Fix NaNs in plot_error_map for zero-error rows

[vkverma9534]

Describe your changes

plot_error_map normalizes each variable by its maximum error before plotting. If a variable has zero error across all prediction horizons, this leads to a division by zero and introduces NaNs in the heatmap.

This change replaces the normalization with a safe division approach to avoid NaNs in this case. A regression test is also added to cover this scenario.

No additional dependencies are required.

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[joeloskarsson]

Does this really get back the normalized errors? I think this code would need some explanations as it is pretty much unpacking the produced figure. Hard to understand without knowing matplotlib internals.

[vkverma9534]

@joeloskarsson , Thanks for your attention and yeah that’s fair.

Right now the test is just guarding against NaNs in the final heatmap for the zero-error case, so I’m pulling the array from ax.images[0] to check that. Do you think that’s acceptable here, or would you rather we restructure things so the normalized values can be tested more directly?

[sadamov]

on a higher level: did you check whether this error is alread solved in #376?