Source code for pymor.discretizers.builtin.grids.oned

#!/usr/bin/env python
# This file is part of the pyMOR project (
# Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

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)