Decode by level¶

# Code source: Lucy Owen
# License: MIT

# load timecorr and other packages
import timecorr as tc
import hypertools as hyp
import numpy as np


# load example data
data = hyp.load('weights').get_data()

# define your weights parameters
width = 10
laplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}

# set your number of levels
# if integer, returns decoding accuracy, error, and rank for specified level
level = 2

# run timecorr with specified functions for calculating correlations, as well as combining and reducing
results = tc.timepoint_decoder(np.array(data), level=level, combine=tc.corrmean_combine,
                               cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],
                               weights_params=laplace['params'])

# returns only decoding results for level 2
print(results)

# set your number of levels
# if list or array of integers, returns decoding accuracy, error, and rank for all levels
levels = np.arange(int(level) + 1)

# run timecorr with specified functions for calculating correlations, as well as combining and reducing
results = tc.timepoint_decoder(np.array(data), level=levels, combine=tc.corrmean_combine,
                               cfun=tc.isfc, rfun='eigenvector_centrality', weights_fun=laplace['weights'],
                               weights_params=laplace['params'])

# returns decoding results for all levels up to level 2
print(results)