For a visual representation of the matplotlib colormaps, see the “Color” section in the gallery.
matplotlib.colors
¶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 01.
This module includes functions and classes for color specification
conversions, and for mapping numbers to colors in a 1D array of colors called
a colormap. Colormapping typically involves two steps: a data array is first
mapped onto the range 01 using an instance of Normalize
or of a
subclass; then this number in the 01 range is mapped to a color using an
instance of a subclass of Colormap
. Two are provided here:
LinearSegmentedColormap
, which is used to generate all the builtin
colormap instances, but is also useful for making custom colormaps, and
ListedColormap
, which is used for generating a custom colormap from a
list of color specifications.
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 HTMLlike 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.
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
: blueg
: greenr
: redc
: cyanm
: magentay
: yellowk
: blackw
: white
To use the colors that are part of the active color cycle in the current style,
use C
followed by a digit. For example:
C0
: The first color in the cycleC1
: The second color in the cycle
Gray shades can be given as a string encoding a float in the 01 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'
(possibly specifying an alpha value as well), or you can pass an (r, g, b)
or (r, g, b, a)
tuple, where each of r
, g
, b
and a
are in the range
[0,1].
Finally, legal html names for colors, like ‘red’, ‘burlywood’ and ‘chartreuse’ are supported.
matplotlib.colors.
BoundaryNorm
(boundaries, ncolors, clip=False)¶Bases: matplotlib.colors.Normalize
Generate a colormap index based on discrete intervals.
Unlike Normalize
or LogNorm
,
BoundaryNorm
maps values to integers instead of to the
interval 01.
Mapping to the 01 interval could have been done via piecewise linear interpolation, but using integers seems simpler, and reduces the number of conversions back and forth between integer and floating point.
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 ncolors1.
Outofrange values are mapped
to 1 if low and ncolors if high; these are converted
to valid indices by
Colormap.__call__()
.
If clip == True, outofrange values
are mapped to 0 if low and ncolors1 if high.
inverse
(value)¶matplotlib.colors.
Colormap
(name, N=256)¶Bases: 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
matplotlib.colors.Normalize
. It is worth noting that
matplotlib.cm.ScalarMappable
subclasses make heavy use of this
data>normalize>maptocolor
processing chain.
Parameters:  name : str
N : int


colorbar_extend
= None¶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.
is_gray
()¶set_bad
(color='k', alpha=None)¶Set color to be used for masked values.
set_over
(color='k', alpha=None)¶Set color to be used for high outofrange values. Requires norm.clip = False
set_under
(color='k', alpha=None)¶Set color to be used for low outofrange values. Requires norm.clip = False
matplotlib.colors.
LightSource
(azdeg=315, altdeg=45, hsv_min_val=0, hsv_max_val=1, hsv_min_sat=1, hsv_max_sat=0)¶Bases: 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 shade()
is used to produce “shaded” rgb values for a data array.
shade_rgb()
can be used to combine an rgb image with
The shade_rgb()
The hillshade()
produces an illumination map of a surface.
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 : number, optional
altdeg : number, optional


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.
blend_hsv
(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 : ndarray
intensity : ndarray
hsv_max_sat : number, optional
hsv_min_sat : number, optional
hsv_max_val : number, optional
hsv_min_val: number, optional


Returns:  rgb : ndarray

blend_overlay
(rgb, intensity)¶Combines an rgb image with an intensity map using “overlay” blending.
Parameters:  rgb : ndarray
intensity : ndarray


Returns:  rgb : ndarray

blend_soft_light
(rgb, intensity)¶Combines an rgb image with an intensity map using “soft light” blending. Uses the “pegtop” formula.
Parameters:  rgb : ndarray
intensity : ndarray


Returns:  rgb : ndarray

hillshade
(elevation, vert_exag=1, dx=1, dy=1, fraction=1.0)¶Calculates the illumination intensity for 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:  elevation : arraylike
vert_exag : number, optional
dx : number, optional
dy : number, optional
fraction : number, optional
Returns —— intensity : ndarray


shade
(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 : arraylike
cmap :
norm :
blend_mode : {‘hsv’, ‘overlay’, ‘soft’} or callable, optional
vmin : scalar or None, optional
vmax : scalar or None, optional
vert_exag : number, optional
dx : number, optional
dy : number, optional
fraction : number, optional
Additional kwargs are passed on to the *blend_mode* function. 

Returns:  rgba : ndarray

shade_rgb
(rgb, elevation, fraction=1.0, blend_mode='hsv', vert_exag=1, dx=1, dy=1, **kwargs)¶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.
Parameters:  rgb : arraylike
elevation : arraylike
fraction : number
blend_mode : {‘hsv’, ‘overlay’, ‘soft’} or callable, optional
vert_exag : number, optional
dx : number, optional
dy : number, optional
Additional kwargs are passed on to the *blend_mode* function. 

Returns:  shaded_rgb : ndarray

matplotlib.colors.
LinearSegmentedColormap
(name, segmentdata, N=256, gamma=1.0)¶Bases: matplotlib.colors.Colormap
Colormap objects based on lookup tables using linear segments.
The lookup table is generated using linear interpolation for each primary color, with the 01 domain divided into any number of segments.
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.
See also
LinearSegmentedColormap.from_list()
Static method; factory function for generating a
smoothlyvarying LinearSegmentedColormap.
makeMappingArray()
For information about making a mapping array.
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.
set_gamma
(gamma)¶Set a new gamma value and regenerate color map.
matplotlib.colors.
ListedColormap
(colors, name='from_list', N=None)¶Bases: matplotlib.colors.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.
Make a colormap from a list of colors.
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.
matplotlib.colors.
LogNorm
(vmin=None, vmax=None, clip=False)¶Bases: matplotlib.colors.Normalize
Normalize a given value to the 01 range on a log scale
If vmin or vmax is not given, they are initialized from the minimum and maximum value respectively of the first input processed. That is, __call__(A) calls autoscale_None(A). 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.
autoscale
(A)¶Set vmin, vmax to min, max of A.
autoscale_None
(A)¶autoscale only Nonevalued vmin or vmax
inverse
(value)¶matplotlib.colors.
NoNorm
(vmin=None, vmax=None, clip=False)¶Bases: matplotlib.colors.Normalize
Dummy replacement for Normalize, for the case where we
want to use indices directly in a
ScalarMappable
.
If vmin or vmax is not given, they are initialized from the minimum and maximum value respectively of the first input processed. That is, __call__(A) calls autoscale_None(A). 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.
inverse
(value)¶matplotlib.colors.
Normalize
(vmin=None, vmax=None, clip=False)¶Bases: object
A class which, when called, can normalize data into
the [0.0, 1.0]
interval.
If vmin or vmax is not given, they are initialized from the minimum and maximum value respectively of the first input processed. That is, __call__(A) calls autoscale_None(A). 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.
autoscale
(A)¶Set vmin, vmax to min, max of A.
autoscale_None
(A)¶autoscale only Nonevalued vmin or vmax
inverse
(value)¶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 inplace operations, can greatly improve speed for large arrays.
Experimental; we may want to add an option to force the use of float32.
scaled
()¶return true if vmin and vmax set
matplotlib.colors.
PowerNorm
(gamma, vmin=None, vmax=None, clip=False)¶Bases: matplotlib.colors.Normalize
Normalize a given value to the [0, 1]
interval with a powerlaw
scaling. This will clip any negative data points to 0.
autoscale
(A)¶Set vmin, vmax to min, max of A.
autoscale_None
(A)¶autoscale only Nonevalued vmin or vmax
inverse
(value)¶matplotlib.colors.
SymLogNorm
(linthresh, linscale=1.0, vmin=None, vmax=None, clip=False)¶Bases: matplotlib.colors.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).
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.
autoscale
(A)¶Set vmin, vmax to min, max of A.
autoscale_None
(A)¶autoscale only Nonevalued vmin or vmax
inverse
(value)¶matplotlib.colors.
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
colors : sequence of colors
extend : {‘neither’, ‘min’, ‘max’, ‘both’}, optional


Returns:  (cmap, norm) : tuple containing a 
matplotlib.colors.
get_named_colors_mapping
()¶Return the global mapping of names to named colors.
matplotlib.colors.
hsv_to_rgb
(hsv)¶convert hsv values in a numpy array to rgb values all values assumed to be in range [0, 1]
Parameters:  hsv : (..., 3) arraylike


Returns:  rgb : (..., 3) ndarray

matplotlib.colors.
is_color_like
(c)¶Return whether c
can be interpreted as an RGB(A) color.
matplotlib.colors.
makeMappingArray
(N, data, gamma=1.0)¶Create an N element 1d 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*(N1)]
gives the closest value for values of x between 0 and 1.
matplotlib.colors.
rgb_to_hsv
(arr)¶convert float rgb values (in the range [0, 1]), in a numpy array to hsv values.
Parameters:  arr : (..., 3) arraylike


Returns:  hsv : (..., 3) ndarray

matplotlib.colors.
to_hex
(c, keep_alpha=False)¶Convert c
to a hex color.
Uses the #rrggbb format if keep_alpha
is False (the default), #rrggbbaa
otherwise.
matplotlib.colors.
to_rgb
(c)¶Convert c
to an RGB color, silently dropping the alpha channel.
matplotlib.colors.
to_rgba
(c, alpha=None)¶Convert c
to an RGBA color.
If alpha
is not None
, it forces the alpha value, except if c
is
“none” (caseinsensitive), which always maps to (0, 0, 0, 0)
.
matplotlib.colors.
to_rgba_array
(c, alpha=None)¶Convert c
to a (n, 4) array of RGBA colors.
If alpha
is not None
, it forces the alpha value. If c
is “none”
(caseinsensitive) or an empty list, an empty array is returned.