%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""


def well2d(x, y, nx, ny, L=1.0):
    """Compute the 2d quantum well wave function."""
    psi = 2*np.sin(nx*np.pi*x/L)*np.sin(ny*np.pi*y/L)/L
    return psi

psi = well2d(np.linspace(0,1,10), np.linspace(0,1,10), 1, 1)
psi

psi = well2d(np.linspace(0,1,10), np.linspace(0,1,10), 1, 1)
assert len(psi)==10
assert psi.shape==(10,)

"""
Explanation: Contour plots of 2d wavefunctions
The wavefunction of a 2d quantum well is:
$$ \psi_{n_x,n_y}(x,y) = \frac{2}{L}
    \sin{\left( \frac{n_x \pi x}{L} \right)} 
    \sin{\left( \frac{n_y \pi y}{L} \right)} $$
This is a scalar field and $n_x$ and $n_y$ are quantum numbers that measure the level of excitation in the x and y directions. $L$ is the size of the well.
Define a function well2d that computes this wavefunction for values of x and y that are NumPy arrays.
End of explanation
"""


x = np.linspace(0,1,100)
y = np.linspace(0,1,100)
fld = np.meshgrid(x, y)    # Sets up coordinates

psi = well2d(fld[0],fld[1],3,2) # Creates 2-d array with well2d's values

f = plt.figure(figsize=(5,5))
plt.contourf(x,y,psi)
plt.tick_params(which='both',direction='out', top=False,right=False)
plt.set_cmap('copper')
plt.colorbar()

assert True # use this cell for grading the contour plot

"""
Explanation: The contour, contourf, pcolor and pcolormesh functions of Matplotlib can be used for effective visualizations of 2d scalar fields. Use the Matplotlib documentation to learn how to use these functions along with the numpy.meshgrid function to visualize the above wavefunction:

Use $n_x=3$, $n_y=2$ and $L=0$.
Use the limits $[0,1]$ for the x and y axis.
Customize your plot to make it effective and beautiful.
Use a non-default colormap.
Add a colorbar to you visualization.

First make a plot using one of the contour functions:
End of explanation
"""


plt.figure(figsize=(5,5))
plt.pcolormesh(x,y,psi)
plt.tick_params(which='both',direction='out', top=False, right=False)
plt.set_cmap('winter')
plt.colorbar()

assert True # use this cell for grading the pcolor plot

"""
Explanation: Next make a visualization using one of the pcolor functions:
End of explanation
"""


