[Scientific / technical calculation by Python] Drawing, visualization, matplotlib of 2D (color) contour lines, etc.

Introduction

Create contour plots using matplotlib's ** pcolor **, ** imshow **, contour methods.

Postscript: August 28, 2017: ** Added content (4) that mixes pcolor mesh and contour. ** **

Contents

(1) Consider $ z = sin (x) + cos (y) $ and draw it in pcolor in the range [0, x, 10], [0, y, 10]. I'm using pcolor mesh. Faster than pcolor ().

(2) Drawing example using impshow

(3) Contour map using contor

(4) A mixture of pcolor mesh and contour

Example (1): Using pcolormesh

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.05) #Generate x-axis drawing range. 0 to 10 0.In increments of 05.
y = np.arange(0, 10, 0.05) #Generation of y-axis drawing range. 0 to 10 0.In increments of 05.

X, Y = np.meshgrid(x, y)
Z = np.sin(X) + np.cos(Y)   #Specify the calculation formula to be displayed. Contour lines are made for Z.

plt.pcolormesh(X, Y, Z, cmap='hsv') #Generate contour maps. Specify the coloring rule with cmap.
#plt.pcolor(X, Y, Z, cmap='hsv') #Generate contour maps. Specify the coloring rule with cmap.

pp=plt.colorbar (orientation="vertical") #Color bar display
pp.set_label("Label", fontname="Arial", fontsize=24) #Color bar label

plt.xlabel('X', fontsize=24)
plt.ylabel('Y', fontsize=24)

plt.show()

Result (1): Diagram using pcolor mesh

Example (2): Using impshow

If you want to visualize a two-dimensional array quickly, use matplotlib.pyplot.imshow ().

import numpy as np
import matplotlib.pyplot as plt

z=np.zeros([100,100])
for i in range(100):
    for j in range(100):
        z[i,j] = i+j

plt.imshow(z)
plt.colorbar () #Color bar display
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Result (2): impshow

Example (3) Contour diagram using contour

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.05) #Generate x-axis drawing range. 0 to 10 0.In increments of 05.
y = np.arange(0, 10, 0.05) #Generation of y-axis drawing range. 0 to 10 0.In increments of 05.

X, Y = np.meshgrid(x, y)
Z = np.sin(X) + np.cos(Y)   #Specify the calculation formula to be displayed. Contour lines are made for Z.

#Generate contour maps.
cont=plt.contour(X,Y,Z,  5, Vmax=1,colors=['black'])
cont.clabel(fmt='%1.1f', fontsize=14)


pp=plt.colorbar (orientation="vertical") #Color bar display
pp.set_label("Label", fontname="Arial", fontsize=24) #Color bar label



plt.xlabel('X', fontsize=24)
plt.ylabel('Y', fontsize=24)

plt.show()

Result (3): contour diagram

Example (4) Mix of pcolor mesh and contour

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.05) #Generate x-axis drawing range. 0 to 10 0.In increments of 05.
y = np.arange(0, 10, 0.05) #Generation of y-axis drawing range. 0 to 10 0.In increments of 05.

X, Y = np.meshgrid(x, y)
Z = np.sin(X) + np.cos(Y)   #Specify the calculation formula to be displayed. Contour lines are made for Z.

#Generate contour maps.
cont=plt.contour(X,Y,Z,  5, vmin=-1,vmax=1, colors=['black'])
cont.clabel(fmt='%1.1f', fontsize=14)


plt.xlabel('X', fontsize=24)
plt.ylabel('Y', fontsize=24)


plt.pcolormesh(X,Y,Z, cmap='cool') #Color contour map
pp=plt.colorbar (orientation="vertical") #Color bar display
pp.set_label("Label",  fontsize=24)

plt.show()

Result (4): Mix of pcolor mesh and contour

[Addendum]

Specify the pattern with ** cmap ** of the color map specification. Dozens of patterns are available. Samples can be found here [https://matplotlib.org/examples/color/colormaps_reference.html):

'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv', 'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'