utils_nd.py

GiveDegreeCandidates(degree_max, ndim, ncandidates=10)

Create a vector of degree candidates in each dimension.

Parameters:

• degree_max (int or array[int]) – The maximum degree to be considered (if an integer), or the maximum degree to be considered in each dimension (if an array of integers).

• ndim (int) – The number of dimensions.

• ncandidates (int, default=10) – The number of degree candidates to consider.

Returns:

degree_candidates – A 2D array containing the vector of degree candidates in each dimension.

Return type:

array[int]

MakePlot(Data, Title, degree_candidates, Interpolate=False, AddContour=False)

Plot a 2D heat-map as a function of the degree candidates in each dimension.

Parameters:

• Data (array[float]) – The 2-d array to plot.

• Title (str) – The title of the figure.

• degree_candidates (array[int] or list[array[int]]) – The array or list containing the vector of degree candidates in each dimension.

• Interpolate (bool, default=False) – Whether to interpolate the heat-map from Data.

• AddContour (bool, default=False) – Whether to add contour levels to the plot.

Returns:

fig – The figure containing the plot.

Return type:

matplotlib.figure.Figure

FlattenGrid(Inputs, ndim)

Create a flattened mesh of Inputs.

Parameters:

• Inputs (list[list[float]] or array[float]) – The input list/array of lists/arrays or 2D array.

• ndim (int) – The number of dimensions in (length of) Inputs.

Returns:

FlattenedMesh – The flattened list of mesh points; see example below.

Return type:

list[list[float]]

Examples

>>> Inputs = [[1,2,3,4], [1,2,3,4], [1,2,3,4]] # 3 dimensions with 4 points
>>> FlattenGrid(Inputs, 3)
[[1,1,1], [1,1,2], [1,1,3], [1,1,4], [2,1,1], [2,1,2], [2,1,3], ..., [4,4,3], [4,4,4]]