matlab和python对应函数
MATLAB
numpy.array numpy.matrix Notes
ndims(a)ndim(a) or a.ndim get the number of dimensions of a (tensor rank) 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, )
[ 1 2 3; 4 5 6 ]array([[1.,2.,3.],
[4.,5.,6.]])
mat([[1.,2.,3.],
[4.,5.,6.]]) or
mat("1 2 3; 4 5 6")
2x3 matrix literal
[ a b; c d ]vstack([hstack([a,b]),
hstack([c,d])])
bmat('a b; c d')construct a matrix from blocks a,b,c, and d
a(end)a[-1]a[:,-1][0,0]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
j().transpose() or
a.H conjugate transpose of a
a *
b dot(a,b)  a * b matrix multiply
a .*
b    a * b multiply(a,b)element-wise multiply
a./b a/b element-wise divide
a.^3a**3power(a,3)element-wise exponentiation
(a>0.5)(a>0.5)matrix whose i,jth element is (a_ij > 0.5) 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]]a[:,nonzero(v.A>0.5)
[0]]
extract the columms of a where vector v > 0.5
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
a(a<0.5)=0a[a<0.5]=0  a with elements less than 0.5 zeroed out
a .* (a>0.5)  a * (a>0.5)mat(a.A * (a>0.5).A)  a with elements less than 0.5 zeroed out
a(:) = 3a[:] = 3set 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(1)turn array into vector (note that this forces a copy)
1:10arange(1.,11.) or
r_[1.:11.] or
r_[1:10:10j]
mat(arange(1.,11.))
or
r_[1.:11.,'r']
create an increasing vector
0:9arange(10.) or
r_[:10.] or
r_[:9:10j]
mat(arange(10.)) or
r_[:10.,'r']
create an increasing vector
[1:10]'arange(1.,11.)[:, newaxis]r_[1.:11.,'c']create a column vector
zeros(3,4)zeros((3,4))mat(...)3x4 rank-2 array full of 64-bit floating point zeros
zeros(3,4,5)zeros((3,4,5))mat(...)3x4x5 rank-3 array full of 64-bit floating point zeros
ones(3,4)ones((3,4))mat(...)3x4 rank-2 array full of 64-bit floating point ones eye(3)eye(3)mat(...)3x3 identity matrix
diag(a)diag(a)mat(...)vector of diagonal elements of a
diag(a,0)diag(a,0)mat(...)square diagonal matrix whose nonzero values are the elements of a
rand(3,4)random.rand(3,4)mat(...)random 3x4 matrix
linspace(1,3,4)linspace(1,3,4)mat(...)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.]
mat(...)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.]
mat(...)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])mat(...)
ix_([1,2,4],[2,4,5])mat(...)the best way to eval functions on a grid
repmat(a, m, n)tile(a, (m, n))mat(...)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((a,b),1)concatenate columns of a and b
[a; b]concatenate((a,b)) or
vstack((a,b)) or
r_[a,b]
concatenate((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(dot(v,v)) or
(v) or
<(v)
sqrt(dot(v.A,v.A)) or
(v)
or
<(v)
L2 norm of vector v
a &
b logical_and(a,b)element-by-element AND operator (Numpy ufunc) a | b logical_or(a,b)element-by-element OR operator (Numpy ufunc)
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)rank of a matrix a
a\b linalg.solve(a,b) if a is square
linalg.lstsq(a,b) otherwise
solution of a x = b for x
linspace numpyb/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)V,D = Sci.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 = Sci.linalg.qr(a)mat(...)QR decomposition
[L,U,P]=lu(a)L,U = Sci.linalg.lu(a) or
mat(...)
LU decomposition (note: P(Matlab) ==
[L,U,P]=lu(a)
LU,P=Sci.linalg.lu_factor(a)mat(...)
transpose(P(numpy)) )
conjgrad mat(...)Conjugate gradients solver
fft(a)fft(a)mat(...)Fourier transform of a
ifft(a)ifft(a)mat(...)inverse Fourier transform of a sort(a)sort(a) or a.sort()mat(...)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)sample(x, len(x)/q)downsample with low-pass filtering unique(a)unique(a)
squeeze(a)  a.squeeze()
MATLAB
numpy Notes
help func info(func) or help(func) or func? (in
Ipython)
get help on the function func
which func()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,1j1j complex numbers
eps spacing(1)Distance between 1 and the nearest floating point number ode45scipy.integrate.ode(f).set_integrator('dopri5')integrate an ODE with Runge-Kutta 4,5
ode15s scipy.integrate.ode(f).\
set_integrator('vode', method='bdf', order=15)
integrate an ODE with BDF

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