def _write_legacy_vtu(x, fname):
"""
Write a legacy VTK unstructured grid file.
"""
# Voxel local points relative to its centre of geometry:
voxel_local_points = asarray([[-1,-1,-1],[ 1,-1,-1],[-1, 1,-1],[ 1, 1,-1],
[-1,-1, 1],[ 1,-1, 1],[-1, 1, 1],[ 1, 1, 1]])\
* 0.5 # scaling
# Voxel world points:
points = []
# Culled input array -- as list:
xculled = []
try:
depth, rows, columns = x.shape
except ValueError:
sys.exit('Array dimensions not equal to 3, possibly 2-dimensional.\n')
for i in xrange(depth):
for j in xrange(rows):
for k in xrange(columns):
if x[i, j, k] > THRESHOLD:
xculled.append(x[i, j, k])
points += (voxel_local_points + [k, j, i]).tolist()
voxels = arange(len(points)).reshape(len(xculled), 8).tolist()
topology = UnstructuredGrid(points, voxel=voxels)
file_header = \
'ToPy data, created '\
+ str(datetime.now()).rsplit('.')[0]
scalars = CellData(Scalars(xculled, name='Densities', lookup_table =\
'default'))
vtk = VtkData(topology, file_header, scalars)
vtk.tofile(fname, 'binary')
def close(self):
if not self.is_closed:
from pyvtk import PointData, VtkData
vtk = VtkData(self.structure, self.description,
PointData(*self.pointdata))
vtk.tofile(self.pathname)
self.is_closed = True
def _write_legacy_vtu(x, fname):
"""
Write a legacy VTK unstructured grid file.
"""
# Voxel local points relative to its centre of geometry:
voxel_local_points = asarray([[-1,-1,-1],[ 1,-1,-1],[-1, 1,-1],[ 1, 1,-1],
[-1,-1, 1],[ 1,-1, 1],[-1, 1, 1],[ 1, 1, 1]])\
* 0.5 # scaling
# Voxel world points:
points = []
# Culled input array -- as list:
xculled = []
try:
depth, rows, columns = x.shape
except ValueError:
sys.exit('Array dimensions not equal to 3, possibly 2-dimensional.\n')
for i in xrange(depth):
for j in xrange(rows):
for k in xrange(columns):
if x[i,j,k] > THRESHOLD:
xculled.append(x[i,j,k])
points += (voxel_local_points + [k,j,i]).tolist()
voxels = arange(len(points)).reshape(len(xculled), 8).tolist()
topology = UnstructuredGrid(points, voxel = voxels)
file_header = \
'ToPy data, created '\
+ str(datetime.now()).rsplit('.')[0]
scalars = CellData(Scalars(xculled, name='Densities', lookup_table =\
'default'))
vtk = VtkData(topology, file_header, scalars)
vtk.tofile(fname, 'binary')
def save_vtk(fname, points, colors):
"""N.B.: Paraview is a good VTK viewer, which supports ray-tracing."""
structure = PolyData(points=points, vertices=np.arange(len(points)))
values = PointData(Scalars(colors, name="colors"))
vtk = VtkData(structure, values)
vtk.tofile(folder + fname, "binary")
def close(self):
if not self.is_closed:
from pyvtk import PointData, VtkData
vtk = VtkData(self.structure,
self.description,
PointData(*self.pointdata))
vtk.tofile(self.pathname)
self.is_closed = True
def file_quiver(self, q, p, name=' ', **kwargs):
if q.shape[1] == 2:
q = np.hstack((q, np.zeros((q.shape[0], 1))))
if p.shape[1] == 2:
p = np.hstack((p, np.zeros((p.shape[0], 1))))
pd = PolyData(points=q)
vec = PointData(Vectors(p, name='momentum'))
vtk = VtkData(pd, vec)
vtk.tofile(self.foldername + name + '.vtk', 'ascii')
def _write_vtu_series(us_grid, data, filename_base, binary_vtk, last_step, is_cell_data):
steps = last_step + 1 if last_step is not None else len(data)
fn_tpl = "{}_{:08d}"
_write_meta_file(filename_base, steps, fn_tpl)
for i in xrange(steps):
fn = fn_tpl.format(filename_base, i)
pd = _data_item(is_cell_data, data, i)
vtk = VtkData(us_grid, pd, 'Unstructured Grid Example')
if binary_vtk:
vtk.tofile(fn, 'binary')
else:
vtk.tofile(fn)
def vtkStructure(self):
"""Generates a vtk mesh structure that can be used in a vtk file.
Returns
-------
out : pyvtk.VtkData
Vtk data structure
"""
# Generate the quad node locations in x
x = self.x.cellEdges
y = self.y.cellEdges
z = self.z.cellEdges
nCells = self.nCells
z = self.z.cellEdges
nNodes = self.x.nEdges * self.z.nEdges
# Constuct the node locations for the vtk file
nodes = np.empty([nNodes, 3])
nodes[:, 0] = self.xMesh('x').reshape(self.nNodes)
nodes[:, 1] = self.xMesh('y').reshape(self.nNodes)
nodes[:, 2] = self.zMesh('absolute').reshape(self.nNodes)
tmp = np.int32([0, 1, self.x.nEdges + 1, self.x.nEdges])
a = np.ones(self.x.nCells, dtype=np.int32)
a[0] = 2
index = (np.repeat(tmp[:, np.newaxis], nCells, 1) +
np.cumsum(np.tile(a, self.z.nCells)) - 2).T
return VtkData(PolyData(points=nodes, polygons=index))
def from_file(fname) : if fname[-4:] == '.png' : return level_curves(fname) elif fname[-4:] == '.vtk' : data = VtkData(fname) points = np.array(data.structure.points)[:,0:2] # Discard "Z" connec = np.array(data.structure.polygons) return Curve((points + 150)/300, connec)
def from_file(fname):
if fname[-4:] == '.png':
(points, connec) = level_curves(fname)
return Curve(points, connec)
elif fname[-4:] == '.vtk':
data = VtkData(fname)
points = np.array(data.structure.points)[:, 0:2] # Discard "Z"
connec = np.array(data.structure.polygons)
return Curve((points + 150) / 300,
connec) # offset for the skull dataset...
def vtkStructure(self):
"""Generates a vtk mesh structure that can be used in a vtk file.
Returns
-------
out : pyvtk.VtkData
Vtk data structure
"""
nodes = np.vstack([self.x, self.y, self.z]).T
vtk = VtkData(UnstructuredGrid(nodes, vertex=np.arange(self._nPoints)))
vtk.point_data.append(Scalars(self.z, self.z.getNameUnits()))
return vtk
def _write_vtu_series(us_grid, data, filename_base, binary_vtk, last_step, is_cell_data):
steps = last_step + 1 if last_step is not None else len(data)
fn_tpl = "{}_{:08d}"
_write_meta_file(filename_base, steps, fn_tpl)
for i in xrange(steps):
fn = fn_tpl.format(filename_base, i)
pd = _data_item(is_cell_data, data, i)
vtk = VtkData(us_grid, pd, 'Unstructured Grid Example')
if binary_vtk:
vtk.tofile(fn, 'binary')
else:
vtk.tofile(fn)
def save_vtk(fname,
xyz,
triangles=None,
values=None,
vectors=None,
triangle_values=None):
"""Saves a point cloud or triangle mesh as a .vtk file.
Files can be opened with Paraview or displayed using the PyVista library.
Args:
fname (string): filename.
xyz (Tensor): (N,3) point cloud or vertices.
triangles (integer Tensor, optional): (T,3) mesh connectivity. Defaults to None.
values (Tensor, optional): (N,D) values, supported by the vertices. Defaults to None.
vectors (Tensor, optional): (N,3) vectors, supported by the vertices. Defaults to None.
triangle_values (Tensor, optional): (T,D) values, supported by the triangles. Defaults to None.
"""
# Encode the points/vertices as a VTK structure:
if triangles is None: # Point cloud
structure = PolyData(points=numpy(xyz), vertices=np.arange(len(xyz)))
else: # Surface mesh
structure = PolyData(points=numpy(xyz), polygons=numpy(triangles))
data = [structure]
pointdata, celldata = [], []
# Point values - one channel per column of the `values` array:
if values is not None:
values = numpy(values)
if len(values.shape) == 1:
values = values[:, None]
features = values.T
pointdata += [
Scalars(f, name=f"features_{i:02d}")
for i, f in enumerate(features)
]
# Point vectors - one vector per point:
if vectors is not None:
pointdata += [Vectors(numpy(vectors), name="vectors")]
# Store in the VTK object:
if pointdata != []:
pointdata = PointData(*pointdata)
data.append(pointdata)
# Triangle values - one channel per column of the `triangle_values` array:
if triangle_values is not None:
triangle_values = numpy(triangle_values)
if len(triangle_values.shape) == 1:
triangle_values = triangle_values[:, None]
features = triangle_values.T
celldata += [
Scalars(f, name=f"features_{i:02d}")
for i, f in enumerate(features)
]
celldata = CellData(*celldata)
data.append(celldata)
# Write to hard drive:
vtk = VtkData(*data)
os.makedirs(os.path.dirname(fname), exist_ok=True)
vtk.tofile(fname)
def runner (parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("Incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path (options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
if str (stack.images.dtype).startswith ('float'):
output_type_name = 'float32'
elif str (stack.images.dtype).startswith ('int'):
output_type_name = 'int32'
elif str (stack.images.dtype).startswith ('uint'):
output_type_name = 'uint32'
else:
output_type_name = 'int32'
else:
output_type_name = options.output_type.lower()
output_type = getattr (numpy, output_type_name, None)
mn, mx = stack.images.min (), stack.images.max ()
print 'Input minimum and maximum: %s, %s' % (mn, mx)
if options.scale and 'int' in output_type_name:
tmn, tmx = get_dtype_min_max(output_type)
new_images = (tmn + float(tmx - tmn) * (stack.images-float(mn)) / (mx - mn)).astype (output_type)
else:
new_images = stack.images.astype (output_type)
print 'Output minimum and maximum: %s, %s' % (new_images.min (), new_images.max ())
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext (bn)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(dn, fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir (options.input_path):
output_path = os.path.join (dn, bn+'_'+output_type_name + '.' + output_ext)
else:
raise NotImplementedError ('%s is not file nor directory' % (options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to',output_path
if output_ext=='tif':
ImageStack(new_images, stack.pathinfo, options=options).save(output_path)
elif output_ext=='data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images, stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete>0)
new_value_resolution = value_resolution * normal_images.max() / new_images.max()
ImageStack(normal_images, stack.pathinfo,
value_resolution = new_value_resolution).save(output_path, zip(*signal_indices))
elif output_ext=='vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData (StructuredPoints (new_images.shape), PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError (`output_ext`)
def toVTK(self, fName, dx, dy, mask=False, clip=False, force=False, method='ct'):
""" Convert a 3D volume of interpolated values to vtk for visualization in Paraview """
print('toVTK')
self.getAttribute(xy=True, elevation=True, force=force)
self.getMean3D(dx=dx, dy=dy, mask=mask, clip=clip, force=force, method=method)
self.getZGrid()
self.points.getBounds()
x,y,intPoints = interpolation.getGridLocations2D(self.points.bounds, dx, dy)
z=self.zGrid
from pyvtk import VtkData, UnstructuredGrid, PointData, CellData, Scalars
# Get the 3D dimensions
mx = x.size
my = y.size
mz = z.size
nPoints = mx*my*mz
nCells = (mx-1)*(my-1)*(mz-1)
# Interpolate the elevation to the grid nodes
if (method == 'ct'):
tx,ty, vals = self.points.interpCloughTocher(self.elevation, dx = dx,dy=dy, mask = mask, clip = clip, extrapolate='nearest')
elif (method == 'mc'):
tx,ty, vals = self.points.interpMinimumCurvature(self.elevation, dx = dx, dy=dy, mask = mask, clip = clip)
vals = vals[:my,:mx]
vals = vals.reshape(mx*my)
# Set up the nodes and voxel indices
points = np.zeros([nPoints,3], order='F')
points[:,0] = np.tile(x,my*mz)
points[:,1] = np.tile(y.repeat(mx),mz)
points[:,2] = np.tile(vals,mz)-z.repeat(mx*my)
# Create the cell indices into the points
p = np.arange(nPoints).reshape((mz,my,mx))
voxels = np.zeros([nCells,8],dtype=np.int)
iCell = 0
for k in range(mz-1):
k1 = k + 1
for j in range(my-1):
j1 = j + 1
for i in range(mx-1):
i1 = i + 1
voxels[iCell,:] = [p[k1,j,i],p[k1,j,i1],p[k1,j1,i1],p[k1,j1,i], p[k,j,i],p[k,j,i1],p[k,j1,i1],p[k,j1,i]]
iCell += 1
# Create the various point data
pointID = Scalars(np.arange(nPoints),name='Point iD')
pointElev = Scalars(points[:,2],name='Point Elevation (m)')
tmp=self.mean3D.reshape(np.size(self.mean3D))
tmp1 = np.log10(1.0/tmp)
pointRes = Scalars(tmp1, name = 'log10(Resistivity) (Ohm m)')
tmp1 = np.log10(tmp)
pointCon = Scalars(tmp1, name = 'log10(Conductivity) (S/m)')
PData = PointData(pointID, pointElev, pointRes, pointCon)
CData = CellData(Scalars(np.arange(nCells),name='Cell iD'))
vtk = VtkData(
UnstructuredGrid(points,
hexahedron=voxels),
# ),
PData,
CData,
'Some Name'
)
vtk.tofile(fName, 'ascii')
def runner (parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("Incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path (options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
output_type_name = stack.images.dtype.name
else:
output_type_name = options.output_type.lower()
output_type = getattr (numpy, output_type_name, None)
nof_stacks = stack.get_nof_stacks()
old_shape = stack.images.shape
new_shape = (nof_stacks, old_shape[0]//nof_stacks) + old_shape[1:]
new_images = numpy.zeros (new_shape[1:], dtype=output_type_name)
first_stack = None
last_stack = None
for i, stacki in enumerate(stack.images.reshape(new_shape)):
if i==0:
first_stack = stacki.astype (float)
new_images[:] = stacki
else:
err_first = abs(stacki - first_stack).mean()
err_last = abs(stacki - last_stack).mean()
print ('Stack %i: mean abs difference from first and last stack: %.3f, %.3f' % (i+1, err_first, err_last))
new_images += stacki
last_stack = stacki.astype(float)
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext (bn)
fn += '_sumstacks%s' % (nof_stacks)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(dn, fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir (options.input_path):
bn += '_sumstacks%s' % (nof_stacks)
output_path = os.path.join (dn, bn+'_'+output_type_name + '.' + output_ext)
else:
raise NotImplementedError ('%s is not file nor directory' % (options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to',output_path
if output_ext=='tif':
ImageStack(new_images, stack.pathinfo, options=options).save(output_path)
elif output_ext=='data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images, stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete>0)
new_value_resolution = value_resolution * normal_images.max() / new_images.max()
ImageStack(normal_images, stack.pathinfo,
value_resolution = new_value_resolution).save(output_path, zip(*signal_indices))
elif output_ext=='vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData (StructuredPoints (new_images.shape), PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError (`output_ext`)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (
options.output_path, args[1])
options.output_path = args[1]
else:
parser.error(
"Incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
if str(stack.images.dtype).startswith('float'):
output_type_name = 'float32'
elif str(stack.images.dtype).startswith('int'):
output_type_name = 'int32'
elif str(stack.images.dtype).startswith('uint'):
output_type_name = 'uint32'
else:
output_type_name = 'int32'
else:
output_type_name = options.output_type.lower()
output_type = getattr(numpy, output_type_name, None)
mn, mx = stack.images.min(), stack.images.max()
print 'Input minimum and maximum: %s, %s' % (mn, mx)
if options.scale and 'int' in output_type_name:
tmn, tmx = get_dtype_min_max(output_type)
new_images = (tmn + float(tmx - tmn) * (stack.images - float(mn)) /
(mx - mn)).astype(output_type)
else:
new_images = stack.images.astype(output_type)
print 'Output minimum and maximum: %s, %s' % (new_images.min(),
new_images.max())
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext(bn)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(
dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(
dn,
fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir(options.input_path):
output_path = os.path.join(
dn, bn + '_' + output_type_name + '.' + output_ext)
else:
raise NotImplementedError('%s is not file nor directory' %
(options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to', output_path
if output_ext == 'tif':
ImageStack(new_images, stack.pathinfo,
options=options).save(output_path)
elif output_ext == 'data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images,
stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete > 0)
new_value_resolution = value_resolution * normal_images.max(
) / new_images.max()
ImageStack(normal_images,
stack.pathinfo,
value_resolution=new_value_resolution).save(
output_path, zip(*signal_indices))
elif output_ext == 'vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData(StructuredPoints(new_images.shape),
PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError( ` output_ext `)
