def bxplot(stats, label):
stats = concat_ts_boxplot_stats(stats)
# XXX need some way to expose whether there were missing subjects and
# report proper IDs -- for now resort to whining
verbose(
0,
"List of outlier time series follows (if any) [note, subject IDs are enumarations and may differ from dataset subject IDs in case of missing subjects]"
)
for i, run in enumerate([
np.where(np.sum(np.logical_not(o.mask), axis=0))
for o in stats[1]
]):
sids = run[0]
if len(sids):
verbose(0,
"%s r%.3i: %s" % (label, i + 1, [s + 1 for s in sids]))
timeseries_boxplot(stats[0]['median'],
mean=stats[0]['mean'],
std=stats[0]['std'],
n=stats[0]['n'],
min=stats[0]['min'],
max=stats[0]['max'],
p25=stats[0]['p25'],
p75=stats[0]['p75'],
outlierd=stats[1],
segment_sizes=segment_sizes)
pl.title(label)
xp, xl = pl.xticks()
pl.xticks(xp, ['' for i in xl])
pl.xlim((0, len(stats[0]['n'])))
pl.ylabel(plt_props[label])
def motionqc_plot(data, outlier_abs_minthresh=None, outlier_stdthresh=None, ylabel=None):
import pylab as pl
from mvpa2.misc.plot import timeseries_boxplot, concat_ts_boxplot_stats
from mvpa2.misc.stats import compute_ts_boxplot_stats
# segments x [subjects x timepoints x props]
segment_sizes = [d.shape[1] for d in data]
# get stats for all segments and concatenate them
stats = concat_ts_boxplot_stats(
[compute_ts_boxplot_stats(
d,
outlier_abs_minthresh=outlier_abs_minthresh,
outlier_thresh=outlier_stdthresh,
aggfx=np.linalg.norm,
greedy_outlier=True)
for d in data])
outlier = None
if outlier_stdthresh:
outlier = [list(np.where(np.sum(np.logical_not(o.mask), axis=0))[0])
for o in stats[1]]
# plot
timeseries_boxplot(
stats[0]['median'],
mean=stats[0]['mean'],
std=stats[0]['std'],
n=stats[0]['n'],
min=stats[0]['min'],
max=stats[0]['max'],
p25=stats[0]['p25'],
p75=stats[0]['p75'],
outlierd=stats[1],
segment_sizes=segment_sizes)
xp, xl = pl.xticks()
pl.xticks(xp, ['' for i in xl])
pl.xlim((0, len(stats[0]['n'])))
if ylabel:
pl.ylabel(ylabel)
pl.xlabel('time')
return outlier
def bxplot(stats, label):
stats = concat_ts_boxplot_stats(stats)
verbose(0, "List of outlier time series follows (if any)")
for i, run in enumerate([np.where(np.sum(np.logical_not(o.mask), axis=0))
for o in stats[1]]):
sids = run[0]
if len(sids):
verbose(0, "%s r%.3i: %s"
% (label, i + 1, [s + 1 for s in sids]))
timeseries_boxplot(stats[0]['median'],
mean=stats[0]['mean'], std=stats[0]['std'], n=stats[0]['n'],
min=stats[0]['min'], max=stats[0]['max'],
p25=stats[0]['p25'], p75=stats[0]['p75'],
outlierd=stats[1], segment_sizes=segment_sizes)
pl.title(label)
xp, xl = pl.xticks()
pl.xticks(xp, ['' for i in xl])
pl.xlim((0, len(stats[0]['n'])))
pl.ylabel(plt_props[label])
def bxplot(stats, label):
stats = concat_ts_boxplot_stats(stats)
# XXX need some way to expose whether there were missing subjects and
# report proper IDs -- for now resort to whining
verbose(0, "List of outlier time series follows (if any) [note, subject IDs are enumarations and may differ from dataset subject IDs in case of missing subjects]")
for i, run in enumerate([np.where(np.sum(np.logical_not(o.mask), axis=0))
for o in stats[1]]):
sids = run[0]
if len(sids):
verbose(0, "%s r%.3i: %s"
% (label, i + 1, [s + 1 for s in sids]))
timeseries_boxplot(stats[0]['median'],
mean=stats[0]['mean'], std=stats[0]['std'], n=stats[0]['n'],
min=stats[0]['min'], max=stats[0]['max'],
p25=stats[0]['p25'], p75=stats[0]['p75'],
outlierd=stats[1], segment_sizes=segment_sizes)
pl.title(label)
xp, xl = pl.xticks()
pl.xticks(xp, ['' for i in xl])
pl.xlim((0, len(stats[0]['n'])))
pl.ylabel(plt_props[label])
def run(args):
import numpy as np
import pylab as pl
from mvpa2.datasets.sources.openfmri import OpenFMRIDataset
from mvpa2.misc.plot import timeseries_boxplot, concat_ts_boxplot_stats
from mvpa2.misc.stats import compute_ts_boxplot_stats
of = OpenFMRIDataset(args.path)
data = of.get_task_bold_attributes(
args.task, args.estimate_fname, np.loadtxt,
exclude_subjs=args.exclude_subjs)
segment_sizes = [len(d[0]) for d in data]
# figure setup
pl.figure(figsize=(12, 5))
ax = pl.subplot(211)
# translation
run_stats = [compute_ts_boxplot_stats(
d[...,:3],
outlier_abs_minthresh=args.outlier_minthresh,
outlier_thresh=args.outlier_stdthresh,
aggfx=np.linalg.norm,
greedy_outlier=True)
for d in data]
stats = concat_ts_boxplot_stats(run_stats)
timeseries_boxplot(stats[0]['median'],
mean=stats[0]['mean'], std=stats[0]['std'], n=stats[0]['n'],
min=stats[0]['min'], max=stats[0]['max'],
p25=stats[0]['p25'], p75=stats[0]['p75'],
outlierd=stats[1], segment_sizes=segment_sizes)
pl.title('translation')
xp, xl = pl.xticks()
pl.xticks(xp, ['' for i in xl])
pl.xlim((0, len(stats[0]['n'])))
#pl.ylim((0,7))
pl.ylabel('estimate L2-norm in mm')
# rotation
ax = pl.subplot(212)
run_stats = [compute_ts_boxplot_stats(
d[...,3:],
outlier_abs_minthresh=args.outlier_minthresh,
outlier_thresh=args.outlier_stdthresh,
aggfx=np.linalg.norm,
greedy_outlier=True)
for d in data]
stats = concat_ts_boxplot_stats(run_stats)
timeseries_boxplot(stats[0]['median'],
mean=stats[0]['mean'], std=stats[0]['std'], n=stats[0]['n'],
min=stats[0]['min'], max=stats[0]['max'],
p25=stats[0]['p25'], p75=stats[0]['p75'],
outlierd=stats[1], segment_sizes=segment_sizes)
pl.xlim((0, len(stats[0]['n'])))
#pl.ylim((0,5))
pl.title('rotation')
pl.ylabel('estimate L2-norm in deg')
pl.xlabel('time in fMRI volumes')
if args.savefig is None:
pl.show()
else:
pl.savefig(args.safefig)