Simulate subject data¶

# Code source: Lucy Owen
# License: MIT

# load timecorr
import timecorr as tc
import seaborn as sns
import matplotlib.pyplot as plt

# simulate some data
data, corrs = tc.simulate_data(datagen='block', return_corrs=True, set_random_seed=True, S=1, T=100, K=10, B=5)

# calculate correlations  - returned squareformed
tc_vec_data = tc.timecorr(tc.simulate_data(), weights_function=tc.gaussian_weights, weights_params={'var': 5}, combine=tc.helpers.corrmean_combine)

# convert from vector to matrix format
tc_mat_data = tc.vec2mat(tc_vec_data)

# plot the 3 correlation matrices different timepoints

sns.heatmap(tc_mat_data[:, :, 48])
plt.show()
plt.clf()
sns.heatmap(tc_mat_data[:, :, 50])
plt.show()
plt.clf()
sns.heatmap(tc_mat_data[:, :, 52])
plt.show()
plt.clf()