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SolverUtils.py
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SolverUtils.py
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from __future__ import print_function
import numpy as np
from scipy.sparse import linalg
from .matutils import mkvc
import warnings
def _checkAccuracy(A, b, X, accuracyTol):
nrm = np.linalg.norm(mkvc(A*X - b), np.inf)
nrm_b = np.linalg.norm(mkvc(b), np.inf)
if nrm_b > 0:
nrm /= nrm_b
if nrm > accuracyTol:
msg = '### SolverWarning ###: Accuracy on solve is above tolerance: {0:e} > {1:e}'.format(nrm, accuracyTol)
print(msg)
warnings.warn(msg, RuntimeWarning)
def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6, name=None):
"""
Wraps a direct Solver.
::
import scipy.sparse as sp
Solver = SolverUtils.SolverWrapD(sp.linalg.spsolve, factorize=False)
SolverLU = SolverUtils.SolverWrapD(sp.linalg.splu, factorize=True)
"""
def __init__(self, A, **kwargs):
self.A = A.tocsc()
self.checkAccuracy = kwargs.get("checkAccuracy", checkAccuracy)
if "checkAccuracy" in kwargs: del kwargs["checkAccuracy"]
self.accuracyTol = kwargs.get("accuracyTol", accuracyTol)
if "accuracyTol" in kwargs: del kwargs["accuracyTol"]
self.kwargs = kwargs
if factorize:
self.solver = fun(self.A, **kwargs)
def __mul__(self, b):
if type(b) is not np.ndarray:
raise TypeError('Can only multiply by a numpy array.')
if len(b.shape) == 1 or b.shape[1] == 1:
b = b.flatten()
# Just one RHS
if b.dtype is np.dtype('O'):
b = b.astype(type(b[0]))
if factorize:
X = self.solver.solve(b, **self.kwargs)
else:
X = fun(self.A, b, **self.kwargs)
else: # Multiple RHSs
if b.dtype is np.dtype('O'):
b = b.astype(type(b[0,0]))
X = np.empty_like(b)
for i in range(b.shape[1]):
if factorize:
X[:,i] = self.solver.solve(b[:,i])
else:
X[:,i] = fun(self.A, b[:,i], **self.kwargs)
if self.checkAccuracy:
_checkAccuracy(self.A, b, X, self.accuracyTol)
return X
def clean(self):
if factorize and hasattr(self.solver, 'clean'):
return self.solver.clean()
return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5, name=None):
"""
Wraps an iterative Solver.
::
import scipy.sparse as sp
SolverCG = SolverUtils.SolverWrapI(sp.linalg.cg)
"""
def __init__(self, A, **kwargs):
self.A = A
self.checkAccuracy = kwargs.get("checkAccuracy", checkAccuracy)
if "checkAccuracy" in kwargs: del kwargs["checkAccuracy"]
self.accuracyTol = kwargs.get("accuracyTol", accuracyTol)
if "accuracyTol" in kwargs: del kwargs["accuracyTol"]
self.kwargs = kwargs
def __mul__(self, b):
if type(b) is not np.ndarray:
raise TypeError('Can only multiply by a numpy array.')
if len(b.shape) == 1 or b.shape[1] == 1:
b = b.flatten()
# Just one RHS
out = fun(self.A, b, **self.kwargs)
if type(out) is tuple and len(out) == 2:
# We are dealing with scipy output with an info!
X = out[0]
self.info = out[1]
else:
X = out
else: # Multiple RHSs
X = np.empty_like(b)
for i in range(b.shape[1]):
out = fun(self.A, b[:,i], **self.kwargs)
if type(out) is tuple and len(out) == 2:
# We are dealing with scipy output with an info!
X[:,i] = out[0]
self.info = out[1]
else:
X[:,i] = out
if self.checkAccuracy:
_checkAccuracy(self.A, b, X, self.accuracyTol)
return X
def clean(self):
pass
return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
Solver = SolverWrapD(linalg.spsolve, factorize=False, name="Solver")
SolverLU = SolverWrapD(linalg.splu, factorize=True, name="SolverLU")
SolverCG = SolverWrapI(linalg.cg, name="SolverCG")
SolverBiCG = SolverWrapI(linalg.bicgstab, name="SolverBiCG")
class SolverDiag(object):
"""docstring for SolverDiag"""
def __init__(self, A):
self.A = A
self._diagonal = A.diagonal()
def __mul__(self, rhs):
n = self.A.shape[0]
assert rhs.size % n == 0, 'Incorrect shape of rhs.'
nrhs = rhs.size // n
if len(rhs.shape) == 1 or rhs.shape[1] == 1:
x = self._solve1(rhs)
else:
x = self._solveM(rhs)
if nrhs == 1:
return x.flatten()
elif nrhs > 1:
return x.reshape((n,nrhs), order='F')
def _solve1(self, rhs):
return rhs.flatten()/self._diagonal
def _solveM(self, rhs):
n = self.A.shape[0]
nrhs = rhs.size // n
return rhs/self._diagonal.repeat(nrhs).reshape((n,nrhs))
def clean(self):
pass