From c6c859a68ff96e17d730b633fd24de35605de129 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=A9line=20Provins?= Date: Wed, 15 May 2024 19:53:49 +0200 Subject: [PATCH 1/5] enh: first draft of an example in form of python code about how to create an command line interface to generate a group report using nireports --- docs/notebooks/data/reports-spec.yml | 38 +++ docs/notebooks/group_report_cli.py | 174 ++++++++++ docs/notebooks/load_save.py | 449 ++++++++++++++++++++++++++ docs/notebooks/reports.py | 453 +++++++++++++++++++++++++++ 4 files changed, 1114 insertions(+) create mode 100644 docs/notebooks/data/reports-spec.yml create mode 100644 docs/notebooks/group_report_cli.py create mode 100644 docs/notebooks/load_save.py create mode 100644 docs/notebooks/reports.py diff --git a/docs/notebooks/data/reports-spec.yml b/docs/notebooks/data/reports-spec.yml new file mode 100644 index 00000000..aaa8dbce --- /dev/null +++ b/docs/notebooks/data/reports-spec.yml @@ -0,0 +1,38 @@ +package: funconn +sections: +- name: Group report + reportlets: + - bids: + desc: censoring + extension: [.html] + suffix: bold + caption: | + fMRI duration after censoring computed as the number of good timepoints multiplied by the repetition time (TR). + The red line corresponds to the QC cutoff of 5 minutes (300 seconds). Less than 5 minutes of fMRI is not enough to + reliably estimate connectivity. + subtitle: Report about censoring. + - bids: {desc: fcdist, suffix: bold} + caption: Density distributions of within-session FC strengths. The FC distribution from each session are overlaid. + subtitle: Functional connectivity density distributions. + style: + max-width: 600px + + - bids: {desc: qcfc, suffix: bold} + caption: | + QC-FC plots tested functional connectivity associations with three image quality metrics (fd_mean, fd_num, and + fd_perc). Plots were generated from functional data from all sessions. Red dotted lines represent a theoretical + artifact-free null-hypothesis distribution obtained through permutation analyses. QC-FC percent match level represents the distance + between the observed and the null-hypothesis distribution and is computed using the R implementation of the two-sample Kolmogorov-Smirnov + test for goodness of fit. Red label indicates that the QC-FC distribution did not reach above the 95% cutoff. + subtitle: QC-FC correlation distributions. + style: + max-width: 1350px + + - bids: {desc: qcfcvseuclidean, suffix: bold} + caption: | + Correlation between QC-FC and Euclidean distance separating nodes. The euclidean distance is computed from the centers of mass + of each region in the atlas used to compute the FC. + subtitle: Distance-dependent effect of motion. + style: + max-width: 1350px + diff --git a/docs/notebooks/group_report_cli.py b/docs/notebooks/group_report_cli.py new file mode 100644 index 00000000..c764ef88 --- /dev/null +++ b/docs/notebooks/group_report_cli.py @@ -0,0 +1,174 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2023 The Axon Lab +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# + +import argparse +import logging + +import numpy as np +import os.path as op +import pandas as pd + +from itertools import chain + +from load_save import ( + get_atlas_data, + find_atlas_dimension, + find_derivative, + check_existing_output, + get_bids_savename, + get_func_filenames_bids, + load_iqms, + FC_FILLS, + FC_PATTERN +) + +from reports import ( + group_report, +) + + +def get_arguments() -> argparse.Namespace: + parser: argparse.ArgumentParser = argparse.ArgumentParser( + description="""Compute functional connectivity group report from functional connectivity matrices.""", + ) + + # Input/Output arguments and options + parser.add_argument( + "output", + help="path to the directory where the functional connectivity matrices are stored", + ) + parser.add_argument( + "--mriqc-path", + default=None, + help="specify the path to the mriqc derivatives", + ) + parser.add_argument( + "--task", + default=["rest"], + action="store", + nargs="+", + help="a space delimited list of task(s)", + ) + parser.add_argument( + "--fc-estimator", + default="sparse inverse covariance", + action="store", + choices=["correlation", "covariance", "sparse", "sparse inverse covariance"], + type=str, + help="""type of connectivity to compute (can be 'correlation', 'covariance' or + 'sparse')""", + ) + parser.add_argument( + "-v", + "--verbosity", + action="count", + default=1, + help="""increase output verbosity (-v: standard logging infos; -vv: logging + infos and NiLearn verbose; -vvv: debug)""", + ) + + args = parser.parse_args() + + return args + + +def main(): + args = get_arguments() + output = args.output + task_filter = args.task + mriqc_path = args.mriqc_path + fc_label = args.fc_estimator.replace(" ", "") + + verbosity_level = args.verbosity + + logging_level_map = { + 0: logging.WARN, + 1: logging.INFO, + 2: logging.INFO, + 3: logging.DEBUG, + } + + logging.basicConfig( + # filename='example.log', + # format='%(asctime)s %(levelname)s:%(message)s', + format="%(levelname)s: %(message)s", + level=logging_level_map[min([verbosity_level, 3])], + ) + + logging.captureWarnings(True) + + # Find the atlas dimension from the output path + atlas_dimension = find_atlas_dimension(output) + atlas_data = get_atlas_data(dimension=atlas_dimension) + atlas_filename = getattr(atlas_data, "maps") + + # Find all existing functional connectivity + input_path = find_derivative(output) + func_filenames, _ = get_func_filenames_bids(input_path, task_filter=task_filter) + all_filenames = list(chain.from_iterable(func_filenames)) + + existing_fc = check_existing_output( + output, + all_filenames, + return_existing=True, + return_output=True, + patterns=FC_PATTERN, + meas=fc_label, + **FC_FILLS, + ) + if not existing_fc: + filename = op.join( + output, + get_bids_savename( + all_filenames[0], patterns=FC_PATTERN, meas=fc_label, **FC_FILLS + ), + ) + raise ValueError( + f"No functional connectivity of type {filename} were found. Please revise the arguments." + ) + + # Load functional connectivity matrices + fc_matrices = [] + for file_path in existing_fc: + fc_matrices.append(np.loadtxt(file_path, delimiter="\t")) + + # Load fMRI duration after censoring + good_timepoints_df = pd.read_csv( + op.join(output, "fMRI_duration_after_censoring.csv") + ) + + # Load IQMs + iqms_df = load_iqms(output, existing_fc, mriqc_path=mriqc_path) + + # Generate group figures + group_report( + good_timepoints_df, + fc_matrices, + iqms_df, + atlas_filename, + output, + ) + + +if __name__ == "__main__": + main() diff --git a/docs/notebooks/load_save.py b/docs/notebooks/load_save.py new file mode 100644 index 00000000..23a2c5a0 --- /dev/null +++ b/docs/notebooks/load_save.py @@ -0,0 +1,449 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2023 The Axon Lab +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +"""Python module for loading and saving fMRI related data""" + +import os +import re +import os.path as op +import pandas as pd +from collections import defaultdict +import logging +from typing import Optional, Union + +from bids import BIDSLayout +import numpy as np + +from pandas import read_csv +from nibabel import loadsave +from bids.layout import parse_file_entities +from bids.layout.writing import build_path +from nilearn.datasets import fetch_atlas_difumo +from nilearn.interfaces.fmriprep.load_confounds import _load_single_confounds_file + +FC_PATTERN: list = [ + "sub-{subject}[/ses-{session}]/func/sub-{subject}" + "[_ses-{session}][_task-{task}][_meas-{meas}]" + "_{suffix}{extension}" +] +FC_FILLS: dict = {"suffix": "connectivity", "extension": ".tsv"} + +TIMESERIES_PATTERN: list = [ + "sub-{subject}[/ses-{session}]/func/sub-{subject}" + "[_ses-{session}][_task-{task}][_desc-{desc}]" + "_{suffix}{extension}" +] +TIMESERIES_FILLS: dict = {"desc": "denoised", "extension": ".tsv"} + +CONFOUND_PATTERN: list = [ + "sub-{subject}[_ses-{session}][_task-{task}][_part-{part}][_desc-{desc}]" + "_{suffix}{extension}" +] +CONFOUND_FILLS: dict = {"desc": "confounds", "suffix": "timeseries", "extension": "tsv"} + + +def separate_by_similar_values( + input_list: list, external_value: Optional[Union[list, np.ndarray]] = None +) -> dict: + """This returns elements of `input_list` with similar values (optionally set by + `external_value`) separated into sub-lists. + + Parameters + ---------- + input_list : list + List to be separated. + external_value : Optional[list], optional + Values corresponding to the elements of `input_list`, by default None + + Returns + ------- + dict + Dictionary where each entry is a list of elements that have similar values and + the keys are the value for each list. + """ + if external_value is None: + external_value = input_list + + data_by_value = defaultdict(list) + + for val, data in zip(external_value, input_list): + data_by_value[val].append(data) + return data_by_value + + +def get_func_filenames_bids( + paths_to_func_dir: str, + task_filter: Optional[list] = None, + ses_filter: Optional[list] = None, + run_filter: Optional[list] = None, +) -> tuple[list[list[str]], list[float]]: + """Return the BIDS functional imaging files matching the specified task and session + filters as well as the first (if multiple) unique repetition time (TR). + + Parameters + ---------- + paths_to_func_dir : str + Path to the BIDS (usually derivatives) directory + task_filter : list, optional + List of task name(s) to consider, by default `None` + ses_filter : list, optional + List of session name(s) to consider, by default `None` + run_filter : list, optional + List of run(s) to consider, by default `None` + + Returns + ------- + tuple[list[list[str]], list[float]] + Returns two lists with: a list of sorted filenames and a list of TRs. + """ + logging.debug("Using BIDS to find functional files...") + + layout = BIDSLayout( + paths_to_func_dir, + validate=False, + ) + + all_derivatives = layout.get( + scope="all", + return_type="file", + extension=["nii.gz", "gz"], + suffix="bold", + task=task_filter or [], + session=ses_filter or [], + run=run_filter or [], + ) + + if not all_derivatives: + raise ValueError( + f"No functional derivatives were found under {paths_to_func_dir} with the following filters:" + f"\nExtension: ['nii.gz', 'gz']" + f"\nSuffix: bold" + f"\nTask: {task_filter or []}" + f"\nSession: {ses_filter or []}" + f"\nRun: {run_filter or []}" + ) + + affines = [] + for file in all_derivatives: + affines.append(loadsave.load(file).affine) + + similar_fov_dict = separate_by_similar_values( + all_derivatives, np.array(affines)[:, 0, 0] + ) + if len(similar_fov_dict) > 1: + logging.warning( + f"{len(similar_fov_dict)} different FoV found ! " + "Files with similar FoV will be computed together. " + "Computation time may increase." + ) + + separated_files = [] + separated_trs = [] + for file_group in similar_fov_dict.values(): + t_rs = [] + for file in file_group: + t_rs.append(layout.get_metadata(file)["RepetitionTime"]) + + similar_tr_dict = separate_by_similar_values(file_group, t_rs) + separated_files += list(similar_tr_dict.values()) + separated_trs += list(similar_tr_dict.keys()) + + if len(similar_tr_dict) > 1: + logging.warning( + "Multiple TR values found ! " + "Files with similar TR will be computed together. " + "Computation time may increase." + ) + + return separated_files, separated_trs + + +def get_bids_savename(filename: str, patterns: list, **kwargs) -> str: + """Return the BIDS filename following the specified patterns and modifying the + entities from the keywords arguments. + + Parameters + ---------- + filename : str + Name of the original BIDS file + patterns : list, optional + Patterns for the output file, by default FC_PATTERN + + Returns + ------- + str + BIDS output filename. + """ + entity = parse_file_entities(filename) + + for key, value in kwargs.items(): + entity[key] = value + + bids_savename = build_path(entity, patterns) + + return str(bids_savename) + + +def get_atlas_data(atlas_name: str = "DiFuMo", **kwargs) -> dict: + """Fetch the specifies atlas filename and data. + + Parameters + ---------- + atlas_name : str, optional + Name of the atlas to fetch, by default "DiFuMo" + + Returns + ------- + dict + Dictionary with keys "maps" (filename) and "labels" (ROI labels). + """ + logging.info("Fetching the DiFuMo atlas ...") + + if kwargs["dimension"] not in [64, 128, 512]: + logging.warning( + "Dimension for DiFuMo atlas is different from 64, 128 or 512 ! Are you" + "certain you want to deviate from those optimized modes? " + ) + + return fetch_atlas_difumo(legacy_format=False, **kwargs) + + +def find_atlas_dimension(path: str, atlas_name: str = "DiFuMo") -> int: + """Fetch the atlas dimension from the path where the functional connectivity are saved. + Parameters + ---------- + path : str + Path to the directory where functional connectivity are saved. + atlas_name : str, optional + Name of the atlas to fetch, by default "DiFuMo" + + Returns + ------- + int + Atlas dimension. + """ + + # Using regular expression to extract the number of dimensions + dimension_match = re.search(rf"{atlas_name}(\d+)", path) + + if dimension_match: + return int(dimension_match.group(1)) + else: + raise ValueError( + f"The output path {path} does not contain the expected pattern: {atlas_name} followed by digits." + ) + + +def find_derivative(path: str, derivatives_name: str = "derivatives") -> str: + """Find the corresponding BIDS derivative folder (if existing, otherwise it will be + created). + + Parameters + ---------- + path : str + Path to the BIDS (usually derivatives) dataset. + derivatives_name : str, optional + Name of the derivatives folder, by default "derivatives" + + Returns + ------- + str + Absolute path to the derivative folder. + """ + splitted_path = path.split("/") + try: + while derivatives_name not in splitted_path[-1]: + splitted_path.pop() + except IndexError: + logging.warning( + f'"{derivatives_name}" could not be found on path - ' + f'creating at: {op.join(path, derivatives_name)}"' + ) + return op.join(path, derivatives_name) + + return "/".join(splitted_path) + + +def find_mriqc(path: str) -> str: + """Find the path to the MRIQC folder (if existing, otherwise it will be + created). + + Parameters + ---------- + path : str + Path to the BIDS (usually derivatives) dataset. + + Returns + ------- + str + Absolute path to the mriqc folder. + """ + logging.debug("Searching for MRIQC path...") + derivative_path = find_derivative(path) + + folders = [ + f for f in os.listdir(derivative_path) if op.isdir(op.join(derivative_path, f)) + ] + + mriqc_path = [f for f in folders if "mriqc" in f] + if len(mriqc_path) >= 2: + logging.warning( + f"More than one mriqc derivative folder was found: {mriqc_path}" + f"The first instance {mriqc_path[0]} is used for the computation." + "In case you want to use another mriqc derivative folder, use the --mriqc-path flag" + ) + return op.join(derivative_path, mriqc_path[0]) + + +def reorder_iqms(iqms_df: pd.DataFrame, fc_paths: list[str]): + """Reorder the IQMs according to the list of filenames + + Parameters + ---------- + iqms_df : pd.Dataframe + Dataframe containing the IQMs value for each image + fc_paths : list [str] + List of paths to the functional connectivity matrices + + Returns + ------- + panda.df + Dataframe containing the IQMs dataframe with reordered rows. + """ + iqms_df[["subject", "session", "task"]] = iqms_df["bids_name"].str.extract( + r"sub-(\d+)_ses-(\d+)_task-(\w+)_" + ) + entities_list = [parse_file_entities(filepath) for filepath in fc_paths] + entities_df = pd.DataFrame(entities_list) + + return pd.merge( + entities_df, iqms_df, on=["subject", "session", "task"], how="inner" + ) + + +def load_iqms( + derivative_path: str, + fc_paths: list[str], + mriqc_path: str = None, + mod="bold", + iqms_name: list = ["fd_mean", "fd_num", "fd_perc"], +) -> str: + """Load the IQMs and match their order with the corresponding functional matrix. + + Parameters + ---------- + derivative_path : str + Path to the BIDS dataset's derivatives. + fc_paths : list [str] + List of paths to the functional connectivity matrices + mriqc_path : str, optional + Name of the MRIQC derivative folder, by default None + mod : str, optional + Load the IQMs of that modality + iqms_name : list, optional + Name of the IQMs to find, by default ["fd_mean", "fd_num", "fd_perc"] + + Returns + ------- + panda.df + Dataframe containing the IQMs loaded from the derivatives folder. + """ + # Find the MRIQC folder + if mriqc_path is None: + mriqc_path = find_mriqc(derivative_path) + + # Load the IQMs from the group tsv + iqms_filename = op.join(mriqc_path, f"group_{mod}.tsv") + iqms_df = read_csv(iqms_filename, sep="\t") + # If multi-echo dataset and the IQMs of interest are motion-related, keep only the IQMs from the second echo + if "echo" in iqms_df["bids_name"][0] and all("fd" in i for i in iqms_name): + iqms_df = iqms_df[iqms_df["bids_name"].str.contains("echo-2")] + logging.info( + "In the case of a multi-echo dataset, the IQMs of the second echo are considered." + ) + + # Match the order of the rows in iqms_df with the corresponding FC + iqms_df = reorder_iqms(iqms_df, fc_paths) + + # Keep only the IQMs of interest + iqms_df = iqms_df[iqms_name] + + return iqms_df + + +def check_existing_output( + output: str, + func_filename: list[str], + return_existing: bool = False, + return_output: bool = False, + **kwargs, +) -> tuple[list[str], list[str]]: + """Check for existing output. + + Parameters + ---------- + output : str + Path to the output directory + func_filename : list[str] + Input files to be processed + return_existing : bool, optional + Condition to return the list of input corresponding to existing outputs, by default + False + return_output: bool, optional + Condition to return the path of existing outputs, by default False + + Returns + ------- + tuple[list[str], list[str]] + List of missing data path (optionally, a second list of existing data path) + """ + if return_output == True and return_existing == False: + raise ValueError( + "Setting return_output=True in check_existing_output requires return_existing=True." + ) + + missing_data_filter = [ + not op.exists(op.join(output, get_bids_savename(filename, **kwargs))) + for filename in func_filename + ] + + missing_data = np.array(func_filename)[missing_data_filter] + logging.debug( + f"\t{sum(missing_data_filter)} missing data found for files:" + "\n\t" + "\n\t".join(missing_data) + ) + + if return_existing: + if return_output: + existing_output = [ + op.join(output, get_bids_savename(filename, **kwargs)) + for filename in func_filename + if op.exists(op.join(output, get_bids_savename(filename, **kwargs))) + ] + return existing_output + else: + existing_data = np.array(func_filename)[ + [not fltr for fltr in missing_data_filter] + ] + return missing_data.tolist(), existing_data.tolist() + + return missing_data.tolist() diff --git a/docs/notebooks/reports.py b/docs/notebooks/reports.py new file mode 100644 index 00000000..57fbf1f5 --- /dev/null +++ b/docs/notebooks/reports.py @@ -0,0 +1,453 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2023 The Axon Lab +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +"""Python module for functional connectivity visual reports""" + +import logging +import os.path as op + +import matplotlib.pyplot as plt +import nibabel as nib +import numpy as np +import pandas as pd +import plotly.offline as pyo +import seaborn as sns +from nireports.assembler.report import Report +from scipy.stats import pearsonr, ks_2samp +from time import strftime +from uuid import uuid4 + + +FIGURE_PATTERN: list = [ + "sub-{subject}/figures/sub-{subject}[_ses-{session}]" + "[_task-{task}][_meas-{meas}][_desc-{desc}]" + "_{suffix}{extension}", + "sub-{subject}/figures/sub-{subject}[_ses-{session}]" + "[_task-{task}][_desc-{desc}]_{suffix}{extension}", +] +FIGURE_FILLS: dict = {"extension": "png"} + +TS_FIGURE_SIZE: tuple = (50, 25) +FC_FIGURE_SIZE: tuple = (70, 45) +LABELSIZE: int = 42 +N_PERMUTATION: int = 10000 +ALPHA = 0.05 +PERCENT_MATCH_CUT_OFF = 95 +DURATION_CUT_OFF = 300 + +def group_report_censoring(good_timepoints_df, output) -> None: + """ + Generate a group report about censoring. + + This function generates an HTML report that includes an interactive scatterplot + showing the fMRI duration after censoring. The scatterplot includes + error bars for the confidence interval and a red line indicating a duration cutoff. + + Parameters: + ----------- + good_timepoints_df: pd.Dataframe + A DataFrame containing information the fMRI duration after censoring. + output : str + Path to the output directory + """ + filenames = good_timepoints_df["filename"] + durations = good_timepoints_df["duration"] + + # Constructing the data for the plot + # Add jitter to x values + jitter = 0.2 # adjust this value to change the amount of jitter + x_values = [1 + np.random.uniform(-jitter, jitter) for _ in range(len(durations))] + data = [ + { + "x": x_values, + "y": durations, + "text": filenames, + "mode": "markers", + "type": "scatter", + "hoverinfo": "text", + "marker": {"opacity": 0.5}, + } + ] + + # Adding a red line at 5 minutes + red_line = { + "type": "line", + "x0": 0, + "y0": DURATION_CUT_OFF, + "x1": 1.5, + "y1": DURATION_CUT_OFF, + "line": {"color": "red", "width": 3, "dash": "dashdot"}, + } + + # Layout settings + layout = { + "hovermode": "closest", + "title": "Duration of fMRI signal after censoring", + "yaxis": {"title": "Duration [s]"}, + "xaxis": {"showticklabels": False, "range": [0.5, 1.5]}, + "shapes": [red_line], + "width": 600, + "height": 600, + "font": {"size": 16}, + "annotations": [ + { + "x": 0.8, + "y": DURATION_CUT_OFF - DURATION_CUT_OFF / 55, + "xref": "x", + "yref": "y", + "text": f"QC cutoff of {DURATION_CUT_OFF/60} min", + "showarrow": False, + "font": {"color": "red"}, + } + ], + } + + fig = {"data": data, "layout": layout} + + # Save the plot as an HTML file + pyo.plot( + fig, + filename=op.join(output, "reportlets", "group_desc-censoring_bold.html"), + auto_open=False, + ) + + +def group_report_fc_dist( + fc_matrices: list[np.ndarray], + output: str, +) -> None: + """Plot and save the functional connectivity density distributions. + + Parameters + ---------- + fc_matrices : list[np.ndarray] + List of functional connectivity matrices + output : str + Path to the output directory + """ + + _, ax = plt.subplots(figsize=FC_FIGURE_SIZE) + + for fc_matrix in fc_matrices: + sns.displot( + fc_matrix, + kind="kde", + fill=True, + linewidth=0.5, + legend=False, + palette="ch:s=.25,rot=-.25", + ) + + ax.tick_params(labelsize=LABELSIZE) + + # Ensure the labels are within the figure + plt.tight_layout() + + savename = op.join("reportlets", "group_desc-fcdist_bold.svg") + + logging.debug("Saving functional connectivity distribution visual report at:") + logging.debug(f"\t{op.join(output, savename)}") + + plt.savefig(op.join(output, savename)) + plt.close() + + +def group_reportlet_fc_dist( + fc_matrices: list[np.ndarray], + output: str, +) -> None: + """Plot and save the functional connectivity density distributions. + + Parameters + ---------- + fc_matrices : list[np.ndarray] + List of functional connectivity matrices + output : str + Path to the output directory + """ + + _, ax = plt.subplots(figsize=FC_FIGURE_SIZE) + + for fc_matrix in fc_matrices: + sns.displot( + fc_matrix, + kind="kde", + fill=True, + linewidth=0.5, + legend=False, + palette="ch:s=.25,rot=-.25", + ) + + ax.tick_params(labelsize=LABELSIZE) + + # Ensure the labels are within the figure + plt.tight_layout() + + savename = op.join("reportlets", "group_desc-fcdist_bold.svg") + + logging.debug("Saving functional connectivity distribution visual report at:") + logging.debug(f"\t{op.join(output, savename)}") + + plt.savefig(op.join(output, savename)) + plt.close() + + +def group_reportlet_qc_fc( + fc_matrices: list[np.ndarray], + iqms_df: pd.DataFrame, + output: str, +) -> dict: + """Plot and save the QC-FC distributions. + + Parameters + ---------- + fc_matrices : list[np.ndarray] + List of functional connectivity matrices + iqms_df : pd.Dataframe + Dataframe containing the image quality metrics to correlate with + output : str + Path to the output directory + """ + + # Stack the list of arrays into a 3D matrix + fc_matrices = np.stack(fc_matrices, axis=2) + + # Keep only upper triangle as the matrix is symmetric + upper_triangle_indices = np.triu_indices(fc_matrices.shape[0], k=1) + fc_matrices = fc_matrices[upper_triangle_indices] + + if fc_matrices.shape[1] != iqms_df.shape[0]: + raise ValueError( + "The number of functional connectivity matrices and IQMs do not match." + ) + + if fc_matrices.shape[1] == 1: + raise ValueError( + "We need at least two functional connectivity matrices to be able to compute its correlation with IQMs." + ) + + fig, axs = plt.subplots(1, 3, figsize=FC_FIGURE_SIZE) + + # Iterate over each IQM + qc_fc_dict = dict() + for i, iqm_column in enumerate(iqms_df.columns): + # Create an empty list to store correlations for each edge + qc_fcs = [] + + # Iterate over each edge + logging.debug("Compute QC-FC correlation for each edge.") + for e in range(fc_matrices.shape[0]): + qc_fc = np.corrcoef(fc_matrices[e, :], iqms_df[iqm_column])[0, 1] + qc_fcs.append(qc_fc) + + # Create a density distribution plot for the current IQM + logging.debug("Create the density distribution plot.") + sns.kdeplot( + qc_fcs, fill=True, label="QC-FC distribution", linewidth=3, ax=axs[i] + ) + + # Save the QC-FC distributions in a dictionary + qc_fc_dict[iqm_column] = qc_fcs + + ## Permutation analyses + logging.debug("Compute QC-FC distribution under the null hypothesis.") + correlations_null = [] + for e in range(fc_matrices.shape[0]): + for _ in range(N_PERMUTATION): + permuted_fc = fc_matrices[ + e, np.random.default_rng(seed=42).permutation(fc_matrices.shape[1]) + ] + # Correlation under null hypothesis + correlation = np.corrcoef(permuted_fc, iqms_df[iqm_column])[0, 1] + correlations_null.append(correlation) + + # Create a density distribution plot for null distribution + logging.debug("Create the density distribution plot for the null distribution.") + sns.kdeplot( + correlations_null, + fill=False, + color="red", + label="Dist under null hypothesis", + linewidth=3, + linestyle="dashed", + ax=axs[i], + ) + plt.legend(fontsize=LABELSIZE + 2) + + # Compute percent match between the two distributions + logging.debug("Compute percent match between the two distributions.") + ks_statistic, _ = ks_2samp(qc_fcs, correlations_null) + percent_match_ks = (1 - ks_statistic) * 100 + + # Plot the box in red if the correlation is significant + facecolor = "red" if percent_match_ks < PERCENT_MATCH_CUT_OFF else "grey" + axs[i].text( + 0.08, + 0.9, + f"QC-FC% = {percent_match_ks:.0f}", + fontsize=LABELSIZE - 2, + bbox=dict(facecolor=facecolor, alpha=0.4, boxstyle="round,pad=0.5"), + transform=axs[i].transAxes, + ) + axs[i].tick_params(labelsize=LABELSIZE) + axs[i].set_title(iqm_column, fontsize=LABELSIZE + 2) + + # Turn off individual plot y-labels + for ax in axs: + ax.set_ylabel("") + + fig.supylabel("Density", fontsize=LABELSIZE + 2) + fig.suptitle("QC-FC correlation distributions", fontsize=LABELSIZE + 4) + + savename = op.join("reportlets", "group_desc-qcfc_bold.svg") + + logging.debug("Saving QC-FC visual report at:") + logging.debug(f"\t{op.join(output, savename)}") + + plt.savefig(op.join(output, savename)) + plt.close() + + return qc_fc_dict + + +def compute_distance(atlas_path: str) -> np.array: + """Compute the euclidean distance between the center of mass of the atlas regions. + + Parameters + ---------- + atlas_path : str + Path to the atlas Nifti + Returns + ------- + np.array + Distance matrix + """ + from scipy.ndimage.measurements import center_of_mass + + logging.debug("Compute distance matrix from atlas centers of mass") + + atlas_img = nib.load(atlas_path) + atlas_data = atlas_img.get_fdata() + # Array to store the center of mass of each region + centroids = np.zeros((atlas_data.shape[3], 3), dtype=float) + for r in range(atlas_data.shape[3]): + centroids[r, ...] = np.array(center_of_mass(atlas_data[..., r])) + + # Compute Euclidean distance matrix using broadcasting + diff = centroids[:, np.newaxis, :] - centroids + distance_matrix = np.sqrt(np.sum(diff**2, axis=-1)) + + return distance_matrix + + +def group_reportlet_qc_fc_euclidean( + qc_fc_dict: dict, atlas_path: str, output: str +) -> None: + """Plot and save the correlations between QC-FC and euclidean distance. + The euclidean distance is computed from the centers of mass of each region. + + Parameters + ---------- + qc_fc_dict : dict + Dictionary containing the qc-fc distribution over the edges for different IQMs. + atlas_path : str + Path to the atlas Nifti + output : str + Path to the output directory + """ + d = compute_distance(atlas_path) + # Keep only upper triangle as the matrix is symmetric + upper_triangle_indices = np.triu_indices(d.shape[0], k=1) + d = d[upper_triangle_indices] + + # Iterate over the IQMs + fig, axs = plt.subplots(1, 3, figsize=FC_FIGURE_SIZE) + for i, iqm in enumerate(qc_fc_dict.keys()): + qc_fc = qc_fc_dict[iqm] + + logging.debug("Compute the correlation between QC-FC and euclidean distance.") + correlation, p_value = pearsonr(qc_fc, d) + + # Plot the box in red if the correlation is significant + facecolor = "red" if p_value < ALPHA else "grey" + axs[i].text( + 0.15, + 0.97, + f"Correlation = {correlation:.2f}, p-value = {p_value:.4f}", + fontsize=LABELSIZE - 2, + bbox=dict(facecolor=facecolor, alpha=0.4, boxstyle="round,pad=0.5"), + transform=axs[i].transAxes, + ) + axs[i].scatter(qc_fc, d) + + # Plot trend line + axs[i].plot( + np.unique(qc_fc), + np.poly1d(np.polyfit(qc_fc, d, 1))(np.unique(qc_fc)), + "r-", + linewidth=3, + ) + axs[i].tick_params(labelsize=LABELSIZE) + axs[i].set_title(iqm, fontsize=LABELSIZE + 2) + + fig.suptitle( + "Dependence between euclidean distance and QC-FC", fontsize=LABELSIZE + 4 + ) + fig.supxlabel("QC-FC correlation of edge between nodes", fontsize=LABELSIZE + 2) + fig.supylabel("Euclidean distance separating nodes", fontsize=LABELSIZE + 2) + # Ensure the labels are within the figure + # plt.tight_layout() + + savename = op.join("reportlets", "group_desc-qcfcvseuclidean_bold.svg") + + logging.debug("Saving QC-FC vs euclidean distance visual report at:") + logging.debug(f"\t{op.join(output, savename)}") + + plt.savefig(op.join(output, savename)) + plt.close() + +def group_report( + good_timepoints_df: pd.DataFrame, + fc_matrices: list[np.ndarray], + iqms_df: pd.DataFrame, + atlas_filename: str, + output: str, +) -> None: + """Generate a group report.""" + + # Generate each reportlets + group_report_censoring(good_timepoints_df, output) + group_reportlet_fc_dist(fc_matrices, output) + qc_fc_dict = group_reportlet_qc_fc(fc_matrices, iqms_df, output) + group_reportlet_qc_fc_euclidean(qc_fc_dict, atlas_filename, output) + + # Assemble reportlets into a single HTML report + logging.debug("Assemble the group report into a single HTML report.") + + run_uuid = "{}_{}".format(strftime("%Y%m%d-%H%M%S"), uuid4()) + robj = Report( + output, + run_uuid, + out_filename="group_report.html", + bootstrap_file=op.join("data", "reports-spec.yml"), + ) + robj.generate_report() \ No newline at end of file From 7e9132aa5fac786aecc1737521f6009199713771 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=A9line=20Provins?= Date: Thu, 16 May 2024 16:07:27 +0200 Subject: [PATCH 2/5] enh: start drafting tutorial notebook enh: add data necessary to run the example --- docs/notebooks/Tutorial.ipynb | 151 ++++++++++++++++++ .../data/fMRI_duration_after_censoring.csv | 16 ++ ...01_ses-001_task-qct_desc-denoised_bold.tsv | 98 ++++++++++++ ...s-sparseinversecovariance_connectivity.tsv | 64 ++++++++ ...01_ses-003_task-qct_desc-denoised_bold.tsv | 98 ++++++++++++ ...s-sparseinversecovariance_connectivity.tsv | 64 ++++++++ ...01_ses-004_task-qct_desc-denoised_bold.tsv | 91 +++++++++++ ...s-sparseinversecovariance_connectivity.tsv | 64 ++++++++ 8 files changed, 646 insertions(+) create mode 100644 docs/notebooks/Tutorial.ipynb create mode 100644 docs/notebooks/data/fMRI_duration_after_censoring.csv create mode 100644 docs/notebooks/data/sub-001/ses-001/func/sub-001_ses-001_task-qct_desc-denoised_bold.tsv create mode 100644 docs/notebooks/data/sub-001/ses-001/func/sub-001_ses-001_task-qct_meas-sparseinversecovariance_connectivity.tsv create mode 100644 docs/notebooks/data/sub-001/ses-003/func/sub-001_ses-003_task-qct_desc-denoised_bold.tsv create mode 100644 docs/notebooks/data/sub-001/ses-003/func/sub-001_ses-003_task-qct_meas-sparseinversecovariance_connectivity.tsv create mode 100644 docs/notebooks/data/sub-001/ses-004/func/sub-001_ses-004_task-qct_desc-denoised_bold.tsv create mode 100644 docs/notebooks/data/sub-001/ses-004/func/sub-001_ses-004_task-qct_meas-sparseinversecovariance_connectivity.tsv diff --git a/docs/notebooks/Tutorial.ipynb b/docs/notebooks/Tutorial.ipynb new file mode 100644 index 00000000..6be1b5d8 --- /dev/null +++ b/docs/notebooks/Tutorial.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nireports" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nireports is a framework to easily combine what we call reportlets, that is data visualizations that help assess the quality of a particular processing step, into HTML visual report. In this notebook, we give you an example about how to build your own visual report using the nireports framework. The goal with this example visual report is to summarize some group properties of functional connectivity (FC) matrices computed from a single dataset in order to assess the quality of connectivity extraction.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step is to generate the visualizations you would like and store them in a file with a BIDS-compatible name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "import pandas as pd\n", + "\n", + "# Define the path where the derivatives are stored\n", + "output = (\"/home/cprovins/derivatives/hcph/\")\n", + "# Load fMRI duration after censoring\n", + "good_timepoints_df = pd.read_csv(\"./data/fMRI_duration_after_censoring.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A reportlet does not have to be stored in an image format, it is also possible to incorporate HTML chunks into the overall report. Let us look at an example together. The goal with the next reportlet is to verify whether each session has enough timepoints after censoring to reliably estimate FC. If not, that session needs to be identified and excluded. We use plotly to generate an interactive plot in which hovering on the points tells you the session it corresponds to. The interactivity would not be possible with an image that is why we store the reportlet in HTML format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.offline as pyo\n", + "# The literature indicates that less than 5 minutes of fMRI signal after censoring is not enough to reliably estimate functional connectivity.\n", + "DURATION_CUT_OFF = 300\n", + "# Generate each reportlets\n", + "def group_report_censoring(good_timepoints_df, output) -> None:\n", + " \"\"\"\n", + " Generate a group report about censoring.\n", + "\n", + " This function generates an HTML report that includes an interactive scatterplot\n", + " showing the fMRI duration after censoring. The scatterplot includes\n", + " error bars for the confidence interval and a red line indicating a duration cutoff.\n", + "\n", + " Parameters:\n", + " -----------\n", + " good_timepoints_df: pd.Dataframe\n", + " A DataFrame containing information the fMRI duration after censoring.\n", + " output : str\n", + " Path to the output directory\n", + " \"\"\"\n", + " filenames = good_timepoints_df[\"filename\"]\n", + " durations = good_timepoints_df[\"duration\"]\n", + "\n", + " # Constructing the data for the plot\n", + " # Add jitter to x values\n", + " jitter = 0.2 # adjust this value to change the amount of jitter\n", + " x_values = [1 + np.random.uniform(-jitter, jitter) for _ in range(len(durations))]\n", + " data = [\n", + " {\n", + " \"x\": x_values,\n", + " \"y\": durations,\n", + " \"text\": filenames,\n", + " \"mode\": \"markers\",\n", + " \"type\": \"scatter\",\n", + " \"hoverinfo\": \"text\",\n", + " \"marker\": {\"opacity\": 0.5},\n", + " }\n", + " ]\n", + "\n", + " # Adding a red line at 5 minutes\n", + " red_line = {\n", + " \"type\": \"line\",\n", + " \"x0\": 0,\n", + " \"y0\": DURATION_CUT_OFF,\n", + " \"x1\": 1.5,\n", + " \"y1\": DURATION_CUT_OFF,\n", + " \"line\": {\"color\": \"red\", \"width\": 3, \"dash\": \"dashdot\"},\n", + " }\n", + "\n", + " # Layout settings\n", + " layout = {\n", + " \"hovermode\": \"closest\",\n", + " \"title\": \"Duration of fMRI signal after censoring\",\n", + " \"yaxis\": {\"title\": \"Duration [s]\"},\n", + " \"xaxis\": {\"showticklabels\": False, \"range\": [0.5, 1.5]},\n", + " \"shapes\": [red_line],\n", + " \"width\": 600,\n", + " \"height\": 600,\n", + " \"font\": {\"size\": 16},\n", + " \"annotations\": [\n", + " {\n", + " \"x\": 0.8,\n", + " \"y\": DURATION_CUT_OFF - DURATION_CUT_OFF / 55,\n", + " \"xref\": \"x\",\n", + " \"yref\": \"y\",\n", + " \"text\": f\"QC cutoff of {DURATION_CUT_OFF/60} min\",\n", + " \"showarrow\": False,\n", + " \"font\": {\"color\": \"red\"},\n", + " }\n", + " ],\n", + " }\n", + "\n", + " fig = {\"data\": data, \"layout\": layout}\n", + "\n", + " # Save the plot as an HTML file\n", + " pyo.plot(\n", + " fig,\n", + " filename=op.join(output, \"reportlets\", \"group_desc-censoring_bold.html\"),\n", + " auto_open=False,\n", + " )\n", + "group_report_censoring(good_timepoints_df, output)\n", + "group_reportlet_fc_dist(fc_matrices, output)\n", + "qc_fc_dict = group_reportlet_qc_fc(fc_matrices, iqms_df, output)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/data/fMRI_duration_after_censoring.csv b/docs/notebooks/data/fMRI_duration_after_censoring.csv new file mode 100644 index 00000000..9a51fce7 --- /dev/null +++ b/docs/notebooks/data/fMRI_duration_after_censoring.csv @@ -0,0 +1,16 @@ +filename,duration 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15:53:03 +0200 Subject: [PATCH 3/5] enh: write a tutorial notebook that replaces the python code I added in a previous PR. The tutorial simplifies the latter code by generating only two reportlets without dependence on MRIQC-generated IQMs. fix: remove session not saved as examples from fMRI_duration_after_censoring.csv --- docs/notebooks/Tutorial.ipynb | 242 +++++++++- .../data/fMRI_duration_after_censoring.csv | 12 - docs/notebooks/group_report_cli.py | 174 ------- docs/notebooks/load_save.py | 449 ----------------- docs/notebooks/reports.py | 453 ------------------ 5 files changed, 225 insertions(+), 1105 deletions(-) delete mode 100644 docs/notebooks/group_report_cli.py delete mode 100644 docs/notebooks/load_save.py delete mode 100644 docs/notebooks/reports.py diff --git a/docs/notebooks/Tutorial.ipynb b/docs/notebooks/Tutorial.ipynb index 6be1b5d8..aef55002 100644 --- a/docs/notebooks/Tutorial.ipynb +++ b/docs/notebooks/Tutorial.ipynb @@ -14,26 +14,125 @@ "Nireports is a framework to easily combine what we call reportlets, that is data visualizations that help assess the quality of a particular processing step, into HTML visual report. In this notebook, we give you an example about how to build your own visual report using the nireports framework. The goal with this example visual report is to summarize some group properties of functional connectivity (FC) matrices computed from a single dataset in order to assess the quality of connectivity extraction.\n" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "import glob\n", + "import os\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os.path as op\n", + "\n", + "# Define the path where the derivatives are stored\n", + "output = \"./data\"\n", + "\n", + "# Load fMRI duration after censoring\n", + "good_timepoints_df = pd.read_csv(op.join(output, \"fMRI_duration_after_censoring.csv\"))\n", + "\n", + "# Load functional connectivity matrices\n", + "connectivity_files = glob.glob(op.join(output, '**', '*connectivity.tsv'), recursive=True)\n", + "\n", + "fc_matrices = []\n", + "for connectivity_file in connectivity_files:\n", + " fc_matrices.append(np.loadtxt(connectivity_file, delimiter=\"\\t\"))\n", + "\n", + "# Create folder to store reportlets\n", + "os.makedirs(op.join(output, \"reportlets\"), exist_ok=True)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The first step is to generate the visualizations you would like and store them in a file with a BIDS-compatible name." + "The first step is to generate the visualizations you would like and store them in a file with a BIDS-compatible name. The reportlets filename need to be BIDS compatible, because the Nireports assembler will use PyBIDS to retrieve the figures it needs to combine. The first visualization we want in this example is the plot the functional connectivity distribution per session." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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JiGhQQm2gje132BtwvWwlBbiZqLIY1ERENCjGWFhjMVBSCwBCCvaoK4xBTUREgxIaC23MgJuvhACkFLCsd1JRDGoiIhoUbQy0tgMGtYRwQQ2wV11BDGoiIhqUUFtobfpdSAa4HrX7HQ/mqCQGNRERDYo2FgZudXdBe3cGYXRYR2lRMs163xXDoCYiokExxkIbWywf2t2bQ9uhFA609xavEcLtow55glbFMKiJiGhQLAAUh7eBju4spjcl0d2bBxDdLgBrLY+6rCAGNRERDUqxznc0vJ3OhvB9BU8J5IvD3wIwPJO6khjUREQ0aFGnGYUclgIIfIXeTFj8vbFu0RlVBoOaiIgGLwrqbD6E7ykAQBBI9KbzxUs49F1ZDGoiIhoCd5BlNqfheW4MPOZ76M2V9KgNg7qSGNRERDRoUgpIIZDNhfA9FyFKiuJQt4ArIcrtWZXDoCYiokGxFsWh70xew/cUjI3mq6MOdOE46pAFTyqGQU1ERINi4fZQCwHkchrWAge6Mkjl8tHvASkELIe+K4pBTUREJ2SthbXudCwB17tO5zVq4wrZnIFSEvlQu6plQnDou4IY1EREdELa2OIxlgV5beBJCSEApQRyeQPA9bhZmaxyGNRERHRC2tji/LSxovi9EAJKSgghkMtrVwxFsOBJJTGoiYjohIo9agCh1lBSQkW9axUlSS7UxfBmUFcOg5qIiE5IG+NWiwk35A0hoEQhqAUMgHxooqAGK5NVkFftBhAR0fhX6CELIZDPG0AAyhUmcyu9YRGGtq9HbdmjrhT2qImI6IS0sYUONfKhq0ImpYuQwmlaoTbFWuCa+6grhkFNREQnpI2FtW5BWS50/yycSw24imQ2+ie3Z1UWg5qIiE5IawNjoh51NPRdSkpXkkxE33N7VuUwqImI6IR0VCdUQCA0GvKopJYCsNGycCEFh74riEFNREQnpK0b+hYCxQplpWRU6MQaAxmdSU2VwaAmIqITCrWBsba4cEwdNfQtFCCERD5011gGdcVwexYREZ2QNhbGut5dcdEYAK01AEBFpcqMtVH1MgZ1pbBHTUREJ6S1dYvFop60lEA6ncXmTduwd1drcSg81G5FGXO6chjURER0QqExsCWHTksh0NXZhSlTm5DOZKLFZcLtpYZgUFcQg5qIiE6oMPRtrStwIqVAZ0cvMhmNWCxAbzpdrAPuFpwxqSuFQU1ERCdUKCFqolO0rHH7qsNQw/N8pLpTUY3v6DrmdMUwqImI6ITC6JANY9zBG13daUilcMr8ZtRNqUc6nQWkO5gDXPVdUQxqIiI6IROVD3WVQQVSXb1omFILndeIJQKE+RASAqF2q74Z1JXDoCYiohPS2u2PDqMzpzPZPGrrkxCeRJg3qG2ogZSuxrcLam7RqhQGNRERnVBoXFWy0FgI6Wp/B4EHawHpKcRrEn17rIWrUqY5UV0RDGoiIjqhwiKyMHRHWXq+ctu0lIIxFvFEHGHoip8Idz4HtOYJWpXAoCYiohMqVCPT2sJqi0RNHCgMhQvXqw7DPIDoYC3r6oPTyLGEKBERDZo2rkcdiwWwAIx2p2oBAIyBlQpSCFhhXS+cRow9aiIiOqFCVTILIMyFiCX8aA7a7ae2tnAwh7teWM5RVwqDmoiITqwkc3PZHIKY31fVxFpXMlREB1LDwoBBXSkMaiIiOiELRJPPQC6voZSCMX2LxYwx8H0/OuLSXcyh78pgUBMR0QlZY91hG4CreiJQEtQW2lj4scCVFy25jUaOQU1ERCdkrNtH7VZ4u+gwoSkOiVtjIZU7Q8toy33UFcSgJiKiE7K2kMkCvq9grYXWGvl8HrCuZKiI5qiNdUddcntWZXB7FhERnVBfOVALz/eKR15KqWCsAULh5rCFhY6KorBHXRnsURMR0XGZYnfare4OEgGsKZxL7Ya6Ldw1UgKhtoBkZbJKYVATEdFxFVZvFzrVsXgAbTRElCDWuoM4jLVQQkLraOibPeqKYFATEdFx6ajOt432S3uecgvGot9LIaN5akBEPerCAR40cgxqIiI6rkLP2BoLawykFLC2ZFhbuBIn1iA66tICQpTts6bhY1ATEdFx6ZKhb6MNxDEhLGAMYKyBUn2/49B3ZTCoiYjouLQxLqSt7Qto3RfCUgKwFkZbSKkK687cojIaMQY1EREdlxvKtq7oCdxctUF5CFtYt5dauplrIYCQq74rgkFNRETHZYxbROaqjxXOpi6QxZ9Kh8MFBPIM6opgUBMR0XFp4w65tBZQylUlAwAIBSFlNPZdWp0MgOA+6kphUBMR0XEVFoUZa6E8icKCbyEFrHF7poUQsNGctI2KlHF7VmWMi6B+8MEHMW/ePMTjcSxevBjPPffcgNc+8sgjuOyyy9DU1ISmpiYsXbr0mOuttVi1ahVmzpyJRCKBpUuXYsuWLaP9MoiIJqS+oAY8X0WFT2TfgRzWAlLBwMBaC6VctGguJquIqgf1U089hZUrV+KOO+7Axo0bcd5552HZsmU4cOBAv9evW7cON9xwA375y19i/fr1mDNnDq688krs3bu3eM0999yDb37zm3j44YexYcMG1NTUYNmyZchkMmP1soiIJgxtolOyrIXylNtDLUTfEDgspBBAdJmMFpRpy6HvShDWVvd4k8WLF+Piiy/GAw88AMAtRpgzZw4+//nP49Zbbz3h/bXWaGpqwgMPPIDly5fDWotZs2bhi1/8Im655RYAQGdnJ5qbm/HYY4/hYx/72DGPkc1mkc1miz93dXVhzpw56OzsRH19fYVeKRHRyWnr/k68tucIurszqKuPI5/XCPMG1rptWwIApIQJQ9Q1JpFNh0jEPQgLfPCiU6vd/JNeVXvUuVwOzz//PJYuXVq8TUqJpUuXYv369YN6jN7eXuTzeUyZMgUAsH37drS2tpY9ZkNDAxYvXjzgY65evRoNDQ3Frzlz5ozgVRERTSxhScETKWVfT9oC1piS1d7WVSdTKB59SSNX1aA+dOgQtNZobm4uu725uRmtra2DeowvfelLmDVrVjGYC/cbymPedttt6OzsLH7t3r17qC+FiGjCKqzejiqDFgO4rDqZdZu2rAWEkIjO06IKOKnPo/7qV7+KJ598EuvWrUM8Hh/248RiMcRisQq2jIho4tCmr9BJYftVMYYFgKhqGaS7RkrBHnUFVbVHPW3aNCil0NbWVnZ7W1sbWlpajnvfe++9F1/96lfx05/+FOeee27x9sL9hvOYRER0rDDaR42SIW+YkqAGAGsgIGFs4dAOC1tSFoWGr6pBHQQBLrzwQqxdu7Z4mzEGa9euxZIlSwa83z333IO77roLa9aswUUXXVT2u/nz56OlpaXsMbu6urBhw4bjPiYREfVPF4a4RV9Gl63njnrV7rAO16M2hQopNGJVH/peuXIlbrzxRlx00UW45JJLcP/99yOVSmHFihUAgOXLl2P27NlYvXo1AODuu+/GqlWr8K//+q+YN29ecd65trYWtbW1EELgC1/4Ar7yla/g9NNPx/z583H77bdj1qxZuPbaa6v1MomITlqFfdTSTVC7G035MZfFPdWFs6uNBSR71JVQ9aC+/vrrcfDgQaxatQqtra1YtGgR1qxZU1wMtmvXLkjZ1/F/6KGHkMvl8NGPfrTsce644w7ceeedAIC//uu/RiqVwqc//Wl0dHTg0ksvxZo1a0Y0j01ENFm5EqIWSonyTnI/Y7LWWggU9lgzqCuh6vuox6Ouri40NDRwHzUREYD/fn0/2nuy6E1nUVsbR28qBx2G5UFtACEVpBKoq48j3ZOD8iQ+vHh+1do9UVS9MhkREY1vhe1Zbg81jtp4JVGIkujoDgDRKnBw5XclMKiJiOi4tHUBrFQ0GV3MXhchAqJvPtqiLMwNg3rEGNRERHRcNtonraIedSF8BQSSiQCNTQm4FWR927hElM+aJ2iNGIOaiIiOy1rryoeqkvKhAvADiUTCh7UW8ZjvgjrqcBcKozCoR45BTUREx2VsX8UxY63bmiUkamti6Ell0dubR22tq+4ogKgouACifdU0MgxqIiI6rkKPWkgB21f7pLi4DHDFqpSvoutdj1rAskddAQxqIiI6PlvYH923SMzzVHE1OADkQ40gcKU53ClbAIRgUFcAg5qIiI7LIOpRl1Qgi8UU8nldvCafN4gFHgqVQ4WQEOAcdSUwqImI6PhK6nZbABACQeAhH/YFtTEWniejBWUWwk1Rc3tWBTCoiYjouGyxxLcodJfdfPVRGVyoGmrhFp5ZsEddCQxqIiI6LhfAbvjbGHcmdX8Vx8JQw/c9GO2OugQY1JXAoCYiogGZqCqZEKJYItTzJLQ+NoC1dr9zwe6C2hhzzHU0NAxqIiIakNsHLYq9aGsA31Mw+tgA1sbA9wplRQFY9qgrgUFNREQDKgStqzTmCp74vkQ4YI9a9Xt/Gj4GNRERDagYtALFld/qqD3UZUTforKy+9OwMaiJiGhA2li3ilv0rfKOpp+PL6r5nQ85Rz1SDGoiIhqQNiaqNFYS1Me53mgLKfpOwszrcLSbOOF51W4AERGNX9pYQERnTgNQqv8V333XG0jlCp8IWORDDn2PFHvUREQ0IOOOzoKQrt6358njbrnS2sJTAoUTq/MDzWXToDGoiYhoQG6OWkAKCWOt61EfZ4GY1gbKk9Gh1EDIoB4xBjUREQ1IF3rUIupRK1l2xvTRC8uMsVBSFk/ZCrnqe8QY1ERENCDXo45YC1kyRy36Sn+XE4jOrRZgYbKRY1ATEdGAdCFpo+1ZSpbU+S4k+FGd5sLCMyGAkEk9YgxqIiIaULEyGQpHXPb9boCcdqVGo5624dD3iDGoiYhoQGVzzNYWc/ro4e7Sn000pw0hjglxGjoGNRERDUhr03futLDH9JD7K35itDttq9gLpxFhUBMR0YC06VvxLUXJim8LSAlIT0EpWZbIxhoIER3k0c+51TQ0DGoiIhpQqE3xiEslZXQ+dbRNWipYrd1qcNnXtzbaQspoVXiV2j2RMKiJiGhAhRKi1gJSCTesDUCWnKblFpmJ4jC4NhZSSoZ0hTCoiYhoQHljigGslOir/S1Kt2lFS7yjC43p62EzrEeOQU1ERAMqzElbIJqjjvZFSxcfQqnCPiw3Jx0ROP4pWzR4DGoiIhpQqE1xsrn0qEsgWixmDEQhtKvUxomOQU1ERAMKTd/e6cI3rudcusy7vJddYC2HviuBQU1ERAMqdKhNSVdaAIA9autVPyFdOMyDRoZBTUREA3J7ovsWjgnAhXK/AWxR2KWloznrwh5sGr5hBfVbb71V6XYQEdE45Gp9R4EbrfjuV6EQStSF7rtWgOW+R2ZYQb1w4UK8973vxb/8y78gk8lUuk1ERDROGG0hpJuXNiUHdBTSV0hZ2FSNvnOzotXiQkAI0XcCFw3LsIJ648aNOPfcc7Fy5Uq0tLTgM5/5DJ577rlKt42IiKqsEM4SIjpso3DQhoVQbghcunqh7g7R2LcxNgqYY+uD09AMK6gXLVqEb3zjG9i3bx8effRR7N+/H5deeine8Y534L777sPBgwcr3U4iIqoCA7d0u9CjLh35FnBD3KUFTgpj431z1KJ4VCYNz4gWk3meh4985CN4+umncffdd2Pr1q245ZZbMGfOHCxfvhz79++vVDuJiKgKCuvAhIzmnVH4WR73vA1jLIR0ZU8MF5ONyIiC+ve//z3+7M/+DDNnzsR9992HW265Bdu2bcPPfvYz7Nu3D9dcc02l2klERFWkZDRHXShuctRq7kIV0bKfo+vYox4Zbzh3uu+++/Dtb38bmzdvxgc+8AE88cQT+MAHPgAZ/QucP38+HnvsMcybN6+SbSUiomoQgJCuZyyBY1MZhWMwRVRqFGUrvRnUIzOsoH7ooYfwP/7H/8BNN92EmTNn9nvNjBkz8E//9E8jahwREVVZtKBbRmVEBQBbXFA2wF1KzqFmj3rkhhXUP/vZzzB37txiD7rAWovdu3dj7ty5CIIAN954Y0UaSUREY89ElcWstdFRlu72khxGECgYaxHmTbRBq7xsqBACWnN71kgMa476tNNOw6FDh465vb29HfPnzx9xo4iIqPoKq7mLC8qi4W5XQtQiCDwoJRAECp4nUUzqQtGT6I4he9QjMqygHqgcXE9PD+Lx+IgaRERE44M2FkJI10O2R/WWpUDgS+TyGtmsRjzmwdpo+1YU1FFRM4QseDIiQxr6XrlyJQD3qWrVqlVIJpPF32mtsWHDBixatKiiDSQioupwQQ0IWFjrwrYQ1oEnkQ8LAWyhtYHnybK91tYaSCkRavaoR2JIQf3CCy8AcD3qV155BUEQFH8XBAHOO+883HLLLZVtIRERVYUrWgJACHeSpRTFpPZ9iUw2LF4bagvfl8hmdfE2YwAlgXyoj3lsGrwhBfUvf/lLAMCKFSvwjW98A/X19aPSKCIiqj5drNddKCUazVH3czCHsQZK+rDQxUVnNtrGlWFQj8iwVn1/+9vfrnQ7iIhonNHRMLYAoK0pfi+VhNau7ndtMkAYaqSzIbSxxT3UMjptSwDI5ThHPRKDDuqPfOQjeOyxx1BfX4+PfOQjx732e9/73ogbRkRE1eXmqN1wty3JWl8JhNqgNukjmwsR+AqektDGQnkKNq8BIaANAAFk2aMekUEHdUNDQ3FpfkNDw6g1iIiIxgetTXQ6VmG3j8sApSSMtTDG7bXO5kIk4j56enMIfIUwH01lWw0Bn6u+R2jQQV063M2hbyKiia849F2oRCb6tmcFvkKotTv+ErZYAUWWTGBbA9ezZod6RIa1jzqdTqO3t7f4886dO3H//ffjpz/9acUaRkRE1ZULdfGoysLJWUqJ4lYsKQSUEpBwQ+GBL2HhtnShpHKlBnvUIzGsoL7mmmvwxBNPAAA6OjpwySWX4Gtf+xquueYaPPTQQxVtIBERVUdvOgegryIZACghXc/aAlpbaG2hlEBeGwS+gtEWSopoZbjrabPW98gMK6g3btyIyy67DADwb//2b2hpacHOnTvxxBNP4Jvf/GZFG0hERNWRyeZLfhLRim935KUumXc2FpAQEBBu5bdy0WJLfk/DN6yg7u3tRV1dHQDgpz/9KT7ykY9ASol3vvOd2LlzZ0UbSERE1dGbDctKhgKAkhKep6DDkuFs43rdJjqEWkoRLSbDcU/ZosEZVlAvXLgQP/jBD7B7927813/9F6688koAwIEDB1gEhYhoguhNRz1q21cWFHALxkqPfLCwkNKtEldS9i0os261uOivQgoN2rCCetWqVbjlllswb948LF68GEuWLAHgetfnn39+RRtIRETV0ZvNAYUjLlGoHmqLBzOJktuNdSVDfa88lK11PWwavmFVJvvoRz+KSy+9FPv378d5551XvP2KK67Ahz/84Yo1joiIqienDRKFFd8CkEpAir5tW4UAtwBE9IOSEqHWEBLFs6wZ1CMzrKAGgJaWFrS0tJTddskll4y4QUREND5oU9ia5eajlZKQUrpCKEezFkK5/dbWWCghEAoBa8p2atEwDOvtS6VSuP322/Gud70LCxcuxIIFC8q+huLBBx/EvHnzEI/HsXjxYjz33HMDXvvaa6/huuuuw7x58yCEwP3333/MNXfeeWdx31/h68wzzxzqSyQimvSEiga7ozlqKVDcU23hvhclvWW3gMy6VeBKFn8W7FGPyLB61J/61Kfwq1/9Cp/4xCcwc+bMYS8UeOqpp7By5Uo8/PDDWLx4Me6//34sW7YMmzdvxowZM465vre3FwsWLMAf//Ef4y//8i8HfNyzzz4bP//5z4s/e96wBw6IiCYtqRSAvu1VUspoOBsQUhQWgsMoAastDAAZ9aBdiEu4UXMG9UgMK8F+8pOf4Ec/+hH+4A/+YERPft999+Hmm2/GihUrAAAPP/wwfvSjH+HRRx/Frbfeesz1F198MS6++GIA6Pf3BZ7nHTMsfzzZbBbZbLb4c1dX16DvS0Q0UUnP7Y4uXeFdCF0pRHT0pQtmAwEYwEoTbc+KDvPgqu8RG9bQd1NTE6ZMmTKiJ87lcnj++eexdOnSvsZIiaVLl2L9+vUjeuwtW7Zg1qxZWLBgAf70T/8Uu3btOu71q1evRkNDQ/Frzpw5I3p+IqKJQElXhcwUVnlLAWMMoKL63hEDQKjoe+vmsgEU91Izp0dmWEF91113YdWqVWX1vofq0KFD0Fqjubm57Pbm5ma0trYO+3EXL16Mxx57DGvWrMFDDz2E7du347LLLkN3d/eA97ntttvQ2dlZ/Nq9e/ewn5+IaKKQUSlQt3K7sA3LQka95yKDqOcdrQ4vS2YX6Nay9MlwDWvo+2tf+xq2bduG5uZmzJs3D77vl/1+48aNFWnccFx99dXF788991wsXrwYp556Kr773e/ik5/8ZL/3icViiMViY9VEIqKTgpDRqu/C4jDh9kr3X2/MDXEXItoYCyH7rtTGwlPsWg/HsIL62muvHfETT5s2DUoptLW1ld3e1tY2pPnlE2lsbMTb3vY2bN26tWKPSUQ0GZT2jFWhLChsv2dhGYuSYHYrxZUQxQM58trAU9ynNRzDCuo77rhjxE8cBAEuvPBCrF27thj8xhisXbsWn/vc50b8+AU9PT3Ytm0bPvGJT1TsMYmIJgMp+w6gFjLaiqWBfpPa9tX7tm7vFqSS0NFh1NlciETAHTjDMeyPNx0dHfjHf/xH3HbbbWhvbwfghrz37t076MdYuXIlHnnkETz++OPYtGkTPvvZzyKVShVXgS9fvhy33XZb8fpcLocXX3wRL774InK5HPbu3YsXX3yxrLd8yy234Fe/+hV27NiBZ555Bh/+8IehlMINN9ww3JdKRDQpCSGKdUL7etcnGL62bh67sO+6MPTd0ZMZpVZOfMP6ePPyyy9j6dKlaGhowI4dO3DzzTdjypQp+N73voddu3YVz6o+keuvvx4HDx7EqlWr0NraikWLFmHNmjXFBWa7du2CLClps2/fvrJa4vfeey/uvfdeXH755Vi3bh0AYM+ePbjhhhtw+PBhTJ8+HZdeeimeffZZTJ8+fTgvlYho8orqhAqUrtwun58WErCm5HfC/Sxk33y1tRZHejKYNwZNnoiEHcZSvKVLl+KCCy7APffcg7q6Orz00ktYsGABnnnmGXz84x/Hjh07RqGpY6erqwsNDQ3o7OzkaWBENGn9+OU98KRAbzqP2poAUgrkQ10M5sCX0MbNRxsDIFoZLuC22xpjkU7nkUwGiJsQl50zt4qv5uQ1rB717373O/z93//9MbfPnj17RFuriIhofNDaQCI6cKOQvtYWQzqZ8GAt4PsCubybuDbG9bABN79tosM8ACCd629imwZjWHPUsVis3+pdb775JoeYiYgmgHQ6G1UWi/ZTA8UKZZ7noiOvDbI5jZgfVTuRQPkcdnENOPKGQT1cwwrqD33oQ/jf//t/I593h4oLIbBr1y586UtfwnXXXVfRBhIR0dg70tHjFpNFe6gRregGgHigkAtNcbo6rw08T0URHR3YAbfnWkq3D5vlToZvWEH9ta99DT09PZg+fTrS6TQuv/xyLFy4EHV1dfjbv/3bSreRiIjGWNuBzr5KZEK4YfDobGlzVOpqbeF7EqXrwq21sFERFHeCFvdQD9ew5qgbGhrws5/9DL/97W/x0ksvoaenBxdccEFZ3W4iIjp5HTzSA9WShLUGKqpKZuEWkIWmrzddSGdb+N72/Swg3FnU1rpeOQ3LkIPaGIPHHnsM3/ve97Bjxw4IITB//ny0tLTwlBQiogmivTOLGS3RHHUhjC3gexKZnCtiUjodrbWBpxTCUBevjQ7QKjuog4ZuSO+ctRYf+tCH8KlPfQp79+7FOeecg7PPPhs7d+7ETTfdhA9/+MOj1U4iIhpDqWxYPJDDEZAQfXumi5uk3Zc2bvi78MvCsLeUIlo5zk7ccA2pR/3YY4/hv//7v7F27Vq8973vLfvdL37xC1x77bV44oknsHz58oo2koiIxlYoBFCYXxYueD1PQpeu3i6dqz56tZi1UFIWD/FgUA/fkHrU3/nOd/A3f/M3x4Q0ALzvfe/Drbfeiv/7f/9vxRpHRETVoYK+EwXdqVkWntd3yEZ/jLWuHjj6tnUVvmdOD9+Qgvrll1/GVVddNeDvr776arz00ksjbhQREVWXF/eKFcYAuAVhQvZ1nPvJa6Nt2QlZ5dnMpB6uIQV1e3t7sQ53f5qbm3HkyJERN4qIiKrLj/lubtltoY6moo+/G1obN9xdJBDNVbsKZWboFasJQwxqrTU8b+BpbaUUwjAccaOIiKi6PM9VG5Nw26yEEDD6xNXF3BB3tOTbuiFzEX0fDuL+dKwhLSaz1uKmm25CLBbr9/fZbLYijSIiouryPOWqkkkX0kqWzE8fp2NcskjczU0XbodFKpNDUJsYzWZPSEMK6htvvPGE13DFNxHRyc/zVHGLFQSgpBhUj9gYV9zEmJLKZMKl95HONJoY1EM2pKD+9re/PVrtICKicUQpCVhbXK092KVg1lqo6OSsQu/a1Qy3ONKdHpW2TnQsFUNERMcoHKZRGMMe7Dqw4oIyCxT2T0sIWAj09OZHr8ETGIOaiIiO4SqK2WjVt4ApFDoZRGAXeuE2OtCj0B3P5LnYeDgY1EREdAwV9agFBKQQ0MPYWWVt4VAOF9p5LvoeFgY1ERGVsSUVxoCh74E2xrqAFtH8dFSK1DJxhoVvGxERleno7HHD19YF7VDLf5ro3OqiwlFaPJN6WPiuERFRmX1tHcVtWYXAHgprS07LiqqTQdi+cqQ0JHzXiIiozIGDnRBAVOtb9A17DzKwTWkpUQtYA0iIkuF0GgoGNRERlTl4pBcQojjsbY9zYtZACj1xi75V4DzqcngY1EREVKYzlXM7qqI90MNZ8V1UUkeUQT08DGoiIirTm3P7qGS0ans4p14VzqAulv4WbuibJ2gNHYOaiIjKmApEg7GmWN1MQBQO00I4jGH0yY5BTUREZWTgjoEoG6ke6spvA0jhqptJKVA4/ZJHXQ4dg5qIiMrE4rFoEZgrVDIcplA0xZZvn86FujKNnEQY1EREVCaWCICoaMlw55StjcqQIhr6huuUd6WylWvoJMGgJiKiMrGYD8ANXZtKzCkLFHvoHZ086nKoGNRERFTGDxQAN2Q94lXa0WIyt6jMoifNHvVQMaiJiKiM77loEBAYSYfabdGyiI7PghVAOsejLoeKQU1ERGWkksUiJQCGvOK7wJ2iJd3dhYUAkA+56nuovGo3gIiIxhelZDSpPLLHsbBui5btW1DG/vTQsUdNRERllIpqfI90etoAQoro9CxEi8pYRnSoGNRERFTU05uBlCLqCY8sqY0t6VFHR19y4HvoGNRERFS0c087pBDF8p8j5U7RcsVP3IFa7FEPFYOaiIiKdu/vcKdmSQFjK9P/tbbv5CyeST10DGoiIio61J5y08lCDOsc6v5YuJO4AEQ9dR7MMRQMaiIiKursyQFwa79stAV6JKy1kEcNd2ueoDUkDGoiIipKh9rNKwtUZOGXO5zDPZ61FkII5BnUQ8KgJiKiIqtUZR/PuprhKJQRlYJHXQ4Rg5qIiIqCwB/R8ZZHs8bNSxeG0IUQyDOoh4RBTURERYnaGGQFip0UFPdSw0IIF9SpdK4yDz5JMKiJiKgomYwVt1JVTCH4hdtX3dmdqezjT3AMaiIiKorF/YpuzSqwhVInQqCHPeohYVATEREAd9pVEFMoTilXMqstAOFqiGd41OWQMKiJiAgA0JvOw4tWfVeyKImNUl8KAQGBXKgr9tiTAYOaiIgAAD3pHDzlYqGSxcOstRCFgy4FoLmNekgY1EREBABI9eaglFuhbSs47m2iQieFgzlYmWxoGNRERAQAaO/ohVISsJXuUUenaEXfM6eHhkFNREQAgL1tncU91BVdR2YtZNSbBmzfCR00KAxqIiICAOxp66j8HmqUH3NpLSAlo2co+G4REREAIJ1zPd/RGpkuPK6QgvPUQ8CgJiIiAECsJo5RHZS2NjqZiwdzDAWDmoiIAADJ2ljfD6PQ4S0sUJNSIG8Y1IPFoCYiIgAlQT0qIW1R6K67E7Q49D1YXrUbQERE1WetRTLpw9rK7qEuKOylhhAQEsjlWZ1ssNijJiIiZLIhgsCDxah0qFHoUMvoBK3eTH4UnmViYlATERF6enPwfVXxYicF1qB4HjUAdPbwqMvBYlATERGOdKbhKemGvUchqW10ILUQACxwqCNV8eeYqKoe1A8++CDmzZuHeDyOxYsX47nnnhvw2tdeew3XXXcd5s2bByEE7r///hE/JhERATv2d0BJAVgxKnPUFoVhb9ej7s3yqMvBqmpQP/XUU1i5ciXuuOMObNy4Eeeddx6WLVuGAwcO9Ht9b28vFixYgK9+9atoaWmpyGMSERGwu7UTUgpAjM7QN4Diqm8LCyuq3k88aVT1nbrvvvtw8803Y8WKFTjrrLPw8MMPI5lM4tFHH+33+osvvhh/93d/h4997GOIxWL9XjPUxwSAbDaLrq6usi8ioskkkx+Dfc19O7QgFOt9D1bVgjqXy+H555/H0qVL+xojJZYuXYr169eP6WOuXr0aDQ0Nxa85c+YM6/mJiE5WKhGM+nMUV5Sz3veQVO2dOnToELTWaG5uLru9ubkZra2tY/qYt912Gzo7O4tfu3fvHtbzExGdrOrq4hidjVl9rLVRj1pAegzqwWLBEwCxWGzAoXQioskgWROM3tx0xK38loC0UJZBPVhVe6emTZsGpRTa2trKbm9raxtwoVg1HpOIaDKIBZ47h3oU09odd2khhYSQAnlWJxuUqgV1EAS48MILsXbt2uJtxhisXbsWS5YsGTePSUQ00aUzOfi+RGHoe7Si2p1s6fZSCwDtXelReqaJpapD3ytXrsSNN96Iiy66CJdccgnuv/9+pFIprFixAgCwfPlyzJ49G6tXrwbgFou9/vrrxe/37t2LF198EbW1tVi4cOGgHpOIiMrtbet2xU4sMJprv21U71tAwAqLg0dSaJ5aO4rPODFUNaivv/56HDx4EKtWrUJraysWLVqENWvWFBeD7dq1q2xl4L59+3D++ecXf7733ntx77334vLLL8e6desG9ZhERFRu574O+FNroxXZozj0jUIZUffz3tYOvGMh/zafiLCjOSFxkurq6kJDQwM6OztRX19f7eYQEY2qR//teZx+3imwFtDWQBs7OuPfAogHHvKhRhgaHNx5CB//wHmj8EQTC5fdERFNcl25vJs0jupwjzYhBCyA3twYFFmZABjURESTXE19sjjiPeo5HVUnswbw4/5oP9uEwKAmIprk6uoTo7otq1Sh6ImFQXwMqqFNBAxqIqJJLpkcu8A0ACAErAFicW/MPiCczBjURESTWDqTRyymxmRuGkBU9cQ9nR946OnNjdETn7wY1EREk9jBw93wPdWX08WTM0aHLZygJQBPSXR0sujJiTCoiYgmsS1vtUFFR066YejRP5gDcOEjlcDhjt5Rfb6JgEFNRDSJvbXzAABX03PUN0vZQr1vASElhBDYufvwaD/rSY9BTUQ0ibUdyUa1t0VUlUyM8jNaCFhIAUAIvLXryCg/38mPQU1ENIklmur6zom2FnbUV5W5YidSug8Enb3ZUX6+kx+DmohoEpsyvR7GAMUO9SizcOVJhXBPqPyqHjlxUmBQExFNUtZa1Df0FTsZqx1axgJSCFgLxBKxMXrWkxeDmohoktrf2o5E3B+D4e5y1lpI6T4Y1NbFkM2FY/r8JxsGNRHRJPXGG7sRBGpsxrxLRedSW2tRUxdHRxf3Uh8Pg5qIaJLatr0NnpKwYrRXepeLpsRhjUWyJoYjLHpyXAxqIqJJat/BbrenOfp5rOLaus3UMMYinvCxcw/3Uh8Pg5qIaJJKawXALexyM8ZiTGarC2VELQx8X2HTm61j8KwnLwY1EdEkVdtY7xaSjdHWrKNZAEpJHOrg0PfxMKiJiCahMB9i2swmGOMWdo16+dAShSF2KSSEFAjN2M6Rn2wY1EREk9DuXfvRNLXOBXXh3MkxyEvXcXdPpKSbHxdKjf4Tn8QY1EREk9DmzbtRUxuLetTA2JU7cawFlHQRpDwJrceyT39yYVATEU1CW7ftQyymYI0LaGPGNqiNtcXjNaVS6OzOjOnzn0wY1EREk9Duve3wPTXmVckAuMVrxkBKt8o8WRfDzt2Hxr4dJwkGNRHRJJRFEJ0LXZ2FXAaFet8WjU1JbNq0uyrtOBkwqImIJhlrLYKaegDROdTFX2DMpqqtBYQUsMairj6Bt3bzXOqBMKiJiCaZI+2dqJ/aGB2OUdqjHrthcLfa3M1VJxI+etJ6zJ77ZMOgJiKaZHZs34spLQ0wNlrxPcbHXBYJwBgDP/BguEVrQAxqIqJJZtuWnWiaUgutjTvFqhqNiJ7UasD3JJTnV6MVJwUGNRHRJLNl237U1cWLw89jHtQlT2gFIJWE8hTyIc+l7g+Dmohokmlt7UQ87kNrd4pVNep8AwAs4Cm3+lt6Ctvf4uEc/WFQExFNMqGIwfMkLCxkFaqSoeQgEKUURNSrfv2NXWPcjpMDg5qIaBLJZLLwaxrcHuroa0x71Db6WCAK1clcDPmBxJatbWPYkJMHg5qIaBLZvXM/ko0NAOxYnMFxXBYWKtoeVlObwMEjvVVu0fjEoCYimkR2bt+DxumNMBZle6irEdrGuH3c1gLJ2hhCBFVoxfjHoCYimkS2bN6Jpun1MMZAitJ4HtttWoXt21IIWFgkkj68RGIMW3DyYFATEU0ib721F01NNcUV34ArKVoVhUIrxiKRCKB8H+lenqJ1NAY1EdEk0pM2qKkLoHW04rtaW7NKntoYiyBQUJ7Czu17q9egcYpBTUQ0SRhjAC+BeMxHqA2EQMkxl1VK7Gj1t+dJSE9h85vconU0r9oNICKisbF/30EEtY3RIjILIQRMFdtjo/8nBQAhIaXA5i37qtii8Yk9aiKiSWLHtt3wkrUoriETArCFqB7bdd+FZ7MWUNKFtFQSrQd6xrQdJwMGNRHRJLFtyy40zWhyZ0ELARnldBXqksGVJgOMNZBSQgggngyQF7Exbs34x6AmIpok3tq6Cw3T62Cic6iFqM5Z1KVP6c7Edj3sRDKAl6it3ir0cYpBTUQ0SRw42IEp0fGWsr+R7mpktUVURlQgFvfhJxI40HZ47BsyjjGoiYgmAWstQviob0i4rVnFpBbVOeoyYkzfwSCJZADP5xatozGoiYgmgSPtnfBrmpBIBAi17Rv2rmLBbxt9FQ4GCWIepOdh25bd1WvUOMSgJiKaBHa8tQdBsg6eJ2CthpCiqsVOCkrnyT1PQimFN7dyi1YpBjUR0SSwbcsuyFjS7Z02Ua3tKie1KPnGRkdeCgkcOpKuZrPGHQY1EdEksGXzDjQ2N8FYAAJ989LjoFddOKPa8wSklNCKh3OUYlATEU0Ce3bvx5TpdW7xFkQ0L2yr2Kvue15j3YIyJSWChAc/UYeeHp5NXcCgJiKaBPKhQOOUWhjjanwfPUddlbiOxr6ttVDSFWBJJGPwkzXY+daearRoXGJQExFNcB0dXfASdWhoSiKMtma586CLNcLGnC08sbWwxvWmhXB7qb3Ax/ZtXPldwKAmIprg3tq6G/G6KUjE/eIe6iruyipj4cqIiqhSWhDzoXwf27bwFK0CBjUR0QS37c2dQCwJJQVsVFu70I+u5lqywocFYwEp3PK2IO5BehLbd7RWsWXjC4OaiGiC27J5B2J1tYAQxUpg1VxGdvTcuCt6AgACvi8hpURW8xTmAgY1EdEEt29PG6Y1N8FY607OisqH9hUbqXJ5shIqOklL+DXI5/LVadM4w6AmIprgsrkQjdPrYXQU1Dh6D/V42EztKOXOpfZrG7B7F4e/AQY1EdGE1tHRBS9Wg6YpNdDGQMiotjZQ1Y50UckHBykElBSIx334iRq8tZULygAGNRHRhPbWll0I6qegtjaGMLRQIpoPrvYeakSfE6Int9ZCCEBKgXhtABUE2LJ5e5VaNr4wqImIJrA339gOEdTA95VbtCVlX1KXJvQYp7Ut+QJQnDuXUiCeCKB8D9veZI8aYFATEU1omze9hXh9I6QEjDFQxWInbuV3NUe/C7u5hRAw1kBGvX0/8KB8Dz29uSq2bvxgUBMRTWCt+w5iSksTjHHDy1K5s5/HzQKyqHNvtCsjKoSA7ytIJSGDWq78xjgJ6gcffBDz5s1DPB7H4sWL8dxzzx33+qeffhpnnnkm4vE4zjnnHPz4xz8u+/1NN90EIUTZ11VXXTWaL4GIaNwxxiDUFlOn18FYA2sBJaQrHToucjrq0QvAAIXN1O64SwHE6qdg1879VWzf+FD1oH7qqaewcuVK3HHHHdi4cSPOO+88LFu2DAcOHOj3+meeeQY33HADPvnJT+KFF17Atddei2uvvRavvvpq2XVXXXUV9u/fX/z6zne+MxYvh4ho3Ni/7yBiiTo0TqmFNoU91Bgfq72PYqMzsgVEMahFwJXfwDgI6vvuuw8333wzVqxYgbPOOgsPP/wwkskkHn300X6v/8Y3voGrrroKf/VXf4W3v/3tuOuuu3DBBRfggQceKLsuFouhpaWl+NXU1DRgG7LZLLq6usq+iIhOdls374BX24jaOrfiG7BRqc6SimAYB53r6LhNt9gNkBJu+DsWx5Y3uPK7qkGdy+Xw/PPPY+nSpcXbpJRYunQp1q9f3+991q9fX3Y9ACxbtuyY69etW4cZM2bgjDPOwGc/+1kcPnx4wHasXr0aDQ0Nxa85c+aM4FUREY0Pmze9BZVsgOdJ15suVCKzfQu5qksA0WmbIvq0oISEFAKJ2hhU4GMbe9TVDepDhw5Ba43m5uay25ubm9Ha2n9FmtbW1hNef9VVV+GJJ57A2rVrcffdd+NXv/oVrr76amit+33M2267DZ2dncWv3bt5vBoRnfy2bN6BmmlTIKPDOJSMyoeK8RDSQLHaCaIiLNZCSledLJ6MQXkeUqlMdZs4DkzIqucf+9jHit+fc845OPfcc3Haaadh3bp1uOKKK465PhaLIRaLjWUTiYhGXXdXD+a2NEEbC2MsPFXSNxNwK7iOrn5SBUfvpVZSIoj7kL6CUAFyuTyCwK9qG6upqj3qadOmQSmFtra2stvb2trQ0tLS731aWlqGdD0ALFiwANOmTcPWrVtH3mgiopNAJpOFNhJN0+pgooVkUopoA3WhHFh121hWalxG28eE6NtLrRRqp8zAjrf2VLGV1VfVoA6CABdeeCHWrl1bvM0Yg7Vr12LJkiX93mfJkiVl1wPAz372swGvB4A9e/bg8OHDmDlzZmUaTkQ0zm17cyfiNXVoaEhAR4dx+ErBmsKBHDaaH65uWpcOwheO4JRSQCkRrfyuw5ub3qpa+8aDqq/6XrlyJR555BE8/vjj2LRpEz772c8ilUphxYoVAIDly5fjtttuK17/F3/xF1izZg2+9rWv4Y033sCdd96J3//+9/jc5z4HAOjp6cFf/dVf4dlnn8WOHTuwdu1aXHPNNVi4cCGWLVtWlddIRDTWNr22DV7DdMQTHkzUgxYKMFHp0PFU86R4MIdb8RYNx7sI1zLAG69P7qCu+hz19ddfj4MHD2LVqlVobW3FokWLsGbNmuKCsV27dkHKvs8T73rXu/Cv//qv+PKXv4y/+Zu/wemnn44f/OAHeMc73gEAUErh5ZdfxuOPP46Ojg7MmjULV155Je666y7OQxPRpLHp1S1ITpsPKSWsDYunU2lrIQoJXfWuWsS6jWKm5IODkoDnS8hYDDu3761a08YDYa0dL5+pxo2uri40NDSgs7MT9fX11W4OEdGQffamL+OMK/8Q5yyai1Q6D6MNGhri0MZC4qh91FVKgcLic6MtlJLQxiAR89CbyeNIZwZ7d7bjwO52HHn9N/j7x79SnUaOA+Pl8xQREVWI1hr5vMXU6Q1uxbe1EEpACnFMSI8PriXGuhlzJSWkFEgkAyhfIVlbh8OHOqrawmpiUBMRTTA7t+9DLJ5EfUPMlQ41FlJIF87R+QfjZn4aKNQORaH6iVQSSgrEouMu65qmTuqzqRnUREQTzBuvb0Vixiwk4r7bmgUBX4m+xVpA3wKuKraz0KsXZbdZSCngeRJ+zIOUAtqrweZJvKCMQU1ENMG8/spW1LW0QApR2IUFz5fQZjx1o48STZRbC7dFS4jiQuKs9bF5Em/RYlATEU0wO97ag+a5zTAArDHFFd/W2OqtHDuBqOaJm08vDM8DboW69NHRPnkPS2JQExFNIMYY9KbzaGyqgQGi4y2Nq/EdLdbqC+txspyspN53YY+WkG5gPoj5UDEfkAK5XL56bawiBjUR0QSye9d+1E2bjrq6OIy2sCZaRS0ELErCGtWvSlZUWNsmAB0tSRfCnaiVrI9D+T6mNc/A1jd3Vrmh1cGgJiKaQF596U3UzZoL33OrvA1M8Q996VnU46UzXWxIYWWZcSdTe0pCCCBRE4PyJBK1jXj9lS3VbGjVMKiJiCaQl194A83zTkEY6mhhloTnSxhjISRKTs2qckOL+g4IcU1zdclVYYtW3If0FLI2htdefrOqLa0WBjUR0QSya8d+NEyphZACRhsAQOB7riJZSQ3t8ZLTpUdc9v3TQkkJ31fwPAUBoCcn0Lr/YHUaWWUMaiKiCSKfy0PVNaGhIQEDEVX6slDKnUwFIY4N6HEwTV0YkC+0zlpASQHPl26y2lpY5UOIybmgjEFNRDRBbNu6C8npLYgFHiAEtDauKpmUMMb1rkvXe4+DjD5WycIyAQDWQgUSXizAjJnTsW3L5FtQxqAmIpogfr7mN5j9tnkItS4OJReKhhSGlMf1OUxRGVFbKMwSfdiorUvCCzw0TJk+KReUMaiJiCaIdb96AVOaamGtC2QhBZRy5zzLaB91n/EySx0Rfe0xUfuVFBCwSNbGoDwFLWOTMqirfh41ERGN3DO//h1mnXkWGptqgGioW0AgFqhoIZlb8l3oUY+LPdQlR3i54zZt8dhNay08T0IIdziHVBKHD2t07D1QxQZXB3vUREQnOWstHvrG45h9+gIoJSGkRBi60qGBp6C1W1DmLsa46kwf/XFBRKVOrQU8JeEpAd93jc9oAaUUMuns2De0ihjUREQnud/8agOmtMxCfUMtcrkQhXleawykktDaQIiSP/fjKKiBvua4Yzjd0Dfg9lLHYl5x5bcXi2H2nBa8/trWqrW1GhjUREQnucf+4SnMWHgGpk1vgFDSDSGLo/ZLCxSHvcflim+JY3r77rhLBWst/MCDF49h6oxmvPT8pmq1sioY1EREJ7GNv3sZM5qnwatrQLI2DuV70NoAAgh8Nz999NnT46xD3cce2zYhgTA0qGtMwgsUjIrh1Zc2V6V51cKgJiIaA9lsDju378XWN3fiSHtnxbZJPfr338H5l1+KmCcRahfKWhtIKRGLKTfsLV1IW3PUncdZt7r0PSms/JZSwGqNZG0MUinsbu1FT09qfG8zqzCu+iYiGiXWWjz725fwb99Zg3wuxNTpjfCUQnd3Cl1dKTS3TMMfXns5Lrj47OJQ9VDs3L4HQgh06hhmzamFFQLGGmhtIaUrHZrJ5CFVtJe6eM9xOfgNoLiVGsZYGGnhKVdCNJYIIATQmwOapjRi/74DmDW7ucqtHRsMaiKiUdDV1YPVd/4D6uqS+PAfvx/JmsQx1xw+1IGf/vi3+KeH/g0f/fhVeO/SxUMK7Mf/8SlcesWlaEvnMee0euSN600LuHOp3dGWgFQumPsWbY2vkC5rTeGEL+O+PM/V/IZyk9gyFsMpU2fi5Rc2T5qg5tA3EVGFtbUexl//+d9h8bvOxVV/+O5+QxoApk5rxLIPXobln/wwNv7uNfzFZ/4WWzYPrkRmT3cKb23ZCTTNRtOMJvR0ZQC4PchSCnhKwkRj3Uq607MAjP+/+lH9UGPd9jJPScTjrk8ppUAQD9AwdTo2/u7VarZyTLFHTURUQUfau7DqS9/EdddfiWnTmwZ1nyDm433vX4Kuzh489I3v4G1nzsOnPvtReP7Af6K///RPcOEV78GRrMCs6QG09JDPhW7htBSIxz03Z43i7ib3PcbroDcAIWDRV/REiCicA4VUOoua+gTyOY0j3Wns2rGv2q0dM+P9sxUR0UkjzIe487Zv4Y8+/N5Bh3Sp+oZa/D8rPgQ/8PCFz67G3j1t/V5njMHLO44gPmsBamQO6VQWQcyHBWC025oVCxTyoS4uoy4N6vGo7wBORMdx9v0shEA+r1HbkIDyFPbs70E8EUNHR1c1mjrmGNRERBXy0De/g/MufDtmzZ4xosc5/8KzcO1Hr8BXbn8I69Y+V/a7dDbEE//5IqY2z4CvJBqaamB9H/lcCKVkNP/szp62BlBKud4pCj3p8RrVTuHkLMAtxjPGlRLVeY1EMgYAyEFhwWlz8MLvXqtaO8cSh76JiCrgxec3Ye+eA/iTj1993OsymTw2vbEPu3a3I5sLISBQXx/H/HnTsWD+dHie6z9NmdqIFZ++Dv/f93+BTa9uw6c/dz0Od2fwi427sfWFjVh4/iJMqfWQ7s2idtoUdHWkkaiJQUoB31PQxkAIwJOAjeaqXVhHXevxOP5d+DQRBbUxFkK4ld9+IKE8BVgLPxHH7FMbseGZl/De9y+pZovHBIOaiGiEstkcHv7mk/jE/7hmwGu0Ntjwu+3YuesQTp0zFWefPRuB78Fai1Qqi7172/H7jTswb+40XHTRqQh8D56n8OE/fj+ee/Zl3HnX4zjrD96JM2bX4oV8HtILUD+lDqmD3dChgecrdxCHEEgmPIShW/UtpCshCmB8jqFawIqoMJkAYPoO0jIWEMbC910p0UKJUT8eQzonsXP7nio2fOwwqImIRuif/+k/sPgPzkMiGe/39z09GfxozSuYPbMR73rnwrItWEII1NbGUVsbx4IFM7B/fwf+7d9/j3PPnYOz3z4LQgjMPfss4HAaz/3k53hrWi3mX3AhZkyJo6szhZr6JFJdGcRrYsjnDYJAQUqBXN5CSFm2kEwiKnoyHgM74k7QEsW91EoJSCkQj/vo6cmjvqkGR3QKb751EPFEDJ0d3WhorKt2s0fVOP7XRUQ0/rW1HsZLG9/AuYvO6Pf37UdS+I//fBFnnzULc+dOdadDCQETi8EkEjBBDLaweEoIzJrVhCVLFqJ1fyf+/fvPY1trLw73Gpw+I4krlr0ThzMCOp1GsqEGOUiYaO90Ydw4FqjicZEC6CvLaVEMwPGu8EHGWjfXLiAQeAq5fIjaxiSkEujsNZi/YM6k2KbFoCYiGoGHv/kdXPnBS/stVHKkI4U1//UKLrpgHurrErBKQTdNgZ4yFTYeh/U82EQCespU6KYpsL4PwO17ftvbWrDgHfOwrTWFw7tbYYzBnr0HMffM03DamafgcEcaQiikU1n4Mb9Y4CQeU9AWgJTRXuqSLVr9FdMej2z56RwGFp4vkc+ESCQDCFioWAzzTpuL3/zq99Vr5xhhUBMRDdOWzTuRTmcx+5RjK2SlerP48ZpXcMH585BIBNCJBHRTE5DJQHZ3Q6QzENkcRDoN2d0NpNMwtXUusKVE2kiEQQJzG30oKbHu128i5dfjzLfPQd4IdBzphfIk8rkQfuDOnI7FFIwBYAAYGw1722hv9fhcP1Zgj/q+EE7GWFht4SkJP6YgpYTVBrGaBDKhwu6d+yd83W/OURMRDdMjD34Xyz546TG3h6HGf/7oJZz7jjlIJgPomhogiEF0dR/ToTVGI92bQS6XBwD4yQTi06Yh05tHXaob+VAjXl+DMxbMxozpdehNZXD4YDemtjSitycDKWXUmzdIJgNYAFprKF+5AiLaFoe9rbF9x0mOVyXTALC2OCLg+wqJuA9jLGI1AbQFNr15ADNapmL7tj1YsHBONVs9qhjURETD8OpLbyKRjKNpSkPZ7dZa/PTnr2P+vGloaEhAJxJAEANSqWJIh/k8du7cj9Z9h2CtRTwewPM8aCuhjUTLWQFqawOo5mkIe7PwjMG0KUl0HDyC9vYU6qc1wWrretHxAEZbBIHbLy0FEB5Vhaw47D3OFWuRF7rUBu7DRbSgLJHw0dGVQePUemQzR9DencdFb1+A36z73YQOag59ExENw6N//+947/sXH3P7Cy/ugu9JtLQ0wvg+bLKmGNJGa7y5aTue+c1LyOfymH/aHExvmQkZa4D1ajBj7myc+74LMaO5Fp416DncgbrGJObNn479ew6hoyODRH0dauvi6EllAQB+zJ053VAXhzYGxrjep9VRx9lGw97l077j11E1TrWxENKNCASBQjaTR7IuDgFAxuKYdcos/O7Zl6vV2jHBHjUR0RC98PvX0dBYh/r62rLb97d2YNtbB3DJxQvcyu6GRohuN9zd1dmFFze+iRkzmrDwbafi8JEs2g6lkUx4OGX+VDTOmoF8TqP3SA/CbBrK8zB7fjOkUti/rwON05qQzuZRWxNH95EUpHQFQKyxqK+LQWsLFaWc0e7ELBMVPREQbh53HHfNip8jhOtFy8IK9WjY3liLwHev1/MVoDXidTXYubcz2oueRs0Ah5+c7BjURERD9MQ//Qeu/ejSstsymTx+uW4zLrl4PoQQ0I2NkL0pwFrs2rEXu3e2YuHpp6ArpdF6MI2GOh+xRBxT582GCUO072+HTCShs1nMmdcCP/DQk84j3dWJTG8GYX0dGhprAAC1jUkAgPIVlBCI+RKptEYy6RaTGWvhSemKnki3avpkUNqZdvPu0QcPawFj4XkK8YQPay38uA8Ngc1b9+P0M+dh/a+fx9Krjl0vMBGM489XRETjzwu/fx1NTfWoq68p3matxZqfvoqzzpqJIPBg4nGXOGGI11/ZioMH2jF/4Vy0HsrBApjaFKB2Si2mLzwVh1qPYN+udsSb6nHKrHqc9raZ6E1l0NGVQefBDmQyWcTq6lFTG0c+m0N3RwqZdIhs1pUfTSZ8pNJ5+IEb1y6cRw2UHHRhSl7AeMzs/tpk+87PNsbNsPueRDLhI5fNo2lGA5QnkdYK71h0Bn72k9+MYYPHFoOaiGgIHv/HH+DypZeU3fbc77ajoSGBKU21bsi7tg7oTeHF5zdBa43pLTOw/2AaDfUBYr6EqqtHYupU7Ni0B/lMDmddsACzW+rR3dmLtn1HYKRCb2cPlO8jVlOLuvoYjNbIpHOQUS3w2to4ahM+enpzUJ5E4Cvk8xrFAzmshZQCZjwG8yBY9J2iZXQU2kKgJhkg25tDTUMcsBZBTRK9aaCzowfp3kw1mzxqGNRERIP0wu9fx5SpDair6+tN79p9GHv2HsGC+dMBAKa+HqK3Fy/+fhP8wEddQwMOH8kh5lscau9FMHUqaprq0LazFc0zG3DeO89EbyqLnW+1oac3D78mgUwqC+n78GM+GptqkM+FyGZykFIhmYxh+vRaxOMeetI5t7/YVzDGorBhWkoB2GjIuxDUJ8Ff+8LiN3cuNfoO57Du6E5jLWIxD8YCnlIw0X7ql17dh7effdqELX7COWoiokEItcF/rlmP91y7FNlkAjY67MJMsXjfH5/itv8KCesppDq6ccmyxZBSIR8aGO1Omogn45BKIMzmsHBhC6wAwtCgaUoNmqbWQioJow3slFooT0JJiVBrJBNesUeZDw2yuRDWugpmvq8AC4Sm74QsqYQrK2qO84LGKwEIC9d2WX67Me7wkVjcg9YGdY1J9HRl0bY/i3ddeQbW/H//jfdfPfHmqRnUREQDCLXBwZ4MDqdy6Ozsxhnnn41YLIDI5xDmQzzzmzexYP50ZLIZWACmoQG739gMncsjnqxBe2cGQkhIKTHjlCnIZkPk0xnMnteMQ20dsL6HfDoH5ftQvodcJgOlFGrrk8il88imc8WJZqkUpJJQygW4EEDgK+hQw/fdFi1ICaUErI0O3zjayTIM3s/Kb6MNLAQC381Tp3qzmDKjHr09B+HFE8iFEl2dPehNpZGcYKu/T4LBECKisZUNNbYc7MKm1k7ktMGs+jh+/t3/wqypdZA6hNUa69dvxZSp9ejKAbva8ziY89DRmYENauE3TEVn2kAoCd+TmDazEbFkDMIazDp1Btr2d8B4PvLZECoIIKSAzuVQ11CDRF0cmWwU0tIVK/E8H77nQQoBKSUQhXQudBXI8toAAu6kKeG2ZZU5GfZPo3yLFlAoUhYN4xtACAtrgfq6ODK9IWLJACbUSDTU4uVX9+Pc88/ET3/y66q1f7SwR01EFAm1wc72FNL5EFNrYphe646t3Pjca5g2Ywq2bjuAAwe6kMpZJBob0JEFbE83dJjHqTPmY/eW3YgFPtJGwo/H4Pk1SCR8JBsS8CWQbKxB6/4OBPEYwlwefsyDFBKxuAdjgGw2DyGkWwjmqWivtAtmGEBI4XrSnkIur6E8EZ2MZaE85a7xolKhpU6SoAbKt2gVtn4bAWhj4HkqquTmQfkuwIO4Bysldm/vwQ3XvB3/8u3/wLUfvbKKr6DyGNRENGkZY7Fl635sfGE7Utpi1mktOLj7MHq6XCWxuroE6uvi+O2vXsSMlqmIWwFZ34h6WDQklOvx2QQQb8CRA+3wlEIuBACNfK9G0FCD+ml1iAceBCza9h6Bl/ABIVBbn4TyJHReI5vJQygJz/ddqU8DQAooKdyQNtwZzb5SLqRDDQDwlURe676Qlm4et//tTmP1rg5TaeU0W3JT6YHaQiDU7sztRDxAOp3D9NlTsX/nIfg1NTh0JIuGxjps27ITp51+ahVexOgQdqIfOzIMXV1daGhoQGdnJ+rr66vdHCIqobVBbyqNIPARiwfDeoze3ix+9vNX8OLLO9HS0oCF5y9APO4jIQU84WYEO7tSeG7Dm+jo6IVUEnXTp8IA8E0e9fVxWGNx5EgKvZkcFpx7Gg7sbIOBAISAkh78hI+WOVORCFzhkZ6eLIJ4gJraGKSSyGXzUbUwCbeHSkBrDSkFPM+DLRxIEQ3/Br5yW7BCDWuBIKZgtYVQAjAWxrrh8FAfFdR9dUPGPxH1oEtH7iWgQ3eD50lYADFfoe1AN/bv78KU6XXY+uIu5PIGouswLj5nCt547S186Y7/txqvYFSwR01E497una34z//4NbZs3gUhBOLxAPl8iHxeo6GxBu+54iL8wbsXwfeP/yftSHsXHvnHn2Dzm60w2S5Mn92I6QtPx0sbXkDnoSPI5/Ow2sLIBPx4DaZMq0NQl0SysRGZVC9MLoRWAuhMo6OzB4GncMoZp6CzPQURxOEV9i8rgZa5U1ET95HPa2hr0TStDkJK6FAjr0N3MpS2sDqElAqAgR94sNYN8xZOkSqEtOe5FeDWusACAKGE22Ms3Epva+2xgXwSDXsD6NuWZV0hGQUBHQ39G22hlNum1VCfQNuBHlgLxGoC2IzGof0azTNn4Mc/XDehSoqyR90P9qiJxoe2/Yfx9w/+O/K5PC5e8g7MmdsclZbs09Pdi5df3IJNr27HZe85H9f+8XsRBH7ZNW+8vhUPPfh9HOmWaJmRxFlnzUHzwrkI6mqROdQOow1SGY0DhzPoTOXge8oNO0uFMJ9HPpN124KCAPnQwMKtuo7XxjFjViMO7jsCqw2MdVXB5pw+E1OakrAAspkQ2loo5fY6W2NdoAq46ltKQgm3UtuY6PbCcK9wIS2juelQW3iegK+kO3Ja2+KlQaAQ5vtZ6j3ej7UsJY6ao46COdS2eOZ0LOZBawvfE9iy9RCMBaSS2Lu1Dd0dKSycpuCZbniewo03X1e1l1JJDOp+MKiJqstaix/82y/x37/YiKv+6F1obpl6wvsYY/DSxi3Y+LtNuP7/uRKXXn4+tm/bhfvueRS9YT1mz56Oc99xCpSnUDdnJozWONx6BEe6QmTzGtl0Djqfw9TpdQhVDAZwC748Nxedy2loY6HzBmFewxqN086Zi4P7O2BCA2sN6hrimDF7KupqY8ik8+hNZREkAlf7u2RI2sJCSAklo8pbpu8QykJQKSmglFtI5is3Lx14bvuVgQsxG13sqcLjHPWmFPb1nCx/5UXfPyzca5RKwJpolAHRljRjEQs8tB3owr59nZg2owFvvrgdxkq079iD5X9yHv7hW9/B/3nsK8d8aDsZMaj7waAmqp5UKo17vvLPqJnahERTA7p68siFBlprGNfJhATgS4u6hIfZM+owb+50QEik8xa92TxefnErdu9uhRf4SNY04W3zp6C2NkDOCDSdOgttezvQ2ZGGUoAXeBC+h1jCzR0bY93wdDYHKSWMKaxlMgAEpJJR0Y0A1lqEuTxqal0hEwiBxvo4crkQ0bRqtKfZwlp3NIaUbi+060Gbkv1Ibp5aAPA8Fd3ZIBb3kctpxGKqmLdGu++EAIQV8Hw3D36Mk6k3DRSDWsJN21vjKpIV56mt24ImpICn3Gt+480DqK2Lo7M9hY6DPWjfdxBLzpmGw/t2Y8bMafjox66u6kuqBAZ1PxjURGMvnQ3x45+/iA0v7oKAhc6mkFBAIhEgkYzB8wJX+ctYhG5fDlRNEkEyCaUUoklNCFhkMhlI4UfzxRJCSiRrArTMqMWhgz0w0e0ulF3AGW1gtY7iWBTniAs5KpUrzWkBxOMBamoDaG0R5kOEoYFSEtOm1iCX08hkQ7dFSgpYbQBEQ9zSPadUotibtsbNVUO6DwGFtkAAibgPrQ18TxbWm5WFNKw7ezo0/VQhO9l60wVHLSgrzPmH2hZXf8djHvLaIOYrbN12CL2pHBqm1mDbS7uR1xY9e/biho+ci79/4En8n2//b8Tjseq9ngpgUPeDQU00dvYf6ML31ryAvfva0b6/DbOnJdE4Yzq6MxbZvLtGSqCmPolkQy1UPAAgEIYa2UwOud5sMWwBoGFKLaY110FK6Xqxxp1jHE8EriBJaQ3saMESEN0WzR9bY2FCA2N0NK/sFnrVNdQgCBSgJPLZPBAVIFGeRFN9HLmcRjYXRluqoupaUhQPyQhiCkKKYti61yaKRzpaGLfAzAKxmCrezwDFld1A3xR2MFBIAydfb7qgME8dlRAtzFNrHa2CB+B7CtoYxGIeursy2LHjCBqm1KB11yGkenJo39OGS89vRtehNmit8enP3VDVlzRSXPVNRFWxa+8RPP3j53HwQDu8fC+62lM4Zd485LRAT8Zi+ox6TJ3ZhJqmumgIui/8jNZI92bR02nRqUNkhat1feppzcjnQhzY2w7pe6ipS6ClxX3Y1tpACItUdwYi6rlCW0AhKvNZPBQSxlpYWChfob42jkQigLEWuZxGXlt4UYUsYwwELJrqk+jtzSEMDYQS8D1VHMa1xvW2PaWKwQ9EQ+KwMPlohXfUY/aUcEU8rEW+n+HsQrWuwJfFedtjnOQ1J6OzRfqG+lGyn9q6wjS+JxHmNerr4vADia6OFJrnTMVbr+xC7bQp+PUz2/GJ68/HPz30NA60HcaM5hOvcxiv2KPuB3vURKPnwOEu/PO/b8DBgx2Y2egjHwL7DuXQ0FCLGTNqMHv+DCTqa5DNG2TSWYShQbI2Dk+5LqKN6j4rJeEHyg39agNrgGwuhJAC1goYa1CTCNDZlUEqlQMA1NYGgAXaj6TdMLmIqn1FtbML68mlFIgnA1emM+d67ka7Fdm1tQmke7OAAOKJANOmJJHO5KG17XsAoJgyynMfAAqLyfq2UEXJHN1HSQE/cAvXjEbfHuqj9kQrKaLDOkyxYlmZk2nfdH+Onqe27pAOoVC2RzwI3LGeyXiAQ4d7sGdvFxqn1qB1pxsKP7L/EM4+tQbTGxWe+e/n8dX7v3TMjoGTBYO6HwxqospLpXN47MlfY29bJ6bXSbTMbMarbx5CPjSYe+pUnHp6CyAltBVQ0oWlMRa5XIh0TxY6dPPHZX+whEBdfQL1DQkoKSClQFd3FgBQWxtDOp2DjFZPWwvkc7q44rqjo9fNg1qULPQSqK2NQymBbCaHMLQu+I0r0ZlIBCisz/aVRH1dDL29eTd/Gm23siUtDALlRgAsjgncAiUF/KiQRyG/C73uUlK5BVRG275tXDj6DcHJOzddUBrUbsGAe/+PGv5WUkJI9/vAU3h9UxvCvEbj1FpsfXEntJA49NZu3PDhc/DrXzyLd5z7NnzouqXVelUjwqDuB4OaCOjqTGHn9v3Yv/8QujpTCEONZCKOaTMaMXvODLTMnOrCxbqw8TwVDR+X01rj8ad+i+1tvRBGAxDIhW5RVGNjHGcvmgsv8CA9BWMMUp1p5EJXDzPM5YtlJKOq1tGqaw/KlwgChSBQSPVk3clSUqCpKekCuyuLjKvnWeQpF9q+ryAF0NmZQW86B0CgrjYGSIFsJoQxFsrvO6kKQLGXGmqD2qSPmmSA7lQuOqXq2D+jQeDmUctOsSoU8kBhP7SCse498jwVrTCPhsbhwsotPJOwsNBhyfNITJwFZEfrZz+1q8AWbdOKBiNiMQ/ZXIhkPEBHZy927epAXUMCPZ1pHNp/BL1dGdiuw7juj96Bf/w/38Wqv/08Tpk7s4ovbHgY1P1gUNNkpLXBSy+8iXVrX0TbkRSSjQ1INjbATySgAh9CqWjVs4XRBvmc28IUZkLoMEQ+E0IKuHBTErm8QQ5AkEwgn8sh19MLCwGpFGpqAlzwzgVI1sZhrEWqO4eO9h4IKeHHfIT5sGz+1fcUvJjvesbG9apiMQ+xQCGTDSGERCxQSCZ9pLMhtDaIBx4CX6EnlUVvOl98rMLwp1ICiUTgtnsJgVyoEeZNNOQsYLSB1hYWBkopKCVhjMGUBlftKtVbeMzyP6ECgB+485KP/usqoj3R0nPD265kqIJSrpgJAEgICOW2cQFujtstJit5oP5C+mQf8i4VvQ4h+o7rtNZCKelOCoveWBnVQw+1RU3Cx6bNbUj35jC1uQHbX9+LUANH9h3E6bMSeNv8ejz1Lz/C1x78X2horKveaxsGBnU/GNQ0VFob5PNh8Y+J73tjOh+Wzuaxt60T+w9240h3BqlMiHw0RKrdYmYU/r65U30dEQ0XS0/C89yKZFgLndfI5/KwoQa0gS9cJShVeE0CbqsULFK9OWS1RiwZh/J95LIanR0p5LMGuVwema40Cucp1zclcP7F81FbF0c6k0fHkTRSXa6WdlDYl5zPw0LA9z0EMQ9CKVit3UIrX0FAwvclPE8iHxpIIZBMeDDaIp3J97UvEo95CAIP6XQeqd6cGwFQwj22cCuy86EuHiGpoi3MobYIQwMdrdwOPIWapI90Jo98fxXA4I5h9H3vmEVeSsqSoiRuKNw9X8l9pYxGz93KcxOtenZ3KlyEiTkvfbSjh79lP8VPol51Ya7aj0Z0XtvUCgmJ2sYEtr20E0YqHNqxD5dfMguByOHna57B1/7P/0IsNrw68dXAoO4Hg5qO1tWVwo7trdi5fT927TqAg20dyOVcMFtYSCGgPAVR6IlFf6hdngnU1CYwa9Y0zD21GafOa8HcuTPgB0PfdKG1wc4drXjljT3Y3daNtBGAkDBaw4Z5SCFhhYSBm5OVEi5gvWgYN/DgJ+KQ0hXPyGVyyOfyEFYgn9cQwgW28iSEFw0xKzfsrMMQuZzrrep8iExvBmE+hPQCSE9BhwY61EjUxpFIxhCLKShfId2TRTzm4dR5U+EHHnpSWRxpTyGbzkEIiSDhw/M8t1BKKUjler2FuV0JQHpuq5XRQCLhw/MEjHFbmJSUyOV1sadtdPTeF4bhowVcni+RiPmurGdOF0+gEhBuTjqrS85xFlCemztOxDx4noLWBtmchjUG2rqeXuE9FrJQyEREmSmKR1IW2nBMze1oCNxGVcns0fPYpSFdGsT9/cWeKEPepUqrlFkUpxiUcgvpCovyhARigYdMNkQi4SObDfHmloOIx314vocdr+2GthKHd+3F+5bMRTbViWf++3ms/vpfoa6+toovcPAY1P1gUE9u6XQWb76xG6+9uh1b3tyDdDqLeDzAjOYmTJvWgGnTG9HYVNtXPeo4rLXIa4vOVBZHOlLo6OhFd3caqd4MgpiHxqZazDllGubMnY5p0xrgewqBkoh5Cr4SSPdm8fLLW/Hs89vQntKI1ddDCIFcbxomn4NSCon6WsTqahCLxxDEXC9URQuxwtC4vcbZPHJZjXwudD1l67YkuRXSgLUayZoAM2Y2oa4xGe1BLvTuUFwdrWQU2lqjtzePzs4MMmm3olp6bt7XWiAW91CT9FFfF0dN0oeQAr3pEJlMHr09WeRzIWIJH57vFwtciELIRQErhCiOSgjh2uv7LvyUdD3qMKddBirXMy4UATFRu7UxEBCQSvTtWIruL4U73CGMhrilFMXqXr4ni1us8qFxq42jD2RuX7RrVLF9pf/O0VeNDBgghEvackwA97e16kT3xwC/P5kVe9Xuv+VCr1oot5Wt2KuG+/epPIFcTqMmEaA3ncOWrYdQUxsDhMDO1/dCQ+Dwrv1457kzUBs3+PEP1+HLX/kcTp03u3qvcZAY1P1gUDvGWGSyIbK5EGHo6hxba6N5IbeQJx7z4XuyKtsejLHI5kJks3lkctrtkwXg+RKxwEci5sH31QnbduDAEbz2yna88vJb2LfnEDxfoWXmVEyb3oiamjiy2RC9vRmke3PI5vKughVcD8oV0oihpiaGIBbAKB9Z5SMvFYSnoKLeqVv05HpmbjuohY56jIVDGoyxxXm5wkplIQGtrfsy5XOe1gKedL1BFS04koWh7KiIRmHLUTE8oufRhWpYwkJGW5MKj2mshRRuKFaJvl6hewzbt6soCs9COBZCyUQHKHieK/HYmw6Ry4bRcC/gBW6+17WtZE9U6YsreV7A/SEOosVdQKHuc19CWWOPmcIVUpT9uy/0uG3fqCkg4YbzRSHzXHhrCzdfEAWhjEI52sJc7KWX/vksG40eKFhLlQbs0eE8wPboY+4/UUO6oLRXXTZX7UZUSofAlef+e83lNWoSPvKhwVs7DkMqt4Vvx6b9CLVF54F2NPghllzQgh88/VO854rF+OjHPxBVtxufxkVQP/jgg/i7v/s7tLa24rzzzsO3vvUtXHLJJQNe//TTT+P222/Hjh07cPrpp+Puu+/GBz7wgeLvrbW444478Mgjj6CjowN/8Ad/gIceeginn376oNozGYLaGIuDh7uxfWcb9uxtx8FDPTjSlUE6k0cYupKIWruDBxBVd+r7axD1IqQEhPujqzwFTylXvSnpY0pTDWa1NGDunOmYN3caEkM8NzjVm8OO3Yexe28HWg92o72jFz29eeTzuriFxUZzrW6eESUraqM/ogBg+4JASlfJyZeAzmXR3dGFXG8PfCmQTLoSg6lU1j2Hse584poY4rEAsZgPP/CglITWQDqn0ZvVkLW1qJtWj/opdfBjHqw2CPMhfF8hWRNHEPNgjEU+r5HPG4TGFOdF+wpiAJ4noiFnASlk8bUYl67RB6NoWDrqtWprEeb7html6OuNWmMRhtYNU0dHIxbC3/cLHxrc84jCEKzt+28jH+riXGpxPrv4b6dvMrRQGMT3lFvw5RVKYFqYqAdqo/bo6FhHrU302LYkMcsnWL1oj7SnXM8XQLSC2tV+Ln5oKNwruuvRn8lKV1wX3lYg+hBQCN6jHdU9LrTfDVOX/E663l7pe9f/A/Y91jE/l4bt0b8b6C/zZAjogmg+XkpAa0Ao16sGUH6qViGslYSnJLJ5jXjgKrt1dKdx5EgaXuCj41APDu7vRC6TQ2frASx62xT0tLfitZffxA3L/wiXX7F4XAZ21YP6qaeewvLly/Hwww9j8eLFuP/++/H0009j8+bNmDFjxjHXP/PMM3j3u9+N1atX4w//8A/xr//6r7j77ruxceNGvOMd7wAA3H333Vi9ejUef/xxzJ8/H7fffjteeeUVvP7664jH4yds00QI6jAf4kDbYby1ow279hzG/tYOtHdlkckamMJ/1yaE77lD2BNxhdpkDDXJAIlkHPF4gCAWFFeeDsRaCx1GvdpMFr2pDLp7sujNhMjkNLJ5A20lRPRX0pNAXdLDtKYaNDTVIYgFyOYEjnRl0dGdRTav4QpRCMQChXjMQzLho74uhoa6OGoSsb65x+O1y1j09KTRcaQLhw514+CBDqRSGbdH1/cBqSCVglBuW1Ah7D0JxH2FmrhE4LvFU5mcRiodQiViqG2qQ/3UWgTJGIQQCPMaAoDyFZLJAL6vYGwUzDld7BhZY938p3VnCXu+KtnO5IpzGGPdKuDoaMMgUNHJTa6MZGEPqbXRKmTpurXu34FBXhvkovYA0d9w43rQvq+iBVjRHyHR15N0z+s+EBR64yrqZRf+ONhCj9kWwrDkw0ZJLz00BkrI4iIpt4jN1d8Woq+3XwjfQtSK6P9KG2+i57PWlowKlJ8yNRgDjRif6PpCD9v28wji6IAeyHAaOZIXNtEMNqiBsg8+gaeQzYVuKilQ6OzKQAiBfGiQzoR484UdsMpHV9thZDs78fHrzsEz/70Rmze9hZtuvg6Xvufiar3iflU9qBcvXoyLL74YDzzwAABXkm/OnDn4/Oc/j1tvvfWY66+//nqkUin853/+Z/G2d77znVi0aBEefvhhWGsxa9YsfPGLX8Qtt9wCAOjs7ERzczMee+wxfOxjHzvmMbPZLLLZbPHnzs5OzJ07F7t37x4XQW2tRS6XR28qjZ7uXhw50o3DR7pxpL0H7R0pdHSk0JPOIZM1sMp3w31CwFMKgRIIYhK1iQAN9XFMaap1c2+j1M5s3iCb1cjk3Fc2GyKdM8jnLfLaoFBYqO8Pf+FLu/8B2jwkDHzPhXQi7iORiD5AJOJIxANIIWEQFcJIZdDVk0Z3Vy96utPoTWURajcMDojiIp/A9xBPBIjFAsTiPnzfj/avGtfzhIUVEirmI5aMI14bIFGTQCzuu/250Ypdt2jMzV960RGEbsW3m8d05xG71bxSuIpXpcFUmkUCrpfrRQu9CmFU6G26/2mKsr3JbojXuprUQFRW0fWk3fNFowuF5yruP7ZlPVsb9WqL/+MfxF8BUdhOLEQUrSW/O+ohCn9VCsP4pYvFC48hSsLaFidxy4eXC/9dHa95pT3q8gYPLvP6feyjgvPoOegxC8mjX8BkCOdSwwhqa90RmJlMCCGBwPdwpD2Fnp4cPE+gaWot0j1pCCnR09GL1h2tuOicZiRjHg63HcTWN3bgc1+8ccxeYl1d3YmnDm0VZbNZq5Sy3//+98tuX758uf3Qhz7U733mzJljv/71r5fdtmrVKnvuuedaa63dtm2bBWBfeOGFsmve/e532z//8z/v9zHvuOOOwl8JfvGLX/ziF7/G7Kuzs/OEWVnVQzkOHToErTWam5vLbm9ubsYbb7zR731aW1v7vb61tbX4+8JtA11ztNtuuw0rV64s/myMQXt7O6ZOnTrsRVJdXV2YM2fOuOmVnyz4vg0f37vh43s3fHzvRqau7sTFV3h6FoBYLIZYrPy80sbGxoo8dn19Pf/jHQa+b8PH9274+N4NH9+70VPVw9CmTZsGpRTa2trKbm9ra0NLS0u/92lpaTnu9YV/DuUxiYiIxquqBnUQBLjwwguxdu3a4m3GGKxduxZLlizp9z5Lliwpux4AfvaznxWvnz9/PlpaWsqu6erqwoYNGwZ8TCIiovGq6kPfK1euxI033oiLLroIl1xyCe6//36kUimsWLECALB8+XLMnj0bq1evBgD8xV/8BS6//HJ87Wtfwwc/+EE8+eST+P3vf49/+Id/AOBWk37hC1/AV77yFZx++unF7VmzZs3CtddeO2avKxaL4Y477jhmSJ2Oj+/b8PG9Gz6+d8PH924MnHC52Rj41re+ZefOnWuDILCXXHKJffbZZ4u/u/zyy+2NN95Ydv13v/td+7a3vc0GQWDPPvts+6Mf/ajs98YYe/vtt9vm5mYbi8XsFVdcYTdv3jwWL4WIiKiiqr6PmoiIiAZW1TlqIiIiOj4GNRER0TjGoCYiIhrHGNRERETjGIO6Qtrb2/Gnf/qnqK+vR2NjIz75yU+ip6fnuPdpbW3FJz7xCbS0tKCmpgYXXHAB/v3f/32MWjx+DOe9A4D169fjfe97H2pqalBfX493v/vdSKfTY9Di8WO47x0AWGtx9dVXQwiBH/zgB6Pb0HFoqO9de3s7Pv/5z+OMM85AIpHA3Llz8ed//ufo7Owcw1ZXx4MPPoh58+YhHo9j8eLFeO655457/dNPP40zzzwT8Xgc55xzDn784x+PUUsnqCqvOp8wrrrqKnveeefZZ5991v7617+2CxcutDfccMNx7/P+97/fXnzxxXbDhg1227Zt9q677rJSSrtx48YxavX4MJz37plnnrH19fV29erV9tVXX7VvvPGGfeqpp2wmkxmjVo8Pw3nvCu677z579dVXWwDHHIwzGQz1vXvllVfsRz7yEfvDH/7Qbt261a5du9aefvrp9rrrrhvDVo+9J5980gZBYB999FH72muv2Ztvvtk2Njbatra2fq//7W9/a5VS9p577rGvv/66/fKXv2x937evvPLKGLd84mBQV8Drr79uAdjf/e53xdt+8pOfWCGE3bt374D3q6mpsU888UTZbVOmTLGPPPLIqLV1vBnue7d48WL75S9/eSyaOG4N972z1toXXnjBzp492+7fv39SBvVI3rtS3/3ud20QBDafz49GM8eFSy65xP7P//k/iz9rre2sWbPs6tWr+73+T/7kT+wHP/jBstsWL15sP/OZz4xqOycyDn1XwPr169HY2IiLLrqoeNvSpUshpcSGDRsGvN+73vUuPPXUU2hvb4cxBk8++SQymQze8573jEGrx4fhvHcHDhzAhg0bMGPGDLzrXe9Cc3MzLr/8cvzmN78Zq2aPC8P97663txcf//jH8eCDD07a+vfDfe+O1tnZifr6enhe1Ys8jopcLofnn38eS5cuLd4mpcTSpUuxfv36fu+zfv36susBYNmyZQNeTyfGoK6A1tZWzJgxo+w2z/MwZcqUAY/WBIDvfve7yOfzmDp1KmKxGD7zmc/g+9//PhYuXDjaTR43hvPevfXWWwCAO++8EzfffDPWrFmDCy64AFdccQW2bNky6m0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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Load data\n", - "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import os.path as op\n", "\n", - "# Define the path where the derivatives are stored\n", - "output = (\"/home/cprovins/derivatives/hcph/\")\n", - "# Load fMRI duration after censoring\n", - "good_timepoints_df = pd.read_csv(\"./data/fMRI_duration_after_censoring.csv\")" + "FC_FIGURE_SIZE: tuple = (70, 45)\n", + "LABELSIZE: int = 42\n", + "\n", + "def group_reportlet_fc_dist(\n", + " fc_matrices: list[np.ndarray],\n", + " output: str,\n", + ") -> None:\n", + " \"\"\"Plot and save the functional connectivity density distributions.\n", + "\n", + " Parameters\n", + " ----------\n", + " fc_matrices : list[np.ndarray]\n", + " List of functional connectivity matrices\n", + " output : str\n", + " Path to the output directory\n", + " \"\"\"\n", + "\n", + " _, ax = plt.subplots(figsize=FC_FIGURE_SIZE)\n", + "\n", + " for fc_matrix in fc_matrices:\n", + " sns.displot(\n", + " fc_matrix,\n", + " kind=\"kde\",\n", + " fill=True,\n", + " linewidth=0.5,\n", + " legend=False,\n", + " palette=\"ch:s=.25,rot=-.25\",\n", + " )\n", + "\n", + " ax.tick_params(labelsize=LABELSIZE)\n", + "\n", + " # Ensure the labels are within the figure\n", + " plt.tight_layout()\n", + "\n", + " # Note that we save the image under a BIDS-compatible name\n", + " savename = op.join(\"reportlets\", \"group_desc-fcdist_bold.svg\")\n", + "\n", + " plt.savefig(op.join(output, savename))\n", + " plt.close()\n", + "group_reportlet_fc_dist(fc_matrices, output)" ] }, { @@ -45,11 +144,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import plotly.offline as pyo\n", + "import os.path as op\n", + "\n", "# The literature indicates that less than 5 minutes of fMRI signal after censoring is not enough to reliably estimate functional connectivity.\n", "DURATION_CUT_OFF = 300\n", "# Generate each reportlets\n", @@ -128,22 +229,129 @@ " filename=op.join(output, \"reportlets\", \"group_desc-censoring_bold.html\"),\n", " auto_open=False,\n", " )\n", - "group_report_censoring(good_timepoints_df, output)\n", - "group_reportlet_fc_dist(fc_matrices, output)\n", - "qc_fc_dict = group_reportlet_qc_fc(fc_matrices, iqms_df, output)" + "group_report_censoring(good_timepoints_df, output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we generated two reportlets, one saved in SVG format and one saved in HTML format, let's combine them into a visual report.\n", + "What makes nireports powerful and easy-to-use is that to assemble a visual report all you need is to define a configuration file. Here is the configuration file for this project. Let's dissect it.\n", + "\n", + "```yaml\n", + "package: funconn\n", + "sections:\n", + "- name: Group report \n", + " reportlets:\n", + " - bids: {desc: fcdist, suffix: bold}\n", + " caption: Density distributions of within-session FC strengths. The FC distribution from each session are overlaid.\n", + " subtitle: Functional connectivity density distributions.\n", + " style:\n", + " max-width: 600px\n", + " - bids:\n", + " desc: censoring\n", + " extension: [.html]\n", + " suffix: bold\n", + " caption: |\n", + " fMRI duration after censoring computed as the number of good timepoints multiplied by the repetition time (TR). \n", + " The red line corresponds to the QC cutoff of 5 minutes (300 seconds). Less than 5 minutes of fMRI is not enough to \n", + " reliably estimate connectivity.\n", + " subtitle: Report about censoring.\n", + "```\n", + "\n", + "A visual report is a collection of reportlets in a particular order.\n", + "\n", + "Those visualizations are organized into sections. Each section has a title encoded under name and a list of reportlets\n", + "```yaml\n", + "package: funconn\n", + "sections:\n", + "- name: Group report \n", + " reportlets:\n", + "```\n", + "\n", + "Each reportlet is defined by a visualization, a title and a caption explaining the reportlet. The corresponding visualization is found by passing a bids filter to PyBIDS.\n", + "```yaml\n", + " bids: {desc: fcdist, suffix: bold}\n", + "```\n", + "In this example we are searching for an image that has bold as suffix (that is ends with `_bold.svg`) and the value associated with desc is fcdist (that is the filename contains `desc-fcdist`). The next two lines define the caption and title of the reportlet\n", + "\n", + "```yaml\n", + "caption: Density distributions of within-session FC strengths. The FC distribution from each session are overlaid.\n", + "subtitle: Functional connectivity density distributions\n", + "```\n", + "\n", + "Similarly, the rest of the configuration file defines the parameters to integrate the HTML snippet we generated earlier into the visual report.\n", + "\n", + "```yaml\n", + "- bids:\n", + " desc: censoring\n", + " extension: [.html]\n", + " suffix: bold\n", + " caption: |\n", + " fMRI duration after censoring computed as the number of good timepoints multiplied by the repetition time (TR). \n", + " The red line corresponds to the QC cutoff of 5 minutes (300 seconds). Less than 5 minutes of fMRI is not enough to \n", + " reliably estimate connectivity.\n", + " subtitle: Report about censoring.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having this configuration file defined, it is now easy to generate the visual report. This works by instantiating a Report object. You have to pass the path to the configuration file to `bootstrap_file`, `out_filename` contains the filename under which you want to save the visual report, `out_dir` corresponds to where to store the output, `run_uuid`is useful to uniquely identify a run. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nireports.assembler.report import Report\n", + "from time import strftime\n", + "from uuid import uuid4\n", + "\n", + "run_uuid = \"{}_{}\".format(strftime(\"%Y%m%d-%H%M%S\"), uuid4())\n", + "robj = Report(\n", + " out_dir=output,\n", + " run_uuid=run_uuid,\n", + " out_filename=\"group_report.html\",\n", + " bootstrap_file=op.join(\"data\", \"reports-spec.yml\"),\n", + ")\n", + "robj.generate_report()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/notebooks/data/fMRI_duration_after_censoring.csv b/docs/notebooks/data/fMRI_duration_after_censoring.csv index 9a51fce7..2a35fbff 100644 --- a/docs/notebooks/data/fMRI_duration_after_censoring.csv +++ b/docs/notebooks/data/fMRI_duration_after_censoring.csv @@ -2,15 +2,3 @@ filename,duration sub-001_ses-001_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 sub-001_ses-003_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 sub-001_ses-004_task-qct_dir-RL_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,145.6 -sub-001_ses-007_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-008_task-qct_dir-RL_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-005_task-qct_dir-PA_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-006_task-qct_dir-PA_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-009_task-qct_dir-AP_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-001_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-001_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-003_task-qct_dir-LR_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,145.6 -sub-001_ses-004_task-qct_dir-RL_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-008_task-qct_dir-RL_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-005_task-qct_dir-PA_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 -sub-001_ses-006_task-qct_dir-PA_part-mag_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz,156.8 diff --git a/docs/notebooks/group_report_cli.py b/docs/notebooks/group_report_cli.py deleted file mode 100644 index c764ef88..00000000 --- a/docs/notebooks/group_report_cli.py +++ /dev/null @@ -1,174 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2023 The Axon Lab -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# - -import argparse -import logging - -import numpy as np -import os.path as op -import pandas as pd - -from itertools import chain - -from load_save import ( - get_atlas_data, - find_atlas_dimension, - find_derivative, - check_existing_output, - get_bids_savename, - get_func_filenames_bids, - load_iqms, - FC_FILLS, - FC_PATTERN -) - -from reports import ( - group_report, -) - - -def get_arguments() -> argparse.Namespace: - parser: argparse.ArgumentParser = argparse.ArgumentParser( - description="""Compute functional connectivity group report from functional connectivity matrices.""", - ) - - # Input/Output arguments and options - parser.add_argument( - "output", - help="path to the directory where the functional connectivity matrices are stored", - ) - parser.add_argument( - "--mriqc-path", - default=None, - help="specify the path to the mriqc derivatives", - ) - parser.add_argument( - "--task", - default=["rest"], - action="store", - nargs="+", - help="a space delimited list of task(s)", - ) - parser.add_argument( - "--fc-estimator", - default="sparse inverse covariance", - action="store", - choices=["correlation", "covariance", "sparse", "sparse inverse covariance"], - type=str, - help="""type of connectivity to compute (can be 'correlation', 'covariance' or - 'sparse')""", - ) - parser.add_argument( - "-v", - "--verbosity", - action="count", - default=1, - help="""increase output verbosity (-v: standard logging infos; -vv: logging - infos and NiLearn verbose; -vvv: debug)""", - ) - - args = parser.parse_args() - - return args - - -def main(): - args = get_arguments() - output = args.output - task_filter = args.task - mriqc_path = args.mriqc_path - fc_label = args.fc_estimator.replace(" ", "") - - verbosity_level = args.verbosity - - logging_level_map = { - 0: logging.WARN, - 1: logging.INFO, - 2: logging.INFO, - 3: logging.DEBUG, - } - - logging.basicConfig( - # filename='example.log', - # format='%(asctime)s %(levelname)s:%(message)s', - format="%(levelname)s: %(message)s", - level=logging_level_map[min([verbosity_level, 3])], - ) - - logging.captureWarnings(True) - - # Find the atlas dimension from the output path - atlas_dimension = find_atlas_dimension(output) - atlas_data = get_atlas_data(dimension=atlas_dimension) - atlas_filename = getattr(atlas_data, "maps") - - # Find all existing functional connectivity - input_path = find_derivative(output) - func_filenames, _ = get_func_filenames_bids(input_path, task_filter=task_filter) - all_filenames = list(chain.from_iterable(func_filenames)) - - existing_fc = check_existing_output( - output, - all_filenames, - return_existing=True, - return_output=True, - patterns=FC_PATTERN, - meas=fc_label, - **FC_FILLS, - ) - if not existing_fc: - filename = op.join( - output, - get_bids_savename( - all_filenames[0], patterns=FC_PATTERN, meas=fc_label, **FC_FILLS - ), - ) - raise ValueError( - f"No functional connectivity of type {filename} were found. Please revise the arguments." - ) - - # Load functional connectivity matrices - fc_matrices = [] - for file_path in existing_fc: - fc_matrices.append(np.loadtxt(file_path, delimiter="\t")) - - # Load fMRI duration after censoring - good_timepoints_df = pd.read_csv( - op.join(output, "fMRI_duration_after_censoring.csv") - ) - - # Load IQMs - iqms_df = load_iqms(output, existing_fc, mriqc_path=mriqc_path) - - # Generate group figures - group_report( - good_timepoints_df, - fc_matrices, - iqms_df, - atlas_filename, - output, - ) - - -if __name__ == "__main__": - main() diff --git a/docs/notebooks/load_save.py b/docs/notebooks/load_save.py deleted file mode 100644 index 23a2c5a0..00000000 --- a/docs/notebooks/load_save.py +++ /dev/null @@ -1,449 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2023 The Axon Lab -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Python module for loading and saving fMRI related data""" - -import os -import re -import os.path as op -import pandas as pd -from collections import defaultdict -import logging -from typing import Optional, Union - -from bids import BIDSLayout -import numpy as np - -from pandas import read_csv -from nibabel import loadsave -from bids.layout import parse_file_entities -from bids.layout.writing import build_path -from nilearn.datasets import fetch_atlas_difumo -from nilearn.interfaces.fmriprep.load_confounds import _load_single_confounds_file - -FC_PATTERN: list = [ - "sub-{subject}[/ses-{session}]/func/sub-{subject}" - "[_ses-{session}][_task-{task}][_meas-{meas}]" - "_{suffix}{extension}" -] -FC_FILLS: dict = {"suffix": "connectivity", "extension": ".tsv"} - -TIMESERIES_PATTERN: list = [ - "sub-{subject}[/ses-{session}]/func/sub-{subject}" - "[_ses-{session}][_task-{task}][_desc-{desc}]" - "_{suffix}{extension}" -] -TIMESERIES_FILLS: dict = {"desc": "denoised", "extension": ".tsv"} - -CONFOUND_PATTERN: list = [ - "sub-{subject}[_ses-{session}][_task-{task}][_part-{part}][_desc-{desc}]" - "_{suffix}{extension}" -] -CONFOUND_FILLS: dict = {"desc": "confounds", "suffix": "timeseries", "extension": "tsv"} - - -def separate_by_similar_values( - input_list: list, external_value: Optional[Union[list, np.ndarray]] = None -) -> dict: - """This returns elements of `input_list` with similar values (optionally set by - `external_value`) separated into sub-lists. - - Parameters - ---------- - input_list : list - List to be separated. - external_value : Optional[list], optional - Values corresponding to the elements of `input_list`, by default None - - Returns - ------- - dict - Dictionary where each entry is a list of elements that have similar values and - the keys are the value for each list. - """ - if external_value is None: - external_value = input_list - - data_by_value = defaultdict(list) - - for val, data in zip(external_value, input_list): - data_by_value[val].append(data) - return data_by_value - - -def get_func_filenames_bids( - paths_to_func_dir: str, - task_filter: Optional[list] = None, - ses_filter: Optional[list] = None, - run_filter: Optional[list] = None, -) -> tuple[list[list[str]], list[float]]: - """Return the BIDS functional imaging files matching the specified task and session - filters as well as the first (if multiple) unique repetition time (TR). - - Parameters - ---------- - paths_to_func_dir : str - Path to the BIDS (usually derivatives) directory - task_filter : list, optional - List of task name(s) to consider, by default `None` - ses_filter : list, optional - List of session name(s) to consider, by default `None` - run_filter : list, optional - List of run(s) to consider, by default `None` - - Returns - ------- - tuple[list[list[str]], list[float]] - Returns two lists with: a list of sorted filenames and a list of TRs. - """ - logging.debug("Using BIDS to find functional files...") - - layout = BIDSLayout( - paths_to_func_dir, - validate=False, - ) - - all_derivatives = layout.get( - scope="all", - return_type="file", - extension=["nii.gz", "gz"], - suffix="bold", - task=task_filter or [], - session=ses_filter or [], - run=run_filter or [], - ) - - if not all_derivatives: - raise ValueError( - f"No functional derivatives were found under {paths_to_func_dir} with the following filters:" - f"\nExtension: ['nii.gz', 'gz']" - f"\nSuffix: bold" - f"\nTask: {task_filter or []}" - f"\nSession: {ses_filter or []}" - f"\nRun: {run_filter or []}" - ) - - affines = [] - for file in all_derivatives: - affines.append(loadsave.load(file).affine) - - similar_fov_dict = separate_by_similar_values( - all_derivatives, np.array(affines)[:, 0, 0] - ) - if len(similar_fov_dict) > 1: - logging.warning( - f"{len(similar_fov_dict)} different FoV found ! " - "Files with similar FoV will be computed together. " - "Computation time may increase." - ) - - separated_files = [] - separated_trs = [] - for file_group in similar_fov_dict.values(): - t_rs = [] - for file in file_group: - t_rs.append(layout.get_metadata(file)["RepetitionTime"]) - - similar_tr_dict = separate_by_similar_values(file_group, t_rs) - separated_files += list(similar_tr_dict.values()) - separated_trs += list(similar_tr_dict.keys()) - - if len(similar_tr_dict) > 1: - logging.warning( - "Multiple TR values found ! " - "Files with similar TR will be computed together. " - "Computation time may increase." - ) - - return separated_files, separated_trs - - -def get_bids_savename(filename: str, patterns: list, **kwargs) -> str: - """Return the BIDS filename following the specified patterns and modifying the - entities from the keywords arguments. - - Parameters - ---------- - filename : str - Name of the original BIDS file - patterns : list, optional - Patterns for the output file, by default FC_PATTERN - - Returns - ------- - str - BIDS output filename. - """ - entity = parse_file_entities(filename) - - for key, value in kwargs.items(): - entity[key] = value - - bids_savename = build_path(entity, patterns) - - return str(bids_savename) - - -def get_atlas_data(atlas_name: str = "DiFuMo", **kwargs) -> dict: - """Fetch the specifies atlas filename and data. - - Parameters - ---------- - atlas_name : str, optional - Name of the atlas to fetch, by default "DiFuMo" - - Returns - ------- - dict - Dictionary with keys "maps" (filename) and "labels" (ROI labels). - """ - logging.info("Fetching the DiFuMo atlas ...") - - if kwargs["dimension"] not in [64, 128, 512]: - logging.warning( - "Dimension for DiFuMo atlas is different from 64, 128 or 512 ! Are you" - "certain you want to deviate from those optimized modes? " - ) - - return fetch_atlas_difumo(legacy_format=False, **kwargs) - - -def find_atlas_dimension(path: str, atlas_name: str = "DiFuMo") -> int: - """Fetch the atlas dimension from the path where the functional connectivity are saved. - Parameters - ---------- - path : str - Path to the directory where functional connectivity are saved. - atlas_name : str, optional - Name of the atlas to fetch, by default "DiFuMo" - - Returns - ------- - int - Atlas dimension. - """ - - # Using regular expression to extract the number of dimensions - dimension_match = re.search(rf"{atlas_name}(\d+)", path) - - if dimension_match: - return int(dimension_match.group(1)) - else: - raise ValueError( - f"The output path {path} does not contain the expected pattern: {atlas_name} followed by digits." - ) - - -def find_derivative(path: str, derivatives_name: str = "derivatives") -> str: - """Find the corresponding BIDS derivative folder (if existing, otherwise it will be - created). - - Parameters - ---------- - path : str - Path to the BIDS (usually derivatives) dataset. - derivatives_name : str, optional - Name of the derivatives folder, by default "derivatives" - - Returns - ------- - str - Absolute path to the derivative folder. - """ - splitted_path = path.split("/") - try: - while derivatives_name not in splitted_path[-1]: - splitted_path.pop() - except IndexError: - logging.warning( - f'"{derivatives_name}" could not be found on path - ' - f'creating at: {op.join(path, derivatives_name)}"' - ) - return op.join(path, derivatives_name) - - return "/".join(splitted_path) - - -def find_mriqc(path: str) -> str: - """Find the path to the MRIQC folder (if existing, otherwise it will be - created). - - Parameters - ---------- - path : str - Path to the BIDS (usually derivatives) dataset. - - Returns - ------- - str - Absolute path to the mriqc folder. - """ - logging.debug("Searching for MRIQC path...") - derivative_path = find_derivative(path) - - folders = [ - f for f in os.listdir(derivative_path) if op.isdir(op.join(derivative_path, f)) - ] - - mriqc_path = [f for f in folders if "mriqc" in f] - if len(mriqc_path) >= 2: - logging.warning( - f"More than one mriqc derivative folder was found: {mriqc_path}" - f"The first instance {mriqc_path[0]} is used for the computation." - "In case you want to use another mriqc derivative folder, use the --mriqc-path flag" - ) - return op.join(derivative_path, mriqc_path[0]) - - -def reorder_iqms(iqms_df: pd.DataFrame, fc_paths: list[str]): - """Reorder the IQMs according to the list of filenames - - Parameters - ---------- - iqms_df : pd.Dataframe - Dataframe containing the IQMs value for each image - fc_paths : list [str] - List of paths to the functional connectivity matrices - - Returns - ------- - panda.df - Dataframe containing the IQMs dataframe with reordered rows. - """ - iqms_df[["subject", "session", "task"]] = iqms_df["bids_name"].str.extract( - r"sub-(\d+)_ses-(\d+)_task-(\w+)_" - ) - entities_list = [parse_file_entities(filepath) for filepath in fc_paths] - entities_df = pd.DataFrame(entities_list) - - return pd.merge( - entities_df, iqms_df, on=["subject", "session", "task"], how="inner" - ) - - -def load_iqms( - derivative_path: str, - fc_paths: list[str], - mriqc_path: str = None, - mod="bold", - iqms_name: list = ["fd_mean", "fd_num", "fd_perc"], -) -> str: - """Load the IQMs and match their order with the corresponding functional matrix. - - Parameters - ---------- - derivative_path : str - Path to the BIDS dataset's derivatives. - fc_paths : list [str] - List of paths to the functional connectivity matrices - mriqc_path : str, optional - Name of the MRIQC derivative folder, by default None - mod : str, optional - Load the IQMs of that modality - iqms_name : list, optional - Name of the IQMs to find, by default ["fd_mean", "fd_num", "fd_perc"] - - Returns - ------- - panda.df - Dataframe containing the IQMs loaded from the derivatives folder. - """ - # Find the MRIQC folder - if mriqc_path is None: - mriqc_path = find_mriqc(derivative_path) - - # Load the IQMs from the group tsv - iqms_filename = op.join(mriqc_path, f"group_{mod}.tsv") - iqms_df = read_csv(iqms_filename, sep="\t") - # If multi-echo dataset and the IQMs of interest are motion-related, keep only the IQMs from the second echo - if "echo" in iqms_df["bids_name"][0] and all("fd" in i for i in iqms_name): - iqms_df = iqms_df[iqms_df["bids_name"].str.contains("echo-2")] - logging.info( - "In the case of a multi-echo dataset, the IQMs of the second echo are considered." - ) - - # Match the order of the rows in iqms_df with the corresponding FC - iqms_df = reorder_iqms(iqms_df, fc_paths) - - # Keep only the IQMs of interest - iqms_df = iqms_df[iqms_name] - - return iqms_df - - -def check_existing_output( - output: str, - func_filename: list[str], - return_existing: bool = False, - return_output: bool = False, - **kwargs, -) -> tuple[list[str], list[str]]: - """Check for existing output. - - Parameters - ---------- - output : str - Path to the output directory - func_filename : list[str] - Input files to be processed - return_existing : bool, optional - Condition to return the list of input corresponding to existing outputs, by default - False - return_output: bool, optional - Condition to return the path of existing outputs, by default False - - Returns - ------- - tuple[list[str], list[str]] - List of missing data path (optionally, a second list of existing data path) - """ - if return_output == True and return_existing == False: - raise ValueError( - "Setting return_output=True in check_existing_output requires return_existing=True." - ) - - missing_data_filter = [ - not op.exists(op.join(output, get_bids_savename(filename, **kwargs))) - for filename in func_filename - ] - - missing_data = np.array(func_filename)[missing_data_filter] - logging.debug( - f"\t{sum(missing_data_filter)} missing data found for files:" - "\n\t" + "\n\t".join(missing_data) - ) - - if return_existing: - if return_output: - existing_output = [ - op.join(output, get_bids_savename(filename, **kwargs)) - for filename in func_filename - if op.exists(op.join(output, get_bids_savename(filename, **kwargs))) - ] - return existing_output - else: - existing_data = np.array(func_filename)[ - [not fltr for fltr in missing_data_filter] - ] - return missing_data.tolist(), existing_data.tolist() - - return missing_data.tolist() diff --git a/docs/notebooks/reports.py b/docs/notebooks/reports.py deleted file mode 100644 index 57fbf1f5..00000000 --- a/docs/notebooks/reports.py +++ /dev/null @@ -1,453 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2023 The Axon Lab -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Python module for functional connectivity visual reports""" - -import logging -import os.path as op - -import matplotlib.pyplot as plt -import nibabel as nib -import numpy as np -import pandas as pd -import plotly.offline as pyo -import seaborn as sns -from nireports.assembler.report import Report -from scipy.stats import pearsonr, ks_2samp -from time import strftime -from uuid import uuid4 - - -FIGURE_PATTERN: list = [ - "sub-{subject}/figures/sub-{subject}[_ses-{session}]" - "[_task-{task}][_meas-{meas}][_desc-{desc}]" - "_{suffix}{extension}", - "sub-{subject}/figures/sub-{subject}[_ses-{session}]" - "[_task-{task}][_desc-{desc}]_{suffix}{extension}", -] -FIGURE_FILLS: dict = {"extension": "png"} - -TS_FIGURE_SIZE: tuple = (50, 25) -FC_FIGURE_SIZE: tuple = (70, 45) -LABELSIZE: int = 42 -N_PERMUTATION: int = 10000 -ALPHA = 0.05 -PERCENT_MATCH_CUT_OFF = 95 -DURATION_CUT_OFF = 300 - -def group_report_censoring(good_timepoints_df, output) -> None: - """ - Generate a group report about censoring. - - This function generates an HTML report that includes an interactive scatterplot - showing the fMRI duration after censoring. The scatterplot includes - error bars for the confidence interval and a red line indicating a duration cutoff. - - Parameters: - ----------- - good_timepoints_df: pd.Dataframe - A DataFrame containing information the fMRI duration after censoring. - output : str - Path to the output directory - """ - filenames = good_timepoints_df["filename"] - durations = good_timepoints_df["duration"] - - # Constructing the data for the plot - # Add jitter to x values - jitter = 0.2 # adjust this value to change the amount of jitter - x_values = [1 + np.random.uniform(-jitter, jitter) for _ in range(len(durations))] - data = [ - { - "x": x_values, - "y": durations, - "text": filenames, - "mode": "markers", - "type": "scatter", - "hoverinfo": "text", - "marker": {"opacity": 0.5}, - } - ] - - # Adding a red line at 5 minutes - red_line = { - "type": "line", - "x0": 0, - "y0": DURATION_CUT_OFF, - "x1": 1.5, - "y1": DURATION_CUT_OFF, - "line": {"color": "red", "width": 3, "dash": "dashdot"}, - } - - # Layout settings - layout = { - "hovermode": "closest", - "title": "Duration of fMRI signal after censoring", - "yaxis": {"title": "Duration [s]"}, - "xaxis": {"showticklabels": False, "range": [0.5, 1.5]}, - "shapes": [red_line], - "width": 600, - "height": 600, - "font": {"size": 16}, - "annotations": [ - { - "x": 0.8, - "y": DURATION_CUT_OFF - DURATION_CUT_OFF / 55, - "xref": "x", - "yref": "y", - "text": f"QC cutoff of {DURATION_CUT_OFF/60} min", - "showarrow": False, - "font": {"color": "red"}, - } - ], - } - - fig = {"data": data, "layout": layout} - - # Save the plot as an HTML file - pyo.plot( - fig, - filename=op.join(output, "reportlets", "group_desc-censoring_bold.html"), - auto_open=False, - ) - - -def group_report_fc_dist( - fc_matrices: list[np.ndarray], - output: str, -) -> None: - """Plot and save the functional connectivity density distributions. - - Parameters - ---------- - fc_matrices : list[np.ndarray] - List of functional connectivity matrices - output : str - Path to the output directory - """ - - _, ax = plt.subplots(figsize=FC_FIGURE_SIZE) - - for fc_matrix in fc_matrices: - sns.displot( - fc_matrix, - kind="kde", - fill=True, - linewidth=0.5, - legend=False, - palette="ch:s=.25,rot=-.25", - ) - - ax.tick_params(labelsize=LABELSIZE) - - # Ensure the labels are within the figure - plt.tight_layout() - - savename = op.join("reportlets", "group_desc-fcdist_bold.svg") - - logging.debug("Saving functional connectivity distribution visual report at:") - logging.debug(f"\t{op.join(output, savename)}") - - plt.savefig(op.join(output, savename)) - plt.close() - - -def group_reportlet_fc_dist( - fc_matrices: list[np.ndarray], - output: str, -) -> None: - """Plot and save the functional connectivity density distributions. - - Parameters - ---------- - fc_matrices : list[np.ndarray] - List of functional connectivity matrices - output : str - Path to the output directory - """ - - _, ax = plt.subplots(figsize=FC_FIGURE_SIZE) - - for fc_matrix in fc_matrices: - sns.displot( - fc_matrix, - kind="kde", - fill=True, - linewidth=0.5, - legend=False, - palette="ch:s=.25,rot=-.25", - ) - - ax.tick_params(labelsize=LABELSIZE) - - # Ensure the labels are within the figure - plt.tight_layout() - - savename = op.join("reportlets", "group_desc-fcdist_bold.svg") - - logging.debug("Saving functional connectivity distribution visual report at:") - logging.debug(f"\t{op.join(output, savename)}") - - plt.savefig(op.join(output, savename)) - plt.close() - - -def group_reportlet_qc_fc( - fc_matrices: list[np.ndarray], - iqms_df: pd.DataFrame, - output: str, -) -> dict: - """Plot and save the QC-FC distributions. - - Parameters - ---------- - fc_matrices : list[np.ndarray] - List of functional connectivity matrices - iqms_df : pd.Dataframe - Dataframe containing the image quality metrics to correlate with - output : str - Path to the output directory - """ - - # Stack the list of arrays into a 3D matrix - fc_matrices = np.stack(fc_matrices, axis=2) - - # Keep only upper triangle as the matrix is symmetric - upper_triangle_indices = np.triu_indices(fc_matrices.shape[0], k=1) - fc_matrices = fc_matrices[upper_triangle_indices] - - if fc_matrices.shape[1] != iqms_df.shape[0]: - raise ValueError( - "The number of functional connectivity matrices and IQMs do not match." - ) - - if fc_matrices.shape[1] == 1: - raise ValueError( - "We need at least two functional connectivity matrices to be able to compute its correlation with IQMs." - ) - - fig, axs = plt.subplots(1, 3, figsize=FC_FIGURE_SIZE) - - # Iterate over each IQM - qc_fc_dict = dict() - for i, iqm_column in enumerate(iqms_df.columns): - # Create an empty list to store correlations for each edge - qc_fcs = [] - - # Iterate over each edge - logging.debug("Compute QC-FC correlation for each edge.") - for e in range(fc_matrices.shape[0]): - qc_fc = np.corrcoef(fc_matrices[e, :], iqms_df[iqm_column])[0, 1] - qc_fcs.append(qc_fc) - - # Create a density distribution plot for the current IQM - logging.debug("Create the density distribution plot.") - sns.kdeplot( - qc_fcs, fill=True, label="QC-FC distribution", linewidth=3, ax=axs[i] - ) - - # Save the QC-FC distributions in a dictionary - qc_fc_dict[iqm_column] = qc_fcs - - ## Permutation analyses - logging.debug("Compute QC-FC distribution under the null hypothesis.") - correlations_null = [] - for e in range(fc_matrices.shape[0]): - for _ in range(N_PERMUTATION): - permuted_fc = fc_matrices[ - e, np.random.default_rng(seed=42).permutation(fc_matrices.shape[1]) - ] - # Correlation under null hypothesis - correlation = np.corrcoef(permuted_fc, iqms_df[iqm_column])[0, 1] - correlations_null.append(correlation) - - # Create a density distribution plot for null distribution - logging.debug("Create the density distribution plot for the null distribution.") - sns.kdeplot( - correlations_null, - fill=False, - color="red", - label="Dist under null hypothesis", - linewidth=3, - linestyle="dashed", - ax=axs[i], - ) - plt.legend(fontsize=LABELSIZE + 2) - - # Compute percent match between the two distributions - logging.debug("Compute percent match between the two distributions.") - ks_statistic, _ = ks_2samp(qc_fcs, correlations_null) - percent_match_ks = (1 - ks_statistic) * 100 - - # Plot the box in red if the correlation is significant - facecolor = "red" if percent_match_ks < PERCENT_MATCH_CUT_OFF else "grey" - axs[i].text( - 0.08, - 0.9, - f"QC-FC% = {percent_match_ks:.0f}", - fontsize=LABELSIZE - 2, - bbox=dict(facecolor=facecolor, alpha=0.4, boxstyle="round,pad=0.5"), - transform=axs[i].transAxes, - ) - axs[i].tick_params(labelsize=LABELSIZE) - axs[i].set_title(iqm_column, fontsize=LABELSIZE + 2) - - # Turn off individual plot y-labels - for ax in axs: - ax.set_ylabel("") - - fig.supylabel("Density", fontsize=LABELSIZE + 2) - fig.suptitle("QC-FC correlation distributions", fontsize=LABELSIZE + 4) - - savename = op.join("reportlets", "group_desc-qcfc_bold.svg") - - logging.debug("Saving QC-FC visual report at:") - logging.debug(f"\t{op.join(output, savename)}") - - plt.savefig(op.join(output, savename)) - plt.close() - - return qc_fc_dict - - -def compute_distance(atlas_path: str) -> np.array: - """Compute the euclidean distance between the center of mass of the atlas regions. - - Parameters - ---------- - atlas_path : str - Path to the atlas Nifti - Returns - ------- - np.array - Distance matrix - """ - from scipy.ndimage.measurements import center_of_mass - - logging.debug("Compute distance matrix from atlas centers of mass") - - atlas_img = nib.load(atlas_path) - atlas_data = atlas_img.get_fdata() - # Array to store the center of mass of each region - centroids = np.zeros((atlas_data.shape[3], 3), dtype=float) - for r in range(atlas_data.shape[3]): - centroids[r, ...] = np.array(center_of_mass(atlas_data[..., r])) - - # Compute Euclidean distance matrix using broadcasting - diff = centroids[:, np.newaxis, :] - centroids - distance_matrix = np.sqrt(np.sum(diff**2, axis=-1)) - - return distance_matrix - - -def group_reportlet_qc_fc_euclidean( - qc_fc_dict: dict, atlas_path: str, output: str -) -> None: - """Plot and save the correlations between QC-FC and euclidean distance. - The euclidean distance is computed from the centers of mass of each region. - - Parameters - ---------- - qc_fc_dict : dict - Dictionary containing the qc-fc distribution over the edges for different IQMs. - atlas_path : str - Path to the atlas Nifti - output : str - Path to the output directory - """ - d = compute_distance(atlas_path) - # Keep only upper triangle as the matrix is symmetric - upper_triangle_indices = np.triu_indices(d.shape[0], k=1) - d = d[upper_triangle_indices] - - # Iterate over the IQMs - fig, axs = plt.subplots(1, 3, figsize=FC_FIGURE_SIZE) - for i, iqm in enumerate(qc_fc_dict.keys()): - qc_fc = qc_fc_dict[iqm] - - logging.debug("Compute the correlation between QC-FC and euclidean distance.") - correlation, p_value = pearsonr(qc_fc, d) - - # Plot the box in red if the correlation is significant - facecolor = "red" if p_value < ALPHA else "grey" - axs[i].text( - 0.15, - 0.97, - f"Correlation = {correlation:.2f}, p-value = {p_value:.4f}", - fontsize=LABELSIZE - 2, - bbox=dict(facecolor=facecolor, alpha=0.4, boxstyle="round,pad=0.5"), - transform=axs[i].transAxes, - ) - axs[i].scatter(qc_fc, d) - - # Plot trend line - axs[i].plot( - np.unique(qc_fc), - np.poly1d(np.polyfit(qc_fc, d, 1))(np.unique(qc_fc)), - "r-", - linewidth=3, - ) - axs[i].tick_params(labelsize=LABELSIZE) - axs[i].set_title(iqm, fontsize=LABELSIZE + 2) - - fig.suptitle( - "Dependence between euclidean distance and QC-FC", fontsize=LABELSIZE + 4 - ) - fig.supxlabel("QC-FC correlation of edge between nodes", fontsize=LABELSIZE + 2) - fig.supylabel("Euclidean distance separating nodes", fontsize=LABELSIZE + 2) - # Ensure the labels are within the figure - # plt.tight_layout() - - savename = op.join("reportlets", "group_desc-qcfcvseuclidean_bold.svg") - - logging.debug("Saving QC-FC vs euclidean distance visual report at:") - logging.debug(f"\t{op.join(output, savename)}") - - plt.savefig(op.join(output, savename)) - plt.close() - -def group_report( - good_timepoints_df: pd.DataFrame, - fc_matrices: list[np.ndarray], - iqms_df: pd.DataFrame, - atlas_filename: str, - output: str, -) -> None: - """Generate a group report.""" - - # Generate each reportlets - group_report_censoring(good_timepoints_df, output) - group_reportlet_fc_dist(fc_matrices, output) - qc_fc_dict = group_reportlet_qc_fc(fc_matrices, iqms_df, output) - group_reportlet_qc_fc_euclidean(qc_fc_dict, atlas_filename, output) - - # Assemble reportlets into a single HTML report - logging.debug("Assemble the group report into a single HTML report.") - - run_uuid = "{}_{}".format(strftime("%Y%m%d-%H%M%S"), uuid4()) - robj = Report( - output, - run_uuid, - out_filename="group_report.html", - bootstrap_file=op.join("data", "reports-spec.yml"), - ) - robj.generate_report() \ No newline at end of file From 9bf54fb3be8d5d3e1a213c7caad662fc4215d2ba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=A9line=20Provins?= Date: Fri, 17 May 2024 21:04:28 +0200 Subject: [PATCH 4/5] fix: the fc distribution plot was not displaying one distribution per session but one distribution per FC matrix row. This has been corrected. enh: add a mention in the README pointing to the tutorial --- README.rst | 2 + docs/notebooks/Tutorial.ipynb | 149 +++++++++++++++++---------- docs/notebooks/data/reports-spec.yml | 2 +- 3 files changed, 97 insertions(+), 56 deletions(-) diff --git a/README.rst b/README.rst index c59f666e..6c450503 100644 --- a/README.rst +++ b/README.rst @@ -29,3 +29,5 @@ * **Assembler**: end-user *NiPreps* write out reportlets to a predetermined folder, which is then queried by the assembler using *PyBIDS*. The assembler follows a *report specification* in YAML format, which states the query to find specific reportlets and their corresponding metadata and text annotations. As a result, one HTML file with a concatenation of reportlets is produced. + + A tutorial is available under the `docs/notebooks` directory of the repository to help you build your own visual report using *NiReports*. diff --git a/docs/notebooks/Tutorial.ipynb b/docs/notebooks/Tutorial.ipynb index aef55002..af9ebe68 100644 --- a/docs/notebooks/Tutorial.ipynb +++ b/docs/notebooks/Tutorial.ipynb @@ -16,10 +16,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "# Silence warnings\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=UserWarning)\n", + "\n", "# Load data\n", "import glob\n", "import os\n", @@ -35,16 +39,25 @@ "good_timepoints_df = pd.read_csv(op.join(output, \"fMRI_duration_after_censoring.csv\"))\n", "\n", "# Load functional connectivity matrices\n", - "connectivity_files = glob.glob(op.join(output, '**', '*connectivity.tsv'), recursive=True)\n", - "\n", "fc_matrices = []\n", - "for connectivity_file in connectivity_files:\n", - " fc_matrices.append(np.loadtxt(connectivity_file, delimiter=\"\\t\"))\n", + "connectivity_files = glob.glob(op.join(output, '**', '*connectivity.tsv'), recursive=True)\n", + "for idx, connectivity_file in enumerate(connectivity_files):\n", + " fc_matrix = np.loadtxt(connectivity_file, delimiter=\"\\t\")\n", + " flattened = fc_matrix.reshape(-1)\n", + " session = [f\"Session {idx+1}\"] * len(flattened)\n", + " fc_matrices.extend(zip(flattened, session))\n", "\n", "# Create folder to store reportlets\n", "os.makedirs(op.join(output, \"reportlets\"), exist_ok=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reportlets" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -54,52 +67,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "# Silence futureWarnings\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import os.path as op\n", "\n", + "import matplotlib as mpl\n", + "\n", "FC_FIGURE_SIZE: tuple = (70, 45)\n", - "LABELSIZE: int = 42\n", + "LABELSIZE: int = 60\n", "\n", - "def group_reportlet_fc_dist(\n", - " fc_matrices: list[np.ndarray],\n", - " output: str,\n", - ") -> None:\n", + "def group_reportlet_fc_dist(fc_matrices: list[np.ndarray], output: str) -> None:\n", " \"\"\"Plot and save the functional connectivity density distributions.\n", "\n", " Parameters\n", @@ -109,32 +94,51 @@ " output : str\n", " Path to the output directory\n", " \"\"\"\n", - "\n", " _, ax = plt.subplots(figsize=FC_FIGURE_SIZE)\n", + " ax.tick_params(labelsize=LABELSIZE)\n", + " mpl.rcParams['legend.fontsize'] = LABELSIZE\n", "\n", - " for fc_matrix in fc_matrices:\n", - " sns.displot(\n", - " fc_matrix,\n", - " kind=\"kde\",\n", - " fill=True,\n", - " linewidth=0.5,\n", - " legend=False,\n", - " palette=\"ch:s=.25,rot=-.25\",\n", - " )\n", + " fc_df = pd.DataFrame(fc_matrices, columns=[\"value\", \"session\"])\n", "\n", - " ax.tick_params(labelsize=LABELSIZE)\n", + " sns.kdeplot(\n", + " data=fc_df,\n", + " x=\"value\",\n", + " hue=\"session\",\n", + " fill=True,\n", + " linewidth=0.5,\n", + " alpha=0.5,\n", + " palette=\"ch:s=.25,rot=-.25\",\n", + " )\n", + "\n", + " plt.tick_params(labelsize=LABELSIZE)\n", + " plt.xlabel('Value', fontsize=LABELSIZE+4)\n", + " plt.ylabel('Density', fontsize=LABELSIZE+4)\n", "\n", " # Ensure the labels are within the figure\n", " plt.tight_layout()\n", "\n", " # Note that we save the image under a BIDS-compatible name\n", - " savename = op.join(\"reportlets\", \"group_desc-fcdist_bold.svg\")\n", + " savename = op.join(\"reportlets\", \"group_desc-fcdist_bold.png\")\n", "\n", " plt.savefig(op.join(output, savename))\n", " plt.close()\n", "group_reportlet_fc_dist(fc_matrices, output)" ] }, + { + "attachments": { + "group_desc-fcdist_bold.png": { + "image/png": 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WETF06NAsTQMAQK6R+SvuxRdfTLlc7a677oq77rorI7843blzZ8vVAAAokcwPAADVm8yffY888sg2l6tFRFxzzTWx9957Z3kiAACqM5m/fHr27JnR5WoREbVr147HHnss+vfvn1i3cuXKGDNmTEZnAQAgd8n85dOwYcP4xS9+EXPnzo277747rcvVIn583ceNGxfbb799Yt38+fPj0UcfTWtvAACqF5k/PfLz86NPnz5x5ZVXxiOPPBIzZsyIlStXxjvvvBN33nlnnHPOObHnnntGXl5eZY8KAECW3HTTTYmLFlq2bBljxowp16KFiIhGjRrFE088Ec2bNy+xZsuWLXHzzTeX6/7KMmPGjBg9enRizTXXXFPu5WoRET/96U9j2LBhiTWjRo2KmTNnlrsHAADVn8xfcfXr14/evXvHxRdfHH/5y19i6tSpsXr16vjggw/i3nvvjYKCgujWrVvUrl27skcFqHQWrFUzDRo0iKuuuiqx5pFHHomxY8dWqM8TTzwRI0eOTKwZNmxY1K9fv0J9AEjt7bffjg8//DCx5t57742mTZtWqE/v3r3j8ssvT6x54IEHYvXq1RXqUx0VFxdHYWFhYk2vXr2ie/fu2RkIAICcIvNX3Pr16+Oyyy5LrLn++utT/ns1AADIBJkfAACqN5k/+77//vv4+c9/vs2z3XbbLeWXYgEAQFnI/FVfrVq14v77749GjRol1j3//PNZmggAgFwi85dd7dq1Y8iQIfHFF1/EbbfdlnIBWkXsuuuuMWrUqJR1I0aMyNgMAADkNpm/Yvbff/8YMWJEfPrpp7Fq1ap477334k9/+lMMHDgwOnfuXO6ldAAA5L45c+bE008/nVjz29/+NnbZZZcK9Wnfvn3cdNNNiTVPPvlkzJs3r0J9sun222+P4uLiEs/bt28fv/nNbyrc55Zbbom2bduWeF5cXBy33357hfsAAFA9yfzlt8cee8SDDz4YkydPjtWrV8ekSZPiv/7rv2Lo0KGx7777Rp06dSp7RIAqyU8eq6Fhw4alDAuDBg2KiRMnluv+Dz74IIYOHZpY0759e19IDZAld999d+J5v3794vjjj09Lr+uvvz6aNGlS4vmKFSvikUceSUuv6mT8+PEp32Cn+mcrAAA1l8xfcbfffnssWLCgxPNjjjkm5Q/OAAAgU2R+AACo3mT+7Bs2bFgsXbp0m2f3339/NGzYMMsTAQBQncn8uaFdu3YxaNCgxJp33nknS9MAAJBLZP6ya926dTz00EOx8847Z6Vfv3794uSTT06see+99+L777/PyjwAAOQWmb9i+vTpE4MHD4699947ateuXdnjAABQhdx3331RVFRU4nmnTp3iwgsvTEuvSy+9NHbbbbcSz4uKiuK+++5LS69MW7p0aYwcOTKx5uabb44GDRpUuFd+fn7K79t57LHHYtmyZRXuBQBA9SPzl1+XLl3iwgsvjF69ekXdunUrexyAnGHBWjWUn58fd955Z2LN6tWr45hjjokXX3yxTHc/99xzceyxx8aaNWsS6+644w5fQAGQBStWrIiXXnopseYXv/hF2vo1b9485SKwxx57LG39qovhw4cnnjds2DDOOeecLE0DAEAukfkrbunSpXHbbbeVeN6oUaN46KGHsjgRAAD8D5kfAACqN5k/+8aNG1fiL5SfddZZceyxx2Z5IgAAqjOZP7eccsopiefLli2LJUuWZGcYAABygsyfO6699tqUNW+//XYWJgEAIJfI/AAAkBlFRUUxatSoxJqrr746bUt669SpE1deeWVizciRI2Pr1q1p6ZdJY8aMiU2bNpV4vvPOO8fZZ5+dtn7nnntubL/99iWeb9q0KZ588sm09QMAoHqQ+QGoDBasVVNnnHFGykUtK1eujJNOOinOPffcmDlzZmLtjBkz4qyzzopTTjklVq1alVh77rnnxumnn17mmQEouyeffDI2btxY4vkuu+wSxx13XFp7ptr6/cEHH8RXX32V1p65bPny5TF27NjEmtNPPz2aNWuWpYkAAMglMn/F3X///bF27doSz4cNGxZt27bN4kQAAPA/ZH4AAKjeZP7sWrNmTVx88cXbPGvevHn88Y9/zPJEAABUdzJ/bjnooINS1nzzzTdZmAQAgFwh8+eOgw46KLbbbrvEmk8//TRL0wAAkCtkfgAAyIwJEybEt99+W+J5gwYN4rzzzktrz0GDBkW9evVKPP/mm2/irbfeSmvPTHj88ccTzwcPHhx169ZNW7/69evHoEGDKjQTAAA1j8wPQGWwYK0ae/DBB2PPPfdMrCkuLo6RI0dG586do2fPnnH55ZfHHXfcEQ8++GD853/+Z1x22WWx7777RteuXWPMmDEpe+61117xwAMPpOspAJDCCy+8kHg+YMCAyMvLS2vPzp07R/fu3RNrXnzxxbT2zGUjR46MDRs2JNYMGTIkS9MAAJBrZP6K2bhxY9x3330lnjdo0CB+/vOfZ3EiAAD4ZzI/AABUbzJ/dv3qV7+K+fPnb/Ps97//feywww5ZnggAgOpO5s8tjRs3jkaNGiXWrF27NkvTAACQC2T+3FG7du3o06dPYs2cOXOyNA0AALlC5gcAgMxIlbX79+8fTZo0SWvP5s2bx/HHH59Yk2quyrZ06dJ4//33E2vOOuustPc9++yzE8/fe++9WLZsWdr7AgCQu2R+ACqDBWvVWOPGjePVV1+NXXbZpVT1U6dOjfvuuy+uvfbauPjii+MXv/hF3H///fHxxx+X6vHt2rWLV199NRo3blyRsQEopS1btsTbb7+dWNO/f/+M9E517+uvv56RvrnooYceSjzfbbfdom/fvtkZBgCAnCLzV9zLL78c3333XYnnAwYMiJYtW2ZxIgAA+B8yPwAAVG8yf3Z9+OGHce+9927z7KCDDoqLLrooyxMBAFDdyfy5qWnTponn+fn5WZoEAICqTubPPTvssEPi+YoVK7IzCAAAOUHmBwCAzHnjjTcSz2XtbRs/fnwUFxeXeN6uXbvYe++909533333jZ133rnE861bt8aECRPS3hcAgNwl8wNQGSxYq+bat28fEyZMiI4dO2a0z+677x4TJkyIdu3aZbQPAP9j0qRJsWrVqhLPGzZsGH369MlI76OPPjrx/O23344tW7ZkpHcumTZtWkydOjWxZsiQIZGXl5eliQAAyCUyf8WNGjUq8fycc87J0iQAAPCvZH4AAKjeZP7s2bx5c5x//vmxdevWfzmrU6dOPPjggz6fAwBA2sn8uae4uDh++OGHxJpWrVplaRoAAKo6mT/3tG7dOvF8/fr1WZoEAIBcIPMDAEBmfPvtt/H5558n1hx11FEZ6Z0qa0+fPj0WL16ckd7pMH78+MTzTL1upbk71QINAABqDpkfgMpiwVoNsPvuu8ekSZPi2GOPzcj9xx13XEyaNCnjS9wA+GcTJ05MPO/du3fUr18/I70POOCAqF27donna9asSfkmtyZ46KGHEs9r164dgwcPzs4wAADkHJm/YtatWxcvvvhiieeNGzeOvn37Zm8gAAD4P2R+AACo3mT+7Ln11lvjs88+2+bZNddcE926dcvyRAAA1AQyf+5ZvHhxbN68ucTzhg0bxg477JDFiQAAqMpk/tyzbt26xPMGDRpkaRIAAHKBzA8AAJmRKmvvsssuscsuu2Skd4cOHWLHHXdMrJk0aVJGeqdDqtfu4IMPzljvVAumq/LrBgBAdsn8AFQWC9ZqiBYtWsS4ceOisLAw2rRpk5Y727RpEw8//HC88sor0bx587TcCUDpTZkyJfG8Z8+eGevdsGHD6NKlS2LN1KlTM9Y/F2zcuDFGjhyZWHPsscfGzjvvnKWJAADINTJ/xbz77ruxfv36Es8PPvjgjP1yCwAAlIbMDwAA1ZvMnx0zZ86MW265ZZtnHTp0iOuvvz7LEwEAUFPI/Lln3LhxiecHHnhg1K1bN0vTAABQ1cn8uWfhwoWJ5y1atMjSJAAA5AKZHwAAMqMys3bEj8uSk1TVrL1p06aYPn16Yk0mX7tUr9tnn30Wmzdvzlh/AAByh8wPQGWxYK2GGTRoUMyZMyfuu+++6Ny5c7nu6NKlS9x3330xd+7c+NnPfpbmCQEorWnTpiWe77PPPhntn+r+mv5GcuzYsbFs2bLEmqFDh2ZpGgAAcpHMXzFvvfVW4vkBBxyQnUEAAKAEMj8AAFRvMn/mFRcXxwUXXBAbN27c5vn9998f+fn5WZ4KAICaQubPPaNGjUo8P+6447I0CQAAuUDmzz2pvsCrY8eOWZoEAIBcIPMDAEBmyNrlM3369MQFZrVr1065qLki9t5776hVq+SvKd+0aVPMmDEjY/0BAMgdMj8AlaVOZQ9A9jVq1CguvfTSuPTSS2P27Nkxbty4mDJlSkyfPj0WLVoUq1evjnXr1kV+fn40adIk2rZtG126dImePXvG8ccfH506darspwBARHzxxReJ55n+/9e777574nmq+aq74cOHJ563bt06TjzxxCxNAwBALpL5K+add95JPO/Zs2ep7vn2229j5syZsWzZsli9enXUq1cv8vPzY4cddogOHTrEDjvskI5xAQCogWT+7Fu0aFHMmzcvFi9eHGvXro3i4uJo2LBhNG7cOHbaaado27ZttGrVqrLHBACgmpD5M++BBx6Id999d5tnAwYMiOOPPz7LEwEAUJPI/Lnl+eefj9dff73E8/z8/Dj//POzOBEAAFWdzJ9bPv/885g7d25izd57752laQAAyAUyPwAAZMbs2bMTz2XtbUv1urVv3z7q1auXsf716tWLXXbZJebPn19izRdffBHdu3fP2AwAAOQGmR+AymLBWg23xx57xB577FHZYwBQRosXL47169cn1qR6o1dRqe5P9YsI1dmCBQti/PjxiTU/+9nPom7dulmaCACAXCPzV0xxcXF88skniTUlPb+tW7fGSy+9FM8880y88sor8d133yXe07p16zjooIPiuOOOi1NPPdXCNQAASkXmz47169fHn/70p3j99dfjww8/jB9++CHlY1q3bh29evWKQw45JPr37x/77rtv5gcFAKDakfkzb9GiRXHddddt86xZs2bxpz/9KcsTAQBQk8j8uWXmzJlx0UUXJdZccskl0bJlyyxNBABAVSfz556RI0emrDn88MOzMAkAALlA5gcAgMwoLi6OefPmJdZUdtZONV9lSfUeINOv2997JC1Y8z4FAACZH4DKVKuyBwAAyi7Vm7TatWvH9ttvn9EZdtppp8TzmvxGcsSIEbF169bEmqFDh2ZpGgAAcpHMXzHz58+P1atXJ9Z07Njxn/7z1q1b4y9/+Ut06tQpTjrppCgsLEy5XC0i4vvvv4/nn38+Lr300mjbtm0MGDAgPvroowrNDwBA9SfzZ8eSJUti2LBh8dJLL5VquVrEjxl/3Lhx8R//8R/Ro0eP6NSpU9x+++2xfPnyDE8LAEB1IvNn3mWXXRarVq3a5tnvf//72GGHHbI8EQAANYnMnzveeeed6Nu3byxevLjEmj333DN+85vfZHEqAACqOpk/t2zYsCEefPDBxJoDDzwwWrdunaWJAACo6mR+AADIjO+++y42bNiQWJMqC1dUqvvXrl0bS5YsyegM5ZHqPUCmX7fS9LBgDQAAmR+AymTBGgDkoG+++SbxvHXr1lGrVmb/MZ/qS4jWrVsXK1asyOgMVVFxcXGMGDEiseaggw6Kzp07Z2kiAABykcxfMTNmzEg8b9q0aTRo0OAf/3nmzJlx4IEHxgUXXBBz5swpd9+ioqJ48sknY7/99ouCgoJSL3AAAKDmkflzx5dffhn/9m//Frvttlv87ne/S/lBPwAAiJD5M+2pp56K5557bptnBx54YFx00UVZnggAgJpG5q/6Pvvssxg8eHAcfvjh8d1335VY16JFixgzZkw0bNgwi9MBAFDVyfy55Y9//GN8//33iTXnnXdelqYBACAXyPwAAJAZqbJ2ROosXFGlub80c2Zbqpky/bqVpkdVfN0AAMgumR+AymTBGgDkoKVLlyaet2nTJuMzbL/99ilrUs1ZHY0fPz7mz5+fWDN06NAsTQMAQK6S+Stm4cKFieetW7f+x//93HPPxf777x+TJk1KW//i4uIoLCyMHj16xPvvv5+2ewEAqD5k/tyzYsWK+NWvfhU9e/ZM6/sHAACqJ5k/c1asWBFXXHHFNs/q1KkTDz74YMa/4AoAAGT+qmPt2rXx7bffxmeffRajR4+O6667Lnr06BHdunWLhx9+OPGxLVu2jDfeeCO6d++epWkBAMgVMn/u+Oabb+LWW29NrGnZsmUMGjQoSxMBAJALZH4AAMiMVBm2adOmUb9+/YzOkJ+fH40bN06sqYpZOxfep1TF1w0AgOyS+QGoTL5BAABy0LJlyxLPmzZtmvEZStMj1ZzV0UMPPZR43rhx4zjzzDOzNA0AALlK5q+Yb7/9NvH878/tySefjNNPPz1Wr16dkTm+/vrrOOKII+LFF1/MyP0AAOQumT93ff7553HIIYek/FJWAABqNpk/c6699tpYvHjxNs+uvvrq2GeffbI8EQAANZHMnz29e/eOvLy8Ev80btw4dtppp+jWrVucffbZ8Yc//CGmTZuW8t5+/frFtGnTomfPnpl/EgAA5ByZP3dcdNFFsWrVqsSaYcOGpfxiLQAAahaZHwAAMqMqZO3S9KmKWbsqvHa5+LoBAJBdVSG3lqaP7ApQPVmwBgA5aPny5YnnTZo0yfgMdevWTbkNvKa9kVy+fHk8++yziTUDBgzwixgAAKQk81dMqgVr9erVi7fffjvOOeecKCoqyugsGzZsiFNPPTVee+21jPYBACC3yPy5bdOmTTF48OC47bbbKnsUAACqKJk/M956660YPnz4Ns86dOgQN954Y3YHAgCgxpL5c9cee+wRI0aMiDfeeCN22WWXyh4HAIAqSubPDX/+85/jxRdfTKxp3759XHvttVmaCACAXCHzAwBAZlSFrF2aPlUxa1eF1y4XXzcAALKrKuTW0vSRXQGqpzqVPQAAUHYbNmxIPG/UqFFW5mjcuHFs3LixxPNUc1Y3jz/+eMrnPHTo0CxNAwBALpP5K2blypUpz88666zYsmVLiTX77rtvnHjiiXHUUUfFzjvvHDvssEMUFxfH4sWLY9GiRfH666/HCy+8EJ988knKebZs2RIDBgyIDz/8MPbcc88yPx8AAKofmT/zWrVqFQcffHDsvffe0a1bt9hzzz2jefPm0axZs2jSpEmsWbMmli5dGsuWLYvp06fHO++8E++880589dVXpe7xy1/+Mpo2bRoXX3xxBp8JAAC5SOZPvw0bNsQFF1wQxcXF2zy/7777Ij8/P8tTAQBQU8n8uaVZs2bRv3//+OlPfxonnXRS1KpVq7JHAgCgipP5q75PPvkkrrzyypR1d911VzRs2DALEwEAkEtkfgAAyIyqlLWTVMWsXRVeu1x83QAAyK6qkFsjZFeAmsqCNQDIQZs2bUo8r1MnO/+IT9Un1ZzVzfDhwxPP99prr+jTp0+WpgEAIJfJ/BWzfv36xPOZM2eWeNa1a9f405/+FEceeeQ2z3fffffYfffd4/DDD4/f/va38eqrr8awYcMS74z4canbwIED4/3334/atWunfhIAAFRrMn9m7LnnnnH66adH//7948ADD0z8gtTmzZtH8+bNo2PHjrHffvvF4MGDIyLivffeiz/84Q/x4osvlri44X+74oorolu3bnHwwQen62kAAFANyPzpd+ONN8aXX365zbMzzjgjTjjhhCxPBABATSbz55add9452rVrF9ttt11ljwIAQI6Q+au25cuXxxlnnJHyy7DOO++8OOWUU7IzFAAAOUXmBwCAzJC1y68qvHa5+LoBAJBdVSG3lqaP7ApQPZX8bWIAQJXljWTVM3Xq1Jg6dWpizdChQ7M0DQAAuU7mr5hUvyhdkosuuiimTZtW4nK1bTn22GPjk08++ccyhiSTJk2Ke+65p1yzAQBQvcj86VOvXr346U9/GhMmTIiZM2fGLbfcEn369Elcrpbk4IMPjueffz4mT54cnTp1Slm/ZcuWOPvss2Pt2rXl6gcAQPUk86fXtGnT4o477tjmWdOmTePuu+/O8kQAANR0Mn9umTFjRtx6661x2GGHRZcuXeIvf/lLbNy4sbLHAgCgCpP5q64tW7bEgAED4osvvkisa9eunc/uAwBQIpkfAAAyQ9Yuv6rw2uXi6wYAQHZVhdxamj6yK0D1ZMEaAOSgrVu3Jp7Xrl07K3Ok6lNUVJSVOaqC4cOHJ57XrVs3fvazn2VpGgAAcp3MXzGbN28u82N+/vOfxwMPPFCuH8zVrVs3hg8fHpdeemnK2ltuuSVWr15d5h4AAFQvMn/63H///fHEE09Ev3790npvz54946OPPooBAwakrF24cGHcfPPNae0PAEBuk/nTp6ioKM4///zYsmXLNs9/97vfxY477pjlqQAAqOlk/tw1a9asuOCCC6JLly7x3nvvVfY4AABUUTJ/1XXVVVfFG2+8kVhTr169eOKJJ6J58+bZGQoAgJwj8wMAQGbI2uVXFV67XHzdAADIrqqQW0vTR3YFqJ6ys8YTANJkxowZccwxx1T2GGn19ddfl/kxqb7wv6QvFEq3VH3q1q2blTkq28aNG+Pxxx9PrPnJT34Sbdq0ydJEAAC5S+b/kcxfMWX94doRRxwR//mf/1mhnnl5eXHPPffEtGnT4m9/+1uJdT/88EMUFhbGFVdcUaF+AAC5Sub/kcyfPuVZklxaTZo0idGjR0d+fn4UFhYm1t59993x85//PLbffvuMzQMAkAtk/h/J/Onzxz/+MT766KNtnh1wwAFxySWXZHkiAICaTeb/kcyfPb/+9a/j+++/3+ZZUVFRrFixIlasWBE//PBDTJkyJT799NPYvHlzynvnzJkThx12WPz617+OG2+8Mc1TAwDkLpn/RzJ/1XTrrbfG/fffn7LujjvuiAMOOCALEwEA5B6Z/0cyPwAAZIasXX516tRJ/MxLNl67XHzdAADILpkfgMpkwRoAOWXTpk2xaNGiyh6j0tWrVy/xPFtvJFP94nGqOauLsWPHxvLlyxNrhg4dmqVpAABym8z/I5m/YsoyV5MmTWL48OGRl5dX4b61atWKwsLC6N69e6xfv77EugceeMCCNQCgxpL5fyTz5468vLz47//+75g9e3biMuUNGzbEvffeG7/5zW+yOB0AQNUj8/9I5k+POXPmxA033LDNszp16sSDDz4YtWrVyvJUAAA1m8z/I5k/e04++eQy1W/cuDHefffd+POf/xxjx46NTZs2lVi7devWuOmmm2L16tVxxx13VHRUAIBqQeb/kcxf9QwfPjz+/d//PWXdlVdeGZdffnkWJgIAyE0y/49kfgAAyAxZu/zq1atX6QvWcvF1AwAgu2R+ACqTbxUAgByUagN20i8Bp5M3kj966KGHEs932mmnOO6447I0DQAA1YHMXzFlmeuiiy6K9u3bp613p06doqCgILFmxowZMXPmzLT1BAAg98j8uaVu3bpRWFiY8vV49NFHszQRAABVncyfHhdddFGsW7dum2dXXXVVdO/ePcsTAQDAj2T+qqt+/fpx5JFHxujRo2PBggVx5plnpnzMnXfeGTfffHMWpgMAIFfI/FXL008/HRdeeGHKunPPPTfuuuuuzA8EAEDOk/kBACAzZO3yqwqvXS6+bgAAZFdVyK0RsitATWXBGgDkoMaNGyeer1mzJitzrF69OvE81ZzVwfz582PChAmJNYMHD47atWtnaSIAAKoDmb9iGjVqVKq6vLy8uPjii9Pe/9JLL01Z8/LLL6e9LwAAuUPmzz2dOnWKiy66KLFm/vz5MWnSpCxNBABAVSbzV9zDDz8cb7zxxjbP2rdvHzfddFOWJwIAgP8h8+eG7bffPkaPHh1PP/105OfnJ9befPPN8cEHH2RpMgAAqjqZv+p49dVX45xzzomioqLEupNOOikKCwsjLy8vS5MBAJDLZH4AAMgMWbv8qsJrl4uvGwAA2VUVcmuE7ApQU1mwBgA5qGXLlonnq1atyvgM69atS/kLCanmrA5GjBgRW7duLfE8Ly8vhgwZksWJAACoDmT+iintXIccckh07Ngx7f27du0a++23X2KNL2QCAKjZZP7cNGzYsJQ1b775ZuYHAQCgypP5K+b777+Pa665psTze++9Nxo1apTFiQAA4J/J/LnltNNOi7Fjx0b9+vVLrCkqKopBgwalfE0BAKgZZP6q4d13343TTjstNm3alFh35JFHxpgxY6JOnTpZmgwAgFwn8wMAQGZUhaxdmj5VMWtXhdcuF183AACyqyrk1tL0kV0BqicL1gAgB2233XaJ5ytWrMj4DKXpkWrOXLd169YoLCxMrDn88MMzsrABAIDqTeavmNLOdfDBB2dshj59+iSeT5kyJWO9AQCo+mT+3LTbbrtF7969E2vef//9LE0DAEBVJvNXzJVXXhlLly7d5tnpp58eP/nJT7I8EQAA/DOZP/ccc8wxcf/99yfWzJ49O5555pksTQQAQFUm81e+yZMnR//+/WPdunWJdQcddFA899xz0aBBgyxNBgBAdSDzAwBAZlSFrB0RsXLlysTzqpi1q8Jrl6pHVXzdAADIrqqQWyNyM/MDUHEWrAFADmrVqlXi+eLFizM+Q2l6VPc3kuPHj4/58+cn1gwZMiRL0wAAUJ3I/BWT6vX7uwMPPDBjMxxwwAGJ5/PmzYuioqKM9QcAoGqT+XNXv379Es9nzpyZpUkAAKjKZP7ye+mll2L06NHbPGvatGncfffdWZ4IAAD+lcyfmwoKClJ+XujOO+/M0jQAAFRlMn/l+uyzz+K4446LVatWJdb16NEjXn755WjUqFGWJgMAoLqQ+QEAIDNSZe2NGzdmfOHCsmXLYtOmTYk1VTFr58L7lKr4ugEAkF0yPwCVqU5lDwAAZbHvvvtGcXFxZY9R6dq1a5d4vnLlytiwYUM0aNAgYzN89913ieetW7eOhg0bZqx/VfDQQw8lnjdr1izOOOOMLE0DAFA9yPw/kvkrJtXr93ddunTJ2Ayp7i4qKopvv/022rZtm7EZAACqIpn/RzJ/7urZs2fi+fz586O4uDjy8vKyNBEAQNUi8/9I5i+fNWvWxCWXXFLi+S233BI77bRTFicCAOD/kvl/JPPnpry8vLj55pvjmGOOKbHmgw8+iCVLlkSbNm2yOBkAQNUh8/9I5q88s2bNiqOOOiqWLl2aWNelS5d47bXXonnz5tkZDACgmpD5fyTzAwBAZpTm+16+++67jP673VRZO6L030uTTalmKs3zqqhUPdq3b5/xGQAAqNpkfgAqU63KHgAAKLsOHTqkrFmwYEFGZ5g3b17i+a677prR/pVt2bJl8eyzzybWnH322T6sBgBAucj8FVPa2TL5w7cWLVqkrFm2bFnG+gMAULXJ/Lkr1d+79evXx5o1a7IzDAAAVZbMXz533XVXLFy4cJtn++23X1x66aVZnggAALZN5s9dffv2TfmZoXfeeSc7wwAAUGXJ/JVj7ty5ceSRR6b8Iqzdd9893njjjWjVqlWWJgMAoLqR+QEAIDMaN24c2223XWLN/PnzMzpDqqzdpk2baNSoUUZnKI9U71My/bpFeJ8CAEBqMj8AlalOZQ8AAJRd48aNo1WrVvHDDz+UWPPFF1/EHnvskbEZvvzyy8Tz6v4DkMcffzw2btyYWDN06NAsTQMAQHUj81fMbrvtVqq6TC5YK83d69aty1h/AACqNpk/dzVr1ixlzbp166JJkyZZmAYAgKpK5i+fpNfrwAMPjOHDh6et19KlS1PW/OUvf0k833HHHaN///7pGgkAgBwi8+euunXrxtFHHx1PPvlkiTWTJk2KM844I4tTAQBQ1cj82ff111/HEUccEYsWLUqsa9++fYwfPz523HHHLE0GAEB1JPMDAEDm7Lrrromf1f7iiy/imGOOyVj/XM3aqeb64osvMj5Drr52AABkl8wPQGWxYA0AclTXrl3j7bffLvF81qxZGf0Cn1mzZiWed+3aNWO9q4JUX9i0zz77RO/evbM0DQAA1ZHMX375+fmx6667xty5c0usqV27dtStWzdjMzRs2DBlzZYtWzLWHwCAqk/mz0316tVLWbN58+YsTAIAQFUn86fXPffck/WeF1xwQeL54YcfbsEaAEANJvPnrlS/NL9kyZIsTQIAQFUm82fP4sWL48gjj4x58+Yl1u20004xfvz4aNeuXXYGAwCgWpP5AQAgM7p27RqTJ08u8TxVFq6oXM3aqeb64YcfYtmyZdGyZcuM9P/7/Umq6msHAEB2yfwAVJZalT0AAFA+PXv2TDyfOnVqRvtPmTIl8bxHjx4Z7V+ZpkyZEtOmTUusGTp0aHaGAQCg2pL5K6ZXr16J50VFRbF27dqM9V+5cmXKmtIsYQMAoPqS+XPT+vXrU9bI+gAARMj8AABQ3cn8uWv77bdPPF+6dGmWJgEAoCqT+bPjhx9+iKOOOipmz56dWNe6det44403omPHjlmaDACA6k7mBwCAzJC1y6dDhw7RokWLxJpMvnapXrftttsudtlll4z1BwAgd8j8AFQWC9YAIEeleiP50UcfZaz3t99+G99++21iTXV+Izl8+PDE8/r168d5552XpWkAAKiuZP6K6d27d8qaFStWZKz/8uXLU9Y0btw4Y/0BAKj6ZP7ctHjx4pQ1sj4AABEyPwAAVHcyf+5q2rRp4vn69euzNAkAAFWZzJ95y5cvj6OPPjqmT5+eWNeiRYt4/fXXo3PnzlmaDACAmkDmBwCAzEiVtadNmxZFRUUZ6b1ly5b4+OOPE2uqctauzPcpqe6uyq8bAADZJfMDUFksWAOAHHXIIYckns+cOTOWLFmSkd5vvvlm4nmHDh1i5513zkjvyrZhw4YYOXJkYs0pp5wSLVu2zNJEAABUVzJ/xaR6/SIiY69fae+u6q8hAACZJfPnpi+//DLxfLvttov69etnaRoAAKoymR8AAKo3mT93rV27NvG8UaNGWZoEAICqTObPrNWrV8dxxx0X06ZNS6xr2rRpvPbaa9G9e/fsDAYAQI0h8wMAQGb07t07GjRoUOL5mjVrMrYobOLEibFu3boSzxs0aBC9evXKSO90SPU+5a233spY71TvU0rzPT4AANQMMj8AlcWCNQDIUR06dIiOHTuWeF5cXByvv/56Rnq/8cYbiedHH310RvpWBWPHjo3ly5cn1gwdOjRL0wAAUJ3J/BVzwAEHRLNmzRJrJk2alLH+kydPTjxv2bJlNG7cOGP9AQCo+mT+3PThhx8mnu+6665ZmgQAgKpO5gcAgOpN5s9dCxcuTDxv0aJFliYBAKAqk/kzZ926ddG/f/+YOHFiYl2jRo3ilVdeid69e2dpMgAAahKZHwAAMqNBgwZx8MEHJ9ZUVtY+9NBDExdBVLajjjoq8fydd96JTZs2pb3vhg0b4t13302s8T4FAIC/k/kBqCwWrAFADkv1g4Znnnkm7T03b94czz33XGLNMccck/a+VcVDDz2UeN6+ffs48sgjszQNAADVncxffnXq1En5wbEPPvggY/1T3d29e/eM9QYAIHfI/Lll69atMW7cuMSaffbZJ0vTAACQC2R+AACo3mT+3PTxxx8nnid9oS4AADWLzJ9+GzdujJNPPjn++te/JtY1bNgwXnjhhejTp0+WJgMAoCaS+QEAIDMqI2tHRDz11FOJ51U9ax944IHRpEmTEs/Xrl0br776atr7vvzyy7F+/foSz5s1axb7779/2vsCAJC7ZH4AKoMFawCQw376058mnr/88suxcuXKtPZ89dVXY9myZSWeN2rUKI4//vi09qwq5s2bFxMmTEisKSgoiFq1RCwAANJD5q+YAQMGJJ6PHz8+tm7dmva+mzdvjjfffDOx5oADDkh7XwAAco/Mn1tee+21WLx4cWKNL3UCAOB/k/nL5q677ori4uKs/Jk7d27KeVLd8dZbb2X+RQEAoEqT+XPP2rVr429/+1tiTefOnbM0DQAAVZ3Mn16bN2+O008/Pd54443Eunr16sUzzzwT/fr1y9JkAADUVDI/AABkxhlnnJF4PmXKlJg1a1Zae3722Wfx6aeflniel5eXcq7KVqdOnTjllFMSa0aOHJn2vqnuPPXUU6NOnTpp7wsAQO6S+QGoDLZ/AEAO69u3b+y8884lnm/YsCH+/Oc/p7Xn3XffnXh+yimnRKNGjdLas6oYMWJEFBcXl3heq1atKCgoyOJEAABUdzJ/xZx00knRvHnzEs8XLFgQL7/8ctr7jh07Nr777rvEmmOPPTbtfQEAyD0yf275/e9/n3iel5cXxxxzTJamAQAgF8j8AABQvcn8ueeJJ56IdevWJdYceuihWZoGAICqTuZPn6KiojjnnHPipZdeSqyrU6dOPPHEE3HcccdlaTIAAGoymR8AADKjY8eOceCBBybW3HPPPWntmSpr9+nTJzp06JDWnplw7rnnJp4//fTT8c0336St34IFC+K5556r0EwAANQ8Mj8AlcGCNQDIYbVq1YpBgwYl1tx1110pfwG4tCZPnhyvv/56Ys3gwYPT0quq2bp1axQWFibWHHXUUdGuXbvsDAQAQI0g81dMgwYN4rzzzkusSfcP30pzZ6tWrXwREwAAESHz55LRo0fHO++8k1hz4IEHxi677JKliQAAyAUyPwAAVG8yf27ZtGlT/O53v0us6dGjR7Rp0yZLEwEAUNXJ/OlRXFwcBQUF8dRTTyXW1a5dOx5//PE4+eSTszQZAAA1ncwPAACZM2TIkMTzESNGxLfffpuWXl9//XU8+uijiTW5krWPOuqoxN9T3bx5c9x+++1p63f77bfHli1bSjxv165d9OvXL239AACoPmR+ALLNgjUAyHGXX3551K9fv8TzRYsWxR/+8IcK9ykuLo5hw4Yl1uy7775x1FFHVbhXVfTGG2/EggULEmuGDh2apWkAAKhJZP6Kueaaa6JOnTolnr/22msxevTotPUbMWJEvPvuu4k1BQUFUbt27bT1BAAgt8n8Vd+XX34Zl1xyScq6Cy+8MAvTAACQa2R+AACo3mT+3PEf//Ef8eWXXybW/OxnP8vSNAAA5AqZv+IuvfTSlF9yVatWrRgxYkQMGDAgS1MBAMCPZH4AAMiMgQMHRps2bUo8X7duXVx33XVp6fXLX/4yNmzYUOL59ttvHwMHDkxLr0yrXbt2yvcO9913X8yaNavCvWbMmBEPPPBAYs3VV1/tO3IAANgmmR+AbLNgDQBy3I477pjyl3h///vfx8SJEyvU5/bbb4/33nsvseaXv/xlhXr83VtvvRV5eXmJfwoLC9PSq7SGDx+eeL7ddtvFKaeckp1hAACoUWT+iunQoUOce+65iTWXXXZZLFq0qMK95s+fH1dffXViTd26deOyyy6rcC8AAKoPmb/svv/++5g9e3ZaZk1lwYIFccwxx8SKFSsS63bZZZeU7z0AAKiZZH4AAKjeZP6ymz17dnz++edpmbW0Hnzwwbj99tsTa5o1axaDBg3K0kQAAOQKmb9irr322pRfzpqXlxcPPPCAL7oCAKBSyPwAAJAZDRo0iKuuuiqx5pFHHomxY8dWqM8TTzwRI0eOTKwZNmxY4mLl0pg3b17KnH3jjTdWqMffXXjhhdGyZcsSzzdv3hznnXdebNq0qdw9Nm7cGOedd15s2bKlxJqWLVvG+eefX+4eAABUbzI/ANlmwRoAVAM33XRTNGnSpMTzzZs3x6mnnhpz5swp1/3PPvts/OpXv0qs2X///ePMM88s1/1V3bJly+LZZ59NrDnvvPOiXr162RkIAIAaR+avmFtuuSUaN25c4vmyZcuiX79+sXDhwnL3mDt3bvTr1y9WrlyZWHfZZZdF+/bty90HAIDqSeYvm0WLFkWXLl1i6NChMWvWrIz1eeWVV6Jnz54xd+7clLW33XZb1K1bN2OzAACQ22R+AACo3mT+spkxY0bsvffece6552b03/NHRGzYsCGuu+66uPjii1PWXnvttdGiRYuMzgMAQG6S+cvnxhtvjDvuuCNl3Z/+9Ke44IILsjARAABsm8wPAACZMWzYsNhll10SawYNGlTuhcYffPBBDB06NLGmffv2KZc+VDWNGzeOm266KbFm8uTJUVBQEFu3bi3z/UVFRTFo0KCYOnVqYt1vfvObxO/rAQAAmR+AbLJgDQCqgR133DGuv/76xJpvvvkmDjvssPjggw/KdPeDDz4YZ555ZmzZsqXEmlq1asU999wTeXl5Zbo7Vzz++OOxcePGxJpUb7QBAKAiZP6K2XnnnePmm29OrPniiy/ikEMOib/+9a9lvv/NN9+MQw89NOXShZ122inl30cAAGommb/sioqKYvjw4dG5c+fo379/PP3007Fp06a03P3tt9/GVVddFf3794+lS5emrD/++OPjrLPOSktvAACqJ5kfAACqN5m/7LZu3RojR46Mrl27xmmnnRZjxoyJtWvXpu3+4uLiePHFF6Nr167xhz/8IWV9t27d4he/+EXa+gMAUL3I/GV3xx13pPzy14iI2267La644oosTAQAACWT+QEAIDPy8/PjzjvvTKxZvXp1HHPMMfHiiy+W6e7nnnsujj322FizZk1i3R133BENGzYs091VwSWXXBL77LNPYs3IkSPjjDPOiFWrVpX63pUrV/7jszpJunfvHhdddFGp7wUAoGaS+QHIpjqVPQAAkB5XX311PP/884nLABYtWhSHHnpoXHzxxfFv//Zvidu9P/zww7j++uvjtddeS9n73//932P//fcv19y5YPjw4Ynn++23X3Tr1i1L0wAAUFPJ/BVz1VVXxSuvvBKvv/56iTULFiyIww47LM4666y49tpro1evXiXWFhcXx+TJk+MPf/hDPP300yn75+XlxcMPPxwtWrQo1/wAAFR/Mn/5FBcXx8svvxwvv/xyNGvWLI455pg4/vjj4+CDD4499tij1Pds2LAhPvzww3j00Ufj0UcfLfWytg4dOsSjjz5a3vEBAKhBZH4AAKjeZP7yKSoqirFjx8bYsWOjYcOGccIJJ8SJJ54Yffr0iU6dOpX5runTp8eTTz4Zjz32WMybN69Uj2vSpEk8/vjjUb9+/XI8AwAAagqZv/SefvrpuPbaa1PW9ejRI1q0aBF/+ctfsjDVj84666xo3Lhx1voBAJA7ZP7y+/bbb+Oll14q1+OSvPPOO4mL6bZlxx13jP79+5d5FgAAMueMM86Ic845J0aOHFlizcqVK+Okk06Ks88+O37961/HXnvtVWLtjBkz4uabb065ICwi4txzz43TTz+9XHNXttq1a8ejjz4aBxxwQGzYsKHEurFjx8akSZPiN7/5TZx11lnRoEGDbdatX78+Ro4cGTfccEMsWrQosXfDhg3j0Ucfjdq1a1foOQAAUDPI/OWzZs2aGD16dJkf98UXXySeT5kypcyfxWnSpEmceeaZZZ4FINvyiouLiyt7CAAgPRYtWhQ9e/aMJUuWpKytVatWHHjggXHAAQfELrvsEg0bNoxVq1bF7Nmz469//WvMnj27VD0PP/zwGD9+fFp/APLWW29Fv379EmtGjBgRgwcPTlvPkkyZMiVxqUJExAMPPBAXXXRRxmcBAACZv2KWLl0avXr1ivnz55eqvm3btnHEEUfEzjvvHDvssEMUFxfHd999F19//XWMHz8+vvnmm1L3vvPOO+Pqq68u7+gAANQQMn/pTJs2LXr06JGyrlmzZrHXXntFp06dYscdd4zWrVtHw4YNo169erF69epYunRpLFu2LGbMmBETJ06MjRs3lmmONm3axJtvvhldunQp1/MAAKDmkfmrjnnz5sWuu+6aWOMj1gAAlJXMXzrPPvtsnHrqqSnrmjVrFnvuuWd06tQpdthhh2jTpk3k5+dHvXr1YsOGDbFq1apYvXp1/PDDD/HJJ5/EZ599lviFUttSr169ePHFF+Poo48u13MBAKBmkflL58Ybb4ybbrqpXI/NtLlz50aHDh0qewwAAKoomT9z/bLl8MMPj7feequyxwAA4P9Ys2ZN9O7dO2bNmlWq+h49ekSfPn1i1113jcaNG8fq1atj7ty58d5778XHH39cqjv22muvmDRpUjRu3Lgio/9DaT5/fsMNN8SNN96Yln5/95e//CUuuOCCUtU2b948+vXrF927d49WrVpFcXFx/PDDD/Hxxx/HhAkTYtWqVaXuOXTo0IqMDQBADSPzZ6ZftrRv3z7mzZtX2WMApFSnsgcAANJn5513jnHjxsURRxwRK1asSKzdunVr/O1vf4u//e1v5e7Xo0ePePbZZ9P6Ia2q5qGHHko8z8/Pj7PPPjtL0wAAUNPJ/BWz3XbbxRtvvBF9+/aNRYsWpaz/+uuv45FHHqlw3//3//6f5WoAAJSKzJ9eK1eujA8//DA+/PDDtN/dtm3bePXVVy1XAwCgTGR+AACo3mT+9Fq5cmVMnDgxJk6cmJH7mzVrFmPHjq0yX/oKAEDVJ/MDAED1JvMDAEBmNG7cOF599dU49NBDY+HChSnrp06dGlOnTi13v3bt2sWrr76atkULlen888+PhQsXxs0335yydsWKFTF27NgYO3ZsufvdeOONlqsBAFBmMj8A2VCrsgcAANKrR48e8eqrr0abNm0y2me//faL1157LZo3b57RPpVpw4YNMXLkyMSaM844I5o2bZqliQAAQOavqN133z3efPPN6NSpU8Z71a5dO+6444645ZZbMt4LAIDqQ+av+vr27RsfffSR5WoAAJSLzA8AANWbzJ8bevXqFR9++KHlagAAlJnMDwAA1ZvMDwAAmdG+ffuYMGFCdOzYMaN9dt9995gwYUK0a9cuo32y6aabborrr78+431uuOGGuOGGGzLeBwCA6knmByDTLFgDgGpo//33j0mTJkWvXr0ycv+gQYPinXfeiVatWmXk/qrimWeeiRUrViTWDB06NDvDAADA/yLzV0ynTp1i0qRJceqpp2asR9u2bWP8+PHx85//PGM9AACovmT+qqlZs2Zx//33x4QJEzL+C/MAAFRvMj8AAFRvMn/V1bRp07j11lvj/fffjz333LOyxwEAIEfJ/AAAUL3J/AAAkBm77757TJo0KY499tiM3H/cccfFpEmTMr7QoTLcdNNNMWbMmGjcuHHa727cuHE8+eSTceONN6b9bgAAahaZH4BMsmANAKqpdu3axQcffBB33HFHNGnSJC137rbbbvHSSy9FYWFhNGjQIC13VmXDhw9PPO/UqVMcdthhWZoGAAD+mcxfMc2aNYtnnnkmxo4dG7vttlva7s3Pz4//9//+X8ycOTMOP/zwtN0LAEDNI/OXbLvttos+ffpE3bp1s9KvRYsW8etf/zrmzp0bl1xySeTl5WWlLwAA1ZvMDwAA1ZvMX7KDDz44br/99jjiiCOiXr16WenZpk2buP766+Orr76KX/7yl1n7GQMAANWXzA8AANWbzA8AAJnRokWLGDduXBQWFkabNm3ScmebNm3i4YcfjldeeSWaN2+eljurogEDBsTnn38ep59+etruPOOMM+Lzzz+PM844I213AgBQs8n8AGSKBWsAUI3VqVMnfv7zn8e8efPi97//fbRr165c9+y///7x6KOPxsyZM+OEE05I85RV07x582LChAmJNQUFBVmaBgAAtk3mr7hTTjklZs6cGaNGjYpDDz203IsSunTpEr/73e9i4cKFccstt0SjRo3SPCkAADWRzL9tu+yyS7z33nuxfPnyePXVV+O6666Lvn37RosWLdLWo1mzZnHqqafGmDFj4ttvv42bb745rfcDAECEzA8AANWdzL9trVu3jmuvvTbGjx8fS5cujbFjx8awYcOiT58+0bBhw7T12XHHHWPQoEHx/PPPx8KFC+Omm26KVq1ape1+AACQ+QEAoHqT+QEAIHMGDRoUc+bMifvuuy86d+5crju6dOkS9913X8ydOzd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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is what the reportlet of FC distribution looks like\n", + "\n", + "![group_desc-fcdist_bold.png](attachment:group_desc-fcdist_bold.png)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -232,6 +236,27 @@ "group_report_censoring(good_timepoints_df, output)" ] }, + { + "attachments": { + "newplot.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot about censoring looks as follows\n", + "\n", + "![newplot.png](attachment:newplot.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assembler" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -333,6 +358,20 @@ ")\n", "robj.generate_report()\n" ] + }, + { + "attachments": { + "Screenshot 2024-05-17 at 21-02-57 Visual report generated by NiReports.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now open the HTML visual report in a web browser\n", + "\n", + "![Screenshot 2024-05-17 at 21-02-57 Visual report generated by NiReports.png]()" + ] } ], "metadata": { diff --git a/docs/notebooks/data/reports-spec.yml b/docs/notebooks/data/reports-spec.yml index aaa8dbce..a68e59e7 100644 --- a/docs/notebooks/data/reports-spec.yml +++ b/docs/notebooks/data/reports-spec.yml @@ -15,7 +15,7 @@ sections: caption: Density distributions of within-session FC strengths. The FC distribution from each session are overlaid. subtitle: Functional connectivity density distributions. style: - max-width: 600px + max-width: 1200px - bids: {desc: qcfc, suffix: bold} caption: | From 56d1e84a1a7730bdef4d62cfcf6cb8287ed8f383 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=A9line=20Provins?= Date: Fri, 17 May 2024 21:09:00 +0200 Subject: [PATCH 5/5] sty: ruff --- docs/notebooks/Tutorial.ipynb | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/docs/notebooks/Tutorial.ipynb b/docs/notebooks/Tutorial.ipynb index af9ebe68..29840f9b 100644 --- a/docs/notebooks/Tutorial.ipynb +++ b/docs/notebooks/Tutorial.ipynb @@ -22,7 +22,8 @@ "source": [ "# Silence warnings\n", "import warnings\n", - "warnings.simplefilter(action='ignore', category=UserWarning)\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=UserWarning)\n", "\n", "# Load data\n", "import glob\n", @@ -40,7 +41,7 @@ "\n", "# Load functional connectivity matrices\n", "fc_matrices = []\n", - "connectivity_files = glob.glob(op.join(output, '**', '*connectivity.tsv'), recursive=True)\n", + "connectivity_files = glob.glob(op.join(output, \"**\", \"*connectivity.tsv\"), recursive=True)\n", "for idx, connectivity_file in enumerate(connectivity_files):\n", " fc_matrix = np.loadtxt(connectivity_file, delimiter=\"\\t\")\n", " flattened = fc_matrix.reshape(-1)\n", @@ -73,7 +74,8 @@ "source": [ "# Silence futureWarnings\n", "import warnings\n", - "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", @@ -84,6 +86,7 @@ "FC_FIGURE_SIZE: tuple = (70, 45)\n", "LABELSIZE: int = 60\n", "\n", + "\n", "def group_reportlet_fc_dist(fc_matrices: list[np.ndarray], output: str) -> None:\n", " \"\"\"Plot and save the functional connectivity density distributions.\n", "\n", @@ -96,7 +99,7 @@ " \"\"\"\n", " _, ax = plt.subplots(figsize=FC_FIGURE_SIZE)\n", " ax.tick_params(labelsize=LABELSIZE)\n", - " mpl.rcParams['legend.fontsize'] = LABELSIZE\n", + " mpl.rcParams[\"legend.fontsize\"] = LABELSIZE\n", "\n", " fc_df = pd.DataFrame(fc_matrices, columns=[\"value\", \"session\"])\n", "\n", @@ -111,8 +114,8 @@ " )\n", "\n", " plt.tick_params(labelsize=LABELSIZE)\n", - " plt.xlabel('Value', fontsize=LABELSIZE+4)\n", - " plt.ylabel('Density', fontsize=LABELSIZE+4)\n", + " plt.xlabel(\"Value\", fontsize=LABELSIZE + 4)\n", + " plt.ylabel(\"Density\", fontsize=LABELSIZE + 4)\n", "\n", " # Ensure the labels are within the figure\n", " plt.tight_layout()\n", @@ -122,6 +125,8 @@ "\n", " plt.savefig(op.join(output, savename))\n", " plt.close()\n", + "\n", + "\n", "group_reportlet_fc_dist(fc_matrices, output)" ] }, @@ -157,6 +162,8 @@ "\n", "# The literature indicates that less than 5 minutes of fMRI signal after censoring is not enough to reliably estimate functional connectivity.\n", "DURATION_CUT_OFF = 300\n", + "\n", + "\n", "# Generate each reportlets\n", "def group_report_censoring(good_timepoints_df, output) -> None:\n", " \"\"\"\n", @@ -233,6 +240,8 @@ " filename=op.join(output, \"reportlets\", \"group_desc-censoring_bold.html\"),\n", " auto_open=False,\n", " )\n", + "\n", + "\n", "group_report_censoring(good_timepoints_df, output)" ] }, @@ -356,7 +365,7 @@ " out_filename=\"group_report.html\",\n", " bootstrap_file=op.join(\"data\", \"reports-spec.yml\"),\n", ")\n", - "robj.generate_report()\n" + "robj.generate_report()" ] }, {