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.ModelBase.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.tools.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, VectorArrayInterface) \
or (isinstance(U, tuple)
and all(isinstance(u, VectorArrayInterface) 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.gui.jupyter import visualize_patch
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, columns=columns)
else:
block = self.block if block is None else block
from pymor.gui.qt import visualize_patch
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)
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.ModelBase.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.tools.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, VectorArrayInterface) \
or (isinstance(U, tuple)
and all(isinstance(u, VectorArrayInterface) 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.gui.jupyter import visualize_patch
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, columns=columns)
else:
block = self.block if block is None else block
from pymor.gui.qt import visualize_patch
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)
def test_vtkio(rect_or_tria_grid):
grid = rect_or_tria_grid
steps = 4
for dim in range(1, 2):
for codim, data in enumerate((NumpyVectorSpace.from_data(np.zeros((steps, grid.size(c)))) for c in range(grid.dim+1))):
with SafeTemporaryFileName('wb') as out_name:
if codim == 1:
with pytest.raises(NotImplementedError):
write_vtk(grid, data, out_name, codim=codim)
else:
write_vtk(grid, data, out_name, codim=codim)
def test_vtkio(rect_or_tria_grid):
grid = rect_or_tria_grid
steps = 4
for dim in range(1, 2):
for codim, data in enumerate((NumpyVectorArray(np.zeros((steps, grid.size(c)))) for c in range(grid.dim+1))):
with NamedTemporaryFile('wb') as out:
if codim == 1:
with pytest.raises(NotImplementedError):
write_vtk(grid, data, out.name, codim=codim)
else:
write_vtk(grid, data, out.name, codim=codim)
def save(self):
if not config.HAVE_PYVTK:
msg = QMessageBox(QMessageBox.Critical, 'Error', 'VTK output disabled. Pleas install pyvtk.')
msg.exec_()
return
filename = QFileDialog.getSaveFileName(self, 'Save as vtk file')[0]
base_name = filename.split('.vtu')[0].split('.vtk')[0].split('.pvd')[0]
if base_name:
if len(self.U) == 1:
write_vtk(self.grid, NumpyVectorSpace.make_array(self.U[0]), base_name, codim=self.codim)
else:
for i, u in enumerate(self.U):
write_vtk(self.grid, NumpyVectorSpace.make_array(u), '{}-{}'.format(base_name, i),
codim=self.codim)
def visualize(self, U, discretization, title=None, legend=None, separate_colorbars=False,
block=None, filename=None):
'''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.
discretization
Filled in :meth:`pymor.discretizations.DiscretizationBase.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.
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.tools.vtkio.write_vtk` instead of displaying it.
'''
assert isinstance(U, VectorArrayInterface) and hasattr(U, 'data') \
or (isinstance(U, tuple) and all(isinstance(u, VectorArrayInterface) and hasattr(u, 'data') 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(self.U):
write_vtk(self.grid, u, '{}-{}'.format(filename, i), codim=self.codim)
else:
block = self.block if block is None else block
visualize_patch(self.grid, U, bounding_box=self.bounding_box, codim=self.codim, title=title,
legend=legend, separate_colorbars=separate_colorbars, backend=self.backend,
block=block)
def save(self):
if not config.HAVE_PYVTK:
msg = QMessageBox(QMessageBox.Critical, 'Error',
'VTK output disabled. Pleas install pyvtk.')
msg.exec_()
return
filename = QFileDialog.getSaveFileName(self, 'Save as vtk file')[0]
base_name = filename.split('.vtu')[0].split('.vtk')[0].split(
'.pvd')[0]
if base_name:
if len(self.U) == 1:
write_vtk(self.grid,
NumpyVectorSpace.make_array(self.U[0]),
base_name,
codim=self.codim)
else:
for i, u in enumerate(self.U):
write_vtk(self.grid,
NumpyVectorSpace.make_array(u),
'{}-{}'.format(base_name, i),
codim=self.codim)
