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Diffstat (limited to 'silx/gui/plot/matplotlib/Colormap.py')
-rw-r--r-- | silx/gui/plot/matplotlib/Colormap.py | 282 |
1 files changed, 282 insertions, 0 deletions
diff --git a/silx/gui/plot/matplotlib/Colormap.py b/silx/gui/plot/matplotlib/Colormap.py new file mode 100644 index 0000000..a86d76e --- /dev/null +++ b/silx/gui/plot/matplotlib/Colormap.py @@ -0,0 +1,282 @@ +# 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) + + if colormap.getNormalization().startswith('log'): + vmin, vmax = None, None + if not colormap.isAutoscale(): + if colormap.getVMin() > 0.: + vmin = colormap.getVMin() + if colormap.getVMax() > 0.: + vmax = colormap.getVMax() + + if vmin is None or vmax is None: + _logger.warning('Log colormap with negative bounds, ' + + 'changing bounds to positive ones.') + elif vmin > vmax: + _logger.warning('Colormap bounds are inverted.') + vmin, vmax = vmax, vmin + + # Set unset/negative bounds to positive bounds + if vmin is None or vmax is None: + # Convert to numpy array + data = numpy.array(data if data is not None else [], copy=False) + + if data.size > 0: + finiteData = data[numpy.isfinite(data)] + posData = finiteData[finiteData > 0] + if vmax is None: + # 1. as an ultimate fallback + vmax = posData.max() if posData.size > 0 else 1. + if vmin is None: + vmin = posData.min() if posData.size > 0 else vmax + if vmin > vmax: + vmin = vmax + else: + vmin, vmax = 1., 1. + + norm = matplotlib.colors.LogNorm(vmin, vmax) + + else: # Linear normalization + if colormap.isAutoscale(): + # Convert to numpy array + data = numpy.array(data if data is not None else [], copy=False) + + if data.size == 0: + vmin, vmax = 1., 1. + else: + finiteData = data[numpy.isfinite(data)] + if finiteData.size > 0: + vmin = finiteData.min() + vmax = finiteData.max() + else: + vmin, vmax = 1., 1. + + else: + vmin = colormap.getVMin() + vmax = colormap.getVMax() + if vmin > vmax: + _logger.warning('Colormap bounds are inverted.') + vmin, vmax = vmax, vmin + + 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)) |