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coh_results_analysis.py
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"""Analyses results."""
import os
import numpy as np
from coh_handle_files import generate_fpath_from_analysed, load_file
from coh_process_results import load_results_of_types
def analyse(
folderpath_processing: str,
folderpath_analysis: str,
analysis: str,
to_analyse: str,
to_analyse_ftype: str,
save: bool,
) -> None:
"""Analyses results.
PARAMETERS
----------
folderpath_processing : str
- Folderpath to the location where the results of the data processing are
located.
folderpath_analysis : str
- Folderpath to the location where the analysis settings are located and
where the output of the analysis should be saved, if applicable.
analysis : str
- Name of the analysis being performed, used for loading the analysis
settings and, optionally, saving the output of the analysis.
to_analyse : str
- Name of the file containing information on the set of results to
analyse.
to_analyse_ftype : str
- The filetype of the results, with the leading period, e.g. JSON would be
specified as ".json".
save : bool
- Whether or not to save the output of the analysis.
"""
## Loads the analysis settings
analysis_settings = load_file(
os.path.join(folderpath_analysis, "Settings", f"{analysis}.json")
)
to_analyse = load_file(
os.path.join(
folderpath_analysis,
"Settings",
"Data File Presets",
f"{to_analyse}.json",
)
)
results = load_results_of_types(
folderpath_processing=os.path.join(folderpath_processing, "Data"),
to_analyse=to_analyse,
result_types=analysis_settings["result_types"],
extract_from_dicts=analysis_settings["extract_from_dicts"],
identical_keys=analysis_settings["identical_keys"],
discard_keys=analysis_settings["discard_keys"],
result_ftype=to_analyse_ftype,
)
accepted_methods = [
"average_within_nodes",
"average_over_nodes",
"subtract",
"log10",
"absolute",
"find_value",
"find_index_of_value",
"isolate_bands",
"percentile",
"interpolate",
"gaussianise",
"zscore_within_nodes",
"zscore_over_nodes",
"project_to_mesh",
"track_fibres_within_radius",
"track_closest_fibres",
]
for step in analysis_settings["steps"]:
if step["method"] not in accepted_methods:
raise NotImplementedError(
f"The method {step['method']} is not recognised. Accepted "
f"methods are {accepted_methods}."
)
if step["method"] == "average_over_nodes":
results.average_over_nodes(
over_key=step["over_key"],
data_keys=step["data_keys"],
group_keys=step["group_keys"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
ignore_nan=step["ignore_nan"],
var_measures=step["var_measures"],
)
elif step["method"] == "average_within_nodes":
results.average_within_nodes(
data_keys=step["data_keys"],
average_dimension=step["average_dimension"],
eligible_entries=step["eligible_entries"],
ignore_nan=step["ignore_nan"],
var_measures=step["var_measures"],
)
elif step["method"] == "subtract":
results.subtract(
over_key=step["over_key"],
data_keys=step["data_keys"],
group_keys=step["group_keys"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
)
elif step["method"] == "log10":
results.log10(
data_keys=step["data_keys"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "absolute":
results.absolute(
data_keys=step["data_keys"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "find_value":
results.find_value(
value_method=step["value_method"],
data_keys=step["data_keys"],
find_in_dimension=step["find_in_dimension"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "find_index_of_value":
results.find_index_of_value(
value_method=step["value_method"],
data_keys=step["data_keys"],
find_in_dimension=step["find_in_dimension"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "isolate_bands":
results.isolate_bands(
data_keys=step["data_keys"],
isolate_dimension=step["isolate_dimension"],
bands=step["bands"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "percentile":
results.percentile(
over_key=step["over_key"],
data_keys=step["data_keys"],
group_keys=step["group_keys"],
percentile_interval=step["percentile_interval"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
ignore_nan=step["ignore_nan"],
)
elif step["method"] == "zscore_within_nodes":
results.zscore_within_nodes(
data_keys=step["data_keys"],
zscore_dimension=step["zscore_dimension"],
eligible_entries=step["eligible_entries"],
ignore_nan=step["ignore_nan"],
)
elif step["method"] == "zscore_over_nodes":
results.zscore_over_nodes(
data_keys=step["data_keys"],
group_keys=step["group_keys"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
ignore_nan=step["ignore_nan"],
)
elif step["method"] == "interpolate":
if isinstance(step["interpolate_to"], np.ndarray):
interpolation_coords = step["interpolate_to"].copy()
else:
supported_interpolations = [
"central_cortex_lh",
"cortex_bl",
"cortex_rh",
"cortex_lh",
"STN_bl",
"STN_rh",
"STN_lh",
]
if step["interpolate_to"] not in supported_interpolations:
raise ValueError(
"The requested interpolation is not recognised. "
"Supported interpolations are "
f"{supported_interpolations}."
)
interpolation_coords = np.load(
os.path.join(
"coherence",
"interpolation_coords",
f"{step['interpolate_to']}.npy",
)
)
results.interpolate(
over_key=step["over_key"],
data_keys=step["data_keys"],
group_keys=step["group_keys"],
coords_key=step["coords_key"],
interpolation_coords=interpolation_coords,
pin_to_hemisphere=step["pin_to_hemisphere"],
interpolation_settings=step["interpolation_settings"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
)
elif step["method"] == "gaussianise":
results.gaussianise(
over_key=step["over_key"],
data_keys=step["data_keys"],
gaussianise_dimension=step["gaussianise_dimension"],
group_keys=step["group_keys"],
eligible_entries=step["eligible_entries"],
identical_keys=step["identical_keys"],
)
elif step["method"] == "project_to_mesh":
results.project_to_mesh(
mesh=step["mesh"],
coords_key=step["coords_key"],
pin_to_hemisphere=step["pin_to_hemisphere"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "track_fibres_within_radius":
results.track_fibres_within_radius(
atlas=step["atlas"],
seeds_key=step["seeds_key"],
targets_key=step["targets_key"],
seeds_coords_key=step["seeds_coords_key"],
targets_coords_key=step["targets_coords_key"],
seeds_types_key=step["seeds_types_key"],
targets_types_key=step["targets_types_key"],
sphere_radii=step["sphere_radii"],
allow_bypassing_fibres=step["allow_bypassing_fibres"],
pin_to_hemisphere=step["pin_to_hemisphere"],
eligible_entries=step["eligible_entries"],
)
elif step["method"] == "track_closest_fibres":
results.track_closest_fibres(
atlas=step["atlas"],
seeds_key=step["seeds_key"],
seeds_coords_key=step["seeds_coords_key"],
normalise_distance=step["normalise_distance"],
pin_to_hemisphere=step["pin_to_hemisphere"],
eligible_entries=step["eligible_entries"],
)
if save:
results_fpath = generate_fpath_from_analysed(
analysed=to_analyse,
parent_folderpath=os.path.join(folderpath_analysis, "Results"),
analysis=analysis,
ftype=".pkl",
fpath_format="personal",
)
results.save_results(fpath=results_fpath)