davrot/pytutorial
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2023-12-15 14:18:46 +01:00 committed by GitHub
parent 2bef5651c3
commit f58e6d0533
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 31 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

32
numpy/fft/README.md
View file

@ -32,7 +32,7 @@ fft.fft(a, n=None, axis=-1, norm=None)[source]
>
> This function computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT].
## [numpy.fft.rfft](https://numpy.org/doc/stable/reference/generated/numpy.fft.rfft.html)
### [numpy.fft.rfft](https://numpy.org/doc/stable/reference/generated/numpy.fft.rfft.html)
```python
fft.rfft(a, n=None, axis=-1, norm=None)[source]
@ -42,6 +42,36 @@ fft.rfft(a, n=None, axis=-1, norm=None)[source]
>
> This function computes the one-dimensional n-point discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT).
### Comparison
If the input array is **real-valued** (i.e. no complex numbers) then use **rfft**. Otherwise use **fft**. However, you can always use **fft** if you want but you might need to add extra steps to remove the complex noise from the results. E.g. if x is real-valued ifft(fft(x)) can be complex, due to numerical noise.
The test signal:
```python
import numpy as np
import matplotlib.pyplot as plt
# Test signal
f: float = 10.0
t = np.linspace(0, 10, 10000)
x = np.sin(t * f * 2 * np.pi)
plt.plot(t, x)
plt.ylabel("sin(x)")
plt.xlabel("sin(x)")
plt.show()
```
![image0](image0.png)
```python
fft_result = np.fft.fft(x)
print(fft_result.shape) # -> (10000,)
rfft_result = np.fft.rfft(x)
print(rfft_result.shape) # -> (5001,)
```
## [Discrete Fourier Transform (numpy.fft)](https://numpy.org/doc/stable/reference/routines.fft.html#discrete-fourier-transform-numpy-fft)