https://numpy.org/devdocs/user/numpy-for-matlab-users.html
Table of Rough MATLAB-NumPy Equivalents
The table below gives rough equivalents for some common MATLAB® expressions. These are not exact equivalents, but rather should be taken as hints to get you going in the right direction. For more detail read the built-in documentation on the NumPy functions.
In the table below, it is assumed that you have executed the following commands in Python:
Also assume below that if the Notes talk about “matrix” that the arguments are two-dimensional entities.
General Purpose Equivalents
| MATLAB | numpy | Notes |
|---|---|---|
help func | info(func) or help(func) or func? (in Ipython) | get help on the function func |
which func | see note HELP | find out where func is defined |
type func | source(func) or func?? (in Ipython) | print source for func (if not a native function) |
a && b | a and b | short-circuiting logical AND operator (Python native operator); scalar arguments only |
a || b | a or b | short-circuiting logical OR operator (Python native operator); scalar arguments only |
1*i, 1*j, 1i, 1j | 1j | complex numbers |
eps | np.spacing(1) | Distance between 1 and the nearest floating point number. |
ode45 | scipy.integrate.solve_ivp(f) | integrate an ODE with Runge-Kutta 4,5 |
ode15s | scipy.integrate.solve_ivp(f, method='BDF') | integrate an ODE with BDF method |
Linear Algebra Equivalents
| MATLAB | NumPy | Notes |
|---|---|---|
ndims(a) | ndim(a) or a.ndim | get the number of dimensions of an array |
numel(a) | size(a) or a.size | get the number of elements of an array |
size(a) | shape(a) or a.shape | get the “size” of the matrix |
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 INDEXING) |
[ 1 2 3; 4 5 6 ] | array([[1.,2.,3.], [4.,5.,6.]]) | 2x3 matrix literal |
[ a b; c d ] | block([[a,b], [c,d]]) | construct a matrix from blocks a, b, c, and d |
a(end) | a[-1] | access last element in the 1xn matrix a |
a(2,5) | a[1,4] | access element in second row, fifth column |
a(2,:) | a[1] or a[1,:] | entire second row of a |
a(1:5,:) | a[0:5] or a[:5] or a[0:5,:] | the first five rows of a |
a(end-4:end,:) | a[-5:] | the last five rows of a |
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. |
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. |
a(3:2:21,:) | a[ 2:21:2,:] | every other row of a, starting with the third and going to the twenty-first |
a(1:2:end,:) | a[ ::2,:] | every other row of a, starting with the first |
a(end:-1:1,:) or flipud(a) | a[ ::-1,:] | a with rows in reverse order |
a([1:end 1],:) | a[r_[:len(a),0]] | a with copy of the first row appended to the end |
a.' | a.transpose() or a.T | transpose of a |
a' | a.conj().transpose() or a.conj().T | conjugate transpose of a |
a * b | a @ b | matrix multiply |
a .* b | a * b | element-wise multiply |
a./b | a/b | element-wise divide |
a.^3 | a**3 | element-wise exponentiation |
(a>0.5) | (a>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) |
a(:,find(v>0.5)) | a[:,nonzero(v>0.5)[0]] | 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 |
a(a<0.5)=0 | a[a<0.5]=0 | 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(:) = 3 | a[:] = 3 | set all values to the same scalar value |
y=x | y = x.copy() | numpy assigns by reference |
y=x(2,:) | y = x[1,:].copy() | numpy slices are by reference |
y=x(:) | y = x.flatten() | turn array into vector (note that this forces a copy) |
1:10 | arange(1.,11.) or r_[1.:11.] or r_[1:10:10j] | create an increasing vector (see note RANGES) |
0:9 | arange(10.) or r_[:10.] or r_[:9:10j] | create an increasing vector (see note RANGES) |
[1:10]' | arange(1.,11.)[:, newaxis] | create a column vector |
zeros(3,4) | zeros((3,4)) | 3x4 two-dimensional array full of 64-bit floating point zeros |
zeros(3,4,5) | zeros((3,4,5)) | 3x4x5 three-dimensional array full of 64-bit floating point zeros |
ones(3,4) | ones((3,4)) | 3x4 two-dimensional array full of 64-bit floating point ones |
eye(3) | eye(3) | 3x3 identity matrix |
diag(a) | diag(a) | vector of diagonal elements of a |
diag(a,0) | diag(a,0) | square diagonal matrix whose nonzero values are the elements of a |
rand(3,4) | random.rand(3,4) or random.random_sample((3, 4)) | random 3x4 matrix |
linspace(1,3,4) | linspace(1,3,4) | 4 equally spaced samples between 1 and 3, inclusive |
[x,y]=meshgrid(0:8,0:5) | mgrid[0:9.,0:6.] or meshgrid(r_[0:9.],r_[0:6.] | two 2D arrays: one of x values, the other of y values |
ogrid[0:9.,0:6.] or ix_(r_[0:9.],r_[0:6.] | the best way to eval functions on a grid | |
[x,y]=meshgrid([1,2,4],[2,4,5]) | meshgrid([1,2,4],[2,4,5]) | |
ix_([1,2,4],[2,4,5]) | the best way to eval functions on a grid | |
repmat(a, m, n) | tile(a, (m, n)) | 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] | concatenate columns of a and b |
[a; b] | concatenate((a,b)) or vstack((a,b)) or r_[a,b] | concatenate rows of a and b |
max(max(a)) | a.max() | maximum element of a (with ndims(a)<=2 for matlab) |
max(a) | a.max(0) | maximum element of each column of matrix a |
max(a,[],2) | a.max(1) | 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 |
norm(v) | sqrt(v @ v) or np.linalg.norm(v) | L2 norm of vector v |
a & b | logical_and(a,b) | element-by-element AND operator (NumPy ufunc) See note LOGICOPS |
a | b | logical_or(a,b) | element-by-element OR operator (NumPy ufunc) See note LOGICOPS |
bitand(a,b) | a & b | bitwise AND operator (Python native and NumPy ufunc) |
bitor(a,b) | a | b | bitwise OR operator (Python native and NumPy ufunc) |
inv(a) | linalg.inv(a) | inverse of square matrix a |
pinv(a) | linalg.pinv(a) | pseudo-inverse of matrix a |
rank(a) | linalg.matrix_rank(a) | matrix rank of a 2D array / matrix a |
a\b | linalg.solve(a,b) if a is square; linalg.lstsq(a,b) otherwise | solution of a x = b for x |
b/a | Solve a.T x.T = b.T instead | solution of x a = b for x |
[U,S,V]=svd(a) | U, S, Vh = linalg.svd(a), V = Vh.T | 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) |
[V,D]=eig(a) | D,V = linalg.eig(a) | eigenvalues and eigenvectors of a |
[V,D]=eig(a,b) | D,V = scipy.linalg.eig(a,b) | eigenvalues and eigenvectors of a, b |
[V,D]=eigs(a,k) | find the k largest eigenvalues and eigenvectors of a | |
[Q,R,P]=qr(a,0) | Q,R = scipy.linalg.qr(a) | QR decomposition |
[L,U,P]=lu(a) | L,U = scipy.linalg.lu(a) or LU,P=scipy.linalg.lu_factor(a) | LU decomposition (note: P(Matlab) == transpose(P(numpy)) ) |
conjgrad | scipy.sparse.linalg.cg | Conjugate gradients solver |
fft(a) | fft(a) | Fourier transform of a |
ifft(a) | ifft(a) | inverse Fourier transform of a |
sort(a) | sort(a) or a.sort() | sort the matrix |
[b,I] = sortrows(a,i) | I = argsort(a[:,i]), b=a[I,:] | sort the rows of the matrix |
regress(y,X) | linalg.lstsq(X,y) | multilinear regression |
decimate(x, q) | scipy.signal.resample(x, len(x)/q) | downsample with low-pass filtering |
unique(a) | unique(a) | |
squeeze(a) | a.squeeze() |
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