Source code for

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Copyright (C) 2012 Computational Neuroscience Group, NMBU.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.


import numpy as np
import scipy.signal as ss
import pickle

[docs]def load(filename): """Generic loading of cPickled objects from file Parameters ---------- filename: str path to pickle file """ with open(filename, 'rb') as f: obj = pickle.load(f) return obj
[docs]def noise_brown(ncols, nrows=1, weight=1., filter=None, filterargs=None): """Return 1/f^2 noise of shape(nrows, ncols obtained by taking the cumulative sum of gaussian white noise, with rms weight. If filter is not None, this function will apply the filter coefficients obtained by: >>> b, a = filter(**filterargs) >>> signal = scipy.signal.lfilter(b, a, signal) Parameters ---------- ncols: int nrows: int weight: float filter: None or function filterargs: **dict parameters passed to `filter` """ def rms_flat(a): """ Return the root mean square of all the elements of *a*, flattened out. """ return np.sqrt(np.mean(np.absolute(a)**2)) if filter is not None: coeff_b, coeff_a = list(filter(**filterargs)) noise = np.zeros((nrows, ncols)) for i in range(nrows): signal = np.random.normal(size=ncols + 10000).cumsum() if filter is not None: signal = ss.lfilter(coeff_b, coeff_a, signal) noise[i, :] = signal[10000:] noise[i, :] /= rms_flat(noise[i, :]) noise[i, :] *= weight return noise