#!/usr/bin/env python
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import numpy as np
from pymor.discretizers.builtin.grids.interfaces import AffineGridWithOrthogonalCenters
from pymor.discretizers.builtin.grids.referenceelements import line
[docs]class OnedGrid(AffineGridWithOrthogonalCenters):
"""One-dimensional |Grid| on an interval.
Parameters
----------
domain
Tuple `(left, right)` containing the left and right boundary of the domain.
num_intervals
The number of codim-0 entities.
"""
dim = 1
reference_element = line
def __init__(self, domain=(0, 1), num_intervals=4, identify_left_right=False):
assert domain[0] < domain[1]
domain = np.array(domain)
self.__auto_init(locals())
self._sizes = [num_intervals, num_intervals] if identify_left_right else [num_intervals, num_intervals + 1]
self._width = np.abs(self.domain[1] - self.domain[0]) / self.num_intervals
self.__subentities = np.vstack((np.arange(self.num_intervals, dtype=np.int32),
np.arange(self.num_intervals, dtype=np.int32) + 1))
if identify_left_right:
self.__subentities[-1, -1] = 0
self.__A = np.ones(self.num_intervals, dtype=np.int32)[:, np.newaxis, np.newaxis] * self._width
self.__B = (self.domain[0] + self._width * (np.arange(self.num_intervals, dtype=np.int32)))[:, np.newaxis]
[docs] def __reduce__(self):
return (OnedGrid,
(self.domain, self.num_intervals, self.identify_left_right))
[docs] def __str__(self):
return (f'OnedGrid, domain [{self.domain[0]},{self.domain[1]}], '
f'{self.size(0)} elements, {self.size(1)} vertices')
[docs] def size(self, codim=0):
assert 0 <= codim <= 1, f'codim has to be between 0 and {self.dim}!'
return self._sizes[codim]
[docs] def subentities(self, codim, subentity_codim):
assert 0 <= codim <= 1, 'Invalid codimension'
assert codim <= subentity_codim <= self.dim, 'Invalid subentity codimension'
if codim == 0:
if subentity_codim == 0:
return np.arange(self.size(0), dtype='int32')[:, np.newaxis]
else:
return self.__subentities.T
else:
return super().subentities(codim, subentity_codim)
[docs] def embeddings(self, codim):
if codim == 0:
return self.__A, self.__B
else:
return super().embeddings(codim)
[docs] def bounding_box(self):
return np.array(self.domain).reshape((2, 1))
[docs] def orthogonal_centers(self):
return self.centers(0)
[docs] 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_matplotlib_1d
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)
visualize_matplotlib_1d(self, U, codim=codim, **kwargs)