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copick
/
generate-torch-basic-features
/ 0.0.7
Generate Multiscale Basic Features with Torch using Copick API (Chunked, Corrected)
Compute multiscale basic features of a tomogram from a Copick run in chunks and save them using Copick's API.
Tags
imaging
cryoet
Python
Solution written by
Kyle Harrington
License of solution
MIT
Source Code
View on GitHub
Arguments
--copick_config_path
Path to the Copick configuration JSON file. (default value: PARAMETER_VALUE)
--run_name
Name of the Copick run to process. (default value: PARAMETER_VALUE)
--voxel_spacing
Voxel spacing to be used. (default value: PARAMETER_VALUE)
--tomo_type
Type of tomogram to process. (default value: PARAMETER_VALUE)
--feature_type
Name for the feature type to be saved. (default value: PARAMETER_VALUE)
--intensity
Include intensity features (default value: 1)
--edges
Include edge features (default value: 1)
--texture
Include texture features (default value: 1)
--sigma_min
Minimum sigma for Gaussian blurring (default value: 0.5)
--sigma_max
Maximum sigma for Gaussian blurring (default value: 16.0)
--num_sigma
Number of sigma values between sigma_min and sigma_max for texture features. (default value: 5)
Usage instructions
Please follow
this link
for details on how to install and run this solution.