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The notation mat(...) means to use the same expression as array, but convert to matrix with the mat() type converter.
The notation asarray(...) means to use the same expression as matrix, but convert to array with the asarray() type converter.
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MATLAB |
numpy.array |
numpy.matrix |
Notes |
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ndims(a) |
a.ndim |
get the number of dimensions of a (tensor rank) |
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size(a,n) |
a.shape[n-1] |
get the number of elements of the nth dimension of array a. (Note that MATLAB® uses 1 based indexing while Python uses 0 based indexing, See note 'INDEXING') |
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[ 1 2 3; 4 5 6 ] |
array([[1.,2.,3.], |
mat([[1.,2.,3.], |
2x3 matrix literal |
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[ a b; c d ] |
vstack([hstack([a,b]), |
bmat('a b; c d') |
construct a matrix from blocks a,b,c, and d |
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a(end) |
a[-1] |
a[:,-1][0,0] |
access last element in the 1xn matrix a |
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a(2,5) |
a[1,4] |
access element in second row, fifth column |
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a(2,:) |
a[1] or a[1,:] |
entire second row of a |
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a(1:5,:) |
a[0:5] or a[:5] or a[0:5,:] |
the first five rows of a |
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a(end-4:end,:) |
a[-5:] |
the last five rows of a |
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a(1:3,5:9) |
a[0:3][:,4:9] |
rows one to three and columns five to nine of a. This gives read-only access. |
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a([2,4,5],[1,3]) |
a[ix_([1,3,4],[0,2])] |
rows 2,4 and 5 and columns 1 and 3. This allows the matrix to be modified, and doesn't require a regular slice. |
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a(3:2:21,:) |
a[ 2:21:2,:] |
every other row of a, starting with the third and going to the twenty-first |
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a(1:2:end,:) |
a[ ::2,:] |
every other row of a, starting with the first |
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a(end:-1:1,:) or flipud(a) |
a[ ::-1,:] |
a with rows in reverse order |
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a([1:end 1],:) |
a[r_[:len(a),0]] |
a with copy of the first row appended to the end |
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a.' |
a.transpose() or a.T |
transpose of a |
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a' |
a.conj().transpose() or a.conj().T |
a.H |
conjugate transpose of a |
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a * b |
dot(a,b) |
a * b |
matrix multiply |
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a .* b |
a * b |
multiply(a,b) |
element-wise multiply |
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a./b |
a/b |
element-wise divide |
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a.^3 |
a**3 |
power(a,3) |
element-wise exponentiation |
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(a>0.5) |
(a>0.5) |
matrix whose i,jth element is (a_ij > 0.5) |
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find(a>0.5) |
nonzero(a>0.5) |
find the indices where (a > 0.5) |
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a(:,find(v>0.5)) |
a[:,nonzero(v>0.5)[0]] |
a[:,nonzero(v.A>0.5)[0]] |
extract the columms of a where vector v > 0.5 |
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a(:,find(v>0.5)) |
a[:,v.T>0.5] |
a[:,v.T>0.5)] |
extract the columms of a where column vector v > 0.5 |
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a(a<0.5)=0 |
a[a<0.5]=0 |
a with elements less than 0.5 zeroed out |
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a .* (a>0.5) |
a * (a>0.5) |
mat(a.A * (a>0.5).A) |
a with elements less than 0.5 zeroed out |
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a(:) = 3 |
a[:] = 3 |
set all values to the same scalar value |
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y=x |
y = x.copy() |
numpy assigns by reference |
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y=x(2,:) |
y = x[1,:].copy() |
numpy slices are by reference |
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y=x(:) |
y = x.flatten(1) |
turn array into vector (note that this forces a copy) |
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1:10 |
arange(1.,11.) or |
mat(arange(1.,11.)) or |
create an increasing vector see note 'RANGES' |
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0:9 |
arange(10.) or |
mat(arange(10.)) or |
create an increasing vector see note 'RANGES' |
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[1:10]' |
arange(1.,11.)[:, newaxis] |
r_[1.:11.,'c'] |
create a column vector |
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zeros(3,4) |
zeros((3,4)) |
mat(...) |
3x4 rank-2 array full of 64-bit floating point zeros |
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zeros(3,4,5) |
zeros((3,4,5)) |
mat(...) |
3x4x5 rank-3 array full of 64-bit floating point zeros |
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ones(3,4) |
ones((3,4)) |
mat(...) |
3x4 rank-2 array full of 64-bit floating point ones |
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eye(3) |
eye(3) |
mat(...) |
3x3 identity matrix |
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diag(a) |
diag(a) |
mat(...) |
vector of diagonal elements of a |
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diag(a,0) |
diag(a,0) |
mat(...) |
square diagonal matrix whose nonzero values are the elements of a |
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rand(3,4) |
random.rand(3,4) |
mat(...) |
random 3x4 matrix |
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linspace(1,3,4) |
linspace(1,3,4) |
mat(...) |
4 equally spaced samples between 1 and 3, inclusive |
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[x,y]=meshgrid(0:8,0:5) |
mgrid[0:9.,0:6.] |
mat(...) |
two 2D arrays: one of x values, the other of y values |
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ogrid[0:9.,0:6.] |
mat(...) |
the best way to eval functions on a grid |
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repmat(a, m, n) |
tile(a, (m, n)) |
mat(...) |
create m by n copies of a |
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[a b] |
concatenate((a,b),1) or |
concatenate((a,b),1) |
concatenate columns of a and b |
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[a; b] |
concatenate((a,b)) or |
concatenate((a,b)) |
concatenate rows of a and b |
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max(max(a)) |
a.max() |
maximum element of a (with ndims(a)<=2 for matlab) |
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max(a) |
a.max(0) |
maximum element of each column of matrix a |
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max(a,[],2) |
a.max(1) |
maximum element of each row of matrix a |
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max(a,b) |
maximum(a, b) |
compares a and b element-wise, and returns the maximum value from each pair |
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norm(v) |
sqrt(dot(v,v)) or |
sqrt(dot(v.A,v.A)) or |
L2 norm of vector v |
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a & b |
logical_and(a,b) |
element-by-element AND operator (Numpy ufunc) see note 'LOGICOPS' |
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a | b |
logical_or(a,b) |
element-by-element OR operator (Numpy ufunc) see note 'LOGICOPS' |
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bitand(a,b) |
a & b |
bitwise AND operator (Python native and Numpy ufunc) |
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bitor(a,b) |
a | b |
bitwise OR operator (Python native and Numpy ufunc) |
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inv(a) |
linalg.inv(a) |
inverse of square matrix a |
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pinv(a) |
linalg.pinv(a) |
pseudo-inverse of matrix a |
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a\b |
linalg.solve(a,b) if a is square |
solution of a x = b for x |
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b/a |
Solve a.T x.T = b.T instead |
solution of x a = b for x |
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[U,S,V]=svd(a) |
U, S, Vh = linalg.svd(a), V = Vh.T |
singular value decomposition of a |
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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) |
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[V,D]=eig(a) |
V,D = linalg.eig(a) |
eigenvalues and eigenvectors of a |
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[V,D]=eig(a,b) |
V,D = Sci.linalg.eig(a,b) |
eigenvalues and eigenvectors of a,b |
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[V,D]=eigs(a,k) |
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find the k largest eigenvalues and eigenvectors of a |
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[Q,R,P]=qr(a,0) |
Q,R = Sci.linalg.qr(a) |
mat(...) |
QR decomposition |
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[L,U,P]=lu(a) |
L,U = Sci.linalg.lu(a) or |
mat(...) |
LU decomposition |
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conjgrad |
Sci.linalg.cg |
mat(...) |
Conjugate gradients solver |
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fft(a) |
fft(a) |
mat(...) |
Fourier transform of a |
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ifft(a) |
ifft(a) |
mat(...) |
inverse Fourier transform of a |
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sort(a) |
sort(a) or a.sort() |
mat(...) |
sort the matrix |
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[b,I] = sortrows(a,i) |
I = argsort(a[:,i]), b=a[I,:] |
sort the rows of the matrix |
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regress(y,X) |
linalg.lstsq(X,y) |
multilinear regression |
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decimate(x, q) |
Sci.signal.resample(x, len(x)/q) |
downsample with low-pass filtering |
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unique(a) |
unique(a) |
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squeeze(a) |
a.squeeze() |
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