def _get_ns(self, do, association):
if association == vtkDataObject.FIELD:
# For FieldData, it gets tricky. In general, one would think we are going
# to look at field data in inputDO directly -- same for composite datasets.
# However, ExodusIIReader likes to put field data on leaf nodes insead.
# So we also check leaf nodes, if the FieldData on the root is empty.
# We explicitly call dsa.DataObject.GetFieldData to ensure that
# when dealing with composite datasets, we get the FieldData on the
# vtkCompositeDataSet itself, not in the leaf nodes.
fieldData = dsa.DataObject.GetFieldData(do)
if len(fieldData.keys()) == 0:
# if this is a composite dataset, use field data from the first block with some
# field data.
if isinstance(do, dsa.CompositeDataSet):
for dataset in do:
fieldData = dataset.GetFieldData()
if (not fieldData is None) and (len(fieldData.keys()) > 0):
break
else:
fieldData = do.GetAttributes(association)
arrays = calculator.get_arrays(fieldData)
ns = {}
ns["input"] = do
if self.GetDataTimeValid():
ns["time_value"] = self.GetDataTime()
ns["t_value"] = ns["time_value"]
if self.GetNumberOfTimeSteps() > 0:
ns["time_steps"] = [
self.GetTimeStep(x) for x in xrange(self.GetNumberOfTimeSteps())
]
ns["t_steps"] = ns["time_steps"]
if self.GetTimeRangeValid():
ns["time_range"] = self.GetTimeRange()
ns["t_range"] = ns["time_range"]
if self.GetDataTimeValid() and self.GetNumberOfTimeSteps() > 0:
try:
ns["time_index"] = ns["time_steps"].index(ns["time_value"])
ns["t_index"] = ns["time_index"]
except ValueError:
pass
ns.update(arrays)
return ns
def _get_ns(self, do, association):
if association == vtkDataObject.FIELD:
# For FieldData, it gets tricky. In general, one would think we are going
# to look at field data in inputDO directly -- same for composite datasets.
# However, ExodusIIReader likes to put field data on leaf nodes insead.
# So we also check leaf nodes, if the FieldData on the root is empty.
# We explicitly call dsa.DataObject.GetFieldData to ensure that
# when dealing with composite datasets, we get the FieldData on the
# vtkCompositeDataSet itself, not in the leaf nodes.
fieldData = dsa.DataObject.GetFieldData(do)
if len(fieldData.keys()) == 0:
# if this is a composite dataset, use field data from the first block with some
# field data.
if isinstance(do, dsa.CompositeDataSet):
for dataset in do:
fieldData = dataset.GetFieldData()
if (not fieldData is None) and (len(fieldData.keys()) > 0):
break
else:
fieldData = do.GetAttributes(association)
arrays = calculator.get_arrays(fieldData)
ns = {}
ns["input"] = do
if self.GetDataTimeValid():
ns["time_value"] = self.GetDataTime()
ns["t_value"] = ns["time_value"]
if self.GetNumberOfTimeSteps() > 0:
ns["time_steps"] = [self.GetTimeStep(x) for x in xrange(self.GetNumberOfTimeSteps())]
ns["t_steps"] = ns["time_steps"]
if self.GetTimeRangeValid():
ns["time_range"] = self.GetTimeRange()
ns["t_range"] = ns["time_range"]
if self.GetDataTimeValid() and self.GetNumberOfTimeSteps() > 0:
try:
ns["time_index"] = ns["time_steps"].index(ns["time_value"])
ns["t_index"] = ns["time_index"]
except ValueError:
pass
ns.update(arrays)
return ns
def execute(self):
inputDO = self.GetInputDataObject(0, 0)
inputSEL = self.GetInputDataObject(1, 0)
outputDO = self.GetOutputDataObject(0)
assert inputSEL.GetNumberOfNodes() >= 1
selectionNode = inputSEL.GetNode(0)
field_type = selectionNode.GetFieldType()
if field_type == selectionNode.CELL:
attributeType = vtk.vtkDataObject.CELL
elif field_type == selectionNode.POINT:
attributeType = vtk.vtkDataObject.POINT
elif field_type == selectionNode.ROW:
attributeType = vtk.vtkDataObject.ROW
else:
raise RuntimeError ("Unsupported field attributeType %r" % field_type)
# evaluate expression on the inputDO.
# this is equivalent to executing the Python Calculator on the input dataset
# to produce a mask array.
inputs = []
inputs.append(dsa.WrapDataObject(inputDO))
query = selectionNode.GetQueryString()
# get a dictionary for arrays in the dataset attributes. We pass that
# as the variables in the eval namespace for calculator.compute().
elocals = calculator.get_arrays(inputs[0].GetAttributes(attributeType))
if not elocals.has_key("id") and re.search(r'\bid\b', query):
# add "id" array if the query string refers to id.
# This is a temporary fix. We should look into
# accelerating id-based selections in the future.
elocals["id"] = _create_id_array(inputs[0], attributeType)
try:
maskArray = calculator.compute(inputs, query, ns=elocals)
except:
from sys import stderr
print ("Error: Failed to evaluate Expression '%s'. "\
"The following exception stack should provide additional developer "\
"specific information. This typically implies a malformed "\
"expression. Verify that the expression is valid.\n" % query, file=sys.stderr)
raise
if not maskarray_is_valid(maskArray):
raise RuntimeError(
"Expression '%s' did not produce a valid mask array. The value "\
"produced is of the type '%s'. This typically implies a malformed "\
"expression. Verify that the expression is valid." % \
(query, type(maskArray)))
# if inverse selection is requested, just logical_not the mask array.
if selectionNode.GetProperties().Has(selectionNode.INVERSE()) and \
selectionNode.GetProperties().Get(selectionNode.INVERSE()) == 1:
maskArray = algos.logical_not(maskArray)
output = dsa.WrapDataObject(outputDO)
if self.GetPreserveTopology():
# when preserving topology, just add the mask array as
# vtkSignedCharArray to the output. vtkPythonExtractSelection should
# have already ensured that the input is shallow copied over properly
# before this method gets called.
# note: since mask array is a bool-array, we multiply it by int8(1) to
# make it a type of array that can be represented as vtkSignedCharArray.
output.GetAttributes(attributeType).append(maskArray * np.int8(1), "vtkInsidedness")
else:
# handle extraction.
# flatnonzero() will give is array of indices where the arrays is
# non-zero (or non-False in our case). We then pass that to
# vtkPythonExtractSelection to extract the selected ids.
nonzero_indices = algos.flatnonzero(maskArray)
output.FieldData.append(nonzero_indices, "vtkSelectedIds");
#print (output.FieldData["vtkSelectedIds"])
self.ExtractElements(attributeType, inputDO, outputDO)
del nonzero_indices
del maskArray
def execute(self):
inputDO = self.GetInputDataObject(0, 0)
inputSEL = self.GetInputDataObject(1, 0)
outputDO = self.GetOutputDataObject(0)
assert inputSEL.GetNumberOfNodes() >= 1
selectionNode = inputSEL.GetNode(0)
field_type = selectionNode.GetFieldType()
if field_type == selectionNode.CELL:
attributeType = vtk.vtkDataObject.CELL
elif field_type == selectionNode.POINT:
attributeType = vtk.vtkDataObject.POINT
elif field_type == selectionNode.ROW:
attributeType = vtk.vtkDataObject.ROW
else:
raise RuntimeError, "Unsupported field attributeType %r" % field_type
# evaluate expression on the inputDO.
# this is equivalent to executing the Python Calculator on the input dataset
# to produce a mask array.
inputs = []
inputs.append(dsa.WrapDataObject(inputDO))
# get a dictionary for arrays in the dataset attributes. We pass that
# as the variables in the eval namespace for calculator.compute().
elocals = calculator.get_arrays(inputs[0].GetAttributes(attributeType))
try:
maskArray = calculator.compute(inputs,
selectionNode.GetQueryString(),
ns=elocals)
except:
from sys import stderr
print >> stderr, "Error: Failed to evaluate Expression '%s'. "\
"The following exception stack should provide additional developer "\
"specific information. This typically implies a malformed "\
"expression. Verify that the expression is valid.\n" % \
selectionNode.GetQueryString()
raise
if not maskarray_is_valid(maskArray):
raise RuntimeError,\
"Expression '%s' did not produce a valid mask array. The value "\
"produced is of the type '%s'. This typically implies a malformed "\
"expression. Verify that the expression is valid." % \
(selectionNode.GetQueryString(), type(maskArray))
# if inverse selection is requested, just logical_not the mask array.
if selectionNode.GetProperties().Has(selectionNode.INVERSE()) and \
selectionNode.GetProperties().Get(selectionNode.INVERSE()) == 1:
maskArray = algos.logical_not(maskArray)
output = dsa.WrapDataObject(outputDO)
if self.GetPreserveTopology():
# when preserving topology, just add the mask array as
# vtkSignedCharArray to the output. vtkPythonExtractSelection should
# have already ensured that the input is shallow copied over properly
# before this method gets called.
# note: since mask array is a bool-array, we multiply it by int8(1) to
# make it a type of array that can be represented as vtkSignedCharArray.
output.GetAttributes(attributeType).append(maskArray * np.int8(1),
"vtkInsidedness")
else:
# handle extraction.
# flatnonzero() will give is array of indices where the arrays is
# non-zero (or non-False in our case). We then pass that to
# vtkPythonExtractSelection to extract the selected ids.
nonzero_indices = algos.flatnonzero(maskArray)
output.FieldData.append(nonzero_indices, "vtkSelectedIds")
#print output.FieldData["vtkSelectedIds"]
self.ExtractElements(attributeType, inputDO, outputDO)
del nonzero_indices
del maskArray
def execute(inputDO, selectionNode, insidednessArrayName, outputDO):
field_type = selectionNode.GetFieldType()
if field_type == selectionNode.CELL:
attributeType = vtkDataObject.CELL
elif field_type == selectionNode.POINT:
attributeType = vtkDataObject.POINT
elif field_type == selectionNode.ROW:
attributeType = vtkDataObject.ROW
else:
raise RuntimeError ("Unsupported field attributeType %r" % field_type)
# Evaluate expression on the inputDO.
# This is equivalent to executing the Python Calculator on the input dataset
# to produce a mask array.
inputs = []
inputs.append(dsa.WrapDataObject(inputDO))
query = selectionNode.GetQueryString()
# Get a dictionary for arrays in the dataset attributes. We pass that
# as the variables in the eval namespace for calculator.compute().
elocals = calculator.get_arrays(inputs[0].GetAttributes(attributeType))
if ("id" not in elocals) and re.search(r'\bid\b', query):
# Add "id" array if the query string refers to id.
# This is a temporary fix. We should look into
# accelerating id-based selections in the future.
elocals["id"] = _create_id_array(inputs[0], attributeType)
try:
maskArray = calculator.compute(inputs, query, ns=elocals)
except:
from sys import stderr
print ("Error: Failed to evaluate Expression '%s'. "\
"The following exception stack should provide additional developer "\
"specific information. This typically implies a malformed "\
"expression. Verify that the expression is valid.\n" % query, file=stderr)
raise
if not maskarray_is_valid(maskArray):
raise RuntimeError(
"Expression '%s' did not produce a valid mask array. The value "\
"produced is of the type '%s'. This typically implies a malformed "\
"expression. Verify that the expression is valid." % \
(query, type(maskArray)))
# Preserve topology. Just add the mask array as vtkSignedCharArray to the
# output.
# Note: we must force the data type to VTK_SIGNED_CHAR or the array will
# be ignored by the freeze selection operation
from paraview.vtk.util import numpy_support
output = dsa.WrapDataObject(outputDO)
if type(maskArray) is not dsa.VTKNoneArray:
if isinstance(maskArray, dsa.VTKCompositeDataArray):
for ds, array in izip(output, maskArray.Arrays):
if array is not None:
insidedness = numpy_support.numpy_to_vtk(array, deep=1, array_type=vtkConstants.VTK_SIGNED_CHAR)
insidedness.SetName(insidednessArrayName)
ds.GetAttributes(attributeType).VTKObject.AddArray(insidedness)
else:
insidedness = numpy_support.numpy_to_vtk(maskArray, deep=1, array_type=vtkConstants.VTK_SIGNED_CHAR)
insidedness.SetName(insidednessArrayName)
output.GetAttributes(attributeType).VTKObject.AddArray(insidedness)
def execute(inputDO, selectionNode, insidednessArrayName, outputDO):
field_type = selectionNode.GetFieldType()
if field_type == selectionNode.CELL:
attributeType = vtkDataObject.CELL
elif field_type == selectionNode.POINT:
attributeType = vtkDataObject.POINT
elif field_type == selectionNode.ROW:
attributeType = vtkDataObject.ROW
else:
raise RuntimeError("Unsupported field attributeType %r" % field_type)
# Evaluate expression on the inputDO.
# This is equivalent to executing the Python Calculator on the input dataset
# to produce a mask array.
inputs = []
inputs.append(dsa.WrapDataObject(inputDO))
query = selectionNode.GetQueryString()
# Get a dictionary for arrays in the dataset attributes. We pass that
# as the variables in the eval namespace for calculator.compute().
elocals = calculator.get_arrays(inputs[0].GetAttributes(attributeType))
if ("id" not in elocals) and re.search(r'\bid\b', query):
# Add "id" array if the query string refers to id.
# This is a temporary fix. We should look into
# accelerating id-based selections in the future.
elocals["id"] = _create_id_array(inputs[0], attributeType)
try:
maskArray = calculator.compute(inputs, query, ns=elocals)
except:
from sys import stderr
print ("Error: Failed to evaluate Expression '%s'. "\
"The following exception stack should provide additional developer "\
"specific information. This typically implies a malformed "\
"expression. Verify that the expression is valid.\n" % query, file=stderr)
raise
if not maskarray_is_valid(maskArray):
raise RuntimeError(
"Expression '%s' did not produce a valid mask array. The value "\
"produced is of the type '%s'. This typically implies a malformed "\
"expression. Verify that the expression is valid." % \
(query, type(maskArray)))
# Preserve topology. Just add the mask array as vtkSignedCharArray to the
# output.
# Note: we must force the data type to VTK_SIGNED_CHAR or the array will
# be ignored by the freeze selection operation
from paraview.vtk.util import numpy_support
output = dsa.WrapDataObject(outputDO)
if type(maskArray) is not dsa.VTKNoneArray:
if isinstance(maskArray, dsa.VTKCompositeDataArray):
for ds, array in izip(output, maskArray.Arrays):
if array is not None:
insidedness = numpy_support.numpy_to_vtk(
array, deep=1, array_type=vtkConstants.VTK_SIGNED_CHAR)
insidedness.SetName(insidednessArrayName)
ds.GetAttributes(attributeType).VTKObject.AddArray(
insidedness)
else:
insidedness = numpy_support.numpy_to_vtk(
maskArray, deep=1, array_type=vtkConstants.VTK_SIGNED_CHAR)
insidedness.SetName(insidednessArrayName)
output.GetAttributes(attributeType).VTKObject.AddArray(insidedness)
def execute(self):
inputDO = self.GetInputDataObject(0, 0)
inputSEL = self.GetInputDataObject(1, 0)
outputDO = self.GetOutputDataObject(0)
assert inputSEL.GetNumberOfNodes() >= 1
selectionNode = inputSEL.GetNode(0)
field_type = selectionNode.GetFieldType()
if field_type == selectionNode.CELL:
attributeType = vtkDataObject.CELL
elif field_type == selectionNode.POINT:
attributeType = vtkDataObject.POINT
elif field_type == selectionNode.ROW:
attributeType = vtkDataObject.ROW
else:
raise RuntimeError("Unsupported field attributeType %r" % field_type)
# evaluate expression on the inputDO.
# this is equivalent to executing the Python Calculator on the input dataset
# to produce a mask array.
inputs = []
inputs.append(dsa.WrapDataObject(inputDO))
query = selectionNode.GetQueryString()
# get a dictionary for arrays in the dataset attributes. We pass that
# as the variables in the eval namespace for calculator.compute().
elocals = calculator.get_arrays(inputs[0].GetAttributes(attributeType))
if ("id" not in elocals) and re.search(r'\bid\b', query):
# add "id" array if the query string refers to id.
# This is a temporary fix. We should look into
# accelerating id-based selections in the future.
elocals["id"] = _create_id_array(inputs[0], attributeType)
try:
maskArray = calculator.compute(inputs, query, ns=elocals)
except:
from sys import stderr
print ("Error: Failed to evaluate Expression '%s'. "\
"The following exception stack should provide additional developer "\
"specific information. This typically implies a malformed "\
"expression. Verify that the expression is valid.\n" % query, file=stderr)
raise
if not maskarray_is_valid(maskArray):
raise RuntimeError(
"Expression '%s' did not produce a valid mask array. The value "\
"produced is of the type '%s'. This typically implies a malformed "\
"expression. Verify that the expression is valid." % \
(query, type(maskArray)))
# if inverse selection is requested, just logical_not the mask array.
if selectionNode.GetProperties().Has(selectionNode.INVERSE()) and \
selectionNode.GetProperties().Get(selectionNode.INVERSE()) == 1:
maskArray = algos.logical_not(maskArray)
output = dsa.WrapDataObject(outputDO)
if self.GetPreserveTopology():
# when preserving topology, just add the mask array as
# vtkSignedCharArray to the output. vtkPythonExtractSelection should
# have already ensured that the input is shallow copied over properly
# before this method gets called.
# Note: we must force the data type to VTK_SIGNED_CHAR or the array will
# be ignored by the freeze selection operation
from vtkmodules.util.numpy_support import numpy_to_vtk
if type(maskArray) is not dsa.VTKNoneArray:
insidedness = numpy_to_vtk(maskArray,
deep=1,
array_type=vtkConstants.VTK_SIGNED_CHAR)
insidedness.SetName("vtkInsidedness")
output.GetAttributes(attributeType).VTKObject.AddArray(insidedness)
else:
# handle extraction.
# flatnonzero() will give is array of indices where the arrays is
# non-zero (or non-False in our case). We then pass that to
# vtkPythonExtractSelection to extract the selected ids.
nonzero_indices = algos.flatnonzero(maskArray)
output.FieldData.append(nonzero_indices, "vtkSelectedIds")
#print (output.FieldData["vtkSelectedIds"])
self.ExtractElements(attributeType, inputDO, outputDO)
del nonzero_indices
del maskArray
