TME
Python implementation of Corrected Fisher Randomization (CFR)
Install:
pip install corrected-fisher-randomization
Usage example:
import numpy as np
import scipy.io
from corrected_fisher_randomization import CFR
model_dim = 10
data = scipy.io.loadmat('./exampleData.mat')
dataTensor = data['dataTensor']
print(dataTensor.shape)
t = data['t']
mask = np.logical_and(t > - 50, t < 350)
CFR(dataTensor, mask, model_dim)
The algorithm description can be found in the following article:
Elsayed, G.F.; Cunningham, J.P. Structure in Neural Population Recordings: An Expected Byproduct of Simpler Phenomena? Nat Neurosci 2017, 20, 1310–1318, doi:10.1038/nn.4617.
A matlab implementation can be found at the following link: https://github.com/gamaleldin/CFR
Metadata
Release files for corrected-fisher-randomization 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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| corrected_fisher_randomization-0.0.2.tar.gz | 6.9 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| corrected_fisher_randomization-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.5 kB
Release files / corrected_fisher_randomization-0.0.2.tar.gz
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