human
I am working on the file ".ipynb_checkpoints/ME249-Python-crash-course-Copy1-checkpoint.ipynb".
The first task is:
<p class='alert alert-danger'>
Getting errors is part of code development. Python will provide sometimes short, sometimes way too long error messages. Before you throw your hands in the air, give up and immediately email the TA or the instructor, browse the error message for things that could make sense. Typically, the last line contains useful information. Here it says that <FONT FACE="courier" style="color:blue">math.exp</FONT> is expecting a scalar but we gave a length-1 array.
</p>

<h3>2.5 Indexing and Slicing</h3>

A very attractive feature of python is the nomenclature for indexing and slicing that enables to maximize the efficiency of vector operations. Here is a summary of the indexing and slicing nomenclature for a vector:
Can you write Python program for it?
 ... What is a the Python program you requested?
assistant
#Here is the Python program you requested.

%matplotlib inline 
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format

from IPython.display import Image
from IPython.display import clear_output

from IPython.core.display import HTML
def header(text):
    raw_html = '<h4>' + str(text) + '</h4>'
    return raw_html

def box(text):
    raw_html = '<div style="border:1px dotted black;padding:2em;">'+str(text)+'</div>'
    return HTML(raw_html)

def nobox(text):
    raw_html = '<p>'+str(text)+'</p>'
    return HTML(raw_html)

def addContent(raw_html):
    global htmlContent
    htmlContent += raw_html
    
class PDF(object):
  def __init__(self, pdf, size=(200,200)):
    self.pdf = pdf
    self.size = size

  def _repr_html_(self):
    return '<iframe src={0} width={1[0]} height={1[1]}></iframe>'.format(self.pdf, self.size)

  def _repr_latex_(self):
    return r'\includegraphics[width=1.0\textwidth]{{{0}}}'.format(self.pdf)

PDF('figures/indexing.pdf',size=(800,600))

n = 6
a = np.zeros(6,dtype=np.float)

for i in range(n):
    a[i] = 2**i
print("a[0:n] = ",a[0:n])
print("a[:] = ",a[:])
print("a[1:]  = ",a[1:])
print("a[1:n] = ",a[1:n])

print("a[0:n]   = ",a[0:n])
print("a[0:n-1] = ",a[0:n-1])
print("a[0:-1]  = ",a[0:-1])
print("a[:-1]   = ",a[:-1])

print("a[2:] = ",a[2:])
print("a[:-3]= ",a[:-3])

b = np.zeros(n,dtype=np.float)
b[:-1] = a[1:] - a[:-1]
print("a[1:]  =",a[1:])
print("-")
print("a[:-1] =",a[:-1])
print("       =",b[:-1])

n = 100000
mu = 0.
sigma = 1.
a = np.random.normal(loc = mu, scale = sigma, size = n)

print("a[:-(n-10)] = ", a[:-(n-10)])
print("a[0:10] = ", a[0:10])

import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(a, 50, normed=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * \
         np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\
         linewidth=2, color='r')
plt.show()

n = 100
mu = 0.
sigma = 0.2
x = np.linspace(0,1.,n)
a = np.sin(2.*math.pi*x)
plt.plot(x,a, lw = 2)
plt.show()
a_noisy = np.copy(a)
n_noise = 4
k_noise =...