_df, intensity_threshold):
|
69 |
return predict_all_cells(image_atp, cellpose_df, intensity_threshold)
|
70 |
|
71 |
|
72 |
+
@st.cache_data
|
73 |
def st_paint_full_image(image_atp, df_cellpose, class_predicted_all):
|
74 |
return paint_full_image(image_atp, df_cellpose, class_predicted_all)
|
75 |
|