def _calcValue(self):
l = len(self.distribution)
bins = self.mesh.cellCenters[0]
n = numerix.searchsorted(numerix.sort(self.distribution), bins)
n = numerix.concatenate([n, [l]])
dx = bins[1:] - bins[:-1]
return (n[1:] - n[:-1]) / numerix.concatenate([dx, [dx[-1]]]) / float(l)
def _ijv2csr(self, i, j, v):
"""Convert arrays of matrix indices and values into CSR format
see: http://netlib.org/linalg/html_templates/node91.html#SECTION00931100000000000000
.. note::
petsc4py only understands CSR formatted matrices (setValuesCSR and
setValuesIJV both inexplicably call the same underlying routine).
Parameters
----------
i : array_like
column indices
j : array_like
row indices
v : array_like
non-zero values
Returns
-------
ptrs : array_like
locations in the val vector that start a row,
terminated with len(val) + 1
cols : array_like
column indices
data : array_like
non-zero values
"""
i = numerix.asarray(i)
j = numerix.asarray(j)
v = numerix.asarray(v)
start_row, end_row = self.matrix.getOwnershipRange()
ix = numerix.lexsort([i, j])
ix = ix[(j[ix] >= start_row) & (j[ix] < end_row)]
cols = i[ix]
row_ptr = numerix.searchsorted(j[ix],
numerix.arange(start_row, end_row + 1))
vals = v[ix]
# note: PETSc (at least via pip) only seems to handle 32 bit addressing
return row_ptr.astype('int32'), cols.astype('int32'), vals
def _ijv2csr(self, i, j, v):
"""Convert arrays of matrix indices and values into CSR format
see: http://netlib.org/linalg/html_templates/node91.html#SECTION00931100000000000000
.. note::
petsc4py only understands CSR formatted matrices (setValuesCSR and
setValuesIJV both inexplicably call the same underlying routine).
Parameters
----------
i : array_like
column indices
j : array_like
row indices
v : array_like
non-zero values
Returns
-------
row_ptr : array_like
locations in the val vector that start a row,
terminated with len(val) + 1
cols : array_like
column indices
val : array_like
non-zero values
"""
# from fipy.tools.debug import PRINT
# PRINT("i:", i)
# PRINT("j:", j)
# PRINT("v:", v)
i = numerix.asarray(i)
j = numerix.asarray(j)
v = numerix.asarray(v)
start_row, end_row = self.matrix.getOwnershipRange()
# PRINT("start_row:", start_row)
# PRINT("end_row:", end_row)
ix = numerix.lexsort([i, j])
ix = ix[(j[ix] >= start_row) & (j[ix] < end_row)]
cols = i[ix]
row_ptr = numerix.searchsorted(
j[ix],
# numerix.arange(0, self._shape[1]+1))
numerix.arange(start_row, end_row + 1))
vals = v[ix]
# PRINT("i[ix]:", i[ix])
# PRINT("j[ix]:", j[ix])
# PRINT("v[ix]:", v[ix])
# PRINT("size:", self._shape)
# PRINT("shape:", self._shape[0])
#
# PRINT("row_ptr:", row_ptr)
# PRINT("cols:", cols)
# PRINT("vals:", vals)
#
# PRINT("size:", self.matrix.sizes)
# PRINT("owns-rows:", self.matrix.getOwnershipRange())
# PRINT("owns-cols:", self.matrix.getOwnershipRangesColumn())
# note: PETSc (at least via pip) only seems to handle 32 bit addressing
return row_ptr.astype('int32'), cols.astype('int32'), vals