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Feat/rt cetsa #541

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Feat/rt cetsa #541

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agerardin
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Add two new tools :

  • rt-cetsa-plate-extraction to rotate and crop rt-cetsa plate images
  • rt-cetsa-intensity-extraction to extract corrected intensities from wells

nishaq503 and others added 26 commits May 22, 2024 05:53
feat: containerized plate extraction and run with cwl.
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The types of certain parameters are different between the README.md, ict.yml, plugin.json and *.cwl files. Other than that, this branhc is ready to merge.

Comment on lines +23 to +25
| `--preview` | Generate JSON file with outputs | Output | JSON | Optional
| `--params` | Plate params to use | Output | JSON | Optional
| `--temp` | Temperature range. Default to [37,90] | Output | JSON | Optional
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These three lines need an additional column in the table.

Comment on lines +37 to +48
{
"name": "params",
"type": "boolean",
"description": "Plate params to use.",
"required": false
},
{
"name": "temp",
"type": "boolean",
"description": "Temperature range. Default to [37,90].",
"required": false
}
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The types for these are listed as JSON in the README, string in the ICT and boolean here. What is the actual type?

bfio = "^2.3.6"
scikit-image = "^0.22.0"
imagecodecs = "^2024.1.1"
polus-images-segmentation-rt-cetsa-plate-extraction = {git = "https://github.com/agerardin/image-tools.git", branch = "feat/rt_cetsa", subdirectory = "segmentation/rt-cetsa-plate-extraction-tool"}
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This git link will need to be updated to either use the main repo from polusai and the master branch, or it should be changed to a local path dependency

| `--inpDir` | Input data collection to be processed by this tool | Input | genericData |
| `--filePattern` | FilePattern to parse input files | Input | string |
| `--outDir` | Output dir | Output | genericData |
| `--preview` | Generate JSON file with outputs | Output | JSON | Optional
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This line needs another column

Comment on lines +214 to +215
This method is degree of magnitude slower than extract_wells_intensity
and is just provided for convenience. Consider using extract_wells_intensity instead.
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I am surprised that numpy indexing with a mask is so much slower. I'll have to keep that in mind if we can assume that masks always have a specific shape

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2 participants