Matplotlib - imshow()

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html

https://matplotlib.org/tutorials/introductory/images.html

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html

IMSHOW

Display data as an image, i.e., on a 2D regular raster.

# matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None,
#     interpolation=None, alpha=None, vmin=None, vmax=None, origin=None,
#     extent=None, filternorm=1, filterrad=4.0, resample=None, url=None, *,
#     data=None, **kwargs)
#
# X : array-like or PIL image
#     The image data. Supported array shapes are:
#     (M, N): an image with scalar data. The data is visualized using a colormap.
#     (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
#     (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
#     i.e. including transparency.
# cmap : str or Colormap, default is 'viridis' ['hot', 'cool', 'gray']
# interpolation : default is 'nearest' (no interpolation) ['bilinear', 'hamming']
#
# Colormaps are used to convert data values (floats) from the interval [0, 1]
# to the RGBA color that the respective Colormap represents.

# heatmap1.py
import numpy as np
import matplotlib.pyplot as plt

data = np.random.random((8, 8))
#data = np.arange(100).reshape((10,10)) # data will be scaled to [0,1]

plt.imshow(data, cmap='hot', interpolation='nearest') # pixelated
# 0.0 black, 0.37 red, 0.75 yellow, 1.0 white

#plt.imshow(data, cmap='cool', interpolation='bilinear') # blurry

plt.colorbar()
#plt.colorbar(label='temperature')

plt.show()

# Creating data using numpy functions.
x = np.linspace(-5, 5, 101)
y = np.linspace(-5, 5, 101)
xx, yy = np.meshgrid(x, y)
# xx.shape is (101, 101)
# yy.shape is (101, 101)

# data will be scaled to the range [0,1]
#data = np.sqrt(xx**2 + yy**2)
#data = np.abs(xx) + np.abs(yy)
#data = np.cos(xx) * np.cos(yy)
data = np.exp(-(xx*0.5)**2 -(yy*0.5)**2)   # heatmap2.py