def UnstructuredScalar(InputCoords, OutFileName):
#mlab.options.backend = 'envisage'
#------------ Writing .vti files ----------------------
# Make the data.
dims = np.array((ngr, ngr, ngr))
vol = np.array((nx1, nx2, ny1, ny2, nz1, nz2))
origin = vol[::2]
spacing = (vol[1::2] - origin) / (dims - 1)
xmin, xmax, ymin, ymax, zmin, zmax = vol
x, y, z = np.ogrid[xmin:xmax:dims[0] * 1j, ymin:ymax:dims[1] * 1j,
zmin:zmax:dims[2] * 1j]
x, y, z = [t.astype('f') for t in (x, y, z)]
scalars = r
# Make the tvtk dataset.
spoints = tvtk.StructuredPoints(origin=origin,
spacing=spacing,
dimensions=dims)
s = scalars.transpose().copy()
spoints.point_data.scalars = np.ravel(s)
spoints.point_data.scalars.name = 'scalars'
fileOut = OutFileName + '.vti'
w = tvtk.XMLImageDataWriter(input=spoints, file_name=fileOut)
w.write()
def save(self, i):
self.mask_nonfluid_nodes()
os.environ['ETS_TOOLKIT'] = 'null'
from tvtk.api import tvtk
from tvtk.common import configure_input
idata = tvtk.ImageData(spacing=(1, 1, 1), origin=(0, 0, 0))
first = True
sample_field = None
for name, field in self._scalar_fields.items():
if first:
idata.point_data.scalars = field.flatten()
idata.point_data.scalars.name = name
first = False
sample_field = field
else:
t = idata.point_data.add_array(field.flatten())
idata.point_data.get_array(t).name = name
idata.update_traits()
dim = len(sample_field.shape)
for name, field in self._vector_fields.items():
if dim == 3:
tmp = idata.point_data.add_array(np.c_[field[0].flatten(),
field[1].flatten(),
field[2].flatten()])
else:
tmp = idata.point_data.add_array(
np.c_[field[0].flatten(), field[1].flatten(),
np.zeros_like(field[0].flatten())])
idata.point_data.get_array(tmp).name = name
if dim == 3:
idata.dimensions = list(reversed(sample_field.shape))
else:
idata.dimensions = list(reversed(sample_field.shape)) + [1]
fname = filename(self.basename,
self.digits,
self.subdomain_id,
i,
suffix='.vti')
w = tvtk.XMLImageDataWriter(file_name=fname)
configure_input(w, idata)
w.write()
def StructuredScalar(InputArray, OutFileName, lim1, lim2):
nGr = np.shape(InputArray)[0]
# Make the data.
nx1IC = ny1 = nz1 = int(lim1)
nx2IC = ny2 = nz2 = int(lim2)
im = jm = km = nGr * 1j
nf0 = nf1 = nf2 = nGr
Xg, Yg, Zg = np.mgrid[nx1IC:nx2IC:im, ny1:ny2:jm, nz1:nz2:km]
# Make the data.
dims = np.array((nf0, nf1, nf2))
vol = np.array((lim1, lim2, lim1, lim2, lim1, lim2))
origin = vol[::2]
spacing = (vol[1::2] - origin) / (dims - 1)
xmin, xmax, ymin, ymax, zmin, zmax = vol
x, y, z = np.ogrid[xmin:xmax:dims[0] * 1j, ymin:ymax:dims[1] * 1j,
zmin:zmax:dims[2] * 1j]
x, y, z = [t.astype('f') for t in (x, y, z)]
scalars = InputArray
# Make the tvtk dataset.
spoints = tvtk.StructuredPoints(origin=origin,
spacing=spacing,
dimensions=dims)
s = scalars.transpose().copy()
spoints.point_data.scalars = np.ravel(s)
spoints.point_data.scalars.name = 'scalars'
spoints.scalar_type = get_vtk_array_type(s.dtype)
fileOut = OutFileName + '.vti'
print fileOut
w = tvtk.XMLImageDataWriter(input=spoints, file_name=fileOut)
w.write()
idata.point_data.scalars = src_data[field].flatten()
idata.point_data.scalars.name = field
first = False
else:
t = idata.point_data.add_array(src_data[field].flatten())
idata.point_data.get_array(t).name = field
# Only process vector fields.
for field in src_data.files:
if len(src_data[field].shape) != max_len:
continue
f = src_data[field]
if dim == 3:
tmp = idata.point_data.add_array(np.c_[f[0].flatten(), f[1].flatten(),
f[2].flatten()])
else:
tmp = idata.point_data.add_array(np.c_[f[0].flatten(), f[1].flatten(),
np.zeros_like(f[0].flatten())])
idata.point_data.get_array(tmp).name = field
if dim == 3:
idata.dimensions = list(reversed(shape))
else:
idata.dimensions = list(reversed(shape)) + [1]
out_filename = filename.replace('.npz', '.vti')
w = tvtk.XMLImageDataWriter(input=idata, file_name=out_filename)
w.write()