I think that there are issues I am having with data starting in NumPy, but not properly “sticking” in my dataset. Peripherally discussed here. The data I am trying to put into VTK lives in a few very large files. One has geometry, and another has scalar values associated with each node (temperature, pressure, etc.)
Schematically, I start out with a `vtkPartitionedDataSet`, and *in a function* I add each block of the geometry to it.
def get_geometry(fname, pdset):
with open(fname, "rb") as f:
# find blocks
for idx, b in enumerate(blocks):
sg = vtk.vtkStructuredGrid()
# ...
pdset.SetPartition(idx, sg)
The geometry creation looks like it works well - I can save the file and plot it in ParaView, it looks fine.
Then, I read in the scalar data as an array of `vtkFloatArray`:
def get_scalars(fname):
"Return list-of-list-of variables."
solution_data = []
with open(fname, "rb") as f:
# ... find blocks
for idx, b in enumerate(blocks):
for _ in range(number_of_variables):
d = np.fromfile(f, dtype=">f4", count=elementcount)
d = d.reshape(size_of_block)
solution_data.append(d.ravel().byteswap())
return solution_data
And in yet another function, I (try to) add the data to the dataset:
def store_scalars(dataset):
variables = get_scalars("huge_file.dat")
namelist = get_namelist("name_list.txt")
for j, g in enumerate(variables):
p = data.GetPartition(j)
for k, v in enumerate(g):
a = numpy_to_vtk(v)
a.SetName(namelist[k])
logging.debug(f"vtkFloatArray.GetValueRange(): {a.GetValueRange()}")
p.GetPointData().AddArray(a)
p.Modified()
So adding the data, the debug statement that prints out the variable array range is nominally correct. Max and min values are about what I would expect.
But in a debug print just before I save the file to disk, the values are not correct? In short, I create vtkFloatArrays to store in the dataset, and a check at creation time shows that the arrays seem to have the right information in them. But, seconds later, when I make a check of the array contents before saving them to disk, they seem to contain garbage … (like numbers on the order of 1e38, and NaN … perhaps some random chunk of memory after a garbage collection? And I get those numbers instead of something worse like a segfault?)
Is there some way to deep copy the data out of the NumPy allocated memory and store them more permenantly inside of the vtkStructuredGrid partitions?