Power spectral density in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the power spectral density (PSD) of the signal using scipy.

SciPy

from scipy.signal import [1]
import numpy as np

fs = 1000  # Sampling frequency
signal = np.sin(2 * np.pi * 50 * np.linspace(0, 1, fs))
f, Pxx = [1](signal, fs)
print(f[:5], Pxx[:5])

Drag options to blanks, or click blank then click option'

Aperiodogram

Bfind_peaks

Cspectrogram

Dwelch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'spectrogram' instead of 'welch' which returns a spectrogram, not PSD directly.

Using 'find_peaks' which is for peak detection, not PSD.

Using 'periodogram' which also computes PSD but is less robust than 'welch'.

The welch function from scipy.signal computes the power spectral density (PSD) of a signal.

2fill in blank

medium

Complete the code to generate a noisy sine wave signal before computing its PSD.

SciPy

import numpy as np
fs = 500
T = 1
n = int(T * fs)
time = np.linspace(0, T, n, endpoint=False)
signal = np.sin(2 * np.pi * 60 * time) + [1]

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Anp.random.normal(0, 0.5, n)

Bnp.random.uniform(0, 1, n)

Cnp.random.binomial(10, 0.5, n)

Dnp.random.poisson(5, n)

Attempts:

3 left

💡 Hint

Common Mistakes

Using uniform noise which has a different distribution.

Using discrete distributions like Poisson or binomial which are not typical for noise.

Adding Gaussian noise with mean 0 and standard deviation 0.5 simulates a noisy sine wave.

3fill in blank

hard

Fix the error in the code to correctly compute the PSD with the correct sampling frequency parameter.

SciPy

from scipy.signal import welch
import numpy as np
fs = 1000
signal = np.sin(2 * np.pi * 100 * np.linspace(0, 1, fs))
f, Pxx = welch(signal, [1]=fs)
print(f[:5], Pxx[:5])

Drag options to blanks, or click blank then click option'

Afs

Bsampling_rate

Cfreq

Drate

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'sampling_rate' or 'freq' causes errors.

Omitting the sampling frequency leads to wrong frequency axis.

The correct parameter name for sampling frequency in welch is fs.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

SciPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword.startswith('s')

Dword == 'fun'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using unrelated conditions like checking the first letter.

The dictionary comprehension maps each word to its length (len(word)) only if the length is greater than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 2.

SciPy

words = ['Data', 'AI', 'ML', 'Science']
result = { [1]: [2] for word in words if [3] }

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Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using incorrect conditions that do not filter by length.

The comprehension maps the uppercase version of each word (word.upper()) to its length (len(word)) only if the length is greater than 2 (len(word) > 2).