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# coding: utf-8
# /*##########################################################################
# Copyright (C) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ############################################################################*/
"""Matplotlib's new colormaps"""
import numpy
import logging
from matplotlib.colors import ListedColormap
import matplotlib.colors
import matplotlib.cm
import silx.resources
_logger = logging.getLogger(__name__)
_AVAILABLE_AS_RESOURCE = ('magma', 'inferno', 'plasma', 'viridis')
"""List available colormap name as resources"""
_AVAILABLE_AS_BUILTINS = ('gray', 'reversed gray',
'temperature', 'red', 'green', 'blue')
"""List of colormaps available through built-in declarations"""
_CMAPS = {}
"""Cache colormaps"""
@property
def magma():
return getColormap('magma')
@property
def inferno():
return getColormap('inferno')
@property
def plasma():
return getColormap('plasma')
@property
def viridis():
return getColormap('viridis')
def getColormap(name):
"""Returns matplotlib colormap corresponding to given name
:param str name: The name of the colormap
:return: The corresponding colormap
:rtype: matplolib.colors.Colormap
"""
if not _CMAPS: # Lazy initialization of own colormaps
cdict = {'red': ((0.0, 0.0, 0.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0))}
_CMAPS['red'] = matplotlib.colors.LinearSegmentedColormap(
'red', cdict, 256)
cdict = {'red': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0))}
_CMAPS['green'] = matplotlib.colors.LinearSegmentedColormap(
'green', cdict, 256)
cdict = {'red': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(1.0, 1.0, 1.0))}
_CMAPS['blue'] = matplotlib.colors.LinearSegmentedColormap(
'blue', cdict, 256)
# Temperature as defined in spslut
cdict = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.25, 1.0, 1.0),
(0.75, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(0.25, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0))}
# but limited to 256 colors for a faster display (of the colorbar)
_CMAPS['temperature'] = \
matplotlib.colors.LinearSegmentedColormap(
'temperature', cdict, 256)
# reversed gray
cdict = {'red': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0))}
_CMAPS['reversed gray'] = \
matplotlib.colors.LinearSegmentedColormap(
'yerg', cdict, 256)
if name in _CMAPS:
return _CMAPS[name]
elif name in _AVAILABLE_AS_RESOURCE:
filename = silx.resources.resource_filename("gui/colormaps/%s.npy" % name)
data = numpy.load(filename)
lut = ListedColormap(data, name=name)
_CMAPS[name] = lut
return lut
else:
# matplotlib built-in
return matplotlib.cm.get_cmap(name)
def getScalarMappable(colormap, data=None):
"""Returns matplotlib ScalarMappable corresponding to colormap
:param :class:`.Colormap` colormap: The colormap to convert
:param numpy.ndarray data:
The data on which the colormap is applied.
If provided, it is used to compute autoscale.
:return: matplotlib object corresponding to colormap
:rtype: matplotlib.cm.ScalarMappable
"""
assert colormap is not None
if colormap.getName() is not None:
cmap = getColormap(colormap.getName())
else: # No name, use custom colors
if colormap.getColormapLUT() is None:
raise ValueError(
'addImage: colormap no name nor list of colors.')
colors = colormap.getColormapLUT()
assert len(colors.shape) == 2
assert colors.shape[-1] in (3, 4)
if colors.dtype == numpy.uint8:
# Convert to float in [0., 1.]
colors = colors.astype(numpy.float32) / 255.
cmap = matplotlib.colors.ListedColormap(colors)
vmin, vmax = colormap.getColormapRange(data)
if colormap.getNormalization().startswith('log'):
norm = matplotlib.colors.LogNorm(vmin, vmax)
else: # Linear normalization
norm = matplotlib.colors.Normalize(vmin, vmax)
return matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
def applyColormapToData(data, colormap):
"""Apply a colormap to the data and returns the RGBA image
This supports data of any dimensions (not only of dimension 2).
The returned array will have one more dimension (with 4 entries)
than the input data to store the RGBA channels
corresponding to each bin in the array.
:param numpy.ndarray data: The data to convert.
:param :class:`.Colormap`: The colormap to apply
"""
# Debian 7 specific support
# No transparent colormap with matplotlib < 1.2.0
# Add support for transparent colormap for uint8 data with
# colormap with 256 colors, linear norm, [0, 255] range
if matplotlib.__version__ < '1.2.0':
if (colormap.getName() is None and
colormap.getColormapLUT() is not None):
colors = colormap.getColormapLUT()
if (colors.shape[-1] == 4 and
not numpy.all(numpy.equal(colors[3], 255))):
# This is a transparent colormap
if (colors.shape == (256, 4) and
colormap.getNormalization() == 'linear' and
not colormap.isAutoscale() and
colormap.getVMin() == 0 and
colormap.getVMax() == 255 and
data.dtype == numpy.uint8):
# Supported case, convert data to RGBA
return colors[data.reshape(-1)].reshape(
data.shape + (4,))
else:
_logger.warning(
'matplotlib %s does not support transparent '
'colormap.', matplotlib.__version__)
scalarMappable = getScalarMappable(colormap, data)
rgbaImage = scalarMappable.to_rgba(data, bytes=True)
return rgbaImage
def getSupportedColormaps():
"""Get the supported colormap names as a tuple of str.
"""
colormaps = set(matplotlib.cm.datad.keys())
colormaps.update(_AVAILABLE_AS_BUILTINS)
colormaps.update(_AVAILABLE_AS_RESOURCE)
return tuple(sorted(colormaps))
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