Python Program to Implement Bug Fixing Assignment Solution.

from types import TracebackType

import numpy as np

'''

QUESTION 1

'''

def prod(S):

prod = 1

for x in S:

prod = prod*x

return prod

'''

QUESTION 2

'''

for k in range(len(exacts)):

if abs(exacts[k] - approx[k])/approx[k] < 0.001:

print(f"Test case {k}: pass")

else:

print(f"Test case {k}: fail")

'''

QUESTION 3

'''

# No question 3

'''

QUESTION 4

'''

def stats(sample):

sample_min = min(sample)

sample_max = max(sample)

sample_mean = sum(sample)/len(sample)

sample_variance = sum([s**2 for s in sample])/len(sample) - sample_mean**2

return (sample_min, sample_max, sample_mean, sample_variance)

def faststats(sample):

N = len(sample)

sample_min = 1E+99

sample_max = -1E+99

sum_of_x = 0

sum_of_xx = 0

for x in sample:

if x < sample_min:

sample_min = x

if x > sample_max:

sample_max = x

sum_of_x += x

sum_of_xx += x**2

sample_mean = sum_of_x/N

sample_variance = sum_of_xx/N - sample_mean**2

return (sample_min, sample_max, sample_mean, sample_variance)

print(stats(sample))

print(faststats(sample))

'''

QUESTION 5

'''

midpts = (x[:-1] + x[1:])/2

'''

QUESTION 6

'''

def trace(A):

assert A.shape[0] == A.shape[1], "Matrix is not square"

return sum([A[i][i] for i in range(A.shape[0])])

'''

QUESTION 7

'''

sorted_sample = np.sort(sample)

N = len(sample)

if N%2 > 0:

sample_median = sorted_sample[int(N/2)]

else:

sample_median = 0.5*(sorted_sample[int(N/2)]+ sorted_sample[int(N/2)+1])

'''

QUESTION 8

'''

positions = np.loadtxt('output-run-2965.dat', delimiter=',')

x = positions[:,0]

y = positions[:,1]

z = positions[:,2]

# Get number of points

N = positions.shape[0]

dists = np.zeros((N,N))

# Compute

for i in range(N):

for j in range(N):

if i != j:

p1 = np.array([x[i], y[i], z[i]])

p2 = np.array([x[j], y[j], z[j]])

distance = np.linalg.norm(p1-p2) # Euclidean distance

dists[i,j] = distance

'''

QUESTION 9

'''

def expectation(dice):

Y = get_value(dice)

P = get_probability(dice)

assert all(P >= 0) and all(P <= 1) and (sum(P) == 1)

return sum(P*Y)

'''

QUESTION 10

'''

R2 = R1.copy() # The bug occurs here. If you use R2 = R1, when changing R2 will also change R1

# So the correct way is using the function copy()

for r in R2:

r[0] = -r[0]

r[1] = -r[1]

dR = np.zeros(R1.shape[0])

for k in range(R1.shape[0]):

dR[k] = np.sqrt(np.sum((R1[k,:] - R2[k,:])**2))

'''

QUESTION 11???

'''

most_emitters = 0

# We need to pre-allocate the variable winner

winner = -1

for k in range(len(galaxies)):

if len(galaxies[k]) > 0 and galaxies[k].count('x') > most_emitters:

winner = k

most_emitters = galaxies[k].count('x')

if winner != -1:

print(f"galaxy {winner} has the most x-ray emitters; {most_emitters} of them")

else:

print("There is no galaxy with 'x's in it")