def line_source(*args, **kwargs):
"""
Creates line data.
**Function signatures**::
line_source(x, y, z, ...)
line_source(x, y, z, s, ...)
line_source(x, y, z, f, ...)
If 4 positional arguments are passed the last one must be an array s,
or a callable, f, that returns an array.
**Keyword arguments**:
:name: the name of the vtk object created.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization."""
if len(args) == 1:
raise ValueError("wrong number of arguments")
x, y, z, s = process_regular_scalars(*args)
data_source = MLineSource()
data_source.reset(x=x, y=y, z=z, scalars=s)
name = kwargs.pop('name', 'LineSource')
ds = tools.add_dataset(data_source.dataset, name, **kwargs)
data_source.m_data = ds
return ds
def vertical_vectors_source(*args, **kwargs):
"""
Creates a set of vectors pointing upward, useful eg for bar graphs.
**Function signatures**::
vertical_vectors_source(s, ...)
vertical_vectors_source(x, y, s, ...)
vertical_vectors_source(x, y, f, ...)
vertical_vectors_source(x, y, z, s, ...)
vertical_vectors_source(x, y, z, f, ...)
If only one positional argument is passed, it can be a 1D, 2D, or 3D
array giving the length of the vectors. The positions of the data
points are deducted from the indices of array, and an
uniformly-spaced data set is created.
If 3 positional arguments (x, y, s) are passed the last one must be
an array s, or a callable, f, that returns an array. x and y give the
2D coordinates of positions corresponding to the s values. The
vertical position is assumed to be 0.
If 4 positional arguments (x, y, z, s) are passed, the 3 first are
arrays giving the 3D coordinates of the data points, and the last one
is an array s, or a callable, f, that returns an array giving the
data value.
**Keyword arguments**:
:name: the name of the vtk object created.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization.
"""
if len(args) == 3:
x, y, data = args
if np.isscalar(x):
z = 0
else:
z = np.zeros_like(x)
args = (x, y, z, data)
x, y, z, s = process_regular_scalars(*args)
if s is not None:
s = np.ravel(s)
data_source = MVerticalGlyphSource()
data_source.reset(x=x, y=y, z=z, scalars=s)
name = kwargs.pop('name', 'VerticalVectorsSource')
ds = tools.add_dataset(data_source.dataset, name, **kwargs)
data_source.m_data = ds
return ds
def __init__(self, parent, **kwargs):
# We are not passing the traits to the parent class
super(PipeFactory, self).__init__()
# Try to find the right engine and scene to work with
ancester = parent
while hasattr(ancester, 'parent'):
ancester = getattr(ancester, 'parent')
if isinstance(ancester, Scene):
self._scene = ancester
self._engine = ancester.parent
break
else:
if self.figure is not None:
self._scene = self.figure
else:
self._scene = tools.gcf()
self._engine = get_engine()
scene = self._scene.scene
if self.figure is not None and self.figure is not self._scene:
warnings.warn('Trying to add a module on the wrong scene')
if isinstance(parent, (Source, tvtk.DataSet)) \
and not isinstance(parent, Filter) and scene is not None:
# Search the current scene to see if the source is already
# in it, if not add it.
if not parent in self._scene.children:
parent = tools.add_dataset(parent, figure=self._scene)
if scene is not None:
self._do_redraw = not scene.disable_render
scene.disable_render = True
if issubclass(self._target.__class__, Filter):
self._engine.add_filter(self._target, obj=parent)
else:
self.add_module(parent, kwargs)
# Inject the magical mlab source trait.
if hasattr(parent, 'mlab_source'):
ms = parent.mlab_source
self._target.add_trait('mlab_source', Instance(ms.__class__))
self._target.mlab_source = ms
traits = self.get(self.class_trait_names())
[
traits.pop(key) for key in traits.keys()
if key[0] == '_' or key is None
]
traits.update(kwargs)
# Now calling the traits setter, so that traits handlers are
# called
self.set(**traits)
if scene is not None:
scene.disable_render = not self._do_redraw
def __init__(self, parent, **kwargs):
# We are not passing the traits to the parent class
super(PipeFactory, self).__init__()
# Try to find the right engine and scene to work with
ancester = parent
while hasattr(ancester, 'parent'):
ancester = getattr(ancester, 'parent')
if isinstance(ancester, Scene):
self._scene = ancester
self._engine = ancester.parent
break
else:
if self.figure is not None:
self._scene = self.figure
else:
self._scene = tools.gcf()
self._engine = get_engine()
scene = self._scene.scene
if self.figure is not None and self.figure is not self._scene:
warnings.warn('Trying to add a module on the wrong scene')
if isinstance(parent, (Source, tvtk.DataSet)) \
and not isinstance(parent, Filter) and scene is not None:
# Search the current scene to see if the source is already
# in it, if not add it.
if not parent in self._scene.children:
parent = tools.add_dataset(parent, figure=self._scene)
if scene is not None:
self._do_redraw = not scene.disable_render
scene.disable_render = True
if issubclass(self._target.__class__, Filter):
self._engine.add_filter(self._target, obj=parent)
else:
self.add_module(parent, kwargs)
# Inject the magical mlab source trait.
if hasattr(parent, 'mlab_source'):
ms = parent.mlab_source
self._target.add_trait('mlab_source', Instance(ms.__class__))
self._target.mlab_source = ms
traits = self.get(self.class_trait_names())
[traits.pop(key) for key in traits.keys()
if key[0]=='_' or key is None]
traits.update(kwargs)
# Now calling the traits setter, so that traits handlers are
# called
self.set(**traits)
if scene is not None:
scene.disable_render = not self._do_redraw
def array2d_source(*args, **kwargs):
"""
Creates structured 2D data from a 2D array.
**Function signatures**::
array2d_source(s, ...)
array2d_source(x, y, s, ...)
array2d_source(x, y, f, ...)
If 3 positional arguments are passed the last one must be an array s,
or a callable, f, that returns an array. x and y give the
coordinnates of positions corresponding to the s values.
x and y can be 1D or 2D arrays (such as returned by numpy.ogrid or
numpy.mgrid), but the points should be located on an orthogonal grid
(possibly non-uniform). In other words, all the points sharing a same
index in the s array need to have the same x or y value.
If only 1 array s is passed the x and y arrays are assumed to be
made from the indices of arrays, and an uniformly-spaced data set is
created.
**Keyword arguments**:
:name: the name of the vtk object created.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization.
:mask: Mask points specified in a boolean masking array.
"""
data_source = MArray2DSource()
mask = kwargs.pop('mask', None)
if len(args) == 1:
args = convert_to_arrays(args)
s = np.atleast_2d(args[0])
data_source.reset(scalars=s, mask=mask)
else:
x, y, s = process_regular_2d_scalars(*args, **kwargs)
data_source.reset(x=x, y=y, scalars=s, mask=mask)
name = kwargs.pop('name', 'Array2DSource')
return tools.add_dataset(data_source.m_data, name, **kwargs)
def vector_field(*args, **kwargs):
""" Creates vector field data.
**Function signatures**::
vector_field(u, v, w, ...)
vector_field(x, y, z, u, v, w, ...)
vector_field(x, y, z, f, ...)
If only 3 arrays u, v, w are passed the x, y and z arrays are assumed to be
made from the indices of vectors.
If the x, y and z arrays are passed, they should have been generated
by `numpy.mgrid` or `numpy.ogrid`. The function builds a scalar field
assuming the points are regularily spaced on an orthogonal grid.
If 4 positional arguments are passed the last one must be a callable, f,
that returns vectors.
**Keyword arguments**:
:name: the name of the vtk object created.
:scalars: optional scalar data.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization."""
if len(args) == 3:
x = y = z = np.atleast_3d(1)
u, v, w = [np.atleast_3d(a) for a in args]
else:
x, y, z, u, v, w = [
np.atleast_3d(a) for a in process_regular_vectors(*args)
]
scalars = kwargs.pop('scalars', None)
if scalars is not None:
scalars = np.atleast_3d(scalars)
data_source = MArraySource()
data_source.reset(x=x, y=y, z=z, u=u, v=v, w=w, scalars=scalars)
name = kwargs.pop('name', 'VectorField')
return tools.add_dataset(data_source.m_data, name, **kwargs)
def vector_field(*args, **kwargs):
""" Creates vector field data.
**Function signatures**::
vector_field(u, v, w, ...)
vector_field(x, y, z, u, v, w, ...)
vector_field(x, y, z, f, ...)
If only 3 arrays u, v, w are passed the x, y and z arrays are assumed to be
made from the indices of vectors.
If the x, y and z arrays are passed, they should have been generated
by `numpy.mgrid` or `numpy.ogrid`. The function builds a scalar field
assuming the points are regularily spaced on an orthogonal grid.
If 4 positional arguments are passed the last one must be a callable, f,
that returns vectors.
**Keyword arguments**:
:name: the name of the vtk object created.
:scalars: optional scalar data.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization."""
if len(args) == 3:
x = y = z = np.atleast_3d(1)
u, v, w = [np.atleast_3d(a) for a in args]
else:
x, y, z, u, v, w = [np.atleast_3d(a)
for a in process_regular_vectors(*args)]
scalars = kwargs.pop('scalars', None)
if scalars is not None:
scalars = np.atleast_3d(scalars)
data_source = MArraySource()
data_source.reset(x=x, y=y, z=z, u=u, v=v, w=w, scalars=scalars)
name = kwargs.pop('name', 'VectorField')
return tools.add_dataset(data_source.m_data, name, **kwargs)
def scalar_field(*args, **kwargs):
"""
Creates a scalar field data.
**Function signatures**::
scalar_field(s, ...)
scalar_field(x, y, z, s, ...)
scalar_field(x, y, z, f, ...)
If only 1 array s is passed the x, y and z arrays are assumed to be
made from the indices of arrays.
If the x, y and z arrays are passed they are supposed to have been
generated by `numpy.mgrid`. The function builds a scalar field assuming
the points are regularily spaced.
If 4 positional arguments are passed the last one must be an array s, or
a callable, f, that returns an array.
**Keyword arguments**:
:name: the name of the vtk object created.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization."""
if len(args) == 1:
# Be lazy, don't create three big arrays for 1 input array. The
# MArraySource is clever-enough to handle flat arrays
x = y = z = np.atleast_1d(1)
s = args[0]
else:
x, y, z, s = process_regular_scalars(*args)
data_source = MArraySource()
data_source.reset(x=x, y=y, z=z, scalars=s)
name = kwargs.pop('name', 'ScalarField')
return tools.add_dataset(data_source.m_data, name, **kwargs)
def vector_scatter(*args, **kwargs):
""" Creates scattered vector data.
**Function signatures**::
vector_scatter(u, v, w, ...)
vector_scatter(x, y, z, u, v, w, ...)
vector_scatter(x, y, z, f, ...)
If only 3 arrays u, v, w are passed the x, y and z arrays are assumed to be
made from the indices of vectors.
If 4 positional arguments are passed the last one must be a callable, f,
that returns vectors.
**Keyword arguments**:
:name: the name of the vtk object created.
:scalars: optional scalar data.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization."""
x, y, z, u, v, w = process_regular_vectors(*args)
scalars = kwargs.pop('scalars', None)
if scalars is not None:
scalars = np.ravel(scalars)
name = kwargs.pop('name', 'VectorScatter')
data_source = MGlyphSource()
data_source.reset(x=x, y=y, z=z, u=u, v=v, w=w, scalars=scalars)
ds = tools.add_dataset(data_source.dataset, name, **kwargs)
data_source.m_data = ds
return ds
def triangular_mesh_source(x, y, z, triangles, **kwargs):
"""
Creates 2D mesh by specifying points and triangle connectivity.
x, y, z are 2D arrays giving the positions of the vertices of the surface.
The connectivity between these points is given by listing triplets of
vertices inter-connected. These vertices are designed by there
position index.
**Keyword arguments**:
:name: the name of the vtk object created.
:scalars: optional scalar data.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization.
"""
x, y, z, triangles = convert_to_arrays((x, y, z, triangles))
if triangles.min() < 0:
raise ValueError('The triangles array has negative values')
if triangles.max() > x.size:
raise ValueError('The triangles array has values larger than' \
'the number of points')
scalars = kwargs.pop('scalars', None)
if scalars is None:
scalars = z
data_source = MTriangularMeshSource()
data_source.reset(x=x, y=y, z=z, triangles=triangles, scalars=scalars)
name = kwargs.pop('name', 'TriangularMeshSource')
ds = tools.add_dataset(data_source.dataset, name, **kwargs)
data_source.m_data = ds
return ds
def grid_source(x, y, z, **kwargs):
"""
Creates 2D grid data.
x, y, z are 2D arrays giving the positions of the vertices of the surface.
The connectivity between these points is implied by the connectivity on
the arrays.
For simple structures (such as orthogonal grids) prefer the array2dsource
function, as it will create more efficient data structures.
**Keyword arguments**:
:name: the name of the vtk object created.
:scalars: optional scalar data.
:figure: optionally, the figure on which to add the data source.
If None, the source is not added to any figure, and will
be added automatically by the modules or
filters. If False, no figure will be created by modules
or filters applied to the source: the source can only
be used for testing, or numerical algorithms, not
visualization.
:mask: Mask points specified in a boolean masking array.
"""
scalars = kwargs.pop('scalars', None)
if scalars is None:
scalars = z
mask = kwargs.pop('mask', None)
x, y, z, scalars = convert_to_arrays((x, y, z, scalars))
data_source = MGridSource()
data_source.reset(x=x, y=y, z=z, scalars=scalars, mask=mask)
name = kwargs.pop('name', 'GridSource')
ds = tools.add_dataset(data_source.dataset, name, **kwargs)
data_source.m_data = ds
return ds