FFT with np.fft module in NumPy

Introduction

FFT helps us quickly find the hidden frequencies in a signal. It turns time data into frequency data so we can understand patterns better.

Analyzing sound waves to find musical notes.

Checking vibrations in machines to detect problems.

Studying heartbeats to find irregular rhythms.

Finding repeating patterns in stock prices.

Syntax

NumPy

numpy.fft.fft(a, n=None, axis=-1, norm=None)

a is the input array (your signal data).

n is the length of the FFT (optional, pads or trims data).

Examples

Basic FFT on a small array of 4 numbers.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4])
fft_result = np.fft.fft(x)

FFT with zero-padding to length 8 for higher frequency resolution.

NumPy

fft_result = np.fft.fft(x, n=8)

FFT along the first axis (default for 1D arrays).

NumPy

fft_result = np.fft.fft(x, axis=0)

Sample Program

This program creates a signal with two sine waves at 50 Hz and 120 Hz. It uses FFT to find these frequencies and prints their values and amplitudes. It also plots the original signal and the frequency spectrum.

NumPy

import numpy as np
import matplotlib.pyplot as plt

# Create a signal with two frequencies
sampling_rate = 1000  # samples per second
T = 1.0 / sampling_rate  # sample spacing
x = np.linspace(0.0, 1.0, sampling_rate, endpoint=False)
signal = 0.5 * np.sin(50.0 * 2.0 * np.pi * x) + 0.3 * np.sin(120.0 * 2.0 * np.pi * x)

# Compute FFT
fft_values = np.fft.fft(signal)

# Compute frequencies for x-axis
freqs = np.fft.fftfreq(len(signal), T)

# Take only the positive half of frequencies and amplitudes
positive_freqs = freqs[:len(freqs)//2]
positive_fft = np.abs(fft_values[:len(fft_values)//2])

# Print main frequency peaks
peak_indices = positive_fft.argsort()[-2:][::-1]
for i in peak_indices:
    print(f"Frequency: {positive_freqs[i]:.1f} Hz, Amplitude: {positive_fft[i]:.2f}")

# Plot the signal and its FFT
plt.subplot(2, 1, 1)
plt.plot(x, signal)
plt.title('Original Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')

plt.subplot(2, 1, 2)
plt.stem(positive_freqs, positive_fft, use_line_collection=True)
plt.title('FFT Magnitude')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Magnitude')
plt.tight_layout()
plt.show()

Important Notes

FFT output is complex numbers; use np.abs() to get amplitude.

Frequencies can be positive or negative; usually, we look at positive frequencies only.

Sampling rate affects frequency resolution and max frequency you can detect.

Summary

FFT converts time signals into frequency signals.

Use np.fft.fft() to perform FFT on data arrays.

Analyze the magnitude of FFT to find main frequencies in your data.

Practice

1. What does the np.fft.fft() function do in numpy?

easy

A. It calculates the mean of an array.

B. It sorts an array in ascending order.

C. It generates random numbers.

D. It computes the Fast Fourier Transform of a signal.

Solution

1. Step 1: Understand the purpose of FFT

   FFT converts time-domain data into frequency-domain data quickly.

2. Step 2: Identify the function role

   np.fft.fft() computes this Fast Fourier Transform on input data.

3. Final Answer:

   It computes the Fast Fourier Transform of a signal. -> Option D

4. Quick Check:

   FFT = Fast Fourier Transform [OK]

Hint: FFT = Fast Fourier Transform, converts time to frequency [OK]

Common Mistakes:

• Confusing FFT with sorting or averaging functions
• Thinking FFT generates random data
• Mixing FFT with statistical calculations

2. Which of the following is the correct syntax to compute the FFT of a numpy array data?

easy

A. np.fft(data).fft()

B. np.fft.fft(data)

C. fft(np.data)

D. np.fft_fft(data)

Solution

1. Step 1: Recall numpy FFT syntax

   The correct function call is np.fft.fft() with the data array as argument.

2. Step 2: Check each option

   Only np.fft.fft(data) matches the correct syntax; others are invalid or incorrect.

3. Final Answer:

   np.fft.fft(data) -> Option B

4. Quick Check:

   Correct function call = np.fft.fft(data) [OK]

Hint: Use np.fft.fft(array) to compute FFT [OK]

Common Mistakes:

• Swapping function and module order
• Using wrong function names
• Passing wrong argument types

3. What is the output of the following code?

import numpy as np
x = np.array([1, 0, 0, 0])
fft_x = np.fft.fft(x)
print(np.round(fft_x, 2))

medium

A. [1.+0.j 0.+0.j 0.+0.j 0.+0.j]

B. [1.+0.j 1.+0.j 1.+0.j 1.+0.j]

C. [4.+0.j 0.+0.j 0.+0.j 0.+0.j]

D. [0.+0.j 0.+0.j 0.+0.j 0.+0.j]

Solution

1. Step 1: Understand input signal

   The input array has one '1' followed by zeros, representing a unit impulse (delta function).

2. Step 2: Compute FFT of impulse

   The FFT of a unit impulse of length 4 is [4.+0.j 0.+0.j 0.+0.j 0.+0.j], because the sum of the input is 1 at position 0, but the FFT sums all elements, so the DC component is 4.

3. Final Answer:

   [4.+0.j 0.+0.j 0.+0.j 0.+0.j] -> Option C

4. Quick Check:

   Impulse FFT = DC component equals sum of input [OK]

Hint: FFT of unit impulse = all ones [Incorrect], actually FFT of [1,0,0,0] = [4,0,0,0] [OK]

Common Mistakes:

• Thinking FFT shows sum only at DC ([4.+0.j 0.+0.j 0.+0.j 0.+0.j])
• Ignoring complex number format
• Misreading array rounding

4. Identify the error in this code snippet:

import numpy as np
signal = [1, 2, 3, 4]
fft_signal = np.fft.fft(signal, n=-1)
print(fft_signal)

medium

A. The FFT length n cannot be smaller than the input length.

B. The print statement syntax is incorrect.

C. The function np.fft.fft does not accept the parameter n.

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

Solution

1. Step 1: Check FFT length parameter

   The input signal length is 4, but n=-1 is smaller than input length.

2. Step 2: Understand FFT behavior with n parameter

   FFT length n must be at least the input length or zero-padded; smaller n truncates data causing error or unexpected results.

3. Final Answer:

   The FFT length n cannot be smaller than the input length. -> Option A

4. Quick Check:

   FFT length n ≥ input length [OK]

Hint: FFT length n ≥ input length [OK]

Common Mistakes:

• Thinking lists are invalid input
• Believing n can be any number
• Assuming print syntax error

5. You have a noisy signal stored in a numpy array noisy_signal. You want to remove high-frequency noise by zeroing FFT components above index 5. Which code correctly applies this filter and returns the cleaned signal?

hard

A. fft_vals = np.fft.fft(noisy_signal)
fft_vals[5:] = 0
clean_signal = np.fft.ifft(fft_vals).real

B. fft_vals = np.fft.fft(noisy_signal)
fft_vals[:5] = 0
clean_signal = np.fft.ifft(fft_vals).real

C. fft_vals = np.fft.ifft(noisy_signal)
fft_vals[5:] = 0
clean_signal = np.fft.fft(fft_vals).real

D. clean_signal = np.fft.fft(noisy_signal)
clean_signal[5:] = 0

Solution

1. Step 1: Compute FFT of noisy signal

   Use np.fft.fft(noisy_signal) to get frequency components.

2. Step 2: Zero out high-frequency components

   Set FFT values from index 5 onward to zero to remove high frequencies.

3. Step 3: Compute inverse FFT to get cleaned signal

   Apply np.fft.ifft() and take real part to get filtered time-domain signal.

4. Final Answer:

   fft_vals = np.fft.fft(noisy_signal) fft_vals[5:] = 0 clean_signal = np.fft.ifft(fft_vals).real -> Option A

5. Quick Check:

   Zero high freq FFT, then inverse FFT [OK]

Hint: Zero high freq FFT components, then inverse FFT [OK]

Common Mistakes:

• Zeroing low-frequency components instead
• Using ifft before fft
• Not taking real part after inverse FFT