def _actor_changed(self, old, new):
if old is None:
# First time the actor is set.
new.mapper = tvtk.DataSetMapper(use_lookup_table_scalar_range=1)
new.scene = self.scene
mm = self.module_manager
if mm is not None:
new.inputs = [mm.source]
self._change_components(old, new)
def gridActor(grid):
"""
Return a TVTK actor representing a grid.
*Parameters:*
- grid (Grid)
A BEM++ grid object.
"""
mesh = getTvtkGrid(grid)
mapper = tvtk.DataSetMapper(input=mesh)
actor = tvtk.Actor(mapper=mapper)
actor.property.representation = 'w'
actor.property.ambient = 1
return actor
def gridFunctionActor(gridFun,dataType='vertex_data',transformation='real'):
"""
Return a TVTK actor representing a grid function.
*Parameters:*
- gridFun (gridFunction).
The grid function to be plotted.
- dataType ('vertex_data' or 'cell_data')
Determines whether the plot should be constructed from the function
values at element vertices or at element centres.
- transformation ('real', 'imag', 'abs' or a callable object)
Determines how the function is transformed before plotting.
"""
if not dataType in ["cell_data", "vertex_data"]:
raise ValueError("Unknown mode specified. Valid modes are 'vertex_data' and 'cell_data'!")
if not hasattr(transformation, '__call__'):
if transformation=='real':
data_transform = lambda x:np.real(x)
elif transformation=='imag':
data_transform = lambda x:np.imag(x)
elif transformation=='abs':
data_transform = lambda x:np.abs(x)
else:
raise ValueError("Unknown value for 'transformation'. It needs to be 'real', 'imag', 'abs' or a Python callable!")
else:
data_transform = transformation
mesh = getTvtkGrid(gridFun.grid())
if dataType=="vertex_data":
values = gridFun.evaluateAtSpecialPoints("vertex_data").flatten()
tvtk_data = mesh.point_data
elif dataType=="cell_data":
values = gridFun.evaluateAtSpecialPoints("cell_data").flatten()
tvtk_data = mesh.cell_data
values = data_transform(values)
tvtk_data.scalars = values
mapper = tvtk.DataSetMapper(input = mesh)
mapper.scalar_range = tvtk_data.scalars.range
actor = tvtk.Actor(mapper=mapper)
return actor
def scalarDataOnRegularGridActor(
points, data, dimensions,
colorRange=None,transformation='real'):
"""
Return a TVTK actor representing the plot of a function interpolated on
a regular grid.
"""
if points.shape[0] != 3 or points.ndim != 2:
raise ValueError("incorrect shape")
data = data.squeeze()
if data.ndim != 1:
raise ValueError("incorrect shape")
if not hasattr(transformation, '__call__'):
if transformation=='real':
data_transform = lambda point,val:np.real(val)
elif transformation=='imag':
data_transform = lambda point,val:np.imag(val)
elif transformation=='abs':
data_transform = lambda point,val:np.abs(val)
else:
raise ValueError("Unknown value for 'transformation'. It needs to be 'real', 'imag', 'abs' or a Python Callable!")
else:
data_transform = transformation
data = data_transform(points,data)
dims = dimensions
if colorRange is None:
minVal = np.min(data)
maxVal = np.max(data)
colorRange = (minVal, maxVal)
g = tvtk.StructuredGrid(dimensions=(dims[1], dims[0], 1), points=points.T)
g.point_data.scalars = data
# Create actors
mapper = tvtk.DataSetMapper(input=g)
mapper.scalar_range = colorRange
return tvtk.Actor(mapper=mapper)
def plotThreePlanes(potentialOp, gridFun, limits, dimensions,
colorRange=None, transformation='real', evalOps=None):
"""
Plot the potential generated by applying a potential operator to a grid
function on the xy, xz and yz planes.
*Parameters:*
- potentialOp (PotentialOperator)
A potential operator.
- gridFun (GridFunction)
A grid function.
- limits (tuple)
Tuple (min, max) or (xmin, xmax, ymin, ymax, zmin, zmax)
specifying the extent of each plane on which the potential
will be plotted.
- dimensions (tuple)
Scalar or tuple (xdim, ydim, zdim) specifying the number of samples
in each direction.
- colorRange (tuple)
Tuple (min, max) determining the range of data to be plotted.
If set to None, the data range is determined automatically.
- transformation ('real', 'imag', 'abs' or a callable object)
Determines how the potential is transformed before plotting.
- evalOps (EvaluationOptions)
Options controlling the evaluation of the potential.
"""
if np.isscalar(dimensions):
dims = (dimensions, dimensions, dimensions)
else:
if len(dimensions) == 3:
dims = dimensions
else:
raise ValueError("dimensions must be a scalar or a tuple with 3 elements")
if len(limits) == 2:
lims = (limits[0], limits[1], limits[0], limits[1], limits[0], limits[1])
elif len(limits) == 6:
lims = limits
else:
raise ValueError("limits must be a tuple with 2 or 6 elements")
origin = ((lims[0] + lims[1]) / 2.,
(lims[2] + lims[3]) / 2.,
(lims[4] + lims[5]) / 2.)
(points1,vals1) = py_ext.evaluatePotentialOnPlane(
potentialOp,gridFun,lims[:4],dims[:2],plane="xy",origin=origin,
evalOps=evalOps)
(points2,vals2) = py_ext.evaluatePotentialOnPlane(
potentialOp,gridFun,lims[:2]+lims[4:],dims[:1]+dims[2:],plane="xz",
origin=origin,evalOps=evalOps)
(points3,vals3) = py_ext.evaluatePotentialOnPlane(
potentialOp,gridFun,lims[2:],dims[1:],plane="yz",origin=origin,
evalOps=evalOps)
if not hasattr(transformation, '__call__'):
if transformation=='real':
data_transform = lambda point,val:np.real(val)
elif transformation=='imag':
data_transform = lambda point,val:np.imag(val)
elif transformation=='abs':
data_transform = lambda point,val:np.abs(val)
else:
raise ValueError("Unknown value for 'transformation'. It needs to be 'real', 'imag', 'abs' or a Python Callable!")
else:
data_transform = transformation
vals1 = data_transform(points1,vals1)
vals2 = data_transform(points2,vals2)
vals3 = data_transform(points3,vals3)
if colorRange is None:
minVal = np.min([vals1,vals2,vals3])
maxVal = np.max([vals1,vals2,vals3])
colorRange = (minVal,maxVal)
g1 = tvtk.StructuredGrid(dimensions=(dims[0],dims[1],1),points=points1)
g2 = tvtk.StructuredGrid(dimensions=(dims[0],dims[2],1),points=points2)
g3 = tvtk.StructuredGrid(dimensions=(dims[1],dims[2],1),points=points3)
# Add data
g1.point_data.scalars = vals1
g2.point_data.scalars = vals2
g3.point_data.scalars = vals3
# Create actors
mapper1 = tvtk.DataSetMapper(input=g1)
mapper1.scalar_range = colorRange
actor1 = tvtk.Actor(mapper=mapper1)
mapper2 = tvtk.DataSetMapper(input=g2)
mapper2.scalar_range = colorRange
actor2 = tvtk.Actor(mapper=mapper2)
mapper3 = tvtk.DataSetMapper(input=g3)
mapper3.scalar_range = colorRange
actor3 = tvtk.Actor(mapper=mapper3)
gActor = gridActor(gridFun.grid())
legend = legendActor(actor1)
plotTvtkActors([actor1,actor2,actor3,gActor,legend])