From ac3728064e00d14bf98b94aeba78678b160295bf Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Fri, 15 Dec 2023 14:26:51 +0100
Subject: [PATCH] Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 numpy/fft/README.md | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/numpy/fft/README.md b/numpy/fft/README.md
index 99c8ab4..b7f0067 100644
--- a/numpy/fft/README.md
+++ b/numpy/fft/README.md
@@ -60,7 +60,7 @@ x = np.sin(t * f * 2 * np.pi)
 
 plt.plot(t, x)
 plt.ylabel("sin(x)")
-plt.xlabel("sin(x)")
+plt.xlabel("t")
 plt.show()
 ```
 
@@ -88,12 +88,27 @@ f = [0, 1, ...,   n/2-1,     -n/2, ..., -1] / (d*n)   if n is even
 f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n)   if n is odd
 ```
 
-## [numpy.fft.rfftfreq](https://numpy.org/doc/stable/reference/generated/numpy.fft.rfftfreq.html#numpy-fft-rfftfreq)
+### [numpy.fft.rfftfreq](https://numpy.org/doc/stable/reference/generated/numpy.fft.rfftfreq.html#numpy-fft-rfftfreq)
 
 ```python
 fft.rfftfreq(n, d=1.0)
 ```
 
+### Comparison (cont.)
+
+The frequency axes:
+
+```python
+dt = t[1] - t[0]
+fft_freq = np.fft.fftfreq(x.shape[0],dt)
+print(fft_freq.shape) # -> (10000,)
+
+rfft_freq = np.fft.rfftfreq(x.shape[0],dt)
+print(rfft_freq.shape) # -> (5001,)
+```
+
+
+
 > Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft).
 >
 > The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second.
@@ -107,6 +122,8 @@ f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n)   if n is odd
 
 > Unlike fftfreq (but like scipy.fftpack.rfftfreq) the Nyquist frequency component is considered to be positive.
 
+
+
 ## [Discrete Fourier Transform (numpy.fft)](https://numpy.org/doc/stable/reference/routines.fft.html#discrete-fourier-transform-numpy-fft)
 
 ## [Standard FFTs](https://numpy.org/doc/stable/reference/routines.fft.html#standard-ffts)