def __init__(
self,
out_dir,
run_uuid,
config=None,
out_filename="report.html",
packagename=None,
reportlets_dir=None,
subject_id=None,
):
self.root = Path(reportlets_dir or out_dir)
# Initialize structuring elements
self.sections = []
self.errors = []
self.out_dir = Path(out_dir)
self.out_filename = out_filename
self.run_uuid = run_uuid
self.packagename = packagename
self.subject_id = subject_id
if subject_id is not None:
self.subject_id = (subject_id[4:] if subject_id.startswith("sub-")
else subject_id)
self.out_filename = f"sub-{self.subject_id}.html"
# Default template from niworkflows
self.template_path = Path(
pkgrf("aslprep", "niworkflows/reports/report.tpl"))
self._load_config(
Path(config
or pkgrf("aslprep", "niworkflows/reports/default.yml")))
assert self.template_path.exists()
def run_reports(reportlets_dir, out_dir, subject_label, run_uuid, report_type='qsiprep'):
"""
Runs the reports
>>> import os
>>> from shutil import copytree
>>> from tempfile import TemporaryDirectory
>>> filepath = os.path.dirname(os.path.realpath(__file__))
>>> test_data_path = os.path.realpath(os.path.join(filepath,
... '../data/tests/work'))
>>> curdir = os.getcwd()
>>> tmpdir = TemporaryDirectory()
>>> os.chdir(tmpdir.name)
>>> data_dir = copytree(test_data_path, os.path.abspath('work'))
>>> os.makedirs('out/qsiprep', exist_ok=True)
>>> run_reports(os.path.abspath('work/reportlets'),
... os.path.abspath('out'),
... '01', 'madeoutuuid')
0
>>> os.chdir(curdir)
>>> tmpdir.cleanup()
"""
reportlet_path = str(Path(reportlets_dir) / report_type / ("sub-%s" % subject_label))
if report_type == 'qsiprep':
config = pkgrf('qsiprep', 'viz/config.json')
else:
config = pkgrf('qsiprep', 'viz/recon_config.json')
out_filename = 'sub-{}.html'.format(subject_label)
report = Report(reportlet_path, config, out_dir, run_uuid, out_filename,
pipeline_type=report_type)
return report.generate_report()
def mock_config():
"""Create a mock config for documentation and testing purposes."""
from .. import config
_old_fs = os.getenv('FREESURFER_HOME')
if not _old_fs:
os.environ['FREESURFER_HOME'] = mkdtemp()
filename = Path(pkgrf('aslprep', 'data/tests/config.toml'))
settings = loads(filename.read_text())
for sectionname, configs in settings.items():
if sectionname != 'environment':
section = getattr(config, sectionname)
section.load(configs, init=False)
config.nipype.init()
config.loggers.init()
config.init_spaces()
config.execution.work_dir = Path(mkdtemp())
config.execution.bids_dir = Path(pkgrf('aslprep', 'data/tests/ds000240')).absolute()
config.execution.init()
yield
if not _old_fs:
del os.environ["FREESURFER_HOME"]
def generate_report(self):
"""
Returns
-------
"""
logs_path = self.out_dir / 'cmp' / 'logs'
boilerplate = []
boiler_idx = 0
if (logs_path / 'CITATION.html').exists():
text = (logs_path / 'CITATION.html').read_text(encoding='UTF-8')
text = '<div class="boiler-html">%s</div>' % re.compile(
'<body>(.*?)</body>',
re.DOTALL | re.IGNORECASE).findall(text)[0].strip()
boilerplate.append((boiler_idx, 'HTML', text))
boiler_idx += 1
if (logs_path / 'CITATION.md').exists():
text = '<pre>%s</pre>\n' % (logs_path / 'CITATION.md').read_text(
encoding='UTF-8')
boilerplate.append((boiler_idx, 'Markdown', text))
boiler_idx += 1
if (logs_path / 'CITATION.tex').exists():
text = (logs_path / 'CITATION.tex').read_text(encoding='UTF-8')
text = re.compile(r'\\begin{document}(.*?)\\end{document}',
re.DOTALL
| re.IGNORECASE).findall(text)[0].strip()
text = '<pre>%s</pre>\n' % text
text += '<h3>Bibliography</h3>\n'
text += '<pre>%s</pre>\n' % Path(
pkgrf('cmp',
'data/boilerplate.bib')).read_text(encoding='UTF-8')
boilerplate.append((boiler_idx, 'LaTeX', text))
boiler_idx += 1
searchpath = pkgrf('cmp', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True,
lstrip_blocks=True)
report_tpl = env.get_template('viz/report.tpl')
report_render = report_tpl.render(sections=self.sections,
errors=self.errors,
boilerplate=boilerplate)
# Write out report
(self.out_dir / 'cmp' / self.out_filename).write_text(report_render,
encoding='UTF-8')
return len(self.errors)
def test_generated_reportlets(bids_sessions, ordering):
# make independent report
out_dir = tempfile.mkdtemp()
report = Report(Path(bids_sessions),
Path(out_dir),
'fakeiuud',
subject_id='01',
packagename='fmriprep')
config = Path(pkgrf('niworkflows', 'reports/fmriprep.yml'))
settings = load(config.read_text())
# change settings to only include some missing ordering
settings['sections'][3]['ordering'] = ordering
report.index(settings['sections'])
# expected number of reportlets
expected_reportlets_num = len(report.layout.get(extension='svg'))
# bids_session uses these entities
needed_entities = ['session', 'task', 'run']
# the last section is the most recently run
reportlets_num = len(report.sections[-1].reportlets)
# if ordering does not contain all the relevent entities
# then there should be fewer reportlets than expected
if all(ent in ordering for ent in needed_entities):
assert reportlets_num == expected_reportlets_num
else:
assert reportlets_num < expected_reportlets_num
def run_reports(reportlets_dir, out_dir, subject_label, run_uuid):
reportlet_path = os.path.join(reportlets_dir, 'fmriprep', "sub-" + subject_label)
config = pkgrf('fmriprep', 'viz/config.json')
out_filename = 'sub-{}.html'.format(subject_label)
report = Report(reportlet_path, config, out_dir, run_uuid, out_filename)
return report.generate_report()
def run_reports(reportlets_dir, out_dir, subject_label, run_uuid):
"""
Runs the reports
>>> import os
>>> from shutil import copytree
>>> from tempfile import TemporaryDirectory
>>> filepath = os.path.dirname(os.path.realpath(__file__))
>>> test_data_path = os.path.realpath(os.path.join(filepath,
... '../data/tests/work'))
>>> curdir = os.getcwd()
>>> tmpdir = TemporaryDirectory()
>>> os.chdir(tmpdir.name)
>>> data_dir = copytree(test_data_path, os.path.abspath('work'))
>>> os.makedirs('out/fmriprep', exist_ok=True)
>>> run_reports(os.path.abspath('work/reportlets'),
... os.path.abspath('out'),
... '01', 'madeoutuuid')
0
>>> os.chdir(curdir)
>>> tmpdir.cleanup()
"""
reportlet_path = os.path.join(reportlets_dir, 'fmriprep', "sub-" + subject_label)
config = pkgrf('fmriprep', 'viz/config.json')
out_filename = 'sub-{}.html'.format(subject_label)
report = Report(reportlet_path, config, out_dir, run_uuid, out_filename)
return report.generate_report()
def get_outputnode_spec():
"""
Generate outputnode's fields from I/O spec file.
Examples
--------
>>> get_outputnode_spec() # doctest: +NORMALIZE_WHITESPACE
['t1w_preproc', 't1w_mask', 't1w_dseg', 't1w_tpms',
'std_preproc', 'std_mask', 'std_dseg', 'std_tpms',
'anat2std_xfm', 'std2anat_xfm',
't1w_aseg', 't1w_aparc',
't1w2fsnative_xfm', 'fsnative2t1w_xfm',
'surfaces']
"""
spec = loads(
Path(pkgrf('aslprep',
'smriprep/data/io_spec.json')).read_text())["queries"]
fields = [
'_'.join((m, s)) for m in ('t1w', 'std')
for s in spec["baseline"].keys()
]
fields += [s for s in spec["std_xfms"].keys()]
fields += [s for s in spec["surfaces"].keys()]
return fields
def __init__(self,
reportlets_dir,
out_dir,
run_uuid,
config=None,
subject_id=None,
out_filename='report.html',
packagename=None):
self.root = reportlets_dir
# Initialize structuring elements
self.sections = []
self.errors = []
self.out_dir = Path(out_dir)
self.out_filename = out_filename
self.run_uuid = run_uuid
self.template_path = None
self.packagename = packagename
self.subject_id = subject_id
if subject_id is not None and subject_id.startswith('sub-'):
self.subject_id = self.subject_id[4:]
if self.subject_id is not None:
self.out_filename = 'sub-{}.html'.format(self.subject_id)
if config is None:
config = pkgrf('niworkflows', 'reports/fmriprep.yml')
self._load_config(Path(config))
def _get_params_dist(self):
preparams = {
'std__by': ['site'],
'std__with_centering': [True, False],
'std__with_scaling': [True, False],
'std__columns': [[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
'ft_sites__disable': [False, True],
'ft_noise__disable': [False, True],
}
prefix = self._model + '__'
if self._multiclass:
prefix += 'estimator__'
clfparams = _load_parameters(
(pkgrf('mriqc', 'data/model_selection.yml')
if self._param_file is None else self._param_file)
)
modparams = {prefix + k: v for k, v in list(clfparams[self._model][0].items())}
if self._debug:
preparams = {
'std__by': ['site'],
'std__with_centering': [True],
'std__with_scaling': [True],
'std__columns': [[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
'ft_sites__disable': [True],
'ft_noise__disable': [True],
}
modparams = {k: [v[0]] for k, v in list(modparams.items())}
return {**preparams, **modparams}
def test_config_spaces():
"""Check that all necessary spaces are recorded in the config."""
filename = Path(pkgrf("fprodents", "data/tests/config.toml"))
settings = loads(filename.read_text())
for sectionname, configs in settings.items():
if sectionname != "environment":
section = getattr(config, sectionname)
section.load(configs, init=False)
config.nipype.init()
config.loggers.init()
config.init_spaces()
spaces = config.workflow.spaces
assert "Fischer344:res-native" not in [
str(s) for s in spaces.get_standard(full_spec=True)
]
config.init_spaces()
spaces = config.workflow.spaces
assert "Fischer344:res-native" in [
str(s) for s in spaces.get_standard(full_spec=True)
]
config.execution.output_spaces = None
config.workflow.use_aroma = False
config.init_spaces()
spaces = config.workflow.spaces
assert not [str(s) for s in spaces.get_standard(full_spec=True)]
assert [
format_reference((s.fullname, s.spec)) for s in spaces.references
if s.standard and s.dim == 3
] == ["Fischer344"]
def save_html(template, report_file_name, unique_string, **kwargs):
''' save an actual html file with name report_file_name. unique_string's
first character must be alphabetical; every call to save_html must have a
unique unique_string. kwargs should all contain valid html that will be sent
to the jinja2 renderer '''
if not unique_string[0].isalpha():
raise ValueError('unique_string must be a valid id value in html; '
'the first character must be alphabetical. Received unique_string={}'
.format(unique_string))
# validate html
validator = HTMLValidator(unique_string=unique_string)
for html in list(kwargs.keys()):
validator.feed(html)
validator.close()
searchpath = pkgrf('niworkflows', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True, lstrip_blocks=True
)
report_tpl = env.get_template('viz/' + template)
kwargs['unique_string'] = unique_string
report_render = report_tpl.render(kwargs)
with open(report_file_name, 'w' if PY3 else 'wb') as handle:
handle.write(report_render)
def run_reports(reportlets_dir, out_dir, subject_label, run_uuid):
reportlet_path = os.path.join(reportlets_dir, 'fmriprep', "sub-" + subject_label)
config = pkgrf('fmriprep', 'viz/config.json')
out_filename = 'sub-{}.html'.format(subject_label)
report = Report(reportlet_path, config, out_dir, run_uuid, out_filename)
return report.generate_report()
def test_generated_reportlets(bids_sessions, ordering):
# make independent report
out_dir = tempfile.mkdtemp()
report = Report(
Path(out_dir),
"fakeuuid",
reportlets_dir=Path(bids_sessions),
subject_id="01",
packagename="fmriprep",
)
config = Path(pkgrf("niworkflows", "reports/default.yml"))
settings = load(config.read_text())
# change settings to only include some missing ordering
settings["sections"][3]["ordering"] = ordering
report.index(settings["sections"])
# expected number of reportlets
expected_reportlets_num = len(report.layout.get(extension=".svg"))
# bids_session uses these entities
needed_entities = ["session", "task", "ceagent", "run"]
# the last section is the most recently run
reportlets_num = len(report.sections[-1].reportlets)
# get the number of figures in the output directory
out_layout = BIDSLayout(out_dir, config="figures", validate=False)
out_figs = len(out_layout.get())
# if ordering does not contain all the relevent entities
# then there should be fewer reportlets than expected
if all(ent in ordering for ent in needed_entities):
assert reportlets_num == expected_reportlets_num == out_figs
else:
assert reportlets_num < expected_reportlets_num == out_figs
def test_report1():
test_data_path = pkgrf(
'niworkflows',
os.path.join('data', 'tests', 'work', 'reportlets'))
out_dir = tempfile.mkdtemp()
return Report(Path(test_data_path), Path(out_dir), 'fakeiuud',
subject_id='01', packagename='aslprep')
def mock_config():
"""Create a mock config for documentation and testing purposes."""
from . import config
filename = Path(pkgrf("mriqc", "data/config-example.toml"))
settings = loads(filename.read_text())
for sectionname, configs in settings.items():
if sectionname != "environment":
section = getattr(config, sectionname)
section.load(configs, init=False)
config.nipype.init()
config.loggers.init()
config.execution.work_dir = Path(mkdtemp())
config.execution.bids_dir = Path(pkgrf("mriqc",
"data/tests/ds000005")).absolute()
config.execution.init()
yield
def generate_report(self):
logs_path = self.out_dir / 'fmriprep' / 'logs'
boilerplate = []
boiler_idx = 0
if (logs_path / 'CITATION.html').exists():
text = (logs_path / 'CITATION.html').read_text(encoding='UTF-8')
text = '<div class="boiler-html">%s</div>' % re.compile(
'<body>(.*?)</body>',
re.DOTALL | re.IGNORECASE).findall(text)[0].strip()
boilerplate.append((boiler_idx, 'HTML', text))
boiler_idx += 1
if (logs_path / 'CITATION.md').exists():
text = '<pre>%s</pre>\n' % (logs_path / 'CITATION.md').read_text(encoding='UTF-8')
boilerplate.append((boiler_idx, 'Markdown', text))
boiler_idx += 1
if (logs_path / 'CITATION.tex').exists():
text = (logs_path / 'CITATION.tex').read_text(encoding='UTF-8')
text = re.compile(
r'\\begin{document}(.*?)\\end{document}',
re.DOTALL | re.IGNORECASE).findall(text)[0].strip()
text = '<pre>%s</pre>\n' % text
text += '<h3>Bibliography</h3>\n'
text += '<pre>%s</pre>\n' % Path(
pkgrf('fmriprep', 'data/boilerplate.bib')).read_text(encoding='UTF-8')
boilerplate.append((boiler_idx, 'LaTeX', text))
boiler_idx += 1
searchpath = pkgrf('fmriprep', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True, lstrip_blocks=True
)
report_tpl = env.get_template('viz/report.tpl')
report_render = report_tpl.render(sections=self.sections, errors=self.errors,
boilerplate=boilerplate)
# Write out report
(self.out_dir / 'fmriprep' / self.out_filename).write_text(report_render, encoding='UTF-8')
return len(self.errors)
def generate_report(self):
searchpath = pkgrf('fmriprep', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True, lstrip_blocks=True
)
report_tpl = env.get_template('viz/report.tpl')
report_render = report_tpl.render(sections=self.sections, errors=self.errors)
with open(os.path.join(self.out_dir, "fmriprep", self.out_filename), 'w') as fp:
fp.write(report_render)
return len(self.errors)
def _get_params(self):
preparams = [
{
'std__by': ['site'],
'std__with_centering': [True],
'std__with_scaling': [True],
'std__columns':
[[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
'ft_sites__disable': [False, True],
'ft_noise__disable': [False, True],
},
{
'std__by': ['site'],
'std__with_centering': [True, False],
'std__with_scaling': [True, False],
'std__columns':
[[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
'ft_sites__disable': [True],
'ft_noise__disable': [True],
},
]
if self._debug:
preparams = [
{
'std__by': ['site'],
'std__with_centering': [False],
'std__with_scaling': [False],
'std__columns':
[[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
'ft_sites__disable': [True],
'ft_noise__disable': [True],
},
]
prefix = self._model + '__'
if self._multiclass:
prefix += 'estimator__'
clfparams = _load_parameters(
(pkgrf('mriqc', 'data/classifier_settings.yml')
if self._param_file is None else self._param_file))
modparams = {
prefix + k: v
for k, v in list(clfparams[self._model][0].items())
}
if self._debug:
modparams = {k: [v[0]] for k, v in list(modparams.items())}
return [{**prep, **modparams} for prep in preparams]
def test_report1():
test_data_path = pkgrf("niworkflows",
os.path.join("data", "tests", "work", "reportlets"))
out_dir = tempfile.mkdtemp()
return Report(
Path(out_dir),
"fakeuuid",
reportlets_dir=Path(test_data_path),
subject_id="01",
packagename="fmriprep",
)
def _get_params(self):
# Some baseline parameters
baseparam = {
'std__by': ['site'],
'std__columns':
[[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
}
# Load in classifier parameters
clfparams = _load_parameters(
(pkgrf('mriqc', 'data/classifier_settings.yml')
if self._param_file is None else self._param_file))
# Read preprocessing parameters
if 'preproc' in clfparams:
preparams = []
for el in clfparams['preproc']:
pcombination = {}
for pref, subel in list(el.items()):
for k, v in list(subel.items()):
pcombination[pref + '__' + k] = v
preparams.append(pcombination)
else:
preparams = [{
'std__with_centering': [True],
'std__with_scaling': [True],
'ft_sites__disable': [False],
'ft_noise__disable': [False],
}]
# Set base parameters
preparams = [{**baseparam, **prep} for prep in preparams]
# Extract this model parameters
prefix = self._model + '__'
if self._multiclass:
prefix += 'estimator__'
modparams = {
prefix + k: v
for k, v in list(clfparams[self._model][0].items())
}
# Merge model parameters + preprocessing
modparams = [{**prep, **modparams} for prep in preparams]
# Evaluate just one model if debug
if self._debug:
modparams = {k: [v[0]] for k, v in list(modparams.items())}
return modparams
def generate_report(self):
searchpath = pkgrf('fmriprep', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True, lstrip_blocks=True
)
report_tpl = env.get_template('viz/report.tpl')
report_render = report_tpl.render(sub_reports=self.sub_reports, errors=self.errors,
date=time.strftime("%Y-%m-%d %H:%M:%S %z"),
version=__version__)
with open(os.path.join(self.out_dir, "fmriprep", self.out_filename), 'w') as fp:
fp.write(report_render)
return len(self.errors)
def _read_model(model_name):
from sys import version_info
import simplejson as json
from pkg_resources import resource_filename as pkgrf
with open(pkgrf('regseg', 'data/model_%s.json' % model_name.lower()),
'rb' if version_info[0] < 3 else 'r') as sfh:
model = json.load(sfh)
name = ['%s.surf' % m for m in model.keys()]
labels = list(model.values())
return name, labels
def test_bunch_hash():
# NOTE: Since the path to the json file is included in the Bunch,
# the hash will be unique to each machine.
json_pth = pkgrf("nipype", os.path.join("testing", "data", "realign_json.json"))
b = nib.Bunch(infile=json_pth, otherthing="blue", yat=True)
newbdict, bhash = b._get_bunch_hash()
assert bhash == "d1f46750044c3de102efc847720fc35f"
# Make sure the hash stored in the json file for `infile` is correct.
jshash = md5()
with open(json_pth, "r") as fp:
jshash.update(fp.read().encode("utf-8"))
assert newbdict["infile"][0][1] == jshash.hexdigest()
assert newbdict["yat"] is True
def _get_params(self):
# Some baseline parameters
baseparam = {
'std__by': ['site'],
'std__columns': [[ft for ft in self._ftnames if ft in FEATURE_NORM]],
'sel_cols__columns': [self._ftnames + ['site']],
}
# Load in classifier parameters
clfparams = _load_parameters(
(pkgrf('mriqc', 'data/classifier_settings.yml')
if self._param_file is None else self._param_file)
)
# Read preprocessing parameters
if 'preproc' in clfparams:
preparams = []
for el in clfparams['preproc']:
pcombination = {}
for pref, subel in list(el.items()):
for k, v in list(subel.items()):
pcombination[pref + '__' + k] = v
preparams.append(pcombination)
else:
preparams = [{
'std__with_centering': [True],
'std__with_scaling': [True],
'ft_sites__disable': [False],
'ft_noise__disable': [False],
}]
# Set base parameters
preparams = [{**baseparam, **prep} for prep in preparams]
# Extract this model parameters
prefix = self._model + '__'
if self._multiclass:
prefix += 'estimator__'
modparams = {prefix + k: v for k, v in list(clfparams[self._model][0].items())}
# Merge model parameters + preprocessing
modparams = [{**prep, **modparams} for prep in preparams]
# Evaluate just one model if debug
if self._debug:
modparams = {k: [v[0]] for k, v in list(modparams.items())}
return modparams
def test_bunch_hash():
# NOTE: Since the path to the json file is included in the Bunch,
# the hash will be unique to each machine.
json_pth = pkgrf('nipype',
os.path.join('testing', 'data', 'realign_json.json'))
b = nib.Bunch(infile=json_pth, otherthing='blue', yat=True)
newbdict, bhash = b._get_bunch_hash()
assert bhash == 'd1f46750044c3de102efc847720fc35f'
# Make sure the hash stored in the json file for `infile` is correct.
jshash = md5()
with open(json_pth, 'r') as fp:
jshash.update(fp.read().encode('utf-8'))
assert newbdict['infile'][0][1] == jshash.hexdigest()
assert newbdict['yat'] is True
def __init__(self,
reportlets_dir,
out_dir,
run_uuid,
config=None,
subject_id=None,
out_filename='report.html',
packagename=None):
self.root = reportlets_dir
# Add a new figures spec
try:
add_config_paths(
figures=pkgrf('niworkflows', 'reports/figures.json'))
except ValueError as e:
if "Configuration 'figures' already exists" != str(e):
raise
# Initialize structuring elements
self.sections = []
self.errors = []
self.out_dir = Path(out_dir)
self.out_filename = out_filename
self.run_uuid = run_uuid
self.template_path = None
self.packagename = packagename
self.subject_id = subject_id
if subject_id is not None and subject_id.startswith('sub-'):
self.subject_id = self.subject_id[4:]
if self.subject_id is not None:
self.out_filename = 'sub-{}.html'.format(self.subject_id)
if config is None:
config = pkgrf('niworkflows', 'reports/fmriprep.yml')
self._load_config(Path(config))
def get_parser():
from argparse import ArgumentParser
from argparse import RawTextHelpFormatter
parser = ArgumentParser(description='MRIQC model selection and held-out evaluation',
formatter_class=RawTextHelpFormatter)
g_clf = parser.add_mutually_exclusive_group()
g_clf.add_argument('--train', nargs='*',
help='training data tables, X and Y, leave empty for ABIDE.')
g_clf.add_argument('--load-classifier', nargs="?", type=str, default='',
help='load a previously saved classifier')
parser.add_argument('--test', nargs='*',
help='test data tables, X and Y, leave empty for DS030.')
parser.add_argument('-X', '--evaluation-data', help='classify this CSV table of IQMs')
parser.add_argument('--train-balanced-leaveout', action='store_true', default=False,
help='leave out a balanced, random, sample of training examples')
parser.add_argument('--multiclass', '--ms', action='store_true', default=False,
help='do not binarize labels')
g_input = parser.add_argument_group('Options')
g_input.add_argument('-P', '--parameters', action='store',
default=pkgrf('mriqc', 'data/classifier_settings.yml'))
g_input.add_argument('-M', '--model', action='store', choices=['rfc', 'xgb'],
default='rfc', help='model')
g_input.add_argument('-S', '--scorer', action='store', default='roc_auc')
g_input.add_argument('--cv', action='store', default='loso',
choices=['kfold', 'loso', 'balanced-kfold', 'batch'])
g_input.add_argument('--debug', action='store_true', default=False)
g_input.add_argument('--log-file', nargs="?", action='store', default='',
help='write log to this file, leave empty for a default log name')
g_input.add_argument("-v", "--verbose", dest="verbose_count",
action="count", default=0,
help="increases log verbosity for each occurence.")
g_input.add_argument('--njobs', action='store', default=-1, type=int,
help='number of jobs')
g_input.add_argument('-o', '--output', action='store', default='predicted_qa.csv',
help='file containing the labels assigned by the classifier')
g_input.add_argument('-t', '--threshold', action='store', default=0.5, type=float,
help='decision threshold of the classifier')
return parser
def test_preproc_plus_controllability(bids_singlescan_data, tmpdir):
preproc_plus_controllability = get_default_cli_args()
preproc_plus_controllability.bids_dir = WORKING_SINGLE_DIR + "/DSCSDSI"
preproc_plus_controllability.work_dir = tmpdir
preproc_plus_controllability.recon_only = False
preproc_plus_controllability.output_dir = op.join(tmpdir,
"preproc_control")
preproc_plus_controllability.recon_spec = pkgrf(
"qsiprep", "data/pipelines/controllability.json")
with mock.patch.object(argparse.ArgumentParser,
'parse_args',
return_value=preproc_plus_controllability):
with pytest.raises(SystemExit):
cli_main()
def test_config_spaces():
"""Check that all necessary spaces are recorded in the config."""
filename = Path(pkgrf('fmriprep', 'data/tests/config.toml'))
settings = loads(filename.read_text())
for sectionname, configs in settings.items():
if sectionname != 'environment':
section = getattr(config, sectionname)
section.load(configs, init=False)
config.nipype.init()
config.loggers.init()
config.init_spaces()
spaces = config.workflow.spaces
assert "MNI152NLin6Asym:res-2" not in [
str(s) for s in spaces.get_standard(full_spec=True)
]
assert "MNI152NLin6Asym_res-2" not in [
format_reference((s.fullname, s.spec)) for s in spaces.references
if s.standard and s.dim == 3
]
config.workflow.use_aroma = True
config.init_spaces()
spaces = config.workflow.spaces
assert "MNI152NLin6Asym:res-2" in [
str(s) for s in spaces.get_standard(full_spec=True)
]
assert "MNI152NLin6Asym_res-2" in [
format_reference((s.fullname, s.spec)) for s in spaces.references
if s.standard and s.dim == 3
]
config.execution.output_spaces = None
config.workflow.use_aroma = False
config.init_spaces()
spaces = config.workflow.spaces
assert [str(s) for s in spaces.get_standard(full_spec=True)] == []
assert [
format_reference((s.fullname, s.spec)) for s in spaces.references
if s.standard and s.dim == 3
] == ['MNI152NLin2009cAsym']
_reset_config()
def _parse_set(arg, default):
if arg is not None and len(arg) == 0:
return [pkgrf('mriqc', 'data/csv/%s' % name) for name in (
'x_%s-0.9.6-2017-06-03-99db97c9be2e.csv' % default,
'y_%s.csv' % default)]
if arg is not None and len(arg) not in (0, 2):
raise RuntimeError('Wrong number of parameters.')
if len(arg) == 2:
train_exists = [isfile(fname) for fname in arg]
if len(train_exists) > 0 and not all(train_exists):
errors = ['file "%s" not found' % fname
for fexists, fname in zip(train_exists, arg)
if not fexists]
raise RuntimeError('Errors (%d) loading training set: %s.' % (
len(errors), ', '.join(errors)))
return arg
def _load_recon_spec(spec_name, sloppy=False):
prepackaged_dir = pkgrf("qsiprep", "data/pipelines")
prepackaged = [op.split(fname)[1][:-5] for fname in glob(prepackaged_dir+"/*.json")]
if op.exists(spec_name):
recon_spec = spec_name
elif spec_name in prepackaged:
recon_spec = op.join(prepackaged_dir + "/{}.json".format(spec_name))
else:
raise Exception("{} is not a file that exists or in {}".format(spec_name, prepackaged))
with open(recon_spec, "r") as f:
try:
spec = json.load(f)
except Exception:
raise Exception("Unable to read JSON spec. Check the syntax.")
if sloppy:
LOGGER.warning("Forcing reconstruction to use unrealistic parameters")
spec = make_sloppy(spec)
return spec
def _load_recon_spec(spec_name):
prepackaged_dir = pkgrf("qsiprep", "data/pipelines")
prepackaged = [
op.split(fname)[1][:-5] for fname in glob(prepackaged_dir + "/*.json")
]
if op.exists(spec_name):
recon_spec = spec_name
elif spec_name in prepackaged:
recon_spec = op.join(prepackaged_dir + "/{}.json".format(spec_name))
else:
raise Exception("{} is not a file that exists or in {}".format(
spec_name, prepackaged))
with open(recon_spec, "r") as f:
try:
spec = json.load(f)
except Exception:
raise Exception("Unable to read JSON spec. Check the syntax.")
return spec
def _parse_set(arg, default):
if arg is not None and len(arg) == 0:
return [pkgrf('mriqc', 'data/csv/%s' % name) for name in (
'x_%s-0.9.6-2017-06-03-99db97c9be2e.csv' % default,
'y_%s.csv' % default)]
if arg is not None and len(arg) not in (0, 2):
raise RuntimeError('Wrong number of parameters.')
if len(arg) == 2:
train_exists = [isfile(fname) for fname in arg]
if len(train_exists) > 0 and not all(train_exists):
errors = ['file "%s" not found' % fname
for fexists, fname in zip(train_exists, arg)
if not fexists]
raise RuntimeError('Errors (%d) loading training set: %s.' % (
len(errors), ', '.join(errors)))
return arg
def _load_config(self, config):
from yaml import safe_load as load
settings = load(config.read_text())
self.packagename = self.packagename or settings.get('package', None)
if self.packagename is not None:
self.root = self.root / self.packagename
self.out_dir = self.out_dir / self.packagename
if self.subject_id is not None:
self.root = self.root / 'sub-{}'.format(self.subject_id)
# Default template from niworkflows
template_path = Path(pkgrf('niworkflows', 'reports/report.tpl'))
if 'template_path' in settings:
template_path = config.parent / settings['template_path']
self.template_path = template_path.absolute()
self.index(settings['sections'])
def generate_report(self):
searchpath = pkgrf('fmriprep', '/')
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=searchpath),
trim_blocks=True,
lstrip_blocks=True)
report_tpl = env.get_template('viz/report.tpl')
# Ignore subreports with no children
sub_reports = [
sub_report for sub_report in self.sub_reports
if len(sub_report.run_reports) > 0 or any(
elem.files_contents for elem in sub_report.elements)
]
report_render = report_tpl.render(sub_reports=sub_reports,
errors=self.errors)
with open(os.path.join(self.out_dir, "fmriprep", self.out_filename),
'w') as fp:
fp.write(report_render)
return len(self.errors)
def test_preproc_pepolar_sdc(tmp_path):
# Get the empty bids data
bids_root = pkgrf('qsiprep', 'data/abcd')
work_dir = str(tmp_path.absolute() / "preproc_pepolar_sdc_work")
output_dir = str(tmp_path.absolute() / "preproc_pepolar_sdc_output")
bids_dir = bids_root
subject_list = ['abcd']
wf = init_qsiprep_wf(subject_list=subject_list,
run_uuid="test",
work_dir=work_dir,
output_dir=output_dir,
bids_dir=bids_dir,
ignore=[],
debug=False,
low_mem=False,
anat_only=False,
longitudinal=False,
freesurfer=False,
hires=False,
force_spatial_normalization=False,
denoise_before_combining=True,
dwi_denoise_window=7,
combine_all_dwis=True,
omp_nthreads=1,
skull_strip_template='OASIS',
skull_strip_fixed_seed=False,
output_spaces=['T1w', 'template'],
template='MNI152NLin2009cAsym',
output_resolution=1.25,
motion_corr_to='iterative',
b0_to_t1w_transform='Rigid',
hmc_transform='Affine',
hmc_model='3dSHORE',
impute_slice_threshold=0,
write_local_bvecs=False,
fmap_bspline=False,
fmap_demean=True,
use_syn=False,
prefer_dedicated_fmaps=True,
force_syn=False)
assert len(wf.list_node_names())
def _parse_set(arg, default):
if arg is not None and len(arg) == 0:
names = f"x_{default}.csv", f"y_{default}.csv"
return [pkgrf("mriqc", f"data/csv/{name}") for name in names]
if arg is not None and len(arg) not in (0, 2):
raise RuntimeError(messages.CLF_WRONG_PARAMETER_COUNT)
if arg is None:
return None
if len(arg) == 2:
train_exists = [isfile(fname) for fname in arg]
if len(train_exists) > 0 and not all(train_exists):
errors = [
f"file '{fname}' not found"
for fexists, fname in zip(train_exists, arg) if not fexists
]
runtime_error = messages.CLF_TRAIN_LOAD_ERROR.format(
n_errors=len(errors), errors=", ".join(errors))
raise RuntimeError(runtime_error)
return arg
def epi_mni_align(settings, name='SpatialNormalization'):
"""
Uses FSL FLIRT with the BBR cost function to find the transform that
maps the EPI space into the MNI152-nonlinear-symmetric atlas.
The input epi_mean is the averaged and brain-masked EPI timeseries
Returns the EPI mean resampled in MNI space (for checking out registration) and
the associated "lobe" parcellation in EPI space.
.. workflow::
from mriqc.workflows.functional import epi_mni_align
wf = epi_mni_align({})
"""
from niworkflows.data import get_mni_icbm152_nlin_asym_09c as get_template
from niworkflows.interfaces.registration import (
RobustMNINormalizationRPT as RobustMNINormalization
)
from pkg_resources import resource_filename as pkgrf
# Get settings
testing = settings.get('testing', False)
n_procs = settings.get('n_procs', 1)
ants_nthreads = settings.get('ants_nthreads', DEFAULTS['ants_nthreads'])
# Init template
mni_template = get_template()
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['epi_mean', 'epi_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['epi_mni', 'epi_parc', 'report']), name='outputnode')
epimask = pe.Node(fsl.ApplyMask(), name='EPIApplyMask')
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='SharpenEPI')
norm = pe.Node(RobustMNINormalization(
num_threads=ants_nthreads,
float=settings.get('ants_float', False),
template='mni_icbm152_nlin_asym_09c',
reference_image=pkgrf('mriqc', 'data/mni/2mm_T2_brain.nii.gz'),
flavor='testing' if testing else 'precise',
moving='EPI',
generate_report=True,),
name='EPI2MNI',
num_threads=n_procs,
mem_gb=3)
# Warp segmentation into EPI space
invt = pe.Node(ants.ApplyTransforms(
float=True,
input_image=str(Path(mni_template) / '1mm_parc.nii.gz'),
dimension=3, default_value=0, interpolation='NearestNeighbor'),
name='ResampleSegmentation')
workflow.connect([
(inputnode, invt, [('epi_mean', 'reference_image')]),
(inputnode, n4itk, [('epi_mean', 'input_image')]),
(inputnode, epimask, [('epi_mask', 'mask_file')]),
(n4itk, epimask, [('output_image', 'in_file')]),
(epimask, norm, [('out_file', 'moving_image')]),
(norm, invt, [
('inverse_composite_transform', 'transforms')]),
(invt, outputnode, [('output_image', 'epi_parc')]),
(norm, outputnode, [('warped_image', 'epi_mni'),
('out_report', 'report')]),
])
return workflow
def main():
"""Entry point"""
import yaml
from io import open
from argparse import ArgumentParser
from argparse import RawTextHelpFormatter
from pkg_resources import resource_filename as pkgrf
from ..classifier.cv import NestedCVHelper
from .. import logging, LOG_FORMAT
warnings.showwarning = warn_redirect
parser = ArgumentParser(description='MRIQC Nested cross-validation evaluation',
formatter_class=RawTextHelpFormatter)
parser.add_argument('training_data', help='input data')
parser.add_argument('training_labels', help='input data')
g_input = parser.add_argument_group('Inputs')
g_input.add_argument('-P', '--parameters', action='store',
default=pkgrf('mriqc', 'data/grid_nested_cv.yml'))
g_input.add_argument('--cv-inner', action='store', default=10,
help='inner loop of cross-validation')
g_input.add_argument('--cv-outer', action='store', default='loso',
help='outer loop of cross-validation')
g_input.add_argument('--log-file', action='store', help='write log to this file')
g_input.add_argument('--log-level', action='store', default='INFO',
choices=['CRITICAL', 'ERROR', 'WARN', 'INFO', 'DEBUG'])
g_input.add_argument('-o', '--output-file', action='store', default='cv_inner_loop.csv',
help='the output table with cross validated scores')
g_input.add_argument('-O', '--output-outer-cv', action='store', default='cv_outer_loop.csv',
help='the output table with cross validated scores')
g_input.add_argument('--njobs', action='store', default=-1, type=int,
help='number of jobs')
g_input.add_argument('--task-id', action='store')
opts = parser.parse_args()
logger = logging.getLogger()
if opts.log_file is not None:
fhl = logging.FileHandler(opts.log_file)
fhl.setFormatter(fmt=logging.Formatter(LOG_FORMAT))
logger.addHandler(fhl)
logger.setLevel(opts.log_level)
parameters = None
if opts.parameters is not None:
with open(opts.parameters) as paramfile:
parameters = yaml.load(paramfile)
cvhelper = NestedCVHelper(opts.training_data, opts.training_labels,
n_jobs=opts.njobs, param=parameters,
task_id=opts.task_id)
cvhelper.cv_inner = read_cv(opts.cv_inner)
cvhelper.cv_outer = read_cv(opts.cv_outer)
# Run inner loop before setting held-out data, for hygene
cvhelper.fit()
with open(opts.output_file, 'a' if PY3 else 'ab') as outfile:
flock(outfile, LOCK_EX)
save_headers = op.getsize(opts.output_file) == 0
cvhelper.get_inner_cv_scores().to_csv(
outfile, index=False, header=save_headers)
flock(outfile, LOCK_UN)
with open(opts.output_outer_cv, 'a' if PY3 else 'ab') as outfile:
flock(outfile, LOCK_EX)
save_headers = op.getsize(opts.output_outer_cv) == 0
cvhelper.get_outer_cv_scores().to_csv(
outfile, index=False, header=save_headers)
flock(outfile, LOCK_UN)
def build_workflow(opts, retval):
"""
Create the Nipype Workflow that supports the whole execution
graph, given the inputs.
All the checks and the construction of the workflow are done
inside this function that has pickleable inputs and output
dictionary (``retval``) to allow isolation using a
``multiprocessing.Process`` that allows fmriprep to enforce
a hard-limited memory-scope.
"""
from subprocess import check_call, CalledProcessError, TimeoutExpired
from pkg_resources import resource_filename as pkgrf
from shutil import copyfile
from nipype import logging, config as ncfg
from niworkflows.utils.bids import collect_participants
from ..__about__ import __version__
from ..workflows.base import init_fmriprep_wf
from ..viz.reports import generate_reports
logger = logging.getLogger('nipype.workflow')
INIT_MSG = """
Running fMRIPREP version {version}:
* BIDS dataset path: {bids_dir}.
* Participant list: {subject_list}.
* Run identifier: {uuid}.
""".format
output_spaces = opts.output_space or []
# Validity of some inputs
# ERROR check if use_aroma was specified, but the correct template was not
if opts.use_aroma and (opts.template != 'MNI152NLin2009cAsym' or
'template' not in output_spaces):
output_spaces.append('template')
logger.warning(
'Option "--use-aroma" requires functional images to be resampled to MNI space. '
'The argument "template" has been automatically added to the list of output '
'spaces (option "--output-space").'
)
if opts.cifti_output and (opts.template != 'MNI152NLin2009cAsym' or
'template' not in output_spaces):
output_spaces.append('template')
logger.warning(
'Option "--cifti-output" requires functional images to be resampled to MNI space. '
'The argument "template" has been automatically added to the list of output '
'spaces (option "--output-space").'
)
# Check output_space
if 'template' not in output_spaces and (opts.use_syn_sdc or opts.force_syn):
msg = ['SyN SDC correction requires T1 to MNI registration, but '
'"template" is not specified in "--output-space" arguments.',
'Option --use-syn will be cowardly dismissed.']
if opts.force_syn:
output_spaces.append('template')
msg[1] = (' Since --force-syn has been requested, "template" has been added to'
' the "--output-space" list.')
logger.warning(' '.join(msg))
# Set up some instrumental utilities
run_uuid = '%s_%s' % (strftime('%Y%m%d-%H%M%S'), uuid.uuid4())
# First check that bids_dir looks like a BIDS folder
bids_dir = os.path.abspath(opts.bids_dir)
subject_list = collect_participants(
bids_dir, participant_label=opts.participant_label)
# Load base plugin_settings from file if --use-plugin
if opts.use_plugin is not None:
from yaml import load as loadyml
with open(opts.use_plugin) as f:
plugin_settings = loadyml(f)
plugin_settings.setdefault('plugin_args', {})
else:
# Defaults
plugin_settings = {
'plugin': 'MultiProc',
'plugin_args': {
'raise_insufficient': False,
'maxtasksperchild': 1,
}
}
# Resource management options
# Note that we're making strong assumptions about valid plugin args
# This may need to be revisited if people try to use batch plugins
nthreads = plugin_settings['plugin_args'].get('n_procs')
# Permit overriding plugin config with specific CLI options
if nthreads is None or opts.nthreads is not None:
nthreads = opts.nthreads
if nthreads is None or nthreads < 1:
nthreads = cpu_count()
plugin_settings['plugin_args']['n_procs'] = nthreads
if opts.mem_mb:
plugin_settings['plugin_args']['memory_gb'] = opts.mem_mb / 1024
omp_nthreads = opts.omp_nthreads
if omp_nthreads == 0:
omp_nthreads = min(nthreads - 1 if nthreads > 1 else cpu_count(), 8)
if 1 < nthreads < omp_nthreads:
logger.warning(
'Per-process threads (--omp-nthreads=%d) exceed total '
'threads (--nthreads/--n_cpus=%d)', omp_nthreads, nthreads)
# Set up directories
output_dir = op.abspath(opts.output_dir)
log_dir = op.join(output_dir, 'fmriprep', 'logs')
work_dir = op.abspath(opts.work_dir or 'work') # Set work/ as default
# Check and create output and working directories
os.makedirs(output_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(work_dir, exist_ok=True)
# Nipype config (logs and execution)
ncfg.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': True
},
'execution': {
'crashdump_dir': log_dir,
'crashfile_format': 'txt',
'get_linked_libs': False,
'stop_on_first_crash': opts.stop_on_first_crash or opts.work_dir is None,
},
'monitoring': {
'enabled': opts.resource_monitor,
'sample_frequency': '0.5',
'summary_append': True,
}
})
if opts.resource_monitor:
ncfg.enable_resource_monitor()
retval['return_code'] = 0
retval['plugin_settings'] = plugin_settings
retval['bids_dir'] = bids_dir
retval['output_dir'] = output_dir
retval['work_dir'] = work_dir
retval['subject_list'] = subject_list
retval['run_uuid'] = run_uuid
retval['workflow'] = None
# Called with reports only
if opts.reports_only:
logger.log(25, 'Running --reports-only on participants %s', ', '.join(subject_list))
if opts.run_uuid is not None:
run_uuid = opts.run_uuid
retval['return_code'] = generate_reports(subject_list, output_dir, work_dir, run_uuid)
return retval
# Build main workflow
logger.log(25, INIT_MSG(
version=__version__,
bids_dir=bids_dir,
subject_list=subject_list,
uuid=run_uuid)
)
template_out_grid = opts.template_resampling_grid
if opts.output_grid_reference is not None:
logger.warning(
'Option --output-grid-reference is deprecated, please use '
'--template-resampling-grid')
template_out_grid = template_out_grid or opts.output_grid_reference
if opts.debug:
logger.warning('Option --debug is deprecated and has no effect')
retval['workflow'] = init_fmriprep_wf(
subject_list=subject_list,
task_id=opts.task_id,
echo_idx=opts.echo_idx,
run_uuid=run_uuid,
ignore=opts.ignore,
debug=opts.sloppy,
low_mem=opts.low_mem,
anat_only=opts.anat_only,
longitudinal=opts.longitudinal,
t2s_coreg=opts.t2s_coreg,
omp_nthreads=omp_nthreads,
skull_strip_template=opts.skull_strip_template,
skull_strip_fixed_seed=opts.skull_strip_fixed_seed,
work_dir=work_dir,
output_dir=output_dir,
bids_dir=bids_dir,
freesurfer=opts.run_reconall,
output_spaces=output_spaces,
template=opts.template,
medial_surface_nan=opts.medial_surface_nan,
cifti_output=opts.cifti_output,
template_out_grid=template_out_grid,
hires=opts.hires,
use_bbr=opts.use_bbr,
bold2t1w_dof=opts.bold2t1w_dof,
fmap_bspline=opts.fmap_bspline,
fmap_demean=opts.fmap_no_demean,
use_syn=opts.use_syn_sdc,
force_syn=opts.force_syn,
use_aroma=opts.use_aroma,
aroma_melodic_dim=opts.aroma_melodic_dimensionality,
ignore_aroma_err=opts.ignore_aroma_denoising_errors,
)
retval['return_code'] = 0
logs_path = Path(output_dir) / 'fmriprep' / 'logs'
boilerplate = retval['workflow'].visit_desc()
if boilerplate:
(logs_path / 'CITATION.md').write_text(boilerplate)
logger.log(25, 'Works derived from this fMRIPrep execution should '
'include the following boilerplate:\n\n%s', boilerplate)
# Generate HTML file resolving citations
cmd = ['pandoc', '-s', '--bibliography',
pkgrf('fmriprep', 'data/boilerplate.bib'),
'--filter', 'pandoc-citeproc',
str(logs_path / 'CITATION.md'),
'-o', str(logs_path / 'CITATION.html')]
try:
check_call(cmd, timeout=10)
except (FileNotFoundError, CalledProcessError, TimeoutExpired):
logger.warning('Could not generate CITATION.html file:\n%s',
' '.join(cmd))
# Generate LaTex file resolving citations
cmd = ['pandoc', '-s', '--bibliography',
pkgrf('fmriprep', 'data/boilerplate.bib'),
'--natbib', str(logs_path / 'CITATION.md'),
'-o', str(logs_path / 'CITATION.tex')]
try:
check_call(cmd, timeout=10)
except (FileNotFoundError, CalledProcessError, TimeoutExpired):
logger.warning('Could not generate CITATION.tex file:\n%s',
' '.join(cmd))
else:
copyfile(pkgrf('fmriprep', 'data/boilerplate.bib'),
(logs_path / 'CITATION.bib'))
return retval
def _run_interface(self, runtime):
nii = nb.load(self.inputs.in_file)
zooms = nii.header.get_zooms()
size_diff = np.array(zooms[:3]) - (self.inputs.pixel_size - 0.1)
if np.all(size_diff >= -1e-3):
IFLOGGER.info('Voxel size is large enough')
self._results['out_file'] = self.inputs.in_file
if isdefined(self.inputs.in_mask):
self._results['out_mask'] = self.inputs.in_mask
return runtime
IFLOGGER.info('One or more voxel dimensions (%f, %f, %f) are smaller than '
'the requested voxel size (%f) - diff=(%f, %f, %f)', zooms[0],
zooms[1], zooms[2], self.inputs.pixel_size, size_diff[0],
size_diff[1], size_diff[2])
# Figure out new matrix
# 1) Get base affine
aff_base = nii.header.get_base_affine()
aff_base_inv = np.linalg.inv(aff_base)
# 2) Find center pixel in mm
center_idx = (np.array(nii.shape[:3]) - 1) * 0.5
center_mm = aff_base.dot(center_idx.tolist() + [1])
# 3) Find extent of each dimension
min_mm = aff_base.dot([-0.5, -0.5, -0.5, 1])
max_mm = aff_base.dot((np.array(nii.shape[:3]) - 0.5).tolist() + [1])
extent_mm = np.abs(max_mm - min_mm)[:3]
# 4) Find new matrix size
new_size = np.array(extent_mm / self.inputs.pixel_size, dtype=int)
# 5) Initialize new base affine
new_base = aff_base[:3, :3] * np.abs(aff_base_inv[:3, :3]) * self.inputs.pixel_size
# 6) Find new center
new_center_idx = (new_size - 1) * 0.5
new_affine_base = np.eye(4)
new_affine_base[:3, :3] = new_base
new_affine_base[:3, 3] = center_mm[:3] - new_base.dot(new_center_idx)
# 7) Rotate new matrix
rotation = nii.affine.dot(aff_base_inv)
new_affine = rotation.dot(new_affine_base)
# 8) Generate new reference image
hdr = nii.header.copy()
hdr.set_data_shape(new_size)
ref_file = 'resample_ref.nii.gz'
nb.Nifti1Image(np.zeros(new_size, dtype=nii.get_data_dtype()),
new_affine, hdr).to_filename(ref_file)
out_prefix, ext = op.splitext(op.basename(self.inputs.in_file))
if ext == '.gz':
out_prefix, ext2 = op.splitext(out_prefix)
ext = ext2 + ext
out_file = op.abspath('%s_resampled%s' % (out_prefix, ext))
# 9) Resample new image
ApplyTransforms(
dimension=3,
input_image=self.inputs.in_file,
reference_image=ref_file,
interpolation='LanczosWindowedSinc',
transforms=[pkgrf('mriqc', 'data/itk_identity.tfm')],
output_image=out_file,
).run()
self._results['out_file'] = out_file
if isdefined(self.inputs.in_mask):
hdr = nii.header.copy()
hdr.set_data_shape(new_size)
hdr.set_data_dtype(np.uint8)
ref_mask = 'mask_ref.nii.gz'
nb.Nifti1Image(np.zeros(new_size, dtype=np.uint8),
new_affine, hdr).to_filename(ref_mask)
out_mask = op.abspath('%s_resmask%s' % (out_prefix, ext))
ApplyTransforms(
dimension=3,
input_image=self.inputs.in_mask,
reference_image=ref_mask,
interpolation='NearestNeighbor',
transforms=[pkgrf('mriqc', 'data/itk_identity.tfm')],
output_image=out_mask,
).run()
self._results['out_mask'] = out_mask
return runtime
def __init__(self):
super(IndividualTemplate, self).__init__(pkgrf('mriqc', 'data/reports/individual.html'))
def main():
"""Entry point"""
import yaml
import re
from io import open
from datetime import datetime
from .. import logging, LOG_FORMAT, __version__
from ..classifier.helper import CVHelper
warnings.showwarning = warn_redirect
opts = get_parser().parse_args()
train_path = _parse_set(opts.train, default='abide')
test_path = _parse_set(opts.test, default='ds030')
log_level = int(max(3 - opts.verbose_count, 0) * 10)
if opts.verbose_count > 1:
log_level = int(max(25 - 5 * opts.verbose_count, 1))
log = logging.getLogger('mriqc.classifier')
log.setLevel(log_level)
base_name = 'mclf_run-%s_mod-%s_ver-%s_class-%d_cv-%s' % (
datetime.now().strftime('%Y%m%d-%H%M%S'), opts.model,
re.sub(r'[\+_@]', '.', __version__),
3 if opts.multiclass else 2, opts.cv,
)
log.info('Results will be saved as %s', abspath(base_name + '*'))
if opts.log_file is None or len(opts.log_file) > 0:
log_file = opts.log_file if opts.log_file else base_name + '.log'
fhl = logging.FileHandler(log_file)
fhl.setFormatter(fmt=logging.Formatter(LOG_FORMAT))
fhl.setLevel(log_level)
log.addHandler(fhl)
parameters = None
if opts.parameters is not None:
with open(opts.parameters) as paramfile:
parameters = yaml.load(paramfile)
clf_loaded = False
if opts.train is not None:
# Initialize model selection helper
cvhelper = CVHelper(
X=train_path[0],
Y=train_path[1],
n_jobs=opts.njobs,
param=parameters,
scorer=opts.scorer,
b_leaveout=opts.train_balanced_leaveout,
multiclass=opts.multiclass,
verbosity=opts.verbose_count,
split=opts.cv,
model=opts.model,
debug=opts.debug,
basename=base_name,
)
if opts.cv == 'batch':
# Do not set x_test unless we are going to run batch exp.
cvhelper.setXtest(test_path[0], test_path[1])
# Perform model selection before setting held-out data, for hygene
cvhelper.fit()
# Pickle if required
cvhelper.save(suffix='data-train_estimator')
# If no training set is given, need a classifier
else:
load_classifier = opts.load_classifier
if load_classifier is None:
load_classifier = pkgrf('mriqc', 'data/rfc-nzs-full-1.0.pklz')
if not isfile(load_classifier):
msg = 'was not provided'
if load_classifier != '':
msg = '("%s") was not found' % load_classifier
raise RuntimeError(
'No training samples were given, and the --load-classifier '
'option %s.' % msg)
cvhelper = CVHelper(load_clf=load_classifier, n_jobs=opts.njobs)
clf_loaded = True
if test_path and opts.cv != 'batch':
# Set held-out data
cvhelper.setXtest(test_path[0], test_path[1])
# Evaluate
cvhelper.evaluate(matrix=True, scoring=[opts.scorer, 'accuracy'],
save_pred=True)
# Pickle if required
if not clf_loaded:
cvhelper.fit_full()
cvhelper.save(suffix='data-all_estimator')
if opts.evaluation_data:
cvhelper.predict_dataset(opts.evaluation_data, out_file=opts.output, thres=opts.threshold)
def __init__(self):
super(GroupTemplate, self).__init__(pkgrf('mriqc', 'data/reports/group.html'))
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
