Why signal processing extracts information in SciPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the signal module from scipy.

SciPy

from scipy import [1]

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

Asignal

Bstats

Coptimize

Dintegrate

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong scipy module like stats or optimize.

Forgetting to import signal before using its functions.

The signal module in scipy contains functions for signal processing.

2fill in blank

medium

Complete the code to create a simple sine wave signal using numpy.

SciPy

import numpy as np
fs = 1000  # Sampling frequency
T = 1.0   # seconds
x = np.linspace(0, T, int(T*fs), endpoint=False)
signal = np.sin(2 * np.pi * 5 * [1])

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Afs

BT

Cx

Dnp.pi

Attempts:

3 left

💡 Hint

Common Mistakes

Using fs or T instead of the time array x.

Using np.pi incorrectly inside the sine function.

The variable x holds the time points for the sine wave.

3fill in blank

hard

Fix the error in the code to compute the Fourier Transform of the signal.

SciPy

from scipy import signal
import numpy as np
fs = 1000
T = 1.0
x = np.linspace(0, T, int(T*fs), endpoint=False)
sig = np.sin(2 * np.pi * 5 * x)
freqs, psd = signal.welch(sig, [1])

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Asig

Bx

CT

Dfs

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the time array or signal instead of the sampling frequency.

Confusing the order of arguments in the function call.

The welch function requires the sampling frequency fs as the second argument.

4fill in blank

hard

Fill both blanks to create a bandpass filter and apply it to the signal.

SciPy

from scipy import signal
fs = 500
nyq = 0.5 * [1]
low = 10 / nyq
high = 50 / [2]
b, a = signal.butter(4, [low, high], btype='band')
filtered = signal.lfilter(b, a, sig)

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Afs

Bsig

Cnyq

Attempts:

3 left

💡 Hint

Common Mistakes

Using the signal variable instead of fs or nyq.

Using fs for the second blank instead of nyq.

The Nyquist frequency is half the sampling frequency fs for the first blank. The second blank requires nyq to normalize the high cutoff frequency.

5fill in blank

hard

Fill both blanks to create a dictionary of frequencies and their power values above a threshold.

SciPy

result = {freq[1]: psd for freq, psd in zip(freqs, psd) if psd [2] 0.01}

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A.round(2)

C>

D.astype(int)

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to convert power values to int which loses precision.

Using wrong comparison operators in the filter condition.

We round the frequency to 2 decimals, keep power spectral density as is, and filter values greater than 0.01.