Allow categorical variables in smpdf plots #1715
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np.unique seems fine to me. At the moment I don't imagine the order matters very much so as long as np.unique keeps things consistent I couldn't care less.
Did you mean to have a different figure for the Ws and Z?
Also, there are double parentheses due to group_label and the title both adding them.
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Can this be merged?
I guess it would be good to add another smpdf test in which the categorical branch is used, maybe using one of the datasets in your report @Zaharid ? ATLAS_WZ_TOT_13TEV for instance
| sm = cm.ScalarMappable(cmap=cm.viridis, norm=norm) | ||
| cmap = cm.viridis | ||
| #TODO: vmin vmax should be global or by figure? | ||
| vmin,vmax = min(plotting_var), max(plotting_var) |
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| vmin,vmax = min(plotting_var), max(plotting_var) | |
| vmin, vmax = min(plotting_var), max(plotting_var) |
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Fix crash in plot_smpdf when the variable in the color axis is categorical, such as for the W and Z total cross sections. Encode the categorical variables as integers and use an appropriate color map to display them. The implementation feels a bit untidy, with some variables acting at a distance, but there doesn't seem to be an easy better way considering that we'd like the color bar to be the same for all figures. Also, the fact that np.unique sorts the output may not be what we want. See numpy/numpy#8621. But don't want to make it more complicated.
Overall it makes little sense to separate 3 points into 2+1
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@scarlehoff @RoyStegeman I don't know what the current test failure with the evolution is, but don't believe it matters for this. Please merge this. |
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I'm happy with this PR. I think the error is because our tests are not compatible with the eko version and I believe this is fixed in #1754. @scarlehoff can you confirm?
Fix crash in plot_smpdf when the variable in the color axis is categorical, such as for the W and Z total cross sections. Encode the categorical variables as integers and use an appropriate color map to display them.
The implementation feels a bit untidy, with some variables acting at a distance, but there doesn't seem to be an easy better way considering that we'd like the color bar to be the same for all figures.
Also, the fact that np.unique sorts the output may not be what we want. See numpy/numpy#8621. But don't want to make it more complicated.