MCALF utils¶

This sub-package contains:

Functions for processing spectra (mcalf.utils.spec).
Functions for smoothing n-dimensional arrays (mcalf.utils.smooth).
Functions for masking the input data to limit the region computed (mcalf.utils.mask).
Functions for helping with plotting (mcalf.utils.plot).
Miscellaneous utility functions (mcalf.utils.misc).

mcalf.utils Package¶

`reinterpolate_spectrum`(spectrum, …)	Reinterpolate the spectrum.
`normalise_spectrum`(spectrum[, …])	Normalise an individual spectrum to have intensities in range [0, 1].
`generate_sigma`(sigma_type, wavelengths, …)	Generate the default sigma profiles.

`moving_average`(array, width)	Boxcar moving average.
`gaussian_kern_3d`([width, sigma])	3D Gaussian kernel.
`smooth_cube`(cube, mask, **kwargs)	Apply Gaussian smoothing to velocities.
`mask_classifications`(class_map[, vmin, …])	Mask 2D and 3D arrays of classifications.

`genmask`(width, height[, radius, …])	Generate a circular mask of specified size.
`radial_distances`(n_cols, n_rows)	Generates a 2D array of specified shape of radial distances from the centre.

`hide_existing_labels`(plot_settings[, axes, fig])	Hides labels for each dictionary provided if label already exists in legend.
`calculate_axis_extent`(resolution, px[, …])	Calculate the extent from a resolution value along a particular axis.
`calculate_extent`(shape, resolution[, …])	Calculate the extent from a particular data shape and resolution.
`class_cmap`(style, n)	Create a listed colormap for a specific number of classifications.

`make_iter`(*args)	Returns each inputted argument, wrapping in a list if not already iterable.
`load_parameter`(parameter[, wl])	Load parameters from file, optionally evaluating variables from strings.
`merge_results`(filenames, output)	Merges files generated by the `mcalf.models.FitResults.save()` method.