colors

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 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 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 Colormap. Two are provided here: LinearSegmentedColormap, which is used to generate all the built-in 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 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.

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

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 cycle
• C1: The second color in the cycle

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'

(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.

class 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 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.

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 Colormap.__call__() . If clip == True, out-of-range values are mapped to 0 if low and ncolors-1 if high.

inverse(value)

class 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->map-to-color processing chain.

Parameters:

name : str

The name of the colormap.

N : int

The number of rgb quantization levels.

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 out-of-range values. Requires norm.clip = False

set_under(color='k', alpha=None)

Set color to be used for low out-of-range values. Requires norm.clip = False

class 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

The azimuth (0-360, degrees clockwise from North) of the light source. Defaults to 315 degrees (from the northwest).

altdeg : number, optional

The altitude (0-90, degrees up from horizontal) of the light source. Defaults to 45 degrees from horizontal.

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

An MxNx3 RGB array of floats ranging from 0 to 1 (color image).

intensity : ndarray

An MxNx1 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. Defaults to 1.

hsv_min_sat : number, optional

The minimum saturation value that the intensity map can shift the output image to. Defaults to 0.

hsv_max_val : number, optional

The maximum value (“v” in “hsv”) that the intensity map can shift the output image to. Defaults to 1.

hsv_min_val: number, optional

The minimum value (“v” in “hsv”) that the intensity map can shift the output image to. Defaults to 0.

Returns:

rgb : ndarray

An MxNx3 RGB array representing the combined images.

blend_overlay(rgb, intensity)

Combines an rgb image with an intensity map using “overlay” blending.

Parameters:

rgb : ndarray

An MxNx3 RGB array of floats ranging from 0 to 1 (color image).

intensity : ndarray

An MxNx1 array of floats ranging from 0 to 1 (grayscale image).

Returns:

rgb : ndarray

An MxNx3 RGB array representing the combined images.

blend_soft_light(rgb, intensity)

Combines an rgb image with an intensity map using “soft light” blending. Uses the “pegtop” formula.

Parameters:

rgb : ndarray

An MxNx3 RGB array of floats ranging from 0 to 1 (color image).

intensity : ndarray

An MxNx1 array of floats ranging from 0 to 1 (grayscale image).

Returns:

rgb : ndarray

An MxNx3 RGB array representing the combined images.

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 : array-like

A 2d array (or equivalent) of 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

——-

intensity : ndarray

A 2d array of illumination values between 0-1, where 0 is completely in shadow and 1 is completely illuminated.

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 : array-like

A 2d array (or equivalent) of the height values used to generate a shaded map.

cmap : Colormap instance

The colormap used to color the data array. Note that this must be a Colormap instance. For example, rather than passing in cmap='gist_earth', use cmap=plt.get_cmap('gist_earth') instead.

norm : 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 MxNx3 RGB array of floats (ranging 0 to 1) with an MxNx1 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 : scalar 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 : scalar 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.

Additional kwargs are passed on to the *blend_mode* function.

Returns:

rgba : ndarray

An MxNx4 array of floats ranging between 0-1.

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 : array-like

An MxNx3 RGB array, assumed to be in the range of 0 to 1.

elevation : array-like

A 2d array (or equivalent) 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 MxNx3 RGB array of floats (ranging 0 to 1) with an MxNx1 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.

Additional kwargs are passed on to the *blend_mode* function.

Returns:

shaded_rgb : ndarray

An MxNx3 array of floats ranging between 0-1.

class 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 0-1 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 smoothly-varying LinearSegmentedColormap.

makeMappingArray() For information about making a mapping array.

static 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.

class 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.

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.

class matplotlib.colors.LogNorm(vmin=None, vmax=None, clip=False)

Bases: matplotlib.colors.Normalize

Normalize a given value to the 0-1 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 None-valued vmin or vmax

inverse(value)

class 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)

class 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 None-valued vmin or vmax

inverse(value)

static 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.

scaled()

return true if vmin and vmax set

class 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 power-law 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 None-valued vmin or vmax

inverse(value)

class 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 None-valued 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

The quantization levels used to construct the 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 contourf() for details.

Returns:

(cmap, norm) : tuple containing a Colormap and a Normalize instance

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) array-like

All values assumed to be in range [0, 1]

Returns:

rgb : (..., 3) ndarray

Colors converted to RGB values in range [0, 1]

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 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.

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) array-like

All values must be in the range [0, 1]

Returns:

hsv : (..., 3) ndarray

Colors converted to hsv values in range [0, 1]

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.

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” (case-insensitive) or an empty list, an empty array is returned.