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import numpy as np
#import scipy.stats
import random
import sys
def zscore(mat, return_unzvals=False):
"""Z-scores the rows of [mat] by subtracting off the mean and dividing
by the standard deviation.
If [return_unzvals] is True, a matrix will be returned that can be used
to return the z-scored values to their original state.
"""
zmat = np.empty(mat.shape, mat.dtype)
unzvals = np.zeros((zmat.shape[0], 2), mat.dtype)
for ri in range(mat.shape[0]):
unzvals[ri,0] = np.std(mat[ri,:])
unzvals[ri,1] = np.mean(mat[ri,:])
zmat[ri,:] = (mat[ri,:]-unzvals[ri,1]) / (1e-10+unzvals[ri,0])
if return_unzvals:
return zmat, unzvals
return zmat
def center(mat, return_uncvals=False):
"""Centers the rows of [mat] by subtracting off the mean, but doesn't
divide by the SD.
Can be undone like zscore.
"""
cmat = np.empty(mat.shape)
uncvals = np.ones((mat.shape[0], 2))
for ri in range(mat.shape[0]):
uncvals[ri,1] = np.mean(mat[ri,:])
cmat[ri,:] = mat[ri,:]-uncvals[ri,1]
if return_uncvals:
return cmat, uncvals
return cmat
def unzscore(mat, unzvals):
"""Un-Z-scores the rows of [mat] by multiplying by unzvals[:,0] (the standard deviations)
and then adding unzvals[:,1] (the row means).
"""
unzmat = np.empty(mat.shape)
for ri in range(mat.shape[0]):
unzmat[ri,:] = mat[ri,:]*(1e-10+unzvals[ri,0])+unzvals[ri,1]
return unzmat
def gaussianize(vec):
"""Uses a look-up table to force the values in [vec] to be gaussian."""
ranks = np.argsort(np.argsort(vec))
cranks = (ranks+1).astype(float)/(ranks.max()+2)
vals = scipy.stats.norm.isf(1-cranks)
zvals = vals/vals.std()
return zvals
def gaussianize_mat(mat):
"""Gaussianizes each column of [mat]."""
gmat = np.empty(mat.shape)
for ri in range(mat.shape[1]):
gmat[:,ri] = gaussianize(mat[:,ri])
return gmat
def make_delayed(stim, delays, circpad=False):
"""Creates non-interpolated concatenated delayed versions of [stim] with the given [delays]
(in samples).
If [circpad], instead of being padded with zeros, [stim] will be circularly shifted.
"""
nt,ndim = stim.shape
dstims = []
for di,d in enumerate(delays):
dstim = np.zeros((nt, ndim))
if d<0: ## negative delay
dstim[:d,:] = stim[-d:,:]
if circpad:
dstim[d:,:] = stim[:-d,:]
elif d>0:
dstim[d:,:] = stim[:-d,:]
if circpad:
dstim[:d,:] = stim[-d:,:]
else: ## d==0
dstim = stim.copy()
dstims.append(dstim)
return np.hstack(dstims)
def mult_diag(d, mtx, left=True):
"""Multiply a full matrix by a diagonal matrix.
This function should always be faster than dot.
Input:
d -- 1D (N,) array (contains the diagonal elements)
mtx -- 2D (N,N) array
Output:
mult_diag(d, mts, left=True) == dot(diag(d), mtx)
mult_diag(d, mts, left=False) == dot(mtx, diag(d))
By Pietro Berkes
From http://mail.scipy.org/pipermail/numpy-discussion/2007-March/026807.html
"""
if left:
return (d*mtx.T).T
else:
return d*mtx
import time
import logging
def counter(iterable, countevery=100, total=None, logger=logging.getLogger("counter")):
"""Logs a status and timing update to [logger] every [countevery] draws from [iterable].
If [total] is given, log messages will include the estimated time remaining.
"""
start_time = time.time()
## Check if the iterable has a __len__ function, use it if no total length is supplied
if total is None:
if hasattr(iterable, "__len__"):
total = len(iterable)
for count, thing in enumerate(iterable):
yield thing
if not count%countevery:
current_time = time.time()
rate = float(count+1)/(current_time-start_time)
if rate>1: ## more than 1 item/second
ratestr = "%0.2f items/second"%rate
else: ## less than 1 item/second
ratestr = "%0.2f seconds/item"%(rate**-1)
if total is not None:
remitems = total-(count+1)
remtime = remitems/rate
timestr = ", %s remaining" % time.strftime('%H:%M:%S', time.gmtime(remtime))
itemstr = "%d/%d"%(count+1, total)
else:
timestr = ""
itemstr = "%d"%(count+1)
formatted_str = "%s items complete (%s%s)"%(itemstr,ratestr,timestr)
if logger is None:
print(formatted_str)
else:
logger.info(formatted_str)
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