DOC: Clarify the frequencies of the rfft using fs, borrowing text from numpy.fft docstring

1 files changed, 8 insertions, 6 deletions

diff --git a/numpy/fft/fftpack.py b/numpy/fft/fftpack.py
index c70e336ea..472d3e06c 100644
--- a/numpy/fft/fftpack.py
+++ b/numpy/fft/fftpack.py
@@ -296,12 +296,14 @@ def rfft(a, n=None, axis=-1):
     compute the negative frequency terms, and the length of the transformed
     axis of the output is therefore ``n//2+1``.
 
-    When ``A = rfft(a)``, ``A[0]`` contains the zero-frequency term, which
-    must be purely real due to the Hermite symmetry.
-
-    If `n` is even, ``A[-1]`` contains the term for frequencies ``n/2`` and
-    ``-n/2``, and must also be purely real.  If `n` is odd, ``A[-1]``
-    contains the term for frequency ``A[(n-1)/2]``, and is complex in the
+    When ``A = rfft(a)`` and fs is the sampling frequency, ``A[0]`` contains 
+    the zero-frequency term 0*fs, which must be purely real due to the Hermite 
+    symmetry.
+
+    If `n` is even, ``A[-1]`` contains the term representing both positive 
+    and negative Nyquist frequency (+fs/2 and -fs/2), and must also be purely 
+    real. If `n` is odd, there is no term at fs/2; ``A[-1]`` contains 
+    the largest positive frequency (fs/2*(n-1)/n), and is complex in the 
     general case.
 
     If the input `a` contains an imaginary part, it is silently discarded.