ahwillia

def bandpass(x, lowcut, highcut, fs, order=5, axis=-1, kind='butter'):
    """
    Parameters
    ----------
    x : ndarray
        1d time series data
    lowcut : float
        Defines lower frequency cutoff (e.g. in Hz)
    highcut : float
        Defines upper frequency cutoff (e.g. in Hz)

ahwillia

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

ahwillia

import numpy as np
from scipy.linalg import solve_circulant, circulant
from numpy.testing import assert_array_almost_equal
import numba


@numba.jit(nopython=True, cache=True)
def rojo_method(c, a, f, x, z):
    """
    Solves symmetric, tridiagonal circulant system, assuming diagonal

ahwillia

import numpy as np
from sklearn.utils.extmath import randomized_svd


def partial_whiten(X, alpha, eigval_tol=1e-7):
    """
    Return regularized whitening transform for a matrix X.

    Parameters
    ----------

ahwillia

# Majority of credit goes to Chris Holdgraf, @choldgraf, and this StackOverflow
# post: http://stackoverflow.com/questions/5320205/matplotlib-text-dimensions

import pylab as plt
import numpy as np

def plot_equation(eq, fontsize=50, outfile=None, padding=0.1, **kwargs):
    """Plot an equation as a matplotlib figure.
    Parameters
    ----------

ahwillia

using PyPlot
using Distributions

function credible_interval(D::UnivariateDistribution; c=0.95, nx=1000)
	# Discretize over the support
	r = support(D)
	lb,ub = r.lb,r.ub

	# Histogram approximation of area under pdf
	x = linspace(lb,ub,nx)

ahwillia

import scipy.io as spio
import numpy as np

def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''

ahwillia

"""
References:

  - B. Plateau, On the stochastic structure of parallelism and synchronization models for distributed algorithms.
  Perform. Eval. Rev., 13 (1985), pp. 147–154.

  - Dayar, T., & Orhan, M. C. (2015). On vector-Kronecker product multiplication with rectangular factors.
  SIAM Journal on Scientific Computing, 37(5), S526-S543.
"""

ahwillia

"""
A simple implementation of a permutation test among two
independent samples.
"""

import numpy as np
from sklearn.utils.validation import check_random_state
from more_itertools import distinct_permutations
from scipy.stats import percentileofscore
from math import factorial

ahwillia

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)