- Syntax:
mobj = h.Matrix(nrow, ncol)mobj = h.Matrix(nrow, ncol, type)- Description:
A class for manipulation of two dimensional arrays of numbers. A companion to the :class:`Vector` class, Matrix contains routines for m*x=b, v1=m*v2, etc.
Individual element values are assigned and evaluated using the syntax:
m.getval(irow, icol) m.setval(irow, icol, new_val)
which may appear anywhere in an expression or on the left hand side of an assignment statement. irow can range from 0 to m.nrow-1 and icol ranges from 0 to m.ncol-1 .
When possible, Matrix methods returning a Matrix use the form, mobj = m.f(args, [mout]), where mobj is a newly constructed matrix (m is unchanged) unless the optional last mout argument is present, in which case the return value is mout and mout is used to store the matrix. This style seems most efficient to me since many matrix operations cannot be done in-situ. Exceptions to this rule, eg m.zero(), are noted in the individual methods.
Similarly, Matrix methods returning a Vector use the form, vobj = m.f(args, [vout]), where vobj is a newly constructed Vector unless the optional last vout is present for storage of the vector elements. Use of vout is extremely useful in those cases where the vector is graphed since the result of the matrix operation does not invalidate the pointers to the vector elements.
Note that the return value allows these operations to be used as members of a filter chain or arguments to other functions.
By default, a new Matrix is of type MFULL (= 1) and allocates storage for all nrow*ncol elements. Scaffolding is in place for matrices of storage type MSPARSE (=2) and MBAND (=3) but not many methods have been interfaced to the meschach library at this time. If a method is called on a matrix type whose method has not been implemented, an error message will be printed. It is intended that implemented methods will be transparent to the user, eg m*x=b (
x = m.solv(b)) will solve the linear system regardless of the type of m and v1 = m*v2 (v1 = m.mulv(v2)) will perform the vector multiplication.Matrix is implemented using the meschach c library by David E. Stewart (discovered at http://www.netlib.org/c/index.html) which contains a large collection of routines for sparse, banded, and full matrices. Many of the useful routines have not been interfaced with the hoc interpreter but can be easily added on request or you can add it yourself by analogy with the code in
nrn/src/ivoc/(matrix.c ocmatrix.[ch])At this time the MFULL matrix type is complete enough to do useful work and MSPARSE can be used to multiply a matrix by a vector and solve Mx=b.
.. data:: Matrix.x
Not currently supported in Python; use :meth:`Matrix.getval` and :meth:`Matrix.setval` instead.
.. method:: Matrix.nrow
Syntax:
``n = m.nrow()``
Description:
Returns the row dimension of the matrix. Row indices range from 0 to m.nrow()-1
.. note::
This method currently returns an integer, but prior to NEURON 7.6, it returned a float.
In older versions, it was thus sometimes necessary to cast the result to an int before
using e.g. range.
.. method:: Matrix.ncol
n = m.ncol()
Description:
returns the column dimension of the matrix. Column indices range
from 0 to m.ncol()-1
.. note::
This method currently returns an integer, but prior to NEURON 7.6, it returned a float.
In older versions, it was thus sometimes necessary to cast the result to an int before
using e.g. range.
.. method:: Matrix.resize
Syntax:
``mobj = m.resize(nrow, ncol)``
Description:
Change the size of the matrix. As many as possible of the former elements
are preserved. New elements are assigned the value of 0. New memory may
not have to be allocated depending on the size history of the matrix.
Example:
.. code-block::
python
>>> from neuron import h
>>> m = h.Matrix(3, 5)
>>> ignore_return = m.printf()
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
>>> for i in range(5):
... ignore_return = m.setcol(i, i)
...
>>> ignore_return = m.printf()
0 1 2 3 4
0 1 2 3 4
0 1 2 3 4
>>> ignore_return = m.resize(7, 7)
>>> ignore_return = m.printf()
0 1 2 3 4 0 0
0 1 2 3 4 0 0
0 1 2 3 4 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
>>> ignore_return = m.resize(4, 2)
>>> ignore_return = m.printf()
0 1
0 1
0 1
0 0
.. warning::
Implemented only for full matrices.
.. method:: Matrix.c
Syntax:
``mdest = msrc.c()``
Description:
Copy the matrix. msrc is unchanged.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.bcopy
Syntax:
``mdest = msrc.bcopy(i0, j0, n, m [, mout])``
``mdest = msrc.bcopy(i0, j0, n, m, i1, j1 [, mout])``
Description:
Copy selected piece of a matrix. msrc is unchanged.
Copies the n x m submatrix with top-left (row i0, col j0) coordinates
to the corresponding submatrix of destination with top-left coordinates
(i1, j1). Out is resized if necessary.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(4,6)
for i in range(m.nrow()):
for j in range(m.ncol()):
m.setval(i, j, 1 + 10*i+j)
m.printf()
print('')
m.bcopy(1,2,2,3).printf()
print('')
m.bcopy(1,2,2,3,2,3).printf()
print('')
m.bcopy(1,2,2,3,2,3, h.Matrix(8,8)).printf()
.. warning::
Implemented only for full matrices.
.. method:: Matrix.getval
Syntax:
``val = m.getval(irow, jcol)``
Description:
Returns the value of the matrix element. If m is sparse and the element
does not exist then 0 is returned without creating the element.
.. method:: Matrix.setval
Syntax:
``val = m.setval(irow, jcol, val)``
Description:
Sets the value of the matrix element. For sparse matrices, if the
element is 0, this method will create the element.
.. method:: Matrix.sprowlen
Syntax:
``n = m.sprowlen(i)``
Description:
Returns the number of existing(usually nonzero)
elements in the ith row of the sparse
matrix. Useful for iterating over a elements of a sparse matrix.
This function works only for sparse matrices.
See :meth:`Matrix.spgetrowval`
.. method:: Matrix.spgetrowval
Syntax:
``x = m.spgetrowval(i, jx, &j)``
Description:
Returns the existing element value and the column index (third pointer arg)
of the ith row and jx item. The latter ranges from 0 to m.sprowlen(i)-1
This function works only for sparse matrices (created with a third argument
of 2)
Example:
To print the elements of a sparse matrix.
.. code-block::
python
from __future__ import print_function
from neuron import h
def sparse_print(m):
m.printf()
print('m.nrow()', m.nrow())
for i in range(m.nrow()):
print("%d " % i, end='')
for jx in range(m.sprowlen(i)):
j = h.ref(0)
x=m.spgetrowval(i, jx, j)
print(" %d:%f" % (j[0], x), end='')
print()
m = h.Matrix(4, 5, 2)
m.setval(0, 2, 1.2)
m.setval(0, 4, 2.4)
m.setval(1, 1, 3.1)
for i in range(4):
m.setval(3, i, i/10.)
sparse_print(m)
.. method:: Matrix.printf
Syntax:
``0 = m.printf()``
``0 = m.printf("element_format")``
``0 = m.printf("element_format", "row_format")``
Description:
Print the matrix to the standard output with a default %-8g element format
and a default "\n" row format.
.. warning::
Needs a separate implementation for sparse and banded matrices. Prints sparse
as though it was full.
.. method:: Matrix.fprint
Syntax:
``0 = m.fprint(fileobj)``
``0 = m.fprint(fileobj, "element_format")``
``0 = m.fprint(fileobj, "element_format", "row_format")``
``0 = m.fprint(0, fileobj [,...])``
Description:
Same as :func:`printf` but prints to the File object (must be open for writing)
with a first line consisting of the two integers, nrow ncol.
Print the matrix to the open file object with a default %-8g element format
and a default "\n" row format.
Because of the "nrow ncol" first line, such a file can be read with :func:`scanf` .
If the first arg is a 0, then the nrow ncol pair of numbers will not
be printed.
.. warning::
Needs a separate implementation for sparse and banded matrices.
.. method:: Matrix.scanf
Syntax:
``0 = m.scanf(File_object)``
``0 = m.scanf(File_object, nrow, ncol)``
Description:
Read a file, including sizes, into a Matrix. The File_object is
an object of type :class:`File` and must be opened for reading prior to
the scanf. If nrow,ncol arguments are not present,
the first two numbers in the file must be nrow and mcol
respectively. In either case those values are used to resize the matrix.
The following nrow*mcol
numbers are row streams, eg it is often natural to have one row on a single line
or else to organize the file as a list of row vectors with only one number
per line. Strings in the file that cannot be parsed as numbers are ignored.
.. code-block::
python
from neuron import h
f = h.File("filename")
f.ropen()
m = h.Matrix()
m.scanf(f)
print('{} {}'.format(m.nrow(), m.ncol()))
.. warning::
Works only for full matrix types
.. seealso::
:meth:`Vector.scanf`, :func:`fscan`
.. method:: Matrix.mulv
Syntax:
``vobj = msrc.mulv(vin)``
``vobj = msrc.mulv(vin, vout)``
Description:
Multiplication of a Matrix by a Vector, vobj = msrc*vin.
Returns a new vector of dimension msrc.nrow. Optional Vector
vout is used for storage of the result. Vector
vin must have dimension msrc.ncol. vin and vout can be the same vector
if the matrix is square.
Example:
.. code-block::
python
from neuron import h
v1 = h.Vector(4)
v1.indgen(1,1)
m = h.Matrix(3, 4)
for i in range(3):
for j in range(3):
m.setval(i, j, i*10 + j)
.. code-block::
python
print("v1 {}".format(v1))
v1.printf()
print("m {}".format(m))
m.printf()
print("m * v1")
m.mulv(v1).printf()
A sparse example
.. code-block::
python
from neuron import h
v1 = h.Vector(100)
v1.indgen(1,1)
m = h.Matrix(100, 100, 2) ##sparse matrix
##reverse permutation
for i in range(100):
m.setval(i, 99 - i, 1)
m.mulv(v1).printf()
.. warning::
Implemented only for full and sparse matrices.
.. method:: Matrix.getrow
Syntax:
``vobj = msrc.getrow(i)``
``vobj = msrc.getrow(i, vout)``
Description:
Return the i'th row of the matrix in a new :class:`Vector` (or use the storage
in the Vector vout if that arg is present). Range of i is from 0 to msrc.nrow-1.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.getcol
Syntax:
``vobj = msrc.getcol(i)``
``vobj = msrc.getcol(i, vout)``
Description:
Return the i'th column of the matrix in a new vector (or use the storage
in vout if that arg is present). Range of i is from 0 to msrc.ncol-1.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.getdiag
Syntax:
``vobj = msrc.getdiag(i)``
``vobj = msrc.getdiag(i, vout)``
Description:
Return the i'th diag of the matrix in a new vector (or use the storage
in vout if that arg is present) of size msrc.nrow.
Range is from -(msrc.nrow-1) to msrc.ncol-1
with 0 being the main diagonal, positive i refers to upper diagonals, and
negative i refers to lower diagonals. Upper diagonals fill the Vector
starting at position 0 and remaining elements are unused.
Lower diagonals fill the Vector ending at msrc.nrow-1 and the first
elements are unused.
Example:
.. code-block::
python
from __future__ import print_function
from neuron import h
m = h.Matrix(4,4)
for i in range(m.nrow()):
for j in range(m.ncol()):
m.setval(i, j, 1 + 10*j + 100*i)
m.printf()
for i in range(1 - m.nrow(), m.ncol()):
print("diagonal %d: " % i, end='')
print(list(m.getdiag(i))[max(0, -i) : (m.nrow() - i)])
.. warning::
Implemented only for full matrices.
.. method:: Matrix.solv
Syntax:
``vx = msrc.solv(vb)``
``vx = msrc.solv(vb, vout and/or 1 in either order)``
Description:
Solves the linear system msrc*vx = vb by LU factorization. msrc must be
a square matrix and vb must have size equal to msrc.nrow. The answer
will be returned in a new Vector of size msrc.nrow.
msrc is not changed.
The LU factorization is stored in case it
is desired for later reuse with a different vb. Re-use of the LU factorization
will actually take place only if the second or third argument is 1 and
msrc has not changed in size.
Note: if the LUfactor is used, changes to the actual values of msrc would
not affect the solution on subsequent calls to solv.
Example:
.. code-block::
python
from neuron import h
b = h.Vector(3)
b.indgen(1,1)
m = h.Matrix(3, 3)
for i in range(m.nrow()):
for j in range(m.ncol()):
m.setval(i, j, i*j + 1)
print("b")
b.printf()
print("m")
m.printf()
print()
print("solution of m*x = b")
print()
m.solv(b).printf()
.. code-block::
python
m = h.Matrix(1000, 1000, 2) ## sparse type
m.setdiag(0, 3)
m.setdiag(-1, -1)
m.setdiag(1, -1)
b = h.Vector(1000)
b[500] = 1
x = m.solv(b)
print()
x.printf("%8.3f", 475, 525)
b[500] = 0
b[499] = 1
print()
m.solv(b,1).printf("%8.3f", 475, 535)
.. warning::
Implemented only for full and sparse matrices.
.. method:: Matrix.det
Syntax:
``mantissa = m.det(_ref_base10exponent)``
Description:
Determinant of matrix m. Returns mantissa in range from -1 to 1 and
integer _ref_base10exponent[0].
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(2,2)
m.setval(0, 1, 20)
m.setval(1, 0, 30)
m.printf()
ex = h.ref(0)
mant = m.det(ex)
print(mant*10**ex[0])
.. method:: Matrix.mulm
Syntax:
``mobj = msrc.mulm(m)``
``mobj = msrc.mulm(m, mout)``
Description:
Multiplication of a Matrix by a Matrix, mobj = msrc*m. msrc and m are
unchanged. A new matrix is returned with size msrc.nrow x m.ncol.
msrc.ncol and m.nrow must be the same. If mout is present, that storage is
used for the result.
Example:
.. code-block::
python
from neuron import h
m1 = h.Matrix(6, 6)
for i in range(-1, 2):
if i == 0:
m1.setdiag(i, 2)
else:
m1.setdiag(i, -1)
m2 = m1.inverse()
print("m1")
m1.printf()
print("m2")
m2.printf(" %8.5f")
print("m1*m2" )
m1.mulm(m2).printf(" %8.5f")
.. warning::
Implemented only for full matrices.
.. method:: Matrix.add
Syntax:
``mobj = m1srcdest.add(m2src)``
Description:
Return m1srcdest + m2src. The matrices must have the same rank.
This is one of those functions that modifies the source matrix (unless the
last optional mout arg is present) instead of
putting the result in a new destination matrix.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.muls
Syntax:
``mobj = msrcdest.muls(scalar)``
Description:
Multiply the matrix by a scalar in place and return the matrix reference.
This is one of those functions that modifies the source matrix instead of
putting the result in a new destination matrix.
Example:
.. code-block::
python
m = h.Matrix(4,4)
m.ident()
m.muls(-10)
m.printf()
.. warning::
Implemented only for full and sparse matrices.
.. method:: Matrix.setrow
Syntax:
``mobj = msrcdest.setrow(i, vin)``
``mobj = msrcdest.setrow(i, scalar)``
Description:
Fill the ith row of the msrcdest matrix with the values of the Vector vin.
The vector must have size msrcdest.ncol
Otherwise fill the matrix row with a constant.
.. warning::
Implemented only for full matrices and sparse.
.. method:: Matrix.setcol
Syntax:
``mobj = msrcdest.setcol(i, vin)``
``mobj = msrcdest.setcol(i, scalar)``
Description:
Fill the ith column of the msrcdest matrix with the values of the Vector vin.
The vector must have size msrcdest.mrow
Otherwise fill the matrix column with a constant.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.setdiag
Syntax:
``mobj = msrcdest.setdiag(i, vin)``
``mobj = msrcdest.setdiag(i, scalar)``
Description:
Fill the ith diagonal of the msrcdest matrix with the values of the
Vector vin. The vector must have size msrcdest.mrow. The ith diagonal
ranges from -(mrow-1) to mcol-1. For positive diagonals, the starting
position of vector elements is 0 and trailing elements are ignored.
For negative diagonals, the ending position of the vector elements is
nrow-1 and beginning elements are ignored.
Otherwise fill the matrix diagonal with a constant.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(5,7)
v1 = h.Vector(5)
for i in range(-4,7):
m.setdiag(i, i)
m.printf()
print
for i in range (-4,7):
v1.indgen(1,1)
m.setdiag(i, v1)
m.printf()
.. warning::
Implemented only for full and sparse matrices.
.. method:: Matrix.zero
Syntax:
``mobj = msrcdest.zero()``
Description:
Fills the matrix with 0.
.. warning::
Implemented only for full matrices.
.. method:: Matrix.ident
Syntax:
``mobj = msrcdest.ident()``
Description:
Fills the principal diagonal with 1. All other elements are set to 0.
Example:
.. code-block::
python
m = h.Matrix(4, 6)
m.ident()
m.printf()
.. warning::
Implemented only for full matrices.
.. method:: Matrix.exp
Syntax:
``mobj = msrc.exp()``
``mobj = msrc.exp(mout)``
Description:
Returns a new matrix which is e^msrc. ie 1 + m + m*m/2 + m*m*m/6 + ...
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(8,8)
v1 = h.Vector(8)
for i in range(-1,2):
v1.fill(2 - 3*abs(i))
m.setdiag(i, v1)
m.exp().printf()
.. warning::
Implemented only for full matrices. But doesn't really make sense for
any other type since the result would normally be full.
.. method:: Matrix.pow
Syntax:
``mobj = msrc.pow(i)``
``mobj = msrc.pow(i, mout)``
Description:
Raise a matrix to a non-negative integer power.
Returns a new matrix which is msrc^i.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(6, 6)
m.ident()
m.setval(0, 5, 1)
m.setval(5, 0, 1)
for i in range(6):
print(i)
m.pow(i).printf()
.. warning::
Implemented only for full matrices. But doesn't really make sense for
any other type since the result would normally be full.
.. method:: Matrix.inverse
Syntax:
``mobj = msrc.inverse()``
``mobj = msrc.inverse(mout)``
Description:
Return 1/msrc in a new matrix. mobj*msrc = msrc*mobj = identity
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(7,7)
v1 = h.Vector(7)
for i in range(-1, 2):
v1.fill(2 - 3*abs(i))
m.setdiag(i, v1)
minv = m.inverse()
print
m.printf()
print
minv.printf()
print
m.mulm(minv).printf()
.. warning::
Implemented only for full matrices. But doesn't really make sense for
any other type since the result would normally be full.
.. method:: Matrix.svd
Syntax:
``dvec = msrc.svd()``
``dvec = msrc.svd(umat, vmat)``
Description:
Singular value decomposition of a rectangular n x m matrix.
On return ut*d*v = m where u is an orthogonal n x n matrix,
v is an orthogonal m x m matrix, and d is a diagonal n x m matrix
(represented as a vector) whose elements are non-negative and sorted
by decreasing value.
Note that if m*x = b then
vmat.mulv(x).mul(dvec) = umat.mulv(b)
Example:
.. code-block::
python
from neuron import h
def svdtest(a):
umat = h.Matrix()
vmat = h.Matrix()
dvec = a.svd(umat, vmat)
dmat = h.Matrix(a.nrow(), a.ncol())
dmat.setdiag(0, dvec)
print("dvec")
dvec.printf()
print("dmat")
dmat.printf()
print("umat")
umat.printf()
print("vmat")
vmat.printf()
print("input ")
a.printf()
print("ut*d*v")
umat.transpose().mulm(dmat).mulm(vmat).printf()
a = h.Matrix(5, 3)
a.setdiag(0, a.getdiag(0).indgen().add(1))
svdtest(a)
a = h.Matrix(6, 6)
r = h.Random()
r.discunif(1,10)
for i in range(a.nrow()):
a.setrow(i, a.getrow(i).setrand(r))
svdtest(a)
a = h.Matrix(2,2)
a.setrow(0, 1)
a.setrow(1, 2)
svdtest(a)
.. warning::
Implemented only for full matrices. umat and vmat are also full.
.. method:: Matrix.transpose
Syntax:
``mdest = msrc.transpose()``
Description:
Return new matrix which is the transpose of the source matrix.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(1,5)
for i in range(5):
m.setval(0, i, i)
m.printf()
print
m.transpose().printf()
print
m.transpose().mulm(m).printf()
print
m.mulm(m.transpose()).printf()
.. warning::
Implemented only for full matrices.
.. method:: Matrix.symmeig
Syntax:
``veigenvalues = msrc.symmeig(eigenvectors)``
Description:
Returns the eigenvalues and eigenvectors of a real symmetric matrix.
On exit the eigenvalues are returned in a new vector and the
eigenvectors are returned as an orthogonal matrix.
Note that the i'th column of the eigenvector matrix is the eigenvector
for the i'th element of the eigenvalue vector.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(5,5)
m.setdiag(0, 2)
m.setdiag(-1, -1)
m.setdiag(1, -1)
m.printf()
q = h.Matrix(1,1)
e = m.symmeig(q)
print("eigenvectors")
q.printf()
print()
print("eigenvalues")
e.printf()
print()
print("qt*m*q")
q.transpose().mulm(m).mulm(q).printf()
print()
print("qt*q")
q.transpose().mulm(q).printf()
.. warning::
Implemented only for full matrices.
msrc must be symmetric but that fact is not checked.
.. method:: Matrix.to_vector
Syntax:
``vobj = msrc.to_vector()``
``vobj = msrc.to_vector(vout)``
Description:
Copies the matrix elements into a :class:`Vector` in column order.
i.e the jth column starts
at vobj[msrc.nrow*j] .
The vector is sized to nrow*ncol.
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(4,5)
m.from_vector(m.to_vector().indgen()).printf()
.. warning::
Works for sparse matrices but the output vector will still be size
nrow*ncol.
Not very efficient since vobj and msrc do not share memory.
.. method:: Matrix.from_vector
Syntax:
``mobj = msrcdest.from_vector(vec)``
Description:
Copies the vector elements into the matrix in column order. I.e
m[i][j] = v[j*nrow + i].
The size of vec must be equal to msrcdest.nrow()*msrcdest.ncol().
Example:
.. code-block::
python
from neuron import h
m = h.Matrix(4,5)
m.from_vector(m.to_vector().indgen()).printf()
.. warning::
Works for sparse matrices but all elements will exist so not really sparse.