4.8. utils.py

General utility/convenience functions

utils.circularize_next_index(i, i_max)

Make an index cyclic.

Parameters

• i (int) – Integer index to be converted to a cyclic index

• i_max (int) – The maximum valid value of i.

Returns

i – If input i is equal to i_max + 1, return 0, otherwise return the input i.

Return type

int

Note

In python, list[-1] will automatically give you the last element of a list, so checking for i == -1 is redundant.

utils.closed_segment_intersect(a, b, c, d)

Verifies if closed segments a, b, c, d do intersect.

utils.covariance(data_1, data_2)

Two-pass numerically stable covariance calculation

utils.cyclic_sort(list_)

Find the lowest member of list and perform cyclic permuting so that the lowest member is first.

Assumes unique members.

utils.gen_de_framed_mat(temp_mat, r)

Return input matrix de-framed by width r.

Parameters

• temp_mat (np.array) – Matrix to be de-padded.

• r (int) – Width of frame

Returns De-framed matrix

Rtype np.array

utils.gen_fft(mat)

Generate the fast Fourier transform of a matrix.

utils.gen_framed_mat(mat, r)

Return input matrix padded with a frame of zeros and width r.

Parameters

• mat (np.array) – Matrix to be padded.

• r (int) – Width of frame

Returns Framed matrix

Rtype np.array

utils.is_circularly_identical(list_1, list_2)

Returns a boolean indicating whether list_1 and list_2 are the same in any cyclic perturbation of each other.

utils.is_point_in_closed_segment(a, b, c)

Returns True if c is inside closed segment, False otherwise. a, b, c are expected to be collinear

utils.make_int_from_list(list_)

Concatenate the menbers of a list of integers into a integer.

utils.mean_val(data)

Return the mean of the given data

Parameters

data ([float] or np.array) – list or 1-d np.array with data values

Returns mean

Rtype float

utils.multivariate_normal_dist(x, means, covar_determinant, inverse_covar_matrix)

All arguments assumed to be python lists

utils.normalize_static(mat)

Normalize matrix such that its elements are in [0, 1]

utils.side(a, b, c)

Returns a position of the point c relative to the line going through a and b Points a, b are expected to be different

utils.variance(data)

Return the var of the given data

Parameters

data ([float] or np.array) – list or 1-d np.array with data values

Returns mean

Rtype float