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])

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

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])

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

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)

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

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}

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

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.

Practice

1. Why do we use signal processing to extract information from data?

easy

A. To make the data more complex and harder to understand

B. To clean and highlight important parts of the data

C. To delete all the data and keep nothing

D. To randomly change the data values

Solution

1. Step 1: Understand the purpose of signal processing

   Signal processing is used to clean data and focus on useful parts.

2. Step 2: Identify the correct purpose from options

   Only To clean and highlight important parts of the data says it cleans and highlights important data, which matches the purpose.

3. Final Answer:

   To clean and highlight important parts of the data -> Option B

4. Quick Check:

   Signal processing cleans data = A [OK]

Hint: Signal processing cleans data to find useful info [OK]

Common Mistakes:

• Thinking signal processing makes data complex
• Believing it deletes all data
• Assuming it changes data randomly

2. Which of the following is the correct way to import the signal module from scipy?

easy

A. import scipy as signal

B. import signal from scipy

C. import scipy.signal

D. from scipy import signal

Solution

1. Step 1: Recall Python import syntax

   The correct syntax to import a submodule is 'from package import module'.

2. Step 2: Match options with correct syntax

   'from scipy import signal' is correct as it imports the signal module directly into the namespace. 'import scipy as signal' only aliases the scipy package. 'import signal from scipy' is invalid syntax. 'import scipy.signal' requires using scipy.signal.function().

3. Final Answer:

   from scipy import signal -> Option D

4. Quick Check:

   Correct import syntax = C [OK]

Hint: Use 'from scipy import signal' to import correctly [OK]

Common Mistakes:

• Using invalid import syntax like 'import signal from scipy'
• Forgetting 'from' keyword
• Trying to import submodule directly without package

3. What will be the output of this code snippet?

from scipy import signal
import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = signal.savgol_filter(x, window_length=3, polyorder=1)
print(np.round(y, 2))

medium

A. [1.33 2. 3. 4. 4.67]

B. [1. 2. 3. 4. 5. ]

C. [1.5 2.5 3.5 4.5 5.5]

D. [0.5 1.5 2.5 3.5 4.5]

Solution

1. Step 1: Understand what savgol_filter does

   The Savitzky-Golay filter smooths data by fitting polynomials in windows.

2. Step 2: Apply filter to input array

   With window_length=3 and polyorder=1, the filter smooths edges slightly, producing values close to but not identical to original.

3. Final Answer:

   [1.33 2. 3. 4. 4.67] -> Option A

4. Quick Check:

   Savgol filter smooths data = A [OK]

Hint: Savgol filter smooths edges, output differs slightly [OK]

Common Mistakes:

• Expecting output to be identical to input
• Confusing filter parameters
• Rounding errors in output

4. Identify the error in this code that tries to apply a low-pass filter using scipy.signal:

from scipy import signal
import numpy as np
fs = 1000
cutoff = 100
b, a = signal.butter(4, cutoff, btype='low')
data = np.sin(2 * np.pi * 50 * np.linspace(0, 1, fs))
filtered = signal.lfilter(b, a, data)
print(filtered[:5])

medium

A. The sampling frequency fs is missing

B. The filter order 4 is too high for this data

C. The cutoff frequency should be normalized between 0 and 1

D. The signal.lfilter function is used incorrectly

Solution

1. Step 1: Check butter function parameters

   scipy.signal.butter expects cutoff (Wn) normalized between 0 and 1 (fraction of Nyquist) when fs is not provided.

2. Step 2: Examine the butter call

   signal.butter(4, cutoff, btype='low') uses cutoff=100 (>1) without fs, causing ValueError.

3. Step 3: Confirm other options

   fs is defined but not passed; order 4 is fine; lfilter usage correct.

4. Final Answer:

   The cutoff frequency should be normalized between 0 and 1 -> Option C

5. Quick Check:

   Without fs, cutoff must be normalized 0-1 = D [OK]

Hint: Check if cutoff frequency needs normalization with butter [OK]

Common Mistakes:

• Forgetting to normalize cutoff frequency when fs is missing
• Confusing filter order with data size
• Misusing lfilter parameters

5. You have noisy heart rate data sampled at 500 Hz. Which scipy.signal method helps extract the true heart rate by removing high-frequency noise?

hard

A. signal.butter with a low-pass filter

B. signal.medfilt to apply a median filter

C. signal.correlate to find repeating patterns

D. signal.spectrogram to visualize frequencies

Solution

1. Step 1: Understand the problem context

   Noisy heart rate data needs noise removal to find true rate, especially removing high-frequency noise.

2. Step 2: Identify suitable signal processing method

   Low-pass filters remove high-frequency noise, preserving slower heart rate signals. Butterworth filter is a common low-pass filter.

3. Step 3: Evaluate other options

   Median filter removes spikes but less effective for frequency noise. Correlate finds patterns but does not filter noise. Spectrogram visualizes frequencies but does not filter.

4. Final Answer:

   signal.butter with a low-pass filter -> Option A

5. Quick Check:

   Low-pass filter removes high-frequency noise = B [OK]

Hint: Use low-pass butter filter to remove high-frequency noise [OK]

Common Mistakes:

• Choosing median filter for frequency noise
• Using correlate to filter noise
• Confusing visualization with filtering