[matplot cheet] subplot bars #matplot

plot_bar.py

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

# Read in and plot the image
image = mpimg.imread('Udacican.jpeg')
plt.imshow(image)

# Take histograms in R, G, and B
r_hist = np.histogram(image[:,:,0], bins=32, range=(0, 256))
g_hist = np.histogram(image[:,:,1], bins=32, range=(0, 256))
b_hist = np.histogram(image[:,:,2], bins=32, range=(0, 256))

# Generating bin centers
bin_edges = r_hist[1]
bin_centers = (bin_edges[1:]  + bin_edges[0:len(bin_edges)-1])/2

fig = plt.figure(figsize=(12,3))
plt.subplot(131)
plt.bar(bin_centers, r_hist[0])
plt.xlim(0, 256)
plt.title('R Histogram')
plt.subplot(132)
plt.bar(bin_centers, g_hist[0])
plt.xlim(0, 256)
plt.title('G Histogram')
plt.subplot(133)
plt.bar(bin_centers, b_hist[0])
plt.xlim(0, 256)
plt.title('B Histogram')
plt.show()

plot_contour.py

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors='black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title('SVC with '+ker+' kernel', fontsize=20)