Ejemplos de get_first_level_dataset en Python

Ejemplo n.º 1

Archivo: example_localizer_glm.py Proyecto: FNNDSC/nipy

from nipy.labs import compute_mask_files
from nibabel import load, save, Nifti1Image
import get_data_light
import nipy.labs.glm
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import \
    load_protocol_from_csv_file
from nipy.labs.viz import plot_map, cm

#######################################
# Data and analysis parameters
#######################################

# volume mask
get_data_light.get_first_level_dataset()
data_path = op.expanduser(op.join('~', '.nipy', 'tests', 'data',
                                 's12069_swaloc1_corr.nii.gz'))
paradigm_file = op.expanduser(op.join('~', '.nipy', 'tests', 'data',
                                      'localizer_paradigm.csv'))

# timing
n_scans = 128
tr = 2.4

# paradigm
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
conditions = ['damier_H', 'damier_V', 'clicDaudio', 'clicGaudio', 'clicDvideo',
              'clicGvideo', 'calculaudio', 'calculvideo', 'phrasevideo',
              'phraseaudio']

Ejemplo n.º 2

Archivo: tmin_statistic.py Proyecto: GaelVaroquaux/nipy

from nipy.modalities.fmri.glm import FMRILinearModel
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import \
    load_paradigm_from_csv_file
from nipy.labs.viz import plot_map, cm

# Local import
from get_data_light import DATA_DIR, get_first_level_dataset

#######################################
# Data and analysis parameters
#######################################

# volume mask
# This dataset is large
get_first_level_dataset()
data_path = path.join(DATA_DIR, 's12069_swaloc1_corr.nii.gz')
paradigm_file = path.join(DATA_DIR, 'localizer_paradigm.csv')

# timing
n_scans = 128
tr = 2.4

# paradigm
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)

# confounds
hrf_model = 'canonical'
drift_model = 'cosine'
hfcut = 128

Ejemplo n.º 3

from nipy.modalities.fmri.glm import FMRILinearModel
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import \
    load_paradigm_from_csv_file
from nipy.labs.viz import plot_map, cm

# Local import
from get_data_light import DATA_DIR, get_first_level_dataset

#######################################
# Data and analysis parameters
#######################################

# volume mask
# This dataset is large
get_first_level_dataset()
data_path = path.join(DATA_DIR, 's12069_swaloc1_corr.nii.gz')
paradigm_file = path.join(DATA_DIR, 'localizer_paradigm.csv')

# timing
n_scans = 128
tr = 2.4

# paradigm
frametimes = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans)

# confounds
hrf_model = 'canonical with derivative'
drift_model = "cosine"
hfcut = 128