Matrix
add · bcopy · c · det · exp · fprint · from_vector · getcol · getdiag · getrow · getval · ident · inverse · mulm · muls · mulv · ncol · nrow · pow · printf · resize · scanf · setcol · setdiag · setrow · setval · solv · spgetrowval · sprowlen · svd · symmeig · to_vector · transpose · x · zero

# Matrix¶↑

class Matrix
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 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.

Matrix.x

Not currently supported in Python; use Matrix.getval() and Matrix.setval() instead.

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.

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.

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:

>>> 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.

Matrix.c()
Syntax:
mdest = msrc.c()
Description:
Copy the matrix. msrc is unchanged.

Warning

Implemented only for full matrices.

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:

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.

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.

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.

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 Matrix.spgetrowval()

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.

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)


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.

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 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 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.

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 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.

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

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:
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)

print("v1 {}".format(v1))
v1.printf()
print("m {}".format(m))
m.printf()
print("m * v1")
m.mulv(v1).printf()


A sparse example

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.

Matrix.getrow()
Syntax:

vobj = msrc.getrow(i)

vobj = msrc.getrow(i, vout)

Description:
Return the i’th row of the matrix in a new 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.

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.

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:

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.

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:

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()

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.

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:

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])


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:

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.

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.

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:

m = h.Matrix(4,4)
m.ident()
m.muls(-10)
m.printf()


Warning

Implemented only for full and sparse matrices.

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.

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.

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:

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.

Matrix.zero()
Syntax:
mobj = msrcdest.zero()
Description:
Fills the matrix with 0.

Warning

Implemented only for full matrices.

Matrix.ident()
Syntax:
mobj = msrcdest.ident()
Description:
Fills the principal diagonal with 1. All other elements are set to 0.

Example:

m = h.Matrix(4, 6)
m.ident()
m.printf()


Warning

Implemented only for full matrices.

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:

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.

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:

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.

Matrix.inverse()
Syntax:

mobj = msrc.inverse()

mobj = msrc.inverse(mout)

Description:
Return 1/msrc in a new matrix. mobj*msrc = msrc*mobj = identity

Example:

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.

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:

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)
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.

Matrix.transpose()
Syntax:
mdest = msrc.transpose()
Description:
Return new matrix which is the transpose of the source matrix.

Example:

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.

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:

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.

Matrix.to_vector()
Syntax:

vobj = msrc.to_vector()

vobj = msrc.to_vector(vout)

Description:
Copies the matrix elements into a Vector in column order. i.e the jth column starts at vobj[msrc.nrow*j] . The vector is sized to nrow*ncol.

Example:

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.

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:

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.