Time-resolved diffraction measurements can benefit from careful plotting, especially for time-series of data. This tutorial will go over some of the functions that make this easier in skued.



Time-resolved data possesses obvious ordering, and as such we can use colors to enhance visualization of such data.

My favourite way to plot time-series data is to make use of rainbow colors to indicate time:

(Source code, png, hires.png, pdf)


This functionality can be accessed through the spectrum_colors() generator:

>>> from skued import spectrum_colors
>>> colors = spectrum_colors(5)
>>> # Colors always go from purple to red (increasing wavelength)
>>> purple = next(colors)
>>> red = list(colors)[-1]

You can see some examples of uses of spectrum_colors() in the baseline tutorial.

Miller indices

Miller indices notation in Matplotlib plots is facilitated by using indices_to_text(). This function renders indices in LaTeX/Mathjax-style, which is compatible with Matplotlib:

>>> from skued import indices_to_text
>>> indices_to_text(1,1,0)
>>> indices_to_text(1,-2,3) 

Here is an example:

(Source code, png, hires.png, pdf)


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