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VectorMappable with data type objects#28428

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5c98354
MultivarColormap and BivarColormap
trygvradJun 25, 2024
8dab3dd
__getitem__ for colors.BivarColormap
trygvradJul 2, 2024
9fd8e4d
Better descriptors for 'Add' and 'Sub' in colors.MultivarColormap
trygvradJul 2, 2024
4d14fb9
minor fixes for MultivarColormap and BivarColormap
trygvradJul 3, 2024
734611c
Allows one to not clip to 0...1 in colors.MultivarColormap.__call__()
trygvradJul 3, 2024
7cfa1b1
Multivariate and bivariate resampling of colormaps
trygvradJul 5, 2024
40dda08
Corrected stubs for mutlivariate and bivariate colormaps
trygvradJul 6, 2024
54ec431
minor fixes to colors.py
trygvradJul 9, 2024
f3c5455
Rename 'Add' to 'sRGB_add'
trygvradJul 18, 2024
44a3b00
name as keyword variable and additional tests to multivariate colormaps
trygvradJul 25, 2024
b4af71e
fix for multivariate tests
trygvradJun 20, 2024
c5b1d06
removed callbacks from ColorizingArtist
trygvradAug 2, 2024
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4 changes: 4 additions & 0 deletionslib/matplotlib/__init__.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -129,6 +129,8 @@
"interactive",
"is_interactive",
"colormaps",
"multivar_colormaps",
"bivar_colormaps",
"color_sequences",
]

Expand DownExpand Up@@ -1513,4 +1515,6 @@ def inner(ax, *args, data=None, **kwargs):
# workaround: we must defer colormaps import to after loading rcParams, because
# colormap creation depends on rcParams
from matplotlib.cm import _colormaps as colormaps # noqa: E402
from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402
from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402
from matplotlib.colors import _color_sequences as color_sequences # noqa: E402
2 changes: 2 additions & 0 deletionslib/matplotlib/__init__.pyi
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Expand Up@@ -112,4 +112,6 @@ def _preprocess_data(
) -> Callable: ...

from matplotlib.cm import _colormaps as colormaps # noqa: E402
from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402
from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402
from matplotlib.colors import _color_sequences as color_sequences # noqa: E402
1,312 changes: 1,312 additions & 0 deletionslib/matplotlib/_cm_bivar.py
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166 changes: 166 additions & 0 deletionslib/matplotlib/_cm_multivar.py
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,166 @@
# auto-genreated by https://github.com/trygvrad/multivariate_colormaps
# date: 2024-05-28

from .colors import LinearSegmentedColormap, MultivarColormap
import matplotlib as mpl
_LUTSIZE = mpl.rcParams['image.lut']

_2VarAddA0_data = [[0.000, 0.000, 0.000],
[0.020, 0.026, 0.031],
[0.049, 0.068, 0.085],
[0.075, 0.107, 0.135],
[0.097, 0.144, 0.183],
[0.116, 0.178, 0.231],
[0.133, 0.212, 0.279],
[0.148, 0.244, 0.326],
[0.161, 0.276, 0.374],
[0.173, 0.308, 0.422],
[0.182, 0.339, 0.471],
[0.190, 0.370, 0.521],
[0.197, 0.400, 0.572],
[0.201, 0.431, 0.623],
[0.204, 0.461, 0.675],
[0.204, 0.491, 0.728],
[0.202, 0.520, 0.783],
[0.197, 0.549, 0.838],
[0.187, 0.577, 0.895]]

_2VarAddA1_data = [[0.000, 0.000, 0.000],
[0.030, 0.023, 0.018],
[0.079, 0.060, 0.043],
[0.125, 0.093, 0.065],
[0.170, 0.123, 0.083],
[0.213, 0.151, 0.098],
[0.255, 0.177, 0.110],
[0.298, 0.202, 0.120],
[0.341, 0.226, 0.128],
[0.384, 0.249, 0.134],
[0.427, 0.271, 0.138],
[0.472, 0.292, 0.141],
[0.517, 0.313, 0.142],
[0.563, 0.333, 0.141],
[0.610, 0.353, 0.139],
[0.658, 0.372, 0.134],
[0.708, 0.390, 0.127],
[0.759, 0.407, 0.118],
[0.813, 0.423, 0.105]]

_2VarSubA0_data = [[1.000, 1.000, 1.000],
[0.959, 0.973, 0.986],
[0.916, 0.948, 0.974],
[0.874, 0.923, 0.965],
[0.832, 0.899, 0.956],
[0.790, 0.875, 0.948],
[0.748, 0.852, 0.940],
[0.707, 0.829, 0.934],
[0.665, 0.806, 0.927],
[0.624, 0.784, 0.921],
[0.583, 0.762, 0.916],
[0.541, 0.740, 0.910],
[0.500, 0.718, 0.905],
[0.457, 0.697, 0.901],
[0.414, 0.675, 0.896],
[0.369, 0.652, 0.892],
[0.320, 0.629, 0.888],
[0.266, 0.604, 0.884],
[0.199, 0.574, 0.881]]

_2VarSubA1_data = [[1.000, 1.000, 1.000],
[0.982, 0.967, 0.955],
[0.966, 0.935, 0.908],
[0.951, 0.902, 0.860],
[0.937, 0.870, 0.813],
[0.923, 0.838, 0.765],
[0.910, 0.807, 0.718],
[0.898, 0.776, 0.671],
[0.886, 0.745, 0.624],
[0.874, 0.714, 0.577],
[0.862, 0.683, 0.530],
[0.851, 0.653, 0.483],
[0.841, 0.622, 0.435],
[0.831, 0.592, 0.388],
[0.822, 0.561, 0.340],
[0.813, 0.530, 0.290],
[0.806, 0.498, 0.239],
[0.802, 0.464, 0.184],
[0.801, 0.426, 0.119]]

_3VarAddA0_data = [[0.000, 0.000, 0.000],
[0.018, 0.023, 0.028],
[0.040, 0.056, 0.071],
[0.059, 0.087, 0.110],
[0.074, 0.114, 0.147],
[0.086, 0.139, 0.183],
[0.095, 0.163, 0.219],
[0.101, 0.187, 0.255],
[0.105, 0.209, 0.290],
[0.107, 0.230, 0.326],
[0.105, 0.251, 0.362],
[0.101, 0.271, 0.398],
[0.091, 0.291, 0.434],
[0.075, 0.309, 0.471],
[0.046, 0.325, 0.507],
[0.021, 0.341, 0.546],
[0.021, 0.363, 0.584],
[0.022, 0.385, 0.622],
[0.023, 0.408, 0.661]]

_3VarAddA1_data = [[0.000, 0.000, 0.000],
[0.020, 0.024, 0.016],
[0.047, 0.058, 0.034],
[0.072, 0.088, 0.048],
[0.093, 0.116, 0.059],
[0.113, 0.142, 0.067],
[0.131, 0.167, 0.071],
[0.149, 0.190, 0.074],
[0.166, 0.213, 0.074],
[0.182, 0.235, 0.072],
[0.198, 0.256, 0.068],
[0.215, 0.276, 0.061],
[0.232, 0.296, 0.051],
[0.249, 0.314, 0.037],
[0.270, 0.330, 0.018],
[0.288, 0.347, 0.000],
[0.302, 0.369, 0.000],
[0.315, 0.391, 0.000],
[0.328, 0.414, 0.000]]

_3VarAddA2_data = [[0.000, 0.000, 0.000],
[0.029, 0.020, 0.023],
[0.072, 0.045, 0.055],
[0.111, 0.067, 0.084],
[0.148, 0.085, 0.109],
[0.184, 0.101, 0.133],
[0.219, 0.115, 0.155],
[0.254, 0.127, 0.176],
[0.289, 0.138, 0.195],
[0.323, 0.147, 0.214],
[0.358, 0.155, 0.232],
[0.393, 0.161, 0.250],
[0.429, 0.166, 0.267],
[0.467, 0.169, 0.283],
[0.507, 0.168, 0.298],
[0.546, 0.168, 0.313],
[0.580, 0.172, 0.328],
[0.615, 0.175, 0.341],
[0.649, 0.178, 0.355]]

cmaps = {
name: LinearSegmentedColormap.from_list(name, data, _LUTSIZE) for name, data in [
('2VarAddA0', _2VarAddA0_data),
('2VarAddA1', _2VarAddA1_data),
('2VarSubA0', _2VarSubA0_data),
('2VarSubA1', _2VarSubA1_data),
('3VarAddA0', _3VarAddA0_data),
('3VarAddA1', _3VarAddA1_data),
('3VarAddA2', _3VarAddA2_data),
]}

cmap_families = {
'2VarAddA': MultivarColormap([cmaps['2VarAddA' + str(i)] for i in range(2)],
'sRGB_add', name='2VarAddA'),
'2VarSubA': MultivarColormap([cmaps['2VarSubA' + str(i)] for i in range(2)],
'sRGB_sub', name='2VarSubA'),
'3VarAddA': MultivarColormap([cmaps['3VarAddA' + str(i)] for i in range(3)],
'sRGB_add', name='3VarAddA'),
}
157 changes: 129 additions & 28 deletionslib/matplotlib/artist.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -12,8 +12,7 @@
import numpy as np

import matplotlib as mpl
from . import _api, cbook
from .colors import BoundaryNorm
from . import _api, cbook, cm
from .cm import ScalarMappable
from .path import Path
from .transforms import (BboxBase, Bbox, IdentityTransform, Transform, TransformedBbox,
Expand DownExpand Up@@ -1346,35 +1345,16 @@ def format_cursor_data(self, data):
--------
get_cursor_data
"""
if np.ndim(data) == 0 and isinstance(self, ScalarMappable):
if isinstance(self, ScalarMappable):
# Internal classes no longer inherit from ScalarMappable, and this
# block should never be executed by the internal API

# This block logically belongs to ScalarMappable, but can't be
# implemented in itbecause mostScalarMappable subclasses inherit
# from Artist first and from ScalarMappable second, so
# implemented in itin case customScalarMappable subclasses
#inheritfrom Artist first and from ScalarMappable second, so
# Artist.format_cursor_data would always have precedence over
# ScalarMappable.format_cursor_data.
n = self.cmap.N
if np.ma.getmask(data):
return "[]"
normed = self.norm(data)
if np.isfinite(normed):
if isinstance(self.norm, BoundaryNorm):
# not an invertible normalization mapping
cur_idx = np.argmin(np.abs(self.norm.boundaries - data))
neigh_idx = max(0, cur_idx - 1)
# use max diff to prevent delta == 0
delta = np.diff(
self.norm.boundaries[neigh_idx:cur_idx + 2]
).max()

else:
# Midpoints of neighboring color intervals.
neighbors = self.norm.inverse(
(int(normed * n) + np.array([0, 1])) / n)
delta = abs(neighbors - data).max()
g_sig_digits = cbook._g_sig_digits(data, delta)
else:
g_sig_digits = 3 # Consistent with default below.
return f"[{data:-#.{g_sig_digits}g}]"
return self.colorizer._format_cursor_data(data)
else:
try:
data[0]
Expand DownExpand Up@@ -1417,6 +1397,127 @@ def set_mouseover(self, mouseover):
mouseover = property(get_mouseover, set_mouseover) # backcompat.


class ColorizingArtist(Artist):
def __init__(self, norm=None, cmap=None):
"""
Parameters
----------
norm : `.Normalize` (or subclass thereof) or str or None
The normalizing object which scales data, typically into the
interval ``[0, 1]``.
If a `str`, a `.Normalize` subclass is dynamically generated based
on the scale with the corresponding name.
If *None*, *norm* defaults to a *colors.Normalize* object which
initializes its scaling based on the first data processed.
cmap : str or `~matplotlib.colors.Colormap`
The colormap used to map normalized data values to RGBA colors.
"""

Artist.__init__(self)

self._A = None
if isinstance(norm, cm.Colorizer):
self._colorizer = norm
else:
self._colorizer = cm.Colorizer(cmap, norm)

# self._id_colorizer = self.colorizer.callbacks.connect('changed', self.changed)
# self.callbacks = cbook.CallbackRegistry(signals=["changed"])

def set_array(self, A):
"""
Set the value array from array-like *A*.

Parameters
----------
A : array-like or None
The values that are mapped to colors.

The base class `.VectorMappable` does not make any assumptions on
the dimensionality and shape of the value array *A*.
"""
if A is None:
self._A = None
return
A = cm._ensure_multivariate_data(self.colorizer.cmap.n_variates, A)

A = cbook.safe_masked_invalid(A, copy=True)
if not np.can_cast(A.dtype, float, "same_kind"):
if A.dtype.fields is None:
raise TypeError(f"Image data of dtype {A.dtype} cannot be "
f"converted to float")
else:
for key in A.dtype.fields:
if not np.can_cast(A[key].dtype, float, "same_kind"):
raise TypeError(f"Image data of dtype {A.dtype} cannot be "
f"converted to a sequence of floats")
self._A = A
self.colorizer.autoscale_None(A)

def get_array(self):
"""
Return the array of values, that are mapped to colors.

The base class `.VectorMappable` does not make any assumptions on
the dimensionality and shape of the array.
"""
return self._A

@property
def colorizer(self):
return self._colorizer

@colorizer.setter
def colorizer(self, colorizer):
self._set_colorizer(colorizer)

def _set_colorizer(self, colorizer):
if isinstance(colorizer, cm.Colorizer):
if self._A is not None:
if not colorizer.cmap.n_variates == self.colorizer.cmap.n_variates:
raise ValueError('The new Colorizer object must have the same'
' number of variates as the existing data.')
else:
# self.colorizer.callbacks.disconnect(self._id_colorizer)
self._colorizer = colorizer
# self._id_colorizer = colorizer.callbacks.connect('changed',
# self.changed)
# self.changed()
else:
raise ValueError('Only a Colorizer object can be set to colorizer.')

def _get_colorizer(self):
"""
Function to get the colorizer.
Useful in edge cases where you want a standalone variable with the colorizer,
but also want the colorizer to update if the colorizer on the artist changes.

used in `contour.ContourLabeler.label_colorizer`
"""
return self._colorizer

'''
def changed(self):
"""
Call this whenever the mappable is changed to notify all the
callbackSM listeners to the 'changed' signal.
"""
self.callbacks.process('changed')
self.stale = True
'''
def format_cursor_data(self, data):
"""
Return a string representation of *data*.

Uses the colorbar's formatter to format the data.

See Also
--------
get_cursor_data
"""
return self.colorizer._format_cursor_data(data)


def _get_tightbbox_for_layout_only(obj, *args, **kwargs):
"""
Matplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a
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