Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.
matplotlib.colors.from_levels_and_colors()
The matplotlib.colors.from_levels_and_colors() function is a helper function that helps create cmap and norm instance whose behavior is similar to that of contourf’s levels and colors argument.
Syntax: matplotlib.colors.from_levels_and_colors(levels, colors, extend=’neither’)
Parameters:
- levels: It is a sequence of numbers that represent quantization levels that are used to construct the BoundaryNorm. A value v is quantized to level k if lev[k] <= v < lev[k+1].
- colors: It is a sequence of colors that are used as fill colors for each level. There must be n_level – 1 colors if extend is “neither”. Add one extra color for an extend of “min” or “max” and for an extend of “both” add two colors.
- extend: It is an optional parameter that accepts one of the four values namely ‘neither’, ‘min’, ‘max’ or ‘both’.
Return Type : This function returns a Normalized cmap and a colormap norm
Example 1:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
data1 = 3 * np.random.random(( 10 , 10 ))
data2 = 5 * np.random.random(( 10 , 10 ))
levels = [ 0 , 1 , 2 , 3 , 4 , 5 ]
colors = [ 'red' , 'brown' ,
'yellow' , 'green' ,
'blue' ]
cmap, norm = matplotlib.colors.from_levels_and_colors(levels,
colors)
fig, axes = plt.subplots(ncols = 2 )
for ax, data in zip (axes, [data1, data2]):
im = ax.imshow(dat,
cmap = cmap,
norm = norm,
interpolation = 'none' )
fig.colorbar(im, ax = ax, orientation = 'horizontal' )
plt.show() |
Output:
Example 2:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import from_levels_and_colors
nvals = np.random.randint( 2 , 20 )
data = np.random.randint( 0 , nvals,
( 10 , 10 ))
colors = np.random.random((nvals, 3 ))
# Make the colors pastels... colors = colors / 2.5 + 0.55
levels = np.arange(nvals + 1 ) - 0.5
cmap, norm = from_levels_and_colors(levels,
colors)
fig, ax = plt.subplots()
im = ax.imshow(data,
interpolation = 'nearest' ,
cmap = cmap,
norm = norm)
fig.colorbar(im, ticks = np.arange(nvals))
plt.show() |
Output: