""" import numpy as np from loader import load_dynacomp, list_of_dicts_to_key_list import matplotlib.pyplot as plt # Load dataset dataset = load_dynacomp() groups = ['av', 'v', 'avn'] # Behavior data behav_data = dataset.behavior # Add deltas for i in range(len(behav_data)): for key in ['Thresh', 'RT', 'HIT_RT', 'Perf', 'Conf_mean']: behav_data[i]['delta' + key] = behav_data[i]['post' + key] - \ behav_data[i]['pre' + key] # for each behav score for key in behav_data[0].keys(): scores = [] # for each group for group in groups: bd = np.array(behav_data) scores.append(list_of_dicts_to_key_list(\ bd[dataset.group_indices[group]], key)) plt.figure() plt.boxplot(scores) plt.xticks(range(1, 4), groups, fontsize=16) plt.title(key, fontsize=16) plt.grid(axis='y')
""" import numpy as np from loader import load_dynacomp, list_of_dicts_to_key_list import matplotlib.pyplot as plt # Load dataset dataset = load_dynacomp() groups = ['av', 'v', 'avn'] # Behavior data behav_data = dataset.behavior # Add deltas for i in range(len(behav_data)): for key in ['Thresh', 'RT', 'HIT_RT', 'Perf', 'Conf_mean']: behav_data[i]['delta' + key] = behav_data[i]['post' + key] - \ behav_data[i]['pre' + key] # for each behav score for key in behav_data[0].keys(): scores = [] # for each group for group in groups: bd = np.array(behav_data) scores.append(list_of_dicts_to_key_list(\ bd[dataset.group_indices[group]], key)) plt.figure() plt.boxplot(scores) plt.xticks(range(1,4), groups, fontsize=16) plt.title(key, fontsize=16) plt.grid(axis='y')
Script to test functions Created on Thu Mar 26 15:02:11 2015 @author: [email protected] """ from loader import load_dynacomp, list_of_dicts_to_key_list, dict_to_list from nilearn.input_data import NiftiMapsMasker import time # Load Dynacomp dataset dataset = load_dynacomp() # Dataset keys print 'keys\n', dataset.keys() # Dataset functional 1 print 'func1\n', dataset.func1 # Dataset behaviordata : prePerf print 'prePerf\n', list_of_dicts_to_key_list(dataset.behavior, 'prePerf') # Generate seed-masker for subject 0 maps_img = dict_to_list(dataset.rois[1]) tic = time.clock() masker = NiftiMapsMasker(maps_img, verbose=5) x = masker.fit_transform(dataset.func1[1]) toc = time.clock() print toc - tic