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_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()
cdata = dsa.WrapDataObject(c)
rtdata = cdata.PointData['RTData']
rtdata = algs.abs(rtdata)
g = algs.gradient(rtdata)
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)
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!")
cdata = dsa.WrapDataObject(c)
rtdata = cdata.PointData['RTData']
rtdata = algs.abs(rtdata)
g = algs.gradient(rtdata)
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
