Utility classes and functions¶

Calculate the z-scores for array x based on the mean and standard deviation in that sample, unless stated

Parameters:

Returns:

Filters the data in trial by time

Parameters:

    Legal values: "in_half" or "odd_even"
    "in_half" filters for first n seconds and last n second
    "odd_even" filters for odd vs even minutes

Returns:

Mean normalise an input array

Parameters:

Returns:

Normalise the input array x to lie between min and max

Parameters:

Returns:

Remap the values of x to the range [new_min, new_max].

Parameters:

Returns:

Flattens a list of lists

Parameters:

Returns:

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 beginning and end part of the output signal.

Parameters:

Returns:

Examples:

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

Smooths all the binned_data in an instance of BinnedData by convolving with a filter.

Parameters:

Returns:

Shifts the elements of a vector by a given amount. A bit like numpys roll function but when the shift goes beyond some limit that limit is subtracted from the shift. The result is then sorted and returned.

Parameters:

Returns:

This function is equivalent to hstack((arange(n_i) for n_i in n)). It seems to be faster for some possible inputs and encapsulates a task in a function.

Examples:

>>> n = np.array([0, 0, 3, 0, 0, 2, 0, 2, 1])
>>> count_to(n)
array([0, 1, 2, 0, 1, 0, 1, 0])

Calculates the root mean square of the input a over a window of size window_size

Parameters:

Returns:

Find runs of consecutive items in an array.

Parameters:

Returns:

Examples:

>>> n = np.array([0, 0, 3, 3, 0, 2, 0,0, 1])
>>> find_runs(n)
(array([0, 3, 0, 2, 0, 1]),
array([0, 2, 4, 5, 6, 8]),
array([2, 2, 1, 1, 2, 1]))

Repeat a given index a specified number of times.

The input specifies how many times to repeat the given index. It is equivalent to something like this:

hstack((zeros(n_i,dtype=int)+i for i, n_i in enumerate(n)))

But this version seems to be faster, and probably scales better. At any rate, it encapsulates a task in a function.

Parameters:

Returns:

Examples:

>>> n = np.array([0, 0, 3, 0, 0, 2, 0, 2, 1])
>>> repeat_ind(n)
array([2, 2, 2, 5, 5, 7, 7, 8])

Convert from polar (r, w) to rectangular (x, y) coordinates.

Parameters:

Returns:

Converts from rectangular coordinates to polar ones.

Parameters:

Returns:

Compute the cumulative sum of an array with labels, resetting the sum at label changes.

Parameters:

Returns:

Convert Cartesian coordinates to polar coordinates.

Parameters:

Returns:

Convert polar coordinates to Cartesian coordinates.

Parameters:

Returns:

M is a logical mask specifying which label numbers to keep x is an array of positive integer labels

This method sets the undesired labels to 0 and renumbers the remaining labels 1 to n when n is the number of trues in M

Get the indices of the start of contiguous runs of non-zero values in a 1D numpy array.

Parameters:

Returns:

Get the indices of the end of contiguous runs of non-zero values in a 1D numpy array.

Parameters:

Returns:

Calculate the absolute value of an angle in radians, normalized to the range [-pi, pi].

Parameters:

Returns:

Label contiguous non-zero runs in a 1D numpy array.

Parameters:

Returns:

Ensure angles lie between -pi and pi.

Parameters:

Returns:

Finds the perimeter of objects in binary images.

A pixel is part of an object perimeter if its value is one and there is at least one zero-valued pixel in its neighborhood.

By default, the neighborhood of a pixel is 4 nearest pixels, but if n is set to 8, the 8 nearest pixels will be considered.

Parameters:

Returns:

Counts the number of continuous runs of numbers in a 1D numpy array.

Parameters:

Returns:

Correlates two rate maps together, ignoring areas that have zero sampling.

Parameters:

Returns: