Question

import numpy as np from numpy import pi, sin import math import matplotlib.pyplot as plt %matplotlib inline import scipy.linalg as lg def V(x, k=1): return 0.5kx**2 N = 500 x = np.linspace(-10, 10, N) dx = x[1] - x[0] V_matrix = np.matrix(np.diag(V(x))) K_matrix = ((-1/2)np.matrix(np.eye(N, k=-1)) + np.eye(N, k=1) - 2np.eye(N))/dx**2 eigen_energies, eigen_modes = lg.eigh(K_matrix + V_matrix) eigen_modes = eigen_modes.T plt.figure(figsize=(8,8)) plt.plot(x, V(x)) plt.show() np.std([2, 3, 4])

          import numpy as np
from numpy import pi, sin
import math
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.linalg as lg
def V(x, k=1):
    return 0.5*k*x**2
N = 500
x = np.linspace(-10, 10, N)
dx = x[1] - x[0]
V_matrix = np.matrix(np.diag(V(x)))
K_matrix = ((-1/2)*np.matrix(np.eye(N, k=-1)) + np.eye(N, k=1) - 2*np.eye(N))/dx**2
eigen_energies, eigen_modes = lg.eigh(K_matrix + V_matrix)
eigen_modes = eigen_modes.T
plt.figure(figsize=(8,8))
plt.plot(x, V(x))
plt.show()
np.std([2, 3, 4])

Show more…

import numpy as np
from numpy import pi, sin
import math
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.linalg as lg
def V(x, k=1):
return 0.5*k*x**2
N = 500
x = np.linspace(-10, 10, N)
dx = x[1] - x[0]
Vmatrix = np.matrix(np.diag(V(x)))
Kmatrix = ((-1/2)*np.matrix(np.eye(N, k=-1)) + np.eye(N, k=1) - 2*np.eye(N))/dx**2
eigenenergies, eigenmodes = lg.eigh(Kmatrix + Vmatrix)
eigenmodes = eigenmodes.T
plt.figure(figsize=(8,8))
plt.plot(x, V(x))
plt.show()
np.std([2, 3, 4])

Added by Beatriz S.

Computer Science and Information Technology

Trishna Knowledge Systems 2018 Edition

Instant Answer

Solved by Expert Harriet O'Brien

Step 1

We only need to import the necessary functions from numpy and scipy.linalg. We can remove the import statement for math and matplotlib.pyplot since they are not used in the code. import numpy as np from numpy import pi, sin import scipy.linalg as lg Show more…

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Thanks for your feedback!

I have this code but can't run it, can anyone help me fix it? import numpy as np from numpy import pi, sin import math import matplotlib.pyplot as plt %matplotlib inline import scipy.linalg as lg def V(x, k): return 0.5 * k * x**2 N = 500 x = np.linspace(-10, 10, N) dx = x[1] - x[0] V_matrix = np.matrix(np.diag(V(x, k))) K_matrix = (-1/2 * np.matrix(np.eye(N, k=-1)) + np.matrix(np.eye(N, k=1)) - 2 * np.matrix(np.eye(N)) / dx**2) eigen_energies, eigen_modes = lg.eigh(K_matrix + V_matrix) eigen_modes = eigen_modes.T plt.figure(figsize=(8, 8)) plt.plot(x, V(x, k)) plt.show() np.std([2, 3, 4])

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