Posts Matplotlib: Hydrogen Wave Function
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Matplotlib: Hydrogen Wave Function

Hydrogen Wave Function

The normalized hydrogen wave function1

\[\begin{equation} \label{eq:hydro_wfc} \psi_{nlm}(r,\theta,\phi) = \sqrt{ \left(\frac{2}{na_0}\right)^3\, \frac{(n-l-1)!}{2n[(n+l)!]} } e^{-r / na_0} \left(\frac{2r}{na_0}\right)^l L_{n-l-1}^{2l+1} \cdot Y_l^m(\theta, \phi) \end{equation}\]
Figure 1. Norm of hydrogen wave function $|\psi_{nlm}(r,\theta,\phi)|$ projected on the $xy$-plane, i.e. $z = 0$. Note that the size of the box in each plot is different, where the distance between two major ticks (long thick sticks) is $5\,a_0$, i.e. 5 times the Bohr radius. The three quantum numbers for each wave function are show in braces. Also, the reason that norm of the wave function was shown is that the nodes, i.e. zeros of the wave functions, can be barely discerned if norm-squared of the wave functions were used.

Codes

Below is the code that generates the above figure, where the analytic hydrogen wave function was obtained from sympy.

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#!/usr/bin/env python

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator

from sympy import lambdify, integrate, oo
from sympy.abc import r, theta, phi
from sympy.physics.hydrogen import R_nl, Psi_nlm

plt.style.use('dark_background')
mpl.rcParams['mathtext.fontset'] = 'cm'


def cart2sphe(x, y, z):
    '''
    3D Cartesian coordinates to spherical coordinates.

    input:
        x, y, z : numpy arrays
    '''
    xy2     = x**2 + y**2
    r       = np.sqrt(xy2 + z**2)    
    theta   = np.arctan2(np.sqrt(xy2), z) # the polar angle in radian angles
    phi     = np.arctan2(y, x)            # the azimuth angle in radian angles
    phi[phi < 0] += np.pi * 2             # np.arctan2 returns the angle in the range [-pi, pi]

    return r, theta, phi


################################################################################
if __name__ == "__main__":
    Nmax = 4
    nlms  = [
        (n, l, m)
        for n in range(1, Nmax+1)
        for l in range(n)
        for m in range(l+1)
    ]
    PSI  = [
        lambdify((r, phi, theta), Psi_nlm(n, l, m, r, phi, theta), 'numpy')
        for n, l, m in nlms
    ]

    ################################################################################
    # print(plt.figaspect(2 / Nmax),)
    fig = plt.figure(
        figsize=(9.6, 4.8),
        # figsize=plt.figaspect(2 / Nmax),
        dpi=300,
        constrained_layout=True
    )

    axes_array = np.zeros((Nmax, (Nmax*(Nmax+1))//2), dtype=int)
    axes_id    = 0
    for ii in range(Nmax):
        lm_sum = ((ii+1) * (ii+2)) // 2
        for jj in range(0, lm_sum):
            axes_id += 1
            axes_array[ii, -(jj+1)] = axes_id 

    axes = fig.subplot_mosaic(
        axes_array,
        empty_sentinel=0,
    )
    axes = [axes[ii+1] for ii in range(len(axes))]

    cut_planes = np.ones(len(axes), dtype=int) * 2
    ################################################################################

    for ii in range(len(axes)):
        n, l, m = nlms[ii]

        # L       = np.ceil(integrate(R_nl(n, l, r, Z=1)**2 * r**3, (r, 0, oo)) / 10) * 10
        # L       = integrate(R_nl(n, l, r, Z=1)**2 * r**3, (r, 0, oo)) * 2
        # N       = 100
        # print(n, l, L)

        if n == 1:
            L, N = 1.5*n**2 + 1.0, 100
        else:
            L, N = 1.5*n**2 + 5.0, 100

        a0, b0 = np.mgrid[
            -L:L:N*1j,
            -L:L:N*1j
        ]
        nx, ny = a0.shape
        xyz_planes_sphe = [
            cart2sphe(a0, b0, 0),
            cart2sphe(0, a0, b0),
            cart2sphe(a0, 0, b0),
        ]

        ax = axes[ii]
        ax.set_aspect(1.0)

        r0, theta, phi = xyz_planes_sphe[cut_planes[ii]]
        wfc = PSI[ii](r0, phi, theta).reshape(nx, ny)
        ax.pcolormesh(a0, b0, np.abs(wfc), cmap='magma') 

        ax.set_xlim(-L, L)
        ax.set_ylim(-L, L)

        STEP = 5
        if n == 1:
            ax.set_xticks([-2.5, 2.5])
            ax.set_yticks([-2.5, 2.5])
            ax.xaxis.set_minor_locator(AutoMinorLocator(n=2))
            ax.yaxis.set_minor_locator(AutoMinorLocator(n=2))
        else:
            ax.set_xticks([-x for x in np.arange(0, L, STEP)] + [x for x in np.arange(0, L, STEP)])
            ax.set_yticks([-x for x in np.arange(0, L, STEP)] + [x for x in np.arange(0, L, STEP)])
        ax.set_xticklabels([])
        ax.set_yticklabels([])

        ax.text(0.05, 0.05, "({}, {}, {})".format(n, l, m), 
                fontsize='xx-small',
                ha='left', va='bottom', transform=ax.transAxes, color='w')


    fig.text(0.0, 1.00,  r'Hydrogen Wave Function',
            fontsize=28,
            ha='left', va='top', transform=fig.transFigure, color='w')

    fig.text(0.0, 0.90,  r'Probability Density',
            fontsize=20,
            ha='left', va='top', transform=fig.transFigure, color='w')

    fig.text(0.0, 0.80,  r'$ \psi_{nlm}(r,\theta,\phi) = \sqrt{ \left(\frac{2}{na_0}\right)^3\, \frac{(n-l-1)!}{2n[(n+l)!]} } e^{-r / na_0} \left(\frac{2r}{na_0}\right)^l L_{n-l-1}^{2l+1}\cdot Y_l^m(\theta, \phi)$',
            fontsize='xx-large',
            ha='left', va='top', transform=fig.transFigure, color='w')

    # fig.text(0.00, 0.64,  "NOTE:\n\t1. The distance between two major ticks in the plot is $5\,a_0$,\n\t2. The number in the braces are the three quantum numbers $(n, l, m)$",
    #         fontsize='small',
    #         linespacing=1.8,
    #         ha='left', va='top', transform=fig.transFigure, color='w')

    fig.text(0.00, 0.64,  "NOTE:",
            fontsize='small',
            ha='left', va='top', transform=fig.transFigure, color='w')

    fig.text(0.00, 0.59,  "    1. The distance between two major ticks in the plot is $5\,a_0$.\n    2. The numbers in the brace are the three quantum numbers $(n,\,l,\,m).$",
            fontsize='small',
            linespacing=1.5,
            ha='left', va='top', transform=fig.transFigure, color='w')

    plt.savefig('hydro_wfc.png')

    # from subprocess import call
    # call('feh -xdF hydro_wfc.png'.split())

References

This post is licensed under CC BY 4.0 by the author.