Matrices (linear algebra)¶
Creating Matrices¶
The linear algebra module is designed to be as simple as possible. First, we
import and declare our first Matrix
object:
>>> from sympy.interactive.printing import init_printing
>>> init_printing(use_unicode=False, wrap_line=False)
>>> from sympy.matrices import Matrix, eye, zeros, ones, diag, GramSchmidt
>>> M = Matrix([[1,0,0], [0,0,0]]); M
[1 0 0]
[ ]
[0 0 0]
>>> Matrix([M, (0, 0, 1)])
[1 0 0 ]
[ ]
[0 0 0 ]
[ ]
[0 0 1]
>>> Matrix([[1, 2, 3]])
[1 2 3]
>>> Matrix([1, 2, 3])
[1]
[ ]
[2]
[ ]
[3]
In addition to creating a matrix from a list of appropriatelysized lists and/or matrices, SymPy also supports more advanced methods of matrix creation including a single list of values and dimension inputs:
>>> Matrix(2, 3, [1, 2, 3, 4, 5, 6])
[1 2 3]
[ ]
[4 5 6]
More interesting (and useful), is the ability to use a 2variable function
(or lambda
) to create a matrix. Here we create an indicator function which
is 1 on the diagonal and then use it to make the identity matrix:
>>> def f(i,j):
... if i == j:
... return 1
... else:
... return 0
...
>>> Matrix(4, 4, f)
[1 0 0 0]
[ ]
[0 1 0 0]
[ ]
[0 0 1 0]
[ ]
[0 0 0 1]
Finally let’s use lambda
to create a 1line matrix with 1’s in the even
permutation entries:
>>> Matrix(3, 4, lambda i,j: 1  (i+j) % 2)
[1 0 1 0]
[ ]
[0 1 0 1]
[ ]
[1 0 1 0]
There are also a couple of special constructors for quick matrix construction:
eye
is the identity matrix, zeros
and ones
for matrices of all
zeros and ones, respectively, and diag
to put matrices or elements along
the diagonal:
>>> eye(4)
[1 0 0 0]
[ ]
[0 1 0 0]
[ ]
[0 0 1 0]
[ ]
[0 0 0 1]
>>> zeros(2)
[0 0]
[ ]
[0 0]
>>> zeros(2, 5)
[0 0 0 0 0]
[ ]
[0 0 0 0 0]
>>> ones(3)
[1 1 1]
[ ]
[1 1 1]
[ ]
[1 1 1]
>>> ones(1, 3)
[1 1 1]
>>> diag(1, Matrix([[1, 2], [3, 4]]))
[1 0 0]
[ ]
[0 1 2]
[ ]
[0 3 4]
Basic Manipulation¶
While learning to work with matrices, let’s choose one where the entries are readily identifiable. One useful thing to know is that while matrices are 2dimensional, the storage is not and so it is allowable  though one should be careful  to access the entries as if they were a 1d list.
>>> M = Matrix(2, 3, [1, 2, 3, 4, 5, 6])
>>> M[4]
5
Now, the more standard entry access is a pair of indices which will always return the value at the corresponding row and column of the matrix:
>>> M[1, 2]
6
>>> M[0, 0]
1
>>> M[1, 1]
5
Since this is Python we’re also able to slice submatrices; slices always give a matrix in return, even if the dimension is 1 x 1:
>>> M[0:2, 0:2]
[1 2]
[ ]
[4 5]
>>> M[2:2, 2]
[]
>>> M[:, 2]
[3]
[ ]
[6]
>>> M[:1, 2]
[3]
In the second example above notice that the slice 2:2 gives an empty range. Note also (in keeping with 0based indexing of Python) the first row/column is 0.
You cannot access rows or columns that are not present unless they are in a slice:
>>> M[:, 10] # the 10th column (not there)
Traceback (most recent call last):
...
IndexError: Index out of range: a[[0, 10]]
>>> M[:, 10:11] # the 10th column (if there)
[]
>>> M[:, :10] # all columns up to the 10th
[1 2 3]
[ ]
[4 5 6]
Slicing an empty matrix works as long as you use a slice for the coordinate that has no size:
>>> Matrix(0, 3, [])[:, 1]
[]
Slicing gives a copy of what is sliced, so modifications of one object do not affect the other:
>>> M2 = M[:, :]
>>> M2[0, 0] = 100
>>> M[0, 0] == 100
False
Notice that changing M2
didn’t change M
. Since we can slice, we can also assign
entries:
>>> M = Matrix(([1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]))
>>> M
[1 2 3 4 ]
[ ]
[5 6 7 8 ]
[ ]
[9 10 11 12]
[ ]
[13 14 15 16]
>>> M[2,2] = M[0,3] = 0
>>> M
[1 2 3 0 ]
[ ]
[5 6 7 8 ]
[ ]
[9 10 0 12]
[ ]
[13 14 15 16]
as well as assign slices:
>>> M = Matrix(([1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]))
>>> M[2:,2:] = Matrix(2,2,lambda i,j: 0)
>>> M
[1 2 3 4]
[ ]
[5 6 7 8]
[ ]
[9 10 0 0]
[ ]
[13 14 0 0]
All the standard arithmetic operations are supported:
>>> M = Matrix(([1,2,3],[4,5,6],[7,8,9]))
>>> M  M
[0 0 0]
[ ]
[0 0 0]
[ ]
[0 0 0]
>>> M + M
[2 4 6 ]
[ ]
[8 10 12]
[ ]
[14 16 18]
>>> M * M
[30 36 42 ]
[ ]
[66 81 96 ]
[ ]
[102 126 150]
>>> M2 = Matrix(3,1,[1,5,0])
>>> M*M2
[11]
[ ]
[29]
[ ]
[47]
>>> M**2
[30 36 42 ]
[ ]
[66 81 96 ]
[ ]
[102 126 150]
As well as some useful vector operations:
>>> M.row_del(0)
>>> M
[4 5 6]
[ ]
[7 8 9]
>>> M.col_del(1)
>>> M
[4 6]
[ ]
[7 9]
>>> v1 = Matrix([1,2,3])
>>> v2 = Matrix([4,5,6])
>>> v3 = v1.cross(v2)
>>> v1.dot(v2)
32
>>> v2.dot(v3)
0
>>> v1.dot(v3)
0
Recall that the row_del()
and col_del()
operations don’t return a value  they
simply change the matrix object. We can also ‘’glue’’ together matrices of the
appropriate size:
>>> M1 = eye(3)
>>> M2 = zeros(3, 4)
>>> M1.row_join(M2)
[1 0 0 0 0 0 0]
[ ]
[0 1 0 0 0 0 0]
[ ]
[0 0 1 0 0 0 0]
>>> M3 = zeros(4, 3)
>>> M1.col_join(M3)
[1 0 0]
[ ]
[0 1 0]
[ ]
[0 0 1]
[ ]
[0 0 0]
[ ]
[0 0 0]
[ ]
[0 0 0]
[ ]
[0 0 0]
Operations on entries¶
We are not restricted to having multiplication between two matrices:
>>> M = eye(3)
>>> 2*M
[2 0 0]
[ ]
[0 2 0]
[ ]
[0 0 2]
>>> 3*M
[3 0 0]
[ ]
[0 3 0]
[ ]
[0 0 3]
but we can also apply functions to our matrix entries using applyfunc()
. Here we’ll declare a function that double any input number. Then we apply it to the 3x3 identity matrix:
>>> f = lambda x: 2*x
>>> eye(3).applyfunc(f)
[2 0 0]
[ ]
[0 2 0]
[ ]
[0 0 2]
If you want to extract a common factor from a matrix you can do so by
applying gcd
to the data of the matrix:
>>> from sympy.abc import x, y
>>> from sympy import gcd
>>> m = Matrix([[x, y], [1, x*y]]).inv(); m
[ x*y y ]
[ ]
[ 2 2 ]
[ x *y + y  x *y + y]
[ ]
[ 1 x ]
[ ]
[ 2 2 ]
[ x *y + y  x *y + y]
>>> gcd(tuple(_))
1

2
 x *y + y
>>> m/_
[x*y y ]
[ ]
[ 1 x]
One more useful matrixwide entry application function is the substitution function. Let’s declare a matrix with symbolic entries then substitute a value. Remember we can substitute anything  even another symbol!:
>>> from sympy import Symbol
>>> x = Symbol('x')
>>> M = eye(3) * x
>>> M
[x 0 0]
[ ]
[0 x 0]
[ ]
[0 0 x]
>>> M.subs(x, 4)
[4 0 0]
[ ]
[0 4 0]
[ ]
[0 0 4]
>>> y = Symbol('y')
>>> M.subs(x, y)
[y 0 0]
[ ]
[0 y 0]
[ ]
[0 0 y]
Linear algebra¶
Now that we have the basics out of the way, let’s see what we can do with the actual matrices. Of course, one of the first things that comes to mind is the determinant:
>>> M = Matrix(( [1, 2, 3], [3, 6, 2], [2, 0, 1] ))
>>> M.det()
28
>>> M2 = eye(3)
>>> M2.det()
1
>>> M3 = Matrix(( [1, 0, 0], [1, 0, 0], [1, 0, 0] ))
>>> M3.det()
0
Another common operation is the inverse: In SymPy, this is computed by Gaussian elimination by default (for dense matrices) but we can specify it be done by \(LU\) decomposition as well:
>>> M2.inv()
[1 0 0]
[ ]
[0 1 0]
[ ]
[0 0 1]
>>> M2.inv(method="LU")
[1 0 0]
[ ]
[0 1 0]
[ ]
[0 0 1]
>>> M.inv(method="LU")
[3/14 1/14 1/2 ]
[ ]
[1/28 5/28 1/4]
[ ]
[ 3/7 1/7 0 ]
>>> M * M.inv(method="LU")
[1 0 0]
[ ]
[0 1 0]
[ ]
[0 0 1]
We can perform a \(QR\) factorization which is handy for solving systems:
>>> A = Matrix([[1,1,1],[1,1,3],[2,3,4]])
>>> Q, R = A.QRdecomposition()
>>> Q
[ ___ ___ ___ ]
[\/ 6 \/ 3 \/ 2 ]
[  ]
[ 6 3 2 ]
[ ]
[ ___ ___ ___ ]
[\/ 6 \/ 3 \/ 2 ]
[   ]
[ 6 3 2 ]
[ ]
[ ___ ___ ]
[\/ 6 \/ 3 ]
[  0 ]
[ 3 3 ]
>>> R
[ ___ ]
[ ___ 4*\/ 6 ___]
[\/ 6  2*\/ 6 ]
[ 3 ]
[ ]
[ ___ ]
[ \/ 3 ]
[ 0  0 ]
[ 3 ]
[ ]
[ ___ ]
[ 0 0 \/ 2 ]
>>> Q*R
[1 1 1]
[ ]
[1 1 3]
[ ]
[2 3 4]
In addition to the solvers in the solver.py
file, we can solve the system Ax=b
by passing the b vector to the matrix A’s LUsolve function. Here we’ll cheat a
little choose A and x then multiply to get b. Then we can solve for x and check
that it’s correct:
>>> A = Matrix([ [2, 3, 5], [3, 6, 2], [8, 3, 6] ])
>>> x = Matrix(3,1,[3,7,5])
>>> b = A*x
>>> soln = A.LUsolve(b)
>>> soln
[3]
[ ]
[7]
[ ]
[5]
There’s also a nice GramSchmidt orthogonalizer which will take a set of
vectors and orthogonalize them with respect to another. There is an
optional argument which specifies whether or not the output should also be
normalized, it defaults to False
. Let’s take some vectors and orthogonalize
them  one normalized and one not:
>>> L = [Matrix([2,3,5]), Matrix([3,6,2]), Matrix([8,3,6])]
>>> out1 = GramSchmidt(L)
>>> out2 = GramSchmidt(L, True)
Let’s take a look at the vectors:
>>> for i in out1:
... print(i)
...
Matrix([[2], [3], [5]])
Matrix([[23/19], [63/19], [47/19]])
Matrix([[1692/353], [1551/706], [423/706]])
>>> for i in out2:
... print(i)
...
Matrix([[sqrt(38)/19], [3*sqrt(38)/38], [5*sqrt(38)/38]])
Matrix([[23*sqrt(6707)/6707], [63*sqrt(6707)/6707], [47*sqrt(6707)/6707]])
Matrix([[12*sqrt(706)/353], [11*sqrt(706)/706], [3*sqrt(706)/706]])
We can spotcheck their orthogonality with dot() and their normality with norm():
>>> out1[0].dot(out1[1])
0
>>> out1[0].dot(out1[2])
0
>>> out1[1].dot(out1[2])
0
>>> out2[0].norm()
1
>>> out2[1].norm()
1
>>> out2[2].norm()
1
So there is quite a bit that can be done with the module including eigenvalues,
eigenvectors, nullspace calculation, cofactor expansion tools, and so on. From
here one might want to look over the matrices.py
file for all functionality.
MatrixDeterminant Class Reference¶

class
sympy.matrices.matrices.
MatrixDeterminant
[source]¶ Provides basic matrix determinant operations. Should not be instantiated directly.

adjugate
(method='berkowitz')[source]¶ Returns the adjugate, or classical adjoint, of a matrix. That is, the transpose of the matrix of cofactors.

charpoly
(x='lambda', simplify=<function simplify>)[source]¶ Computes characteristic polynomial det(x*I  self) where I is the identity matrix.
A PurePoly is returned, so using different variables for
x
does not affect the comparison or the polynomials:Examples
>>> from sympy import Matrix >>> from sympy.abc import x, y >>> A = Matrix([[1, 3], [2, 0]]) >>> A.charpoly(x) == A.charpoly(y) True
Specifying
x
is optional; a symbol namedlambda
is used by default (which looks good when prettyprinted in unicode):>>> A.charpoly().as_expr() lambda**2  lambda  6
And if
x
clashes with an existing symbol, underscores will be prepended to the name to make it unique:>>> A = Matrix([[1, 2], [x, 0]]) >>> A.charpoly(x).as_expr() _x**2  _x  2*x
Whether you pass a symbol or not, the generator can be obtained with the gen attribute since it may not be the same as the symbol that was passed:
>>> A.charpoly(x).gen _x >>> A.charpoly(x).gen == x False
Notes
The SamuelsonBerkowitz algorithm is used to compute the characteristic polynomial efficiently and without any division operations. Thus the characteristic polynomial over any commutative ring without zero divisors can be computed.
See also

cofactor_matrix
(method='berkowitz')[source]¶ Return a matrix containing the cofactor of each element.
See also

det
(method='bareiss', iszerofunc=None)[source]¶ Computes the determinant of a matrix.
 Parameters
method : string, optional
Specifies the algorithm used for computing the matrix determinant.
If the matrix is at most 3x3, a hardcoded formula is used and the specified method is ignored. Otherwise, it defaults to
'bareiss'
.If it is set to
'bareiss'
, Bareiss’ fractionfree algorithm will be used.If it is set to
'berkowitz'
, Berkowitz’ algorithm will be used.Otherwise, if it is set to
'lu'
, LU decomposition will be used.Note
For backward compatibility, legacy keys like “bareis” and “det_lu” can still be used to indicate the corresponding methods. And the keys are also caseinsensitive for now. However, it is suggested to use the precise keys for specifying the method.
iszerofunc : FunctionType or None, optional
If it is set to
None
, it will be defaulted to_iszero
if the method is set to'bareiss'
, and_is_zero_after_expand_mul
if the method is set to'lu'
.It can also accept any userspecified zero testing function, if it is formatted as a function which accepts a single symbolic argument and returns
True
if it is tested as zero andFalse
if it tested as nonzero, and alsoNone
if it is undecidable. Returns
det : Basic
Result of determinant.
 Raises
ValueError
If unrecognized keys are given for
method
oriszerofunc
.NonSquareMatrixError
If attempted to calculate determinant from a nonsquare matrix.

MatrixReductions Class Reference¶

class
sympy.matrices.matrices.
MatrixReductions
[source]¶ Provides basic matrix row/column operations. Should not be instantiated directly.

echelon_form
(iszerofunc=<function _iszero>, simplify=False, with_pivots=False)[source]¶ Returns a matrix rowequivalent to
self
that is in echelon form. Note that echelon form of a matrix is not unique, however, properties like the row space and the null space are preserved.

elementary_col_op
(op='n>kn', col=None, k=None, col1=None, col2=None)[source]¶ Performs the elementary column operation \(op\).
\(op\) may be one of
“n>kn” (column n goes to k*n)
“n<>m” (swap column n and column m)
“n>n+km” (column n goes to column n + k*column m)
 Parameters
op : string; the elementary row operation
col : the column to apply the column operation
k : the multiple to apply in the column operation
col1 : one column of a column swap
col2 : second column of a column swap or column “m” in the column operation
“n>n+km”

elementary_row_op
(op='n>kn', row=None, k=None, row1=None, row2=None)[source]¶ Performs the elementary row operation \(op\).
\(op\) may be one of
“n>kn” (row n goes to k*n)
“n<>m” (swap row n and row m)
“n>n+km” (row n goes to row n + k*row m)
 Parameters
op : string; the elementary row operation
row : the row to apply the row operation
k : the multiple to apply in the row operation
row1 : one row of a row swap
row2 : second row of a row swap or row “m” in the row operation
“n>n+km”

property
is_echelon
¶ Returns \(True\) if the matrix is in echelon form. That is, all rows of zeros are at the bottom, and below each leading nonzero in a row are exclusively zeros.

rank
(iszerofunc=<function _iszero>, simplify=False)[source]¶ Returns the rank of a matrix
>>> from sympy import Matrix >>> from sympy.abc import x >>> m = Matrix([[1, 2], [x, 1  1/x]]) >>> m.rank() 2 >>> n = Matrix(3, 3, range(1, 10)) >>> n.rank() 2

rref
(iszerofunc=<function _iszero>, simplify=False, pivots=True, normalize_last=True)[source]¶ Return reduced rowechelon form of matrix and indices of pivot vars.
 Parameters
iszerofunc : Function
A function used for detecting whether an element can act as a pivot.
lambda x: x.is_zero
is used by default.simplify : Function
A function used to simplify elements when looking for a pivot. By default SymPy’s
simplify
is used.pivots : True or False
If
True
, a tuple containing the rowreduced matrix and a tuple of pivot columns is returned. IfFalse
just the rowreduced matrix is returned.normalize_last : True or False
If
True
, no pivots are normalized to \(1\) until after all entries above and below each pivot are zeroed. This means the row reduction algorithm is fraction free until the very last step. IfFalse
, the naive row reduction procedure is used where each pivot is normalized to be \(1\) before row operations are used to zero above and below the pivot.
Notes
The default value of
normalize_last=True
can provide significant speedup to row reduction, especially on matrices with symbols. However, if you depend on the form row reduction algorithm leaves entries of the matrix, setnoramlize_last=False
Examples
>>> from sympy import Matrix >>> from sympy.abc import x >>> m = Matrix([[1, 2], [x, 1  1/x]]) >>> m.rref() (Matrix([ [1, 0], [0, 1]]), (0, 1)) >>> rref_matrix, rref_pivots = m.rref() >>> rref_matrix Matrix([ [1, 0], [0, 1]]) >>> rref_pivots (0, 1)

MatrixSubspaces Class Reference¶

class
sympy.matrices.matrices.
MatrixSubspaces
[source]¶ Provides methods relating to the fundamental subspaces of a matrix. Should not be instantiated directly.

columnspace
(simplify=False)[source]¶ Returns a list of vectors (Matrix objects) that span columnspace of
self
Examples
>>> from sympy.matrices import Matrix >>> m = Matrix(3, 3, [1, 3, 0, 2, 6, 0, 3, 9, 6]) >>> m Matrix([ [ 1, 3, 0], [2, 6, 0], [ 3, 9, 6]]) >>> m.columnspace() [Matrix([ [ 1], [2], [ 3]]), Matrix([ [0], [0], [6]])]

nullspace
(simplify=False, iszerofunc=<function _iszero>)[source]¶ Returns list of vectors (Matrix objects) that span nullspace of
self
Examples
>>> from sympy.matrices import Matrix >>> m = Matrix(3, 3, [1, 3, 0, 2, 6, 0, 3, 9, 6]) >>> m Matrix([ [ 1, 3, 0], [2, 6, 0], [ 3, 9, 6]]) >>> m.nullspace() [Matrix([ [3], [ 1], [ 0]])]
See also

classmethod
orthogonalize
(*vecs, **kwargs)[source]¶ Apply the GramSchmidt orthogonalization procedure to vectors supplied in
vecs
. Parameters
vecs
vectors to be made orthogonal
normalize : bool
If
True
, return an orthonormal basis.rankcheck : bool
If
True
, the computation does not stop when encountering linearly dependent vectors.If
False
, it will raiseValueError
when any zero or linearly dependent vectors are found. Returns
list
List of orthogonal (or orthonormal) basis vectors.
See also
References

MatrixEigen Class Reference¶

class
sympy.matrices.matrices.
MatrixEigen
[source]¶ Provides basic matrix eigenvalue/vector operations. Should not be instantiated directly.

diagonalize
(reals_only=False, sort=False, normalize=False)[source]¶ Return (P, D), where D is diagonal and
D = P^1 * M * P
where M is current matrix.
 Parameters
reals_only : bool. Whether to throw an error if complex numbers are need
to diagonalize. (Default: False)
sort : bool. Sort the eigenvalues along the diagonal. (Default: False)
normalize : bool. If True, normalize the columns of P. (Default: False)
Examples
>>> from sympy import Matrix >>> m = Matrix(3, 3, [1, 2, 0, 0, 3, 0, 2, 4, 2]) >>> m Matrix([ [1, 2, 0], [0, 3, 0], [2, 4, 2]]) >>> (P, D) = m.diagonalize() >>> D Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> P Matrix([ [1, 0, 1], [ 0, 0, 1], [ 2, 1, 2]]) >>> P.inv() * m * P Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]])
See also

eigenvals
(error_when_incomplete=True, **flags)[source]¶ Return eigenvalues using the Berkowitz agorithm to compute the characteristic polynomial.
 Parameters
error_when_incomplete : bool, optional
If it is set to
True
, it will raise an error if not all eigenvalues are computed. This is caused byroots
not returning a full list of eigenvalues.simplify : bool or function, optional
If it is set to
True
, it attempts to return the most simplified form of expressions returned by applying default simplification method in every routine.If it is set to
False
, it will skip simplification in this particular routine to save computation resources.If a function is passed to, it will attempt to apply the particular function as simplification method.
rational : bool, optional
If it is set to
True
, every floating point numbers would be replaced with rationals before computation. It can solve some issues ofroots
routine not working well with floats.multiple : bool, optional
If it is set to
True
, the result will be in the form of a list.If it is set to
False
, the result will be in the form of a dictionary. Returns
eigs : list or dict
Eigenvalues of a matrix. The return format would be specified by the key
multiple
. Raises
MatrixError
If not enough roots had got computed.
NonSquareMatrixError
If attempted to compute eigenvalues from a nonsquare matrix.
Notes
Eigenvalues of a matrix \(A\) can be computed by solving a matrix equation \(\det(A  \lambda I) = 0\)
See also

eigenvects
(error_when_incomplete=True, iszerofunc=<function _iszero>, **flags)[source]¶ Return list of triples (eigenval, multiplicity, eigenspace).
 Parameters
error_when_incomplete : bool, optional
Raise an error when not all eigenvalues are computed. This is caused by
roots
not returning a full list of eigenvalues.iszerofunc : function, optional
Specifies a zero testing function to be used in
rref
.Default value is
_iszero
, which uses SymPy’s naive and fast default assumption handler.It can also accept any userspecified zero testing function, if it is formatted as a function which accepts a single symbolic argument and returns
True
if it is tested as zero andFalse
if it is tested as nonzero, andNone
if it is undecidable.simplify : bool or function, optional
If
True
,as_content_primitive()
will be used to tidy up normalization artifacts.It will also be used by the
nullspace
routine.chop : bool or positive number, optional
If the matrix contains any Floats, they will be changed to Rationals for computation purposes, but the answers will be returned after being evaluated with evalf. The
chop
flag is passed toevalf
. Whenchop=True
a default precision will be used; a number will be interpreted as the desired level of precision. Returns
ret : [(eigenval, multiplicity, eigenspace), …]
A ragged list containing tuples of data obtained by
eigenvals
andnullspace
.eigenspace
is a list containing theeigenvector
for each eigenvalue.eigenvector
is a vector in the form of aMatrix
. e.g. a vector of length 3 is returned asMatrix([a_1, a_2, a_3])
. Raises
NotImplementedError
If failed to compute nullspace.
See also

is_diagonalizable
(reals_only=False, **kwargs)[source]¶ Returns true if a matrix is diagonalizable.
 Parameters
reals_only : bool. If reals_only=True, determine whether the matrix can be
diagonalized without complex numbers. (Default: False)
Kwargs
 clear_cachebool. If True, clear the result of any computations when finished.
(Default: True)
Examples
>>> from sympy import Matrix >>> m = Matrix(3, 3, [1, 2, 0, 0, 3, 0, 2, 4, 2]) >>> m Matrix([ [1, 2, 0], [0, 3, 0], [2, 4, 2]]) >>> m.is_diagonalizable() True >>> m = Matrix(2, 2, [0, 1, 0, 0]) >>> m Matrix([ [0, 1], [0, 0]]) >>> m.is_diagonalizable() False >>> m = Matrix(2, 2, [0, 1, 1, 0]) >>> m Matrix([ [ 0, 1], [1, 0]]) >>> m.is_diagonalizable() True >>> m.is_diagonalizable(reals_only=True) False
See also

property
is_indefinite
¶ Finds out the definiteness of a matrix.
Examples
An example of numeric positive definite matrix:
>>> from sympy import Matrix >>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite True >>> A.is_positive_semidefinite True >>> A.is_negative_definite False >>> A.is_negative_semidefinite False >>> A.is_indefinite False
An example of numeric negative definite matrix:
>>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
An example of numeric indefinite matrix:
>>> A = Matrix([[1, 2], [2, 1]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
Notes
Definitiveness is not very commonly discussed for nonhermitian matrices.
However, computing the definitiveness of a matrix can be generalized over any real matrix by taking the symmetric part:
\(A_S = 1/2 (A + A^{T})\)
Or over any complex matrix by taking the hermitian part:
\(A_H = 1/2 (A + A^{H})\)
And computing the eigenvalues.
References
 R498
https://en.wikipedia.org/wiki/Definiteness_of_a_matrix#Eigenvalues
 R499
 R500
Johnson, C. R. “Positive Definite Matrices.” Amer. Math. Monthly 77, 259264 1970.

property
is_negative_definite
¶ Finds out the definiteness of a matrix.
Examples
An example of numeric positive definite matrix:
>>> from sympy import Matrix >>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite True >>> A.is_positive_semidefinite True >>> A.is_negative_definite False >>> A.is_negative_semidefinite False >>> A.is_indefinite False
An example of numeric negative definite matrix:
>>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
An example of numeric indefinite matrix:
>>> A = Matrix([[1, 2], [2, 1]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
Notes
Definitiveness is not very commonly discussed for nonhermitian matrices.
However, computing the definitiveness of a matrix can be generalized over any real matrix by taking the symmetric part:
\(A_S = 1/2 (A + A^{T})\)
Or over any complex matrix by taking the hermitian part:
\(A_H = 1/2 (A + A^{H})\)
And computing the eigenvalues.
References
 R501
https://en.wikipedia.org/wiki/Definiteness_of_a_matrix#Eigenvalues
 R502
 R503
Johnson, C. R. “Positive Definite Matrices.” Amer. Math. Monthly 77, 259264 1970.

property
is_negative_semidefinite
¶ Finds out the definiteness of a matrix.
Examples
An example of numeric positive definite matrix:
>>> from sympy import Matrix >>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite True >>> A.is_positive_semidefinite True >>> A.is_negative_definite False >>> A.is_negative_semidefinite False >>> A.is_indefinite False
An example of numeric negative definite matrix:
>>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
An example of numeric indefinite matrix:
>>> A = Matrix([[1, 2], [2, 1]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
Notes
Definitiveness is not very commonly discussed for nonhermitian matrices.
However, computing the definitiveness of a matrix can be generalized over any real matrix by taking the symmetric part:
\(A_S = 1/2 (A + A^{T})\)
Or over any complex matrix by taking the hermitian part:
\(A_H = 1/2 (A + A^{H})\)
And computing the eigenvalues.
References
 R504
https://en.wikipedia.org/wiki/Definiteness_of_a_matrix#Eigenvalues
 R505
 R506
Johnson, C. R. “Positive Definite Matrices.” Amer. Math. Monthly 77, 259264 1970.

property
is_positive_definite
¶ Finds out the definiteness of a matrix.
Examples
An example of numeric positive definite matrix:
>>> from sympy import Matrix >>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite True >>> A.is_positive_semidefinite True >>> A.is_negative_definite False >>> A.is_negative_semidefinite False >>> A.is_indefinite False
An example of numeric negative definite matrix:
>>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
An example of numeric indefinite matrix:
>>> A = Matrix([[1, 2], [2, 1]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
Notes
Definitiveness is not very commonly discussed for nonhermitian matrices.
However, computing the definitiveness of a matrix can be generalized over any real matrix by taking the symmetric part:
\(A_S = 1/2 (A + A^{T})\)
Or over any complex matrix by taking the hermitian part:
\(A_H = 1/2 (A + A^{H})\)
And computing the eigenvalues.
References
 R507
https://en.wikipedia.org/wiki/Definiteness_of_a_matrix#Eigenvalues
 R508
 R509
Johnson, C. R. “Positive Definite Matrices.” Amer. Math. Monthly 77, 259264 1970.

property
is_positive_semidefinite
¶ Finds out the definiteness of a matrix.
Examples
An example of numeric positive definite matrix:
>>> from sympy import Matrix >>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite True >>> A.is_positive_semidefinite True >>> A.is_negative_definite False >>> A.is_negative_semidefinite False >>> A.is_indefinite False
An example of numeric negative definite matrix:
>>> A = Matrix([[1, 2], [2, 6]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
An example of numeric indefinite matrix:
>>> A = Matrix([[1, 2], [2, 1]]) >>> A.is_positive_definite False >>> A.is_positive_semidefinite False >>> A.is_negative_definite True >>> A.is_negative_semidefinite True >>> A.is_indefinite False
Notes
Definitiveness is not very commonly discussed for nonhermitian matrices.
However, computing the definitiveness of a matrix can be generalized over any real matrix by taking the symmetric part:
\(A_S = 1/2 (A + A^{T})\)
Or over any complex matrix by taking the hermitian part:
\(A_H = 1/2 (A + A^{H})\)
And computing the eigenvalues.
References
 R510
https://en.wikipedia.org/wiki/Definiteness_of_a_matrix#Eigenvalues
 R511
 R512
Johnson, C. R. “Positive Definite Matrices.” Amer. Math. Monthly 77, 259264 1970.

jordan_form
(calc_transform=True, **kwargs)[source]¶ Return
(P, J)
where \(J\) is a Jordan block matrix and \(P\) is a matrix such thatself == P*J*P**1
 Parameters
calc_transform : bool
If
False
, then only \(J\) is returned.chop : bool
All matrices are converted to exact types when computing eigenvalues and eigenvectors. As a result, there may be approximation errors. If
chop==True
, these errors will be truncated.
Examples
>>> from sympy import Matrix >>> m = Matrix([[ 6, 5, 2, 3], [3, 1, 3, 3], [ 2, 1, 2, 3], [1, 1, 5, 5]]) >>> P, J = m.jordan_form() >>> J Matrix([ [2, 1, 0, 0], [0, 2, 0, 0], [0, 0, 2, 1], [0, 0, 0, 2]])
See also

left_eigenvects
(**flags)[source]¶ Returns left eigenvectors and eigenvalues.
This function returns the list of triples (eigenval, multiplicity, basis) for the left eigenvectors. Options are the same as for eigenvects(), i.e. the
**flags
arguments gets passed directly to eigenvects().Examples
>>> from sympy import Matrix >>> M = Matrix([[0, 1, 1], [1, 0, 0], [1, 1, 1]]) >>> M.eigenvects() [(1, 1, [Matrix([ [1], [ 1], [ 0]])]), (0, 1, [Matrix([ [ 0], [1], [ 1]])]), (2, 1, [Matrix([ [2/3], [1/3], [ 1]])])] >>> M.left_eigenvects() [(1, 1, [Matrix([[2, 1, 1]])]), (0, 1, [Matrix([[1, 1, 1]])]), (2, 1, [Matrix([[1, 1, 1]])])]

MatrixCalculus Class Reference¶

class
sympy.matrices.matrices.
MatrixCalculus
[source]¶ Provides calculusrelated matrix operations.

diff
(*args, **kwargs)[source]¶ Calculate the derivative of each element in the matrix.
args
will be passed to theintegrate
function.Examples
>>> from sympy.matrices import Matrix >>> from sympy.abc import x, y >>> M = Matrix([[x, y], [1, 0]]) >>> M.diff(x) Matrix([ [1, 0], [0, 0]])

integrate
(*args)[source]¶ Integrate each element of the matrix.
args
will be passed to theintegrate
function.Examples
>>> from sympy.matrices import Matrix >>> from sympy.abc import x, y >>> M = Matrix([[x, y], [1, 0]]) >>> M.integrate((x, )) Matrix([ [x**2/2, x*y], [ x, 0]]) >>> M.integrate((x, 0, 2)) Matrix([ [2, 2*y], [2, 0]])

jacobian
(X)[source]¶ Calculates the Jacobian matrix (derivative of a vectorvalued function).
 Parameters
``self`` : vector of expressions representing functions f_i(x_1, …, x_n).
X : set of x_i’s in order, it can be a list or a Matrix
Both ``self`` and X can be a row or a column matrix in any order
(i.e., jacobian() should always work).
Examples
>>> from sympy import sin, cos, Matrix >>> from sympy.abc import rho, phi >>> X = Matrix([rho*cos(phi), rho*sin(phi), rho**2]) >>> Y = Matrix([rho, phi]) >>> X.jacobian(Y) Matrix([ [cos(phi), rho*sin(phi)], [sin(phi), rho*cos(phi)], [ 2*rho, 0]]) >>> X = Matrix([rho*cos(phi), rho*sin(phi)]) >>> X.jacobian(Y) Matrix([ [cos(phi), rho*sin(phi)], [sin(phi), rho*cos(phi)]])

MatrixBase Class Reference¶

class
sympy.matrices.matrices.
MatrixBase
[source]¶ Base class for matrix objects.

property
D
¶ Return Dirac conjugate (if
self.rows == 4
).Examples
>>> from sympy import Matrix, I, eye >>> m = Matrix((0, 1 + I, 2, 3)) >>> m.D Matrix([[0, 1  I, 2, 3]]) >>> m = (eye(4) + I*eye(4)) >>> m[0, 3] = 2 >>> m.D Matrix([ [1  I, 0, 0, 0], [ 0, 1  I, 0, 0], [ 0, 0, 1 + I, 0], [ 2, 0, 0, 1 + I]])
If the matrix does not have 4 rows an AttributeError will be raised because this property is only defined for matrices with 4 rows.
>>> Matrix(eye(2)).D Traceback (most recent call last): ... AttributeError: Matrix has no attribute D.
See also
sympy.matrices.common.MatrixCommon.conjugate
Byelement conjugation
sympy.matrices.common.MatrixCommon.H
Hermite conjugation

LDLdecomposition
(hermitian=True)[source]¶ Returns the LDL Decomposition (L, D) of matrix A, such that L * D * L.H == A if hermitian flag is True, or L * D * L.T == A if hermitian is False. This method eliminates the use of square root. Further this ensures that all the diagonal entries of L are 1. A must be a Hermitian positivedefinite matrix if hermitian is True, or a symmetric matrix otherwise.
Examples
>>> from sympy.matrices import Matrix, eye >>> A = Matrix(((25, 15, 5), (15, 18, 0), (5, 0, 11))) >>> L, D = A.LDLdecomposition() >>> L Matrix([ [ 1, 0, 0], [ 3/5, 1, 0], [1/5, 1/3, 1]]) >>> D Matrix([ [25, 0, 0], [ 0, 9, 0], [ 0, 0, 9]]) >>> L * D * L.T * A.inv() == eye(A.rows) True
The matrix can have complex entries:
>>> from sympy import I >>> A = Matrix(((9, 3*I), (3*I, 5))) >>> L, D = A.LDLdecomposition() >>> L Matrix([ [ 1, 0], [I/3, 1]]) >>> D Matrix([ [9, 0], [0, 4]]) >>> L*D*L.H == A True
See also

LDLsolve
(rhs)[source]¶ Solves
Ax = B
using LDL decomposition, for a general square and nonsingular matrix.For a nonsquare matrix with rows > cols, the least squares solution is returned.
Examples
>>> from sympy.matrices import Matrix, eye >>> A = eye(2)*2 >>> B = Matrix([[1, 2], [3, 4]]) >>> A.LDLsolve(B) == B/2 True

LUdecomposition
(iszerofunc=<function _iszero>, simpfunc=None, rankcheck=False)[source]¶ Returns (L, U, perm) where L is a lower triangular matrix with unit diagonal, U is an upper triangular matrix, and perm is a list of row swap index pairs. If A is the original matrix, then A = (L*U).permuteBkwd(perm), and the row permutation matrix P such that P*A = L*U can be computed by P=eye(A.row).permuteFwd(perm).
See documentation for LUCombined for details about the keyword argument rankcheck, iszerofunc, and simpfunc.
Examples
>>> from sympy import Matrix >>> a = Matrix([[4, 3], [6, 3]]) >>> L, U, _ = a.LUdecomposition() >>> L Matrix([ [ 1, 0], [3/2, 1]]) >>> U Matrix([ [4, 3], [0, 3/2]])

LUdecompositionFF
()[source]¶ Compute a fractionfree LU decomposition.
Returns 4 matrices P, L, D, U such that PA = L D**1 U. If the elements of the matrix belong to some integral domain I, then all elements of L, D and U are guaranteed to belong to I.
 Reference
W. Zhou & D.J. Jeffrey, “Fractionfree matrix factors: new forms for LU and QR factors”. Frontiers in Computer Science in China, Vol 2, no. 1, pp. 6780, 2008.
See also

LUdecomposition_Simple
(iszerofunc=<function _iszero>, simpfunc=None, rankcheck=False)[source]¶ Compute an lu decomposition of m x n matrix A, where P*A = L*U
L is m x m lower triangular with unit diagonal
U is m x n upper triangular
P is an m x m permutation matrix
Returns an m x n matrix lu, and an m element list perm where each element of perm is a pair of row exchange indices.
The factors L and U are stored in lu as follows: The subdiagonal elements of L are stored in the subdiagonal elements of lu, that is lu[i, j] = L[i, j] whenever i > j. The elements on the diagonal of L are all 1, and are not explicitly stored. U is stored in the upper triangular portion of lu, that is lu[i ,j] = U[i, j] whenever i <= j. The output matrix can be visualized as:
 Matrix([
[u, u, u, u], [l, u, u, u], [l, l, u, u], [l, l, l, u]])
where l represents a subdiagonal entry of the L factor, and u represents an entry from the upper triangular entry of the U factor.
perm is a list row swap index pairs such that if A is the original matrix, then A = (L*U).permuteBkwd(perm), and the row permutation matrix P such that
P*A = L*U
can be computed byP=eye(A.row).permuteFwd(perm)
.The keyword argument rankcheck determines if this function raises a ValueError when passed a matrix whose rank is strictly less than min(num rows, num cols). The default behavior is to decompose a rank deficient matrix. Pass rankcheck=True to raise a ValueError instead. (This mimics the previous behavior of this function).
The keyword arguments iszerofunc and simpfunc are used by the pivot search algorithm. iszerofunc is a callable that returns a boolean indicating if its input is zero, or None if it cannot make the determination. simpfunc is a callable that simplifies its input. The default is simpfunc=None, which indicate that the pivot search algorithm should not attempt to simplify any candidate pivots. If simpfunc fails to simplify its input, then it must return its input instead of a copy.
When a matrix contains symbolic entries, the pivot search algorithm differs from the case where every entry can be categorized as zero or nonzero. The algorithm searches column by column through the submatrix whose top left entry coincides with the pivot position. If it exists, the pivot is the first entry in the current search column that iszerofunc guarantees is nonzero. If no such candidate exists, then each candidate pivot is simplified if simpfunc is not None. The search is repeated, with the difference that a candidate may be the pivot if
iszerofunc()
cannot guarantee that it is nonzero. In the second search the pivot is the first candidate that iszerofunc can guarantee is nonzero. If no such candidate exists, then the pivot is the first candidate for which iszerofunc returns None. If no such candidate exists, then the search is repeated in the next column to the right. The pivot search algorithm differs from the one inrref()
, which relies on_find_reasonable_pivot()
. Future versions ofLUdecomposition_simple()
may use_find_reasonable_pivot()
.See also

LUsolve
(rhs, iszerofunc=<function _iszero>)[source]¶ Solve the linear system
Ax = rhs
forx
whereA = self
.This is for symbolic matrices, for real or complex ones use mpmath.lu_solve or mpmath.qr_solve.

QRdecomposition
()[source]¶ Return Q, R where A = Q*R, Q is orthogonal and R is upper triangular.
Examples
This is the example from wikipedia:
>>> from sympy import Matrix >>> A = Matrix([[12, 51, 4], [6, 167, 68], [4, 24, 41]]) >>> Q, R = A.QRdecomposition() >>> Q Matrix([ [ 6/7, 69/175, 58/175], [ 3/7, 158/175, 6/175], [2/7, 6/35, 33/35]]) >>> R Matrix([ [14, 21, 14], [ 0, 175, 70], [ 0, 0, 35]]) >>> A == Q*R True
QR factorization of an identity matrix:
>>> A = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) >>> Q, R = A.QRdecomposition() >>> Q Matrix([ [1, 0, 0], [0, 1, 0], [0, 0, 1]]) >>> R Matrix([ [1, 0, 0], [0, 1, 0], [0, 0, 1]])
See also

QRsolve
(b)[source]¶ Solve the linear system
Ax = b
.self
is the matrixA
, the method argument is the vectorb
. The method returns the solution vectorx
. Ifb
is a matrix, the system is solved for each column ofb
and the return value is a matrix of the same shape asb
.This method is slower (approximately by a factor of 2) but more stable for floatingpoint arithmetic than the LUsolve method. However, LUsolve usually uses an exact arithmetic, so you don’t need to use QRsolve.
This is mainly for educational purposes and symbolic matrices, for real (or complex) matrices use mpmath.qr_solve.

cholesky
(hermitian=True)[source]¶ Returns the Choleskytype decomposition L of a matrix A such that L * L.H == A if hermitian flag is True, or L * L.T == A if hermitian is False.
A must be a Hermitian positivedefinite matrix if hermitian is True, or a symmetric matrix if it is False.
Examples
>>> from sympy.matrices import Matrix >>> A = Matrix(((25, 15, 5), (15, 18, 0), (5, 0, 11))) >>> A.cholesky() Matrix([ [ 5, 0, 0], [ 3, 3, 0], [1, 1, 3]]) >>> A.cholesky() * A.cholesky().T Matrix([ [25, 15, 5], [15, 18, 0], [5, 0, 11]])
The matrix can have complex entries:
>>> from sympy import I >>> A = Matrix(((9, 3*I), (3*I, 5))) >>> A.cholesky() Matrix([ [ 3, 0], [I, 2]]) >>> A.cholesky() * A.cholesky().H Matrix([ [ 9, 3*I], [3*I, 5]])
Nonhermitian Choleskytype decomposition may be useful when the matrix is not positivedefinite.
>>> A = Matrix([[1, 2], [2, 1]]) >>> L = A.cholesky(hermitian=False) >>> L Matrix([ [1, 0], [2, sqrt(3)*I]]) >>> L*L.T == A True
See also

cholesky_solve
(rhs)[source]¶ Solves
Ax = B
using Cholesky decomposition, for a general square nonsingular matrix. For a nonsquare matrix with rows > cols, the least squares solution is returned.

condition_number
()[source]¶ Returns the condition number of a matrix.
This is the maximum singular value divided by the minimum singular value
Examples
>>> from sympy import Matrix, S >>> A = Matrix([[1, 0, 0], [0, 10, 0], [0, 0, S.One/10]]) >>> A.condition_number() 100
See also

copy
()[source]¶ Returns the copy of a matrix.
Examples
>>> from sympy import Matrix >>> A = Matrix(2, 2, [1, 2, 3, 4]) >>> A.copy() Matrix([ [1, 2], [3, 4]])

cross
(b)[source]¶ Return the cross product of
self
andb
relaxing the condition of compatible dimensions: if each has 3 elements, a matrix of the same type and shape asself
will be returned. Ifb
has the same shape asself
then common identities for the cross product (like \(a \times b =  b \times a\)) will hold. Parameters
b : 3x1 or 1x3 Matrix
See also

diagonal_solve
(rhs)[source]¶ Solves
Ax = B
efficiently, where A is a diagonal Matrix, with nonzero diagonal entries.Examples
>>> from sympy.matrices import Matrix, eye >>> A = eye(2)*2 >>> B = Matrix([[1, 2], [3, 4]]) >>> A.diagonal_solve(B) == B/2 True

dot
(b, hermitian=None, conjugate_convention=None)[source]¶ Return the dot or inner product of two vectors of equal length. Here
self
must be aMatrix
of size 1 x n or n x 1, andb
must be either a matrix of size 1 x n, n x 1, or a list/tuple of length n. A scalar is returned.By default,
dot
does not conjugateself
orb
, even if there are complex entries. Sethermitian=True
(and optionally aconjugate_convention
) to compute the hermitian inner product.Possible kwargs are
hermitian
andconjugate_convention
.If
conjugate_convention
is"left"
,"math"
or"maths"
, the conjugate of the first vector (self
) is used. If"right"
or"physics"
is specified, the conjugate of the second vectorb
is used.Examples
>>> from sympy import Matrix >>> M = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> v = Matrix([1, 1, 1]) >>> M.row(0).dot(v) 6 >>> M.col(0).dot(v) 12 >>> v = [3, 2, 1] >>> M.row(0).dot(v) 10
>>> from sympy import I >>> q = Matrix([1*I, 1*I, 1*I]) >>> q.dot(q, hermitian=False) 3
>>> q.dot(q, hermitian=True) 3
>>> q1 = Matrix([1, 1, 1*I]) >>> q.dot(q1, hermitian=True, conjugate_convention="maths") 1  2*I >>> q.dot(q1, hermitian=True, conjugate_convention="physics") 1 + 2*I
See also

dual
()[source]¶ Returns the dual of a matrix, which is:
(1/2)*levicivita(i, j, k, l)*M(k, l)
summed over indices \(k\) and \(l\)Since the levicivita method is anti_symmetric for any pairwise exchange of indices, the dual of a symmetric matrix is the zero matrix. Strictly speaking the dual defined here assumes that the ‘matrix’ \(M\) is a contravariant anti_symmetric second rank tensor, so that the dual is a covariant second rank tensor.

exp
()[source]¶ Return the exponential of a square matrix
Examples
>>> from sympy import Symbol, Matrix
>>> t = Symbol('t') >>> m = Matrix([[0, 1], [1, 0]]) * t >>> m.exp() Matrix([ [ exp(I*t)/2 + exp(I*t)/2, I*exp(I*t)/2 + I*exp(I*t)/2], [I*exp(I*t)/2  I*exp(I*t)/2, exp(I*t)/2 + exp(I*t)/2]])

gauss_jordan_solve
(B, freevar=False)[source]¶ Solves
Ax = B
using Gauss Jordan elimination.There may be zero, one, or infinite solutions. If one solution exists, it will be returned. If infinite solutions exist, it will be returned parametrically. If no solutions exist, It will throw ValueError.
 Parameters
B : Matrix
The right hand side of the equation to be solved for. Must have the same number of rows as matrix A.
freevar : List
If the system is underdetermined (e.g. A has more columns than rows), infinite solutions are possible, in terms of arbitrary values of free variables. Then the index of the free variables in the solutions (column Matrix) will be returned by freevar, if the flag \(freevar\) is set to \(True\).
 Returns
x : Matrix
The matrix that will satisfy
Ax = B
. Will have as many rows as matrix A has columns, and as many columns as matrix B.params : Matrix
If the system is underdetermined (e.g. A has more columns than rows), infinite solutions are possible, in terms of arbitrary parameters. These arbitrary parameters are returned as params Matrix.
Examples
>>> from sympy import Matrix >>> A = Matrix([[1, 2, 1, 1], [1, 2, 2, 1], [2, 4, 0, 6]]) >>> B = Matrix([7, 12, 4]) >>> sol, params = A.gauss_jordan_solve(B) >>> sol Matrix([ [2*tau0  3*tau1 + 2], [ tau0], [ 2*tau1 + 5], [ tau1]]) >>> params Matrix([ [tau0], [tau1]]) >>> taus_zeroes = { tau:0 for tau in params } >>> sol_unique = sol.xreplace(taus_zeroes) >>> sol_unique Matrix([ [2], [0], [5], [0]])
>>> A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 10]]) >>> B = Matrix([3, 6, 9]) >>> sol, params = A.gauss_jordan_solve(B) >>> sol Matrix([ [1], [ 2], [ 0]]) >>> params Matrix(0, 1, [])
>>> A = Matrix([[2, 7], [1, 4]]) >>> B = Matrix([[21, 3], [12, 2]]) >>> sol, params = A.gauss_jordan_solve(B) >>> sol Matrix([ [0, 2], [3, 1]]) >>> params Matrix(0, 2, [])
See also
lower_triangular_solve
,upper_triangular_solve
,cholesky_solve
,diagonal_solve
,LDLsolve
,LUsolve
,QRsolve
,pinv
References

inv
(method=None, **kwargs)[source]¶ Return the inverse of a matrix.
CASE 1: If the matrix is a dense matrix.
Return the matrix inverse using the method indicated (default is Gauss elimination).
 Parameters
method : (‘GE’, ‘LU’, or ‘ADJ’)
 Raises
ValueError
If the determinant of the matrix is zero.
Notes
According to the
method
keyword, it calls the appropriate method:LDL … inverse_LDL(); default CH …. inverse_CH()
Kwargs
method : (‘CH’, ‘LDL’)
See also

inv_mod
(m)[source]¶ Returns the inverse of the matrix \(K\) (mod \(m\)), if it exists.
Method to find the matrix inverse of \(K\) (mod \(m\)) implemented in this function:
Compute \(\mathrm{adj}(K) = \mathrm{cof}(K)^t\), the adjoint matrix of \(K\).
Compute \(r = 1/\mathrm{det}(K) \pmod m\).
\(K^{1} = r\cdot \mathrm{adj}(K) \pmod m\).
Examples
>>> from sympy import Matrix >>> A = Matrix(2, 2, [1, 2, 3, 4]) >>> A.inv_mod(5) Matrix([ [3, 1], [4, 2]]) >>> A.inv_mod(3) Matrix([ [1, 1], [0, 1]])

inverse_ADJ
(iszerofunc=<function _iszero>)[source]¶ Calculates the inverse using the adjugate matrix and a determinant.
See also

inverse_GE
(iszerofunc=<function _iszero>)[source]¶ Calculates the inverse using Gaussian elimination.
See also

inverse_LU
(iszerofunc=<function _iszero>)[source]¶ Calculates the inverse using LU decomposition.
See also

classmethod
irregular
(ntop, *matrices, **kwargs)[source]¶ Return a matrix filled by the given matrices which are listed in order of appearance from left to right, top to bottom as they first appear in the matrix. They must fill the matrix completely.
Examples
>>> from sympy import ones, Matrix >>> Matrix.irregular(3, ones(2,1), ones(3,3)*2, ones(2,2)*3, ... ones(1,1)*4, ones(2,2)*5, ones(1,2)*6, ones(1,2)*7) Matrix([ [1, 2, 2, 2, 3, 3], [1, 2, 2, 2, 3, 3], [4, 2, 2, 2, 5, 5], [6, 6, 7, 7, 5, 5]])

is_nilpotent
()[source]¶ Checks if a matrix is nilpotent.
A matrix B is nilpotent if for some integer k, B**k is a zero matrix.
Examples
>>> from sympy import Matrix >>> a = Matrix([[0, 0, 0], [1, 0, 0], [1, 1, 0]]) >>> a.is_nilpotent() True
>>> a = Matrix([[1, 0, 1], [1, 0, 0], [1, 1, 0]]) >>> a.is_nilpotent() False

key2bounds
(keys)[source]¶ Converts a key with potentially mixed types of keys (integer and slice) into a tuple of ranges and raises an error if any index is out of
self
’s range.See also

key2ij
(key)[source]¶ Converts key into canonical form, converting integers or indexable items into valid integers for
self
’s range or returning slices unchanged.See also

log
(simplify=<function cancel>)[source]¶ Return the logarithm of a square matrix
 Parameters
simplify : function, bool
The function to simplify the result with.
Default is
cancel
, which is effective to reduce the expression growing for taking reciprocals and inverses for symbolic matrices.
Examples
>>> from sympy import S, Matrix
Examples for positivedefinite matrices:
>>> m = Matrix([[1, 1], [0, 1]]) >>> m.log() Matrix([ [0, 1], [0, 0]])
>>> m = Matrix([[S(5)/4, S(3)/4], [S(3)/4, S(5)/4]]) >>> m.log() Matrix([ [ 0, log(2)], [log(2), 0]])
Examples for non positivedefinite matrices:
>>> m = Matrix([[S(3)/4, S(5)/4], [S(5)/4, S(3)/4]]) >>> m.log() Matrix([ [ I*pi/2, log(2)  I*pi/2], [log(2)  I*pi/2, I*pi/2]])
>>> m = Matrix( ... [[0, 0, 0, 1], ... [0, 0, 1, 0], ... [0, 1, 0, 0], ... [1, 0, 0, 0]]) >>> m.log() Matrix([ [ I*pi/2, 0, 0, I*pi/2], [ 0, I*pi/2, I*pi/2, 0], [ 0, I*pi/2, I*pi/2, 0], [I*pi/2, 0, 0, I*pi/2]])

norm
(ord=None)[source]¶ Return the Norm of a Matrix or Vector. In the simplest case this is the geometric size of the vector Other norms can be specified by the ord parameter
ord
norm for matrices
norm for vectors
None
Frobenius norm
2norm
‘fro’
Frobenius norm
does not exist
inf
maximum row sum
max(abs(x))
inf
–
min(abs(x))
1
maximum column sum
as below
1
–
as below
2
2norm (largest sing. value)
as below
2
smallest singular value
as below
other
does not exist
sum(abs(x)**ord)**(1./ord)
Examples
>>> from sympy import Matrix, Symbol, trigsimp, cos, sin, oo >>> x = Symbol('x', real=True) >>> v = Matrix([cos(x), sin(x)]) >>> trigsimp( v.norm() ) 1 >>> v.norm(10) (sin(x)**10 + cos(x)**10)**(1/10) >>> A = Matrix([[1, 1], [1, 1]]) >>> A.norm(1) # maximum sum of absolute values of A is 2 2 >>> A.norm(2) # Spectral norm (max of Ax/x under 2vectornorm) 2 >>> A.norm(2) # Inverse spectral norm (smallest singular value) 0 >>> A.norm() # Frobenius Norm 2 >>> A.norm(oo) # Infinity Norm 2 >>> Matrix([1, 2]).norm(oo) 2 >>> Matrix([1, 2]).norm(oo) 1
See also

normalized
(iszerofunc=<function _iszero>)[source]¶ Return the normalized version of
self
. Parameters
iszerofunc : Function, optional
A function to determine whether
self
is a zero vector. The default_iszero
tests to see if each element is exactly zero. Returns
Matrix
Normalized vector form of
self
. It has the same length as a unit vector. However, a zero vector will be returned for a vector with norm 0. Raises
ShapeError
If the matrix is not in a vector form.
See also

pinv
(method='RD')[source]¶ Calculate the MoorePenrose pseudoinverse of the matrix.
The MoorePenrose pseudoinverse exists and is unique for any matrix. If the matrix is invertible, the pseudoinverse is the same as the inverse.
 Parameters
method : String, optional
Specifies the method for computing the pseudoinverse.
If
'RD'
, RankDecomposition will be used.If
'ED'
, Diagonalization will be used.
Examples
Computing pseudoinverse by rank decomposition :
>>> from sympy import Matrix >>> A = Matrix([[1, 2, 3], [4, 5, 6]]) >>> A.pinv() Matrix([ [17/18, 4/9], [ 1/9, 1/9], [ 13/18, 2/9]])
Computing pseudoinverse by diagonalization :
>>> B = A.pinv(method='ED') >>> B.simplify() >>> B Matrix([ [17/18, 4/9], [ 1/9, 1/9], [ 13/18, 2/9]])
See also
References

pinv_solve
(B, arbitrary_matrix=None)[source]¶ Solve
Ax = B
using the MoorePenrose pseudoinverse.There may be zero, one, or infinite solutions. If one solution exists, it will be returned. If infinite solutions exist, one will be returned based on the value of arbitrary_matrix. If no solutions exist, the leastsquares solution is returned.
 Parameters
B : Matrix
The right hand side of the equation to be solved for. Must have the same number of rows as matrix A.
arbitrary_matrix : Matrix
If the system is underdetermined (e.g. A has more columns than rows), infinite solutions are possible, in terms of an arbitrary matrix. This parameter may be set to a specific matrix to use for that purpose; if so, it must be the same shape as x, with as many rows as matrix A has columns, and as many columns as matrix B. If left as None, an appropriate matrix containing dummy symbols in the form of
wn_m
will be used, with n and m being row and column position of each symbol. Returns
x : Matrix
The matrix that will satisfy
Ax = B
. Will have as many rows as matrix A has columns, and as many columns as matrix B.
Examples
>>> from sympy import Matrix >>> A = Matrix([[1, 2, 3], [4, 5, 6]]) >>> B = Matrix([7, 8]) >>> A.pinv_solve(B) Matrix([ [ _w0_0/6  _w1_0/3 + _w2_0/6  55/18], [_w0_0/3 + 2*_w1_0/3  _w2_0/3 + 1/9], [ _w0_0/6  _w1_0/3 + _w2_0/6 + 59/18]]) >>> A.pinv_solve(B, arbitrary_matrix=Matrix([0, 0, 0])) Matrix([ [55/18], [ 1/9], [ 59/18]])
Notes
This may return either exact solutions or least squares solutions. To determine which, check
A * A.pinv() * B == B
. It will be True if exact solutions exist, and False if only a leastsquares solution exists. Be aware that the left hand side of that equation may need to be simplified to correctly compare to the right hand side.See also
lower_triangular_solve
,upper_triangular_solve
,gauss_jordan_solve
,cholesky_solve
,diagonal_solve
,LDLsolve
,LUsolve
,QRsolve
,pinv
References

print_nonzero
(symb='X')[source]¶ Shows location of nonzero entries for fast shape lookup.
Examples
>>> from sympy.matrices import Matrix, eye >>> m = Matrix(2, 3, lambda i, j: i*3+j) >>> m Matrix([ [0, 1, 2], [3, 4, 5]]) >>> m.print_nonzero() [ XX] [XXX] >>> m = eye(4) >>> m.print_nonzero("x") [x ] [ x ] [ x ] [ x]

project
(v)[source]¶ Return the projection of
self
onto the line containingv
.Examples
>>> from sympy import Matrix, S, sqrt >>> V = Matrix([sqrt(3)/2, S.Half]) >>> x = Matrix([[1, 0]]) >>> V.project(x) Matrix([[sqrt(3)/2, 0]]) >>> V.project(x) Matrix([[sqrt(3)/2, 0]])

rank_decomposition
(iszerofunc=<function _iszero>, simplify=False)[source]¶ Returns a pair of matrices (\(C\), \(F\)) with matching rank such that \(A = C F\).
 Parameters
iszerofunc : Function, optional
A function used for detecting whether an element can act as a pivot.
lambda x: x.is_zero
is used by default.simplify : Bool or Function, optional
A function used to simplify elements when looking for a pivot. By default SymPy’s
simplify
is used. Returns
(C, F) : Matrices
\(C\) and \(F\) are fullrank matrices with rank as same as \(A\), whose product gives \(A\).
See Notes for additional mathematical details.
Examples
>>> from sympy.matrices import Matrix >>> A = Matrix([ ... [1, 3, 1, 4], ... [2, 7, 3, 9], ... [1, 5, 3, 1], ... [1, 2, 0, 8] ... ]) >>> C, F = A.rank_decomposition() >>> C Matrix([ [1, 3, 4], [2, 7, 9], [1, 5, 1], [1, 2, 8]]) >>> F Matrix([ [1, 0, 2, 0], [0, 1, 1, 0], [0, 0, 0, 1]]) >>> C * F == A True
Notes
Obtaining \(F\), an RREF of \(A\), is equivalent to creating a product
\[E_n E_{n1} ... E_1 A = F\]where \(E_n, E_{n1}, ... , E_1\) are the elimination matrices or permutation matrices equivalent to each rowreduction step.
The inverse of the same product of elimination matrices gives \(C\):
\[C = (E_n E_{n1} ... E_1)^{1}\]It is not necessary, however, to actually compute the inverse: the columns of \(C\) are those from the original matrix with the same column indices as the indices of the pivot columns of \(F\).
See also
References

solve
(rhs, method='GJ')[source]¶ Solves linear equation where the unique solution exists.
 Parameters
rhs : Matrix
Vector representing the right hand side of the linear equation.
method : string, optional
If set to
'GJ'
, the GaussJordan elimination will be used, which is implemented in the routinegauss_jordan_solve
.If set to
'LU'
,LUsolve
routine will be used.If set to
'QR'
,QRsolve
routine will be used.If set to
'PINV'
,pinv_solve
routine will be used.It also supports the methods available for special linear systems
For positive definite systems:
If set to
'CH'
,cholesky_solve
routine will be used.If set to
'LDL'
,LDLsolve
routine will be used.To use a different method and to compute the solution via the inverse, use a method defined in the .inv() docstring.
 Returns
solutions : Matrix
Vector representing the solution.
 Raises
ValueError
If there is not a unique solution then a
ValueError
will be raised.If
self
is not square, aValueError
and a different routine for solving the system will be suggested.

solve_least_squares
(rhs, method='CH')[source]¶ Return the leastsquare fit to the data.
 Parameters
rhs : Matrix
Vector representing the right hand side of the linear equation.
method : string or boolean, optional
If set to
'CH'
,cholesky_solve
routine will be used.If set to
'LDL'
,LDLsolve
routine will be used.If set to
'QR'
,QRsolve
routine will be used.If set to
'PINV'
,pinv_solve
routine will be used.Otherwise, the conjugate of
self
will be used to create a system of equations that is passed tosolve
along with the hint defined bymethod
. Returns
solutions : Matrix
Vector representing the solution.
Examples
>>> from sympy.matrices import Matrix, ones >>> A = Matrix([1, 2, 3]) >>> B = Matrix([2, 3, 4]) >>> S = Matrix(A.row_join(B)) >>> S Matrix([ [1, 2], [2, 3], [3, 4]])
If each line of S represent coefficients of Ax + By and x and y are [2, 3] then S*xy is:
>>> r = S*Matrix([2, 3]); r Matrix([ [ 8], [13], [18]])
But let’s add 1 to the middle value and then solve for the leastsquares value of xy:
>>> xy = S.solve_least_squares(Matrix([8, 14, 18])); xy Matrix([ [ 5/3], [10/3]])
The error is given by S*xy  r:
>>> S*xy  r Matrix([ [1/3], [1/3], [1/3]]) >>> _.norm().n(2) 0.58
If a different xy is used, the norm will be higher:
>>> xy += ones(2, 1)/10 >>> (S*xy  r).norm().n(2) 1.5

table
(printer, rowstart='[', rowend=']', rowsep='\n', colsep=', ', align='right')[source]¶ String form of Matrix as a table.
printer
is the printer to use for on the elements (generally something like StrPrinter())rowstart
is the string used to start each row (by default ‘[‘).rowend
is the string used to end each row (by default ‘]’).rowsep
is the string used to separate rows (by default a newline).colsep
is the string used to separate columns (by default ‘, ‘).align
defines how the elements are aligned. Must be one of ‘left’, ‘right’, or ‘center’. You can also use ‘<’, ‘>’, and ‘^’ to mean the same thing, respectively.This is used by the string printer for Matrix.
Examples
>>> from sympy import Matrix >>> from sympy.printing.str import StrPrinter >>> M = Matrix([[1, 2], [33, 4]]) >>> printer = StrPrinter() >>> M.table(printer) '[ 1, 2]\n[33, 4]' >>> print(M.table(printer)) [ 1, 2] [33, 4] >>> print(M.table(printer, rowsep=',\n')) [ 1, 2], [33, 4] >>> print('[%s]' % M.table(printer, rowsep=',\n')) [[ 1, 2], [33, 4]] >>> print(M.table(printer, colsep=' ')) [ 1 2] [33 4] >>> print(M.table(printer, align='center')) [ 1 , 2] [33, 4] >>> print(M.table(printer, rowstart='{', rowend='}')) { 1, 2} {33, 4}

vech
(diagonal=True, check_symmetry=True)[source]¶ Return the unique elements of a symmetric Matrix as a one column matrix by stacking the elements in the lower triangle.
Arguments: diagonal – include the diagonal cells of
self
or not check_symmetry – checks symmetry ofself
but not completely reliablyExamples
>>> from sympy import Matrix >>> m=Matrix([[1, 2], [2, 3]]) >>> m Matrix([ [1, 2], [2, 3]]) >>> m.vech() Matrix([ [1], [2], [3]]) >>> m.vech(diagonal=False) Matrix([[2]])
See also

property
Matrix Exceptions Reference¶
Matrix Functions Reference¶

sympy.matrices.dense.
matrix_multiply_elementwise
(A, B)[source]¶ Return the Hadamard product (elementwise product) of A and B
>>> from sympy.matrices import matrix_multiply_elementwise >>> from sympy.matrices import Matrix >>> A = Matrix([[0, 1, 2], [3, 4, 5]]) >>> B = Matrix([[1, 10, 100], [100, 10, 1]]) >>> matrix_multiply_elementwise(A, B) Matrix([ [ 0, 10, 200], [300, 40, 5]])

sympy.matrices.dense.
zeros
(*args, **kwargs)[source]¶ Returns a matrix of zeros with
rows
rows andcols
columns; ifcols
is omitted a square matrix will be returned.

sympy.matrices.dense.
ones
(*args, **kwargs)[source]¶ Returns a matrix of ones with
rows
rows andcols
columns; ifcols
is omitted a square matrix will be returned.

sympy.matrices.dense.
diag
(*values, **kwargs)[source]¶ Returns a matrix with the provided values placed on the diagonal. If nonsquare matrices are included, they will produce a blockdiagonal matrix.
Examples
This version of diag is a thin wrapper to Matrix.diag that differs in that it treats all lists like matrices – even when a single list is given. If this is not desired, either put a \(*\) before the list or set \(unpack=True\).
>>> from sympy import diag
>>> diag([1, 2, 3], unpack=True) # = diag(1,2,3) or diag(*[1,2,3]) Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]])
>>> diag([1, 2, 3]) # a column vector Matrix([ [1], [2], [3]])

sympy.matrices.dense.
jordan_cell
(eigenval, n)[source]¶ Create a Jordan block:
Examples
>>> from sympy.matrices import jordan_cell >>> from sympy.abc import x >>> jordan_cell(x, 4) Matrix([ [x, 1, 0, 0], [0, x, 1, 0], [0, 0, x, 1], [0, 0, 0, x]])

sympy.matrices.dense.
hessian
(f, varlist, constraints=[])[source]¶ Compute Hessian matrix for a function f wrt parameters in varlist which may be given as a sequence or a row/column vector. A list of constraints may optionally be given.
Examples
>>> from sympy import Function, hessian, pprint >>> from sympy.abc import x, y >>> f = Function('f')(x, y) >>> g1 = Function('g')(x, y) >>> g2 = x**2 + 3*y >>> pprint(hessian(f, (x, y), [g1, g2])) [ d d ] [ 0 0 (g(x, y)) (g(x, y)) ] [ dx dy ] [ ] [ 0 0 2*x 3 ] [ ] [ 2 2 ] [d d d ] [(g(x, y)) 2*x (f(x, y)) (f(x, y))] [dx 2 dy dx ] [ dx ] [ ] [ 2 2 ] [d d d ] [(g(x, y)) 3 (f(x, y)) (f(x, y)) ] [dy dy dx 2 ] [ dy ]
References

sympy.matrices.dense.
GramSchmidt
(vlist, orthonormal=False)[source]¶ Apply the GramSchmidt process to a set of vectors.
 Parameters
vlist : List of Matrix
Vectors to be orthogonalized for.
orthonormal : Bool, optional
If true, return an orthonormal basis.
 Returns
vlist : List of Matrix
Orthogonalized vectors
Notes
This routine is mostly duplicate from
Matrix.orthogonalize
, except for some difference that this always raises error when linearly dependent vectors are found, and the keywordnormalize
has been named asorthonormal
in this function.References

sympy.matrices.dense.
wronskian
(functions, var, method='bareiss')[source]¶ Compute Wronskian for [] of functions
 f1 f2 ... fn   f1' f2' ... fn'   . . . .  W(f1, ..., fn) =  . . . .   . . . .   (n) (n) (n)   D (f1) D (f2) ... D (fn) 

sympy.matrices.dense.
casoratian
(seqs, n, zero=True)[source]¶ Given linear difference operator L of order ‘k’ and homogeneous equation Ly = 0 we want to compute kernel of L, which is a set of ‘k’ sequences: a(n), b(n), … z(n).
Solutions of L are linearly independent iff their Casoratian, denoted as C(a, b, …, z), do not vanish for n = 0.
Casoratian is defined by k x k determinant:
+ a(n) b(n) . . . z(n) +  a(n+1) b(n+1) . . . z(n+1)   . . . .   . . . .   . . . .  + a(n+k1) b(n+k1) . . . z(n+k1) +
It proves very useful in rsolve_hyper() where it is applied to a generating set of a recurrence to factor out linearly dependent solutions and return a basis:
>>> from sympy import Symbol, casoratian, factorial >>> n = Symbol('n', integer=True)
Exponential and factorial are linearly independent:
>>> casoratian([2**n, factorial(n)], n) != 0 True

sympy.matrices.dense.
randMatrix
(r, c=None, min=0, max=99, seed=None, symmetric=False, percent=100, prng=None)[source]¶ Create random matrix with dimensions
r
xc
. Ifc
is omitted the matrix will be square. Ifsymmetric
is True the matrix must be square. Ifpercent
is less than 100 then only approximately the given percentage of elements will be nonzero.The pseudorandom number generator used to generate matrix is chosen in the following way.
If
prng
is supplied, it will be used as random number generator. It should be an instance ofrandom.Random
, or at least haverandint
andshuffle
methods with same signatures.if
prng
is not supplied butseed
is supplied, then newrandom.Random
with givenseed
will be created;otherwise, a new
random.Random
with default seed will be used.
Examples
>>> from sympy.matrices import randMatrix >>> randMatrix(3) [25, 45, 27] [44, 54, 9] [23, 96, 46] >>> randMatrix(3, 2) [87, 29] [23, 37] [90, 26] >>> randMatrix(3, 3, 0, 2) [0, 2, 0] [2, 0, 1] [0, 0, 1] >>> randMatrix(3, symmetric=True) [85, 26, 29] [26, 71, 43] [29, 43, 57] >>> A = randMatrix(3, seed=1) >>> B = randMatrix(3, seed=2) >>> A == B False >>> A == randMatrix(3, seed=1) True >>> randMatrix(3, symmetric=True, percent=50) [77, 70, 0], [70, 0, 0], [ 0, 0, 88]
Numpy Utility Functions Reference¶

sympy.matrices.dense.
list2numpy
(l, dtype=<class 'object'>)[source]¶ Converts python list of SymPy expressions to a NumPy array.
See also

sympy.matrices.dense.
matrix2numpy
(m, dtype=<class 'object'>)[source]¶ Converts SymPy’s matrix to a NumPy array.
See also

sympy.matrices.dense.
symarray
(prefix, shape, **kwargs)[source]¶ Create a numpy ndarray of symbols (as an object array).
The created symbols are named
prefix_i1_i2_
… You should thus provide a nonempty prefix if you want your symbols to be unique for different output arrays, as SymPy symbols with identical names are the same object. Parameters
prefix : string
A prefix prepended to the name of every symbol.
shape : int or tuple
Shape of the created array. If an int, the array is onedimensional; for more than one dimension the shape must be a tuple.
**kwargs : dict
keyword arguments passed on to Symbol
Examples
These doctests require numpy.
>>> from sympy import symarray >>> symarray('', 3) [_0 _1 _2]
If you want multiple symarrays to contain distinct symbols, you must provide unique prefixes:
>>> a = symarray('', 3) >>> b = symarray('', 3) >>> a[0] == b[0] True >>> a = symarray('a', 3) >>> b = symarray('b', 3) >>> a[0] == b[0] False
Creating symarrays with a prefix:
>>> symarray('a', 3) [a_0 a_1 a_2]
For more than one dimension, the shape must be given as a tuple:
>>> symarray('a', (2, 3)) [[a_0_0 a_0_1 a_0_2] [a_1_0 a_1_1 a_1_2]] >>> symarray('a', (2, 3, 2)) [[[a_0_0_0 a_0_0_1] [a_0_1_0 a_0_1_1] [a_0_2_0 a_0_2_1]] [[a_1_0_0 a_1_0_1] [a_1_1_0 a_1_1_1] [a_1_2_0 a_1_2_1]]]
For setting assumptions of the underlying Symbols:
>>> [s.is_real for s in symarray('a', 2, real=True)] [True, True]

sympy.matrices.dense.
rot_axis1
(theta)[source]¶ Returns a rotation matrix for a rotation of theta (in radians) about the 1axis.
Examples
>>> from sympy import pi >>> from sympy.matrices import rot_axis1
A rotation of pi/3 (60 degrees):
>>> theta = pi/3 >>> rot_axis1(theta) Matrix([ [1, 0, 0], [0, 1/2, sqrt(3)/2], [0, sqrt(3)/2, 1/2]])
If we rotate by pi/2 (90 degrees):
>>> rot_axis1(pi/2) Matrix([ [1, 0, 0], [0, 0, 1], [0, 1, 0]])

sympy.matrices.dense.
rot_axis2
(theta)[source]¶ Returns a rotation matrix for a rotation of theta (in radians) about the 2axis.
Examples
>>> from sympy import pi >>> from sympy.matrices import rot_axis2
A rotation of pi/3 (60 degrees):
>>> theta = pi/3 >>> rot_axis2(theta) Matrix([ [ 1/2, 0, sqrt(3)/2], [ 0, 1, 0], [sqrt(3)/2, 0, 1/2]])
If we rotate by pi/2 (90 degrees):
>>> rot_axis2(pi/2) Matrix([ [0, 0, 1], [0, 1, 0], [1, 0, 0]])

sympy.matrices.dense.
rot_axis3
(theta)[source]¶ Returns a rotation matrix for a rotation of theta (in radians) about the 3axis.
Examples
>>> from sympy import pi >>> from sympy.matrices import rot_axis3
A rotation of pi/3 (60 degrees):
>>> theta = pi/3 >>> rot_axis3(theta) Matrix([ [ 1/2, sqrt(3)/2, 0], [sqrt(3)/2, 1/2, 0], [ 0, 0, 1]])
If we rotate by pi/2 (90 degrees):
>>> rot_axis3(pi/2) Matrix([ [ 0, 1, 0], [1, 0, 0], [ 0, 0, 1]])