Functions
def	equalize_times
	Given a list of 1d time arrays, find the sequence that started first and subtract all sequences from its first time recording.
def	filter
	filter a list given indices
def	gradient
	calc dx/dt
def	histogram
def	histogram_get_bin_numb
	Given a histogram with params, calculate.
def	interpolate_1d
	1D interpolation
def	signal_list_variance
	TODO docs Returns the variance of the series x given mean function y over a window of size window_len.
def	signal_smooth
	smooth the data using a window with requested size.
def	signal_variance
	Returns the variance of the series x given mean function y over a window of size window_len.

Function Documentation

def hrl_lib.data_process.equalize_times ( list_of_time_arrays )

Given a list of 1d time arrays, find the sequence that started first and subtract all sequences from its first time recording.

Parameters:

list_of_time_arrays a list of 1d arrays

Returns:: list_of_time_arrays adjusted so that time arrays would start at 0

Definition at line 20 of file data_process.py.

def hrl_lib.data_process.filter	(	alist,
		indices
	)

filter a list given indices

Parameters:

alist	a list
indices	indices in that list to select

Definition at line 8 of file data_process.py.

def hrl_lib.data_process.gradient	(	t,
		x
	)

calc dx/dt

Parameters:

t	matrix 1xn
x	matrix mxn

Definition at line 44 of file data_process.py.

def hrl_lib.data_process.histogram	(	index_list_list,
		elements_list_list,
		bin_size,
		min_index = `None`,
		max_index = `None`
	)

Parameters:

index_list_list	a list of list of indices to histogram by
elements_list_list	a list of list of elements to place in histogram bins
bin_size	size of bins in index_list_list units
min_index	optional argument for mininum index to create histogram over
max_index	optional argument for maximum index to create histogram over

Definition at line 101 of file data_process.py.

def hrl_lib.data_process.histogram_get_bin_numb	(	n,
		min_index,
		bin_size,
		nbins
	)

Given a histogram with params, calculate.

Definition at line 87 of file data_process.py.

def hrl_lib.data_process.interpolate_1d	(	x,
		y,
		xquery
	)

1D interpolation

Parameters:

x	1xn mat x to interpolate from
y	1xn mat y to interpolate from
xquery	1xn mat of query x's

Definition at line 60 of file data_process.py.

def hrl_lib.data_process.signal_list_variance	(	x_list,
		means,
		window_len = `10`,
		num_samples = `30`,
		resample = `1`
	)

TODO docs Returns the variance of the series x given mean function y over a window of size window_len.

Parameters:

x	the original signal
y	the smoothed signal function
window_len	size of the window to calculate variances over

Returns:: the variance function

Definition at line 218 of file data_process.py.

def hrl_lib.data_process.signal_smooth	(	x,
		window_len = `11`,
		window = `'hamming'`
	)

smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal.

output: the smoothed signal

example:

t=linspace(-2,2,0.1) x=sin(t)+randn(len(t))*0.1 y=smooth(x)

see also:

numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve scipy.signal.lfilter

Copied from http://www.scipy.org/Cookbook/SignalSmooth

Parameters:

x	the input signal
window_len	the dimension of the smoothing window; should be an odd integer
window	the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' flat window will produce a moving average smoothing.

Returns:: the smoothed signal function

Definition at line 156 of file data_process.py.

def hrl_lib.data_process.signal_variance	(	x,
		y,
		window_len = `10`
	)

Returns the variance of the series x given mean function y over a window of size window_len.

Parameters:

x	the original signal
y	the smoothed signal function
window_len	size of the window to calculate variances over

Returns:: the variance function

Definition at line 191 of file data_process.py.

Functions

Function Documentation