Spectrogram generation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the spectrogram function from scipy.signal.

SciPy

from scipy.signal import [1]

# Now you can use spectrogram() to generate spectrograms.

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

Aspectrogram

Bfft

Cstft

Dwelch

Attempts:

3 left

💡 Hint

Common Mistakes

Importing fft instead of spectrogram

Using stft or welch which are different functions

The spectrogram function is imported from scipy.signal to generate spectrograms.

2fill in blank

medium

Complete the code to generate a spectrogram from the signal array using the spectrogram function.

SciPy

frequencies, times, Sxx = spectrogram([1])

# frequencies: frequency bins
# times: time bins
# Sxx: spectrogram intensity

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

Aaudio

Bnp.array

Cdata

Dsignal

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable like np.array or data

Passing the function name instead of the signal variable

The input to spectrogram() should be the signal array, here named signal.

3fill in blank

hard

Fix the error in the code to correctly generate a spectrogram with a window size of 256 samples.

SciPy

frequencies, times, Sxx = spectrogram(signal, nperseg=[1])

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

A512

B128

C256

D1024

Attempts:

3 left

💡 Hint

Common Mistakes

Using a window size too large or too small

Not setting nperseg at all

The nperseg parameter sets the window size. To use 256 samples, set nperseg=256.

4fill in blank

hard

Fill both blanks to generate a spectrogram with a sampling frequency of 1000 Hz and a window size of 128 samples.

SciPy

frequencies, times, Sxx = spectrogram(signal, fs=[1], nperseg=[2])

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

A1000

B500

C128

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up fs and nperseg values

Using wrong values for sampling frequency or window size

Set fs=1000 for 1000 Hz sampling frequency and nperseg=128 for window size.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each frequency to its maximum spectrogram intensity if the intensity is greater than 0.5.

SciPy

max_intensity = { [1]: max(Sxx[i]) for i, [2] in enumerate(frequencies) if max(Sxx[i]) [3] 0.5 }

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

Afreq

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names

Using '<' instead of '>' in the condition

Not using max(Sxx[i]) correctly

The dictionary comprehension uses freq as key, iterates over frequencies, and filters intensities greater than 0.5.

Practice

1. What does the scipy.signal.spectrogram function primarily compute from a signal?

easy

A. The frequency content of the signal over time

B. The total energy of the signal

C. The average amplitude of the signal

D. The phase shift of the signal

Solution

1. Step 1: Understand the purpose of spectrogram

   A spectrogram shows how frequencies in a signal change over time, so it computes frequency content over time.

2. Step 2: Compare with other options

   Total energy, average amplitude, and phase shift are different signal properties, not what spectrogram calculates.

3. Final Answer:

   The frequency content of the signal over time -> Option A

4. Quick Check:

   Spectrogram = frequency over time [OK]

Hint: Spectrogram = frequency changes over time [OK]

Common Mistakes:

• Confusing spectrogram with total energy calculation
• Thinking it returns average amplitude only
• Assuming it gives phase information

2. Which of the following is the correct way to import the spectrogram function from scipy?

easy

A. from scipy import spectrogram

B. import scipy.spectrogram

C. from scipy.signal import spectrogram

D. import spectrogram from scipy.signal

Solution

1. Step 1: Recall correct import syntax for functions

   Functions inside submodules are imported using 'from module.submodule import function'.

2. Step 2: Check each option

   from scipy.signal import spectrogram matches this syntax exactly. Options B, C, and D are invalid Python import statements.

3. Final Answer:

   from scipy.signal import spectrogram -> Option C

4. Quick Check:

   Correct import syntax = from scipy.signal import spectrogram [OK]

Hint: Use 'from scipy.signal import spectrogram' [OK]

Common Mistakes:

• Using 'import scipy.spectrogram' which is invalid
• Trying 'from scipy import spectrogram' ignoring submodule
• Incorrect import order like 'import spectrogram from ...'

3. What will be the shape of the frequency array returned by f, t, Sxx = spectrogram(signal, fs=1000, nperseg=256) if signal length is 1024?

medium

A. (256,)

B. (129,)

C. (512,)

D. (1024,)

Solution

1. Step 1: Understand frequency bins from nperseg

   The frequency array length is nperseg/2 + 1 due to FFT symmetry, so 256/2 + 1 = 129.

2. Step 2: Confirm other options

   256 is the segment length, not frequency bins. 512 and 1024 are unrelated to frequency array size here.

3. Final Answer:

   (129,) -> Option B

4. Quick Check:

   Frequency bins = nperseg/2 + 1 = 129 [OK]

Hint: Frequency bins = nperseg/2 + 1 [OK]

Common Mistakes:

• Assuming frequency array length equals nperseg
• Confusing signal length with frequency bins
• Ignoring FFT symmetry in frequency output

4. Given this code snippet, what is the error?

from scipy.signal import spectrogram
signal = [1, 2, 3, 4, 5]
fs = 100
f, t, Sxx = spectrogram(signal, nperseg=256, fs=fs)

medium

A. nperseg is larger than signal length causing a warning or error

B. fs parameter is missing

C. spectrogram function is not imported correctly

D. signal must be a numpy array, not a list

Solution

1. Step 1: Check parameters and signal length

   Signal length is 5, but nperseg is 256, which is larger than signal length.

2. Step 2: Understand spectrogram behavior

   When nperseg > signal length, scipy issues a warning or error because segment size can't exceed signal length.

3. Final Answer:

   nperseg is larger than signal length causing a warning or error -> Option A

4. Quick Check:

   nperseg > signal length causes error [OK]

Hint: nperseg must be ≤ signal length [OK]

Common Mistakes:

• Ignoring nperseg size relative to signal length
• Thinking fs is missing when it is provided
• Assuming list input causes error (it works but numpy array preferred)

5. You want to generate a spectrogram for a noisy signal sampled at 2000 Hz. To improve time resolution, which parameter should you decrease in scipy.signal.spectrogram?

hard

A. window

B. fs

C. noverlap

D. nperseg

Solution

1. Step 1: Understand time vs frequency resolution trade-off

   Decreasing nperseg (segment length) improves time resolution but reduces frequency resolution.

2. Step 2: Check other parameters

   fs is sample rate, noverlap controls overlap, window shapes segments but doesn't directly improve time resolution.

3. Final Answer:

   nperseg -> Option D

4. Quick Check:

   Decrease nperseg for better time resolution [OK]

Hint: Lower nperseg for better time resolution [OK]

Common Mistakes:

• Trying to change fs which is fixed sample rate
• Confusing noverlap with time resolution
• Changing window type expecting time resolution change