本文整理汇总了Python中pyhrf.FmriData类的典型用法代码示例。如果您正苦于以下问题：Python FmriData类的具体用法？Python FmriData怎么用？Python FmriData使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了FmriData类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setDummyInputData
def setDummyInputData(self, xmlFile):
f = open(xmlFile, "r")
xml = f.read()
t = xmlio.fromXML(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.toXML(t, handler=NumpyXMLHandler()))
f.close()示例2: setDummyInputData
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()示例3: test_fir_glm
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))示例4: test_glm_default_real_data
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata)示例5: setUp
def setUp(self):
np.random.seed(8652761)
tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_dir = tmpDir
simu = simulate_bold(self.tmp_dir, spatial_size='random_small')
self.data_simu = FmriData.from_simulation_dict(simu)示例6: test_glm_contrasts
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
fdata = FmriData.from_vol_ui()
g, dm, cons = glm_nipy(fdata, contrasts=cons)示例7: test_hrf_with_var_sampler_2
def test_hrf_with_var_sampler_2(self):
# estimation of HRF and its variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
pyhrf.verbose.set_verbosity(2)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance','HRF'], simu_scenario=simu,
check_fv='print', nb_its=100,
hrf_prior_type='voxelwiseIID')示例8: setSimulationData
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()示例9: test_parcellation
def test_parcellation(self):
p_size = 300
np.random.seed(125437)
parcellation, _ = parcellation_for_jde(FmriData.from_vol_ui(), p_size,
output_dir=self.tmp_dir)
ms = np.mean([(parcellation == i).sum()
for i in np.unique(parcellation) if i != 0])
size_tol = 50
if np.abs(ms - p_size) > size_tol:
raise Exception('Mean size of parcellation seems too '
'large: %1.2f >%d+-%d ' % (ms, p_size, size_tol))
if 0:
print parcellation_report(parcellation)示例10: setSimulationData
def setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, "r")
xml = f.read()
t = xmlio.fromXML(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.toXML(t, handler=NumpyXMLHandler())
# print 'sxml:'
# print sxml
f.write(sxml)
f.close()示例11: test_hrf_var_sampler
def test_hrf_var_sampler(self):
# estimation of HRF variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
# -> else the simulated HRF variance is not consistent
pyhrf.verbose.set_verbosity(2)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance'], simu_scenario=simu,
check_fv='raise', nb_its=100,
hrf_prior_type='singleHRF')示例12: test_default_jde_small_simulation
def test_default_jde_small_simulation(self):
""" Test ASL sampler on small simulation with small nb of iterations.
Estimation accuracy is not tested.
"""
simu = simulate_asl(spatial_size='random_small')
fdata = FmriData.from_simulation_dict(simu)
sampler = jde_asl.ASLSampler()
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_', randomSeed=None)
treatment = FMRITreatment(fmri_data=fdata, analyser=analyser,
output_dir=None)
treatment.run()示例13: test_default_jde_small_simulation
def test_default_jde_small_simulation(self):
""" Test ASL Physio sampler on small simulation with small nb of
iterations. Estimation accuracy is not tested.
"""
pyhrf.verbose.set_verbosity(0)
sampler_params = {
jde_asl_physio.ASLPhysioSampler.P_NB_ITERATIONS : 100,
jde_asl_physio.ASLPhysioSampler.P_SMPL_HIST_PACE : 1,
jde_asl_physio.ASLPhysioSampler.P_OBS_HIST_PACE : 1,
'brf' : jde_asl_physio.PhysioBOLDResponseSampler(zero_constraint=False),
'brf_var' : jde_asl_physio.PhysioBOLDResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'prf' : jde_asl_physio.PhysioPerfResponseSampler(zero_constraint=False),
'prf_var' : jde_asl_physio.PhysioPerfResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'noise_var' : jde_asl_physio.NoiseVarianceSampler(),
'drift_var' : jde_asl_physio.DriftVarianceSampler(),
'drift_coeff' : jde_asl_physio.DriftCoeffSampler(),
'brl' : jde_asl_physio.BOLDResponseLevelSampler(),
'prl' : jde_asl_physio.PerfResponseLevelSampler(),
'bold_mixt_params' : jde_asl_physio.BOLDMixtureSampler(),
'perf_mixt_params' : jde_asl_physio.PerfMixtureSampler(),
'label' : jde_asl_physio.LabelSampler(),
'perf_baseline' : jde_asl_physio.PerfBaselineSampler(),
'perf_baseline_var' : jde_asl_physio.PerfBaselineVarianceSampler(),
'assert_final_value_close_to_true' : False,
}
sampler = jde_asl_physio.ASLPhysioSampler(sampler_params)
simu_items = phym.simulate_asl_physio_rfs(spatial_size='random_small')
simu_fdata = FmriData.from_simulation_dict(simu_items)
dt = simu_items['dt']
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=dt, driftParam=4, driftType='polynomial',
outputFile=None,outputPrefix='jde_mcmc_',
randomSeed=None)
treatment = FMRITreatment(fmri_data=simu_fdata, analyser=analyser)
treatment.run()示例14: test_default_jde_small_simulation
def test_default_jde_small_simulation(self):
""" Test ASL Physio sampler on small simulation with small nb of
iterations. Estimation accuracy is not tested.
"""
pyhrf.verbose.set_verbosity(0)
sampler_params = {
'nb_iterations' : 3,
'smpl_hist_pace' : 1,
'obs_hist_pace' : 1,
'brf' : jde_asl_physio.PhysioBOLDResponseSampler(zero_constraint=False),
'brf_var' : jde_asl_physio.PhysioBOLDResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'prf' : jde_asl_physio.PhysioPerfResponseSampler(zero_constraint=False),
'prf_var' : jde_asl_physio.PhysioPerfResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'noise_var' : jde_asl_physio.NoiseVarianceSampler(),
'drift_var' : jde_asl_physio.DriftVarianceSampler(),
'drift' : jde_asl_physio.DriftCoeffSampler(),
'bold_response_levels' : jde_asl_physio.BOLDResponseLevelSampler(),
'perf_response_levels' : jde_asl_physio.PerfResponseLevelSampler(),
'bold_mixt_params' : jde_asl_physio.BOLDMixtureSampler(),
'perf_mixt_params' : jde_asl_physio.PerfMixtureSampler(),
'labels' : jde_asl_physio.LabelSampler(),
'perf_baseline' : jde_asl_physio.PerfBaselineSampler(),
'perf_baseline_var' : jde_asl_physio.PerfBaselineVarianceSampler(),
'check_final_value' : None,
}
sampler = jde_asl_physio.ASLPhysioSampler(**sampler_params)
simu_items = phym.simulate_asl_physio_rfs(spatial_size='random_small')
simu_fdata = FmriData.from_simulation_dict(simu_items)
dt = simu_items['dt']
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=dt, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_')
treatment = FMRITreatment(fmri_data=simu_fdata, analyser=analyser,
output_dir=None)
treatment.run()示例15: test_full_sampler
def test_full_sampler(self):
""" Test JDE on simulation with normal size.
Estimation accuracy is tested.
"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
simu = simulate_bold(self.tmp_dir, spatial_size='normal',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
sampler = BG(self.sampler_params_for_full_test)
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_',
randomSeed=None)
treatment = FMRITreatment(fmri_data=simu,
analyser=analyser, output_dir=self.tmp_dir)
treatment.run()
print 'output_dir:', self.tmp_dir