neuron_morphology.feature_extractor.__main__
¶
Module Contents¶
Functions¶
extract_multiple (reconstructions: List[Dict[str, Any]], feature_set: str, heavy_output_path: str, required_marks: Optional[List[str]] = None, only_marks: Optional[List[str]] = None, num_processes: Optional[int] = None, global_parameters: Optional[Dict[str, Any]] = None, output_table_path: Optional[str] = None) |
For each path in swc_paths, load the file into a morphology and (attempt |
main () |
-
neuron_morphology.feature_extractor.__main__.
extract_multiple
(reconstructions: List[Dict[str, Any]], feature_set: str, heavy_output_path: str, required_marks: Optional[List[str]] = None, only_marks: Optional[List[str]] = None, num_processes: Optional[int] = None, global_parameters: Optional[Dict[str, Any]] = None, output_table_path: Optional[str] = None)¶ For each path in swc_paths, load the file into a morphology and (attempt to) extract each feature in the set specified by feature_set.
Because of how Windows handles multiprocessing, run_feature_extraction must be in another py file.
Parameters: - reconstructions : specify the reconstructions on which to compute features
- feature_set : names the set of features for which calculation will be
attempted
- heavy_output_path : write “heavy” outputs, such as arrays, to this h5 file
- only_marks : names marks to which calculation will be restricted
- required_marks : raise an exception if these named marks fail validation
- num_processes : use this many cores in the multiprocessing pool.
- global_parameters : a dictionary specifying cross-reconstruction
parameters
- output_table_path : if not none, write a flattened table of features here
Returns: - a dictionary whose keys are reconstruction identifers and whose values are
the outputs of run_feature_extraction for those reconstructions.
-
neuron_morphology.feature_extractor.__main__.
main
()¶