fissa.neuropil module

Functions for removal of neuropil from calcium signals.

Authors:
Created:
2015-05-15
fissa.neuropil.lowPassFilter(F, fs=40, nfilt=40, fw_base=10, axis=0)[source]

Low pass filters a fluorescence imaging trace line.

Parameters:
  • F (array_like) – Fluorescence signal.
  • fs (float, optional) – Sampling frequency of F, in Hz. Default is 40.
  • nfilt (int, optional) – Number of taps to use in FIR filter. Default is 40.
  • fw_base (float, optional) – Cut-off frequency for lowpass filter, in Hz. Default is 10.
  • axis (int, optional) – Along which axis to apply low pass filtering. Default is 0.
Returns:

Low pass filtered signal with the same shape as F.
Return type:

numpy.ndarray
fissa.neuropil.separate(S, sep_method='nmf', n=None, maxiter=10000, tol=0.0001, random_state=892, maxtries=10, W0=None, H0=None, alpha=0.1)[source]

For the signals in S, finds the independent signals underlying it, using ica or nmf.

Parameters:
  • S (array_like) – 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 = each signal, i = signal content. 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
  • n (int, optional) – How many components to estimate. If None (default), use PCA to estimate how many components would explain at least 99% of the variance and adopt this value for n.
  • maxiter (int, optional) – Number of maximally allowed iterations. Default is 500.
  • tol (float, optional) – Error tolerance for termination. Default is 1e-5.
  • random_state (int, optional) – Initial state for the random number generator. Set to None to use the numpy.random default. Default seed is 892.
  • maxtries (int, optional) – Maximum number of tries before algorithm should terminate. Default is 10.
  • W0 (array_like, optional) – Optional starting condition for W in NMF algorithm. (Ignored when using the ICA method.)
  • H0 (array_like, optional) – Optional starting condition for H in NMF algorithm. (Ignored when using the ICA method.)
  • alpha (float, optional) – Sparsity regularizaton weight for NMF algorithm. Set to zero to remove regularization. Default is 0.1. (Ignored when using the ICA method.)
Returns:

  • numpy.ndarray – The raw separated traces.
  • numpy.ndarray – The separated traces matched to the primary signal, in order of matching quality (see Methods below).
  • numpy.ndarray – Mixing matrix.
  • dict – Metadata for the convergence result, with keys:
    • ’random_state’: seed for ICA initiation
    • ’iterations’: number of iterations needed for convergence
    • ’max_iterations’: maximum number of iterations allowed
    • ’converged’: whether the algorithm converged or not (bool)

Notes

Concept by Scott Lowe and Sander Keemink. Normalize the columns in estimated mixing matrix A so that sum(column)=1. This results in a relative score of how strongly each separated signal is represented in each ROI signal.