163 lines
7.2 KiB
Python
163 lines
7.2 KiB
Python
"""
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Filename: intergroupbrainimage.py
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Description: Logic for the Inter-Group Brain & Image analysis window
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Note: Compliant with pylance strict type checking
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Author: Tyler de Zeeuw
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License: GPL-3.0
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"""
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# Built-in imports
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from typing import Any, cast
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# External library imports
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from mne.io.base import BaseRaw
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import pandas as pd
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from pandas import DataFrame
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from flares import aggregate_fnirs_group_geometry, plot_2d_3d_contrasts_between_groups
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from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
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from src.shared.shareddata import APP_NAME
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PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
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0: [
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{
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"key": "show_optodes",
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"label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
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"default": "all",
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"type": str,
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},
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{
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"key": "t_or_theta",
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"label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
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"default": "theta",
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"type": str,
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},
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{
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"key": "show_text",
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"label": "Display informative text on the top left corner about the contrast.",
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"default": "True",
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"type": bool,
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},
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{
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"key": "brain_bounds",
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"label": "Graph Upper/Lower Limit",
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"default": "1.0",
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"type": float,
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},
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{
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"key": "is_3d",
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"label": "Should we display the results in a 3D interactive window?",
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"default": "True",
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"type": bool,
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}
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],
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}
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DESCRIPTION = """\n1. Group Contrast 2D/3D (plot_2d_3d_contrasts_between_groups)
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\nCompares two participant groups' contrast results (e.g. condition-vs-baseline effects) channel-by-channel, fitting a mixed-effects model with group, channel, and chromophore as factors. Produces BOTH directions of the contrast (Group A minus Group B, and Group B minus Group A) as separate plots, so the sign convention is explicit either way you read it.
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\nis_3d controls the display: True renders a 3D weighted brain map per contrast direction (same rendering as intra method 1, but showing the between-group difference rather than a single group's estimate); False renders a 2D topographic map instead, which is faster and sometimes easier to read at a glance for a whole-head pattern.
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\nA channel is only included if BOTH groups have at least min_participants_per_group (default 2) contributing participants for that channel - channels present in only one group, or with too few participants in either group to estimate within-group variance, are dropped before fitting. If this drops too many channels, check that both groups have enough participants with usable data for the selected event/channels.
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\nAs with other mixed-effects models in this app, small participant counts can produce convergence warnings; when that happens, the model falls back to pooled OLS, which does not account for the repeated-measures structure of the data and may understate uncertainty - treat results run this way with extra caution.
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"""
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class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
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def __init__(
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self,
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haemo_dict: dict[str, BaseRaw],
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df_ind_dict: dict[str, DataFrame],
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design_matrix_dict: dict[str, DataFrame],
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contrast_results_dict: dict[str, dict[str, Any]],
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group_dict: dict[str, str],
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) -> None:
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super().__init__("InterGroupBrainImage")
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self.setWindowTitle(f"Inter-Group Brain & Image Viewer - {APP_NAME.upper()}")
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self.haemo_dict = haemo_dict
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self.df_ind_dict = df_ind_dict
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self.design_matrix_dict = design_matrix_dict
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self.contrast_results_dict = contrast_results_dict
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self.group_dict = group_dict
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self.setup_inter_group_ui(["0 (Group Contrast 2D/3D)"], placeholder_text=DESCRIPTION)
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def process_request(self):
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request = self.get_common_request_data(PARAMETERIZED_INDEXES)
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if request is None:
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return
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(selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, raw_params) = request
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param_values = cast(dict[int | str, dict[str, Any]], raw_params)
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# Build group-level contrast DataFrames
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def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
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group_df = pd.DataFrame()
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for fp in file_paths:
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print(f"Looking up contrast for: {fp}")
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event_con_dict = self.contrast_results_dict.get(fp, {})
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print("Available events for this file:", list(event_con_dict.keys()))
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if event and event in event_con_dict:
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df = event_con_dict[event]
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print(f"Appending contrast df for event: {event}")
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group_df = pd.concat([group_df, df], ignore_index=True)
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else:
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print(f"Event '{event}' not found for {fp}")
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return group_df
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print("Selected event:", selected_event)
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print("File paths A:", file_paths_a)
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print("File paths B:", file_paths_b)
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contrast_df_a = concat_group_contrasts(file_paths_a, selected_event)
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contrast_df_b = concat_group_contrasts(file_paths_b, selected_event)
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print("contrast_df_a empty?", contrast_df_a.empty)
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print("contrast_df_b empty?", contrast_df_b.empty)
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all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
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if len(all_raw_objs) > 1:
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processed_raw = aggregate_fnirs_group_geometry(all_raw_objs)
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elif len(all_raw_objs) == 1 and all_raw_objs[0] is not None:
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processed_raw = all_raw_objs[0].copy()
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processed_raw.pick(picks="hbo") # type: ignore
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else:
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processed_raw = None
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# Visualizations
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for idx in selected_indexes:
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if idx == 0:
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params = param_values.get(idx, {})
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show_optodes = params.get("show_optodes", None)
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t_or_theta = params.get("t_or_theta", None)
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show_text = params.get("show_text", None)
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brain_bounds = params.get("brain_bounds", None)
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is_3d = params.get("is_3d", None)
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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:
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print(f"Missing parameters for index {idx}, skipping.")
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continue
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if not contrast_df_a.empty and not contrast_df_b.empty and processed_raw:
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plot_2d_3d_contrasts_between_groups(
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contrast_df_a,
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contrast_df_b,
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raw_haemo=processed_raw,
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group_a_name=self.group_a_dropdown.currentText(),
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group_b_name=self.group_b_dropdown.currentText(),
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is_3d=is_3d,
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t_or_theta=t_or_theta,
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show_optodes=show_optodes,
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show_text=show_text,
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brain_bounds=brain_bounds
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)
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else:
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print(f"No method defined for index {idx}") |