diff options
| -rw-r--r-- | doc/source/user/numpy-for-matlab-users.rst | 68 |
1 files changed, 35 insertions, 33 deletions
diff --git a/doc/source/user/numpy-for-matlab-users.rst b/doc/source/user/numpy-for-matlab-users.rst index be0b2cbd9..d94233a2e 100644 --- a/doc/source/user/numpy-for-matlab-users.rst +++ b/doc/source/user/numpy-for-matlab-users.rst @@ -42,7 +42,7 @@ Some Key Differences 'array' or 'matrix'? Which should I use? ======================================== -Numpy provides, in addition to `np.ndarray`` an additional matrix type +Numpy provides, in addition to ``np.ndarray``, an additional matrix type that you may see used in some existing code. Which one to use? Short answer @@ -51,7 +51,7 @@ Short answer **Use arrays**. - They are the standard vector/matrix/tensor type of numpy. Many numpy - function return arrays, not matrices. + functions return arrays, not matrices. - There is a clear distinction between element-wise operations and linear algebra operations. - You can have standard vectors or row/column vectors if you like. @@ -123,7 +123,7 @@ There are pros and cons to using both: - ``:)`` Is quite at home handling data of any rank. - ``:)`` Closer in semantics to tensor algebra, if you are familiar with that. - - ``:)`` *All* operations (``*``, ``/``, ``+``, ```` etc.) are + - ``:)`` *All* operations (``*``, ``/``, ``+``, ``-`` etc.) are elementwise - ``matrix`` @@ -159,7 +159,7 @@ which hopefully make things easier for Matlab converts. return ``array``\ s, but the ``matlib`` versions return ``matrix`` objects. - ``mat`` has been changed to be a synonym for ``asmatrix``, rather - than ``matrix``, thus making it concise way to convert an ``array`` + than ``matrix``, thus making it a concise way to convert an ``array`` to a ``matrix`` without copying the data. - Some top-level functions have been removed. For example ``numpy.rand()`` now needs to be accessed as ``numpy.random.rand()``. @@ -252,7 +252,7 @@ Linear Algebra Equivalents * - ``ndims(a)`` - ``ndim(a)`` or ``a.ndim`` - - get the number of dimensions of a (tensor rank) + - get the number of dimensions of ``a`` (tensor rank) * - ``numel(a)`` - ``size(a)`` or ``a.size`` @@ -264,7 +264,7 @@ Linear Algebra Equivalents * - ``size(a,n)`` - ``a.shape[n-1]`` - - get the number of elements of the n-th dimension of array a. (Note that MATLAB® uses 1 based indexing while Python uses 0 based indexing, See note :ref:`INDEXING <numpy-for-matlab-users.notes>`) + - get the number of elements of the n-th dimension of array ``a``. (Note that MATLAB® uses 1 based indexing while Python uses 0 based indexing, See note :ref:`INDEXING <numpy-for-matlab-users.notes>`) * - ``[ 1 2 3; 4 5 6 ]`` - ``array([[1.,2.,3.], [4.,5.,6.]])`` @@ -273,7 +273,7 @@ Linear Algebra Equivalents * - ``[ a b; c d ]`` - ``vstack([hstack([a,b]), hstack([c,d])])`` or ``bmat('a b; c d').A`` - - construct a matrix from blocks a,b,c, and d + - construct a matrix from blocks ``a``, ``b``, ``c``, and ``d`` * - ``a(end)`` - ``a[-1]`` @@ -345,27 +345,29 @@ Linear Algebra Equivalents * - ``(a>0.5)`` - ``(a>0.5)`` - - matrix whose i,jth element is (a_ij > 0.5) + - matrix whose i,jth element is (a_ij > 0.5). The Matlab result is + an array of 0s and 1s. The NumPy result is an array of the boolean + values ``False`` and ``True``. * - ``find(a>0.5)`` - ``nonzero(a>0.5)`` - - find the indices where (a > 0.5) + - find the indices where (``a`` > 0.5) * - ``a(:,find(v>0.5))`` - ``a[:,nonzero(v>0.5)[0]]`` - - extract the columms of a where vector v > 0.5 + - extract the columms of ``a`` where vector v > 0.5 * - ``a(:,find(v>0.5))`` - ``a[:,v.T>0.5]`` - - extract the columms of a where column vector v > 0.5 + - extract the columms of ``a`` where column vector v > 0.5 * - ``a(a<0.5)=0`` - ``a[a<0.5]=0`` - - a with elements less than 0.5 zeroed out + - ``a`` with elements less than 0.5 zeroed out * - ``a .* (a>0.5)`` - ``a * (a>0.5)`` - - a with elements less than 0.5 zeroed out + - ``a`` with elements less than 0.5 zeroed out * - ``a(:) = 3`` - ``a[:] = 3`` @@ -380,7 +382,7 @@ Linear Algebra Equivalents - numpy slices are by reference * - ``y=x(:)`` - - ``y = x.flatten(1)`` + - ``y = x.flatten()`` - turn array into vector (note that this forces a copy) * - ``1:10`` @@ -413,11 +415,11 @@ Linear Algebra Equivalents * - ``diag(a)`` - ``diag(a)`` - - vector of diagonal elements of a + - vector of diagonal elements of ``a`` * - ``diag(a,0)`` - ``diag(a,0)`` - - square diagonal matrix whose nonzero values are the elements of a + - square diagonal matrix whose nonzero values are the elements of ``a`` * - ``rand(3,4)`` - ``random.rand(3,4)`` @@ -445,7 +447,7 @@ Linear Algebra Equivalents * - ``repmat(a, m, n)`` - ``tile(a, (m, n))`` - - create m by n copies of a + - create m by n copies of ``a`` * - ``[a b]`` - ``concatenate((a,b),1)`` or ``hstack((a,b))`` or ``column_stack((a,b))`` or ``c_[a,b]`` @@ -453,27 +455,27 @@ Linear Algebra Equivalents * - ``[a; b]`` - ``concatenate((a,b))`` or ``vstack((a,b))`` or ``r_[a,b]`` - - concatenate rows of a and b + - concatenate rows of ``a`` and ``b`` * - ``max(max(a))`` - ``a.max()`` - - maximum element of a (with ndims(a)<=2 for matlab) + - maximum element of ``a`` (with ndims(a)<=2 for matlab) * - ``max(a)`` - ``a.max(0)`` - - maximum element of each column of matrix a + - maximum element of each column of matrix ``a`` * - ``max(a,[],2)`` - ``a.max(1)`` - - maximum element of each row of matrix a + - maximum element of each row of matrix ``a`` * - ``max(a,b)`` - ``maximum(a, b)`` - - compares a and b element-wise, and returns the maximum value from each pair + - compares ``a`` and ``b`` element-wise, and returns the maximum value from each pair * - ``norm(v)`` - ``sqrt(dot(v,v))`` or ``np.linalg.norm(v)`` - - L2 norm of vector v + - L2 norm of vector ``v`` * - ``a & b`` - ``logical_and(a,b)`` @@ -493,15 +495,15 @@ Linear Algebra Equivalents * - ``inv(a)`` - ``linalg.inv(a)`` - - inverse of square matrix a + - inverse of square matrix ``a`` * - ``pinv(a)`` - ``linalg.pinv(a)`` - - pseudo-inverse of matrix a + - pseudo-inverse of matrix ``a`` * - ``rank(a)`` - ``linalg.matrix_rank(a)`` - - rank of a matrix a + - rank of a matrix ``a`` * - ``a\b`` - ``linalg.solve(a,b)`` if ``a`` is square; ``linalg.lstsq(a,b)`` otherwise @@ -513,23 +515,23 @@ Linear Algebra Equivalents * - ``[U,S,V]=svd(a)`` - ``U, S, Vh = linalg.svd(a), V = Vh.T`` - - singular value decomposition of a + - singular value decomposition of ``a`` * - ``chol(a)`` - ``linalg.cholesky(a).T`` - - cholesky factorization of a matrix (chol(a) in matlab returns an upper triangular matrix, but linalg.cholesky(a) returns a lower triangular matrix) + - cholesky factorization of a matrix (``chol(a)`` in matlab returns an upper triangular matrix, but ``linalg.cholesky(a)`` returns a lower triangular matrix) * - ``[V,D]=eig(a)`` - ``D,V = linalg.eig(a)`` - - eigenvalues and eigenvectors of a + - eigenvalues and eigenvectors of ``a`` * - ``[V,D]=eig(a,b)`` - ``V,D = np.linalg.eig(a,b)`` - - eigenvalues and eigenvectors of a,b + - eigenvalues and eigenvectors of ``a``, ``b`` * - ``[V,D]=eigs(a,k)`` - - - find the k largest eigenvalues and eigenvectors of a + - find the ``k`` largest eigenvalues and eigenvectors of ``a`` * - ``[Q,R,P]=qr(a,0)`` - ``Q,R = scipy.linalg.qr(a)`` @@ -545,11 +547,11 @@ Linear Algebra Equivalents * - ``fft(a)`` - ``fft(a)`` - - Fourier transform of a + - Fourier transform of ``a`` * - ``ifft(a)`` - ``ifft(a)`` - - inverse Fourier transform of a + - inverse Fourier transform of ``a`` * - ``sort(a)`` - ``sort(a)`` or ``a.sort()`` |