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pylance standardization

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tyler
2026-07-21 00:54:51 -07:00
parent 2b019c1bc0
commit 8b017005c5
20 changed files with 608 additions and 277 deletions
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src/analysis/crossgroupbrainimage.py
+24 -4
View File
@@ -1,20 +1,28 @@
"""
Filename: crossgroupbrainimage.py
Description: Logic for the Cross-Group Brain & Image analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in Imports
from pathlib import Path
from typing import Any, cast
# External library imports
from mne.io.base import BaseRaw
import pandas as pd
from pandas import DataFrame
from flares import aggregate_fnirs_group_geometry, plot_2d_3d_contrasts_between_groups
from src.shared.flaresbasewidget import CrossGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "show_optodes",
@@ -52,7 +60,15 @@ PARAMETERIZED_INDEXES = {
class CrossGroupBrainImageWidget(CrossGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
df_ind_dict: dict[str, DataFrame],
design_matrix_dict: dict[str, DataFrame],
contrast_results_dict: dict[str, dict[str, Any]],
group_dict: dict[str, str],
) -> None:
super().__init__("CrossGroupBrainImage")
self.setWindowTitle(f"Cross-Group Brain & Image Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
@@ -70,8 +86,9 @@ class CrossGroupBrainImageWidget(CrossGroupUIMixin, FlaresBaseWidget):
if request is None:
return
(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = request
(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, raw_params) = request
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
# Build group-level contrast DataFrames
def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
@@ -102,8 +119,11 @@ class CrossGroupBrainImageWidget(CrossGroupUIMixin, FlaresBaseWidget):
if len(all_raw_objs) > 1:
processed_raw = aggregate_fnirs_group_geometry(all_raw_objs)
elif len(all_raw_objs) == 1 and all_raw_objs[0] is not None:
processed_raw = all_raw_objs[0].copy()
processed_raw.pick(picks="hbo") # type: ignore
else:
processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
processed_raw = None
# Visualizations
for idx in selected_indexes:
src/analysis/crossgroupstats.py
+49 -25
View File
@@ -1,20 +1,28 @@
"""
Filename: crossgroupstats.py
Description: Cross-Group stats analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
from pathlib import Path
from typing import Any, cast
# External library imports
import pandas as pd
from pandas import DataFrame
from mne.io.base import BaseRaw
from flares import run_cross_group_contrast_analysis, run_cross_group_laterality_analysis, run_cross_group_second_level_analysis
from src.shared.flaresbasewidget import CrossGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "p_threshold",
@@ -136,7 +144,18 @@ DESCRIPTION = """0. Raw ROI Comparison (run_cross_group_second_level_analysis)
class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict, json_location):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
cha_dict: dict[str, DataFrame],
df_ind_dict: dict[str, DataFrame],
design_matrix_dict: dict[str, DataFrame],
contrast_results_dict: dict[str, dict[str, Any]],
group_dict: dict[str, str],
json_location: str | Path
) -> None:
super().__init__("CrossGroupStats")
self.setWindowTitle(f"Cross-Group Stats Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
@@ -144,7 +163,7 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
self.df_ind_dict = df_ind_dict
self.design_matrix_dict = design_matrix_dict
self.contrast_results_dict = contrast_results_dict
self.group_dict = group_dict
# self.group_dict = group_dict
self.json_location = json_location
self.setup_cross_group_ui(["0 (Raw ROI Comparison)", "1 (Laterality Comparison)", "2 (Contrast Comparison)",], placeholder_text=DESCRIPTION)
@@ -155,23 +174,18 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
if request is None:
return
(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = request
(selected_event, file_paths_a, file_paths_b, _, selected_indexes, raw_params) = request
if isinstance(self.df_ind_dict, dict):
# Filter out empty entries and concatenate
valid_dfs = [df for df in self.df_ind_dict.values() if isinstance(df, pd.DataFrame) and not df.empty]
if valid_dfs:
df_ind_combined = pd.concat(valid_dfs, ignore_index=True)
else:
df_ind_combined = pd.DataFrame()
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
valid_dfs = [df for df in self.df_ind_dict.values() if not df.empty]
if valid_dfs:
df_ind_combined = pd.concat(valid_dfs, ignore_index=True)
else:
df_ind_combined = self.df_ind_dict
if isinstance(self.cha_dict, dict):
valid_chas = [df for df in self.cha_dict.values() if isinstance(df, pd.DataFrame) and not df.empty]
cha_combined = pd.concat(valid_chas, ignore_index=True) if valid_chas else pd.DataFrame()
else:
cha_combined = self.cha_dict
df_ind_combined = pd.DataFrame()
valid_chas = [df for df in self.cha_dict.values() if not df.empty]
cha_combined = pd.concat(valid_chas, ignore_index=True) if valid_chas else pd.DataFrame()
sample_path = file_paths_a[0]
p_haemo = self.haemo_dict.get(sample_path)
@@ -213,8 +227,8 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
min_subjects = params.get("min_subjects", 3)
correction_method = params.get("correction_method", "None")
target_chroma = params.get("target_chroma", "hbo")
roi_a = params.get("roi_a", "").strip()
roi_b = params.get("roi_b", "").strip()
roi_a: str = params.get("roi_a", "").strip()
roi_b: str = params.get("roi_b", "").strip()
if not roi_a or not roi_b:
print("Both a contralateral and ipsilateral ROI name must be specified.")
@@ -225,14 +239,19 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
# Build each group's dataframe directly from the dict using
# the file-path lists as keys - no ID cleaning/matching needed.
def _build_group_df(file_paths, dict_source):
def _build_group_df(
file_paths: list[str],
dict_source: dict[str, DataFrame]
) -> DataFrame:
valid_dfs = [
dict_source[fp] for fp in file_paths
if fp in dict_source and isinstance(dict_source[fp], pd.DataFrame)
and not dict_source[fp].empty
if fp in dict_source and not dict_source[fp].empty
]
return pd.concat(valid_dfs, ignore_index=True) if valid_dfs else pd.DataFrame()
df_roi_a = _build_group_df(file_paths_a, self.df_ind_dict)
df_roi_b = _build_group_df(file_paths_b, self.df_ind_dict)
@@ -271,8 +290,13 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
# directly from contrast_results_dict, keyed by file path -
# same dict-key approach as the laterality patch, avoids
# any ID-string matching.
def _build_group_contrast_df(file_paths, contrast_dict, name):
all_rows = []
def _build_group_contrast_df(
file_paths: list[str],
contrast_dict: dict[str, dict[str, pd.DataFrame]],
name: str,
) -> pd.DataFrame:
all_rows: list[DataFrame] = []
for fp in file_paths:
condition_dfs = contrast_dict.get(fp)
if condition_dfs is None:
src/analysis/exporttocsv.py
+26 -33
View File
@@ -1,6 +1,7 @@
"""
Filename: exporttocsv.py
Description: Logic for the Export To CSV analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
@@ -8,39 +9,51 @@ License: GPL-3.0
# Built-in imports
import os
from pathlib import Path
from typing import Any
# External library imports
import numpy as np
import pandas as pd
from pandas import DataFrame
from mne.io.base import BaseRaw
from PySide6.QtWidgets import QFileDialog, QMessageBox
from flares import sparks_csv_export
from src.shared.flaresbasewidget import CSVUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict, df_ind, design_matrix, group, contrast_results_dict):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
cha_dict: dict[str, DataFrame],
df_ind_dict: dict[str, DataFrame],
design_matrix_dict: dict[str, DataFrame],
contrast_results_dict: dict[str, dict[str, Any]],
group_dict: dict[str, str],
) -> None:
super().__init__("ExportToCSV")
self.setWindowTitle(f"Export To CSV Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.cha_dict = cha_dict
self.df_ind = df_ind
self.design_matrix = design_matrix
self.group = group
self.contrast_results_dict = contrast_results_dict
# self.df_ind = df_ind_dict
# self.design_matrix = design_matrix_dict
# self.contrast_results_dict = contrast_results_dict
# self.group = group_dict
self.setup_csv_ui(["0 (Export Data to CSV)", "1 (CSV for SPARKS)",])
def process_request(self):
# TODO: Move this into flares for the call?
selected_display_names = self._get_checked_items(self.participant_dropdown)
selected_file_paths = []
selected_file_paths: list[str] = []
for display_name in selected_display_names:
for fp, short_label in self.participant_map.items():
expected_display = f"{short_label} ({os.path.basename(fp)})"
if display_name == expected_display:
if display_name == expected_display:
selected_file_paths.append(fp)
break
@@ -52,7 +65,6 @@ class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
QMessageBox.warning(self, "Selection Missing", "Please select at least one participant and one export type.")
return
# 2. ASK ONCE: Select Output Directory
output_dir = QFileDialog.getExistingDirectory(self, "Select Output Folder for CSV Exports")
if not output_dir:
@@ -78,29 +90,11 @@ class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
cha.to_csv(save_path)
success_count += 1
elif idx == 1:
# SPARKS Export
save_path = os.path.join(output_dir, f"{base_filename}_sparks.csv")
if haemo_obj is not None:
raw = haemo_obj
data, times = raw.get_data(return_times=True)
ann_col = np.full(times.shape, "", dtype=object)
if raw.annotations is not None and len(raw.annotations) > 0:
for onset, duration, desc in zip(
raw.annotations.onset,
raw.annotations.duration,
raw.annotations.description
):
mask = (times >= onset) & (times < onset + duration)
ann_col[mask] = desc
df = pd.DataFrame(data.T, columns=raw.ch_names)
df.insert(0, "annotation", ann_col)
df.insert(0, "time", times)
df.to_csv(save_path, index=False)
success_count += 1
sparks_csv_export(haemo_obj, save_path)
success_count += 1
else:
print(f"No method defined for index {idx}")
@@ -119,5 +113,4 @@ class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
# mode=EventUpdateMode.WRITE_JSON,
# caller="Video Alignment Tool"
# )
# win.show()
# win.show()
src/analysis/intergroupbrainimage.py
+55 -24
View File
@@ -1,20 +1,29 @@
"""
Filename: intergroupbrainimage.py
Description: Logic for the Inter-Group Brain & Image analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in Imports
from pathlib import Path
from typing import Any, cast
# External library imports
import pandas as pd
from pandas import DataFrame
from mne import Annotations
from mne.io.base import BaseRaw
from flares import aggregate_fnirs_group_geometry, plot_fir_model_results, brain_3d_visualization
from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
from mne.io import BaseRaw
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "lower_bound",
@@ -74,15 +83,24 @@ PARAMETERIZED_INDEXES = {
class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
cha_dict: dict[str, DataFrame],
df_ind_dict: dict[str, DataFrame],
design_matrix_dict: dict[str, DataFrame],
contrast_results_dict: dict[str, dict[str, Any]],
group_dict: dict[str, str]
) -> None:
super().__init__("InterGroupBrainImage")
self.setWindowTitle(f"Inter-Group Brain & Image Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.cha = cha
self.df_ind = df_ind
self.design_matrix = design_matrix
self.contrast_results = contrast_results
self.group = group
self.cha_dict = cha_dict
self.df_ind_dict = df_ind_dict
self.design_matrix_dict = design_matrix_dict
self.contrast_results_dict = contrast_results_dict
# self.group_dict = group_dict
self.setup_inter_group_ui(["0 (GLM Results)", "1 (Significance)", "2 (Brain Activity Visualization)",])
@@ -92,35 +110,45 @@ class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
(selected_event, selected_file_paths, selected_indexes, raw_params) = request
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
all_cha = pd.DataFrame()
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if haemo_obj is None:
continue
if selected_event:
participant_events = set(haemo_obj.annotations.description)
raw_annotations = getattr(haemo_obj, "annotations", None)
if raw_annotations is not None:
annotations = cast(Annotations, raw_annotations)
descriptions = cast(list[str], list(annotations.description))
participant_events: set[str] = set(descriptions)
else:
participant_events: set[str] = set()
if selected_event not in participant_events:
print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
continue
if haemo_obj is None:
continue
cha_df = self.cha.get(file_path)
cha_df = self.cha_dict.get(file_path)
if cha_df is not None:
all_cha = pd.concat([all_cha, cha_df], ignore_index=True)
# Pass the necessary arguments to each method
file_path = selected_file_paths[0]
p_haemo = self.haemo_dict.get(file_path)
p_design_matrix = self.design_matrix.get(file_path)
p_design_matrix = self.design_matrix_dict.get(file_path)
df_group = pd.DataFrame()
if selected_file_paths:
for file_path in selected_file_paths:
df = self.df_ind.get(file_path)
df = self.df_ind_dict.get(file_path)
if df is not None:
df_group = pd.concat([df_group, df], ignore_index=True)
@@ -147,9 +175,9 @@ class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
print(f"Missing parameters for index {idx}, skipping.")
continue
all_contrasts = []
all_contrasts: list[DataFrame] = []
for fp in selected_file_paths:
condition_dfs = self.contrast_results.get(fp, {})
condition_dfs = self.contrast_results_dict.get(fp, {})
if selected_event in condition_dfs:
df = condition_dfs[selected_event].copy()
df["ID"] = fp
@@ -159,7 +187,8 @@ class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
print("No contrast data found for selected participants and event.")
return
df_contrasts = pd.concat(all_contrasts, ignore_index=True)
# TODO: look at intergroupstats and figure out what to do
_ = pd.concat(all_contrasts, ignore_index=True)
#flares.run_second_level_analysis(df_contrasts, p_haemo, p_val, graph_bounds)
elif idx == 2:
@@ -173,13 +202,15 @@ class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
print(f"Missing parameters for index {idx}, skipping.")
continue
raw_list = [self.haemo_dict.get(fp) for fp in selected_file_paths]
all_raw_objs = [self.haemo_dict.get(fp) for fp in selected_file_paths if self.haemo_dict.get(fp)]
if len(selected_file_paths) > 1:
print(f"Aggregating geometry for {len(selected_file_paths)} participants...")
processed_raw = aggregate_fnirs_group_geometry(raw_list)
if len(all_raw_objs) > 1:
processed_raw = aggregate_fnirs_group_geometry(all_raw_objs)
elif len(all_raw_objs) == 1 and all_raw_objs[0] is not None:
processed_raw = all_raw_objs[0].copy()
processed_raw.pick(picks="hbo") # type: ignore
else:
processed_raw = raw_list[0].copy().pick(picks="hbo")
processed_raw = None
brain_3d_visualization(processed_raw, all_cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
src/analysis/intergroupfunctionalconnectivity.py
+19 -4
View File
@@ -1,20 +1,27 @@
"""
Filename: intergroupfunctionalconnectivity.py
Description: Logic for the Inter-Group Functional Connectivity analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
from pathlib import Path
from typing import Any, cast
# External library imports
from PySide6.QtWidgets import QMessageBox
from mne.io.base import BaseRaw
from flares import run_group_functional_connectivity
from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "n_lines",
@@ -34,11 +41,17 @@ PARAMETERIZED_INDEXES = {
class InterGroupFunctionalConnectivityWidget(InterGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, group, config_dict):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
group_dict: dict[str, str],
config_dict: dict[str, str]
) -> None:
super().__init__("InterGroupFunctionalConnectivity")
self.setWindowTitle(f"Inter-Group Functional Connectivity Viewer [BETA] - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.group = group
#self.group_dict = group_dict
self.config_dict = config_dict
QMessageBox.warning(self, f"Warning - {APP_NAME.upper()}", f"Functional Connectivity is still in development and the results should currently be taken with a grain of salt. "
@@ -52,7 +65,9 @@ class InterGroupFunctionalConnectivityWidget(InterGroupUIMixin, FlaresBaseWidget
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
(selected_event, selected_file_paths, selected_indexes, raw_params) = request
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
for idx in selected_indexes:
if idx == 0:
src/analysis/intergroupstats.py
+52 -19
View File
@@ -1,20 +1,29 @@
"""
Filename: intergroupstats.py
Description: Logic for the Inter-Group Stats analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
from pathlib import Path
from typing import Any, cast
# External library imports
import pandas as pd
from pandas import DataFrame
from mne import Annotations
from mne.io.base import BaseRaw
from flares import run_roi_paired_contrast_analysis, run_roi_second_level_analysis, aggregate_channel_contrasts_to_roi
from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "p_threshold",
@@ -148,40 +157,64 @@ DESCRIPTION = """0. ROI vs. Zero (run_roi_second_level_analysis)
class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group, json_location):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
cha_dict: dict[str, DataFrame],
df_ind_dict: dict[str, DataFrame],
design_matrix_dict: dict[str, DataFrame],
contrast_results_dict: dict[str, dict[str, Any]],
group_dict: dict[str, str],
json_location: str | Path
) -> None:
super().__init__("InterGroupStats")
self.setWindowTitle(f"Inter-Group Stats Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.cha = cha
self.df_ind = df_ind
self.design_matrix = design_matrix
self.contrast_results = contrast_results
self.group = group
self.cha_dict = cha_dict
self.df_ind_dict = df_ind_dict
self.design_matrix_dict = design_matrix_dict
self.contrast_results_dict = contrast_results_dict
self.group_dict = group_dict
self.json_location = json_location
self.setup_inter_group_ui(["0 (ROI vs. Zero)", "1 (Paired ROI Contrast)", "2 (Joint Contrast, ROI-Aggregated)"], placeholder_text=DESCRIPTION)
def process_request(self):
request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.json_location, self.contrast_results)
request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.json_location, self.contrast_results_dict)
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
(selected_event, selected_file_paths, selected_indexes, raw_params) = request
all_cha = pd.DataFrame()
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
all_cha = DataFrame()
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if haemo_obj is None:
continue
if selected_event:
participant_events = set(haemo_obj.annotations.description)
raw_annotations = getattr(haemo_obj, "annotations", None)
if raw_annotations is not None:
annotations = cast(Annotations, raw_annotations)
descriptions = cast(list[str], list(annotations.description))
participant_events: set[str] = set(descriptions)
else:
participant_events: set[str] = set()
if selected_event not in participant_events:
print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
continue
if haemo_obj is None:
continue
cha_df = self.cha.get(file_path)
cha_df = self.cha_dict.get(file_path)
if cha_df is not None:
all_cha = pd.concat([all_cha, cha_df], ignore_index=True)
@@ -189,10 +222,10 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
p_haemo = self.haemo_dict.get(file_path)
# Concatenate individual ROI stats (df_ind) for all chosen subjects
df_group = pd.DataFrame()
df_group = DataFrame()
if selected_file_paths:
for file_path in selected_file_paths:
df = self.df_ind.get(file_path)
df = self.df_ind_dict.get(file_path)
if df is not None:
df_group = pd.concat([df_group, df], ignore_index=True)
@@ -226,7 +259,7 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
print(f"No ROI data matches the condition '{selected_event}'.")
continue
all_cha_filtered = pd.DataFrame()
all_cha_filtered = DataFrame()
if not all_cha.empty:
if selected_event and 'Condition' in all_cha.columns:
all_cha_filtered = all_cha[all_cha['Condition'] == selected_event]
@@ -304,9 +337,9 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
continue
all_contrasts = []
all_contrasts: list[DataFrame] = []
for fp in selected_file_paths:
condition_dfs = self.contrast_results.get(fp)
condition_dfs = self.contrast_results_dict.get(fp)
if condition_dfs is None:
print(f" [MISSING] '{fp}' not found in contrast_results.")
continue
src/analysis/participantbrain.py
+32 -7
View File
@@ -1,18 +1,28 @@
"""
Filename: participantbrain.py
Description: Logic for the Participant Brain analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
from pathlib import Path
from typing import Any, cast
# External library imports
from mne import Annotations
from pandas import DataFrame
from mne.io.base import BaseRaw
from flares import brain_3d_visualization, brain_landmarks_3d
from src.shared.flaresbasewidget import ParticipantUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "show_optodes",
@@ -57,7 +67,12 @@ PARAMETERIZED_INDEXES = {
class ParticipantBrainViewerWidget(ParticipantUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
cha_dict: dict[str, DataFrame],
) -> None:
super().__init__("ParticipantBrain")
self.setWindowTitle(f"Participant Brain Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
@@ -72,21 +87,31 @@ class ParticipantBrainViewerWidget(ParticipantUIMixin, FlaresBaseWidget):
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
(selected_event, selected_file_paths, selected_indexes, raw_params) = request
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
# Pass the necessary arguments to each method
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if haemo_obj is None:
continue
if selected_event:
participant_events = set(haemo_obj.annotations.description)
raw_annotations = getattr(haemo_obj, "annotations", None)
if raw_annotations is not None:
annotations = cast(Annotations, raw_annotations)
descriptions = cast(list[str], list(annotations.description))
participant_events: set[str] = set(descriptions)
else:
participant_events: set[str] = set()
if selected_event not in participant_events:
print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
continue
if haemo_obj is None:
raise Exception("How did we get here?")
cha = self.cha_dict.get(file_path)
for idx in selected_indexes:
src/analysis/participantfoldchannels.py
+1 -1
View File
@@ -71,7 +71,7 @@ def single_participant_worker(file_path, raw_data, result_queue, progress_queue)
try:
from flares import fold_channels
# Perform the heavy fold_channels logic
channel_results = fold_channels(raw_data, p_name, progress_queue)
channel_results = fold_channels(raw=raw_data, p_name=p_name, progress_queue=progress_queue)
# Hand back results and signal completion
result_queue.put({file_path: channel_results})
src/analysis/participantfunctionalconnectivity.py
+32 -7
View File
@@ -1,20 +1,30 @@
"""
Filename: participantfunctionalconnectivity.py
Description: Logic for the Participant Functional Connectivity analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in Imports
from pathlib import Path
from typing import Any, cast
# External library imports
from PySide6.QtWidgets import QMessageBox
from pandas import DataFrame
from mne import Annotations
from mne.io.base import BaseRaw
from flares import functional_connectivity_betas, functional_connectivity_envelope, functional_connectivity_spectral_epochs, functional_connectivity_spectral_time
from src.shared.flaresbasewidget import ParticipantUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
0: [
{
"key": "n_lines",
@@ -79,7 +89,12 @@ PARAMETERIZED_INDEXES = {
class ParticipantFunctionalConnectivityWidget(ParticipantUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, epochs_dict):
def __init__(
self,
haemo_dict: dict[str | Path, BaseRaw],
epochs_dict: dict[str, DataFrame],
) -> None:
super().__init__("ParticipantFunctionalConnectivity")
self.setWindowTitle(f"Participant Functional Connectivity Viewer [BETA] - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
@@ -97,22 +112,32 @@ class ParticipantFunctionalConnectivityWidget(ParticipantUIMixin, FlaresBaseWidg
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
(selected_event, selected_file_paths, selected_indexes, raw_params) = request
param_values = cast(dict[int | str, dict[str, Any]], raw_params)
# Pass the necessary arguments to each method
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
epochs_obj = self.epochs_dict.get(file_path)
if haemo_obj is None:
continue
if selected_event:
participant_events = set(haemo_obj.annotations.description)
raw_annotations = getattr(haemo_obj, "annotations", None)
if raw_annotations is not None:
annotations = cast(Annotations, raw_annotations)
descriptions = cast(list[str], list(annotations.description))
participant_events: set[str] = set(descriptions)
else:
participant_events: set[str] = set()
if selected_event not in participant_events:
print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
continue
if haemo_obj is None:
raise Exception("How did we get here?")
for idx in selected_indexes:
if idx == 0:
src/analysis/participantimage.py
+23 -13
View File
@@ -1,17 +1,20 @@
"""
Filename: participantimage.py
Description: Logic for the Participant Image analysis window
Note: Compliant with pylance strict type checking
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in Imports
import os
import os.path as op
from pathlib import Path
from datetime import datetime
# External library imports
from mne.io.base import BaseRaw
from PySide6.QtWidgets import QGridLayout, QHBoxLayout, QMessageBox, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
from PySide6.QtCore import Qt, QSize
from PySide6.QtGui import QPixmap
@@ -21,7 +24,13 @@ from src.shared.shareddata import APP_NAME
class ParticipantImageViewerWidget(FlaresBaseWidget):
def __init__(self, haemo_dict, fig_bytes_dict):
def __init__(
self,
haemo_dict: dict[str, BaseRaw],
fig_bytes_dict: dict[str, dict[str, bytes]]
) -> None:
super().__init__("ParticipantImage")
self.setAttribute(Qt.WidgetAttribute.WA_DeleteOnClose)
self.setWindowTitle(f"Participant Image Viewer - {APP_NAME.upper()}")
@@ -29,12 +38,12 @@ class ParticipantImageViewerWidget(FlaresBaseWidget):
self.fig_bytes_dict = fig_bytes_dict
# Create mappings: file_path -> participant label and dropdown display text
self.participant_map = {} # file_path -> "Participant 1"
self.participant_dropdown_items = [] # "Participant 1 (filename)"
self.participant_map: dict[str, str] = {}
self.participant_dropdown_items: list[str] = []
for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
short_label = f"Participant {i}"
display_label = f"{short_label} ({os.path.basename(file_path)})"
display_label = f"{short_label} ({op.basename(file_path)})"
self.participant_map[file_path] = short_label
self.participant_dropdown_items.append(display_label)
@@ -87,23 +96,24 @@ class ParticipantImageViewerWidget(FlaresBaseWidget):
selected_display_names = self._get_checked_items(self.participant_dropdown)
# Map from display names back to file paths
selected_file_paths = []
selected_file_paths: list[str] = []
for display_name in selected_display_names:
# Find file_path by matching display name
for fp, short_label in self.participant_map.items():
expected_display = f"{short_label} ({os.path.basename(fp)})"
expected_display = f"{short_label} ({Path(fp).name})"
if display_name == expected_display:
selected_file_paths.append(fp)
selected_file_paths.append(str(fp))
break
selected_labels = self._get_checked_items(self.image_index_dropdown)
row, col = 0, 0
for file_path in selected_file_paths:
fig_list = self.fig_bytes_dict.get(file_path, [])
participant_label = self.participant_map[file_path]
fig_map: dict[str, bytes] = self.fig_bytes_dict.get(file_path, {})
participant_label: str = self.participant_map.get(file_path, "Unknown")
for label in selected_labels:
fig_bytes = fig_list.get(label)
fig_bytes: bytes | None = fig_map.get(label)
if not fig_bytes:
continue
@@ -149,7 +159,7 @@ class ParticipantImageViewerWidget(FlaresBaseWidget):
for display_name in selected_display_names:
# Match display name to file path
for file_path, short_label in self.participant_map.items():
expected_display = f"{short_label} ({os.path.basename(file_path)})"
expected_display = f"{short_label} ({op.basename(file_path)})"
if display_name == expected_display:
fig_dict = self.fig_bytes_dict.get(file_path, {})
for label in selected_image_labels:
@@ -157,7 +167,7 @@ class ParticipantImageViewerWidget(FlaresBaseWidget):
continue
fig_bytes = fig_dict[label]
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"{os.path.basename(file_path)}_{label}_{timestamp}.png"
filename = f"{op.basename(file_path)}_{label}_{timestamp}.png"
output_path = save_dir / filename
with open(output_path, "wb") as f:
f.write(fig_bytes)