def capture_output():
if sys.version_info < (3, 0):
from io import StringIO
class TextIO(StringIO):
def write(self, data):
if not isinstance(data, unicode): # noqa F821
data = unicode(data, getattr(self, '_encoding', 'utf-8'),
'replace') # noqa F821
StringIO.write(self, data)
else:
from io import StringIO as TextIO
out = TextIO()
err = TextIO()
stdout = sys.stdout
sys.stdout = out
stderr = sys.stderr
sys.stderr = err
try:
yield out, err
finally:
sys.stdout = stdout
sys.stderr = stderr
def test_maximum_rows(self):
"""
Any single string passed to ``Sink.write`` gets limited to the number of
rows available on the sink.
"""
size = Size(rows=3, columns=80, xpixels=3, ypixels=7)
outfile = TextIO()
sink = Sink(
terminal=StubTerminal(size=size),
outfile=outfile,
)
lines = list("Hello {}\n".format(n) for n in range(size.rows + 1))
sink.write("".join(lines))
self.assertEqual(
lines[:size.rows],
list(line + "\n" for line in outfile.getvalue().splitlines()),
)
def gen_html(csv_file, mod, csv_failed=None, out_file=None):
import os.path as op
from os import remove
from shutil import copy
import datetime
from pkg_resources import resource_filename as pkgrf
from .. import __version__ as ver
from ..data import GroupTemplate
from ..utils.misc import check_folder
if version_info[0] > 2:
from io import StringIO as TextIO
else:
from io import BytesIO as TextIO
QCGROUPS = {
'T1w':
[(['cjv'], None), (['cnr'], None), (['efc'], None), (['fber'], None),
(['wm2max'], None), (['snr_csf', 'snr_gm', 'snr_wm'], None),
(['snrd_csf', 'snrd_gm', 'snrd_wm'], None),
(['fwhm_avg', 'fwhm_x', 'fwhm_y', 'fwhm_z'], 'vox'),
(['qi_1', 'qi_2'], None), (['inu_range', 'inu_med'], None),
(['icvs_csf', 'icvs_gm', 'icvs_wm'], None),
(['rpve_csf', 'rpve_gm', 'rpve_wm'], None),
(['tpm_overlap_csf', 'tpm_overlap_gm', 'tpm_overlap_wm'], None),
([
'summary_bg_mean', 'summary_bg_median', 'summary_bg_stdv',
'summary_bg_mad', 'summary_bg_k', 'summary_bg_p05',
'summary_bg_p95'
], None),
([
'summary_csf_mean', 'summary_csf_median', 'summary_csf_stdv',
'summary_csf_mad', 'summary_csf_k', 'summary_csf_p05',
'summary_csf_p95'
], None),
([
'summary_gm_mean', 'summary_gm_median', 'summary_gm_stdv',
'summary_gm_mad', 'summary_gm_k', 'summary_gm_p05',
'summary_gm_p95'
], None),
([
'summary_wm_mean', 'summary_wm_median', 'summary_wm_stdv',
'summary_wm_mad', 'summary_wm_k', 'summary_wm_p05',
'summary_wm_p95'
], None)],
'T2w': [(['cjv'], None), (['cnr'], None), (['efc'], None),
(['fber'], None), (['wm2max'], None),
(['snr_csf', 'snr_gm', 'snr_wm'], None),
(['snrd_csf', 'snrd_gm', 'snrd_wm'], None),
(['fwhm_avg', 'fwhm_x', 'fwhm_y', 'fwhm_z'], 'mm'),
(['qi_1', 'qi_2'], None), (['inu_range', 'inu_med'], None),
(['icvs_csf', 'icvs_gm', 'icvs_wm'], None),
(['rpve_csf', 'rpve_gm', 'rpve_wm'], None),
(['tpm_overlap_csf', 'tpm_overlap_gm',
'tpm_overlap_wm'], None),
([
'summary_bg_mean', 'summary_bg_stdv', 'summary_bg_k',
'summary_bg_p05', 'summary_bg_p95'
], None),
([
'summary_csf_mean', 'summary_csf_stdv', 'summary_csf_k',
'summary_csf_p05', 'summary_csf_p95'
], None),
([
'summary_gm_mean', 'summary_gm_stdv', 'summary_gm_k',
'summary_gm_p05', 'summary_gm_p95'
], None),
([
'summary_wm_mean', 'summary_wm_stdv', 'summary_wm_k',
'summary_wm_p05', 'summary_wm_p95'
], None)],
'bold': [
(['efc'], None),
(['fber'], None),
(['fwhm', 'fwhm_x', 'fwhm_y', 'fwhm_z'], 'mm'),
(['gsr_%s' % a for a in ['x', 'y']], None),
(['snr'], None),
(['dvars_std', 'dvars_vstd'], None),
(['dvars_nstd'], None),
(['fd_mean'], 'mm'),
(['fd_num'], '# timepoints'),
(['fd_perc'], '% timepoints'),
(['spikes_num'], '# slices'),
(['dummy_trs'], '# TRs'),
(['gcor'], None),
(['tsnr'], None),
(['aor'], None),
(['aqi'], None),
([
'summary_bg_mean', 'summary_bg_stdv', 'summary_bg_k',
'summary_bg_p05', 'summary_bg_p95'
], None),
([
'summary_fg_mean', 'summary_fg_stdv', 'summary_fg_k',
'summary_fg_p05', 'summary_fg_p95'
], None),
]
}
def_comps = list(BIDS_COMP.keys())
dataframe = pd.read_csv(csv_file,
index_col=False,
dtype={comp: object
for comp in def_comps})
id_labels = list(set(def_comps) & set(dataframe.columns.ravel().tolist()))
dataframe['label'] = dataframe[id_labels].apply(_format_labels, axis=1)
nPart = len(dataframe)
failed = None
if csv_failed is not None and op.isfile(csv_failed):
MRIQC_REPORT_LOG.warn('Found failed-workflows table "%s"', csv_failed)
failed_df = pd.read_csv(csv_failed, index_col=False)
cols = list(set(id_labels) & set(failed_df.columns.ravel().tolist()))
try:
failed_df = failed_df.sort_values(by=cols)
except AttributeError:
#pylint: disable=E1101
failed_df = failed_df.sort(columns=cols)
failed = failed_df[cols].apply(myfmt, args=(cols, ),
axis=1).ravel().tolist()
csv_groups = []
datacols = dataframe.columns.ravel().tolist()
for group, units in QCGROUPS[mod]:
dfdict = {'iqm': [], 'value': [], 'label': [], 'units': []}
for iqm in group:
if iqm in datacols:
values = dataframe[[iqm]].values.ravel().tolist()
if values:
dfdict['iqm'] += [iqm] * nPart
dfdict['units'] += [units] * nPart
dfdict['value'] += values
dfdict['label'] += dataframe[['label'
]].values.ravel().tolist()
# Save only if there are values
if dfdict['value']:
csv_df = pd.DataFrame(dfdict)
csv_str = TextIO()
csv_df[['iqm', 'value', 'label', 'units']].to_csv(csv_str,
index=False)
csv_groups.append(csv_str.getvalue())
if out_file is None:
out_file = op.abspath('group.html')
tpl = GroupTemplate()
tpl.generate_conf(
{
'modality':
mod,
'timestamp':
datetime.datetime.now().strftime("%Y-%m-%d, %H:%M"),
'version':
ver,
'csv_groups':
csv_groups,
'failed':
failed,
'boxplots_js':
open(
pkgrf(
'mriqc',
op.join('data', 'reports', 'embed_resources',
'boxplots.js'))).read(),
'd3_js':
open(
pkgrf(
'mriqc',
op.join('data', 'reports', 'embed_resources',
'd3.min.js'))).read(),
'boxplots_css':
open(
pkgrf(
'mriqc',
op.join('data', 'reports', 'embed_resources',
'boxplots.css'))).read()
}, out_file)
return out_file
try: # python 2.6
import fractions
import number
import io
from io import StringIO as TextIO
# built-in functions (CH 2)
a['ByteArrayType'] = bytearray([1])
# numeric and mathematical types (CH 9)
a['FractionType'] = fractions.Fraction()
a['NumberType'] = numbers.Number()
# generic operating system services (CH 15)
a['IOBaseType'] = io.IOBase()
a['RawIOBaseType'] = io.RawIOBase()
a['TextIOBaseType'] = io.TextIOBase()
a['BufferedIOBaseType'] = io.BufferedIOBase()
a['UnicodeIOType'] = TextIO() # the new StringIO
a['LoggingAdapterType'] = logging.LoggingAdapter(_logger,
_dict) # pickle ok
if HAS_CTYPES:
a['CBoolType'] = ctypes.c_bool(1)
a['CLongDoubleType'] = ctypes.c_longdouble()
except ImportError:
pass
try: # python 2.7
import argparse
# data types (CH 8)
a['OrderedDictType'] = collections.OrderedDict(_dict)
a['CounterType'] = collections.Counter(_dict)
if HAS_CTYPES:
a['CSSizeTType'] = ctypes.c_ssize_t()
# generic operating system services (CH 15)
def gen_html(csv_file, mod, csv_failed=None, out_file=None):
import datetime
import os.path as op
from pkg_resources import resource_filename as pkgrf
from .. import __version__ as ver
from ..data import GroupTemplate
if version_info[0] > 2:
from io import StringIO as TextIO
else:
from io import BytesIO as TextIO
QCGROUPS = {
"T1w": [
(["cjv"], None),
(["cnr"], None),
(["efc"], None),
(["fber"], None),
(["wm2max"], None),
(["snr_csf", "snr_gm", "snr_wm"], None),
(["snrd_csf", "snrd_gm", "snrd_wm"], None),
(["fwhm_avg", "fwhm_x", "fwhm_y", "fwhm_z"], "vox"),
(["qi_1", "qi_2"], None),
(["inu_range", "inu_med"], None),
(["icvs_csf", "icvs_gm", "icvs_wm"], None),
(["rpve_csf", "rpve_gm", "rpve_wm"], None),
(["tpm_overlap_csf", "tpm_overlap_gm", "tpm_overlap_wm"], None),
(
[
"summary_bg_mean",
"summary_bg_median",
"summary_bg_stdv",
"summary_bg_mad",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_csf_mean",
"summary_csf_median",
"summary_csf_stdv",
"summary_csf_mad",
"summary_csf_k",
"summary_csf_p05",
"summary_csf_p95",
],
None,
),
(
[
"summary_gm_mean",
"summary_gm_median",
"summary_gm_stdv",
"summary_gm_mad",
"summary_gm_k",
"summary_gm_p05",
"summary_gm_p95",
],
None,
),
(
[
"summary_wm_mean",
"summary_wm_median",
"summary_wm_stdv",
"summary_wm_mad",
"summary_wm_k",
"summary_wm_p05",
"summary_wm_p95",
],
None,
),
],
"T2w": [
(["cjv"], None),
(["cnr"], None),
(["efc"], None),
(["fber"], None),
(["wm2max"], None),
(["snr_csf", "snr_gm", "snr_wm"], None),
(["snrd_csf", "snrd_gm", "snrd_wm"], None),
(["fwhm_avg", "fwhm_x", "fwhm_y", "fwhm_z"], "mm"),
(["qi_1", "qi_2"], None),
(["inu_range", "inu_med"], None),
(["icvs_csf", "icvs_gm", "icvs_wm"], None),
(["rpve_csf", "rpve_gm", "rpve_wm"], None),
(["tpm_overlap_csf", "tpm_overlap_gm", "tpm_overlap_wm"], None),
(
[
"summary_bg_mean",
"summary_bg_stdv",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_csf_mean",
"summary_csf_stdv",
"summary_csf_k",
"summary_csf_p05",
"summary_csf_p95",
],
None,
),
(
[
"summary_gm_mean",
"summary_gm_stdv",
"summary_gm_k",
"summary_gm_p05",
"summary_gm_p95",
],
None,
),
(
[
"summary_wm_mean",
"summary_wm_stdv",
"summary_wm_k",
"summary_wm_p05",
"summary_wm_p95",
],
None,
),
],
"bold": [
(["efc"], None),
(["fber"], None),
(["fwhm", "fwhm_x", "fwhm_y", "fwhm_z"], "mm"),
(["gsr_%s" % a for a in ["x", "y"]], None),
(["snr"], None),
(["dvars_std", "dvars_vstd"], None),
(["dvars_nstd"], None),
(["fd_mean"], "mm"),
(["fd_num"], "# timepoints"),
(["fd_perc"], "% timepoints"),
(["spikes_num"], "# slices"),
(["dummy_trs"], "# TRs"),
(["gcor"], None),
(["tsnr"], None),
(["aor"], None),
(["aqi"], None),
(
[
"summary_bg_mean",
"summary_bg_stdv",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_fg_mean",
"summary_fg_stdv",
"summary_fg_k",
"summary_fg_p05",
"summary_fg_p95",
],
None,
),
],
}
if csv_file.suffix == ".csv":
def_comps = list(BIDS_COMP.keys())
dataframe = pd.read_csv(csv_file,
index_col=False,
dtype={comp: object
for comp in def_comps})
id_labels = list(
set(def_comps) & set(dataframe.columns.ravel().tolist()))
dataframe["label"] = dataframe[id_labels].apply(_format_labels,
args=(id_labels, ),
axis=1)
else:
dataframe = pd.read_csv(csv_file,
index_col=False,
sep="\t",
dtype={"bids_name": object})
dataframe = dataframe.rename(index=str, columns={"bids_name": "label"})
nPart = len(dataframe)
failed = None
if csv_failed is not None and op.isfile(csv_failed):
config.loggers.cli.warning(
f'Found failed-workflows table "{csv_failed}"')
failed_df = pd.read_csv(csv_failed, index_col=False)
cols = list(set(id_labels) & set(failed_df.columns.ravel().tolist()))
try:
failed_df = failed_df.sort_values(by=cols)
except AttributeError:
failed_df = failed_df.sort(columns=cols)
# myfmt not defined
# failed = failed_df[cols].apply(myfmt, args=(cols,), axis=1).ravel().tolist()
csv_groups = []
datacols = dataframe.columns.ravel().tolist()
for group, units in QCGROUPS[mod]:
dfdict = {"iqm": [], "value": [], "label": [], "units": []}
for iqm in group:
if iqm in datacols:
values = dataframe[[iqm]].values.ravel().tolist()
if values:
dfdict["iqm"] += [iqm] * nPart
dfdict["units"] += [units] * nPart
dfdict["value"] += values
dfdict["label"] += dataframe[["label"
]].values.ravel().tolist()
# Save only if there are values
if dfdict["value"]:
csv_df = pd.DataFrame(dfdict)
csv_str = TextIO()
csv_df[["iqm", "value", "label", "units"]].to_csv(csv_str,
index=False)
csv_groups.append(csv_str.getvalue())
if out_file is None:
out_file = op.abspath("group.html")
tpl = GroupTemplate()
tpl.generate_conf(
{
"modality":
mod,
"timestamp":
datetime.datetime.now().strftime("%Y-%m-%d, %H:%M"),
"version":
ver,
"csv_groups":
csv_groups,
"failed":
failed,
"boxplots_js":
open(
pkgrf(
"mriqc",
op.join("data", "reports", "embed_resources",
"boxplots.js"),
)).read(),
"d3_js":
open(
pkgrf(
"mriqc",
op.join("data", "reports", "embed_resources",
"d3.min.js"))).read(),
"boxplots_css":
open(
pkgrf(
"mriqc",
op.join("data", "reports", "embed_resources",
"boxplots.css"),
)).read(),
},
out_file,
)
return out_file
