384 lines
12 KiB
Python
384 lines
12 KiB
Python
|
"""
|
||
|
|
Builtin colormaps, colormap handling utilities, and the `ScalarMappable` mixin.
|
||
|
|
|
||
|
|
.. seealso::
|
||
|
|
|
||
|
|
:doc:`/gallery/color/colormap_reference` for a list of builtin colormaps.
|
||
|
|
|
||
|
|
:doc:`/tutorials/colors/colormap-manipulation` for examples of how to
|
||
|
|
make colormaps.
|
||
|
|
|
||
|
|
:doc:`/tutorials/colors/colormaps` an in-depth discussion of
|
||
|
|
choosing colormaps.
|
||
|
|
|
||
|
|
:doc:`/tutorials/colors/colormapnorms` for more details about data
|
||
|
|
normalization.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import functools
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy import ma
|
||
|
|
|
||
|
|
import matplotlib as mpl
|
||
|
|
import matplotlib.colors as colors
|
||
|
|
import matplotlib.cbook as cbook
|
||
|
|
from matplotlib._cm import datad
|
||
|
|
from matplotlib._cm_listed import cmaps as cmaps_listed
|
||
|
|
|
||
|
|
|
||
|
|
def _reverser(f, x): # Deprecated, remove this at the same time as revcmap.
|
||
|
|
return f(1 - x) # Toplevel helper for revcmap ensuring cmap picklability.
|
||
|
|
|
||
|
|
|
||
|
|
@cbook.deprecated("3.2", alternative="Colormap.reversed()")
|
||
|
|
def revcmap(data):
|
||
|
|
"""Can only handle specification *data* in dictionary format."""
|
||
|
|
data_r = {}
|
||
|
|
for key, val in data.items():
|
||
|
|
if callable(val):
|
||
|
|
# Return a partial object so that the result is picklable.
|
||
|
|
valnew = functools.partial(_reverser, val)
|
||
|
|
else:
|
||
|
|
# Flip x and exchange the y values facing x = 0 and x = 1.
|
||
|
|
valnew = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)]
|
||
|
|
data_r[key] = valnew
|
||
|
|
return data_r
|
||
|
|
|
||
|
|
|
||
|
|
LUTSIZE = mpl.rcParams['image.lut']
|
||
|
|
|
||
|
|
|
||
|
|
def _gen_cmap_d():
|
||
|
|
"""
|
||
|
|
Generate a dict mapping standard colormap names to standard colormaps, as
|
||
|
|
well as the reversed colormaps.
|
||
|
|
"""
|
||
|
|
cmap_d = {**cmaps_listed}
|
||
|
|
for name, spec in datad.items():
|
||
|
|
cmap_d[name] = ( # Precache the cmaps at a fixed lutsize..
|
||
|
|
colors.LinearSegmentedColormap(name, spec, LUTSIZE)
|
||
|
|
if 'red' in spec else
|
||
|
|
colors.ListedColormap(spec['listed'], name)
|
||
|
|
if 'listed' in spec else
|
||
|
|
colors.LinearSegmentedColormap.from_list(name, spec, LUTSIZE))
|
||
|
|
# Generate reversed cmaps.
|
||
|
|
for cmap in list(cmap_d.values()):
|
||
|
|
rmap = cmap.reversed()
|
||
|
|
cmap_d[rmap.name] = rmap
|
||
|
|
return cmap_d
|
||
|
|
|
||
|
|
|
||
|
|
cmap_d = _gen_cmap_d()
|
||
|
|
locals().update(cmap_d)
|
||
|
|
|
||
|
|
|
||
|
|
# Continue with definitions ...
|
||
|
|
|
||
|
|
|
||
|
|
def register_cmap(name=None, cmap=None, data=None, lut=None):
|
||
|
|
"""
|
||
|
|
Add a colormap to the set recognized by :func:`get_cmap`.
|
||
|
|
|
||
|
|
It can be used in two ways::
|
||
|
|
|
||
|
|
register_cmap(name='swirly', cmap=swirly_cmap)
|
||
|
|
|
||
|
|
register_cmap(name='choppy', data=choppydata, lut=128)
|
||
|
|
|
||
|
|
In the first case, *cmap* must be a :class:`matplotlib.colors.Colormap`
|
||
|
|
instance. The *name* is optional; if absent, the name will
|
||
|
|
be the :attr:`~matplotlib.colors.Colormap.name` attribute of the *cmap*.
|
||
|
|
|
||
|
|
In the second case, the three arguments are passed to
|
||
|
|
the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer,
|
||
|
|
and the resulting colormap is registered.
|
||
|
|
"""
|
||
|
|
cbook._check_isinstance((str, None), name=name)
|
||
|
|
if name is None:
|
||
|
|
try:
|
||
|
|
name = cmap.name
|
||
|
|
except AttributeError:
|
||
|
|
raise ValueError("Arguments must include a name or a Colormap")
|
||
|
|
if isinstance(cmap, colors.Colormap):
|
||
|
|
cmap_d[name] = cmap
|
||
|
|
return
|
||
|
|
# For the remainder, let exceptions propagate.
|
||
|
|
if lut is None:
|
||
|
|
lut = mpl.rcParams['image.lut']
|
||
|
|
cmap = colors.LinearSegmentedColormap(name, data, lut)
|
||
|
|
cmap_d[name] = cmap
|
||
|
|
|
||
|
|
|
||
|
|
def get_cmap(name=None, lut=None):
|
||
|
|
"""
|
||
|
|
Get a colormap instance, defaulting to rc values if *name* is None.
|
||
|
|
|
||
|
|
Colormaps added with :func:`register_cmap` take precedence over
|
||
|
|
built-in colormaps.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
name : `matplotlib.colors.Colormap` or str or None, default: None
|
||
|
|
If a `Colormap` instance, it will be returned. Otherwise, the name of
|
||
|
|
a colormap known to Matplotlib, which will be resampled by *lut*. The
|
||
|
|
default, None, means :rc:`image.cmap`.
|
||
|
|
lut : int or None, default: None
|
||
|
|
If *name* is not already a Colormap instance and *lut* is not None, the
|
||
|
|
colormap will be resampled to have *lut* entries in the lookup table.
|
||
|
|
"""
|
||
|
|
if name is None:
|
||
|
|
name = mpl.rcParams['image.cmap']
|
||
|
|
if isinstance(name, colors.Colormap):
|
||
|
|
return name
|
||
|
|
cbook._check_in_list(sorted(cmap_d), name=name)
|
||
|
|
if lut is None:
|
||
|
|
return cmap_d[name]
|
||
|
|
else:
|
||
|
|
return cmap_d[name]._resample(lut)
|
||
|
|
|
||
|
|
|
||
|
|
class ScalarMappable:
|
||
|
|
"""
|
||
|
|
This is a mixin class to support scalar data to RGBA mapping.
|
||
|
|
The ScalarMappable makes use of data normalization before returning
|
||
|
|
RGBA colors from the given colormap.
|
||
|
|
|
||
|
|
"""
|
||
|
|
def __init__(self, norm=None, cmap=None):
|
||
|
|
"""
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
norm : :class:`matplotlib.colors.Normalize` instance
|
||
|
|
The normalizing object which scales data, typically into the
|
||
|
|
interval ``[0, 1]``.
|
||
|
|
If *None*, *norm* defaults to a *colors.Normalize* object which
|
||
|
|
initializes its scaling based on the first data processed.
|
||
|
|
cmap : str or :class:`~matplotlib.colors.Colormap` instance
|
||
|
|
The colormap used to map normalized data values to RGBA colors.
|
||
|
|
"""
|
||
|
|
|
||
|
|
self.callbacksSM = cbook.CallbackRegistry()
|
||
|
|
|
||
|
|
if cmap is None:
|
||
|
|
cmap = get_cmap()
|
||
|
|
if norm is None:
|
||
|
|
norm = colors.Normalize()
|
||
|
|
|
||
|
|
self._A = None
|
||
|
|
#: The Normalization instance of this ScalarMappable.
|
||
|
|
self.norm = norm
|
||
|
|
#: The Colormap instance of this ScalarMappable.
|
||
|
|
self.cmap = get_cmap(cmap)
|
||
|
|
#: The last colorbar associated with this ScalarMappable. May be None.
|
||
|
|
self.colorbar = None
|
||
|
|
self.update_dict = {'array': False}
|
||
|
|
|
||
|
|
def to_rgba(self, x, alpha=None, bytes=False, norm=True):
|
||
|
|
"""
|
||
|
|
Return a normalized rgba array corresponding to *x*.
|
||
|
|
|
||
|
|
In the normal case, *x* is a 1-D or 2-D sequence of scalars, and
|
||
|
|
the corresponding ndarray of rgba values will be returned,
|
||
|
|
based on the norm and colormap set for this ScalarMappable.
|
||
|
|
|
||
|
|
There is one special case, for handling images that are already
|
||
|
|
rgb or rgba, such as might have been read from an image file.
|
||
|
|
If *x* is an ndarray with 3 dimensions,
|
||
|
|
and the last dimension is either 3 or 4, then it will be
|
||
|
|
treated as an rgb or rgba array, and no mapping will be done.
|
||
|
|
The array can be uint8, or it can be floating point with
|
||
|
|
values in the 0-1 range; otherwise a ValueError will be raised.
|
||
|
|
If it is a masked array, the mask will be ignored.
|
||
|
|
If the last dimension is 3, the *alpha* kwarg (defaulting to 1)
|
||
|
|
will be used to fill in the transparency. If the last dimension
|
||
|
|
is 4, the *alpha* kwarg is ignored; it does not
|
||
|
|
replace the pre-existing alpha. A ValueError will be raised
|
||
|
|
if the third dimension is other than 3 or 4.
|
||
|
|
|
||
|
|
In either case, if *bytes* is *False* (default), the rgba
|
||
|
|
array will be floats in the 0-1 range; if it is *True*,
|
||
|
|
the returned rgba array will be uint8 in the 0 to 255 range.
|
||
|
|
|
||
|
|
If norm is False, no normalization of the input data is
|
||
|
|
performed, and it is assumed to be in the range (0-1).
|
||
|
|
|
||
|
|
"""
|
||
|
|
# First check for special case, image input:
|
||
|
|
try:
|
||
|
|
if x.ndim == 3:
|
||
|
|
if x.shape[2] == 3:
|
||
|
|
if alpha is None:
|
||
|
|
alpha = 1
|
||
|
|
if x.dtype == np.uint8:
|
||
|
|
alpha = np.uint8(alpha * 255)
|
||
|
|
m, n = x.shape[:2]
|
||
|
|
xx = np.empty(shape=(m, n, 4), dtype=x.dtype)
|
||
|
|
xx[:, :, :3] = x
|
||
|
|
xx[:, :, 3] = alpha
|
||
|
|
elif x.shape[2] == 4:
|
||
|
|
xx = x
|
||
|
|
else:
|
||
|
|
raise ValueError("third dimension must be 3 or 4")
|
||
|
|
if xx.dtype.kind == 'f':
|
||
|
|
if norm and (xx.max() > 1 or xx.min() < 0):
|
||
|
|
raise ValueError("Floating point image RGB values "
|
||
|
|
"must be in the 0..1 range.")
|
||
|
|
if bytes:
|
||
|
|
xx = (xx * 255).astype(np.uint8)
|
||
|
|
elif xx.dtype == np.uint8:
|
||
|
|
if not bytes:
|
||
|
|
xx = xx.astype(np.float32) / 255
|
||
|
|
else:
|
||
|
|
raise ValueError("Image RGB array must be uint8 or "
|
||
|
|
"floating point; found %s" % xx.dtype)
|
||
|
|
return xx
|
||
|
|
except AttributeError:
|
||
|
|
# e.g., x is not an ndarray; so try mapping it
|
||
|
|
pass
|
||
|
|
|
||
|
|
# This is the normal case, mapping a scalar array:
|
||
|
|
x = ma.asarray(x)
|
||
|
|
if norm:
|
||
|
|
x = self.norm(x)
|
||
|
|
rgba = self.cmap(x, alpha=alpha, bytes=bytes)
|
||
|
|
return rgba
|
||
|
|
|
||
|
|
def set_array(self, A):
|
||
|
|
"""Set the image array from numpy array *A*.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
A : ndarray
|
||
|
|
"""
|
||
|
|
self._A = A
|
||
|
|
self.update_dict['array'] = True
|
||
|
|
|
||
|
|
def get_array(self):
|
||
|
|
'Return the array'
|
||
|
|
return self._A
|
||
|
|
|
||
|
|
def get_cmap(self):
|
||
|
|
'return the colormap'
|
||
|
|
return self.cmap
|
||
|
|
|
||
|
|
def get_clim(self):
|
||
|
|
'return the min, max of the color limits for image scaling'
|
||
|
|
return self.norm.vmin, self.norm.vmax
|
||
|
|
|
||
|
|
def set_clim(self, vmin=None, vmax=None):
|
||
|
|
"""
|
||
|
|
Set the norm limits for image scaling.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
vmin, vmax : float
|
||
|
|
The limits.
|
||
|
|
|
||
|
|
The limits may also be passed as a tuple (*vmin*, *vmax*) as a
|
||
|
|
single positional argument.
|
||
|
|
|
||
|
|
.. ACCEPTS: (vmin: float, vmax: float)
|
||
|
|
"""
|
||
|
|
if vmax is None:
|
||
|
|
try:
|
||
|
|
vmin, vmax = vmin
|
||
|
|
except (TypeError, ValueError):
|
||
|
|
pass
|
||
|
|
if vmin is not None:
|
||
|
|
self.norm.vmin = colors._sanitize_extrema(vmin)
|
||
|
|
if vmax is not None:
|
||
|
|
self.norm.vmax = colors._sanitize_extrema(vmax)
|
||
|
|
self.changed()
|
||
|
|
|
||
|
|
def get_alpha(self):
|
||
|
|
"""
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
alpha : float
|
||
|
|
Always returns 1.
|
||
|
|
"""
|
||
|
|
# This method is intended to be overridden by Artist sub-classes
|
||
|
|
return 1.
|
||
|
|
|
||
|
|
def set_cmap(self, cmap):
|
||
|
|
"""
|
||
|
|
set the colormap for luminance data
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
cmap : colormap or registered colormap name
|
||
|
|
"""
|
||
|
|
cmap = get_cmap(cmap)
|
||
|
|
self.cmap = cmap
|
||
|
|
self.changed()
|
||
|
|
|
||
|
|
def set_norm(self, norm):
|
||
|
|
"""Set the normalization instance.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
norm : `.Normalize`
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
If there are any colorbars using the mappable for this norm, setting
|
||
|
|
the norm of the mappable will reset the norm, locator, and formatters
|
||
|
|
on the colorbar to default.
|
||
|
|
|
||
|
|
"""
|
||
|
|
cbook._check_isinstance((colors.Normalize, None), norm=norm)
|
||
|
|
if norm is None:
|
||
|
|
norm = colors.Normalize()
|
||
|
|
self.norm = norm
|
||
|
|
self.changed()
|
||
|
|
|
||
|
|
def autoscale(self):
|
||
|
|
"""
|
||
|
|
Autoscale the scalar limits on the norm instance using the
|
||
|
|
current array
|
||
|
|
"""
|
||
|
|
if self._A is None:
|
||
|
|
raise TypeError('You must first set_array for mappable')
|
||
|
|
self.norm.autoscale(self._A)
|
||
|
|
self.changed()
|
||
|
|
|
||
|
|
def autoscale_None(self):
|
||
|
|
"""
|
||
|
|
Autoscale the scalar limits on the norm instance using the
|
||
|
|
current array, changing only limits that are None
|
||
|
|
"""
|
||
|
|
if self._A is None:
|
||
|
|
raise TypeError('You must first set_array for mappable')
|
||
|
|
self.norm.autoscale_None(self._A)
|
||
|
|
self.changed()
|
||
|
|
|
||
|
|
def add_checker(self, checker):
|
||
|
|
"""
|
||
|
|
Add an entry to a dictionary of boolean flags
|
||
|
|
that are set to True when the mappable is changed.
|
||
|
|
"""
|
||
|
|
self.update_dict[checker] = False
|
||
|
|
|
||
|
|
def check_update(self, checker):
|
||
|
|
"""
|
||
|
|
If mappable has changed since the last check,
|
||
|
|
return True; else return False
|
||
|
|
"""
|
||
|
|
if self.update_dict[checker]:
|
||
|
|
self.update_dict[checker] = False
|
||
|
|
return True
|
||
|
|
return False
|
||
|
|
|
||
|
|
def changed(self):
|
||
|
|
"""
|
||
|
|
Call this whenever the mappable is changed to notify all the
|
||
|
|
callbackSM listeners to the 'changed' signal
|
||
|
|
"""
|
||
|
|
self.callbacksSM.process('changed', self)
|
||
|
|
|
||
|
|
for key in self.update_dict:
|
||
|
|
self.update_dict[key] = True
|
||
|
|
self.stale = True
|