def run_perm_test(row):
row_array = row.split(';')
perm_ind = row_array[0]
perm_y = row_array[1]
perm_y = np.asarray(perm_y.split(',')).astype(int)
analysis_name = 'whole_brain_noTiv_perm_' + str(perm_ind)
project_folder = './perm/'
# get data
data = IQData(tiv_rescaled=False)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_whole_brain(use_cached=True)
# run analysis
pipe = construct_hyperpipe(analysis_name, project_folder)
pipe.groups = data.fsiq
pipe.fit(data.whole_brain, perm_y, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)
def run_perm_test(row):
row_array = row.split(';')
perm_ind = row_array[0]
perm_y = row_array[1]
perm_y = np.asarray(perm_y.split(',')).astype(int)
analysis_name = 'whole_brain_perm_' + str(perm_ind)
data_folder = '/scratch/tmp/wintern/iq_frankfurt/'
project_folder = '/scratch/tmp/wintern/iq_frankfurt/results/perm/whole_brain/'
cache_dir = '/scratch/tmp/wintern/cache'
# get data
data = IQData(data_folder=data_folder)
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_whole_brain(use_cached=True)
# run analysis
pipe = construct_hyperpipe(analysis_name, project_folder, cache_dir)
pipe.groups = data.fsiq
pipe.fit(data.whole_brain, perm_y, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)
This scripts implements the global (whole brain) analysis without TiV rescaling Version ------- Created: 28-01-2019 Last updated: 29-09-2019 Author ------ Nils R. Winter [email protected] Translationale Psychiatrie Universitaetsklinikum Muenster """ from analyses.analysis_base import construct_hyperpipe from data.data import IQData import numpy as np import os analysis_name = 'whole_brain_no_tiv_rescaling' project_folder = '.' # get data data = IQData() covariates = np.asarray([data.age, data.gender, data.handedness]).T data.load_whole_brain(use_cached=True) # run analysis pipe = construct_hyperpipe(analysis_name, project_folder) pipe.fit(data.whole_brain, data.fsiq, **{'covariates': covariates}) os.remove(pipe.output_settings.pretrained_model_filename)