"""
A module for converting numbers or color arguments to *RGB* or *RGBA*.
*RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the
range 0-1.
This module includes functions and classes for color specification conversions,
and for mapping numbers to colors in a 1-D array of colors called a colormap.
Mapping data onto colors using a colormap typically involves two steps: a data
array is first mapped onto the range 0-1 using a subclass of `Normalize`,
then this number is mapped to a color using a subclass of `Colormap`. Two
subclasses of `Colormap` provided here: `LinearSegmentedColormap`, which uses
piecewise-linear interpolation to define colormaps, and `ListedColormap`, which
makes a colormap from a list of colors.
.. seealso::
:ref:`colormap-manipulation` for examples of how to
make colormaps and
:ref:`colormaps` for a list of built-in colormaps.
:ref:`colormapnorms` for more details about data
normalization
More colormaps are available at palettable_.
The module also provides functions for checking whether an object can be
interpreted as a color (`is_color_like`), for converting such an object
to an RGBA tuple (`to_rgba`) or to an HTML-like hex string in the
"#rrggbb" format (`to_hex`), and a sequence of colors to an (n, 4)
RGBA array (`to_rgba_array`). Caching is used for efficiency.
Colors that Matplotlib recognizes are listed at
:ref:`colors_def`.
.. _palettable: https://jiffyclub.github.io/palettable/
.. _xkcd color survey: https://xkcd.com/color/rgb/
"""
import base64
from collections.abc import Sized, Sequence, Mapping
import functools
import importlib
import inspect
import io
import itertools
from numbers import Real
import re
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import matplotlib as mpl
import numpy as np
from matplotlib import _api, _cm, cbook, scale
from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS
class _ColorMapping(dict):
def __init__(self, mapping):
super().__init__(mapping)
self.cache = {}
def __setitem__(self, key, value):
super().__setitem__(key, value)
self.cache.clear()
def __delitem__(self, key):
super().__delitem__(key)
self.cache.clear()
_colors_full_map = {}
# Set by reverse priority order.
_colors_full_map.update(XKCD_COLORS)
_colors_full_map.update({k.replace('grey', 'gray'): v
for k, v in XKCD_COLORS.items()
if 'grey' in k})
_colors_full_map.update(CSS4_COLORS)
_colors_full_map.update(TABLEAU_COLORS)
_colors_full_map.update({k.replace('gray', 'grey'): v
for k, v in TABLEAU_COLORS.items()
if 'gray' in k})
_colors_full_map.update(BASE_COLORS)
_colors_full_map = _ColorMapping(_colors_full_map)
_REPR_PNG_SIZE = (512, 64)
def get_named_colors_mapping():
"""Return the global mapping of names to named colors."""
return _colors_full_map
class ColorSequenceRegistry(Mapping):
r"""
Container for sequences of colors that are known to Matplotlib by name.
The universal registry instance is `matplotlib.color_sequences`. There
should be no need for users to instantiate `.ColorSequenceRegistry`
themselves.
Read access uses a dict-like interface mapping names to lists of colors::
import matplotlib as mpl
cmap = mpl.color_sequences['tab10']
The returned lists are copies, so that their modification does not change
the global definition of the color sequence.
Additional color sequences can be added via
`.ColorSequenceRegistry.register`::
mpl.color_sequences.register('rgb', ['r', 'g', 'b'])
"""
_BUILTIN_COLOR_SEQUENCES = {
'tab10': _cm._tab10_data,
'tab20': _cm._tab20_data,
'tab20b': _cm._tab20b_data,
'tab20c': _cm._tab20c_data,
'Pastel1': _cm._Pastel1_data,
'Pastel2': _cm._Pastel2_data,
'Paired': _cm._Paired_data,
'Accent': _cm._Accent_data,
'Dark2': _cm._Dark2_data,
'Set1': _cm._Set1_data,
'Set2': _cm._Set2_data,
'Set3': _cm._Set3_data,
}
def __init__(self):
self._color_sequences = {**self._BUILTIN_COLOR_SEQUENCES}
def __getitem__(self, item):
try:
return list(self._color_sequences[item])
except KeyError:
raise KeyError(f"{item!r} is not a known color sequence name")
def __iter__(self):
return iter(self._color_sequences)
def __len__(self):
return len(self._color_sequences)
def __str__(self):
return ('ColorSequenceRegistry; available colormaps:\n' +
', '.join(f"'{name}'" for name in self))
def register(self, name, color_list):
"""
Register a new color sequence.
The color sequence registry stores a copy of the given *color_list*, so
that future changes to the original list do not affect the registered
color sequence. Think of this as the registry taking a snapshot
of *color_list* at registration.
Parameters
----------
name : str
The name for the color sequence.
color_list : list of :mpltype:`color`
An iterable returning valid Matplotlib colors when iterating over.
Note however that the returned color sequence will always be a
list regardless of the input type.
"""
if name in self._BUILTIN_COLOR_SEQUENCES:
raise ValueError(f"{name!r} is a reserved name for a builtin "
"color sequence")
color_list = list(color_list) # force copy and coerce type to list
for color in color_list:
try:
to_rgba(color)
except ValueError:
raise ValueError(
f"{color!r} is not a valid color specification")
self._color_sequences[name] = color_list
def unregister(self, name):
"""
Remove a sequence from the registry.
You cannot remove built-in color sequences.
If the name is not registered, returns with no error.
"""
if name in self._BUILTIN_COLOR_SEQUENCES:
raise ValueError(
f"Cannot unregister builtin color sequence {name!r}")
self._color_sequences.pop(name, None)
_color_sequences = ColorSequenceRegistry()
def _sanitize_extrema(ex):
if ex is None:
return ex
try:
ret = ex.item()
except AttributeError:
ret = float(ex)
return ret
_nth_color_re = re.compile(r"\AC[0-9]+\Z")
def _is_nth_color(c):
"""Return whether *c* can be interpreted as an item in the color cycle."""
return isinstance(c, str) and _nth_color_re.match(c)
def is_color_like(c):
"""Return whether *c* can be interpreted as an RGB(A) color."""
# Special-case nth color syntax because it cannot be parsed during setup.
if _is_nth_color(c):
return True
try:
to_rgba(c)
except (TypeError, ValueError):
return False
else:
return True
def _has_alpha_channel(c):
"""Return whether *c* is a color with an alpha channel."""
# 4-element sequences are interpreted as r, g, b, a
return not isinstance(c, str) and len(c) == 4
def _check_color_like(**kwargs):
"""
For each *key, value* pair in *kwargs*, check that *value* is color-like.
"""
for k, v in kwargs.items():
if not is_color_like(v):
raise ValueError(
f"{v!r} is not a valid value for {k}: supported inputs are "
f"(r, g, b) and (r, g, b, a) 0-1 float tuples; "
f"'#rrggbb', '#rrggbbaa', '#rgb', '#rgba' strings; "
f"named color strings; "
f"string reprs of 0-1 floats for grayscale values; "
f"'C0', 'C1', ... strings for colors of the color cycle; "
f"and pairs combining one of the above with an alpha value")
def same_color(c1, c2):
"""
Return whether the colors *c1* and *c2* are the same.
*c1*, *c2* can be single colors or lists/arrays of colors.
"""
c1 = to_rgba_array(c1)
c2 = to_rgba_array(c2)
n1 = max(c1.shape[0], 1) # 'none' results in shape (0, 4), but is 1-elem
n2 = max(c2.shape[0], 1) # 'none' results in shape (0, 4), but is 1-elem
if n1 != n2:
raise ValueError('Different number of elements passed.')
# The following shape test is needed to correctly handle comparisons with
# 'none', which results in a shape (0, 4) array and thus cannot be tested
# via value comparison.
return c1.shape == c2.shape and (c1 == c2).all()
def to_rgba(c, alpha=None):
"""
Convert *c* to an RGBA color.
Parameters
----------
c : Matplotlib color or ``np.ma.masked``
alpha : float, optional
If *alpha* is given, force the alpha value of the returned RGBA tuple
to *alpha*.
If None, the alpha value from *c* is used. If *c* does not have an
alpha channel, then alpha defaults to 1.
*alpha* is ignored for the color value ``"none"`` (case-insensitive),
which always maps to ``(0, 0, 0, 0)``.
Returns
-------
tuple
Tuple of floats ``(r, g, b, a)``, where each channel (red, green, blue,
alpha) can assume values between 0 and 1.
"""
if isinstance(c, tuple) and len(c) == 2:
if alpha is None:
c, alpha = c
else:
c = c[0]
# Special-case nth color syntax because it should not be cached.
if _is_nth_color(c):
prop_cycler = mpl.rcParams['axes.prop_cycle']
colors = prop_cycler.by_key().get('color', ['k'])
c = colors[int(c[1:]) % len(colors)]
try:
rgba = _colors_full_map.cache[c, alpha]
except (KeyError, TypeError): # Not in cache, or unhashable.
rgba = None
if rgba is None: # Suppress exception chaining of cache lookup failure.
rgba = _to_rgba_no_colorcycle(c, alpha)
try:
_colors_full_map.cache[c, alpha] = rgba
except TypeError:
pass
return rgba
def _to_rgba_no_colorcycle(c, alpha=None):
"""
Convert *c* to an RGBA color, with no support for color-cycle syntax.
If *alpha* is given, force the alpha value of the returned RGBA tuple
to *alpha*. Otherwise, the alpha value from *c* is used, if it has alpha
information, or defaults to 1.
*alpha* is ignored for the color value ``"none"`` (case-insensitive),
which always maps to ``(0, 0, 0, 0)``.
"""
if alpha is not None and not 0 <= alpha <= 1:
raise ValueError("'alpha' must be between 0 and 1, inclusive")
orig_c = c
if c is np.ma.masked:
return (0., 0., 0., 0.)
if isinstance(c, str):
if c.lower() == "none":
return (0., 0., 0., 0.)
# Named color.
try:
# This may turn c into a non-string, so we check again below.
c = _colors_full_map[c]
except KeyError:
if len(orig_c) != 1:
try:
c = _colors_full_map[c.lower()]
except KeyError:
pass
if isinstance(c, str):
# hex color in #rrggbb format.
match = re.match(r"\A#[a-fA-F0-9]{6}\Z", c)
if match:
return (tuple(int(n, 16) / 255
for n in [c[1:3], c[3:5], c[5:7]])
+ (alpha if alpha is not None else 1.,))
# hex color in #rgb format, shorthand for #rrggbb.
match = re.match(r"\A#[a-fA-F0-9]{3}\Z", c)
if match:
return (tuple(int(n, 16) / 255
for n in [c[1]*2, c[2]*2, c[3]*2])
+ (alpha if alpha is not None else 1.,))
# hex color with alpha in #rrggbbaa format.
match = re.match(r"\A#[a-fA-F0-9]{8}\Z", c)
if match:
color = [int(n, 16) / 255
for n in [c[1:3], c[3:5], c[5:7], c[7:9]]]
if alpha is not None:
color[-1] = alpha
return tuple(color)
# hex color with alpha in #rgba format, shorthand for #rrggbbaa.
match = re.match(r"\A#[a-fA-F0-9]{4}\Z", c)
if match:
color = [int(n, 16) / 255
for n in [c[1]*2, c[2]*2, c[3]*2, c[4]*2]]
if alpha is not None:
color[-1] = alpha
return tuple(color)
# string gray.
try:
c = float(c)
except ValueError:
pass
else:
if not (0 <= c <= 1):
raise ValueError(
f"Invalid string grayscale value {orig_c!r}. "
f"Value must be within 0-1 range")
return c, c, c, alpha if alpha is not None else 1.
raise ValueError(f"Invalid RGBA argument: {orig_c!r}")
# turn 2-D array into 1-D array
if isinstance(c, np.ndarray):
if c.ndim == 2 and c.shape[0] == 1:
c = c.reshape(-1)
# tuple color.
if not np.iterable(c):
raise ValueError(f"Invalid RGBA argument: {orig_c!r}")
if len(c) not in [3, 4]:
raise ValueError("RGBA sequence should have length 3 or 4")
if not all(isinstance(x, Real) for x in c):
# Checks that don't work: `map(float, ...)`, `np.array(..., float)` and
# `np.array(...).astype(float)` would all convert "0.5" to 0.5.
raise ValueError(f"Invalid RGBA argument: {orig_c!r}")
# Return a tuple to prevent the cached value from being modified.
c = tuple(map(float, c))
if len(c) == 3 and alpha is None:
alpha = 1
if alpha is not None:
c = c[:3] + (alpha,)
if any(elem < 0 or elem > 1 for elem in c):
raise ValueError("RGBA values should be within 0-1 range")
return c
def to_rgba_array(c, alpha=None):
"""
Convert *c* to a (n, 4) array of RGBA colors.
Parameters
----------
c : Matplotlib color or array of colors
If *c* is a masked array, an `~numpy.ndarray` is returned with a
(0, 0, 0, 0) row for each masked value or row in *c*.
alpha : float or sequence of floats, optional
If *alpha* is given, force the alpha value of the returned RGBA tuple
to *alpha*.
If None, the alpha value from *c* is used. If *c* does not have an
alpha channel, then alpha defaults to 1.
*alpha* is ignored for the color value ``"none"`` (case-insensitive),
which always maps to ``(0, 0, 0, 0)``.
If *alpha* is a sequence and *c* is a single color, *c* will be
repeated to match the length of *alpha*.
Returns
-------
array
(n, 4) array of RGBA colors, where each channel (red, green, blue,
alpha) can assume values between 0 and 1.
"""
if isinstance(c, tuple) and len(c) == 2 and isinstance(c[1], Real):
if alpha is None:
c, alpha = c
else:
c = c[0]
# Special-case inputs that are already arrays, for performance. (If the
# array has the wrong kind or shape, raise the error during one-at-a-time
# conversion.)
if np.iterable(alpha):
alpha = np.asarray(alpha).ravel()
if (isinstance(c, np.ndarray) and c.dtype.kind in "if"
and c.ndim == 2 and c.shape[1] in [3, 4]):
mask = c.mask.any(axis=1) if np.ma.is_masked(c) else None
c = np.ma.getdata(c)
if np.iterable(alpha):
if c.shape[0] == 1 and alpha.shape[0] > 1:
c = np.tile(c, (alpha.shape[0], 1))
elif c.shape[0] != alpha.shape[0]:
raise ValueError("The number of colors must match the number"
" of alpha values if there are more than one"
" of each.")
if c.shape[1] == 3:
result = np.column_stack([c, np.zeros(len(c))])
result[:, -1] = alpha if alpha is not None else 1.
elif c.shape[1] == 4:
result = c.copy()
if alpha is not None:
result[:, -1] = alpha
if mask is not None:
result[mask] = 0
if np.any((result < 0) | (result > 1)):
raise ValueError("RGBA values should be within 0-1 range")
return result
# Handle single values.
# Note that this occurs *after* handling inputs that are already arrays, as
# `to_rgba(c, alpha)` (below) is expensive for such inputs, due to the need
# to format the array in the ValueError message(!).
if cbook._str_lower_equal(c, "none"):
return np.zeros((0, 4), float)
try:
if np.iterable(alpha):
return np.array([to_rgba(c, a) for a in alpha], float)
else:
return np.array([to_rgba(c, alpha)], float)
except TypeError:
pass
except ValueError as e:
if e.args == ("'alpha' must be between 0 and 1, inclusive", ):
# ValueError is from _to_rgba_no_colorcycle().
raise e
if isinstance(c, str):
raise ValueError(f"{c!r} is not a valid color value.")
if len(c) == 0:
return np.zeros((0, 4), float)
# Quick path if the whole sequence can be directly converted to a numpy
# array in one shot.
if isinstance(c, Sequence):
lens = {len(cc) if isinstance(cc, (list, tuple)) else -1 for cc in c}
if lens == {3}:
rgba = np.column_stack([c, np.ones(len(c))])
elif lens == {4}:
rgba = np.array(c)
else:
rgba = np.array([to_rgba(cc) for cc in c])
else:
rgba = np.array([to_rgba(cc) for cc in c])
if alpha is not None:
rgba[:, 3] = alpha
if isinstance(c, Sequence):
# ensure that an explicit alpha does not overwrite full transparency
# for "none"
none_mask = [cbook._str_equal(cc, "none") for cc in c]
rgba[:, 3][none_mask] = 0
return rgba
def to_rgb(c):
"""Convert *c* to an RGB color, silently dropping the alpha channel."""
return to_rgba(c)[:3]
def to_hex(c, keep_alpha=False):
"""
Convert *c* to a hex color.
Parameters
----------
c : :ref:`color <colors_def>` or `numpy.ma.masked`
keep_alpha : bool, default: False
If False, use the ``#rrggbb`` format, otherwise use ``#rrggbbaa``.
Returns
-------
str
``#rrggbb`` or ``#rrggbbaa`` hex color string
"""
c = to_rgba(c)
if not keep_alpha:
c = c[:3]
return "#" + "".join(format(round(val * 255), "02x") for val in c)
### Backwards-compatible color-conversion API
cnames = CSS4_COLORS
hexColorPattern = re.compile(r"\A#[a-fA-F0-9]{6}\Z")
rgb2hex = to_hex
hex2color = to_rgb
class ColorConverter:
"""
A class only kept for backwards compatibility.
Its functionality is entirely provided by module-level functions.
"""
colors = _colors_full_map
cache = _colors_full_map.cache
to_rgb = staticmethod(to_rgb)
to_rgba = staticmethod(to_rgba)
to_rgba_array = staticmethod(to_rgba_array)
colorConverter = ColorConverter()
### End of backwards-compatible color-conversion API
def _create_lookup_table(N, data, gamma=1.0):
r"""
Create an *N* -element 1D lookup table.
This assumes a mapping :math:`f : [0, 1] \rightarrow [0, 1]`. The returned
data is an array of N values :math:`y = f(x)` where x is sampled from
[0, 1].
By default (*gamma* = 1) x is equidistantly sampled from [0, 1]. The
*gamma* correction factor :math:`\gamma` distorts this equidistant
sampling by :math:`x \rightarrow x^\gamma`.
Parameters
----------
N : int
The number of elements of the created lookup table; at least 1.
data : (M, 3) array-like or callable
Defines the mapping :math:`f`.
If a (M, 3) array-like, the rows define values (x, y0, y1). The x
values must start with x=0, end with x=1, and all x values be in
increasing order.
A value between :math:`x_i` and :math:`x_{i+1}` is mapped to the range
:math:`y^1_{i-1} \ldots y^0_i` by linear interpolation.
For the simple case of a y-continuous mapping, y0 and y1 are identical.
The two values of y are to allow for discontinuous mapping functions.
E.g. a sawtooth with a period of 0.2 and an amplitude of 1 would be::
[(0, 1, 0), (0.2, 1, 0), (0.4, 1, 0), ..., [(1, 1, 0)]
In the special case of ``N == 1``, by convention the returned value
is y0 for x == 1.
If *data* is a callable, it must accept and return numpy arrays::
data(x : ndarray) -> ndarray
and map values between 0 - 1 to 0 - 1.
gamma : float
Gamma correction factor for input distribution x of the mapping.
See also https://en.wikipedia.org/wiki/Gamma_correction.
Returns
-------
array
The lookup table where ``lut[x * (N-1)]`` gives the closest value
for values of x between 0 and 1.
Notes
-----
This function is internally used for `.LinearSegmentedColormap`.
"""
if callable(data):
xind = np.linspace(0, 1, N) ** gamma
lut = np.clip(np.array(data(xind), dtype=float), 0, 1)
return lut
try:
adata = np.array(data)
except Exception as err:
raise TypeError("data must be convertible to an array") from err
_api.check_shape((None, 3), data=adata)
x = adata[:, 0]
y0 = adata[:, 1]
y1 = adata[:, 2]
if x[0] != 0. or x[-1] != 1.0:
raise ValueError(
"data mapping points must start with x=0 and end with x=1")
if (np.diff(x) < 0).any():
raise ValueError("data mapping points must have x in increasing order")
# begin generation of lookup table
if N == 1:
# convention: use the y = f(x=1) value for a 1-element lookup table
lut = np.array(y0[-1])
else:
x = x * (N - 1)
xind = (N - 1) * np.linspace(0, 1, N) ** gamma
ind = np.searchsorted(x, xind)[1:-1]
distance = (xind[1:-1] - x[ind - 1]) / (x[ind] - x[ind - 1])
lut = np.concatenate([
[y1[0]],
distance * (y0[ind] - y1[ind - 1]) + y1[ind - 1],
[y0[-1]],
])
# ensure that the lut is confined to values between 0 and 1 by clipping it
return np.clip(lut, 0.0, 1.0)
class Colormap:
"""
Baseclass for all scalar to RGBA mappings.
Typically, Colormap instances are used to convert data values (floats)
from the interval ``[0, 1]`` to the RGBA color that the respective
Colormap represents. For scaling of data into the ``[0, 1]`` interval see
`matplotlib.colors.Normalize`. Subclasses of `matplotlib.cm.ScalarMappable`
make heavy use of this ``data -> normalize -> map-to-color`` processing
chain.
"""
def __init__(self, name, N=256):
"""
Parameters
----------
name : str
The name of the colormap.
N : int
The number of RGB quantization levels.
"""
self.name = name
self.N = int(N) # ensure that N is always int
self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything.
self._rgba_under = None
self._rgba_over = None
self._i_under = self.N
self._i_over = self.N + 1
self._i_bad = self.N + 2
self._isinit = False
#: When this colormap exists on a scalar mappable and colorbar_extend
#: is not False, colorbar creation will pick up ``colorbar_extend`` as
#: the default value for the ``extend`` keyword in the
#: `matplotlib.colorbar.Colorbar` constructor.
self.colorbar_extend = False
def __call__(self, X, alpha=None, bytes=False):
r"""
Parameters
----------
X : float or int, `~numpy.ndarray` or scalar
The data value(s) to convert to RGBA.
For floats, *X* should be in the interval ``[0.0, 1.0]`` to
return the RGBA values ``X*100`` percent along the Colormap line.
For integers, *X* should be in the interval ``[0, Colormap.N)`` to
return RGBA values *indexed* from the Colormap with index ``X``.
alpha : float or array-like or None
Alpha must be a scalar between 0 and 1, a sequence of such
floats with shape matching X, or None.
bytes : bool
If False (default), the returned RGBA values will be floats in the
interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the
interval ``[0, 255]``.
Returns
-------
Tuple of RGBA values if X is scalar, otherwise an array of
RGBA values with a shape of ``X.shape + (4, )``.
"""
if not self._isinit:
self._init()
xa = np.array(X, copy=True)
if not xa.dtype.isnative:
# Native byteorder is faster.
xa = xa.byteswap().view(xa.dtype.newbyteorder())
if xa.dtype.kind == "f":
xa *= self.N
# xa == 1 (== N after multiplication) is not out of range.
xa[xa == self.N] = self.N - 1
# Pre-compute the masks before casting to int (which can truncate
# negative values to zero or wrap large floats to negative ints).
mask_under = xa < 0
mask_over = xa >= self.N
# If input was masked, get the bad mask from it; else mask out nans.
mask_bad = X.mask if np.ma.is_masked(X) else np.isnan(xa)
with np.errstate(invalid="ignore"):
# We need this cast for unsigned ints as well as floats
xa = xa.astype(int)
xa[mask_under] = self._i_under
xa[mask_over] = self._i_over
xa[mask_bad] = self._i_bad
lut = self._lut
if bytes:
lut = (lut * 255).astype(np.uint8)
rgba = lut.take(xa, axis=0, mode='clip')
if alpha is not None:
alpha = np.clip(alpha, 0, 1)
if bytes:
alpha *= 255 # Will be cast to uint8 upon assignment.
if alpha.shape not in [(), xa.shape]:
raise ValueError(
f"alpha is array-like but its shape {alpha.shape} does "
f"not match that of X {xa.shape}")
rgba[..., -1] = alpha
# If the "bad" color is all zeros, then ignore alpha input.
if (lut[-1] == 0).all():
rgba[mask_bad] = (0, 0, 0, 0)
if not np.iterable(X):
rgba = tuple(rgba)
return rgba
def __copy__(self):
cls = self.__class__
cmapobject = cls.__new__(cls)
cmapobject.__dict__.update(self.__dict__)
if self._isinit:
cmapobject._lut = np.copy(self._lut)
return cmapobject
def __eq__(self, other):
if (not isinstance(other, Colormap) or
self.colorbar_extend != other.colorbar_extend):
return False
# To compare lookup tables the Colormaps have to be initialized
if not self._isinit:
self._init()
if not other._isinit:
other._init()
return np.array_equal(self._lut, other._lut)
[docs]
def get_bad(self):
"""Get the color for masked values."""
if not self._isinit:
self._init()
return np.array(self._lut[self._i_bad])
[docs]
def set_bad(self, color='k', alpha=None):
"""Set the color for masked values."""
self._rgba_bad = to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
[docs]
def get_under(self):
"""Get the color for low out-of-range values."""
if not self._isinit:
self._init()
return np.array(self._lut[self._i_under])
[docs]
def set_under(self, color='k', alpha=None):
"""Set the color for low out-of-range values."""
self._rgba_under = to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
[docs]
def get_over(self):
"""Get the color for high out-of-range values."""
if not self._isinit:
self._init()
return np.array(self._lut[self._i_over])
[docs]
def set_over(self, color='k', alpha=None):
"""Set the color for high out-of-range values."""
self._rgba_over = to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
[docs]
def set_extremes(self, *, bad=None, under=None, over=None):
"""
Set the colors for masked (*bad*) values and, when ``norm.clip =
False``, low (*under*) and high (*over*) out-of-range values.
"""
if bad is not None:
self.set_bad(bad)
if under is not None:
self.set_under(under)
if over is not None:
self.set_over(over)
[docs]
def with_extremes(self, *, bad=None, under=None, over=None):
"""
Return a copy of the colormap, for which the colors for masked (*bad*)
values and, when ``norm.clip = False``, low (*under*) and high (*over*)
out-of-range values, have been set accordingly.
"""
new_cm = self.copy()
new_cm.set_extremes(bad=bad, under=under, over=over)
return new_cm
def _set_extremes(self):
if self._rgba_under:
self._lut[self._i_under] = self._rgba_under
else:
self._lut[self._i_under] = self._lut[0]
if self._rgba_over:
self._lut[self._i_over] = self._rgba_over
else:
self._lut[self._i_over] = self._lut[self.N - 1]
self._lut[self._i_bad] = self._rgba_bad
def _init(self):
"""Generate the lookup table, ``self._lut``."""
raise NotImplementedError("Abstract class only")
[docs]
def is_gray(self):
"""Return whether the colormap is grayscale."""
if not self._isinit:
self._init()
return (np.all(self._lut[:, 0] == self._lut[:, 1]) and
np.all(self._lut[:, 0] == self._lut[:, 2]))
[docs]
def resampled(self, lutsize):
"""Return a new colormap with *lutsize* entries."""
if hasattr(self, '_resample'):
_api.warn_external(
"The ability to resample a color map is now public API "
f"However the class {type(self)} still only implements "
"the previous private _resample method. Please update "
"your class."
)
return self._resample(lutsize)
raise NotImplementedError()
def reversed(self, name=None):
"""
Return a reversed instance of the Colormap.
.. note:: This function is not implemented for the base class.
Parameters
----------
name : str, optional
The name for the reversed colormap. If None, the
name is set to ``self.name + "_r"``.
See Also
--------
LinearSegmentedColormap.reversed
ListedColormap.reversed
"""
raise NotImplementedError()
def _repr_png_(self):
"""Generate a PNG representation of the Colormap."""
X = np.tile(np.linspace(0, 1, _REPR_PNG_SIZE[0]),
(_REPR_PNG_SIZE[1], 1))
pixels = self(X, bytes=True)
png_bytes = io.BytesIO()
title = self.name + ' colormap'
author = f'Matplotlib v{mpl.__version__}, https://matplotlib.org'
pnginfo = PngInfo()
pnginfo.add_text('Title', title)
pnginfo.add_text('Description', title)
pnginfo.add_text('Author', author)
pnginfo.add_text('Software', author)
Image.fromarray(pixels).save(png_bytes, format='png', pnginfo=pnginfo)
return png_bytes.getvalue()
def _repr_html_(self):
"""Generate an HTML representation of the Colormap."""
png_bytes = self._repr_png_()
png_base64 = base64.b64encode(png_bytes).decode('ascii')
def color_block(color):
hex_color = to_hex(color, keep_alpha=True)
return (f'<div title="{hex_color}" '
'style="display: inline-block; '
'width: 1em; height: 1em; '
'margin: 0; '
'vertical-align: middle; '
'border: 1px solid #555; '
f'background-color: {hex_color};"></div>')
return ('<div style="vertical-align: middle;">'
f'<strong>{self.name}</strong> '
'</div>'
'<div class="cmap"><img '
f'alt="{self.name} colormap" '
f'title="{self.name}" '
'style="border: 1px solid #555;" '
f'src="data:image/png;base64,{png_base64}"></div>'
'<div style="vertical-align: middle; '
f'max-width: {_REPR_PNG_SIZE[0]+2}px; '
'display: flex; justify-content: space-between;">'
'<div style="float: left;">'
f'{color_block(self.get_under())} under'
'</div>'
'<div style="margin: 0 auto; display: inline-block;">'
f'bad {color_block(self.get_bad())}'
'</div>'
'<div style="float: right;">'
f'over {color_block(self.get_over())}'
'</div>'
'</div>')
[docs]
def copy(self):
"""Return a copy of the colormap."""
return self.__copy__()
class LinearSegmentedColormap(Colormap):
"""
Colormap objects based on lookup tables using linear segments.
The lookup table is generated using linear interpolation for each
primary color, with the 0-1 domain divided into any number of
segments.
"""
def __init__(self, name, segmentdata, N=256, gamma=1.0):
"""
Create colormap from linear mapping segments
segmentdata argument is a dictionary with a red, green and blue
entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
forming rows in a table. Entries for alpha are optional.
Example: suppose you want red to increase from 0 to 1 over
the bottom half, green to do the same over the middle half,
and blue over the top half. Then you would use::
cdict = {'red': [(0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0),
(0.25, 0.0, 0.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'blue': [(0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(1.0, 1.0, 1.0)]}
Each row in the table for a given color is a sequence of
*x*, *y0*, *y1* tuples. In each sequence, *x* must increase
monotonically from 0 to 1. For any input value *z* falling
between *x[i]* and *x[i+1]*, the output value of a given color
will be linearly interpolated between *y1[i]* and *y0[i+1]*::
row i: x y0 y1
/
/
row i+1: x y0 y1
Hence y0 in the first row and y1 in the last row are never used.
See Also
--------
LinearSegmentedColormap.from_list
Static method; factory function for generating a smoothly-varying
LinearSegmentedColormap.
"""
# True only if all colors in map are identical; needed for contouring.
self.monochrome = False
super().__init__(name, N)
self._segmentdata = segmentdata
self._gamma = gamma
def _init(self):
self._lut = np.ones((self.N + 3, 4), float)
self._lut[:-3, 0] = _create_lookup_table(
self.N, self._segmentdata['red'], self._gamma)
self._lut[:-3, 1] = _create_lookup_table(
self.N, self._segmentdata['green'], self._gamma)
self._lut[:-3, 2] = _create_lookup_table(
self.N, self._segmentdata['blue'], self._gamma)
if 'alpha' in self._segmentdata:
self._lut[:-3, 3] = _create_lookup_table(
self.N, self._segmentdata['alpha'], 1)
self._isinit = True
self._set_extremes()
def set_gamma(self, gamma):
"""Set a new gamma value and regenerate colormap."""
self._gamma = gamma
self._init()
@staticmethod
def from_list(name, colors, N=256, gamma=1.0):
"""
Create a `LinearSegmentedColormap` from a list of colors.
Parameters
----------
name : str
The name of the colormap.
colors : list of :mpltype:`color` or list of (value, color)
If only colors are given, they are equidistantly mapped from the
range :math:`[0, 1]`; i.e. 0 maps to ``colors[0]`` and 1 maps to
``colors[-1]``.
If (value, color) pairs are given, the mapping is from *value*
to *color*. This can be used to divide the range unevenly.
N : int
The number of RGB quantization levels.
gamma : float
"""
if not np.iterable(colors):
raise ValueError('colors must be iterable')
if (isinstance(colors[0], Sized) and len(colors[0]) == 2
and not isinstance(colors[0], str)):
# List of value, color pairs
vals, colors = zip(*colors)
else:
vals = np.linspace(0, 1, len(colors))
r, g, b, a = to_rgba_array(colors).T
cdict = {
"red": np.column_stack([vals, r, r]),
"green": np.column_stack([vals, g, g]),
"blue": np.column_stack([vals, b, b]),
"alpha": np.column_stack([vals, a, a]),
}
return LinearSegmentedColormap(name, cdict, N, gamma)
def resampled(self, lutsize):
"""Return a new colormap with *lutsize* entries."""
new_cmap = LinearSegmentedColormap(self.name, self._segmentdata,
lutsize)
new_cmap._rgba_over = self._rgba_over
new_cmap._rgba_under = self._rgba_under
new_cmap._rgba_bad = self._rgba_bad
return new_cmap
# Helper ensuring picklability of the reversed cmap.
@staticmethod
def _reverser(func, x):
return func(1 - x)
def reversed(self, name=None):
"""
Return a reversed instance of the Colormap.
Parameters
----------
name : str, optional
The name for the reversed colormap. If None, the
name is set to ``self.name + "_r"``.
Returns
-------
LinearSegmentedColormap
The reversed colormap.
"""
if name is None:
name = self.name + "_r"
# Using a partial object keeps the cmap picklable.
data_r = {key: (functools.partial(self._reverser, data)
if callable(data) else
[(1.0 - x, y1, y0) for x, y0, y1 in reversed(data)])
for key, data in self._segmentdata.items()}
new_cmap = LinearSegmentedColormap(name, data_r, self.N, self._gamma)
# Reverse the over/under values too
new_cmap._rgba_over = self._rgba_under
new_cmap._rgba_under = self._rgba_over
new_cmap._rgba_bad = self._rgba_bad
return new_cmap
class ListedColormap(Colormap):
"""
Colormap object generated from a list of colors.
This may be most useful when indexing directly into a colormap,
but it can also be used to generate special colormaps for ordinary
mapping.
Parameters
----------
colors : list, array
Sequence of Matplotlib color specifications (color names or RGB(A)
values).
name : str, optional
String to identify the colormap.
N : int, optional
Number of entries in the map. The default is *None*, in which case
there is one colormap entry for each element in the list of colors.
If ::
N < len(colors)
the list will be truncated at *N*. If ::
N > len(colors)
the list will be extended by repetition.
"""
def __init__(self, colors, name='from_list', N=None):
self.monochrome = False # Are all colors identical? (for contour.py)
if N is None:
self.colors = colors
N = len(colors)
else:
if isinstance(colors, str):
self.colors = [colors] * N
self.monochrome = True
elif np.iterable(colors):
if len(colors) == 1:
self.monochrome = True
self.colors = list(
itertools.islice(itertools.cycle(colors), N))
else:
try:
gray = float(colors)
except TypeError:
pass
else:
self.colors = [gray] * N
self.monochrome = True
super().__init__(name, N)
def _init(self):
self._lut = np.zeros((self.N + 3, 4), float)
self._lut[:-3] = to_rgba_array(self.colors)
self._isinit = True
self._set_extremes()
def resampled(self, lutsize):
"""Return a new colormap with *lutsize* entries."""
colors = self(np.linspace(0, 1, lutsize))
new_cmap = ListedColormap(colors, name=self.name)
# Keep the over/under values too
new_cmap._rgba_over = self._rgba_over
new_cmap._rgba_under = self._rgba_under
new_cmap._rgba_bad = self._rgba_bad
return new_cmap
def reversed(self, name=None):
"""
Return a reversed instance of the Colormap.
Parameters
----------
name : str, optional
The name for the reversed colormap. If None, the
name is set to ``self.name + "_r"``.
Returns
-------
ListedColormap
A reversed instance of the colormap.
"""
if name is None:
name = self.name + "_r"
colors_r = list(reversed(self.colors))
new_cmap = ListedColormap(colors_r, name=name, N=self.N)
# Reverse the over/under values too
new_cmap._rgba_over = self._rgba_under
new_cmap._rgba_under = self._rgba_over
new_cmap._rgba_bad = self._rgba_bad
return new_cmap
class Normalize:
"""
A class which, when called, maps values within the interval
``[vmin, vmax]`` linearly to the interval ``[0.0, 1.0]``. The mapping of
values outside ``[vmin, vmax]`` depends on *clip*.
Examples
--------
::
x = [-2, -1, 0, 1, 2]
norm = mpl.colors.Normalize(vmin=-1, vmax=1, clip=False)
norm(x) # [-0.5, 0., 0.5, 1., 1.5]
norm = mpl.colors.Normalize(vmin=-1, vmax=1, clip=True)
norm(x) # [0., 0., 0.5, 1., 1.]
See Also
--------
:ref:`colormapnorms`
"""
def __init__(self, vmin=None, vmax=None, clip=False):
"""
Parameters
----------
vmin, vmax : float or None
Values within the range ``[vmin, vmax]`` from the input data will be
linearly mapped to ``[0, 1]``. If either *vmin* or *vmax* is not
provided, they default to the minimum and maximum values of the input,
respectively.
clip : bool, default: False
Determines the behavior for mapping values outside the range
``[vmin, vmax]``.
If clipping is off, values outside the range ``[vmin, vmax]`` are
also transformed, resulting in values outside ``[0, 1]``. This
behavior is usually desirable, as colormaps can mark these *under*
and *over* values with specific colors.
If clipping is on, values below *vmin* are mapped to 0 and values
above *vmax* are mapped to 1. Such values become indistinguishable
from regular boundary values, which may cause misinterpretation of
the data.
Notes
-----
If ``vmin == vmax``, input data will be mapped to 0.
"""
self._vmin = _sanitize_extrema(vmin)
self._vmax = _sanitize_extrema(vmax)
self._clip = clip
self._scale = None
self.callbacks = cbook.CallbackRegistry(signals=["changed"])
@property
def vmin(self):
return self._vmin
@vmin.setter
def vmin(self, value):
value = _sanitize_extrema(value)
if value != self._vmin:
self._vmin = value
self._changed()
@property
def vmax(self):
return self._vmax
@vmax.setter
def vmax(self, value):
value = _sanitize_extrema(value)
if value != self._vmax:
self._vmax = value
self._changed()
@property
def clip(self):
return self._clip
@clip.setter
def clip(self, value):
if value != self._clip:
self._clip = value
self._changed()
def _changed(self):
"""
Call this whenever the norm is changed to notify all the
callback listeners to the 'changed' signal.
"""
self.callbacks.process('changed')
@staticmethod
def process_value(value):
"""
Homogenize the input *value* for easy and efficient normalization.
*value* can be a scalar or sequence.
Parameters
----------
value
Data to normalize.
Returns
-------
result : masked array
Masked array with the same shape as *value*.
is_scalar : bool
Whether *value* is a scalar.
Notes
-----
Float dtypes are preserved; integer types with two bytes or smaller are
converted to np.float32, and larger types are converted to np.float64.
Preserving float32 when possible, and using in-place operations,
greatly improves speed for large arrays.
"""
is_scalar = not np.iterable(value)
if is_scalar:
value = [value]
dtype = np.min_scalar_type(value)
if np.issubdtype(dtype, np.integer) or dtype.type is np.bool_:
# bool_/int8/int16 -> float32; int32/int64 -> float64
dtype = np.promote_types(dtype, np.float32)
# ensure data passed in as an ndarray subclass are interpreted as
# an ndarray. See issue #6622.
mask = np.ma.getmask(value)
data = np.asarray(value)
result = np.ma.array(data, mask=mask, dtype=dtype, copy=True)
return result, is_scalar
def __call__(self, value, clip=None):
"""
Normalize the data and return the normalized data.
Parameters
----------
value
Data to normalize.
clip : bool, optional
See the description of the parameter *clip* in `.Normalize`.
If ``None``, defaults to ``self.clip`` (which defaults to
``False``).
Notes
-----
If not already initialized, ``self.vmin`` and ``self.vmax`` are
initialized using ``self.autoscale_None(value)``.
"""
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
if self.vmin is None or self.vmax is None:
self.autoscale_None(result)
# Convert at least to float, without losing precision.
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
if vmin == vmax:
result.fill(0) # Or should it be all masked? Or 0.5?
elif vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
else:
if clip:
mask = np.ma.getmask(result)
result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
mask=mask)
# ma division is very slow; we can take a shortcut
resdat = result.data
resdat -= vmin
resdat /= (vmax - vmin)
result = np.ma.array(resdat, mask=result.mask, copy=False)
if is_scalar:
result = result[0]
return result
def inverse(self, value):
"""
Maps the normalized value (i.e., index in the colormap) back to image
data value.
Parameters
----------
value
Normalized value.
"""
if not self.scaled():
raise ValueError("Not invertible until both vmin and vmax are set")
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
if np.iterable(value):
val = np.ma.asarray(value)
return vmin + val * (vmax - vmin)
else:
return vmin + value * (vmax - vmin)
def autoscale(self, A):
"""Set *vmin*, *vmax* to min, max of *A*."""
with self.callbacks.blocked():
# Pause callbacks while we are updating so we only get
# a single update signal at the end
self.vmin = self.vmax = None
self.autoscale_None(A)
self._changed()
def autoscale_None(self, A):
"""If *vmin* or *vmax* are not set, use the min/max of *A* to set them."""
A = np.asanyarray(A)
if isinstance(A, np.ma.MaskedArray):
# we need to make the distinction between an array, False, np.bool_(False)
if A.mask is False or not A.mask.shape:
A = A.data
if self.vmin is None and A.size:
self.vmin = A.min()
if self.vmax is None and A.size:
self.vmax = A.max()
def scaled(self):
"""Return whether *vmin* and *vmax* are both set."""
return self.vmin is not None and self.vmax is not None
class TwoSlopeNorm(Normalize):
def __init__(self, vcenter, vmin=None, vmax=None):
"""
Normalize data with a set center.
Useful when mapping data with an unequal rates of change around a
conceptual center, e.g., data that range from -2 to 4, with 0 as
the midpoint.
Parameters
----------
vcenter : float
The data value that defines ``0.5`` in the normalization.
vmin : float, optional
The data value that defines ``0.0`` in the normalization.
Defaults to the min value of the dataset.
vmax : float, optional
The data value that defines ``1.0`` in the normalization.
Defaults to the max value of the dataset.
Examples
--------
This maps data value -4000 to 0., 0 to 0.5, and +10000 to 1.0; data
between is linearly interpolated::
>>> import matplotlib.colors as mcolors
>>> offset = mcolors.TwoSlopeNorm(vmin=-4000.,
... vcenter=0., vmax=10000)
>>> data = [-4000., -2000., 0., 2500., 5000., 7500., 10000.]
>>> offset(data)
array([0., 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])
"""
super().__init__(vmin=vmin, vmax=vmax)
self._vcenter = vcenter
if vcenter is not None and vmax is not None and vcenter >= vmax:
raise ValueError('vmin, vcenter, and vmax must be in '
'ascending order')
if vcenter is not None and vmin is not None and vcenter <= vmin:
raise ValueError('vmin, vcenter, and vmax must be in '
'ascending order')
@property
def vcenter(self):
return self._vcenter
@vcenter.setter
def vcenter(self, value):
if value != self._vcenter:
self._vcenter = value
self._changed()
def autoscale_None(self, A):
"""
Get vmin and vmax.
If vcenter isn't in the range [vmin, vmax], either vmin or vmax
is expanded so that vcenter lies in the middle of the modified range
[vmin, vmax].
"""
super().autoscale_None(A)
if self.vmin >= self.vcenter:
self.vmin = self.vcenter - (self.vmax - self.vcenter)
if self.vmax <= self.vcenter:
self.vmax = self.vcenter + (self.vcenter - self.vmin)
def __call__(self, value, clip=None):
"""
Map value to the interval [0, 1]. The *clip* argument is unused.
"""
result, is_scalar = self.process_value(value)
self.autoscale_None(result) # sets self.vmin, self.vmax if None
if not self.vmin <= self.vcenter <= self.vmax:
raise ValueError("vmin, vcenter, vmax must increase monotonically")
# note that we must extrapolate for tick locators:
result = np.ma.masked_array(
np.interp(result, [self.vmin, self.vcenter, self.vmax],
[0, 0.5, 1], left=-np.inf, right=np.inf),
mask=np.ma.getmask(result))
if is_scalar:
result = np.atleast_1d(result)[0]
return result
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until both vmin and vmax are set")
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
(vcenter,), _ = self.process_value(self.vcenter)
result = np.interp(value, [0, 0.5, 1], [vmin, vcenter, vmax],
left=-np.inf, right=np.inf)
return result
class CenteredNorm(Normalize):
def __init__(self, vcenter=0, halfrange=None, clip=False):
"""
Normalize symmetrical data around a center (0 by default).
Unlike `TwoSlopeNorm`, `CenteredNorm` applies an equal rate of change
around the center.
Useful when mapping symmetrical data around a conceptual center
e.g., data that range from -2 to 4, with 0 as the midpoint, and
with equal rates of change around that midpoint.
Parameters
----------
vcenter : float, default: 0
The data value that defines ``0.5`` in the normalization.
halfrange : float, optional
The range of data values that defines a range of ``0.5`` in the
normalization, so that *vcenter* - *halfrange* is ``0.0`` and
*vcenter* + *halfrange* is ``1.0`` in the normalization.
Defaults to the largest absolute difference to *vcenter* for
the values in the dataset.
clip : bool, default: False
Determines the behavior for mapping values outside the range
``[vmin, vmax]``.
If clipping is off, values outside the range ``[vmin, vmax]`` are
also transformed, resulting in values outside ``[0, 1]``. This
behavior is usually desirable, as colormaps can mark these *under*
and *over* values with specific colors.
If clipping is on, values below *vmin* are mapped to 0 and values
above *vmax* are mapped to 1. Such values become indistinguishable
from regular boundary values, which may cause misinterpretation of
the data.
Examples
--------
This maps data values -2 to 0.25, 0 to 0.5, and 4 to 1.0
(assuming equal rates of change above and below 0.0):
>>> import matplotlib.colors as mcolors
>>> norm = mcolors.CenteredNorm(halfrange=4.0)
>>> data = [-2., 0., 4.]
>>> norm(data)
array([0.25, 0.5 , 1. ])
"""
super().__init__(vmin=None, vmax=None, clip=clip)
self._vcenter = vcenter
# calling the halfrange setter to set vmin and vmax
self.halfrange = halfrange
def autoscale(self, A):
"""
Set *halfrange* to ``max(abs(A-vcenter))``, then set *vmin* and *vmax*.
"""
A = np.asanyarray(A)
self.halfrange = max(self._vcenter-A.min(),
A.max()-self._vcenter)
def autoscale_None(self, A):
"""Set *vmin* and *vmax*."""
A = np.asanyarray(A)
if self.halfrange is None and A.size:
self.autoscale(A)
@property
def vmin(self):
return self._vmin
@vmin.setter
def vmin(self, value):
value = _sanitize_extrema(value)
if value != self._vmin:
self._vmin = value
self._vmax = 2*self.vcenter - value
self._changed()
@property
def vmax(self):
return self._vmax
@vmax.setter
def vmax(self, value):
value = _sanitize_extrema(value)
if value != self._vmax:
self._vmax = value
self._vmin = 2*self.vcenter - value
self._changed()
@property
def vcenter(self):
return self._vcenter
@vcenter.setter
def vcenter(self, vcenter):
if vcenter != self._vcenter:
self._vcenter = vcenter
# Trigger an update of the vmin/vmax values through the setter
self.halfrange = self.halfrange
self._changed()
@property
def halfrange(self):
if self.vmin is None or self.vmax is None:
return None
return (self.vmax - self.vmin) / 2
@halfrange.setter
def halfrange(self, halfrange):
if halfrange is None:
self.vmin = None
self.vmax = None
else:
self.vmin = self.vcenter - abs(halfrange)
self.vmax = self.vcenter + abs(halfrange)
def make_norm_from_scale(scale_cls, base_norm_cls=None, *, init=None):
"""
Decorator for building a `.Normalize` subclass from a `~.scale.ScaleBase`
subclass.
After ::
@make_norm_from_scale(scale_cls)
class norm_cls(Normalize):
...
*norm_cls* is filled with methods so that normalization computations are
forwarded to *scale_cls* (i.e., *scale_cls* is the scale that would be used
for the colorbar of a mappable normalized with *norm_cls*).
If *init* is not passed, then the constructor signature of *norm_cls*
will be ``norm_cls(vmin=None, vmax=None, clip=False)``; these three
parameters will be forwarded to the base class (``Normalize.__init__``),
and a *scale_cls* object will be initialized with no arguments (other than
a dummy axis).
If the *scale_cls* constructor takes additional parameters, then *init*
should be passed to `make_norm_from_scale`. It is a callable which is
*only* used for its signature. First, this signature will become the
signature of *norm_cls*. Second, the *norm_cls* constructor will bind the
parameters passed to it using this signature, extract the bound *vmin*,
*vmax*, and *clip* values, pass those to ``Normalize.__init__``, and
forward the remaining bound values (including any defaults defined by the
signature) to the *scale_cls* constructor.
"""
if base_norm_cls is None:
return functools.partial(make_norm_from_scale, scale_cls, init=init)
if isinstance(scale_cls, functools.partial):
scale_args = scale_cls.args
scale_kwargs_items = tuple(scale_cls.keywords.items())
scale_cls = scale_cls.func
else:
scale_args = scale_kwargs_items = ()
if init is None:
def init(vmin=None, vmax=None, clip=False): pass
return _make_norm_from_scale(
scale_cls, scale_args, scale_kwargs_items,
base_norm_cls, inspect.signature(init))
@functools.cache
def _make_norm_from_scale(
scale_cls, scale_args, scale_kwargs_items,
base_norm_cls, bound_init_signature,
):
"""
Helper for `make_norm_from_scale`.
This function is split out to enable caching (in particular so that
different unpickles reuse the same class). In order to do so,
- ``functools.partial`` *scale_cls* is expanded into ``func, args, kwargs``
to allow memoizing returned norms (partial instances always compare
unequal, but we can check identity based on ``func, args, kwargs``;
- *init* is replaced by *init_signature*, as signatures are picklable,
unlike to arbitrary lambdas.
"""
class Norm(base_norm_cls):
def __reduce__(self):
cls = type(self)
# If the class is toplevel-accessible, it is possible to directly
# pickle it "by name". This is required to support norm classes
# defined at a module's toplevel, as the inner base_norm_cls is
# otherwise unpicklable (as it gets shadowed by the generated norm
# class). If either import or attribute access fails, fall back to
# the general path.
try:
if cls is getattr(importlib.import_module(cls.__module__),
cls.__qualname__):
return (_create_empty_object_of_class, (cls,), vars(self))
except (ImportError, AttributeError):
pass
return (_picklable_norm_constructor,
(scale_cls, scale_args, scale_kwargs_items,
base_norm_cls, bound_init_signature),
vars(self))
def __init__(self, *args, **kwargs):
ba = bound_init_signature.bind(*args, **kwargs)
ba.apply_defaults()
super().__init__(
**{k: ba.arguments.pop(k) for k in ["vmin", "vmax", "clip"]})
self._scale = functools.partial(
scale_cls, *scale_args, **dict(scale_kwargs_items))(
axis=None, **ba.arguments)
self._trf = self._scale.get_transform()
__init__.__signature__ = bound_init_signature.replace(parameters=[
inspect.Parameter("self", inspect.Parameter.POSITIONAL_OR_KEYWORD),
*bound_init_signature.parameters.values()])
def __call__(self, value, clip=None):
value, is_scalar = self.process_value(value)
if self.vmin is None or self.vmax is None:
self.autoscale_None(value)
if self.vmin > self.vmax:
raise ValueError("vmin must be less or equal to vmax")
if self.vmin == self.vmax:
return np.full_like(value, 0)
if clip is None:
clip = self.clip
if clip:
value = np.clip(value, self.vmin, self.vmax)
t_value = self._trf.transform(value).reshape(np.shape(value))
t_vmin, t_vmax = self._trf.transform([self.vmin, self.vmax])
if not np.isfinite([t_vmin, t_vmax]).all():
raise ValueError("Invalid vmin or vmax")
t_value -= t_vmin
t_value /= (t_vmax - t_vmin)
t_value = np.ma.masked_invalid(t_value, copy=False)
return t_value[0] if is_scalar else t_value
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
if self.vmin > self.vmax:
raise ValueError("vmin must be less or equal to vmax")
t_vmin, t_vmax = self._trf.transform([self.vmin, self.vmax])
if not np.isfinite([t_vmin, t_vmax]).all():
raise ValueError("Invalid vmin or vmax")
value, is_scalar = self.process_value(value)
rescaled = value * (t_vmax - t_vmin)
rescaled += t_vmin
value = (self._trf
.inverted()
.transform(rescaled)
.reshape(np.shape(value)))
return value[0] if is_scalar else value
def autoscale_None(self, A):
# i.e. A[np.isfinite(...)], but also for non-array A's
in_trf_domain = np.extract(np.isfinite(self._trf.transform(A)), A)
if in_trf_domain.size == 0:
in_trf_domain = np.ma.masked
return super().autoscale_None(in_trf_domain)
if base_norm_cls is Normalize:
Norm.__name__ = f"{scale_cls.__name__}Norm"
Norm.__qualname__ = f"{scale_cls.__qualname__}Norm"
else:
Norm.__name__ = base_norm_cls.__name__
Norm.__qualname__ = base_norm_cls.__qualname__
Norm.__module__ = base_norm_cls.__module__
Norm.__doc__ = base_norm_cls.__doc__
return Norm
def _create_empty_object_of_class(cls):
return cls.__new__(cls)
def _picklable_norm_constructor(*args):
return _create_empty_object_of_class(_make_norm_from_scale(*args))
@make_norm_from_scale(
scale.FuncScale,
init=lambda functions, vmin=None, vmax=None, clip=False: None)
class FuncNorm(Normalize):
"""
Arbitrary normalization using functions for the forward and inverse.
Parameters
----------
functions : (callable, callable)
two-tuple of the forward and inverse functions for the normalization.
The forward function must be monotonic.
Both functions must have the signature ::
def forward(values: array-like) -> array-like
vmin, vmax : float or None
If *vmin* and/or *vmax* is not given, they are initialized from the
minimum and maximum value, respectively, of the first input
processed; i.e., ``__call__(A)`` calls ``autoscale_None(A)``.
clip : bool, default: False
Determines the behavior for mapping values outside the range
``[vmin, vmax]``.
If clipping is off, values outside the range ``[vmin, vmax]`` are also
transformed by the function, resulting in values outside ``[0, 1]``.
This behavior is usually desirable, as colormaps can mark these *under*
and *over* values with specific colors.
If clipping is on, values below *vmin* are mapped to 0 and values above
*vmax* are mapped to 1. Such values become indistinguishable from
regular boundary values, which may cause misinterpretation of the data.
"""
LogNorm = make_norm_from_scale(
functools.partial(scale.LogScale, nonpositive="mask"))(Normalize)
LogNorm.__name__ = LogNorm.__qualname__ = "LogNorm"
LogNorm.__doc__ = "Normalize a given value to the 0-1 range on a log scale."
@make_norm_from_scale(
scale.SymmetricalLogScale,
init=lambda linthresh, linscale=1., vmin=None, vmax=None, clip=False, *,
base=10: None)
class SymLogNorm(Normalize):
"""
The symmetrical logarithmic scale is logarithmic in both the
positive and negative directions from the origin.
Since the values close to zero tend toward infinity, there is a
need to have a range around zero that is linear. The parameter
*linthresh* allows the user to specify the size of this range
(-*linthresh*, *linthresh*).
Parameters
----------
linthresh : float
The range within which the plot is linear (to avoid having the plot
go to infinity around zero).
linscale : float, default: 1
This allows the linear range (-*linthresh* to *linthresh*) to be
stretched relative to the logarithmic range. Its value is the
number of decades to use for each half of the linear range. For
example, when *linscale* == 1.0 (the default), the space used for
the positive and negative halves of the linear range will be equal
to one decade in the logarithmic range.
base : float, default: 10
"""
@property
def linthresh(self):
return self._scale.linthresh
@linthresh.setter
def linthresh(self, value):
self._scale.linthresh = value
@make_norm_from_scale(
scale.AsinhScale,
init=lambda linear_width=1, vmin=None, vmax=None, clip=False: None)
class AsinhNorm(Normalize):
"""
The inverse hyperbolic sine scale is approximately linear near
the origin, but becomes logarithmic for larger positive
or negative values. Unlike the `SymLogNorm`, the transition between
these linear and logarithmic regions is smooth, which may reduce
the risk of visual artifacts.
.. note::
This API is provisional and may be revised in the future
based on early user feedback.
Parameters
----------
linear_width : float, default: 1
The effective width of the linear region, beyond which
the transformation becomes asymptotically logarithmic
"""
@property
def linear_width(self):
return self._scale.linear_width
@linear_width.setter
def linear_width(self, value):
self._scale.linear_width = value
class PowerNorm(Normalize):
r"""
Linearly map a given value to the 0-1 range and then apply
a power-law normalization over that range.
Parameters
----------
gamma : float
Power law exponent.
vmin, vmax : float or None
If *vmin* and/or *vmax* is not given, they are initialized from the
minimum and maximum value, respectively, of the first input
processed; i.e., ``__call__(A)`` calls ``autoscale_None(A)``.
clip : bool, default: False
Determines the behavior for mapping values outside the range
``[vmin, vmax]``.
If clipping is off, values above *vmax* are transformed by the power
function, resulting in values above 1, and values below *vmin* are linearly
transformed resulting in values below 0. This behavior is usually desirable, as
colormaps can mark these *under* and *over* values with specific colors.
If clipping is on, values below *vmin* are mapped to 0 and values above
*vmax* are mapped to 1. Such values become indistinguishable from
regular boundary values, which may cause misinterpretation of the data.
Notes
-----
The normalization formula is
.. math::
\left ( \frac{x - v_{min}}{v_{max} - v_{min}} \right )^{\gamma}
For input values below *vmin*, gamma is set to one.
"""
def __init__(self, gamma, vmin=None, vmax=None, clip=False):
super().__init__(vmin, vmax, clip)
self.gamma = gamma
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
self.autoscale_None(result)
gamma = self.gamma
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
result.fill(0)
else:
if clip:
mask = np.ma.getmask(result)
result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
mask=mask)
resdat = result.data
resdat -= vmin
resdat /= (vmax - vmin)
resdat[resdat > 0] = np.power(resdat[resdat > 0], gamma)
result = np.ma.array(resdat, mask=result.mask, copy=False)
if is_scalar:
result = result[0]
return result
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
result, is_scalar = self.process_value(value)
gamma = self.gamma
vmin, vmax = self.vmin, self.vmax
resdat = result.data
resdat[resdat > 0] = np.power(resdat[resdat > 0], 1 / gamma)
resdat *= (vmax - vmin)
resdat += vmin
result = np.ma.array(resdat, mask=result.mask, copy=False)
if is_scalar:
result = result[0]
return result
class BoundaryNorm(Normalize):
"""
Generate a colormap index based on discrete intervals.
Unlike `Normalize` or `LogNorm`, `BoundaryNorm` maps values to integers
instead of to the interval 0-1.
"""
# Mapping to the 0-1 interval could have been done via piece-wise linear
# interpolation, but using integers seems simpler, and reduces the number
# of conversions back and forth between int and float.
def __init__(self, boundaries, ncolors, clip=False, *, extend='neither'):
"""
Parameters
----------
boundaries : array-like
Monotonically increasing sequence of at least 2 bin edges: data
falling in the n-th bin will be mapped to the n-th color.
ncolors : int
Number of colors in the colormap to be used.
clip : bool, optional
If clip is ``True``, out of range values are mapped to 0 if they
are below ``boundaries[0]`` or mapped to ``ncolors - 1`` if they
are above ``boundaries[-1]``.
If clip is ``False``, out of range values are mapped to -1 if
they are below ``boundaries[0]`` or mapped to *ncolors* if they are
above ``boundaries[-1]``. These are then converted to valid indices
by `Colormap.__call__`.
extend : {'neither', 'both', 'min', 'max'}, default: 'neither'
Extend the number of bins to include one or both of the
regions beyond the boundaries. For example, if ``extend``
is 'min', then the color to which the region between the first
pair of boundaries is mapped will be distinct from the first
color in the colormap, and by default a
`~matplotlib.colorbar.Colorbar` will be drawn with
the triangle extension on the left or lower end.
Notes
-----
If there are fewer bins (including extensions) than colors, then the
color index is chosen by linearly interpolating the ``[0, nbins - 1]``
range onto the ``[0, ncolors - 1]`` range, effectively skipping some
colors in the middle of the colormap.
"""
if clip and extend != 'neither':
raise ValueError("'clip=True' is not compatible with 'extend'")
super().__init__(vmin=boundaries[0], vmax=boundaries[-1], clip=clip)
self.boundaries = np.asarray(boundaries)
self.N = len(self.boundaries)
if self.N < 2:
raise ValueError("You must provide at least 2 boundaries "
f"(1 region) but you passed in {boundaries!r}")
self.Ncmap = ncolors
self.extend = extend
self._scale = None # don't use the default scale.
self._n_regions = self.N - 1 # number of colors needed
self._offset = 0
if extend in ('min', 'both'):
self._n_regions += 1
self._offset = 1
if extend in ('max', 'both'):
self._n_regions += 1
if self._n_regions > self.Ncmap:
raise ValueError(f"There are {self._n_regions} color bins "
"including extensions, but ncolors = "
f"{ncolors}; ncolors must equal or exceed the "
"number of bins")
def __call__(self, value, clip=None):
"""
This method behaves similarly to `.Normalize.__call__`, except that it
returns integers or arrays of int16.
"""
if clip is None:
clip = self.clip
xx, is_scalar = self.process_value(value)
mask = np.ma.getmaskarray(xx)
# Fill masked values a value above the upper boundary
xx = np.atleast_1d(xx.filled(self.vmax + 1))
if clip:
np.clip(xx, self.vmin, self.vmax, out=xx)
max_col = self.Ncmap - 1
else:
max_col = self.Ncmap
# this gives us the bins in the lookup table in the range
# [0, _n_regions - 1] (the offset is set in the init)
iret = np.digitize(xx, self.boundaries) - 1 + self._offset
# if we have more colors than regions, stretch the region
# index computed above to full range of the color bins. This
# will make use of the full range (but skip some of the colors
# in the middle) such that the first region is mapped to the
# first color and the last region is mapped to the last color.
if self.Ncmap > self._n_regions:
if self._n_regions == 1:
# special case the 1 region case, pick the middle color
iret[iret == 0] = (self.Ncmap - 1) // 2
else:
# otherwise linearly remap the values from the region index
# to the color index spaces
iret = (self.Ncmap - 1) / (self._n_regions - 1) * iret
# cast to 16bit integers in all cases
iret = iret.astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = max_col
ret = np.ma.array(iret, mask=mask)
if is_scalar:
ret = int(ret[0]) # assume python scalar
return ret
def inverse(self, value):
"""
Raises
------
ValueError
BoundaryNorm is not invertible, so calling this method will always
raise an error
"""
raise ValueError("BoundaryNorm is not invertible")
class NoNorm(Normalize):
"""
Dummy replacement for `Normalize`, for the case where we want to use
indices directly in a `~matplotlib.cm.ScalarMappable`.
"""
def __call__(self, value, clip=None):
if np.iterable(value):
return np.ma.array(value)
return value
def inverse(self, value):
if np.iterable(value):
return np.ma.array(value)
return value
def rgb_to_hsv(arr):
"""
Convert an array of float RGB values (in the range [0, 1]) to HSV values.
Parameters
----------
arr : (..., 3) array-like
All values must be in the range [0, 1]
Returns
-------
(..., 3) `~numpy.ndarray`
Colors converted to HSV values in range [0, 1]
"""
arr = np.asarray(arr)
# check length of the last dimension, should be _some_ sort of rgb
if arr.shape[-1] != 3:
raise ValueError("Last dimension of input array must be 3; "
f"shape {arr.shape} was found.")
in_shape = arr.shape
arr = np.array(
arr, copy=False,
dtype=np.promote_types(arr.dtype, np.float32), # Don't work on ints.
ndmin=2, # In case input was 1D.
)
out = np.zeros_like(arr)
arr_max = arr.max(-1)
ipos = arr_max > 0
delta = np.ptp(arr, -1)
s = np.zeros_like(delta)
s[ipos] = delta[ipos] / arr_max[ipos]
ipos = delta > 0
# red is max
idx = (arr[..., 0] == arr_max) & ipos
out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
# green is max
idx = (arr[..., 1] == arr_max) & ipos
out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
# blue is max
idx = (arr[..., 2] == arr_max) & ipos
out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]
out[..., 0] = (out[..., 0] / 6.0) % 1.0
out[..., 1] = s
out[..., 2] = arr_max
return out.reshape(in_shape)
def hsv_to_rgb(hsv):
"""
Convert HSV values to RGB.
Parameters
----------
hsv : (..., 3) array-like
All values assumed to be in range [0, 1]
Returns
-------
(..., 3) `~numpy.ndarray`
Colors converted to RGB values in range [0, 1]
"""
hsv = np.asarray(hsv)
# check length of the last dimension, should be _some_ sort of rgb
if hsv.shape[-1] != 3:
raise ValueError("Last dimension of input array must be 3; "
f"shape {hsv.shape} was found.")
in_shape = hsv.shape
hsv = np.array(
hsv, copy=False,
dtype=np.promote_types(hsv.dtype, np.float32), # Don't work on ints.
ndmin=2, # In case input was 1D.
)
h = hsv[..., 0]
s = hsv[..., 1]
v = hsv[..., 2]
r = np.empty_like(h)
g = np.empty_like(h)
b = np.empty_like(h)
i = (h * 6.0).astype(int)
f = (h * 6.0) - i
p = v * (1.0 - s)
q = v * (1.0 - s * f)
t = v * (1.0 - s * (1.0 - f))
idx = i % 6 == 0
r[idx] = v[idx]
g[idx] = t[idx]
b[idx] = p[idx]
idx = i == 1
r[idx] = q[idx]
g[idx] = v[idx]
b[idx] = p[idx]
idx = i == 2
r[idx] = p[idx]
g[idx] = v[idx]
b[idx] = t[idx]
idx = i == 3
r[idx] = p[idx]
g[idx] = q[idx]
b[idx] = v[idx]
idx = i == 4
r[idx] = t[idx]
g[idx] = p[idx]
b[idx] = v[idx]
idx = i == 5
r[idx] = v[idx]
g[idx] = p[idx]
b[idx] = q[idx]
idx = s == 0
r[idx] = v[idx]
g[idx] = v[idx]
b[idx] = v[idx]
rgb = np.stack([r, g, b], axis=-1)
return rgb.reshape(in_shape)
def _vector_magnitude(arr):
# things that don't work here:
# * np.linalg.norm: drops mask from ma.array
# * np.sum: drops mask from ma.array unless entire vector is masked
sum_sq = 0
for i in range(arr.shape[-1]):
sum_sq += arr[..., i, np.newaxis] ** 2
return np.sqrt(sum_sq)
class LightSource:
"""
Create a light source coming from the specified azimuth and elevation.
Angles are in degrees, with the azimuth measured
clockwise from north and elevation up from the zero plane of the surface.
`shade` is used to produce "shaded" RGB values for a data array.
`shade_rgb` can be used to combine an RGB image with an elevation map.
`hillshade` produces an illumination map of a surface.
"""
def __init__(self, azdeg=315, altdeg=45, hsv_min_val=0, hsv_max_val=1,
hsv_min_sat=1, hsv_max_sat=0):
"""
Specify the azimuth (measured clockwise from south) and altitude
(measured up from the plane of the surface) of the light source
in degrees.
Parameters
----------
azdeg : float, default: 315 degrees (from the northwest)
The azimuth (0-360, degrees clockwise from North) of the light
source.
altdeg : float, default: 45 degrees
The altitude (0-90, degrees up from horizontal) of the light
source.
hsv_min_val : number, default: 0
The minimum value ("v" in "hsv") that the *intensity* map can shift the
output image to.
hsv_max_val : number, default: 1
The maximum value ("v" in "hsv") that the *intensity* map can shift the
output image to.
hsv_min_sat : number, default: 1
The minimum saturation value that the *intensity* map can shift the output
image to.
hsv_max_sat : number, default: 0
The maximum saturation value that the *intensity* map can shift the output
image to.
Notes
-----
For backwards compatibility, the parameters *hsv_min_val*,
*hsv_max_val*, *hsv_min_sat*, and *hsv_max_sat* may be supplied at
initialization as well. However, these parameters will only be used if
"blend_mode='hsv'" is passed into `shade` or `shade_rgb`.
See the documentation for `blend_hsv` for more details.
"""
self.azdeg = azdeg
self.altdeg = altdeg
self.hsv_min_val = hsv_min_val
self.hsv_max_val = hsv_max_val
self.hsv_min_sat = hsv_min_sat
self.hsv_max_sat = hsv_max_sat
@property
def direction(self):
"""The unit vector direction towards the light source."""
# Azimuth is in degrees clockwise from North. Convert to radians
# counterclockwise from East (mathematical notation).
az = np.radians(90 - self.azdeg)
alt = np.radians(self.altdeg)
return np.array([
np.cos(az) * np.cos(alt),
np.sin(az) * np.cos(alt),
np.sin(alt)
])
def hillshade(self, elevation, vert_exag=1, dx=1, dy=1, fraction=1.):
"""
Calculate the illumination intensity for a surface using the defined
azimuth and elevation for the light source.
This computes the normal vectors for the surface, and then passes them
on to `shade_normals`
Parameters
----------
elevation : 2D array-like
The height values used to generate an illumination map
vert_exag : number, optional
The amount to exaggerate the elevation values by when calculating
illumination. This can be used either to correct for differences in
units between the x-y coordinate system and the elevation
coordinate system (e.g. decimal degrees vs. meters) or to
exaggerate or de-emphasize topographic effects.
dx : number, optional
The x-spacing (columns) of the input *elevation* grid.
dy : number, optional
The y-spacing (rows) of the input *elevation* grid.
fraction : number, optional
Increases or decreases the contrast of the hillshade. Values
greater than one will cause intermediate values to move closer to
full illumination or shadow (and clipping any values that move
beyond 0 or 1). Note that this is not visually or mathematically
the same as vertical exaggeration.
Returns
-------
`~numpy.ndarray`
A 2D array of illumination values between 0-1, where 0 is
completely in shadow and 1 is completely illuminated.
"""
# Because most image and raster GIS data has the first row in the array
# as the "top" of the image, dy is implicitly negative. This is
# consistent to what `imshow` assumes, as well.
dy = -dy
# compute the normal vectors from the partial derivatives
e_dy, e_dx = np.gradient(vert_exag * elevation, dy, dx)
# .view is to keep subclasses
normal = np.empty(elevation.shape + (3,)).view(type(elevation))
normal[..., 0] = -e_dx
normal[..., 1] = -e_dy
normal[..., 2] = 1
normal /= _vector_magnitude(normal)
return self.shade_normals(normal, fraction)
def shade_normals(self, normals, fraction=1.):
"""
Calculate the illumination intensity for the normal vectors of a
surface using the defined azimuth and elevation for the light source.
Imagine an artificial sun placed at infinity in some azimuth and
elevation position illuminating our surface. The parts of the surface
that slope toward the sun should brighten while those sides facing away
should become darker.
Parameters
----------
fraction : number, optional
Increases or decreases the contrast of the hillshade. Values
greater than one will cause intermediate values to move closer to
full illumination or shadow (and clipping any values that move
beyond 0 or 1). Note that this is not visually or mathematically
the same as vertical exaggeration.
Returns
-------
`~numpy.ndarray`
A 2D array of illumination values between 0-1, where 0 is
completely in shadow and 1 is completely illuminated.
"""
intensity = normals.dot(self.direction)
# Apply contrast stretch
imin, imax = intensity.min(), intensity.max()
intensity *= fraction
# Rescale to 0-1, keeping range before contrast stretch
# If constant slope, keep relative scaling (i.e. flat should be 0.5,
# fully occluded 0, etc.)
if (imax - imin) > 1e-6:
# Strictly speaking, this is incorrect. Negative values should be
# clipped to 0 because they're fully occluded. However, rescaling
# in this manner is consistent with the previous implementation and
# visually appears better than a "hard" clip.
intensity -= imin
intensity /= (imax - imin)
intensity = np.clip(intensity, 0, 1)
return intensity
def shade(self, data, cmap, norm=None, blend_mode='overlay', vmin=None,
vmax=None, vert_exag=1, dx=1, dy=1, fraction=1, **kwargs):
"""
Combine colormapped data values with an illumination intensity map
(a.k.a. "hillshade") of the values.
Parameters
----------
data : 2D array-like
The height values used to generate a shaded map.
cmap : `~matplotlib.colors.Colormap`
The colormap used to color the *data* array. Note that this must be
a `~matplotlib.colors.Colormap` instance. For example, rather than
passing in ``cmap='gist_earth'``, use
``cmap=plt.get_cmap('gist_earth')`` instead.
norm : `~matplotlib.colors.Normalize` instance, optional
The normalization used to scale values before colormapping. If
None, the input will be linearly scaled between its min and max.
blend_mode : {'hsv', 'overlay', 'soft'} or callable, optional
The type of blending used to combine the colormapped data
values with the illumination intensity. Default is
"overlay". Note that for most topographic surfaces,
"overlay" or "soft" appear more visually realistic. If a
user-defined function is supplied, it is expected to
combine an (M, N, 3) RGB array of floats (ranging 0 to 1) with
an (M, N, 1) hillshade array (also 0 to 1). (Call signature
``func(rgb, illum, **kwargs)``) Additional kwargs supplied
to this function will be passed on to the *blend_mode*
function.
vmin : float or None, optional
The minimum value used in colormapping *data*. If *None* the
minimum value in *data* is used. If *norm* is specified, then this
argument will be ignored.
vmax : float or None, optional
The maximum value used in colormapping *data*. If *None* the
maximum value in *data* is used. If *norm* is specified, then this
argument will be ignored.
vert_exag : number, optional
The amount to exaggerate the elevation values by when calculating
illumination. This can be used either to correct for differences in
units between the x-y coordinate system and the elevation
coordinate system (e.g. decimal degrees vs. meters) or to
exaggerate or de-emphasize topography.
dx : number, optional
The x-spacing (columns) of the input *elevation* grid.
dy : number, optional
The y-spacing (rows) of the input *elevation* grid.
fraction : number, optional
Increases or decreases the contrast of the hillshade. Values
greater than one will cause intermediate values to move closer to
full illumination or shadow (and clipping any values that move
beyond 0 or 1). Note that this is not visually or mathematically
the same as vertical exaggeration.
**kwargs
Additional kwargs are passed on to the *blend_mode* function.
Returns
-------
`~numpy.ndarray`
An (M, N, 4) array of floats ranging between 0-1.
"""
if vmin is None:
vmin = data.min()
if vmax is None:
vmax = data.max()
if norm is None:
norm = Normalize(vmin=vmin, vmax=vmax)
rgb0 = cmap(norm(data))
rgb1 = self.shade_rgb(rgb0, elevation=data, blend_mode=blend_mode,
vert_exag=vert_exag, dx=dx, dy=dy,
fraction=fraction, **kwargs)
# Don't overwrite the alpha channel, if present.
rgb0[..., :3] = rgb1[..., :3]
return rgb0
def shade_rgb(self, rgb, elevation, fraction=1., blend_mode='hsv',
vert_exag=1, dx=1, dy=1, **kwargs):
"""
Use this light source to adjust the colors of the *rgb* input array to
give the impression of a shaded relief map with the given *elevation*.
Parameters
----------
rgb : array-like
An (M, N, 3) RGB array, assumed to be in the range of 0 to 1.
elevation : array-like
An (M, N) array of the height values used to generate a shaded map.
fraction : number
Increases or decreases the contrast of the hillshade. Values
greater than one will cause intermediate values to move closer to
full illumination or shadow (and clipping any values that move
beyond 0 or 1). Note that this is not visually or mathematically
the same as vertical exaggeration.
blend_mode : {'hsv', 'overlay', 'soft'} or callable, optional
The type of blending used to combine the colormapped data values
with the illumination intensity. For backwards compatibility, this
defaults to "hsv". Note that for most topographic surfaces,
"overlay" or "soft" appear more visually realistic. If a
user-defined function is supplied, it is expected to combine an
(M, N, 3) RGB array of floats (ranging 0 to 1) with an (M, N, 1)
hillshade array (also 0 to 1). (Call signature
``func(rgb, illum, **kwargs)``)
Additional kwargs supplied to this function will be passed on to
the *blend_mode* function.
vert_exag : number, optional
The amount to exaggerate the elevation values by when calculating
illumination. This can be used either to correct for differences in
units between the x-y coordinate system and the elevation
coordinate system (e.g. decimal degrees vs. meters) or to
exaggerate or de-emphasize topography.
dx : number, optional
The x-spacing (columns) of the input *elevation* grid.
dy : number, optional
The y-spacing (rows) of the input *elevation* grid.
**kwargs
Additional kwargs are passed on to the *blend_mode* function.
Returns
-------
`~numpy.ndarray`
An (m, n, 3) array of floats ranging between 0-1.
"""
# Calculate the "hillshade" intensity.
intensity = self.hillshade(elevation, vert_exag, dx, dy, fraction)
intensity = intensity[..., np.newaxis]
# Blend the hillshade and rgb data using the specified mode
lookup = {
'hsv': self.blend_hsv,
'soft': self.blend_soft_light,
'overlay': self.blend_overlay,
}
if blend_mode in lookup:
blend = lookup[blend_mode](rgb, intensity, **kwargs)
else:
try:
blend = blend_mode(rgb, intensity, **kwargs)
except TypeError as err:
raise ValueError('"blend_mode" must be callable or one of '
f'{lookup.keys}') from err
# Only apply result where hillshade intensity isn't masked
if np.ma.is_masked(intensity):
mask = intensity.mask[..., 0]
for i in range(3):
blend[..., i][mask] = rgb[..., i][mask]
return blend
def blend_hsv(self, rgb, intensity, hsv_max_sat=None, hsv_max_val=None,
hsv_min_val=None, hsv_min_sat=None):
"""
Take the input data array, convert to HSV values in the given colormap,
then adjust those color values to give the impression of a shaded
relief map with a specified light source. RGBA values are returned,
which can then be used to plot the shaded image with imshow.
The color of the resulting image will be darkened by moving the (s, v)
values (in HSV colorspace) toward (hsv_min_sat, hsv_min_val) in the
shaded regions, or lightened by sliding (s, v) toward (hsv_max_sat,
hsv_max_val) in regions that are illuminated. The default extremes are
chose so that completely shaded points are nearly black (s = 1, v = 0)
and completely illuminated points are nearly white (s = 0, v = 1).
Parameters
----------
rgb : `~numpy.ndarray`
An (M, N, 3) RGB array of floats ranging from 0 to 1 (color image).
intensity : `~numpy.ndarray`
An (M, N, 1) array of floats ranging from 0 to 1 (grayscale image).
hsv_max_sat : number, optional
The maximum saturation value that the *intensity* map can shift the output
image to. If not provided, use the value provided upon initialization.
hsv_min_sat : number, optional
The minimum saturation value that the *intensity* map can shift the output
image to. If not provided, use the value provided upon initialization.
hsv_max_val : number, optional
The maximum value ("v" in "hsv") that the *intensity* map can shift the
output image to. If not provided, use the value provided upon
initialization.
hsv_min_val : number, optional
The minimum value ("v" in "hsv") that the *intensity* map can shift the
output image to. If not provided, use the value provided upon
initialization.
Returns
-------
`~numpy.ndarray`
An (M, N, 3) RGB array representing the combined images.
"""
# Backward compatibility...
if hsv_max_sat is None:
hsv_max_sat = self.hsv_max_sat
if hsv_max_val is None:
hsv_max_val = self.hsv_max_val
if hsv_min_sat is None:
hsv_min_sat = self.hsv_min_sat
if hsv_min_val is None:
hsv_min_val = self.hsv_min_val
# Expects a 2D intensity array scaled between -1 to 1...
intensity = intensity[..., 0]
intensity = 2 * intensity - 1
# Convert to rgb, then rgb to hsv
hsv = rgb_to_hsv(rgb[:, :, 0:3])
hue, sat, val = np.moveaxis(hsv, -1, 0)
# Modify hsv values (in place) to simulate illumination.
# putmask(A, mask, B) <=> A[mask] = B[mask]
np.putmask(sat, (np.abs(sat) > 1.e-10) & (intensity > 0),
(1 - intensity) * sat + intensity * hsv_max_sat)
np.putmask(sat, (np.abs(sat) > 1.e-10) & (intensity < 0),
(1 + intensity) * sat - intensity * hsv_min_sat)
np.putmask(val, intensity > 0,
(1 - intensity) * val + intensity * hsv_max_val)
np.putmask(val, intensity < 0,
(1 + intensity) * val - intensity * hsv_min_val)
np.clip(hsv[:, :, 1:], 0, 1, out=hsv[:, :, 1:])
# Convert modified hsv back to rgb.
return hsv_to_rgb(hsv)
def blend_soft_light(self, rgb, intensity):
"""
Combine an RGB image with an intensity map using "soft light" blending,
using the "pegtop" formula.
Parameters
----------
rgb : `~numpy.ndarray`
An (M, N, 3) RGB array of floats ranging from 0 to 1 (color image).
intensity : `~numpy.ndarray`
An (M, N, 1) array of floats ranging from 0 to 1 (grayscale image).
Returns
-------
`~numpy.ndarray`
An (M, N, 3) RGB array representing the combined images.
"""
return 2 * intensity * rgb + (1 - 2 * intensity) * rgb**2
def blend_overlay(self, rgb, intensity):
"""
Combine an RGB image with an intensity map using "overlay" blending.
Parameters
----------
rgb : `~numpy.ndarray`
An (M, N, 3) RGB array of floats ranging from 0 to 1 (color image).
intensity : `~numpy.ndarray`
An (M, N, 1) array of floats ranging from 0 to 1 (grayscale image).
Returns
-------
ndarray
An (M, N, 3) RGB array representing the combined images.
"""
low = 2 * intensity * rgb
high = 1 - 2 * (1 - intensity) * (1 - rgb)
return np.where(rgb <= 0.5, low, high)
def from_levels_and_colors(levels, colors, extend='neither'):
"""
A helper routine to generate a cmap and a norm instance which
behave similar to contourf's levels and colors arguments.
Parameters
----------
levels : sequence of numbers
The quantization levels used to construct the `BoundaryNorm`.
Value ``v`` is quantized to level ``i`` if ``lev[i] <= v < lev[i+1]``.
colors : sequence of colors
The fill color to use for each level. If *extend* is "neither" there
must be ``n_level - 1`` colors. For an *extend* of "min" or "max" add
one extra color, and for an *extend* of "both" add two colors.
extend : {'neither', 'min', 'max', 'both'}, optional
The behaviour when a value falls out of range of the given levels.
See `~.Axes.contourf` for details.
Returns
-------
cmap : `~matplotlib.colors.Colormap`
norm : `~matplotlib.colors.Normalize`
"""
slice_map = {
'both': slice(1, -1),
'min': slice(1, None),
'max': slice(0, -1),
'neither': slice(0, None),
}
_api.check_in_list(slice_map, extend=extend)
color_slice = slice_map[extend]
n_data_colors = len(levels) - 1
n_expected = n_data_colors + color_slice.start - (color_slice.stop or 0)
if len(colors) != n_expected:
raise ValueError(
f'With extend == {extend!r} and {len(levels)} levels, '
f'expected {n_expected} colors, but got {len(colors)}')
cmap = ListedColormap(colors[color_slice], N=n_data_colors)
if extend in ['min', 'both']:
cmap.set_under(colors[0])
else:
cmap.set_under('none')
if extend in ['max', 'both']:
cmap.set_over(colors[-1])
else:
cmap.set_over('none')
cmap.colorbar_extend = extend
norm = BoundaryNorm(levels, ncolors=n_data_colors)
return cmap, norm
