def LIerrorSFEMvsAF(analyticalFunction,quadraturePointArray,functionValueArray,T=None):
error=0.0
range_nQuadraturePoints_element = range(quadraturePointArray.shape[1])
for eN in range(quadraturePointArray.shape[0]):
for k in range_nQuadraturePoints_element:
AF = analyticalFunction.uOfXT(quadraturePointArray[eN,k],T)
error = max(error,abs(functionValueArray[eN,k] - AF))
return globalMax(error)
def LIerrorVFEMvsAF(analyticalFunction,quadraturePointArray,quadratureWeightArray,functionValueArray,T=None):
error=0.0
range_nQuadraturePoints_element = range(quadraturePointArray.shape[1])
for eN in range(quadraturePointArray.shape[0]):
for k in range_nQuadraturePoints_element:
AF = analyticalFunction.uOfXT(quadraturePointArray[eN,k],T)
e = numpy.absolute(functionValueArray[eN,k,:] - AF)
error = max(error,max(e.flat))
return globalMax(error)
def LIerrorSFEM(quadratureWeightArray,
aSolutionValueArray,
nSolutionValueArray,
T=None):
error = 0.0
range_nQuadraturePoints_element = range(aSolutionValueArray.shape[1])
error = max(
numpy.absolute(aSolutionValueArray.flat - nSolutionValueArray.flat))
return globalMax(error)
def LIerrorSFEMvsAF(analyticalFunction,
quadraturePointArray,
functionValueArray,
T=None):
error = 0.0
range_nQuadraturePoints_element = range(quadraturePointArray.shape[1])
for eN in range(quadraturePointArray.shape[0]):
for k in range_nQuadraturePoints_element:
AF = analyticalFunction.uOfXT(quadraturePointArray[eN, k], T)
error = max(error, abs(functionValueArray[eN, k] - AF))
return globalMax(error)
def LIerrorVFEMvsAF(analyticalFunction,
quadraturePointArray,
quadratureWeightArray,
functionValueArray,
T=None):
error = 0.0
range_nQuadraturePoints_element = range(quadraturePointArray.shape[1])
for eN in range(quadraturePointArray.shape[0]):
for k in range_nQuadraturePoints_element:
AF = analyticalFunction.uOfXT(quadraturePointArray[eN, k], T)
e = numpy.absolute(functionValueArray[eN, k, :] - AF)
error = max(error, max(e.flat))
return globalMax(error)
def lInfNorm(x):
"""
Compute the parallel :math:`l_{\infty}` norm
The :math:`l_{\infty}` norm of a vector :math:`\mathbf{x} \in
\mathbb{R}^n` is
.. math::
\|x\|_{\infty} = \max_i |x_i|
This implemtation works for a distributed array with no ghost
components (each component must be on a single processor).
:param x: numpy array of length n
:return: float
"""
return flcbdfWrappers.globalMax(numpy.linalg.norm(x, numpy.inf))
def lInfNorm(x):
"""
Compute the parallel :math:`l_{\infty}` norm
The :math:`l_{\infty}` norm of a vector :math:`\mathbf{x} \in
\mathbb{R}^n` is
.. math::
\|x\|_{\infty} = \max_i |x_i|
This implemtation works for a distributed array with no ghost
components (each component must be on a single processor).
:param x: numpy array of length n
:return: float
"""
return flcbdfWrappers.globalMax(numpy.linalg.norm(x,numpy.inf))
def wlInfNorm(x, h):
"""
Compute the parallel weighted l_{\infty} norm with weight h
"""
return flcbdfWrappers.globalMax(numpy.linalg.norm(h * x, numpy.inf))
def LIerrorSFEM(quadratureWeightArray,aSolutionValueArray,nSolutionValueArray,T=None):
error=0.0
range_nQuadraturePoints_element = range(aSolutionValueArray.shape[1])
error = max(numpy.absolute(aSolutionValueArray.flat - nSolutionValueArray.flat))
return globalMax(error)
def wlInfNorm(x,h):
"""
Compute the parallel weighted l_{\infty} norm with weight h
"""
return flcbdfWrappers.globalMax(numpy.linalg.norm(h*x,numpy.inf))
def wlInfNorm(x,h):
"""
Compute the parallel weighted l_{\infty} norm with weight h
"""
return flcbdfWrappers.globalMax(numpy.max(h*numpy.abs(x)))
def lInfNorm(x):
"""
Compute the parallel l_{\infty} norm
"""
return flcbdfWrappers.globalMax(numpy.linalg.norm(x,numpy.inf))
