def test_fetch_localizer():
dataset = datasets.fetch_localizer_first_level()
assert_true(isinstance(dataset['events'], _basestring))
assert_true(isinstance(dataset.epi_img, _basestring))
import numpy as np
import pandas as pd
from nilearn import plotting
from nistats.first_level_model import FirstLevelModel
from nistats import datasets
#########################################################################
# Prepare data and analysis parameters
# -------------------------------------
# Prepare timing
t_r = 2.4
slice_time_ref = 0.5
# Prepare data
data = datasets.fetch_localizer_first_level()
paradigm_file = data.paradigm
paradigm = pd.read_csv(paradigm_file, sep=' ', header=None, index_col=None)
paradigm.columns = ['session', 'trial_type', 'onset']
fmri_img = data.epi_img
#########################################################################
# Perform first level analysis
# ----------------------------
# Setup and fit GLM
first_level_model = FirstLevelModel(t_r, slice_time_ref,
hrf_model='glover + derivative')
first_level_model = first_level_model.fit(fmri_img, paradigm)
#########################################################################
# Estimate contrasts
def test_fetch_localizer():
dataset = datasets.fetch_localizer_first_level(data_dir=tmpdir)
assert_true(isinstance(dataset.paradigm, _basestring))
assert_true(isinstance(dataset.epi_img, _basestring))
from nilearn import plotting
from nistats.glm import FirstLevelGLM
from nistats.design_matrix import make_design_matrix
from nistats import datasets
### Data and analysis parameters #######################################
# timing
n_scans = 128
tr = 2.4
frame_times = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans)
# data
data = datasets.fetch_localizer_first_level()
paradigm_file = data.paradigm
fmri_img = data.epi_img
### Design matrix ########################################
paradigm = pd.read_csv(paradigm_file, sep=' ', header=None, index_col=None)
paradigm.columns = ['session', 'name', 'onset']
n_conditions = len(paradigm.name.unique())
design_matrix = make_design_matrix(
frame_times, paradigm, hrf_model='glover + derivative',
drift_model='cosine', period_cut=128)
### Perform a GLM analysis ########################################
fmri_glm = FirstLevelGLM().fit(fmri_img, design_matrix)
obviously less accurate than using a subject-tailored mesh. """ ######################################################################### # Prepare data and analysis parameters # ------------------------------------- # Prepare timing parameters t_r = 2.4 slice_time_ref = 0.5 ######################################################################### # Prepare data # First the volume-based fMRI data. from nistats.datasets import fetch_localizer_first_level data = fetch_localizer_first_level() fmri_img = data.epi_img ######################################################################### # Second the experimental paradigm. events_file = data['events'] import pandas as pd events = pd.read_table(events_file) ######################################################################### # Project the fMRI image to the surface # ------------------------------------- # # For this we need to get a mesh representing the geometry of the # surface. we could use an individual mesh, but we first resort to a # standard mesh, the so-called fsaverage5 template from the Freesurfer
def test_fetch_localizer():
dataset = datasets.fetch_localizer_first_level(data_dir=tmpdir)
assert_true(isinstance(dataset.paradigm, _basestring))
assert_true(isinstance(dataset.epi_img, _basestring))
obviously less accurate than using a subject-tailored mesh. """ ######################################################################### # Prepare data and analysis parameters # ------------------------------------- # Prepare timing parameters t_r = 2.4 slice_time_ref = 0.5 ######################################################################### # Prepare data # First the volume-based fMRI data. from nistats.datasets import fetch_localizer_first_level data = fetch_localizer_first_level() fmri_img = data.epi_img ######################################################################### # Second the experimental paradigm. events_file = data.events import pandas as pd events = pd.read_table(events_file) ######################################################################### # Project the fMRI image to the surface # ------------------------------------- # # For this we need to get a mesh representing the geometry of the # surface. we could use an individual mesh, but we first resort to a # standard mesh, the so-called fsaverage5 template from the Freesurfer
