def _big_plot(in_func,
in_mask,
in_segm,
in_spikes,
in_spikes_bg,
fd,
dvars,
outliers,
out_file=None):
import os.path as op
import numpy as np
from mriqc.viz.fmriplots import fMRIPlot
if out_file is None:
fname, ext = op.splitext(op.basename(in_func))
if ext == '.gz':
fname, _ = op.splitext(fname)
out_file = op.abspath('{}_fmriplot.svg'.format(fname))
title = 'fMRI Summary plot'
myplot = fMRIPlot(in_func, in_mask, in_segm, title=title)
# myplot.add_spikes(np.loadtxt(in_spikes), title='Axial slice homogeneity (brain mask)')
myplot.add_spikes(np.loadtxt(in_spikes_bg), zscored=False)
# Add AFNI ouliers plot
myplot.add_confounds([np.nan] + np.loadtxt(outliers, usecols=[0]).tolist(),
{
'name': 'ouliers',
'units': '%',
'normalize': False,
'ylims': (0.0, None)
})
# Pick non-standardize dvars
myplot.add_confounds([np.nan] +
np.loadtxt(dvars, skiprows=1, usecols=[1]).tolist(), {
'name': 'DVARS',
'units': None,
'normalize': False
})
# Add FD
myplot.add_confounds(
[np.nan] + np.loadtxt(fd, skiprows=1, usecols=[0]).tolist(), {
'name': 'FD',
'units': 'mm',
'normalize': False,
'cutoff': [0.2],
'ylims': (0.0, 0.2)
})
myplot.plot()
myplot.fig.savefig(out_file, bbox_inches='tight')
myplot.fig.clf()
return out_file
def _big_plot(in_func, in_mask, in_segm, in_spikes, in_spikes_bg, fd, dvars, out_file=None):
import os.path as op
import numpy as np
from mriqc.viz.fmriplots import fMRIPlot
if out_file is None:
fname, ext = op.splitext(op.basename(in_func))
if ext == ".gz":
fname, _ = op.splitext(fname)
out_file = op.abspath("{}_fmriplot.pdf".format(fname))
myplot = fMRIPlot(in_func, in_mask, in_segm)
# myplot.add_spikes(np.loadtxt(in_spikes), title='Axial slice homogeneity (brain mask)')
myplot.add_spikes(np.loadtxt(in_spikes_bg), zscored=False)
myplot.add_confounds([np.nan] + np.loadtxt(fd).tolist(), {"name": "FD", "units": "mm"})
myplot.add_confounds([np.nan] + np.loadtxt(dvars).tolist(), {"name": "DVARS", "units": None, "normalize": False})
myplot.plot()
myplot.fig.savefig(out_file, dpi=300, bbox_inches="tight")
myplot.fig.clf()
return out_file
def _big_plot(in_func, in_mask, in_segm, in_spikes_bg,
fd, fd_thres, dvars, outliers, out_file=None):
import os.path as op
import numpy as np
from mriqc.viz.fmriplots import fMRIPlot
if out_file is None:
fname, ext = op.splitext(op.basename(in_func))
if ext == '.gz':
fname, _ = op.splitext(fname)
out_file = op.abspath('{}_fmriplot.svg'.format(fname))
title = 'fMRI Summary plot'
myplot = fMRIPlot(
in_func, in_mask, in_segm, title=title)
myplot.add_spikes(np.loadtxt(in_spikes_bg), zscored=False)
# Add AFNI ouliers plot
myplot.add_confounds([np.nan] + np.loadtxt(outliers, usecols=[0]).tolist(),
{'name': 'ouliers', 'units': '%', 'normalize': False,
'ylims': (0.0, None)})
# Pick non-standardize dvars
myplot.add_confounds([np.nan] + np.loadtxt(dvars, skiprows=1,
usecols=[1]).tolist(),
{'name': 'DVARS', 'units': None, 'normalize': False})
# Add FD
myplot.add_confounds([np.nan] + np.loadtxt(fd, skiprows=1,
usecols=[0]).tolist(),
{'name': 'FD', 'units': 'mm', 'normalize': False,
'cutoff': [fd_thres], 'ylims': (0.0, fd_thres)})
myplot.plot()
myplot.fig.savefig(out_file, bbox_inches='tight')
myplot.fig.clf()
return out_file