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small rewrite to impose dry principles

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tyler
2026-07-15 21:21:35 -07:00
parent ffa14693b3
commit 12afc5d3bc
14 changed files with 1825 additions and 1716 deletions
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src/analysis/crossgroupbrainimage.py
+137
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"""
Filename: crossgroupbrainimage.py
Description: Logic for the Cross-Group Brain & Image analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# External library imports
import pandas as pd
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 = {
0: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner about the contrast.",
"default": "True",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
},
{
"key": "is_3d",
"label": "Should we display the results in a 3D interactive window?",
"default": "True",
"type": bool,
}
],
}
class CrossGroupBrainImageWidget(CrossGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict):
super().__init__("CrossGroupBrainImage")
self.setWindowTitle(f"Cross-Group Brain & Image Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_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_cross_group_ui(["0 (Contrast Image)"])
def proccess_request(self):
request = self.get_common_request_data(PARAMETERIZED_INDEXES)
if request is None:
return
(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = request
# Build group-level contrast DataFrames
def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
group_df = pd.DataFrame()
for fp in file_paths:
print(f"Looking up contrast for: {fp}")
event_con_dict = self.contrast_results_dict.get(fp, {})
print("Available events for this file:", list(event_con_dict.keys()))
if event and event in event_con_dict:
df = event_con_dict[event]
print(f"Appending contrast df for event: {event}")
group_df = pd.concat([group_df, df], ignore_index=True)
else:
print(f"Event '{event}' not found for {fp}")
return group_df
print("Selected event:", selected_event)
print("File paths A:", file_paths_a)
print("File paths B:", file_paths_b)
contrast_df_a = concat_group_contrasts(file_paths_a, selected_event)
contrast_df_b = concat_group_contrasts(file_paths_b, selected_event)
print("contrast_df_a empty?", contrast_df_a.empty)
print("contrast_df_b empty?", contrast_df_b.empty)
all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
if len(all_raw_objs) > 1:
processed_raw = aggregate_fnirs_group_geometry(all_raw_objs)
else:
processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
# Visualizations
for idx in selected_indexes:
if idx == 0:
params = param_values.get(idx, {})
show_optodes = params.get("show_optodes", None)
t_or_theta = params.get("t_or_theta", None)
show_text = params.get("show_text", None)
brain_bounds = params.get("brain_bounds", None)
is_3d = params.get("is_3d", None)
if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None or is_3d is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
if not contrast_df_a.empty and not contrast_df_b.empty and processed_raw:
plot_2d_3d_contrasts_between_groups(
contrast_df_a,
contrast_df_b,
raw_haemo=processed_raw,
group_a_name=self.group_a_dropdown.currentText(),
group_b_name=self.group_b_dropdown.currentText(),
is_3d=is_3d,
t_or_theta=t_or_theta,
show_optodes=show_optodes,
show_text=show_text,
brain_bounds=brain_bounds
)
else:
print(f"No method defined for index {idx}")
src/analysis/crossgroupstats.py
+114
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"""
Filename: crossgroupstats.py
Description: Cross-Group stats analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# External library imports
import pandas as pd
from flares import run_cross_group_second_level_analysis
from src.shared.flaresbasewidget import CrossGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
0: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner about the contrast.",
"default": "True",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
},
{
"key": "is_3d",
"label": "Should we display the results in a 3D interactive window?",
"default": "True",
"type": bool,
}
],
}
class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict):
super().__init__("CrossGroupStats")
self.setWindowTitle(f"Cross-Group Stats Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
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_cross_group_ui(["0 (Compute Statistics)"])
def process_request(self):
request = self.get_common_request_data(PARAMETERIZED_INDEXES)
if request is None:
return
(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = 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()
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
sample_path = file_paths_a[0]
p_haemo = self.haemo_dict.get(sample_path)
# Visualizations
for idx in selected_indexes:
if idx == 0:
run_cross_group_second_level_analysis(
df_roi_all=df_ind_combined, # Individual stats dataframe
file_paths_a=file_paths_a,
file_paths_b=file_paths_b,
group_a_name=self.group_a_dropdown.currentText(),
group_b_name=self.group_b_dropdown.currentText(),
df_cha_all=cha_combined,
raw_haemo=p_haemo,
p_threshold=0.05,
min_subjects=3,
correction_method='fdr_bh',
target_chroma='hbo',
selected_event=selected_event,
roi_config=r"C:\Users\tyler\Desktop\research\flares\regions.json",
threshold_topo=False # Shows the raw difference map (Unthresholded)
)
else:
print("no")
src/analysis/exportcsv.py → src/analysis/exporttocsv.py
+13 -56
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@@ -1,27 +1,28 @@
"""
Filename: exportcsv.py
Description: Export data as csv analysis window for FLARES
Filename: exporttocsv.py
Description: Logic for the Export To CSV analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
import os
# External library imports
import numpy as np
import pandas as pd
from PySide6.QtWidgets import QFileDialog, QGridLayout, QHBoxLayout, QMessageBox, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
from PySide6.QtCore import QSize
from PySide6.QtWidgets import QFileDialog, QMessageBox
from src.shared.flaresbasewidget import FlaresBaseWidget
from src.shared.flaresbasewidget import CSVUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict, df_ind, design_matrix, group, contrast_results_dict):
super().__init__("ExportDataAsCSVViewer")
self.setWindowTitle(f"Export Data As CSV Viewer - {APP_NAME.upper()}")
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
@@ -29,55 +30,11 @@ class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
self.group = group
self.contrast_results_dict = contrast_results_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)"
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)})"
self.participant_map[file_path] = short_label
self.participant_dropdown_items.append(display_label)
self.layout = QVBoxLayout(self)
self.top_bar = QHBoxLayout()
self.layout.addLayout(self.top_bar)
self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
self.index_texts = [
"0 (Export Data to CSV)",
"1 (CSV for SPARKS)",
# "2 (third image)",
# "3 (fourth image)",
]
self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
self.submit_button = QPushButton("Submit")
self.submit_button.clicked.connect(self.generate_and_save_csv)
self.top_bar.addWidget(QLabel("Participants:"))
self.top_bar.addWidget(self.participant_dropdown)
self.top_bar.addWidget(QLabel("Export Type:"))
self.top_bar.addWidget(self.image_index_dropdown)
self.top_bar.addWidget(self.submit_button)
self.scroll = QScrollArea()
self.scroll.setWidgetResizable(True)
self.scroll_content = QWidget()
self.grid_layout = QGridLayout(self.scroll_content)
self.scroll.setWidget(self.scroll_content)
self.layout.addWidget(self.scroll)
self.thumb_size = QSize(280, 180)
self.showMaximized()
def generate_and_save_csv(self):
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 = []
for display_name in selected_display_names:
src/analysis/group.py
-306
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@@ -1,306 +0,0 @@
"""
Filename: group.py
Description: Group analysis window for FLARES
Author: Tyler de Zeeuw
License: GPL-3.0
"""
import os
import pandas as pd
from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
from PySide6.QtCore import QSize
from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
from src.shared.shareddata import APP_NAME
class GroupViewerWidget(FlaresBaseWidget):
def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
super().__init__("GroupViewer")
self.setWindowTitle(f"Group 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.show_all_events = True
self._updating_checkstates = False
# Create mappings: file_path -> participant label and dropdown display text
self.participant_map = {} # file_path -> "Participant 1"
self.participant_dropdown_items = [] # "Participant 1 (filename)"
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)})"
self.participant_map[file_path] = short_label
self.participant_dropdown_items.append(display_label)
self.layout = QVBoxLayout(self)
self.top_bar = QHBoxLayout()
self.layout.addLayout(self.top_bar)
self.group_to_paths = {}
for file_path, group_name in self.group.items():
self.group_to_paths.setdefault(group_name, []).append(file_path)
self.group_names = sorted(self.group_to_paths.keys())
self.group_dropdown = QComboBox()
self.group_dropdown.addItem("<None Selected>")
self.group_dropdown.addItems(self.group_names)
self.group_dropdown.setCurrentIndex(0)
self.group_dropdown.currentIndexChanged.connect(self.update_participant_list_for_group)
self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
self.participant_dropdown.setEnabled(False)
self.event_dropdown = QComboBox()
self.event_dropdown.addItem("<None Selected>")
self.index_texts = [
"0 (GLM Results)",
"1 (Significance)",
"2 (Brain Activity Visualization)",
# "3 (fourth image)",
]
self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
self.submit_button = QPushButton("Submit")
self.submit_button.clicked.connect(self.show_brain_images)
self.top_bar.addWidget(QLabel("Group:"))
self.top_bar.addWidget(self.group_dropdown)
self.top_bar.addWidget(QLabel("Participants:"))
self.top_bar.addWidget(self.participant_dropdown)
self.top_bar.addWidget(QLabel("Event:"))
self.top_bar.addWidget(self.event_dropdown)
self.top_bar.addWidget(QLabel("Image Indexes:"))
self.top_bar.addWidget(self.image_index_dropdown)
self.top_bar.addWidget(self.submit_button)
self.scroll = QScrollArea()
self.scroll.setWidgetResizable(True)
self.scroll_content = QWidget()
self.grid_layout = QGridLayout(self.scroll_content)
self.scroll.setWidget(self.scroll_content)
self.layout.addWidget(self.scroll)
self.thumb_size = QSize(280, 180)
self.showMaximized()
def show_brain_images(self):
import flares as flares
selected_event = self.event_dropdown.currentText()
if selected_event == "<None Selected>":
selected_event = None
selected_display_names = self._get_checked_items(self.participant_dropdown)
selected_file_paths = []
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:
selected_file_paths.append(fp)
break
if selected_event:
valid_paths = []
for fp in selected_file_paths:
raw = self.haemo_dict.get(fp)
# Check if this participant actually has the event in their annotations
if raw is not None and hasattr(raw, "annotations"):
if selected_event in raw.annotations.description:
valid_paths.append(fp)
selected_file_paths = valid_paths
selected_indexes = [
int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
]
if not selected_file_paths:
print("No participants selected.")
return
# Only keep indexes 0 and 1 that need parameters
parameterized_indexes = {
0: [
{
"key": "lower_bound",
"label": "Lower bound + <description>",
"default": "-0.3",
"type": float, # specify int here
},
{
"key": "upper_bound",
"label": "Upper bound + <description>",
"default": "0.8",
"type": float, # specify int here
}
],
1: [
{
"key": "p_value",
"label": "Significance threshold P-value (e.g. 0.05)",
"default": "0.05",
"type": float,
},
{
"key": "graph_bounds",
"label": "Graph Upper/Lower Limit",
"default": "3.0",
"type": float,
}
],
2: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
"default": "False",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
}
],
}
# Inject full_text from index_texts
for idx, params_list in parameterized_indexes.items():
full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
for param_info in params_list:
param_info["full_text"] = full_text
indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
param_values = {}
if indexes_needing_params:
dialog = ParameterInputDialog(indexes_needing_params, parent=self)
if dialog.exec_() == QDialog.Accepted:
param_values = dialog.get_values()
if param_values is None:
return
else:
return
all_cha = pd.DataFrame()
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if selected_event:
participant_events = set(haemo_obj.annotations.description)
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)
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)
df_group = pd.DataFrame()
if selected_file_paths:
for file_path in selected_file_paths:
df = self.df_ind.get(file_path)
if df is not None:
df_group = pd.concat([df_group, df], ignore_index=True)
for idx in selected_indexes:
if idx == 0:
params = param_values.get(idx, {})
lower_bound = params.get("lower_bound", None)
upper_bound = params.get("upper_bound", None)
if lower_bound is None or upper_bound is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
flares.plot_fir_model_results(df_group, p_haemo, p_design_matrix, selected_event, lower_bound, upper_bound)
elif idx == 1:
params = param_values.get(idx, {})
p_val = params.get("p_value", None)
graph_bounds = params.get("graph_bounds", None)
if p_val is None or graph_bounds is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
all_contrasts = []
for fp in selected_file_paths:
condition_dfs = self.contrast_results.get(fp, {})
if selected_event in condition_dfs:
df = condition_dfs[selected_event].copy()
df["ID"] = fp
all_contrasts.append(df)
if not all_contrasts:
print("No contrast data found for selected participants and event.")
return
df_contrasts = pd.concat(all_contrasts, ignore_index=True)
flares.run_second_level_analysis(df_contrasts, p_haemo, p_val, graph_bounds)
elif idx == 2:
params = param_values.get(idx, {})
show_optodes = params.get("show_optodes", None)
t_or_theta = params.get("t_or_theta", None)
show_text = params.get("show_text", None)
brain_bounds = params.get("brain_bounds", None)
if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
raw_list = [self.haemo_dict.get(fp) for fp in selected_file_paths]
if len(selected_file_paths) > 1:
print(f"Aggregating geometry for {len(selected_file_paths)} participants...")
processed_raw = flares.aggregate_fnirs_group_geometry(raw_list)
else:
processed_raw = raw_list[0].copy().pick(picks="hbo")
flares.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)
elif idx == 3:
pass
else:
print(f"No method defined for index {idx}")
src/analysis/groupbrain.py
-311
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@@ -1,311 +0,0 @@
"""
Filename: groupbrain.py
Description: Group brain analysis window for FLARES
Author: Tyler de Zeeuw
License: GPL-3.0
"""
import os
import pandas as pd
from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
from PySide6.QtCore import QSize
from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
from src.shared.shareddata import APP_NAME
class GroupBrainViewerWidget(FlaresBaseWidget):
def __init__(self, haemo_dict, df_ind, design_matrix, group, contrast_results_dict):
super().__init__("GroupBrainViewer")
self.setWindowTitle(f"Group Brain Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.df_ind = df_ind
self.design_matrix = design_matrix
self.group = group
self.contrast_results_dict = contrast_results_dict
self.group_to_paths = {}
for file_path, group_name in self.group.items():
self.group_to_paths.setdefault(group_name, []).append(file_path)
self.group_names = sorted(self.group_to_paths.keys())
self.layout = QVBoxLayout(self)
self.top_bar = QHBoxLayout()
self.layout.addLayout(self.top_bar)
self.group_a_dropdown = QComboBox()
self.group_a_dropdown.addItem("<None Selected>")
self.group_a_dropdown.addItems(self.group_names)
self.group_a_dropdown.currentIndexChanged.connect(self._update_group_a_options)
self.group_b_dropdown = QComboBox()
self.group_b_dropdown.addItem("<None Selected>")
self.group_b_dropdown.addItems(self.group_names)
self.group_b_dropdown.currentIndexChanged.connect(self._update_group_b_options)
self.event_dropdown = QComboBox()
self.event_dropdown.addItem("<None Selected>")
self.participant_dropdown_a = self._create_multiselect_dropdown([])
self.participant_dropdown_a.lineEdit().setPlaceholderText("Select participants (Group A)")
self.participant_dropdown_a.model().itemChanged.connect(self._on_participants_changed)
self.participant_dropdown_b = self._create_multiselect_dropdown([])
self.participant_dropdown_b.lineEdit().setPlaceholderText("Select participants (Group B)")
self.participant_dropdown_b.model().itemChanged.connect(self._on_participants_changed)
self.index_texts = [
"0 (Contrast Image)",
# "1 (3D Brain Contrast)",
# "2 (third image)",
# "3 (fourth image)",
]
self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
self.submit_button = QPushButton("Submit")
self.submit_button.clicked.connect(self.show_brain_images)
self.top_bar.addWidget(QLabel("Group A:"))
self.top_bar.addWidget(self.group_a_dropdown)
self.top_bar.addWidget(QLabel("Participants (Group A):"))
self.top_bar.addWidget(self.participant_dropdown_a)
self.top_bar.addWidget(QLabel("Group B:"))
self.top_bar.addWidget(self.group_b_dropdown)
self.top_bar.addWidget(QLabel("Participants (Group B):"))
self.top_bar.addWidget(self.participant_dropdown_b)
self.top_bar.addWidget(QLabel("Event:"))
self.top_bar.addWidget(self.event_dropdown)
self.top_bar.addWidget(QLabel("Image Indexes:"))
self.top_bar.addWidget(self.image_index_dropdown)
self.top_bar.addWidget(self.submit_button)
self.scroll = QScrollArea()
self.scroll.setWidgetResizable(True)
self.scroll_content = QWidget()
self.grid_layout = QGridLayout(self.scroll_content)
self.scroll.setWidget(self.scroll_content)
self.layout.addWidget(self.scroll)
self.thumb_size = QSize(280, 180)
self.showMaximized()
def _update_group_b_options(self):
"""Triggered when Group B changes: Update Group A to exclude B's choice"""
selected_b = self.group_b_dropdown.currentText()
# Refresh Group A and exclude what was just picked in Group B
self._refresh_group_dropdown(self.group_a_dropdown, exclude=selected_b)
# Update the participants for Group B
self.update_participant_list_for_group(selected_b, self.participant_dropdown_b)
self._update_event_dropdown()
def _update_group_a_options(self):
"""Triggered when Group A changes: Update Group B to exclude A's choice"""
selected_a = self.group_a_dropdown.currentText()
# Refresh Group B and exclude what was just picked in Group A
self._refresh_group_dropdown(self.group_b_dropdown, exclude=selected_a)
# Update the participants for Group A
self.update_participant_list_for_group(selected_a, self.participant_dropdown_a)
self._update_event_dropdown()
def _on_participants_changed(self, item=None):
self._update_event_dropdown()
def _refresh_group_dropdown(self, dropdown, exclude):
current = dropdown.currentText()
dropdown.blockSignals(True)
dropdown.clear()
dropdown.addItem("<None Selected>")
for group in self.group_names:
if group != exclude:
dropdown.addItem(group)
# Restore previous selection if still valid
if current != "<None Selected>" and current != exclude and dropdown.findText(current) != -1:
dropdown.setCurrentText(current)
else:
dropdown.setCurrentIndex(0) # Reset to "<None Selected>"
dropdown.blockSignals(False)
def _get_file_paths_from_labels(self, labels, group_name):
file_paths = []
if group_name == self.group_a_dropdown.currentText():
participant_map = self.participant_map_a
elif group_name == self.group_b_dropdown.currentText():
participant_map = self.participant_map_b
else:
return []
# Reverse map: display label -> file path
reverse_map = {
f"{label} ({os.path.basename(fp)})": fp
for fp, label in participant_map.items()
}
for label in labels:
file_path = reverse_map.get(label)
if file_path:
file_paths.append(file_path)
return file_paths
def show_brain_images(self):
import flares as flares
selected_event = self.event_dropdown.currentText()
if selected_event == "<None Selected>":
selected_event = None
# Group A
participants_a = self._get_checked_items(self.participant_dropdown_a)
file_paths_a = self._get_file_paths_from_labels(participants_a, self.group_a_dropdown.currentText())
# Group B
participants_b = self._get_checked_items(self.participant_dropdown_b)
file_paths_b = self._get_file_paths_from_labels(participants_b, self.group_b_dropdown.currentText())
selected_indexes = [
int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
]
all_selected_paths = list(set(file_paths_a + file_paths_b))
if not all_selected_paths:
print("No participants selected.")
return
parameterized_indexes = {
0: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner about the contrast.",
"default": "True",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
},
{
"key": "is_3d",
"label": "Should we display the results in a 3D interactive window?",
"default": "True",
"type": bool,
}
],
}
# Inject full_text from index_texts
for idx, params_list in parameterized_indexes.items():
full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
for param_info in params_list:
param_info["full_text"] = full_text
indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
param_values = {}
if indexes_needing_params:
dialog = ParameterInputDialog(indexes_needing_params, parent=self)
if dialog.exec_() == QDialog.Accepted:
param_values = dialog.get_values()
if param_values is None:
return
else:
return
# Build group-level contrast DataFrames
def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
group_df = pd.DataFrame()
for fp in file_paths:
print(f"Looking up contrast for: {fp}")
event_con_dict = self.contrast_results_dict.get(fp, {})
print("Available events for this file:", list(event_con_dict.keys()))
if event and event in event_con_dict:
df = event_con_dict[event]
print(f"Appending contrast df for event: {event}")
group_df = pd.concat([group_df, df], ignore_index=True)
else:
print(f"Event '{event}' not found for {fp}")
return group_df
print("Selected event:", selected_event)
print("File paths A:", file_paths_a)
print("File paths B:", file_paths_b)
contrast_df_a = concat_group_contrasts(file_paths_a, selected_event)
contrast_df_b = concat_group_contrasts(file_paths_b, selected_event)
print("contrast_df_a empty?", contrast_df_a.empty)
print("contrast_df_b empty?", contrast_df_b.empty)
all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
if len(all_raw_objs) > 1:
processed_raw = flares.aggregate_fnirs_group_geometry(all_raw_objs)
else:
processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
# Visualizations
for idx in selected_indexes:
if idx == 0:
params = param_values.get(idx, {})
show_optodes = params.get("show_optodes", None)
t_or_theta = params.get("t_or_theta", None)
show_text = params.get("show_text", None)
brain_bounds = params.get("brain_bounds", None)
is_3d = params.get("is_3d", None)
if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None or is_3d is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
if not contrast_df_a.empty and not contrast_df_b.empty and processed_raw:
flares.plot_2d_3d_contrasts_between_groups(
contrast_df_a,
contrast_df_b,
raw_haemo=processed_raw,
group_a_name=self.group_a_dropdown.currentText(),
group_b_name=self.group_b_dropdown.currentText(),
is_3d=is_3d,
t_or_theta=t_or_theta,
show_optodes=show_optodes,
show_text=show_text,
brain_bounds=brain_bounds
)
else:
print("no")
src/analysis/intergroupbrainimage.py
+190
View File
@@ -0,0 +1,190 @@
"""
Filename: intergroupbrainimage.py
Description: Logic for the Inter-Group Brain & Image analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# External library imports
import pandas as pd
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
PARAMETERIZED_INDEXES = {
0: [
{
"key": "lower_bound",
"label": "Lower bound + <description>",
"default": "-0.3",
"type": float, # specify int here
},
{
"key": "upper_bound",
"label": "Upper bound + <description>",
"default": "0.8",
"type": float, # specify int here
}
],
1: [
{
"key": "p_value",
"label": "Significance threshold P-value (e.g. 0.05)",
"default": "0.05",
"type": float,
},
{
"key": "graph_bounds",
"label": "Graph Upper/Lower Limit",
"default": "3.0",
"type": float,
}
],
2: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
"default": "False",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
}
],
}
class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
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.setup_inter_group_ui(["0 (GLM Results)", "1 (Significance)", "2 (Brain Activity Visualization)",])
def process_request(self):
request = self.get_common_request_data(PARAMETERIZED_INDEXES)
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
all_cha = pd.DataFrame()
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if selected_event:
participant_events = set(haemo_obj.annotations.description)
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)
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)
df_group = pd.DataFrame()
if selected_file_paths:
for file_path in selected_file_paths:
df = self.df_ind.get(file_path)
if df is not None:
df_group = pd.concat([df_group, df], ignore_index=True)
for idx in selected_indexes:
if idx == 0:
params = param_values.get(idx, {})
lower_bound = params.get("lower_bound", None)
upper_bound = params.get("upper_bound", None)
if lower_bound is None or upper_bound is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
plot_fir_model_results(df_group, p_haemo, p_design_matrix, selected_event, lower_bound, upper_bound)
elif idx == 1:
params = param_values.get(idx, {})
p_val = params.get("p_value", None)
graph_bounds = params.get("graph_bounds", None)
if p_val is None or graph_bounds is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
all_contrasts = []
for fp in selected_file_paths:
condition_dfs = self.contrast_results.get(fp, {})
if selected_event in condition_dfs:
df = condition_dfs[selected_event].copy()
df["ID"] = fp
all_contrasts.append(df)
if not all_contrasts:
print("No contrast data found for selected participants and event.")
return
df_contrasts = pd.concat(all_contrasts, ignore_index=True)
#flares.run_second_level_analysis(df_contrasts, p_haemo, p_val, graph_bounds)
elif idx == 2:
params = param_values.get(idx, {})
show_optodes = params.get("show_optodes", None)
t_or_theta = params.get("t_or_theta", None)
show_text = params.get("show_text", None)
brain_bounds = params.get("brain_bounds", None)
if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None:
print(f"Missing parameters for index {idx}, skipping.")
continue
raw_list = [self.haemo_dict.get(fp) for fp in selected_file_paths]
if len(selected_file_paths) > 1:
print(f"Aggregating geometry for {len(selected_file_paths)} participants...")
processed_raw = aggregate_fnirs_group_geometry(raw_list)
else:
processed_raw = raw_list[0].copy().pick(picks="hbo")
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)
elif idx == 3:
pass
else:
print(f"No method defined for index {idx}")
src/analysis/groupfunctionalconnectivity.py → src/analysis/intergroupfunctionalconnectivity.py
View File
src/analysis/intergroupstats.py
+130
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@@ -0,0 +1,130 @@
"""
Filename: intergroupstats.py
Description: Logic for the Inter-Group Stats analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# External library imports
import pandas as pd
from flares import run_roi_second_level_analysis
from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
PARAMETERIZED_INDEXES = {
0: [
{
"key": "p_value",
"label": "Significance threshold P-value (e.g. 0.05)",
"default": "0.05",
"type": float,
},
{
"key": "graph_bounds",
"label": "Graph Y-Limit (Optional, e.g. 1e-5)",
"default": "0.0", # Set to 0.0 to auto-scale
"type": float,
}
],
}
class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
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.setup_inter_group_ui(["0 (Significance)",])
def process_request(self):
request = self.get_common_request_data(PARAMETERIZED_INDEXES)
if request is None:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
all_cha = pd.DataFrame()
for file_path in selected_file_paths:
haemo_obj = self.haemo_dict.get(file_path)
if selected_event:
participant_events = set(haemo_obj.annotations.description)
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)
if cha_df is not None:
all_cha = pd.concat([all_cha, cha_df], ignore_index=True)
file_path = selected_file_paths[0]
p_haemo = self.haemo_dict.get(file_path)
# Concatenate individual ROI stats (df_ind) for all chosen subjects
df_group = pd.DataFrame()
if selected_file_paths:
for file_path in selected_file_paths:
df = self.df_ind.get(file_path)
if df is not None:
df_group = pd.concat([df_group, df], ignore_index=True)
for idx in selected_indexes:
if idx == 0:
params = param_values.get(idx, {})
p_val = params.get("p_value", 0.05)
graph_bounds = params.get("graph_bounds", 0.0)
if df_group.empty:
print("No ROI data (df_ind) found for selected participants.")
continue
# Filter down to the selected experimental event/condition
if selected_event:
if 'Condition' in df_group.columns:
df_filtered = df_group[df_group['Condition'] == selected_event]
else:
print("Warning: 'Condition' column not found in ROI data.")
df_filtered = df_group
else:
df_filtered = df_group
if df_filtered.empty:
print(f"No ROI data matches the condition '{selected_event}'.")
continue
all_cha_filtered = pd.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]
else:
all_cha_filtered = all_cha
# Call your new custom group ROI method!
run_roi_second_level_analysis(
df_roi_all=df_filtered,
df_cha_all=all_cha_filtered,
raw_haemo=p_haemo,
p_threshold=p_val,
min_subjects=len(selected_file_paths),
correction_method='fdr_bh',
target_chroma='hbo',
graph_bounds=graph_bounds if graph_bounds > 0.0 else None,
roi_config=r"C:\Users\tyler\Desktop\research\flares\regions.json"
)
else:
print(f"No method defined for index {idx}")
src/analysis/participantbrain.py
+57 -140
View File
@@ -1,161 +1,78 @@
"""
Filename: participantbrain.py
Description: Participant brain analysis window for FLARES
Description: Logic for the Participant Brain analysis window
Author: Tyler de Zeeuw
License: GPL-3.0
"""
import os
from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
from PySide6.QtCore import QSize
from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
# External library imports
from flares import brain_3d_visualization, brain_landmarks_3d
from src.shared.flaresbasewidget import ParticipantUIMixin, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
class ParticipantBrainViewerWidget(FlaresBaseWidget):
PARAMETERIZED_INDEXES = {
0: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "show_brodmann",
"label": "Show common brodmann areas on the brain.",
"default": "True",
"type": bool,
}
],
1: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
"default": "False",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
}
],
}
class ParticipantBrainViewerWidget(ParticipantUIMixin, FlaresBaseWidget):
def __init__(self, haemo_dict, cha_dict):
super().__init__("ParticipantBrainViewer")
self.setWindowTitle(f"Participant Brain Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict
self.cha_dict = cha_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)"
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)})"
self.participant_map[file_path] = short_label
self.participant_dropdown_items.append(display_label)
self.layout = QVBoxLayout(self)
self.top_bar = QHBoxLayout()
self.layout.addLayout(self.top_bar)
self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
self.event_dropdown = QComboBox()
self.event_dropdown.addItem("<None Selected>")
self.index_texts = [
"0 (Brain Landmarks)",
"1 (Brain Activity Visualization)",
# "2 (third image)",
# "3 (fourth image)",
]
self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
self.submit_button = QPushButton("Submit")
self.submit_button.clicked.connect(self.show_brain_images)
self.top_bar.addWidget(QLabel("Participants:"))
self.top_bar.addWidget(self.participant_dropdown)
self.top_bar.addWidget(QLabel("Event:"))
self.top_bar.addWidget(self.event_dropdown)
self.top_bar.addWidget(QLabel("Image Indexes:"))
self.top_bar.addWidget(self.image_index_dropdown)
self.top_bar.addWidget(self.submit_button)
self.scroll = QScrollArea()
self.scroll.setWidgetResizable(True)
self.scroll_content = QWidget()
self.grid_layout = QGridLayout(self.scroll_content)
self.scroll.setWidget(self.scroll_content)
self.layout.addWidget(self.scroll)
self.thumb_size = QSize(280, 180)
self.showMaximized()
def show_brain_images(self):
import flares as flares
selected_event = self.event_dropdown.currentText()
if selected_event == "<None Selected>":
selected_event = None
selected_display_names = self._get_checked_items(self.participant_dropdown)
selected_file_paths = []
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:
selected_file_paths.append(fp)
break
selected_indexes = [
int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
]
self.setup_participant_ui(["0 (Brain Landmarks)", "1 (Brain Activity Visualization)",])
parameterized_indexes = {
0: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "show_brodmann",
"label": "Show common brodmann areas on the brain.",
"default": "True",
"type": bool,
}
],
1: [
{
"key": "show_optodes",
"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
"default": "all",
"type": str,
},
{
"key": "t_or_theta",
"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
"default": "theta",
"type": str,
},
{
"key": "show_text",
"label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
"default": "False",
"type": bool,
},
{
"key": "brain_bounds",
"label": "Graph Upper/Lower Limit",
"default": "1.0",
"type": float,
}
],
}
def process_request(self):
# Inject full_text from index_texts
for idx, params_list in parameterized_indexes.items():
full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
for param_info in params_list:
param_info["full_text"] = full_text
request = self.get_common_request_data(PARAMETERIZED_INDEXES)
if request is None:
return
indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
param_values = {}
if indexes_needing_params:
dialog = ParameterInputDialog(indexes_needing_params, parent=self)
if dialog.exec_() == QDialog.Accepted:
param_values = dialog.get_values()
if param_values is None:
return
else:
return
(selected_event, selected_file_paths, selected_indexes, param_values,) = request
# Pass the necessary arguments to each method
for file_path in selected_file_paths:
@@ -183,7 +100,7 @@ class ParticipantBrainViewerWidget(FlaresBaseWidget):
print(f"Missing parameters for index {idx}, skipping.")
continue
flares.brain_landmarks_3d(haemo_obj, show_optodes, show_brodmann)
brain_landmarks_3d(haemo_obj, show_optodes, show_brodmann)
elif idx == 1:
params = param_values.get(idx, {})
@@ -196,7 +113,7 @@ class ParticipantBrainViewerWidget(FlaresBaseWidget):
print(f"Missing parameters for index {idx}, skipping.")
continue
flares.brain_3d_visualization(haemo_obj, cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
brain_3d_visualization(haemo_obj, cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
else:
print(f"No method defined for index {idx}")
src/analysis/participant.py → src/analysis/participantimage.py
+2 -2
View File
@@ -18,9 +18,9 @@ from src.shared.flaresbasewidget import ClickableLabel, FlaresBaseWidget
from src.shared.shareddata import APP_NAME
class ParticipantViewerWidget(FlaresBaseWidget):
class ParticipantImageViewerWidget(FlaresBaseWidget):
def __init__(self, haemo_dict, fig_bytes_dict):
super().__init__("ParticipantViewer")
super().__init__("ParticipantImage")
self.setAttribute(Qt.WidgetAttribute.WA_DeleteOnClose)
self.setWindowTitle(f"Participant Viewer - {APP_NAME.upper()}")
self.haemo_dict = haemo_dict