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Preparations for multivariate plotting #29877

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Preparations for multivariate plotting #29877

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5e0266a
MultiNorm class
trygvrad Apr 6, 2025
6985111
updates based on feedback from review, @oscargus, @anntzer
trygvrad Apr 10, 2025
f42d65b
Apply suggestions from code review
trygvrad Apr 17, 2025
73713e7
Updates based on feedback from @anntzer
trygvrad May 4, 2025
78b173e
change MultiNorm.n_intput to n_variables
trygvrad May 7, 2025
3979e09
Changes to colorizer to prepare for multivariate plotting
trygvrad Apr 6, 2025
adb4ee3
Updates based on feedback from review
trygvrad Apr 10, 2025
a276d89
update following change to MultiNorm
trygvrad May 7, 2025
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12 changes: 11 additions & 1 deletion lib/matplotlib/cbook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -690,7 +690,17 @@
try:
xm = np.ma.masked_where(~(np.isfinite(x)), x, copy=False)
except TypeError:
return x
if len(x.dtype.descr) == 1:
return x
else:
# in case of a dtype with multiple fields:
try:
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Would be good to get at least partial coverage for this branch.

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I haven't really been involved in this work nor understand how it works, but there is quite a bit of introduced code to deal with multiple datatypes? If this will be covered by tests/functionality in later PRs, that is fine, if not, please add tests for (most of) it.

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I was asked to split #29221 into multiple PRs, and this PR is one of them.
There is tests for this functionality in #29221 using the top-level plotting functions (axes.imshow() etc.)
In my mind it is better to test using the top-level API, but if you wish I could add dedicated testing to this PR.

mask = np.empty(x.shape, dtype=np.dtype('bool, '*len(x.dtype.descr)))

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Could the dtype be e.g. [("mask", bool, len(x.dtype.descr))] (with a slightly different API)?

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This is an interesting idea. I'll make a prototype and see if this would add unnecessary complexity somewhere else.

for dd, dm in zip(x.dtype.descr, mask.dtype.descr):
mask[dm[0]] = ~(np.isfinite(x[dd[0]]))
xm = np.ma.array(x, mask=mask, copy=False)
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Do numpy masked arrays actually support struct arrays as mask, with possibly different masking of the fields?

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I have found that this is the only way numpy supports masking dtypes with multiple fields, but I will see if [("mask", bool, len(x.dtype.descr))] as you suggest bellow is a reasonable approach to using a single mask.

except TypeError:
return x

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return xm


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29 changes: 0 additions & 29 deletions lib/matplotlib/cm.py
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Expand Up @@ -278,32 +278,3 @@ def get_cmap(name=None, lut=None):
return _colormaps[name]
else:
return _colormaps[name].resampled(lut)


def _ensure_cmap(cmap):
"""
Ensure that we have a `.Colormap` object.

For internal use to preserve type stability of errors.

Parameters
----------
cmap : None, str, Colormap

- if a `Colormap`, return it
- if a string, look it up in mpl.colormaps
- if None, look up the default color map in mpl.colormaps

Returns
-------
Colormap

"""
if isinstance(cmap, colors.Colormap):
return cmap
cmap_name = mpl._val_or_rc(cmap, "image.cmap")
# use check_in_list to ensure type stability of the exception raised by
# the internal usage of this (ValueError vs KeyError)
if cmap_name not in _colormaps:
_api.check_in_list(sorted(_colormaps), cmap=cmap_name)
return mpl.colormaps[cmap_name]
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