def visualize(self, U, codim=2, **kwargs):
"""Visualize scalar data associated to the grid as a patch plot.
Parameters
----------
U
|NumPy array| of the data to visualize. If `U.dim == 2 and len(U) > 1`, the
data is visualized as a time series of plots. Alternatively, a tuple of
|Numpy arrays| can be provided, in which case a subplot is created for
each entry of the tuple. The lengths of all arrays have to agree.
codim
The codimension of the entities the data in `U` is attached to (either 0 or 2).
kwargs
See :func:`~pymor.discretizers.builtin.gui.qt.visualize_patch`
"""
from pymor.discretizers.builtin.gui.qt import visualize_patch
from pymor.vectorarrays.interface import VectorArray
from pymor.vectorarrays.numpy import NumpyVectorSpace, NumpyVectorArray
if isinstance(U, (np.ndarray, VectorArray)):
U = (U, )
assert all(isinstance(u, (np.ndarray, VectorArray)) for u in U)
U = tuple(
NumpyVectorSpace.make_array(u) if isinstance(u, np.ndarray) else
u if isinstance(u, NumpyVectorArray
) else NumpyVectorSpace.make_array(u.to_numpy())
for u in U)
bounding_box = kwargs.pop('bounding_box', self.domain)
visualize_patch(self,
U,
codim=codim,
bounding_box=bounding_box,
**kwargs)
def test_visualize_patch(backend_gridtype):
backend, gridtype = backend_gridtype
domain = LineDomain() if gridtype is OnedGrid else RectDomain()
dim = 1 if gridtype is OnedGrid else 2
rhs = GenericFunction(lambda X: np.ones(X.shape[:-1]) * 10, dim) # NOQA
dirichlet = GenericFunction(lambda X: np.zeros(X.shape[:-1]), dim) # NOQA
diffusion = GenericFunction(lambda X: np.ones(X.shape[:-1]), dim) # NOQA
problem = StationaryProblem(domain=domain, rhs=rhs, dirichlet_data=dirichlet, diffusion=diffusion)
grid, bi = discretize_domain_default(problem.domain, grid_type=gridtype)
m, data = discretize_stationary_cg(analytical_problem=problem, grid=grid, boundary_info=bi)
U = m.solve()
try:
visualize_patch(data['grid'], U=U, backend=backend)
except QtMissing as ie:
pytest.xfail("Qt missing")
finally:
stop_gui_processes()
def visualize(self, U, m, title=None, legend=None, separate_colorbars=False,
rescale_colorbars=False, block=None, filename=None, columns=2):
"""Visualize the provided data.
Parameters
----------
U
|VectorArray| of the data to visualize. If `len(U) > 1`, the data is visualized
as a time series of plots. Alternatively, a tuple of |VectorArrays| can be
provided, in which case a subplot is created for each entry of the tuple. The
lengths of all arrays have to agree.
m
Filled in by :meth:`pymor.models.interface.Model.visualize` (ignored).
title
Title of the plot.
legend
Description of the data that is plotted. Most useful if `U` is a tuple in which
case `legend` has to be a tuple of strings of the same length.
separate_colorbars
If `True`, use separate colorbars for each subplot.
rescale_colorbars
If `True`, rescale colorbars to data in each frame.
block
If `True`, block execution until the plot window is closed. If `None`, use the
default provided during instantiation.
filename
If specified, write the data to a VTK-file using
:func:`~pymor.discretizers.builtin.grids.vtkio.write_vtk` instead of displaying it.
columns
The number of columns in the visualizer GUI in case multiple plots are displayed
at the same time.
"""
assert isinstance(U, VectorArray) \
or (isinstance(U, tuple)
and all(isinstance(u, VectorArray) for u in U)
and all(len(u) == len(U[0]) for u in U))
if filename:
if not isinstance(U, tuple):
write_vtk(self.grid, U, filename, codim=self.codim)
else:
for i, u in enumerate(U):
write_vtk(self.grid, u, f'{filename}-{i}', codim=self.codim)
else:
if self.backend == 'jupyter':
from pymor.discretizers.builtin.gui.jupyter import get_visualizer
return get_visualizer()(self.grid, U, bounding_box=self.bounding_box, codim=self.codim, title=title,
legend=legend, separate_colorbars=separate_colorbars,
rescale_colorbars=rescale_colorbars, columns=columns)
else:
block = self.block if block is None else block
from pymor.discretizers.builtin.gui.qt import visualize_patch
return visualize_patch(self.grid, U, bounding_box=self.bounding_box, codim=self.codim, title=title,
legend=legend, separate_colorbars=separate_colorbars,
rescale_colorbars=rescale_colorbars, backend=self.backend, block=block,
columns=columns)
