def generate_templates_refactor(order=1, cov_list=None, **kwargs):
kwargs['return_corrs'] = True
T = kwargs['T']
templates = []
for n in range(order):
print(n)
if cov_list:
kwargs['datagen'] = cov_list[n]
_, next_template = tc.simulate_data(**kwargs)
if n >= 1:
expanded_corrmats_last = tc.vec2mat(templates[n - 1])
expanded_corrmats_next = tc.vec2mat(next_template)
K2 = expanded_corrmats_last.shape[0]**2
next_template = np.zeros([K2, K2, T])
for t in range(T):
x_last = expanded_corrmats_last[:, :, t]
x_next = expanded_corrmats_next[:, :, t]
next_template[:, :, t] = np.kron(x_last, x_next)
next_template = tc.mat2vec(next_template)
templates.append(next_template)
return templates
def generate_templates(order=1, **kwargs):
kwargs['return_corrs'] = True
_, next_template = tc.simulate_data(**kwargs)
T = kwargs['T']
templates = []
for n in range(order - 1):
templates.append(next_template)
expanded_corrmats = tc.vec2mat(next_template)
K2 = expanded_corrmats.shape[0]**2
next_template = np.zeros([K2, K2, T])
for t in range(T):
x = np.atleast_2d(expanded_corrmats[:, :, t].ravel())
next_template[:, :, t] = x * x.T
next_template = tc.mat2vec(next_template)
templates.append(next_template)
return templates
"""
=============================
Simulate subject data
=============================
In this example, we simulate data
"""
# Code source: Lucy Owen
# License: MIT
# load timecorr
import timecorr as tc
import seaborn as sns
# 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])
sns.heatmap(tc_mat_data[:, :, 50])
sns.heatmap(tc_mat_data[:, :, 52])
=============================
In this example, we calculate dynamic correlations
"""
# Code source: Lucy Owen
# License: MIT
# load timecorr and other packages
import timecorr as tc
import numpy as np
S = 1
T = 1000
K = 10
B = 5
# define your weights parameters
width = 100
laplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}
# calculate the dynamic correlation of the two datasets
subs_data_2 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B)
subs_data_1 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B)
wcorred_data = tc.wcorr(np.array(subs_data_1), np.array(subs_data_2), weights=laplace['weights'](T))