Examples

[jwindhager]

No description provided.

[codecov-commenter]

Codecov Report

Base: 32.41% // Head: 32.60% // Increases project coverage by +0.18% 🎉

Coverage data is based on head (50529d5) compared to base (f12591d).
Patch coverage: 33.10% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #135      +/-   ##
==========================================
+ Coverage   32.41%   32.60%   +0.18%     
==========================================
  Files          60       60              
  Lines        2184     2288     +104     
==========================================
+ Hits          708      746      +38     
- Misses       1476     1542      +66     

Impacted Files	Coverage Δ
steinbock/export/_cli/_cli.py	0.00% <0.00%> (ø)
steinbock/export/_cli/data.py	0.00% <0.00%> (ø)
steinbock/measurement/_cli/regionprops.py	0.00% <ø> (ø)
steinbock/preprocessing/_cli/imc.py	0.00% <0.00%> (ø)
steinbock/utils/_cli/mosaics.py	0.00% <ø> (ø)
steinbock/utils/mosaics.py	0.00% <0.00%> (ø)
steinbock/segmentation/deepcell.py	35.71% <6.25%> (+0.52%)	⬆️
steinbock/io.py	52.73% <19.04%> (-4.17%)	⬇️
steinbock/export/data.py	74.35% <36.36%> (-14.93%)	⬇️
steinbock/preprocessing/imc.py	57.19% <44.08%> (-12.75%)	⬇️

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[jwindhager]

Hi @ndamond, I just took a quick look - thanks for your efforts, looks quite complete to me already! Looking forward to merging this PR once ready.

A few important things before I can start the review process:

1. Example notebooks should be self-contained - please do not use separate download/helper scripts; if you need additional functionality exposed in steinbock or have questions regarding how to do certain tasks, please let me know
2. Please do not store data (panel.csv) in this repository - all necessary data should be created by the respective notebook; for example, for the panel file, you could automatically create it from MCD metadata using steinbock's panel preprocessing/download it from its source (Zenodo?) and edit it as needed (e.g. for segmentation) using pandas
3. As this is the steinbock repository, the notebooks should exemplify the use of steinbock whenever possible; for example. for deepcell-based segmentation, please use steinbock functionality instead of re-implementing segmentation using the deepcell API; similarly, for all I/O (e.g. for reading panel files, writing intensities/regionprops/neighbors, ...), please use steinbock.io
4. Example notebooks should be as simple/short as possible - essentially, they should only consist of documentation + some simple steinbock calls; e.g. is the creation of the images.csv file really needed?
5. The only difference between steinbock_preprocessing.ipynb and steinbock_preprocessing_IF.ipynb I see is the starting point (i.e., MCD/TXT files vs TIFF files) - could the two notebooks be merged?
6. Please make use of jupytext to keep the Jupyter notebooks and associated Python scripts in sync - this greatly simplifies code review, as I can directly suggest changes in the code

[jwindhager]

1. Example notebooks should be self-contained - please do not use separate download/helper scripts; if you need additional functionality exposed in steinbock or have questions regarding how to do certain tasks, please let me know

#139, #140, #141

5. The only difference between steinbock_preprocessing.ipynb and steinbock_preprocessing_IF.ipynb I see is the starting point (i.e., MCD/TXT files vs TIFF files) - could the two notebooks be merged?

As discussed offline, there are quite a few differences that all relate to preprocessing/data extraction. We therefore decided to split up the notebooks as follows (file/directory names should be defined at the beginning of each notebook, with preconfigured defaults, see https://bodenmillergroup.github.io/steinbock/latest/directories/):

• preprocessing_{modality}.ipynb
• segmentation_{approach}.ipynb
• measurement.ipynb (including optional data export steps)