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