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,
