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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 builtin 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.
from __future__ import print_function
import re
import numpy as np
from numpy import ma
import matplotlib.cbook as cbook
parts = np.__version__.split('.')
NP_MAJOR, NP_MINOR = map(int, parts[:2])
# true if clip supports the out kwarg
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',
'palevioletred' : '#AFEEEE',
'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 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*'
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([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, basestring):
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:
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),))
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)
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')
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*.
if arg.lower() == 'none':
return (0.0, 0.0, 0.0, 0.0)
except AttributeError:
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')
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.
nc = len(c)
except TypeError:
raise ValueError(
"Cannot convert argument type %s to rgba array" % type(c))
if nc == 0 or c.lower() == 'none':
return np.zeros((0,4), dtype=np.float)
except AttributeError:
# 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 callable(data):
xind = np.linspace(0, 1, N)**gamma
lut = np.clip(np.array(data(xind), dtype=np.float), 0, 1)
return lut
adata = np.array(data)
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:
"""Base class for all scalar to rgb mappings
Important methods:
* :meth:`set_bad`
* :meth:`set_under`
* :meth:`set_over`
def __init__(self, name, N=256):
Public class attributes:
:attr:`N` : number of rgb quantization levels
:attr:`name` : name of colormap
""" = 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
def __call__(self, X, alpha=None, bytes=False):
*X* is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). If the X-values
are integers, then they are used as indices into the array.
If they are floating point, then they must be in the
interval (0.0, 1.0).
Alpha must be a scalar between 0 and 1, or None.
If bytes is False, the rgba values will be floats on a
0-1 scale; if True, they will be uint8, 0-255.
if not self._isinit: self._init()
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
xa = np.array([X])
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
np.clip(xa, -1, self.N, out=xa)
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:
if bytes:
lut = (self._lut * 255).astype(np.uint8)
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.
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
self._lut[self._i_under] = self._lut[0]
if self._rgba_over:
self._lut[self._i_over] = self._rgba_over
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
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::
Static method; factory function for generating a
smoothly-varying LinearSegmentedColormap.
For information about making a mapping array.
self.monochrome = False # True only if all colors in map are identical;
# needed for contouring.
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
def set_gamma(self, gamma):
Set a new gamma value and regenerate color map.
self._gamma = gamma
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
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 = zip(*colors)
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
def __init__(self, colors, name = 'from_list', N = None):
Make a colormap from a list of colors.
a list of matplotlib color specifications,
or an equivalent Nx3 or Nx4 floating point array
(*N* rgb or rgba values)
a string to identify the colormap
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)
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
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
class Normalize:
Normalize a given value to the 0-1 range
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::
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
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)
result = result.astype(np.float32)
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)
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) # Or should it be all masked? Or 0.5?
vmin = float(vmin)
vmax = float(vmax)
if clip:
mask = ma.getmask(result)
result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
# ma division is very slow; we can take a shortcut
resdat =
resdat -= vmin
resdat /= (vmax - vmin)
result =, 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)
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:
self.vmin = ma.min(A)
if self.vmax is None:
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)
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:
if clip:
mask = ma.getmask(result)
val = ma.array(np.clip(result.filled(vmax), vmin, vmax),
#result = (ma.log(result)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
# in-place equivalent of above can be much faster
resdat =
mask = result.mask
if mask is
mask = (resdat <= 0)
mask |= resdat <= 0
cbook._putmask(resdat, mask, 1)
np.log(resdat, resdat)
resdat -= np.log(vmin)
resdat /= (np.log(vmax) - np.log(vmin))
result =, 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)
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:
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 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):
a monotonically increasing sequence
number of colors in the colormap to be used
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
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:
iret = (iret * (float(self.Ncmap-1)/(self.N-2))).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:`` .
def __call__(self, value, clip=None):
return value
def inverse(self, value):
return value
# compatibility with earlier class names that violated convention:
normalize = Normalize
no_norm = 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_like(arr)
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(
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,\
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)
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