Authors: Tim Stasevich.
This module is intended for creating and manipulating an array of crops (or regions of interest) that were generated from a multicolor TIF video obtained from single-molecule microscopy.
- Documentation is accessible via croparray.readthedocs
- Implementation in Google Colab
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Install anaconda.
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Clone the Github repository
git clone https://github.com/Colorado-State-University-Stasevich-Lab/croparray.git- To create a virtual environment navigate to your local repository and use:
conda create -n croparray_env python=3.8 -y
source activate croparray_env- To install the rest of requirements use:
python3 -m pip install -r requirements.txt- To install napari use:
python3 -m pip install "napari[all]"- To ensure the docstrings of functions in crop_array_tools.py appear in jupyter notebooks that import crop_array_tools:
conda develop .\croparray\- To install Pytorch for GPU usage in Cellpose (Optional step). Only for Linux and Windows users check the specific version for your computer on this link :
conda install pytorch cudatoolkit=10.2 -c pytorch -y- To install Pytorch for CPU usage in Cellpose (Optional step). Only for Mac users check the specific version for your computer on this link :
conda install pytorch -c pytorch- To create a virtual environment using:
conda create -n croparray_env python=3.8 -y
source activate croparray_env- Open the terminal and use pip for the installation:
python3 -m pip install croparray- To install napari use:
python3 -m pip install "napari[all]"- To deactivate or remove the environment from your computer use:
conda deactivate- To remove the environment use:
conda env remove -n croparray_env- To unistall croparray use
pip uninstall croparray- If you cannot see the package installed on your computer, try using
pip3. For example:
pip3 install croparray- To creating an environment file (yml) use:
source activate croparray_env
conda env export > croparray_env.yml- ToCreate an environment from this yml file.
conda env create -f croparray_env.yml- Organizes crops and measurements of spots of interest from tif images in a convenient x-array format for reduced filesize and more open and reproducible analyses.
- Visualizes crops of detected spots from super-resolution microscope images.
- Calculates the best maximum projection for each crop containing a detected spot.
- Measures intensity of detected spots within crops.
- Calculates the correlation between two equal-length, 1D signals.
- Saves the crop array as a netcdf file at output_direction/output_filename.
- Integrates with Napari for fast and convenient review of crops of detected spots.