def test_basic_types(self):
dictObj = {'clef1': 'val1', 'clef2': 2}
sXml = xmlio.to_xml(dictObj)
dictObj2 = xmlio.from_xml(sXml)
assert dictObj2 == dictObj
obj = ['ggg', 5, (677, 56), 4.5, 'ff']
sXml = xmlio.to_xml(obj)
obj2 = xmlio.from_xml(sXml)
assert obj2 == obj
def test_basic_types(self):
dictObj = {'clef1' : 'val1', 'clef2': 2}
sXml = xmlio.to_xml(dictObj)
dictObj2 = xmlio.from_xml(sXml)
assert dictObj2 == dictObj
obj = ['ggg', 5, (677,56), 4.5, 'ff']
sXml = xmlio.to_xml(obj)
obj2 = xmlio.from_xml(sXml)
assert obj2 == obj
def setDummyInputData(self, xmlFile):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
if t.data.data_type == 'volume':
dataFn = pyhrf.get_data_file_name('dummySmallBOLD.nii.gz')
maskFn = pyhrf.get_data_file_name('dummySmallMask.nii.gz')
sd = FMRISessionVolumicData(bold_file=dataFn)
t.set_init_param('fmri_data',
FmriData.from_vol_ui(mask_file=maskFn,
sessions_data=[sd]))
elif t.data.data_type == 'surface':
fn = 'real_data_surf_tiny_bold.gii'
dataFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_parcellation.gii'
maskFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_mesh.gii'
meshFn = pyhrf.get_data_file_name(fn)
sd = FMRISessionSurfacicData(bold_file=dataFn)
t.set_init_param('fmri_data',
FmriData.from_surf_ui(mask_file=maskFn,
mesh_file=meshFn,
sessions_data=[sd]))
else:
raise Exception('Unsupported class ... todo')
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def test_JDEMCMCAnalyzerXML(self):
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
axml = xmlio.to_xml(a, pretty=True)
a2 = xmlio.from_xml(axml)
self.assertEqual(a2.sampler.nbIterations, 42)
def setDummyInputData(self, xmlFile):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
if t.data.data_type == 'volume':
dataFn = pyhrf.get_data_file_name('dummySmallBOLD.nii.gz')
maskFn = pyhrf.get_data_file_name('dummySmallMask.nii.gz')
sd = FMRISessionVolumicData(bold_file=dataFn)
t.set_init_param(
'fmri_data',
FmriData.from_vol_ui(mask_file=maskFn, sessions_data=[sd]))
elif t.data.data_type == 'surface':
fn = 'real_data_surf_tiny_bold.gii'
dataFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_parcellation.gii'
maskFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_mesh.gii'
meshFn = pyhrf.get_data_file_name(fn)
sd = FMRISessionSurfacicData(bold_file=dataFn)
t.set_init_param(
'fmri_data',
FmriData.from_surf_ui(mask_file=maskFn,
mesh_file=meshFn,
sessions_data=[sd]))
else:
raise Exception('Unsupported class ... todo')
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def test_JDEMCMCAnalyzerXML(self):
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
axml = xmlio.to_xml(a, pretty=True)
a2 = xmlio.from_xml(axml)
self.assertEqual(a2.sampler.nbIterations, 42)
def makeQuietOutputs(self, xmlFile):
from pyhrf import xmlio
t = xmlio.from_xml(file(xmlFile).read())
t.set_init_param('output_dir', None)
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def makeQuietOutputs(self, xmlFile):
from pyhrf import xmlio
t = xmlio.from_xml(file(xmlFile).read())
t.set_init_param('output_dir', None)
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def makeQuietOutputs(self, xmlFile):
# print 'makeQuietOutputs ...'
# print 'xmlFile:', xmlFile
t = xmlio.from_xml(file(xmlFile).read())
t.set_init_param('output_dir', None)
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def test_TreatmentXML(self):
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
from pyhrf.ui.treatment import FMRITreatment
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
t = FMRITreatment(analyser=a)
txml = xmlio.to_xml(t, pretty=True)
t2 = xmlio.from_xml(txml)
self.assertEqual(t2.analyser.sampler.nbIterations, 42)
def test_TreatmentXML(self):
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
from pyhrf.ui.treatment import FMRITreatment
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
t = FMRITreatment(analyser=a)
txml = xmlio.to_xml(t, pretty=True)
t2 = xmlio.from_xml(txml)
self.assertEqual(t2.analyser.sampler.nbIterations, 42)
def test_set_init_param(self):
a = A()
a.set_init_param('c', 'new_c')
# print 'after override ...'
# print a._init_parameters
xml = xmlio.to_xml(a, pretty=True)
# print xml
new_a = xmlio.from_xml(xml)
self.assertEqual(new_a.param2, 'new_c')
def testNumpy(self):
obj = \
{'array1': np.random.randn(10),
'val1': np.int(5),
'array2D': np.ones((4, 4)),
}
sXml = xmlio.to_xml(obj)
obj2 = xmlio.from_xml(sXml)
assert obj2.keys() == obj.keys()
assert np.allclose(obj2["array1"], obj["array1"])
def testNumpy(self):
obj = \
{ 'array1' : np.random.randn(10),
'val1' : np.int(5),
'array2D' : np.ones((4,4)),
}
sXml = xmlio.to_xml(obj)
obj2 = xmlio.from_xml(sXml)
assert obj2.keys() == obj.keys()
assert np.allclose(obj2["array1"],obj["array1"])
def test_ordered_dict(self):
try:
from collections import OrderedDict
except ImportError:
from pyhrf.tools.backports import OrderedDict
d = OrderedDict([('a', 1), ('b', 2)])
sXml = xmlio.to_xml(d)
d2 = xmlio.from_xml(sXml)
self.assertEqual(d, d2)
def test_ordered_dict(self):
try:
from collections import OrderedDict
except ImportError:
from pyhrf.tools.backports import OrderedDict
d = OrderedDict([('a', 1), ('b', 2)])
sXml = xmlio.to_xml(d)
d2 = xmlio.from_xml(sXml)
self.assertEqual(d, d2)
def test_JDEMCMCAnalyzerXML(self):
pyhrf.verbose.set_verbosity(0)
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
# print 'a -- nbIterations:', a.sampler.nbIterations
axml = xmlio.to_xml(a, pretty=True)
# print 'axml:'
# print axml
a2 = xmlio.from_xml(axml)
# print 'a2 -- nbIterations:', a2.sampler.nbIterations
self.assertEqual(a2.sampler.nbIterations, 42)
def setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
sd = FMRISessionSimulationData(simulation_file=simu_file)
t.set_init_param(
'fmri_data', FmriData.from_simu_ui(sessions_data=[sd]))
f = open(xmlFile, 'w')
sxml = xmlio.to_xml(t)
f.write(sxml)
f.close()
def setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
sd = FMRISessionSimulationData(simulation_file=simu_file)
t.set_init_param('fmri_data',
FmriData.from_simu_ui(sessions_data=[sd]))
f = open(xmlFile, 'w')
sxml = xmlio.to_xml(t)
f.write(sxml)
f.close()
def test_TreatmentXML(self):
pyhrf.verbose.set_verbosity(0)
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
from pyhrf.ui.treatment import FMRITreatment
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
t = FMRITreatment(analyser=a)
# print 't -- nbIterations:', t.analyser.sampler.nbIterations
txml = xmlio.to_xml(t, pretty=True)
# print 'txml:'
# print txml
t2 = xmlio.from_xml(txml)
# print 't2 -- nbIterations:', t2.analyser.sampler.nbIterations
self.assertEqual(t2.analyser.sampler.nbIterations, 42)
def create_treatment(boldFiles, parcelFile, dt, tr, paradigmFile,
nbIterations=4000,
writeXmlSetup=True, parallelize=False,
outputDir=None, outputSuffix=None, outputPrefix=None,
contrasts=None, beta=.6, estimBeta=True,
pfMethod='ps', estimHrf=True, hrfVar=.01,
roiIds=None,
nbClasses=2,gzip_rdump=False, make_outputs=True,
vbjde=False, simulation_file=None):
if roiIds is None:
roiIds = np.array([],dtype=int)
outDump = make_outfile(DEFAULT_DUMP_FILE, outputDir, outputPrefix,
outputSuffix)
if gzip_rdump:
outDump += '.gz'
fmri_data = FmriData.from_vol_files(parcelFile, paradigmFile, boldFiles, tr)
if simulation_file is not None:
f_simu = open(simulation_file)
simulation = cPickle.load(f_simu)
f_simu.close()
fmri_data.simulation = simulation
if contrasts is not None:
cons = dict( ("con_%d"%i, ce) \
for i,ce in enumerate(";".split(contrasts)) )
else:
cons = {}
if(vbjde):
analyser = VBJDE(dt=dt)
else:
if nbClasses == 2:
sampler = BG(**{
'nb_iterations' : nbIterations,
# level of spatial correlation = beta
'beta' : BS(**{
'val_ini' : np.array([beta]),
'do_sampling' : estimBeta,
'pf_method' : pfMethod,
}),
# HRF
'hrf' : HS(**{
'do_sampling' : estimHrf,
}),
# HRF variance
'hrf_var' : HVS(**{
'do_sampling' : False,
'val_ini' : np.array([hrfVar]),
}),
# neural response levels (stimulus-induced effects)
'response_levels' : NS(**{
'contrasts' : cons,
}),
})
elif nbClasses == 3:
raise NotImplementedError('Model with 3 classes not maintained')
# from pyhrf.jde.models import GGG_BOLDGibbsSampler as BG3
# sampler = BG3({
# BG.P_NB_ITERATIONS : nbIterations,
# # level of spatial correlation = beta
# BG.P_BETA : BS({
# BS.P_VAL_INI : np.array([beta]),
# BS.P_SAMPLE_FLAG : estimBeta,
# BS.P_PARTITION_FUNCTION_METH : pfMethod,
# }),
# # HRF
# BG.P_HRF : HS({
# HS.P_SAMPLE_FLAG : estimHrf,
# }),
# # HRF variance
# BG.P_RH : HVS({
# HVS.P_SAMPLE_FLAG : False,
# HVS.P_VAL_INI : np.array([hrfVar]),
# }),
# # neural response levels (stimulus-induced effects)
# BG.P_NRLS : NS3({
# NS.P_CONTRASTS : cons,
# }),
# })
analyser = JDEMCMCAnalyser(sampler, dt=dt)
tjde = FMRITreatment(fmri_data, analyser, outputDir)
sxml = xmlio.to_xml(tjde)
if writeXmlSetup and outputDir is not None:
outSetupXml = make_outfile(DEFAULT_CFG_FILE_JDE, outputDir,
outputPrefix, outputSuffix)
pyhrf.verbose(1, "Writing XML setup to: " + outSetupXml )
f = open(outSetupXml, 'w')
f.write(sxml)
f.close()
else:
outSetupXml = None
return tjde, outSetupXml
def create_treatment_surf(boldFiles, parcelFile, meshFile, dt, tr, paradigmFile,
nbIterations=4000,
writeXmlSetup=True, parallelize=False,
outputDir=None, outputSuffix=None,
outputPrefix=None,
contrasts=';', beta=.6, estimBeta=True,
pfMethod='ps', estimHrf=True, hrfVar=.01,
roiIds=None,
nbClasses=2,gzip_rdump=False,
simulation_file=None, make_outputs=True):
if roiIds is None:
roiIds = np.array([],dtype=int)
outDump = make_outfile(DEFAULT_DUMP_FILE, outputDir, outputPrefix,
outputSuffix)
if gzip_rdump:
outDump += '.gz'
if contrasts is not None:
cons = dict( ("con_%d"%i, ce) \
for i,ce in enumerate(";".split(contrasts)) )
else:
cons = {}
if nbClasses == 2:
sampler = BG(**{
'nb_iterations' : nbIterations,
# level of spatial correlation = beta
'beta' : BS(**{
'val_ini' : np.array([beta]),
'do_sampling' : estimBeta,
'pf_method' : pfMethod,
}),
# HRF
'hrf' : HS(**{
'do_sampling' : estimHrf,
}),
# HRF variance
'hrf_var' : HVS(**{
'do_sampling' : False,
'val_ini' : np.array([hrfVar]),
}),
# neural response levels (stimulus-induced effects)
'response_levels' : NS(**{
'contrasts' : cons,
}),
})
elif nbClasses == 3:
raise NotImplementedError('3 class model not maintained')
analyser = JDEMCMCAnalyser(sampler, dt=dt)
fmri_data = FmriData.from_surf_files(paradigmFile, boldFiles, tr, meshFile,
parcelFile)
if simulation_file is not None:
f_simu = open(simulation_file)
simulation = cPickle.load(f_simu)
f_simu.close()
fmri_data.simulation = simulation
tjde = FMRITreatment(fmri_data, analyser, outputDir)
#print 'make_outputs:', make_outputs
sxml = xmlio.to_xml(tjde)
if writeXmlSetup is not None and outputDir is not None:
outSetupXml = make_outfile(DEFAULT_CFG_FILE_JDE, outputDir,
outputPrefix,
outputSuffix)
pyhrf.verbose(1, "Writing XML setup to: " + outSetupXml )
f = open(outSetupXml, 'w')
f.write(sxml)
f.close()
else:
outSetupXml = None
return tjde, outSetupXml
def test_set_init_param(self):
a = A()
a.set_init_param('c', 'new_c')
xml = xmlio.to_xml(a, pretty=True)
new_a = xmlio.from_xml(xml)
self.assertEqual(new_a.param2, 'new_c')
def run_pyhrf_cmd_treatment(cfg_cmd, exec_cmd, default_cfg_file,
default_profile_file, label_for_cluster):
usage = 'usage: %%prog [options]'
description = 'Manage a joint detection-estimation treatment of fMRI data.' \
'This command runs the treatment defined in an xml '\
'parameter file. See pyhrf_jde_buildcfg command to build a'\
'template of such a file. If no xml file found, then runs a '\
'default example analysis.'
parser = OptionParser(usage=usage, description=description)
parser.add_option('-c','--input-cfg-file', metavar='XMLFILE', dest='cfgFile',
default=default_cfg_file,
help='Configuration file: XML file containing parameters'\
' defining input data and analysis to perform.')
parser.add_option('-r','--roi-data', metavar='PICKLEFILE', dest='roidata',
default=None, help='Input fMRI ROI data. The data '\
'definition part in the config file is ignored.')
parser.add_option('-t','--treatment_pck',
metavar='PICKLEFILE', dest='treatment_pck',
default=None, help='Input treatment as a pickle dump.' \
'The XML cfg file is ignored')
parser.add_option('-s','--stop-on-error', dest='stop_on_error',
action='store_true',
default=False, help='For debug: do not continue if error' \
' during one ROI analysis')
parser.add_option('-v','--verbose',dest='verbose',metavar='INTEGER',
type='int',default=0,
help=dictToString(pyhrf.verboseLevels))
parser.add_option('-p','--profile',action='store_true', default=False,
help='Enable profiling of treatment. Store profile data in '\
'%s. NOTE: not avalaible in parallel mode.'\
%default_profile_file)
parallel_choices = ['LAN','local','cluster']
parser.add_option('-x','--parallel', choices=parallel_choices,
help='Parallel processing. Choices are %s'\
%string.join(parallel_choices,', '))
(options,args) = parser.parse_args()
pyhrf.verbose.set_verbosity(options.verbose)
t0 = time.time()
if options.treatment_pck is not None:
f = open(options.treatment_pck)
treatment = cPickle.load(f)
f.close()
else:
if not os.path.exists(options.cfgFile):
print 'Error: could not find default configuration file "%s"\n'\
'Consider running "%s" to generate it.' \
%(options.cfgFile, cfg_cmd)
sys.exit(1)
else:
pyhrf.verbose(1, 'Loading configuration from: "%s" ...' \
%options.cfgFile)
f = open(options.cfgFile, 'r')
sXml = string.join(f.readlines())
f.close()
treatment = xmlio.from_xml(sXml)
if 0:
sXml = xmlio.to_xml(treatment)
f = './treatment_cmd.xml'
fOut = open(f,'w')
fOut.write(sXml)
fOut.close()
#f = open(fOut, 'w')
#cPickle.dump(treatment, f)
#f.close()
treatment.analyser.set_pass_errors(not options.stop_on_error)
if options.parallel is not None:
# tmpDir = tempfile.mkdtemp(prefix='pyhrf',
# dir=pyhrf.cfg['global']['tmp_path'])
# pyhrf.verbose(1, 'Tmpdir: %s' %tmpDir)
treatment.run(parallel=options.parallel)
else:
if options.roidata is not None:
#treatment.set_roidata(options.roidata)
pyhrf.verbose(1, 'Loading ROI data from: "%s" ...' \
%options.roidata)
roidata = cPickle.load(open(options.roidata))
roidata.verbosity = pyhrf.verbose.verbosity
if pyhrf.verbose > 1:
print roidata.getSummary()
#TODO: enable profiling
pyhrf.verbose(1, 'Launching analysis ...')
if options.profile:
cProfile.runctx("result = treatment.analyser(roidata)",
globals(),
{'treatment':treatment,'roidata': roidata},
default_profile_file)
else:
result = treatment.analyser(roidata)
outPath = op.dirname(op.abspath(options.roidata))
fOut = op.join(outPath,"result_%04d.pck" %roidata.get_roi_id())
pyhrf.verbose(1, 'Dumping results to %s ...' %fOut)
f = open(fOut, 'w')
cPickle.dump(result, f)
f.close()
else:
pyhrf.verbose(1, 'ROI data is none')
if options.profile:
cProfile.runctx("treatment.run()", globals(),
{'treatment':treatment}, default_profile_file)
else:
#print 'treatment:', treatment
treatment.run()
pyhrf.verbose(1, 'Estimation done, took %s' %format_duration(time.time() - t0))
def save_treatment(t,f):
sXml = xmlio.to_xml(t, handler=xmlio.xmlnumpy.NumpyXMLHandler())
fOut = open(f,'w')
fOut.write(sXml)
fOut.close()
def run_pyhrf_cmd_treatment(cfg_cmd, exec_cmd, default_cfg_file,
default_profile_file, label_for_cluster):
usage = 'usage: %%prog [options]'
description = 'Manage a joint detection-estimation treatment of fMRI data.' \
'This command runs the treatment defined in an xml '\
'parameter file. See pyhrf_jde_buildcfg command to build a'\
'template of such a file. If no xml file found, then runs a '\
'default example analysis.'
parser = OptionParser(usage=usage, description=description)
parser.add_option('-c',
'--input-cfg-file',
metavar='XMLFILE',
dest='cfgFile',
default=default_cfg_file,
help='Configuration file: XML file containing parameters'
' defining input data and analysis to perform.')
parser.add_option('-r',
'--roi-data',
metavar='PICKLEFILE',
dest='roidata',
default=None,
help='Input fMRI ROI data. The data '
'definition part in the config file is ignored.')
parser.add_option('-t',
'--treatment_pck',
metavar='PICKLEFILE',
dest='treatment_pck',
default=None,
help='Input treatment as a pickle dump.'
'The XML cfg file is ignored')
parser.add_option('-s',
'--stop-on-error',
dest='stop_on_error',
action='store_true',
default=False,
help='For debug: do not continue if error'
' during one ROI analysis')
parser.add_option('-v',
'--verbose',
dest='verbose',
metavar='INTEGER',
type='int',
default=0,
help=pformat(pyhrf.verbose_levels))
parser.add_option(
'-p',
'--profile',
action='store_true',
default=False,
help='Enable profiling of treatment. Store profile data in '
'%s. NOTE: not avalaible in parallel mode.' % default_profile_file)
parallel_choices = ['LAN', 'local', 'cluster']
parser.add_option('-x',
'--parallel',
choices=parallel_choices,
help='Parallel processing. Choices are %s' %
string.join(parallel_choices, ', '))
(options, args) = parser.parse_args()
# pyhrf.verbose.set_verbosity(options.verbose)
pyhrf.logger.setLevel(options.verbose)
t0 = time.time()
if options.treatment_pck is not None:
f = open(options.treatment_pck)
treatment = cPickle.load(f)
f.close()
else:
if not os.path.exists(options.cfgFile):
print 'Error: could not find default configuration file "%s"\n'\
'Consider running "%s" to generate it.' \
% (options.cfgFile, cfg_cmd)
sys.exit(1)
else:
logger.info('Loading configuration from: "%s" ...',
options.cfgFile)
f = open(options.cfgFile, 'r')
sXml = string.join(f.readlines())
f.close()
treatment = xmlio.from_xml(sXml)
if 0:
sXml = xmlio.to_xml(treatment)
f = './treatment_cmd.xml'
fOut = open(f, 'w')
fOut.write(sXml)
fOut.close()
treatment.analyser.set_pass_errors(not options.stop_on_error)
if options.parallel is not None:
treatment.run(parallel=options.parallel)
else:
if options.roidata is not None:
logger.info('Loading ROI data from: "%s" ...', options.roidata)
roidata = cPickle.load(open(options.roidata))
roidata.verbosity = logger.getEffectiveLevel()
if logger.getEffectiveLevel() <= logging.INFO:
print roidata.getSummary()
# TODO: enable profiling
logger.info('Launching analysis ...')
if options.profile:
cProfile.runctx("result = treatment.analyser(roidata)",
globals(), {
'treatment': treatment,
'roidata': roidata
}, default_profile_file)
else:
result = treatment.analyser(roidata)
outPath = op.dirname(op.abspath(options.roidata))
fOut = op.join(outPath, "result_%04d.pck" % roidata.get_roi_id())
logger.info('Dumping results to %s ...', fOut)
f = open(fOut, 'w')
cPickle.dump(result, f)
f.close()
else:
logger.info('ROI data is none')
if options.profile:
cProfile.runctx("treatment.run()", globals(),
{'treatment': treatment}, default_profile_file)
else:
treatment.run()
logger.info('Estimation done, took %s', format_duration(time.time() - t0))
def _test_xml_bijection(self, o):
xml = to_xml(o)
pyhrf.verbose(6, 'xml from %s:' % str(o))
pyhrf.verbose(6, xml)
o2 = from_xml(xml)
self.assertEqual(o, o2)
def test_set_init_param(self):
a = A()
a.set_init_param('c', 'new_c')
xml = xmlio.to_xml(a, pretty=True)
new_a = xmlio.from_xml(xml)
self.assertEqual(new_a.param2, 'new_c')
def test_simple_bijection(self):
a = A(c='e')
a_xml = to_xml(a)
self.assertEqual(a, from_xml(a_xml))
def _test_xml_bijection(self, o):
xml = to_xml(o)
logger.debug('xml from %s:', str(o))
logger.debug(xml)
o2 = from_xml(xml)
self.assertEqual(o, o2)
def test_simple_bijection(self):
a = A(c='e')
a_xml = to_xml(a)
self.assertEqual(a, from_xml(a_xml))
def create_treatment_surf(boldFiles,
parcelFile,
meshFile,
dt,
tr,
paradigmFile,
nbIterations=4000,
writeXmlSetup=True,
parallelize=False,
outputDir=None,
outputSuffix=None,
outputPrefix=None,
contrasts=';',
beta=.6,
estimBeta=True,
pfMethod='ps',
estimHrf=True,
hrfVar=.01,
roiIds=None,
nbClasses=2,
gzip_rdump=False,
simulation_file=None,
make_outputs=True):
if roiIds is None:
roiIds = np.array([], dtype=int)
outDump = make_outfile(DEFAULT_DUMP_FILE, outputDir, outputPrefix,
outputSuffix)
if gzip_rdump:
outDump += '.gz'
if contrasts is not None:
cons = dict(
("con_%d" % i, ce) for i, ce in enumerate(";".split(contrasts)))
else:
cons = {}
if nbClasses == 2:
sampler = BG(
**{
'nb_iterations':
nbIterations,
# level of spatial correlation = beta
'beta':
BS(
**{
'val_ini': np.array([beta]),
'do_sampling': estimBeta,
'pf_method': pfMethod,
}),
# HRF
'hrf':
HS(**{
'do_sampling': estimHrf,
}),
# HRF variance
'hrf_var':
HVS(**{
'do_sampling': False,
'val_ini': np.array([hrfVar]),
}),
# neural response levels (stimulus-induced effects)
'response_levels':
NS(**{
'contrasts': cons,
}),
})
elif nbClasses == 3:
raise NotImplementedError('3 class model not maintained')
analyser = JDEMCMCAnalyser(sampler, dt=dt)
fmri_data = FmriData.from_surf_files(paradigmFile, boldFiles, tr, meshFile,
parcelFile)
if simulation_file is not None:
f_simu = open(simulation_file)
simulation = cPickle.load(f_simu)
f_simu.close()
fmri_data.simulation = simulation
tjde = FMRITreatment(fmri_data, analyser, outputDir)
# print 'make_outputs:', make_outputs
sxml = xmlio.to_xml(tjde)
if writeXmlSetup is not None and outputDir is not None:
outSetupXml = make_outfile(DEFAULT_CFG_FILE_JDE, outputDir,
outputPrefix, outputSuffix)
logger.info("Writing XML setup to: %s", outSetupXml)
f = open(outSetupXml, 'w')
f.write(sxml)
f.close()
else:
outSetupXml = None
return tjde, outSetupXml
def _test_xml_bijection(self, o):
xml = to_xml(o)
logger.debug('xml from %s:', str(o))
logger.debug(xml)
o2 = from_xml(xml)
self.assertEqual(o, o2)
