Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
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from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
In quantum statistics, the Fermi-Dirac distribution is related to the probability that a particle will be in a quantum state with energy $\epsilon$. The equation for the distribution $F(\epsilon)$ is:
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Image('fermidist.png')
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In this equation:
In the cell below, typeset this equation using LaTeX:
$F(\epsilon)=\frac{1}{e^\frac{(\epsilon-\mu)}{kT}+1}$
Define a function fermidist(energy, mu, kT)
that computes the distribution function for a given value of energy
, chemical potential mu
and temperature kT
. Note here, kT
is a single variable with units of energy. Make sure your function works with an array and don't use any for
or while
loops in your code.
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def fermidist(energy, mu, kT):
"""Compute the Fermi distribution at energy, mu and kT."""
# YOUR CODE HERE
F = 1/(np.exp((energy-mu)/kT)+1)
return F
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assert np.allclose(fermidist(0.5, 1.0, 10.0), 0.51249739648421033)
assert np.allclose(fermidist(np.linspace(0.0,1.0,10), 1.0, 10.0),
np.array([ 0.52497919, 0.5222076 , 0.51943465, 0.5166605 , 0.51388532,
0.51110928, 0.50833256, 0.50555533, 0.50277775, 0.5 ]))
Write a function plot_fermidist(mu, kT)
that plots the Fermi distribution $F(\epsilon)$ as a function of $\epsilon$ as a line plot for the parameters mu
and kT
.
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def plot_fermidist(mu, kT):
ax = plt.gca()
energy = np.arange(0,11.0)
plt.plot(energy,fermidist(energy,mu,kT))
plt.ylim(0,2.0)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
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plot_fermidist(4.0, 1.0)
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assert True # leave this for grading the plot_fermidist function
Use interact
with plot_fermidist
to explore the distribution:
mu
use a floating point slider over the range $[0.0,5.0]$.kT
use a floating point slider over the range $[0.1,10.0]$.
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interact(plot_fermidist,mu = [0.0,5.0], kT = [0.1,10.0])
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Provide complete sentence answers to the following questions in the cell below:
Use LaTeX to typeset any mathematical symbols in your answer.
When kT is low it is mostly likely that the energy will be low. When kT is high the probability to be in higher $\epsilon$ is linearly decreasing as kT increases. Changing $\mu$ shifts the graph up when $\mu$ increases and down when $\mu$ decreases. As $\mu$ increases the number of particles increases and as $\mu$ decreases the number of particles decreses.
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