data-prototype/data_prototype/axes.py at text · matplotlib/data-prototype · GitHub

Name: data-prototype/data_prototype/axes.py at text · matplotlib/data-prototype · GitHub
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importnumpyasnp


importmatplotlibasmpl
frommatplotlib.axes._axesimportAxesasMPLAxes, _preprocess_data
importmatplotlib.collectionsasmcoll
importmatplotlib.cbookascbook
importmatplotlib.markersasmmarkers
importmatplotlib.projectionsasmprojections

from .containersimportArrayContainer, DataUnion
from .conversion_nodeimport (
DelayedConversionNode,
FunctionConversionNode,
RenameConversionNode,
)
from .wrappersimportPathCollectionWrapper


classAxes(MPLAxes):
# Name for registering as a projection so we can experiment with it
name="data-prototype"

@_preprocess_data(
replace_names=[
"x",
"y",
"s",
"linewidths",
"edgecolors",
"c",
"facecolor",
"facecolors",
"color",
 ],
label_namer="y",
 )
defscatter(
self,
x,
y,
s=None,
c=None,
marker=None,
cmap=None,
norm=None,
vmin=None,
vmax=None,
alpha=None,
linewidths=None,
*,
edgecolors=None,
plotnonfinite=False,
**kwargs
 ):
# TODO implement normalize kwargs as a pipeline stage
# add edgecolors and linewidths to kwargs so they can be processed by
# normalize_kwargs
ifedgecolorsisnotNone:
kwargs.update({"edgecolors": edgecolors})
iflinewidthsisnotNone:
kwargs.update({"linewidths": linewidths})

kwargs=cbook.normalize_kwargs(kwargs, mcoll.Collection)
c, colors, edgecolors=self._parse_scatter_color_args(
c,
edgecolors,
kwargs,
np.ma.ravel(x).size,
get_next_color_func=self._get_patches_for_fill.get_next_color,
 )

inputs=ArrayContainer(
x=x,
y=y,
s=s,
c=c,
marker=marker,
cmap=cmap,
norm=norm,
vmin=vmin,
vmax=vmax,
alpha=alpha,
plotnonfinite=plotnonfinite,
facecolors=colors,
edgecolors=edgecolors,
**kwargs
 )
# TODO should more go in here?
# marker/s are always in Container, but require overriding if None
# Color handling is odd too
defaults=ArrayContainer(
linewidths=mpl.rcParams["lines.linewidth"],
 )

cont=DataUnion(defaults, inputs)

pipeline= []
xconvert=DelayedConversionNode.from_keys(("x",), converter_key="xunits")
yconvert=DelayedConversionNode.from_keys(("y",), converter_key="yunits")
pipeline.extend([xconvert, yconvert])
pipeline.append(lambdax: np.ma.ravel(x))
pipeline.append(lambday: np.ma.ravel(y))
pipeline.append(
lambdas: (
np.ma.ravel(s)
ifsisnotNone
else (
 [20]
ifmpl.rcParams["_internal.classic_mode"]
else [mpl.rcParams["lines.markersize"] **2.0]
 )
 )
 )
# TODO plotnonfinite/mask combining
pipeline.append(
lambdamarker: (
markerifmarkerisnotNoneelsempl.rcParams["scatter.marker"]
 )
 )
pipeline.append(
lambdamarker: (
marker
ifisinstance(marker, mmarkers.MarkerStyle)
elsemmarkers.MarkerStyle(marker)
 )
 )
pipeline.append(
FunctionConversionNode.from_funcs(
 {
"paths": lambdamarker: [
marker.get_path().transformed(marker.get_transform())
 ]
 }
 )
 )
pipeline.append(RenameConversionNode.from_mapping({"s": "sizes"}))

# TODO classic mode margin override?
pcw=PathCollectionWrapper(cont, pipeline, offset_transform=self.transData)
self.add_artist(pcw)
self._request_autoscale_view()
returnpcw


# This is a handy trick to allow e.g. plt.subplots(subplot_kw={'projection': 'data-prototype'})
mprojections.register_projection(Axes)