fissa.neuropil module#

Functions for removal of neuropil from calcium signals.

Authors:

Sander W Keemink <swkeemink@scimail.eu>
Scott C Lowe <scott.code.lowe@gmail.com>

Created:

2015-05-15

fissa.neuropil.separate(S, sep_method='nmf', n=None, max_iter=10000, tol=0.0001, random_state=892, max_tries=10, W0=None, H0=None, alpha=0.1, verbosity=1, prefix='')[source]#

Find independent signals, sorted by matching score against the first input signal.

Parameters

Sarray_like shaped (signals, observations)

2-d array containing mixed input signals. Each column of S should be a different signal, and each row an observation of the signals. For S[i, j], j is a signal, and i is an observation. The first column, j = 0, is considered the primary signal and the one for which we will try to extract a decontaminated equivalent.

sep_method{“ica”, “nmf”}

Which source separation method to use, either ICA or NMF.

"ica": Independent Component Analysis
"nmf": Non-negative Matrix Factorization

nint, optional

How many components to estimate. If None (default), for the NMF method, n is the number of input signals; for the ICA method, we use PCA to estimate how many components would explain at least 99% of the variance and adopt this value for n.

max_iterint, default=10000

Maximum number of iterations before timing out on an attempt.

Changed in version 1.0.0: Argument maxiter renamed to max_iter.

tolfloat, default=1e-4

Tolerance of the stopping condition.

random_stateint or None, default=892

Initial state for the random number generator. Set to None to use the numpy.random default. Default seed is 892.

max_triesint, default=10

Maximum number of random initial states to try. Each random state will be optimized for max_iter iterations before timing out.

Changed in version 1.0.0: Argument maxtries renamed to max_tries.

W0array_like, optional

Optional starting condition for W in NMF algorithm. (Ignored when using the ICA method.)

H0array_like, optional

Optional starting condition for H in NMF algorithm. (Ignored when using the ICA method.)

alphafloat, default=0.1

Sparsity regularizaton weight for NMF algorithm. Set to zero to remove regularization. Default is 0.1. (Ignored when using the ICA method.)

verbosityint, default=1

Level of verbosity. The options are:

0: No outputs.
1: Print separation progress.

prefixstr, optional

String to include before any progress statements.

Returns

S_sepnumpy.ndarray shaped (signals, observations)

The raw separated traces.

S_matchednumpy.ndarray shaped (signals, observations)

The separated traces matched to the primary signal, in order of matching quality (see Notes below).

A_sepnumpy.ndarray shaped (signals, signals)

Mixing matrix.

convergencedict

Metadata for the convergence result, with the following keys and values:

convergence["random_state"]: Seed for estimator initiation.
convergence["iterations"]: Number of iterations needed for convergence.
convergence["max_iterations"]: Maximum number of iterations allowed.
convergence["converged"]: Whether the algorithm converged or not (bool).