def __init__(self,dim,dicto=None,shifts=None):
"""
attributes: shifts, dims, data, is1D, length
methods : dump
"""
self.shifts=shifts
if type(dim)==type(()) or type(dim)==type([]):
self.data = spmatrix.ll_mat(reduce(operator.mul, dim), 1)
self.dims = dim
if dicto:
for k, v in dicto.iteritems():
shk = map(operator.__sub__, k, shifts)
self.data[self.comp(shk), 0]=v
elif type(dim)==IntType:
self.data = spmatrix.ll_mat(dim,1)
self.dims = dim
if dicto:
for k, v in dicto.iteritems():
shk = k - shifts
self.data[shk,0] = v
self.is1D = type(self.dims)==IntType
def assemble(fname, topo, mapping):
nnodes = len(mapping)
f = file(fname)
A = spmatrix.ll_mat(nnodes, nnodes)
M = spmatrix.ll_mat(nnodes, nnodes)
for el in topo:
n = len(el)
matK = readmat(f, n)
matV = readmat(f, n)
matM = readmat(f, n)
el = [mapping[x] for x in el]
A.update_add_mask_sym(matK, Numeric.array(el), Numeric.ones(n))
A.update_add_mask_sym(matV, Numeric.array(el), Numeric.ones(n))
M.update_add_mask_sym(matM, Numeric.array(el), Numeric.ones(n))
return A, M
def sparsefeaturematrix(f, sample, format='csc_matrix'):
"""Returns an (m x n) sparse matrix of non-zero evaluations of the scalar
or vector functions f_1,...,f_m in the list f at the points
x_1,...,x_n in the sequence 'sample'.
If format='ll_mat', the PySparse module (or a symlink to it) must be
available in the Python site-packages/ directory. A trimmed-down
version, patched for NumPy compatibility, is available in the SciPy
sandbox/pysparse directory.
"""
m = len(f)
n = len(sample)
if format == 'll_mat':
import spmatrix
sparseF = spmatrix.ll_mat(m, n)
elif format in ('dok_matrix', 'csc_matrix', 'csr_matrix'):
sparseF = sparse.dok_matrix((m, n))
else:
raise ValueError("sparse matrix format not recognized")
for i in xrange(m):
f_i = f[i]
for j in xrange(n):
x = sample[j]
f_i_x = f_i(x)
if f_i_x != 0:
sparseF[i, j] = f_i_x
if format == 'csc_matrix':
return sparseF.tocsc()
elif format == 'csr_matrix':
return sparseF.tocsr()
else:
return sparseF
def sparsefeatures(f, x, format='csc_matrix'):
""" Returns an Mx1 sparse matrix of non-zero evaluations of the
scalar functions f_1,...,f_m in the list f at the point x.
If format='ll_mat', the PySparse module (or a symlink to it) must be
available in the Python site-packages/ directory. A trimmed-down
version, patched for NumPy compatibility, is available in the SciPy
sandbox/pysparse directory.
"""
m = len(f)
if format == 'll_mat':
import spmatrix
sparsef = spmatrix.ll_mat(m, 1)
elif format in ('dok_matrix', 'csc_matrix', 'csr_matrix'):
sparsef = sparse.dok_matrix((m, 1))
for i in xrange(m):
f_i_x = f[i](x)
if f_i_x != 0:
sparsef[i, 0] = f_i_x
if format == 'csc_matrix':
print "Converting to CSC matrix ..."
return sparsef.tocsc()
elif format == 'csr_matrix':
print "Converting to CSR matrix ..."
return sparsef.tocsr()
else:
return sparsef
def sparsefeaturematrix(f, sample, format='csc_matrix'):
"""Returns an (m x n) sparse matrix of non-zero evaluations of the scalar
or vector functions f_1,...,f_m in the list f at the points
x_1,...,x_n in the sequence 'sample'.
If format='ll_mat', the PySparse module (or a symlink to it) must be
available in the Python site-packages/ directory. A trimmed-down
version, patched for NumPy compatibility, is available in the SciPy
sandbox/pysparse directory.
"""
m = len(f)
n = len(sample)
if format == 'll_mat':
import spmatrix
sparseF = spmatrix.ll_mat(m, n)
elif format in ('dok_matrix', 'csc_matrix', 'csr_matrix'):
sparseF = sparse.dok_matrix((m, n))
else:
raise ValueError("sparse matrix format not recognized")
for i in xrange(m):
f_i = f[i]
for j in xrange(n):
x = sample[j]
f_i_x = f_i(x)
if f_i_x != 0:
sparseF[i,j] = f_i_x
if format == 'csc_matrix':
return sparseF.tocsc()
elif format == 'csr_matrix':
return sparseF.tocsr()
else:
return sparseF
def sparsefeatures(f, x, format='csc_matrix'):
""" Returns an Mx1 sparse matrix of non-zero evaluations of the
scalar functions f_1,...,f_m in the list f at the point x.
If format='ll_mat', the PySparse module (or a symlink to it) must be
available in the Python site-packages/ directory. A trimmed-down
version, patched for NumPy compatibility, is available in the SciPy
sandbox/pysparse directory.
"""
m = len(f)
if format == 'll_mat':
import spmatrix
sparsef = spmatrix.ll_mat(m, 1)
elif format in ('dok_matrix', 'csc_matrix', 'csr_matrix'):
sparsef = sparse.dok_matrix((m, 1))
for i in xrange(m):
f_i_x = f[i](x)
if f_i_x != 0:
sparsef[i, 0] = f_i_x
if format == 'csc_matrix':
print "Converting to CSC matrix ..."
return sparsef.tocsc()
elif format == 'csr_matrix':
print "Converting to CSR matrix ..."
return sparsef.tocsr()
else:
return sparsef
def poisson1d(n):
L = spmatrix.ll_mat(n, n, 3*n-2)
for i in range(n):
L[i,i] = 2
if i > 0:
L[i,i-1] = -1
if i < n-1:
L[i,i+1] = -1
return L
def poisson2d(n):
n2 = n*n
L = spmatrix.ll_mat(n2, n2, 5*n2-4*n)
for i in range(n):
for j in range(n):
k = i + n*j
L[k,k] = 4
if i > 0:
L[k,k-1] = -1
if i < n-1:
L[k,k+1] = -1
if j > 0:
L[k,k-n] = -1
if j < n-1:
L[k,k+n] = -1
return L
