def __init__(self, name, parameters=[], ordering=0,
initializer=None,
srepr=None, tip=None, discussion=None):
op = output.Output(name=name,
callback=self.opfunc,
otype=outputval.OutputValPtr,
instancefn=self.instancefn,
params=parameters,
srepr=srepr, tip=tip,
discussion=discussion)
ArithmeticPropertyOutputRegistration.__init__(self, name, op,
initializer)
output.defineAggregateOutput(name, op, ordering=ordering)
compout = outputClones.ComponentOutput.clone(
name=name+" Component",
tip='Compute components of %s' % name,
discussion=
"""
<para>Compute the specified component of <link
linkend='Output-%s'>%s</link> on a &mesh;.</para>
"""
% (name, name))
compout.connect('field', op)
for param in parameters:
compout.aliasParam('field:'+param.name, param.name)
output.defineScalarOutput(name+":Component", compout, ordering=ordering)
def __init__(self, name, parameters=[], ordering=0,
initializer=None,
srepr=None, tip=None, discussion=None):
ScalarPropertyOutputRegBase.__init__(self, name, parameters, ordering,
initializer, srepr,
tip, discussion)
output.defineScalarOutput(name, self.output, ordering=ordering)
output.defineAggregateOutput(name, self.output, ordering=ordering)
def __init__(self, name, parameters=[], ordering=0,
initializer=None,
srepr=None, tip=None, discussion=None):
op = output.Output(name=name,
callback=self.opfunc,
otype=outputval.OutputValPtr,
instancefn=self.instancefn,
srepr=srepr,
column_names=_symmmatrix3_column_names,
params=parameters,
tip=tip, discussion=discussion)
ArithmeticPropertyOutputRegistration.__init__(self, name, op,
initializer)
output.defineAggregateOutput(name+":Value", op, ordering=ordering)
def comprepr(s):
comp = s.resolveAlias("component").value
# We have to pass s to op.shortrepr so that the shortrepr
# will be computed for the actual Output, not the Output
# defined above. The actual output will be a clone of the
# one defined there.
return "%s[%s]" % (op.shortrepr(s), comp)
compout = outputClones.ComponentOutput.clone(
name=name+" Component",
tip='Compute components of %s' % name,
srepr=comprepr,
discussion=
"""
<para>Compute the specified component of %s on a &mesh;.</para>
"""
% name)
compout.connect('field', op)
for param in parameters:
compout.aliasParam('field:' + param.name, param.name)
output.defineScalarOutput(name+":Component", compout, ordering=ordering)
def invariantrepr(s):
invariant = s.resolveAlias("invariant").value.shortrepr()
# See comment above about op.shortrepr(s)
return "%s(%s)" % (invariant, op.shortrepr(s))
invout = outputClones.InvariantOutput.clone(
name=name+" Invariant",
srepr=invariantrepr,
tip='Compute invariants of %s' % name,
discussion="""
<para>Compute the specified invariant of %s on a &mesh;.</para>
"""
% name)
invout.connect('field', op)
for param in parameters:
invout.aliasParam('field:' + param.name, param.name)
output.defineScalarOutput(name+":Invariant", invout, ordering=ordering)
output.defineAggregateOutput(name+":Invariant", invout,
ordering=ordering)
def __init__(self, name, symbol, parameters=[], initializer=None,
ordering=1,tip=None, discussion=None):
self.symbol = symbol
op = output.Output(name=name,
callback=self.opfunc,
otype=outputval.ListOutputValPtr,
instancefn=self.instancefn,
srepr=_twovector_srepr,
column_names=_twovector_column_names,
params=parameters,
tip=tip, discussion=discussion,
symbol=symbol)
NonArithmeticPropertyOutputRegistration.__init__(self, name, op,
initializer)
output.defineAggregateOutput(name, op, ordering=ordering)
def __init__(self, name, parameters=[], ordering=0,
initializer=None,
tip=None, discussion=None):
param = enum.EnumParameter(
"format",
orientationmatrix.OrientationEnum,
tip="How to print the orientation.")
op = output.Output(name=name,
callback=self.opfunc,
otype=corientation.COrientationPtr,
instancefn=self.instancefn,
srepr=_orientation_srepr,
column_names=_orientation_column_names,
params=[param] + parameters,
tip=tip, discussion=discussion)
NonArithmeticPropertyOutputRegistration.__init__(self, name, op,
initializer)
output.defineAggregateOutput(name, op, ordering=ordering)
callback=_difference,
otype=outputval.OutputValPtr,
srepr=_difference_shortrepr,
instancefn=_difference_instancefn,
column_names=_aggdiff_column_names,
params=[
output.AggregateOutputParameter(
'minuend',
tip='The quantity from which the subtrahend is subtracted.'),
output.AggregateOutputParameter(
'subtrahend', tip='The quantity to subtract from the minuend.')
],
tip="Compute the difference between two quantities.")
output.defineAggregateOutput('Difference',
AggregateDifferenceOutput,
ordering=1000)
#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#
# The Concatenate Output allows two Outputs to be printed on the same
# line of the output file. Its value type is the
# ConcatenatedOutputVal class, which defines a bunch of arithmetic
# operations that it just passes through to the OutputVals that it's
# concatenating. ConcatenatedOutputVal is itself *not* an OutputVal.
# Maybe it should be, but that would mean defining it in C++.
## TODO: Allow more than two outputs to be concatenated, so that it's
## not necesary to nest the concatenations. We'll need a Parameter
## class for multiple Outputs, and a widget for it.
tip='Displaced position.',
discussion=xmlmenudump.loadFile(
'DISCUSSIONS/engine/output/actualPosOutput.xml'))
originalPosition = outputClones.posOutput.clone(
discussion=xmlmenudump.loadFile(
'DISCUSSIONS/engine/output/originalPosOutput.xml'))
output.definePositionOutput('original', originalPosition)
output.definePositionOutput('actual', actualPosition)
output.definePositionOutput('enhanced', enhancedPosition)
######################
output.defineAggregateOutput('Field:Value',
outputClones.FieldOutput,
ordering=1.0)
output.defineAggregateOutput('Field:Derivative',
outputClones.FieldDerivOutput,
ordering=1.1)
output.defineAggregateOutput('Field:Invariant',
outputClones.FieldInvariantOutput,
ordering=1.2)
output.defineAggregateOutput('Flux:Value',
outputClones.FluxOutput,
ordering=2.0)
output.defineAggregateOutput('Flux:Invariant',
outputClones.FluxInvariantOutput,
ordering=2.1)
output.defineScalarOutput('Field:Component',
AggregateDifferenceOutput = output.Output(
name="difference",
callback=_difference,
otype=outputval.OutputValPtr,
srepr=_difference_shortrepr,
instancefn=_difference_instancefn,
column_names=_aggdiff_column_names,
params=[
output.AggregateOutputParameter('minuend',
tip='The quantity from which the subtrahend is subtracted.'),
output.AggregateOutputParameter(
'subtrahend',
tip='The quantity to subtract from the minuend.')],
tip="Compute the difference between two quantities.")
output.defineAggregateOutput('Difference', AggregateDifferenceOutput,
ordering=1000)
#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#=-=#
# Scale values to lie between 0 and 1.
def _rescaleOutput(mesh, elements, coords, minimum, maximum, inputdata):
minval = min(inputdata)
maxval = max(inputdata)
# TODO OPT: Doing this math in python on ScalarOutputVals is
# inefficient. Can it be done in C++ instead? This is a low
# priority until this function is actually used.
def rescale(x, mn=minval, mx=maxval, tmin=minimum, tmax=maximum):
if mx == mn:
return 0.5*(tmin + tmax) # arbitrary
name='actual position',
params=dict(factor=1.0),
tip='Displaced position.',
discussion=xmlmenudump.loadFile('DISCUSSIONS/engine/output/actualPosOutput.xml')
)
originalPosition = outputClones.posOutput.clone(
discussion=xmlmenudump.loadFile('DISCUSSIONS/engine/output/originalPosOutput.xml'))
output.definePositionOutput('original', originalPosition)
output.definePositionOutput('actual', actualPosition)
output.definePositionOutput('enhanced', enhancedPosition)
######################
output.defineAggregateOutput('Field:Value', outputClones.FieldOutput,
ordering=1.0)
output.defineAggregateOutput('Field:Derivative', outputClones.FieldDerivOutput,
ordering=1.1)
output.defineAggregateOutput('Field:Invariant',
outputClones.FieldInvariantOutput,
ordering=1.2)
output.defineAggregateOutput('Flux:Value', outputClones.FluxOutput,
ordering=2.0)
output.defineAggregateOutput('Flux:Invariant', outputClones.FluxInvariantOutput,
ordering=2.1)
output.defineScalarOutput('Field:Component', outputClones.FieldCompOutput,
ordering=1.0)
output.defineScalarOutput('Field:Invariant', outputClones.FieldInvariantOutput,
ordering=1.1)
output.defineScalarOutput('Field:Derivative:Component',
