import pandas as pd from prepare import make_summary from tmp.analysis import get_path, pj data = make_summary() depends = ['v', 'a', 't', 'z'] include = ['sv', 'st'] path = get_path(depends, include) samples = pd.read_csv(pj(path, 'all_samples.csv'), index_col=0) samples['s_age'] = data.age.std() print data.age.std()
import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sb from scipy.stats import linregress from prepare import load_data from tmp.analysis import get_path, pj depends = ['v', 'a', 't', 'z'] include = ['sv', 'st'] path = get_path(depends, include) data = load_data() ppc_data = pd.read_csv(pj(path, 'ppc_data.csv')) simulations = 250 results = {'real': []} _results = {'simulation %i' %k: [] for k in xrange(simulations)} results.update(_results) for node, _ppc_data in ppc_data.groupby('node'): subj = int(node.split('.')[1]) subj_data = data[data.subj_idx == subj] corr_array = (subj_data.answer == subj_data.response).astype(int) * 2 - 1 accuracy_coded_rts = corr_array * subj_data.rt ntrials = len(accuracy_coded_rts) ncorrect = len(accuracy_coded_rts[accuracy_coded_rts > 0]) entry = { 'subj': subj,