hfcut = 128 # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) print('Computation will be performed in directory: %s' % write_dir) ######################################## # Design matrix ######################################## print('Loading design matrix...') paradigm = load_paradigm_from_csv_file(paradigm_file)['0'] design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model, hfcut=hfcut) ######################################### # Specify the contrasts ######################################### # simplest ones contrasts = {} n_columns = len(design_matrix.names) for i in range(paradigm.n_conditions): contrasts['%s' % design_matrix.names[i]] = np.eye(n_columns)[i] # and more complex/ interesting ones
hfcut = 128 # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) print('Computation will be performed in directory: %s' % write_dir) ######################################## # Design matrix ######################################## print('Loading design matrix...') paradigm = load_paradigm_from_csv_file(paradigm_file)['0'] design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model, hfcut=hfcut) ax = design_matrix.show() ax.set_position([.05, .25, .9, .65]) ax.set_title('Design matrix') plt.savefig(path.join(write_dir, 'design_matrix.png')) ######################################### # Specify the contrasts
hfcut = 128 # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) print 'Computation will be performed in directory: %s' % write_dir ######################################## # Design matrix ######################################## print 'Loading design matrix...' paradigm = load_paradigm_from_csv_file(paradigm_file).values()[0] design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model, hfcut=hfcut) ax = design_matrix.show() ax.set_position([.05, .25, .9, .65]) ax.set_title('Design matrix') plt.savefig(path.join(write_dir, 'design_matrix.png')) ######################################### # Specify the contrasts ######################################### # simplest ones
hfcut = 128 # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) print 'Computation will be performed in directory: %s' % write_dir ######################################## # Design matrix ######################################## print 'Loading design matrix...' paradigm = load_paradigm_from_csv_file(paradigm_file).values()[0] design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model, hfcut=hfcut) ax = design_matrix.show() ax.set_position([.05, .25, .9, .65]) ax.set_title('Design matrix') plt.savefig(path.join(write_dir, 'design_matrix.png')) # design_matrix.write_csv(...) ########################################
# Paradigm # Fix bug in nipy/modalities/fmri/experimental_paradigm.py # -> amplitudes are not converted to floats # @@ -187,7 +187,7 @@ def load_paradigm_from_csv_file(path, session=None): # if len(row) > 3: # duration.append(float(row[3])) # if len(row) > 4: # - amplitude.append(row[4]) # + amplitude.append(float(row[4])) if 0: paradigm = load_paradigm_from_csv_file(paradigm_csv_file, session=str(session)) if paradigm is None: raise Exception('Failed to load paradigm data from %s (session=%d)' \ %(paradigm_csv_file, session)) pyhrf.verbose(1, 'Loaded paradigm: condition=%s, nb events=%d' %(str(list(set(paradigm.con_id))),paradigm.n_events)) assert op.exists(mask_file) # # Functional mask # if not op.exists(mask_file): # pyhrf.verbose(1, 'Mask file does not exist. Computing mask from '\ # 'BOLD data') # compute_mask_files(data_path, mask_file, False, 0.4, 0.9) # mask_array = compute_mask_files(bold_file, mask_file, # False, 0.4, 0.9)
# Data and analysis parameters ####################################### # timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans) fmri_data = nibabel.load('s12069_swaloc1_corr.nii.gz') ######################################## # Design matrix ######################################## paradigm = load_paradigm_from_csv_file('localizer_paradigm.csv')['0'] design_matrix = make_dmtx(frametimes, paradigm, hrf_model='canonical with derivative', drift_model="cosine", hfcut=128) ######################################### # Specify the contrasts ######################################### # simplest ones contrasts = {} n_columns = len(design_matrix.names) for i in range(paradigm.n_conditions): contrasts['%s' % design_matrix.names[2 * i]] = np.eye(n_columns)[2 * i]
# timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0.5 * tr, (n_scans - 0.5) * tr, n_scans) # write directory write_dir = "results" if not path.exists(write_dir): mkdir(write_dir) ######################################## # Design matrix ######################################## paradigm = load_paradigm_from_csv_file("localizer_paradigm.csv")["0"] design_matrix = make_dmtx(frametimes, paradigm, hrf_model="canonical with derivative", drift_model="cosine", hfcut=128) # Plot the design matrix ax = design_matrix.show() ax.set_position([0.05, 0.25, 0.9, 0.65]) ax.set_title("Design matrix") plt.savefig(path.join(write_dir, "design_matrix.png")) ######################################### # Specify the contrasts ######################################### # simplest ones contrasts = {}
# Data and analysis parameters ####################################### # timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans) fmri_data = nibabel.load('s12069_swaloc1_corr.nii.gz') ######################################## # Design matrix ######################################## paradigm = load_paradigm_from_csv_file('localizer_paradigm.csv')['0'] design_matrix = make_dmtx(frametimes, paradigm, hrf_model='canonical with derivative', drift_model="cosine", hfcut=128) ######################################### # Specify the contrasts ######################################### # simplest ones contrasts = {} n_columns = len(design_matrix.names) for i in range(paradigm.n_conditions):