# 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 NiPreps Developers <nipreps@gmail.com>
#
# 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
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#
# We support and encourage derived works from this project, please read
# about our expectations at
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# https://www.nipreps.org/community/licensing/
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# STATEMENT OF CHANGES: This file was ported carrying over full git history from
# other NiPreps projects licensed under the Apache-2.0 terms.
"""Visualizations specific to functional imaging."""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.gridspec import GridSpec
from nireports.reportlets.nuisance import confoundplot, plot_carpet, spikesplot
[docs]
class fMRIPlot:
"""Generates the fMRI Summary Plot."""
__slots__ = (
"confounds",
"nskip",
"paired_carpet",
"segments",
"sort_carpet",
"spikes",
"timeseries",
"tr",
)
def __init__(
self,
timeseries,
segments,
confounds=None,
conf_file=None,
tr=None,
usecols=None,
units=None,
vlines=None,
spikes_files=None,
nskip=0,
sort_carpet=True,
paired_carpet=False,
):
self.timeseries = timeseries
self.segments = segments
self.tr = tr
self.nskip = nskip
self.sort_carpet = sort_carpet
self.paired_carpet = paired_carpet
if units is None:
units = {}
if vlines is None:
vlines = {}
self.confounds = {}
if confounds is None and conf_file:
confounds = pd.read_csv(conf_file, sep=r"[\t\s]+", usecols=usecols, index_col=False)
if confounds is not None:
for name in confounds.columns:
self.confounds[name] = {
"values": confounds[[name]].values.squeeze().tolist(),
"units": units.get(name),
"cutoff": vlines.get(name),
}
self.spikes = []
if spikes_files:
for sp_file in spikes_files:
self.spikes.append((np.loadtxt(sp_file), None, False))
[docs]
def plot(self, figure=None, out_file=None, fontsize=24):
"""Main plotter"""
plt.rcParams.update({"font.size": fontsize})
nconfounds = len(self.confounds)
nspikes = len(self.spikes)
nrows = 1 + nconfounds + nspikes
# Calculate height ratios in figure points
height_ratios = [0.8] * (nconfounds + nspikes) + [10]
if figure is None:
figure = plt.figure(figsize=(19.2, sum(height_ratios)))
# Create grid
grid = GridSpec(
nrows,
1,
figure=figure,
wspace=0.0,
hspace=0.05,
height_ratios=height_ratios,
)
grid_id = 0
for tsz, name, iszs in self.spikes:
spikesplot(tsz, title=name, outer_gs=grid[grid_id], tr=self.tr, zscored=iszs)
grid_id += 1
if self.confounds:
from seaborn import color_palette
palette = color_palette("husl", nconfounds)
for i, (name, kwargs) in enumerate(self.confounds.items()):
tseries = kwargs.pop("values")
confoundplot(tseries, grid[grid_id], tr=self.tr, color=palette[i], name=name, **kwargs)
grid_id += 1
plot_carpet(
self.timeseries,
segments=self.segments,
subplot=grid[-1],
tr=self.tr,
sort_rows=self.sort_carpet,
drop_trs=self.nskip,
cmap="paired" if self.paired_carpet else None,
)
if out_file is not None:
figure.savefig(out_file, bbox_inches="tight")
plt.close(figure)
figure = None
return out_file
return figure