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-# coding: utf-8
-# /*##########################################################################
-# Copyright (C) 2017-2018 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
-from silx.utils.deprecation import deprecated
-
-
-_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)
-
-
-@deprecated(replacement='silx.colors.Colormap.applyToData',
- since_version='0.8.0')
-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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