Academic Year 2022 Actual
Python actual test – session 2
General note: The following code is executed as the first cell of the notebook in which all other problems’ codes are run.
import numpy as np from scipy.integrate
import solve_ivp
import scipy.stats as st from scipy
import signal
import matplotlib.pyplot as plt 1. What is the output of this code?
A = np.array([ [1, 0],[0, -1] ])
b = np.array([-42, 42])
print(sum(np.dot(A, b)))| A | B | C | D | E | F | G |
|-----|-----|-----|---|----|----|----|
| -84 | -42 | -21 | 0 | 21 | 45 | 84 |2. What is the output of this code?
A = np.arange(9).reshape(3, -1)
print((A - 3)[:, 1])A. [-3 0 3] B. [0 1 2] C. [-3 -2 -1] D. [-2 1 4] E. [-1 2 5] F. [1 4 7] G. [3 4 5]
| A | B | C | D | E | F | G |
|----------|---------|------------|----------|----------|---------|---------|
| [-3 0 3] | [0 1 2] | [-3 -2 -1] | [-2 1 4] | [-1 2 5] | [1 4 7] | [3 4 5] |3. What is the output of this code?
I = np.array([
[1, 1, 1, 1, -1, -1, -1, -1],
[1, 1, 1, 1, -1, -1, -1, -1],
[1, 1, 1, 1, -1, -1, -1, -1],
[1, 1, 1, 1, -1, -1, -1, -1],
[-1, -1, -1, -1, 1, 1, 1, 1],
[-1, -1, -1, -1, 1, 1, 1, 1],
[-1, -1, -1, -1, 1, 1, 1, 1],
[-1, -1, -1, -1, 1, 1, 1, 1]
])
S_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
print(signal.convolve2d(I, S_y, boundary='symm'))A.
[[0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 8 8 8 8 -8 -8 8 8 8 8]
[ 8 8 8 8 -8 -8 8 8 8 8]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]]B.
[[0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 4 4 4 4 -4 -4 -4 -4 -4 -4]
[ 4 4 4 4 -4 -4 -4 -4 -4 -4]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]]C.
[[0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 8 8 8 8 4 -4 -8 -8 -8 -8]
[ 8 8 8 8 4 -4 -8 -8 -8 -8]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]]D.
[[0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 1 1 1 1 1 -1 -1 -1 -1 -1]
[ 1 1 1 1 1 -1 -1 -1 -1 -1]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]]E.
[[0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 1 1 1 1 1 1 1 1 1 1]
[ 1 1 1 1 1 1 1 1 1 1]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0]]4. What is the output of this code?
from sympy import Symbol, Limit, sin
x = Symbol('x')
ans = Limit((3 * x**2) / (x**2), x, np.inf).doit()
print(round(ans, 2))| A | B | C | D |
|---|---|----|--------|
| 0 | 3 | oo | np.inf |5. What is the output of this code?
def count_cell_neighbors(M, row, col):
neighbors = M[max(0, row-1):min(row+2, M.shape[0]),
max(0, col-1):min(col+2, M.shape[1])]
return neighbors.sum() - M[row, col] M = np.array([
[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 1, 1],
[0, 0, 0, 0], ])
A = np.zeros_like(M)
for row in range(M.shape[0]):
for col in range(M.shape[1]):
A[row, col] = count_cell_neighbors(M, row, col)
plt.matshow(A, cmap='gray')
plt.colorbar()A.
B.
C.
D.
6. What is the output of this code?
D = np.zeros((100))
for i in range(int(1e6)):
rand = int(random.gauss(mu=50, sigma=10))
if 0 <= rand < 100:
D[rand] += 1
for i in range(int(1e6)):
rand = int(random.gauss(mu=40, sigma=3))
if 0 <= rand < 100:
if D[rand] > 0:
D[rand] -= 1
D /= D.max()
C = np.cumsum(D)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6))
ax1.plot(list(range(len(D))), D)
ax1.set_title('Gaussian mixture')
ax2.plot(list(range(len(C))), C)
ax2.set_title('Cumulative sum');A.
B.
C.
D.
E.
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