Introduction to Data Science with Python/

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Coloured and 3-D Graphs

Coloured and 3-D Graphs

Let’s learn about creating colored and 3-D graphs using matplotlib.

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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:

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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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