Merge pull request numpy#29775 from star1327p/link-cross-ref

DOC: Link cross references in numpy documentation

Author: mattip

numpy/fft/_pocketfft.py

@@ -124,7 +124,7 @@ def fft(a, n=None, axis=-1, norm=None, out=None):
 
     This function computes the one-dimensional *n*-point discrete Fourier
     Transform (DFT) with the efficient Fast Fourier Transform (FFT)
-    algorithm [CT].
+    algorithm [CT]_.
 
     Parameters
     ----------

numpy/lib/_shape_base_impl.py

@@ -1110,7 +1110,7 @@ def kron(a, b):
     -----
     The function assumes that the number of dimensions of `a` and `b`
     are the same, if necessary prepending the smallest with ones.
-    If ``a.shape = (r0,r1,..,rN)`` and ``b.shape = (s0,s1,...,sN)``,
+    If ``a.shape = (r0,r1,...,rN)`` and ``b.shape = (s0,s1,...,sN)``,
     the Kronecker product has shape ``(r0*s0, r1*s1, ..., rN*SN)``.
     The elements are products of elements from `a` and `b`, organized
     explicitly by::

numpy/linalg/_linalg.py

@@ -2073,9 +2073,9 @@ def matrix_rank(A, tol=None, hermitian=False, *, rtol=None):
     The default threshold to detect rank deficiency is a test on the magnitude
     of the singular values of `A`.  By default, we identify singular values
     less than ``S.max() * max(M, N) * eps`` as indicating rank deficiency
-    (with the symbols defined above). This is the algorithm MATLAB uses [1].
+    (with the symbols defined above). This is the algorithm MATLAB uses [1]_.
     It also appears in *Numerical recipes* in the discussion of SVD solutions
-    for linear least squares [2].
+    for linear least squares [2]_.
 
     This default threshold is designed to detect rank deficiency accounting
     for the numerical errors of the SVD computation. Imagine that there