Mercurial > repos > imgteam > 3d_tensor_feature_dimension_reduction
changeset 0:e8f64a98cbc6 draft default tip
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/3d_tensor_feature_dimension_reduction/ commit e82400162e337b36c29d6e79fb2deb9871475397"
| author | imgteam |
|---|---|
| date | Thu, 20 Jan 2022 00:45:01 +0000 |
| parents | |
| children | |
| files | 3d_tensor_feature_dimension_reduction.py 3d_tensor_feature_dimension_reduction.xml test-data/tensor.h5 test-data/tensor_r.tif |
| diffstat | 4 files changed, 77 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/3d_tensor_feature_dimension_reduction.py Thu Jan 20 00:45:01 2022 +0000 @@ -0,0 +1,39 @@ +""" +Copyright 2022 Biomedical Computer Vision Group, Heidelberg University. + +Distributed under the MIT license. +See file LICENSE for detail or copy at https://opensource.org/licenses/MIT + +""" + +import argparse +import warnings + +import h5py +import numpy as np +import tifffile +import umap + + +def feature_dimension_reduction(tensor_fn, tiff_fn, nCh=5): + with h5py.File(tensor_fn, 'r') as hf: + ts = np.array(hf[list(hf.keys())[0]]) + + assert len(ts.shape) == 3 and ts.shape[-1] > nCh, \ + 'the input tensor data must be three-dimensional' + + embedding = umap.UMAP(n_components=nCh).fit_transform(np.reshape(ts, (-1, ts.shape[-1]))) + img = np.reshape(embedding, (ts.shape[0], ts.shape[1], -1)).astype(np.float32) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + tifffile.imwrite(tiff_fn, np.transpose(img, (2, 0, 1)), imagej=True) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Dimensionality reduction for features (channels) of 3D tensor using UMAP") + parser.add_argument("tensor_fn", help="Path to the 3D tensor data") + parser.add_argument("nCh", type=int, help="The reduced dimension of features") + parser.add_argument("tiff_fn", help="Path to the output file") + args = parser.parse_args() + feature_dimension_reduction(args.tensor_fn, args.tiff_fn, args.nCh)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/3d_tensor_feature_dimension_reduction.xml Thu Jan 20 00:45:01 2022 +0000 @@ -0,0 +1,38 @@ +<tool id="ip_3d_tensor_feature_dimension_reduction" name="Dimensionality Reduction" version="0.0.1" profile="20.05"> + <description>for features of 3D tensor data using UMAP</description> + <requirements> + <requirement type="package" version="1.20.2">numpy</requirement> + <requirement type="package" version="3.6.0">h5py</requirement> + <requirement type="package" version="2020.10.1">tifffile</requirement> + <requirement type="package" version="0.5.2">umap-learn</requirement> + </requirements> + <command detect_errors="aggressive"><![CDATA[ + ln -s '$fn_in' ./input.h5 && + python '$__tool_directory__/3d_tensor_feature_dimension_reduction.py' + ./input.h5 + '$nCh' + ./output.tif + ]]> + </command> + <inputs> + <param name="fn_in" type="data" format="hdf5" label="3D tensor data (rows x cols x features)" /> + <param name="nCh" type="integer" value="5" optional="true" min="1" max="10" label="Reduced dimensions of features" /> + </inputs> + <outputs> + <data format="tiff" name="fn_out" from_work_dir="output.tif" /> + </outputs> + <tests> + <test> + <param name="fn_in" value="tensor.h5"/> + <param name="nCh" value="3"/> + <output name="fn_out" value="tensor_r.tif" ftype="tiff" compare="sim_size"/> + </test> + </tests> + <help> + **What it does** + + This tool performs dimensionality reduction for features of 3D tensor data (rows x cols x features) using UMAP. The results will be saved as a multi-channel 32-bit TIFF image (features x rows x cols). + </help> + <citations> + </citations> +</tool>