def get_range(self, attr='scalars', mode='point'):
assert mode in ('point', 'cell')
assert attr in ('scalars', 'vectors')
dataset = self.dataset
da = dataset.PointData if mode == 'point' else dataset.CellData
x = self._get_attr(da, attr, mode)
if x is None:
return None, [0.0, 1.0]
name, x = x
if self._composite:
# Don't bother with Nans for composite data for now.
if isinstance(x, dsa.VTKNoneArray):
res = [0.0, 1.0]
elif attr == 'scalars':
res = [algs.min(x), algs.max(x)]
else:
max_norm = np.sqrt(algs.max(algs.sum(x * x, axis=1)))
res = [0.0, max_norm]
else:
has_nan = np.isnan(x).any()
if attr == 'scalars':
if has_nan:
res = [float(np.nanmin(x)), float(np.nanmax(x))]
else:
res = list(x.GetRange())
else:
if has_nan:
d_mag = np.sqrt((x * x).sum(axis=1))
res = [float(np.nanmin(d_mag)), float(np.nanmax(d_mag))]
else:
res = [0.0, x.GetMaxNorm()]
return name, res
def testArrays(rtData, rtData2, grad, grad2, total_npts):
" Test various parallel algorithms."
if rank == 0:
print('-----------------------')
PRINT("SUM ones:", algs.sum(rtData / rtData) - total_npts)
PRINT(
"SUM sin:",
(algs.sum(algs.sin(rtData) + 1) - numpy.sum(numpy.sin(rtData2) + 1)) /
numpy.sum(numpy.sin(rtData2) + 1))
PRINT("rtData min:", algs.min(rtData) - numpy.min(rtData2))
PRINT("rtData max:", algs.max(rtData) - numpy.max(rtData2))
PRINT("rtData sum:",
(algs.sum(rtData) - numpy.sum(rtData2)) / (2 * numpy.sum(rtData2)))
PRINT("rtData mean:", (algs.mean(rtData) - numpy.mean(rtData2)) /
(2 * numpy.mean(rtData2)))
PRINT("rtData var:",
(algs.var(rtData) - numpy.var(rtData2)) / numpy.var(rtData2))
PRINT("rtData std:",
(algs.std(rtData) - numpy.std(rtData2)) / numpy.std(rtData2))
PRINT("grad min:", algs.min(grad) - numpy.min(grad2))
PRINT("grad max:", algs.max(grad) - numpy.max(grad2))
PRINT("grad min 0:", algs.min(grad, 0) - numpy.min(grad2, 0))
PRINT("grad max 0:", algs.max(grad, 0) - numpy.max(grad2, 0))
PRINT("grad min 1:",
algs.sum(algs.min(grad, 1)) - numpy.sum(numpy.min(grad2, 1)))
PRINT("grad max 1:",
algs.sum(algs.max(grad, 1)) - numpy.sum(numpy.max(grad2, 1)))
PRINT("grad sum 1:",
algs.sum(algs.sum(grad, 1)) - numpy.sum(numpy.sum(grad2, 1)))
PRINT("grad var:", (algs.var(grad) - numpy.var(grad2)) / numpy.var(grad2))
PRINT("grad var 0:",
(algs.var(grad, 0) - numpy.var(grad2, 0)) / numpy.var(grad2, 0))
def testArrays(rtData, rtData2, grad, grad2, total_npts):
" Test various parallel algorithms."
if rank == 0:
print "-----------------------"
PRINT("SUM ones:", algs.sum(rtData / rtData) - total_npts)
PRINT(
"SUM sin:",
(algs.sum(algs.sin(rtData) + 1) - numpy.sum(numpy.sin(rtData2) + 1)) / numpy.sum(numpy.sin(rtData2) + 1),
)
PRINT("rtData min:", algs.min(rtData) - numpy.min(rtData2))
PRINT("rtData max:", algs.max(rtData) - numpy.max(rtData2))
PRINT("rtData sum:", (algs.sum(rtData) - numpy.sum(rtData2)) / (2 * numpy.sum(rtData2)))
PRINT("rtData mean:", (algs.mean(rtData) - numpy.mean(rtData2)) / (2 * numpy.mean(rtData2)))
PRINT("rtData var:", (algs.var(rtData) - numpy.var(rtData2)) / numpy.var(rtData2))
PRINT("rtData std:", (algs.std(rtData) - numpy.std(rtData2)) / numpy.std(rtData2))
PRINT("grad min:", algs.min(grad) - numpy.min(grad2))
PRINT("grad max:", algs.max(grad) - numpy.max(grad2))
PRINT("grad min 0:", algs.min(grad, 0) - numpy.min(grad2, 0))
PRINT("grad max 0:", algs.max(grad, 0) - numpy.max(grad2, 0))
PRINT("grad min 1:", algs.sum(algs.min(grad, 1)) - numpy.sum(numpy.min(grad2, 1)))
PRINT("grad max 1:", algs.sum(algs.max(grad, 1)) - numpy.sum(numpy.max(grad2, 1)))
PRINT("grad sum 1:", algs.sum(algs.sum(grad, 1)) - numpy.sum(numpy.sum(grad2, 1)))
PRINT("grad var:", (algs.var(grad) - numpy.var(grad2)) / numpy.var(grad2))
PRINT("grad var 0:", (algs.var(grad, 0) - numpy.var(grad2, 0)) / numpy.var(grad2, 0))
def get_range(self, attr='scalars', mode='point'):
assert mode in ('point', 'cell')
assert attr in ('scalars', 'vectors')
dataset = self.dataset
da = dataset.PointData if mode == 'point' else dataset.CellData
x = self._get_attr(da, attr, mode)
if x is None:
return None, [0.0, 1.0]
name, x = x
if self._composite:
# Don't bother with Nans for composite data for now.
if isinstance(x, dsa.VTKNoneArray):
res = [0.0, 1.0]
elif attr == 'scalars':
res = [algs.min(x), algs.max(x)]
else:
max_norm = np.sqrt(algs.max(algs.sum(x*x, axis=1)))
res = [0.0, max_norm]
else:
has_nan = np.isnan(x).any()
if attr == 'scalars':
if has_nan:
res = [float(np.nanmin(x)), float(np.nanmax(x))]
else:
res = list(x.GetRange())
else:
if has_nan:
d_mag = np.sqrt((x*x).sum(axis=1))
res = [float(np.nanmin(d_mag)),
float(np.nanmax(d_mag))]
else:
res = [0.0, x.GetMaxNorm()]
return name, res
def get_bounds(self):
"""Return the bounds of the data.
"""
if self._composite:
c1 = algs.min(self.dataset.Points, axis=0)
c2 = algs.max(self.dataset.Points, axis=0)
result = np.zeros(6)
result[::2] = c1
result[1::2] = c2
return result
else:
return self.dataset.GetBounds()
def get_bounds(self):
"""Return the bounds of the data.
"""
if self._composite:
c1 = algs.min(self.dataset.Points, axis=0)
c2 = algs.max(self.dataset.Points, axis=0)
result = np.zeros(6)
result[::2] = c1
result[1::2] = c2
return result
else:
return self.dataset.GetBounds()
def test_dataset(ds): p2c = vtk.vtkPointDataToCellData() p2c.SetInputData(ds) p2c.Update() d1 = dsa.WrapDataObject(p2c.GetOutput()) vtkm_p2c = vtk.vtkmAverageToCells() vtkm_p2c.SetInputData(ds) vtkm_p2c.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS, "RTData") vtkm_p2c.Update() d2 = dsa.WrapDataObject(vtkm_p2c.GetOutput()) rtD1 = d1.PointData['RTData'] rtD2 = d2.PointData['RTData'] assert (algs.max(algs.abs(rtD1 - rtD2)) < 10E-4)
def test_dataset(ds): p2c = vtk.vtkPointDataToCellData() p2c.SetInputData(ds) p2c.Update() c2p = vtk.vtkCellDataToPointData() c2p.SetInputConnection(p2c.GetOutputPort()) c2p.Update() d1 = dsa.WrapDataObject(c2p.GetOutput()) c2p = vtk.vtkmAverageToPoints() c2p.SetInputData(p2c.GetOutput()) c2p.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, "RTData") c2p.Update() d2 = dsa.WrapDataObject(c2p.GetOutput()) rtD1 = d1.PointData['RTData'] rtD2 = d2.PointData['RTData'] assert (algs.max(algs.abs(rtD1 - rtD2)) < 10E-4)
def test_dataset(ds): p2c = vtk.vtkPointDataToCellData() p2c.SetInputData(ds) p2c.Update() c2p = vtk.vtkCellDataToPointData() c2p.SetInputConnection(p2c.GetOutputPort()) c2p.Update() d1 = dsa.WrapDataObject(c2p.GetOutput()) c2p = vtk.vtkmAverageToPoints() c2p.SetInputData(p2c.GetOutput()) c2p.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, "RTData") c2p.Update() d2 = dsa.WrapDataObject(c2p.GetOutput()) rtD1 = d1.PointData['RTData'] rtD2 = d2.PointData['RTData'] assert (algs.max(algs.abs(rtD1 - rtD2)) < 10E-4)
dbgRt.SetValue(3, 19.47)
dbgRt.SetValue(4, 3.350)
dbgRt.SetValue(5, 0.212)
dbgRt.SetValue(6, 1023.)
dbg.GetPointData().AddArray(dbgRt)
test_dataset(dbg)
print("Success!")
print("Testing homogeneous image data...")
source = vtk.vtkRTAnalyticSource()
source.Update()
imgData = source.GetOutput()
test_dataset(imgData)
print("Success!")
d = dsa.WrapDataObject(imgData)
rtData = d.PointData['RTData']
rtMin = algs.min(rtData)
rtMax = algs.max(rtData)
clipScalar = 0.5 * (rtMin + rtMax)
print("Testing non-homogenous unstructured grid...")
clip = vtk.vtkClipDataSet()
clip.SetInputData(imgData)
clip.SetValue(clipScalar)
clip.Update()
ugrid = clip.GetOutput()
test_dataset(ugrid)
print("Success!")
g2 = algs.gradient(g)
res = True
dummy = vtk.vtkDummyController()
for axis in [None, 0]:
for array in [rtdata, g, g2]:
if rank == 0:
array2 = array/2
min = algs.min_per_block(array2, axis=axis)
res &= numpy.all(min.Arrays[NUM_BLOCKS - 1] == numpy.min(array, axis=axis))
all_min = algs.min(min, controller=dummy)
all_min_true = numpy.min([algs.min(array, controller=dummy), algs.min(array2, controller=dummy)])
res &= all_min == all_min_true
max = algs.max_per_block(array2, axis=axis)
res &= numpy.all(max.Arrays[NUM_BLOCKS - 1] == numpy.max(array, axis=axis))
all_max = algs.max(max, controller=dummy)
all_max_true = numpy.max([algs.max(array, controller=dummy), algs.max(array2, controller=dummy)])
res &= all_max == all_max_true
sum = algs.sum_per_block(array2, axis=axis)
sum_true = numpy.sum(array2.Arrays[0]) * (NUM_BLOCKS-1)
sum_true += numpy.sum(array.Arrays[0]) * 3
res &= numpy.sum(algs.sum(sum, controller=dummy) - algs.sum(sum_true, controller=dummy)) == 0
mean = algs.mean_per_block(array2, axis=axis)
res &= numpy.sum(mean.Arrays[0] - numpy.mean(array2.Arrays[0], axis=axis)) < 1E-6
if len(array.Arrays[0].shape) == 1:
stk = numpy.hstack
else:
stk = numpy.vstack
res &= numpy.sum(mean.Arrays[NUM_BLOCKS-2] - numpy.mean(stk((array.Arrays[0], array2.Arrays[0])), axis=axis)) < 1E-4
elif rank == 2:
min = algs.min_per_block(dsa.NoneArray, axis=axis)
g2 = algs.gradient(g)
res = True
dummy = vtk.vtkDummyController()
for axis in [None, 0]:
for array in [rtdata, g, g2]:
if rank == 0:
array2 = array/2
min = algs.min_per_block(array2, axis=axis)
res &= numpy.all(min.Arrays[NUM_BLOCKS - 1] == numpy.min(array, axis=axis))
all_min = algs.min(min, controller=dummy)
all_min_true = numpy.min([algs.min(array, controller=dummy), algs.min(array2, controller=dummy)])
res &= all_min == all_min_true
max = algs.max_per_block(array2, axis=axis)
res &= numpy.all(max.Arrays[NUM_BLOCKS - 1] == numpy.max(array, axis=axis))
all_max = algs.max(max, controller=dummy)
all_max_true = numpy.max([algs.max(array, controller=dummy), algs.max(array2, controller=dummy)])
res &= all_max == all_max_true
sum = algs.sum_per_block(array2, axis=axis)
sum_true = numpy.sum(array2.Arrays[0]) * (NUM_BLOCKS-1)
sum_true += numpy.sum(array.Arrays[0]) * 3
res &= numpy.sum(algs.sum(sum, controller=dummy) - algs.sum(sum_true, controller=dummy)) == 0
mean = algs.mean_per_block(array2, axis=axis)
res &= numpy.sum(mean.Arrays[0] - numpy.mean(array2.Arrays[0], axis=axis)) < 1E-6
if len(array.Arrays[0].shape) == 1:
stk = numpy.hstack
else:
stk = numpy.vstack
res &= numpy.sum(mean.Arrays[NUM_BLOCKS-2] - numpy.mean(stk((array.Arrays[0], array2.Arrays[0])), axis=axis)) < 1E-4
elif rank == 2:
min = algs.min_per_block(dsa.NoneArray, axis=axis)
print("This test requires numpy!")
from vtk.test import Testing
Testing.skip()
import vtk
from vtk.numpy_interface import dataset_adapter as dsa
from vtk.numpy_interface import algorithms as algs
rt = vtk.vtkRTAnalyticSource()
p2c = vtk.vtkPointDataToCellData()
p2c.SetInputConnection(rt.GetOutputPort())
p2c.Update()
c2p = vtk.vtkCellDataToPointData()
c2p.SetInputConnection(p2c.GetOutputPort())
c2p.Update()
d1 = dsa.WrapDataObject(c2p.GetOutput())
c2p = vtk.vtkmAverageToPoints()
c2p.SetInputData(p2c.GetOutput())
c2p.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS,
"RTData")
c2p.Update()
d2 = dsa.WrapDataObject(c2p.GetOutput())
assert (algs.max(algs.abs(d1.PointData['RTData'] - d2.PointData['RTData'])) <
10E-4)
mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, pd) pd2 = vtk.vtkPolyData() mb.SetBlock(1, pd2) mbw = dsa.WrapDataObject(mb) mbw.PointData.append(dsa.NoneArray, 'foo') assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 0 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 0 mbw.PointData.append(na2, 'foo') assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 1 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 0 assert mbw.GetBlock(0).GetPointData().GetArray(0).GetName() == 'foo' mbw.PointData.append(algs.max(na2), "maxfoo") assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 2 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 1 assert mbw.GetBlock(0).GetPointData().GetArray(1).GetName() == 'maxfoo' # -------------------------------------- mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, vtk.vtkImageData()) mb.SetBlock(1, vtk.vtkImageData()) assert dsa.WrapDataObject(mb).Points is na mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, vtk.vtkStructuredGrid()) mb.SetBlock(1, vtk.vtkImageData()) assert dsa.WrapDataObject(mb).Points is na
import sys
try:
import numpy
except ImportError:
print("Numpy (http://numpy.scipy.org) not found.")
print("This test requires numpy!")
from vtk.test import Testing
Testing.skip()
import vtk
from vtk.numpy_interface import dataset_adapter as dsa
from vtk.numpy_interface import algorithms as algs
rt = vtk.vtkRTAnalyticSource()
p2c = vtk.vtkPointDataToCellData()
p2c.SetInputConnection(rt.GetOutputPort())
p2c.Update()
d1 = dsa.WrapDataObject(p2c.GetOutput())
vtkm_p2c = vtk.vtkmAverageToCells()
vtkm_p2c.SetInputData(rt.GetOutput())
vtkm_p2c.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS, "RTData")
vtkm_p2c.Update()
d2 = dsa.WrapDataObject(vtkm_p2c.GetOutput())
assert (algs.max(algs.abs(d1.CellData['RTData'] - d2.CellData['RTData'])) < 10E-4)
for array in [rtdata, g, g2]:
if rank == 0:
array2 = array / 2
min = algs.min_per_block(array2, axis=axis)
res &= numpy.all(min.Arrays[NUM_BLOCKS -
1] == numpy.min(array, axis=axis))
all_min = algs.min(min, controller=dummy)
all_min_true = numpy.min([
algs.min(array, controller=dummy),
algs.min(array2, controller=dummy)
])
res &= all_min == all_min_true
max = algs.max_per_block(array2, axis=axis)
res &= numpy.all(max.Arrays[NUM_BLOCKS -
1] == numpy.max(array, axis=axis))
all_max = algs.max(max, controller=dummy)
all_max_true = numpy.max([
algs.max(array, controller=dummy),
algs.max(array2, controller=dummy)
])
res &= all_max == all_max_true
sum = algs.sum_per_block(array2, axis=axis)
sum_true = numpy.sum(array2.Arrays[0]) * (NUM_BLOCKS - 1)
sum_true += numpy.sum(array.Arrays[0]) * 3
res &= numpy.sum(
algs.sum(sum, controller=dummy) -
algs.sum(sum_true, controller=dummy)) == 0
mean = algs.mean_per_block(array2, axis=axis)
res &= numpy.sum(mean.Arrays[0] -
numpy.mean(array2.Arrays[0], axis=axis)) < 1E-6
if len(array.Arrays[0].shape) == 1:
try:
import numpy
except ImportError:
print("Numpy (http://numpy.scipy.org) not found.")
print("This test requires numpy!")
sys.exit(0)
import vtk
from vtk.numpy_interface import dataset_adapter as dsa
from vtk.numpy_interface import algorithms as algs
rt = vtk.vtkRTAnalyticSource()
p2c = vtk.vtkPointDataToCellData()
p2c.SetInputConnection(rt.GetOutputPort())
p2c.Update()
d1 = dsa.WrapDataObject(p2c.GetOutput())
vtkm_p2c = vtk.vtkmAverageToCells()
vtkm_p2c.SetInputData(rt.GetOutput())
vtkm_p2c.SetInputArrayToProcess(0, 0, 0,
vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS,
"RTData")
vtkm_p2c.Update()
d2 = dsa.WrapDataObject(vtkm_p2c.GetOutput())
assert (algs.max(algs.abs(d1.CellData['RTData'] - d2.CellData['RTData'])) <
10E-4)
mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, pd) pd2 = vtk.vtkPolyData() mb.SetBlock(1, pd2) mbw = dsa.WrapDataObject(mb) mbw.PointData.append(dsa.NoneArray, 'foo') assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 0 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 0 mbw.PointData.append(na2, 'foo') assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 1 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 0 assert mbw.GetBlock(0).GetPointData().GetArray(0).GetName() == 'foo' mbw.PointData.append(algs.max(na2), "maxfoo") assert mbw.GetBlock(0).GetPointData().GetNumberOfArrays() == 2 assert mbw.GetBlock(1).GetPointData().GetNumberOfArrays() == 1 assert mbw.GetBlock(0).GetPointData().GetArray(1).GetName() == 'maxfoo' # -------------------------------------- mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, vtk.vtkImageData()) mb.SetBlock(1, vtk.vtkImageData()) assert dsa.WrapDataObject(mb).Points is na mb = vtk.vtkMultiBlockDataSet() mb.SetBlock(0, vtk.vtkStructuredGrid()) mb.SetBlock(1, vtk.vtkImageData()) assert dsa.WrapDataObject(mb).Points is na
import vtk
from vtk.numpy_interface import dataset_adapter as dsa
from vtk.numpy_interface import algorithms as algs
reader = vtk.vtkMPASReader()
reader.SetFileName('MPASReader.nc')
# Have reader examine what information is available
reader.UpdateInformation()
# Print out available arrays
for i in range(reader.GetNumberOfCellArrays()):
print reader.GetCellArrayName(i)
# Don't read in the ke cell data array
reader.SetCellArrayStatus('ke', 0)
reader.Update()
# Wrap the reader output to make simplify access
wrappedreader = dsa.WrapDataObject(reader.GetOutput())
print wrappedreader.PointData.keys()
print wrappedreader.CellData['vorticity']
vorticity = wrappedreader.CellData['vorticity']
# Perform some operations on the data
wrappedreader.CellData.append(vorticity + 1, 'vorticity plus one')
print algs.max(vorticity)
print algs.max(wrappedreader.CellData['vorticity plus one'])
