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Introduction to Data Science with Python

Coloured and 3D Graphs

Explore creating colored and 3D graphs using Matplotlib in Python. Learn to apply pcolor, imshow, contour, and 3D plotting methods like plot_surface and plot_wireframe to visualize complex data in two and three dimensions.

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Color and contour

Colormaps and contour figures are very useful for plotting functions of two variables. In most of these functions, we encode one dimension of the data using a colormap.

Let’s learn with a simple example:

Python 3.5
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
# creating custom function which returns some computation
def flux_qubit_potential(phi_m, phi_p):
    return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)
phi_m = np.linspace(0, 2*np.pi, 100)# create an evenly spaced sequence in a specified interval.
phi_p = np.linspace(0, 2*np.pi, 100)# create an evenly spaced sequence in a specified interval.
X,Y = np.meshgrid(phi_p, phi_m) # creating a rectangular grid with the help of the given 1-D arrays that represent the Matrix indexing or Cartesian indexing. 
Z = flux_qubit_potential(X, Y).T # Function call for the function in above cell
print("X : ", X)
print("Y : ", Y)
print("Z : ", Z)

The

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