Matplotlib is a library to visualize the stuff you are interested in. It offers a number of different visualization tools ranging from simple 1d plots to interactive 3d colorful plots. This blog is still in development and contains the visualization techniques that at some point I’ve used. Whenever I learn a new technique, I will add that to here.
For conda users following command is good to go:
conda install -c conda-forge matplotlib
For old-school pip:
pip install matplotlib
In order to plot multiple images and use a common color bar, we can use the following codes. I’ve tried to explain every functionality I’ve used.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
filters = np.random.randn(25,5,5)
min_weight = np.min(filters)
max_weight = np.max(filters)
fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(7, 7))
for i, ax in enumerate(axes.flat):
ax.set_axis_off() # since I am plotting images, I don't want to see axis numbers.
im = ax.imshow(filters[i], cmap='viridis',
fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
cbar = fig.colorbar(im, cax=cb_ax)
#set the colorbar ticks and tick labels
cbar.set_ticks(np.arange(min_weight, max_weight, (max_weight-min_weight-0.001)/2))
cbar.set_ticklabels([min_weight, (max_weight+min_weight)/2, max_weight])