/usr/lib/python3/dist-packages/matplotlib/colors.py is in python3-matplotlib 1.3.1-1ubuntu5.
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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. Colormapping typically involves two steps: a data array is first
mapped onto the range 0-1 using an instance of :class:`Normalize` or of a
subclass; then this number in the 0-1 range is mapped to a color using an
instance of a subclass of :class:`Colormap`. Two are provided here:
:class:`LinearSegmentedColormap`, which is used to generate all the built-in
colormap instances, but is also useful for making custom colormaps, and
:class:`ListedColormap`, which is used for generating a custom colormap from a
list of color specifications.
The module also provides a single instance, *colorConverter*, of the
:class:`ColorConverter` class providing methods for converting single color
specifications or sequences of them to *RGB* or *RGBA*.
Commands which take color arguments can use several formats to specify
the colors. For the basic built-in colors, you can use a single letter
- b: blue
- g: green
- r: red
- c: cyan
- m: magenta
- y: yellow
- k: black
- w: white
Gray shades can be given as a string encoding a float in the 0-1 range, e.g.::
color = '0.75'
For a greater range of colors, you have two options. You can specify the
color using an html hex string, as in::
color = '#eeefff'
or you can pass an *R* , *G* , *B* tuple, where each of *R* , *G* , *B* are in
the range [0,1].
Finally, legal html names for colors, like 'red', 'burlywood' and 'chartreuse'
are supported.
"""
import re
import numpy as np
from numpy import ma
import matplotlib.cbook as cbook
import collections
parts = np.__version__.split('.')
NP_MAJOR, NP_MINOR = list(map(int, parts[:2]))
# true if clip supports the out kwarg
NP_CLIP_OUT = NP_MAJOR >= 1 and NP_MINOR >= 2
cnames = {
'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgreen': '#90EE90',
'lightgray': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#FAA460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}
# add british equivs
for k, v in list(cnames.items()):
if k.find('gray') >= 0:
k = k.replace('gray', 'grey')
cnames[k] = v
def is_color_like(c):
'Return *True* if *c* can be converted to *RGB*'
try:
colorConverter.to_rgb(c)
return True
except ValueError:
return False
def rgb2hex(rgb):
'Given an rgb or rgba sequence of 0-1 floats, return the hex string'
return '#%02x%02x%02x' % tuple([np.round(val * 255) for val in rgb[:3]])
hexColorPattern = re.compile("\A#[a-fA-F0-9]{6}\Z")
def hex2color(s):
"""
Take a hex string *s* and return the corresponding rgb 3-tuple
Example: #efefef -> (0.93725, 0.93725, 0.93725)
"""
if not isinstance(s, str):
raise TypeError('hex2color requires a string argument')
if hexColorPattern.match(s) is None:
raise ValueError('invalid hex color string "%s"' % s)
return tuple([int(n, 16) / 255.0 for n in (s[1:3], s[3:5], s[5:7])])
class ColorConverter(object):
"""
Provides methods for converting color specifications to *RGB* or *RGBA*
Caching is used for more efficient conversion upon repeated calls
with the same argument.
Ordinarily only the single instance instantiated in this module,
*colorConverter*, is needed.
"""
colors = {
'b': (0.0, 0.0, 1.0),
'g': (0.0, 0.5, 0.0),
'r': (1.0, 0.0, 0.0),
'c': (0.0, 0.75, 0.75),
'm': (0.75, 0, 0.75),
'y': (0.75, 0.75, 0),
'k': (0.0, 0.0, 0.0),
'w': (1.0, 1.0, 1.0), }
cache = {}
def to_rgb(self, arg):
"""
Returns an *RGB* tuple of three floats from 0-1.
*arg* can be an *RGB* or *RGBA* sequence or a string in any of
several forms:
1) a letter from the set 'rgbcmykw'
2) a hex color string, like '#00FFFF'
3) a standard name, like 'aqua'
4) a float, like '0.4', indicating gray on a 0-1 scale
if *arg* is *RGBA*, the *A* will simply be discarded.
"""
try:
return self.cache[arg]
except KeyError:
pass
except TypeError: # could be unhashable rgb seq
arg = tuple(arg)
try:
return self.cache[arg]
except KeyError:
pass
except TypeError:
raise ValueError(
'to_rgb: arg "%s" is unhashable even inside a tuple'
% (str(arg),))
try:
if cbook.is_string_like(arg):
argl = arg.lower()
color = self.colors.get(argl, None)
if color is None:
str1 = cnames.get(argl, argl)
if str1.startswith('#'):
color = hex2color(str1)
else:
fl = float(argl)
if fl < 0 or fl > 1:
raise ValueError(
'gray (string) must be in range 0-1')
color = tuple([fl] * 3)
elif cbook.iterable(arg):
if len(arg) > 4 or len(arg) < 3:
raise ValueError(
'sequence length is %d; must be 3 or 4' % len(arg))
color = tuple(arg[:3])
if [x for x in color if (float(x) < 0) or (x > 1)]:
# This will raise TypeError if x is not a number.
raise ValueError(
'number in rbg sequence outside 0-1 range')
else:
raise ValueError(
'cannot convert argument to rgb sequence')
self.cache[arg] = color
except (KeyError, ValueError, TypeError) as exc:
raise ValueError(
'to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc))
# Error messages could be improved by handling TypeError
# separately; but this should be rare and not too hard
# for the user to figure out as-is.
return color
def to_rgba(self, arg, alpha=None):
"""
Returns an *RGBA* tuple of four floats from 0-1.
For acceptable values of *arg*, see :meth:`to_rgb`.
In addition, if *arg* is "none" (case-insensitive),
then (0,0,0,0) will be returned.
If *arg* is an *RGBA* sequence and *alpha* is not *None*,
*alpha* will replace the original *A*.
"""
try:
if arg.lower() == 'none':
return (0.0, 0.0, 0.0, 0.0)
except AttributeError:
pass
try:
if not cbook.is_string_like(arg) and cbook.iterable(arg):
if len(arg) == 4:
if [x for x in arg if (float(x) < 0) or (x > 1)]:
# This will raise TypeError if x is not a number.
raise ValueError(
'number in rbga sequence outside 0-1 range')
if alpha is None:
return tuple(arg)
if alpha < 0.0 or alpha > 1.0:
raise ValueError("alpha must be in range 0-1")
return arg[0], arg[1], arg[2], alpha
r, g, b = arg[:3]
if [x for x in (r, g, b) if (float(x) < 0) or (x > 1)]:
raise ValueError(
'number in rbg sequence outside 0-1 range')
else:
r, g, b = self.to_rgb(arg)
if alpha is None:
alpha = 1.0
return r, g, b, alpha
except (TypeError, ValueError) as exc:
raise ValueError(
'to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
def to_rgba_array(self, c, alpha=None):
"""
Returns a numpy array of *RGBA* tuples.
Accepts a single mpl color spec or a sequence of specs.
Special case to handle "no color": if *c* is "none" (case-insensitive),
then an empty array will be returned. Same for an empty list.
"""
try:
nc = len(c)
except TypeError:
raise ValueError(
"Cannot convert argument type %s to rgba array" % type(c))
try:
if nc == 0 or c.lower() == 'none':
return np.zeros((0, 4), dtype=np.float)
except AttributeError:
pass
try:
# Single value? Put it in an array with a single row.
return np.array([self.to_rgba(c, alpha)], dtype=np.float)
except ValueError:
if isinstance(c, np.ndarray):
if c.ndim != 2 and c.dtype.kind not in 'SU':
raise ValueError("Color array must be two-dimensional")
if (c.ndim == 2 and c.shape[1] == 4 and c.dtype.kind == 'f'):
if (c.ravel() > 1).any() or (c.ravel() < 0).any():
raise ValueError(
"number in rgba sequence is outside 0-1 range")
result = np.asarray(c, np.float)
if alpha is not None:
if alpha > 1 or alpha < 0:
raise ValueError("alpha must be in 0-1 range")
result[:, 3] = alpha
return result
# This alpha operation above is new, and depends
# on higher levels to refrain from setting alpha
# to values other than None unless there is
# intent to override any existing alpha values.
# It must be some other sequence of color specs.
result = np.zeros((nc, 4), dtype=np.float)
for i, cc in enumerate(c):
result[i] = self.to_rgba(cc, alpha)
return result
colorConverter = ColorConverter()
def makeMappingArray(N, data, gamma=1.0):
"""Create an *N* -element 1-d lookup table
*data* represented by a list of x,y0,y1 mapping correspondences.
Each element in this list represents how a value between 0 and 1
(inclusive) represented by x is mapped to a corresponding value
between 0 and 1 (inclusive). The two values of y are to allow
for discontinuous mapping functions (say as might be found in a
sawtooth) where y0 represents the value of y for values of x
<= to that given, and y1 is the value to be used for x > than
that given). The list must start with x=0, end with x=1, and
all values of x must be in increasing order. Values between
the given mapping points are determined by simple linear interpolation.
Alternatively, data can be a function mapping values between 0 - 1
to 0 - 1.
The function returns an array "result" where ``result[x*(N-1)]``
gives the closest value for values of x between 0 and 1.
"""
if isinstance(data, collections.Callable):
xind = np.linspace(0, 1, N) ** gamma
lut = np.clip(np.array(data(xind), dtype=np.float), 0, 1)
return lut
try:
adata = np.array(data)
except:
raise TypeError("data must be convertable to an array")
shape = adata.shape
if len(shape) != 2 and shape[1] != 3:
raise ValueError("data must be nx3 format")
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.sometrue(np.sort(x) - x):
raise ValueError(
"data mapping points must have x in increasing order")
# begin generation of lookup table
x = x * (N - 1)
lut = np.zeros((N,), np.float)
xind = (N - 1) * np.linspace(0, 1, N) ** gamma
ind = np.searchsorted(x, xind)[1:-1]
lut[1:-1] = (((xind[1:-1] - x[ind - 1]) / (x[ind] - x[ind - 1])) *
(y0[ind] - y1[ind - 1]) + y1[ind - 1])
lut[0] = y1[0]
lut[-1] = y0[-1]
# ensure that the lut is confined to values between 0 and 1 by clipping it
np.clip(lut, 0.0, 1.0)
#lut = where(lut > 1., 1., lut)
#lut = where(lut < 0., 0., lut)
return lut
class Colormap(object):
"""
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
:class:`matplotlib.colors.Normalize`. It is worth noting that
:class:`matplotlib.cm.ScalarMappable` subclasses make heavy use of this
``data->normalize->map-to-color`` processing chain.
"""
def __init__(self, name, N=256):
r"""
Parameters
----------
name : str
The name of the colormap.
N : int
The number of rgb quantization levels.
"""
self.name = name
self.N = N
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 = N
self._i_over = N + 1
self._i_bad = 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
#: :class:`matplotlib.colorbar.Colorbar` constructor.
self.colorbar_extend = False
def __call__(self, X, alpha=None, bytes=False):
"""
Parameters
----------
X : scalar, ndarray
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, None
Alpha must be a scalar between 0 and 1, or None.
bytes : bool
If False (default), the returned RGBA values will be floats in the
interval ``[0, 1]`` otherwise they will be uint8s in the interval
``[0, 255]``.
Returns
-------
Tuple of RGBA values if X is scalar, othewise an array of
RGBA values with a shape of ``X.shape + (4, )``.
"""
# See class docstring for arg/kwarg documentation.
if not self._isinit:
self._init()
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
xa = np.array([X])
else:
vtype = 'array'
xma = ma.array(X, copy=True) # Copy here to avoid side effects.
mask_bad = xma.mask # Mask will be used below.
xa = xma.filled() # Fill to avoid infs, etc.
del xma
# Calculations with native byteorder are faster, and avoid a
# bug that otherwise can occur with putmask when the last
# argument is a numpy scalar.
if not xa.dtype.isnative:
xa = xa.byteswap().newbyteorder()
if xa.dtype.kind == "f":
# Treat 1.0 as slightly less than 1.
vals = np.array([1, 0], dtype=xa.dtype)
almost_one = np.nextafter(*vals)
cbook._putmask(xa, xa == 1.0, almost_one)
# The following clip is fast, and prevents possible
# conversion of large positive values to negative integers.
xa *= self.N
if NP_CLIP_OUT:
np.clip(xa, -1, self.N, out=xa)
else:
xa = np.clip(xa, -1, self.N)
# ensure that all 'under' values will still have negative
# value after casting to int
cbook._putmask(xa, xa < 0.0, -1)
xa = xa.astype(int)
# Set the over-range indices before the under-range;
# otherwise the under-range values get converted to over-range.
cbook._putmask(xa, xa > self.N - 1, self._i_over)
cbook._putmask(xa, xa < 0, self._i_under)
if mask_bad is not None:
if mask_bad.shape == xa.shape:
cbook._putmask(xa, mask_bad, self._i_bad)
elif mask_bad:
xa.fill(self._i_bad)
if bytes:
lut = (self._lut * 255).astype(np.uint8)
else:
lut = self._lut.copy() # Don't let alpha modify original _lut.
if alpha is not None:
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
if bytes:
alpha = int(alpha * 255)
if (lut[-1] == 0).all():
lut[:-1, -1] = alpha
# All zeros is taken as a flag for the default bad
# color, which is no color--fully transparent. We
# don't want to override this.
else:
lut[:, -1] = alpha
# If the bad value is set to have a color, then we
# override its alpha just as for any other value.
rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)
lut.take(xa, axis=0, mode='clip', out=rgba)
# twice as fast as lut[xa];
# using the clip or wrap mode and providing an
# output array speeds it up a little more.
if vtype == 'scalar':
rgba = tuple(rgba[0, :])
return rgba
def set_bad(self, color='k', alpha=None):
'''Set color to be used for masked values.
'''
self._rgba_bad = colorConverter.to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
def set_under(self, color='k', alpha=None):
'''Set color to be used for low out-of-range values.
Requires norm.clip = False
'''
self._rgba_under = colorConverter.to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
def set_over(self, color='k', alpha=None):
'''Set color to be used for high out-of-range values.
Requires norm.clip = False
'''
self._rgba_over = colorConverter.to_rgba(color, alpha)
if self._isinit:
self._set_extremes()
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")
def is_gray(self):
if not self._isinit:
self._init()
return (np.alltrue(self._lut[:, 0] == self._lut[:, 1]) and
np.alltrue(self._lut[:, 0] == self._lut[:, 2]))
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 color map 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.
.. seealso::
:meth:`LinearSegmentedColormap.from_list`
Static method; factory function for generating a
smoothly-varying LinearSegmentedColormap.
:func:`makeMappingArray`
For information about making a mapping array.
"""
# True only if all colors in map are identical; needed for contouring.
self.monochrome = False
Colormap.__init__(self, name, N)
self._segmentdata = segmentdata
self._gamma = gamma
def _init(self):
self._lut = np.ones((self.N + 3, 4), np.float)
self._lut[:-3, 0] = makeMappingArray(
self.N, self._segmentdata['red'], self._gamma)
self._lut[:-3, 1] = makeMappingArray(
self.N, self._segmentdata['green'], self._gamma)
self._lut[:-3, 2] = makeMappingArray(
self.N, self._segmentdata['blue'], self._gamma)
if 'alpha' in self._segmentdata:
self._lut[:-3, 3] = makeMappingArray(
self.N, self._segmentdata['alpha'], 1)
self._isinit = True
self._set_extremes()
def set_gamma(self, gamma):
"""
Set a new gamma value and regenerate color map.
"""
self._gamma = gamma
self._init()
@staticmethod
def from_list(name, colors, N=256, gamma=1.0):
"""
Make a linear segmented colormap with *name* from a sequence
of *colors* which evenly transitions from colors[0] at val=0
to colors[-1] at val=1. *N* is the number of rgb quantization
levels.
Alternatively, a list of (value, color) tuples can be given
to divide the range unevenly.
"""
if not cbook.iterable(colors):
raise ValueError('colors must be iterable')
if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
not cbook.is_string_like(colors[0]):
# List of value, color pairs
vals, colors = list(zip(*colors))
else:
vals = np.linspace(0., 1., len(colors))
cdict = dict(red=[], green=[], blue=[], alpha=[])
for val, color in zip(vals, colors):
r, g, b, a = colorConverter.to_rgba(color)
cdict['red'].append((val, r, r))
cdict['green'].append((val, g, g))
cdict['blue'].append((val, b, b))
cdict['alpha'].append((val, a, a))
return LinearSegmentedColormap(name, cdict, N, gamma)
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.
"""
def __init__(self, colors, name='from_list', N=None):
"""
Make a colormap from a list of colors.
*colors*
a list of matplotlib color specifications,
or an equivalent Nx3 or Nx4 floating point array
(*N* rgb or rgba values)
*name*
a string to identify the colormap
*N*
the 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.
"""
self.colors = colors
self.monochrome = False # True only if all colors in map are
# identical; needed for contouring.
if N is None:
N = len(self.colors)
else:
if cbook.is_string_like(self.colors):
self.colors = [self.colors] * N
self.monochrome = True
elif cbook.iterable(self.colors):
self.colors = list(self.colors) # in case it was a tuple
if len(self.colors) == 1:
self.monochrome = True
if len(self.colors) < N:
self.colors = list(self.colors) * N
del(self.colors[N:])
else:
try:
gray = float(self.colors)
except TypeError:
pass
else:
self.colors = [gray] * N
self.monochrome = True
Colormap.__init__(self, name, N)
def _init(self):
rgba = colorConverter.to_rgba_array(self.colors)
self._lut = np.zeros((self.N + 3, 4), np.float)
self._lut[:-3] = rgba
self._isinit = True
self._set_extremes()
class Normalize(object):
"""
A class which, when called, can normalize data into
the ``[0.0, 1.0]`` interval.
"""
def __init__(self, vmin=None, vmax=None, clip=False):
"""
If *vmin* or *vmax* is not given, they are taken from the input's
minimum and maximum value respectively. If *clip* is *True* and
the given value falls outside the range, the returned value
will be 0 or 1, whichever is closer. Returns 0 if::
vmin==vmax
Works with scalars or arrays, including masked arrays. If
*clip* is *True*, masked values are set to 1; otherwise they
remain masked. Clipping silently defeats the purpose of setting
the over, under, and masked colors in the colormap, so it is
likely to lead to surprises; therefore the default is
*clip* = *False*.
"""
self.vmin = vmin
self.vmax = vmax
self.clip = clip
@staticmethod
def process_value(value):
"""
Homogenize the input *value* for easy and efficient normalization.
*value* can be a scalar or sequence.
Returns *result*, *is_scalar*, where *result* is a
masked array matching *value*. Float dtypes are preserved;
integer types with two bytes or smaller are converted to
np.float32, and larger types are converted to np.float.
Preserving float32 when possible, and using in-place operations,
can greatly improve speed for large arrays.
Experimental; we may want to add an option to force the
use of float32.
"""
if cbook.iterable(value):
is_scalar = False
result = ma.asarray(value)
if result.dtype.kind == 'f':
if isinstance(value, np.ndarray):
result = result.copy()
elif result.dtype.itemsize > 2:
result = result.astype(np.float)
else:
result = result.astype(np.float32)
else:
is_scalar = True
result = ma.array([value]).astype(np.float)
return result, is_scalar
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
self.autoscale_None(result)
vmin, vmax = self.vmin, 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:
vmin = float(vmin)
vmax = float(vmax)
if clip:
mask = ma.getmask(result)
result = 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):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin = float(self.vmin)
vmax = float(self.vmax)
if cbook.iterable(value):
val = 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*.
'''
self.vmin = ma.min(A)
self.vmax = ma.max(A)
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None and np.size(A) > 0:
self.vmin = ma.min(A)
if self.vmax is None and np.size(A) > 0:
self.vmax = ma.max(A)
def scaled(self):
'return true if vmin and vmax set'
return (self.vmin is not None and self.vmax is not None)
class LogNorm(Normalize):
"""
Normalize a given value to the 0-1 range on a log scale
"""
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
result = ma.masked_less_equal(result, 0, copy=False)
self.autoscale_None(result)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin <= 0:
raise ValueError("values must all be positive")
elif vmin == vmax:
result.fill(0)
else:
if clip:
mask = ma.getmask(result)
result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
mask=mask)
# in-place equivalent of above can be much faster
resdat = result.data
mask = result.mask
if mask is np.ma.nomask:
mask = (resdat <= 0)
else:
mask |= resdat <= 0
cbook._putmask(resdat, mask, 1)
np.log(resdat, resdat)
resdat -= np.log(vmin)
resdat /= (np.log(vmax) - np.log(vmin))
result = np.ma.array(resdat, mask=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")
vmin, vmax = self.vmin, self.vmax
if cbook.iterable(value):
val = ma.asarray(value)
return vmin * ma.power((vmax / vmin), val)
else:
return vmin * pow((vmax / vmin), value)
def autoscale(self, A):
'''
Set *vmin*, *vmax* to min, max of *A*.
'''
A = ma.masked_less_equal(A, 0, copy=False)
self.vmin = ma.min(A)
self.vmax = ma.max(A)
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is not None and self.vmax is not None:
return
A = ma.masked_less_equal(A, 0, copy=False)
if self.vmin is None:
self.vmin = ma.min(A)
if self.vmax is None:
self.vmax = ma.max(A)
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*).
"""
def __init__(self, linthresh, linscale=1.0,
vmin=None, vmax=None, clip=False):
"""
*linthresh*:
The range within which the plot is linear (to
avoid having the plot go to infinity around zero).
*linscale*:
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. Defaults to 1.
"""
Normalize.__init__(self, vmin, vmax, clip)
self.linthresh = linthresh
self._linscale_adj = (linscale / (1.0 - np.e ** -1))
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
self.autoscale_None(result)
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 = ma.getmask(result)
result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
mask=mask)
# in-place equivalent of above can be much faster
resdat = self._transform(result.data)
resdat -= self._lower
resdat /= (self._upper - self._lower)
if is_scalar:
result = result[0]
return result
def _transform(self, a):
"""
Inplace transformation.
"""
masked = np.abs(a) > self.linthresh
sign = np.sign(a[masked])
log = (self._linscale_adj + np.log(np.abs(a[masked]) / self.linthresh))
log *= sign * self.linthresh
a[masked] = log
a[~masked] *= self._linscale_adj
return a
def _inv_transform(self, a):
"""
Inverse inplace Transformation.
"""
masked = np.abs(a) > (self.linthresh * self._linscale_adj)
sign = np.sign(a[masked])
exp = np.exp(sign * a[masked] / self.linthresh - self._linscale_adj)
exp *= sign * self.linthresh
a[masked] = exp
a[~masked] /= self._linscale_adj
return a
def _transform_vmin_vmax(self):
"""
Calculates vmin and vmax in the transformed system.
"""
vmin, vmax = self.vmin, self.vmax
arr = np.array([vmax, vmin])
self._upper, self._lower = self._transform(arr)
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
val = ma.asarray(value)
val = val * (self._upper - self._lower) + self._lower
return self._inv_transform(val)
def autoscale(self, A):
"""
Set *vmin*, *vmax* to min, max of *A*.
"""
self.vmin = ma.min(A)
self.vmax = ma.max(A)
self._transform_vmin_vmax()
def autoscale_None(self, A):
""" autoscale only None-valued vmin or vmax """
if self.vmin is not None and self.vmax is not None:
pass
if self.vmin is None:
self.vmin = ma.min(A)
if self.vmax is None:
self.vmax = ma.max(A)
self._transform_vmin_vmax()
class BoundaryNorm(Normalize):
'''
Generate a colormap index based on discrete intervals.
Unlike :class:`Normalize` or :class:`LogNorm`,
:class:`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 integer and floating point.
'''
def __init__(self, boundaries, ncolors, clip=False):
'''
*boundaries*
a monotonically increasing sequence
*ncolors*
number of colors in the colormap to be used
If::
b[i] <= v < b[i+1]
then v is mapped to color j;
as i varies from 0 to len(boundaries)-2,
j goes from 0 to ncolors-1.
Out-of-range values are mapped to -1 if low and ncolors
if high; these are converted to valid indices by
:meth:`Colormap.__call__` .
'''
self.clip = clip
self.vmin = boundaries[0]
self.vmax = boundaries[-1]
self.boundaries = np.asarray(boundaries)
self.N = len(self.boundaries)
self.Ncmap = ncolors
if self.N - 1 == self.Ncmap:
self._interp = False
else:
self._interp = True
def __call__(self, x, clip=None):
if clip is None:
clip = self.clip
x = ma.asarray(x)
mask = ma.getmaskarray(x)
xx = x.filled(self.vmax + 1)
if clip:
np.clip(xx, self.vmin, self.vmax)
iret = np.zeros(x.shape, dtype=np.int16)
for i, b in enumerate(self.boundaries):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self.N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
ret = ma.array(iret, mask=mask)
if ret.shape == () and not mask:
ret = int(ret) # assume python scalar
return ret
def inverse(self, value):
return ValueError("BoundaryNorm is not invertible")
class NoNorm(Normalize):
'''
Dummy replacement for Normalize, for the case where we
want to use indices directly in a
:class:`~matplotlib.cm.ScalarMappable` .
'''
def __call__(self, value, clip=None):
return value
def inverse(self, value):
return value
# compatibility with earlier class names that violated convention:
normalize = cbook.deprecated('1.3', alternative='Normalize',
name='normalize',
obj_type='class alias')(Normalize)
no_norm = cbook.deprecated('1.3', alternative='NoNorm',
name='no_norm',
obj_type='class alias')(NoNorm)
def rgb_to_hsv(arr):
"""
convert rgb values in a numpy array to hsv values
input and output arrays should have shape (M,N,3)
"""
out = np.zeros(arr.shape, dtype=np.float)
arr_max = arr.max(-1)
ipos = arr_max > 0
delta = arr.ptp(-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
def hsv_to_rgb(hsv):
"""
convert hsv values in a numpy array to rgb values
both input and output arrays have shape (M,N,3)
"""
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(np.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.empty_like(hsv)
rgb[:, :, 0] = r
rgb[:, :, 1] = g
rgb[:, :, 2] = b
return rgb
class LightSource(object):
"""
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.
The :meth:`shade` is used to produce rgb values for a shaded relief image
given a data array.
"""
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.
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).
"""
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
def shade(self, data, cmap):
"""
Take the input data array, convert to HSV values in the
given colormap, then adjust those color values
to given 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.
"""
rgb0 = cmap((data - data.min()) / (data.max() - data.min()))
rgb1 = self.shade_rgb(rgb0, elevation=data)
rgb0[:, :, 0:3] = rgb1
return rgb0
def shade_rgb(self, rgb, elevation, fraction=1.):
"""
Take the input RGB array (ny*nx*3) adjust their color values
to given the impression of a shaded relief map with a
specified light source using the elevation (ny*nx).
A new RGB array ((ny*nx*3)) is returned.
"""
# 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. convert alt, az to radians
az = self.azdeg * np.pi / 180.0
alt = self.altdeg * np.pi / 180.0
# gradient in x and y directions
dx, dy = np.gradient(elevation)
slope = 0.5 * np.pi - np.arctan(np.hypot(dx, dy))
aspect = np.arctan2(dx, dy)
intensity = (np.sin(alt) * np.sin(slope) + np.cos(alt) *
np.cos(slope) * np.cos(-az - aspect - 0.5 * np.pi))
# rescale to interval -1,1
# +1 means maximum sun exposure and -1 means complete shade.
intensity = (intensity - intensity.min()) / \
(intensity.max() - intensity.min())
intensity = (2. * intensity - 1.) * fraction
# convert to rgb, then rgb to hsv
#rgb = cmap((data-data.min())/(data.max()-data.min()))
hsv = rgb_to_hsv(rgb[:, :, 0:3])
# modify hsv values to simulate illumination.
hsv[:, :, 1] = np.where(np.logical_and(np.abs(hsv[:, :, 1]) > 1.e-10,
intensity > 0),
((1. - intensity) * hsv[:, :, 1] +
intensity * self.hsv_max_sat),
hsv[:, :, 1])
hsv[:, :, 2] = np.where(intensity > 0,
((1. - intensity) * hsv[:, :, 2] +
intensity * self.hsv_max_val),
hsv[:, :, 2])
hsv[:, :, 1] = np.where(np.logical_and(np.abs(hsv[:, :, 1]) > 1.e-10,
intensity < 0),
((1. + intensity) * hsv[:, :, 1] -
intensity * self.hsv_min_sat),
hsv[:, :, 1])
hsv[:, :, 2] = np.where(intensity < 0,
((1. + intensity) * hsv[:, :, 2] -
intensity * self.hsv_min_val),
hsv[:, :, 2])
hsv[:, :, 1:] = np.where(hsv[:, :, 1:] < 0., 0, hsv[:, :, 1:])
hsv[:, :, 1:] = np.where(hsv[:, :, 1:] > 1., 1, hsv[:, :, 1:])
# convert modified hsv back to rgb.
return hsv_to_rgb(hsv)
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 :class:`BoundaryNorm`.
Values ``v`` are quantizized 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 :func:`~matplotlib.pyplot.contourf` for details.
Returns
-------
(cmap, norm) : tuple containing a :class:`Colormap` and a \
:class:`Normalize` instance
"""
colors_i0 = 0
colors_i1 = None
if extend == 'both':
colors_i0 = 1
colors_i1 = -1
extra_colors = 2
elif extend == 'min':
colors_i0 = 1
extra_colors = 1
elif extend == 'max':
colors_i1 = -1
extra_colors = 1
elif extend == 'neither':
extra_colors = 0
else:
raise ValueError('Unexpected value for extend: {0!r}'.format(extend))
n_data_colors = len(levels) - 1
n_expected_colors = n_data_colors + extra_colors
if len(colors) != n_expected_colors:
raise ValueError('With extend == {0!r} and n_levels == {1!r} expected'
' n_colors == {2!r}. Got {3!r}.'
''.format(extend, len(levels), n_expected_colors,
len(colors)))
cmap = ListedColormap(colors[colors_i0:colors_i1], 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
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