Add plotting functions to make plotting RSoXS data (already processed into Xarray) in Jupyter notebooks straightforward. The plots to be added are below. The first one reads in cartesian Qy vs Qx (or pix_y vs pix_x) data while the rest read in polar data (Q vs Chi):
[ ] 2D detector images at chosen energies (single image at set energy, facet plots at multiple energies, all energies via slide bar or mp4).
For all below plots, there should also be an option to select specific chi ranges (to be parallel or perpendicular to X-ray polarization). It is also often beneficial to average between scans of different polarizations if the anisotropy rotates with polarization, though this feature likely fits better somewhere outside of these plotting tools.
[x] Integrated scattered intensity (1D)
[x] Intensity 2D heatmap (energy along one axis and Q along the other, perceptually uniform colormap representing intensity)
[x] Intensity vs Q 1D linecuts, with selected energies overlayed (and/or an option with a slide bar for all energies)
[x] Anisotropy 2D heatmap (energy along one axis and Q along the other, diverging blue/red colormap representing anisotropy)
[x] Anisotropy vs Q linecuts, with selected energies overlayed (and/or an option with a slide bar for all energies)
Add plotting functions to make plotting RSoXS data (already processed into Xarray) in Jupyter notebooks straightforward. The plots to be added are below. The first one reads in cartesian Qy vs Qx (or pix_y vs pix_x) data while the rest read in polar data (Q vs Chi):
For all below plots, there should also be an option to select specific chi ranges (to be parallel or perpendicular to X-ray polarization). It is also often beneficial to average between scans of different polarizations if the anisotropy rotates with polarization, though this feature likely fits better somewhere outside of these plotting tools.