/usr/lib/petscdir/3.7.7/x86_64-linux-gnu-complex/bin/PetscBinaryIO.py is in libpetsc-complex-3.7.7-dev 3.7.7+dfsg1-2build5.
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===============
Provides
1. PETSc-named objects Vec, Mat, and IS that inherit numpy.ndarray
2. A class to read and write these objects from PETSc binary files.
The standard usage of this module should look like:
>>> import PetscBinaryIO
>>> io = PetscBinaryIO.PetscBinaryIO()
>>> objects = io.readBinaryFile('file.dat')
or
>>> import PetscBinaryIO
>>> import numpy
>>> vec = numpy.array([1., 2., 3.]).view(PetscBinaryIO.Vec)
>>> io = PetscBinaryIO.PetscBinaryIO()
>>> io.writeBinaryFile('file.dat', [vec,])
to read in objects one at a time use such as
>>> import PetscBinaryIO
>>> io = PetscBinaryIO.PetscBinaryIO()
>>> fh = open('file.dat')
>>> objecttype = io.readObjectType(fh)
>>> if objecttype == 'Vec':
>>> v = io.readVec(fh)
Note that one must read in the object type first and then call readVec(), readMat() etc.
See also PetscBinaryIO.__doc__ and methods therein.
"""
import numpy as np
import functools
try:
basestring # Python-2 has basestring as a common parent of unicode and str
except NameError:
basestring = str # Python-3 is unicode through and through
def update_wrapper_with_doc(wrapper, wrapped):
"""Similar to functools.update_wrapper, but also gets the wrapper's __doc__ string"""
wdoc = wrapper.__doc__
functools.update_wrapper(wrapper, wrapped)
if wdoc is not None:
if wrapper.__doc__ is None:
wrapper.__doc__ = wdoc
else:
wrapper.__doc__ = wrapper.__doc__ + wdoc
return wrapper
def wraps_with_doc(wrapped):
"""Similar to functools.wraps, but also gets the wrapper's __doc__ string"""
return functools.partial(update_wrapper_with_doc, wrapped=wrapped)
def decorate_with_conf(f):
"""Decorates methods to take kwargs for precisions."""
@wraps_with_doc(f)
def decorated_f(self, *args, **kwargs):
"""
Additional kwargs:
precision: 'single', 'double', '__float128' for scalars
indices: '32bit', '64bit' integer size
complexscalars: True/False
Note these are set in order of preference:
1. kwargs if given here
2. PetscBinaryIO class __init__ arguments
3. PETSC_DIR/PETSC_ARCH defaults
"""
changed = False
old_precision = self.precision
old_indices = self.indices
old_complexscalars = self.complexscalars
try:
self.precision = kwargs.pop('precision')
except KeyError:
pass
else:
changed = True
try:
self.indices = kwargs.pop('indices')
except KeyError:
pass
else:
changed = True
try:
self.complexscalars = kwargs.pop('complexscalars')
except KeyError:
pass
else:
changed = True
if changed:
self._update_dtypes()
result = f(self, *args, **kwargs)
if changed:
self.precision = old_precision
self.indices = old_indices
self.complexscalars = old_complexscalars
self._update_dtypes()
return result
return decorated_f
class DoneWithFile(Exception): pass
class Vec(np.ndarray):
"""Vec represented as 1D numpy array
The best way to instantiate this class for use with writeBinaryFile()
is through the numpy view method:
vec = numpy.array([1,2,3]).view(Vec)
"""
_classid = 1211214
class MatDense(np.matrix):
"""Mat represented as 2D numpy array
The best way to instantiate this class for use with writeBinaryFile()
is through the numpy view method:
mat = numpy.array([[1,0],[0,1]]).view(Mat)
"""
_classid = 1211216
class MatSparse(tuple):
"""Mat represented as CSR tuple ((M, N), (rowindices, col, val))
This should be instantiated from a tuple:
mat = MatSparse( ((M,N), (rowindices,col,val)) )
"""
_classid = 1211216
def __repr__(self):
return 'MatSparse: %s'%super(MatSparse, self).__repr__()
class IS(np.ndarray):
"""IS represented as 1D numpy array
The best way to instantiate this class for use with writeBinaryFile()
is through the numpy "view" method:
an_is = numpy.array([3,4,5]).view(IS)
"""
_classid = 1211218
class PetscBinaryIO(object):
"""Reader/Writer class for PETSc binary files.
Note that by default, precisions for both scalars and indices, as well as
complex scalars, are picked up from the PETSC_DIR/PETSC_ARCH configuration
as set by environmental variables.
Alternatively, defaults can be overridden at class instantiation, or for
a given method call.
"""
_classid = {1211216:'Mat',
1211214:'Vec',
1211218:'IS',
1211219:'Bag'}
def __init__(self, precision=None, indices=None, complexscalars=None):
if (precision is None) or (indices is None) or (complexscalars is None):
import petsc_conf
defaultprecision, defaultindices, defaultcomplexscalars = petsc_conf.get_conf()
if precision is None:
if defaultprecision is None:
precision = 'double'
else:
precision = defaultprecision
if indices is None:
if defaultindices is None:
indices = '32bit'
else:
indices = defaultindices
if complexscalars is None:
if defaultcomplexscalars is None:
complexscalars = False
else:
complexscalars = defaultcomplexscalars
self.precision = precision
if self.precision == '__float128' :
raise RuntimeError('__float128 (quadruple) precision is not properly supported. One may use double precision by using -binary_write_double in PETSc and precision=\'double\' here')
self.indices = indices
self.complexscalars = complexscalars
self._update_dtypes()
def _update_dtypes(self):
if self.indices == '64bit':
self._inttype = np.dtype('>i8')
else:
self._inttype = np.dtype('>i4')
if self.precision == '__float128':
nbyte = 16
elif self.precision == 'single':
nbyte = 4
else:
nbyte = 8
if self.complexscalars:
name = 'c'
nbyte = nbyte * 2 # complex scalar takes twice as many bytes
else:
name = 'f'
self._scalartype = '>{0}{1}'.format(name, nbyte)
@decorate_with_conf
def readVec(self, fh):
"""Reads a PETSc Vec from a binary file handle, must be called after readObjectType()."""
nz = np.fromfile(fh, dtype=self._inttype, count=1)[0]
try:
vals = np.fromfile(fh, dtype=self._scalartype, count=nz)
except MemoryError:
raise IOError('Inconsistent or invalid Vec data in file')
if (len(vals) is 0):
raise IOError('Inconsistent or invalid Vec data in file')
return vals.view(Vec)
@decorate_with_conf
def writeVec(self, fh, vec):
"""Writes a PETSc Vec to a binary file handle."""
metadata = np.array([Vec._classid, len(vec)], dtype=self._inttype)
metadata.tofile(fh)
vec.astype(self._scalartype).tofile(fh)
return
@decorate_with_conf
def readMatSparse(self, fh):
"""Reads a PETSc Mat, returning a sparse representation of the data. Must be called after readObjectType()
(M,N), (I,J,V) = readMatSparse(fid)
Input:
fid : file handle to open binary file.
Output:
M,N : matrix size
I,J : arrays of row and column for each nonzero
V: nonzero value
"""
try:
M,N,nz = np.fromfile(fh, dtype=self._inttype, count=3)
I = np.empty(M+1, dtype=self._inttype)
I[0] = 0
rownz = np.fromfile(fh, dtype=self._inttype, count=M)
np.cumsum(rownz, out=I[1:])
assert I[-1] == nz
J = np.fromfile(fh, dtype=self._inttype, count=nz)
assert len(J) == nz
V = np.fromfile(fh, dtype=self._scalartype, count=nz)
assert len(V) == nz
except (AssertionError, MemoryError, IndexError):
raise IOError('Inconsistent or invalid Mat data in file')
return MatSparse(((M, N), (I, J, V)))
@decorate_with_conf
def writeMatSparse(self, fh, mat):
"""Writes a Mat into a PETSc binary file handle"""
((M,N), (I,J,V)) = mat
metadata = np.array([MatSparse._classid,M,N,I[-1]], dtype=self._inttype)
rownz = I[1:] - I[:-1]
assert len(J.shape) == len(V.shape) == len(I.shape) == 1
assert len(J) == len(V) == I[-1] == rownz.sum()
assert (rownz > -1).all()
metadata.tofile(fh)
rownz.astype(self._inttype).tofile(fh)
J.astype(self._inttype).tofile(fh)
V.astype(self._scalartype).tofile(fh)
return
@decorate_with_conf
def readMatDense(self, fh):
"""Reads a PETSc Mat, returning a dense represention of the data, must be called after readObjectType()"""
try:
M,N,nz = np.fromfile(fh, dtype=self._inttype, count=3)
I = np.empty(M+1, dtype=self._inttype)
I[0] = 0
rownz = np.fromfile(fh, dtype=self._inttype, count=M)
np.cumsum(rownz, out=I[1:])
assert I[-1] == nz
J = np.fromfile(fh, dtype=self._inttype, count=nz)
assert len(J) == nz
V = np.fromfile(fh, dtype=self._scalartype, count=nz)
assert len(V) == nz
except (AssertionError, MemoryError, IndexError):
raise IOError('Inconsistent or invalid Mat data in file')
mat = np.zeros((M,N), dtype=self._scalartype)
for row in range(M):
rstart, rend = I[row:row+2]
mat[row, J[rstart:rend]] = V[rstart:rend]
return mat.view(MatDense)
@decorate_with_conf
def readMatSciPy(self, fh):
from scipy.sparse import csr_matrix
(M, N), (I, J, V) = self.readMatSparse(fh)
return csr_matrix((V, J, I), shape=(M, N))
@decorate_with_conf
def writeMatSciPy(self, fh, mat):
from scipy.sparse import csr_matrix
if hasattr(mat, 'tocsr'):
mat = mat.tocsr()
assert isinstance(mat, csr_matrix)
V = mat.data
M,N = mat.shape
J = mat.indices
I = mat.indptr
return self.writeMatSparse(fh, (mat.shape, (mat.indptr,mat.indices,mat.data)))
@decorate_with_conf
def readMat(self, fh, mattype='sparse'):
"""Reads a PETSc Mat from binary file handle, must be called after readObjectType()
optional mattype: 'sparse" or 'dense'
See also: readMatSparse, readMatDense
"""
if mattype == 'sparse':
return self.readMatSparse(fh)
elif mattype == 'dense':
return self.readMatDense(fh)
elif mattype == 'scipy.sparse':
return self.readMatSciPy(fh)
else:
raise RuntimeError('Invalid matrix type requested: choose sparse/dense/scipy.sparse')
@decorate_with_conf
def readIS(self, fh):
"""Reads a PETSc Index Set from binary file handle, must be called after readObjectType()"""
try:
nz = np.fromfile(fh, dtype=self._inttype, count=1)[0]
v = np.fromfile(fh, dtype=self._inttype, count=nz)
assert len(v) == nz
except (MemoryError,IndexError):
raise IOError('Inconsistent or invalid IS data in file')
return v.view(IS)
@decorate_with_conf
def writeIS(self, fh, anis):
"""Writes a PETSc IS to binary file handle."""
metadata = np.array([IS._classid, len(anis)], dtype=self._inttype)
metadata.tofile(fh)
anis.astype(self._inttype).tofile(fh)
return
@decorate_with_conf
def readObjectType(self, fid):
"""Returns the next object type as a string in the file"""
try:
header = np.fromfile(fid, dtype=self._inttype, count=1)[0]
except (MemoryError, IndexError):
raise DoneWithFile
try:
objecttype = self._classid[header]
except KeyError:
raise IOError('Invalid PetscObject CLASSID or object not implemented for python')
return objecttype
@decorate_with_conf
def readBinaryFile(self, fid, mattype='sparse'):
"""Reads a PETSc binary file, returning a tuple of the contained objects.
objects = self.readBinaryFile(fid, **kwargs)
Input:
fid : either file name or handle to an open binary file.
Output:
objects : tuple of objects representing the data in numpy arrays.
Optional:
mattype :
'sparse': Return matrices as raw CSR: (M, N), (row, col, val).
'dense': Return matrices as MxN numpy arrays.
'scipy.sparse': Return matrices as scipy.sparse objects.
"""
close = False
if isinstance(fid, basestring):
fid = open(fid, 'rb')
close = True
objects = []
try:
while True:
objecttype = self.readObjectType(fid)
if objecttype == 'Vec':
objects.append(self.readVec(fid))
elif objecttype == 'IS':
objects.append(self.readIS(fid))
elif objecttype == 'Mat':
objects.append(self.readMat(fid,mattype))
elif objecttype == 'Bag':
raise NotImplementedError('Bag Reader not yet implemented')
except DoneWithFile:
pass
finally:
if close:
fid.close()
return tuple(objects)
@decorate_with_conf
def writeBinaryFile(self, fid, objects):
"""Writes a PETSc binary file containing the objects given.
readBinaryFile(fid, objects)
Input:
fid : either file handle to an open binary file, or filename.
objects : list of objects representing the data in numpy arrays,
which must be of type Vec, IS, MatSparse, or MatSciPy.
"""
close = False
if isinstance(fid, basestring):
fid = open(fid, 'wb')
close = True
for petscobj in objects:
if (isinstance(petscobj, Vec)):
self.writeVec(fid, petscobj)
elif (isinstance(petscobj, IS)):
self.writeIS(fid, petscobj)
elif (isinstance(petscobj, MatSparse)):
self.writeMatSparse(fid, petscobj)
elif (isinstance(petscobj, MatDense)):
if close:
fid.close()
raise NotImplementedError('Writing a dense matrix is not yet supported')
else:
try:
self.writeMatSciPy(fid, petscobj)
except AssertionError:
if close:
fid.close()
raise TypeError('Object %s is not a valid PETSc object'%(petscobj.__repr__()))
if close:
fid.close()
return
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