def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the grid to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
Indexing with integers along all axes produces an array (a point):
>>> g = TensorGrid([-1, 0, 3], [2, 4], [5], [2, 4, 7])
>>> g[0, 0, 0, 0]
array([-1., 2., 5., 2.])
Otherwise, a new TensorGrid is returned:
>>> g[:, 0, 0, 0]
TensorGrid([-1.0, 0.0, 3.0], [2.0], [5.0], [2.0])
>>> g[0, ..., 1:]
TensorGrid([-1.0], [2.0, 4.0], [5.0], [4.0, 7.0])
>>> g[::2, ..., ::2]
TensorGrid([-1.0, 3.0], [2.0, 4.0], [5.0], [2.0, 7.0])
Too few indices are filled up with an ellipsis from the right:
>>> g[0]
TensorGrid([-1.0], [2.0, 4.0], [5.0], [2.0, 4.0, 7.0])
>>> g[0] == g[0, :, :, :] == g[0, ...]
True
"""
if isinstance(indices, list):
new_coord_vecs = [self.coord_vectors[0][indices]]
new_coord_vecs += self.coord_vectors[1:]
return TensorGrid(*new_coord_vecs)
indices = normalized_index_expression(indices,
self.shape,
int_to_slice=False)
# If all indices are integers, return an array (a point). Otherwise,
# create a new grid.
if all(np.isscalar(idx) for idx in indices):
return np.fromiter(
(v[int(idx)] for idx, v in zip(indices, self.coord_vectors)),
dtype=float)
else:
new_coord_vecs = [
vec[idx] for idx, vec in zip(indices, self.coord_vectors)
]
return TensorGrid(*new_coord_vecs)
def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the grid to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
Indexing with integers along all axes produces an array (a point):
>>> g = TensorGrid([-1, 0, 3], [2, 4], [5], [2, 4, 7])
>>> g[0, 0, 0, 0]
array([-1., 2., 5., 2.])
Otherwise, a new TensorGrid is returned:
>>> g[:, 0, 0, 0]
TensorGrid([-1.0, 0.0, 3.0], [2.0], [5.0], [2.0])
>>> g[0, ..., 1:]
TensorGrid([-1.0], [2.0, 4.0], [5.0], [4.0, 7.0])
>>> g[::2, ..., ::2]
TensorGrid([-1.0, 3.0], [2.0, 4.0], [5.0], [2.0, 7.0])
Too few indices are filled up with an ellipsis from the right:
>>> g[0]
TensorGrid([-1.0], [2.0, 4.0], [5.0], [2.0, 4.0, 7.0])
>>> g[0] == g[0, :, :, :] == g[0, ...]
True
"""
if isinstance(indices, list):
new_coord_vecs = [self.coord_vectors[0][indices]]
new_coord_vecs += self.coord_vectors[1:]
return TensorGrid(*new_coord_vecs)
indices = normalized_index_expression(indices, self.shape,
int_to_slice=False)
# If all indices are integers, return an array (a point). Otherwise,
# create a new grid.
if all(np.isscalar(idx) for idx in indices):
return np.fromiter(
(v[int(idx)] for idx, v in zip(indices, self.coord_vectors)),
dtype=float)
else:
new_coord_vecs = [vec[idx]
for idx, vec in zip(indices, self.coord_vectors)]
return TensorGrid(*new_coord_vecs)
def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the partition to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
>>> intvp = IntervalProd([-1, 1, 4, 2], [3, 6, 5, 7])
>>> grid = TensorGrid([-1, 0, 3], [2, 4], [5], [2, 4, 7])
>>> part = RectPartition(intvp, grid)
>>> part.cell_boundary_vecs
(array([-1. , -0.5, 1.5, 3. ]),
array([ 1., 3., 6.]),
array([ 4., 5.]),
array([ 2. , 3. , 5.5, 7. ]))
Indexing picks out sub-intervals (compare with the boundary
vectors):
>>> part[0, 0, 0, 0]
RectPartition(
IntervalProd([-1.0, 1.0, 4.0, 2.0], [-0.5, 3.0, 5.0, 3.0]),
TensorGrid([-1.0], [2.0], [5.0], [2.0]))
Taking an advanced slice (every second along the first axis,
the last in the last axis and everything in between):
>>> part[::2, ..., -1]
RectPartition(
IntervalProd([-1.0, 1.0, 4.0, 5.5], [3.0, 6.0, 5.0, 7.0]),
TensorGrid([-1.0, 3.0], [2.0, 4.0], [5.0], [7.0]))
Too few indices are filled up with an ellipsis from the right:
>>> part[1]
RectPartition(
IntervalProd([-0.5, 1.0, 4.0, 2.0], [1.5, 6.0, 5.0, 7.0]),
TensorGrid([0.0], [2.0, 4.0], [5.0], [2.0, 4.0, 7.0]))
"""
# Special case of index list: slice along first axis
if isinstance(indices, list):
new_begin = [self.cell_boundary_vecs[0][:-1][indices][0]]
new_end = [self.cell_boundary_vecs[0][1:][indices][-1]]
for cvec in self.cell_boundary_vecs[1:]:
new_begin.append(cvec[0])
new_end.append(cvec[-1])
new_intvp = IntervalProd(new_begin, new_end)
new_grid = self.grid[indices]
return RectPartition(new_intvp, new_grid)
indices = normalized_index_expression(indices, self.shape,
int_to_slice=True)
# Build the new partition
new_begin, new_end = [], []
for cvec, idx in zip(self.cell_boundary_vecs, indices):
# Determine the subinterval begin and end vectors. Take the
# first begin as new begin and the last end as new end.
sub_begin = cvec[:-1][idx]
sub_end = cvec[1:][idx]
new_begin.append(sub_begin[0])
new_end.append(sub_end[-1])
new_intvp = IntervalProd(new_begin, new_end)
new_grid = self.grid[indices]
return RectPartition(new_intvp, new_grid)
def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the grid to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
Indexing with integers along all axes produces an array (a point):
>>> g = RegularGrid([-1.5, -3, -1], [-0.5, 7, 4], (2, 3, 6))
>>> g[0, 0, 0]
array([-1.5, -3. , -1. ])
Otherwise, a new RegularGrid is returned:
>>> g[:, 0, 0]
RegularGrid([-1.5, -3.0, -1.0], [-0.5, -3.0, -1.0], (2, 1, 1))
Ellipsis can be used, too:
>>> g[..., ::3]
RegularGrid([-1.5, -3.0, -1.0], [-0.5, 7.0, 2.0], (2, 3, 2))
"""
# Index list results in a tensor grid
if isinstance(indices, list):
return super().__getitem__(indices)
indices = normalized_index_expression(indices,
self.shape,
int_to_slice=False)
# If all indices are integers, return an array (a point). Otherwise,
# create a new grid.
if all(np.isscalar(idx) for idx in indices):
return np.fromiter(
(v[int(idx)] for idx, v in zip(indices, self.coord_vectors)),
dtype=float)
else:
new_minpt = []
new_maxpt = []
new_shape = []
for idx, minval, maxval, n, cvec in zip(indices, self.min_pt,
self.max_pt, self.shape,
self.coord_vectors):
if np.isscalar(idx):
idx = slice(idx, idx + 1)
if idx == slice(None): # Take all along this axis
new_minpt.append(minval)
new_maxpt.append(maxval)
new_shape.append(n)
else: # Slice
istart, istop, istep = idx.indices(n)
if istart == istop:
num = 1
last = istart
else:
num = int(np.ceil((istop - istart) / istep))
last = istart + (num - 1) * istep
new_minpt.append(cvec[istart])
new_maxpt.append(cvec[last])
new_shape.append(num)
return RegularGrid(new_minpt, new_maxpt, new_shape)
def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the grid to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
Indexing with integers along all axes produces an array (a point):
>>> g = RegularGrid([-1.5, -3, -1], [-0.5, 7, 4], (2, 3, 6))
>>> g[0, 0, 0]
array([-1.5, -3. , -1. ])
Otherwise, a new RegularGrid is returned:
>>> g[:, 0, 0]
RegularGrid([-1.5, -3.0, -1.0], [-0.5, -3.0, -1.0], (2, 1, 1))
Ellipsis can be used, too:
>>> g[..., ::3]
RegularGrid([-1.5, -3.0, -1.0], [-0.5, 7.0, 2.0], (2, 3, 2))
"""
# Index list results in a tensor grid
if isinstance(indices, list):
return super().__getitem__(indices)
indices = normalized_index_expression(indices, self.shape,
int_to_slice=False)
# If all indices are integers, return an array (a point). Otherwise,
# create a new grid.
if all(np.isscalar(idx) for idx in indices):
return np.fromiter(
(v[int(idx)] for idx, v in zip(indices, self.coord_vectors)),
dtype=float)
else:
new_minpt = []
new_maxpt = []
new_shape = []
for idx, minval, maxval, n, cvec in zip(
indices, self.min_pt, self.max_pt, self.shape,
self.coord_vectors):
if np.isscalar(idx):
idx = slice(idx, idx + 1)
if idx == slice(None): # Take all along this axis
new_minpt.append(minval)
new_maxpt.append(maxval)
new_shape.append(n)
else: # Slice
istart, istop, istep = idx.indices(n)
if istart == istop:
num = 1
last = istart
else:
num = int(np.ceil((istop - istart) / istep))
last = istart + (num - 1) * istep
new_minpt.append(cvec[istart])
new_maxpt.append(cvec[last])
new_shape.append(num)
return RegularGrid(new_minpt, new_maxpt, new_shape)
def __getitem__(self, indices):
"""Return ``self[indices]``.
Parameters
----------
indices : index expression
Object determining which parts of the partition to extract.
`None` (new axis) and empty axes are not supported.
Examples
--------
>>> intvp = IntervalProd([-1, 1, 4, 2], [3, 6, 5, 7])
>>> grid = TensorGrid([-1, 0, 3], [2, 4], [5], [2, 4, 7])
>>> part = RectPartition(intvp, grid)
>>> part.cell_boundary_vecs
(array([-1. , -0.5, 1.5, 3. ]),
array([ 1., 3., 6.]),
array([ 4., 5.]),
array([ 2. , 3. , 5.5, 7. ]))
Indexing picks out sub-intervals (compare with the boundary
vectors):
>>> part[0, 0, 0, 0]
RectPartition(
IntervalProd([-1.0, 1.0, 4.0, 2.0], [-0.5, 3.0, 5.0, 3.0]),
TensorGrid([-1.0], [2.0], [5.0], [2.0]))
Taking an advanced slice (every second along the first axis,
the last in the last axis and everything in between):
>>> part[::2, ..., -1]
RectPartition(
IntervalProd([-1.0, 1.0, 4.0, 5.5], [3.0, 6.0, 5.0, 7.0]),
TensorGrid([-1.0, 3.0], [2.0, 4.0], [5.0], [7.0]))
Too few indices are filled up with an ellipsis from the right:
>>> part[1]
RectPartition(
IntervalProd([-0.5, 1.0, 4.0, 2.0], [1.5, 6.0, 5.0, 7.0]),
TensorGrid([0.0], [2.0, 4.0], [5.0], [2.0, 4.0, 7.0]))
"""
# Special case of index list: slice along first axis
if isinstance(indices, list):
new_begin = [self.cell_boundary_vecs[0][:-1][indices][0]]
new_end = [self.cell_boundary_vecs[0][1:][indices][-1]]
for cvec in self.cell_boundary_vecs[1:]:
new_begin.append(cvec[0])
new_end.append(cvec[-1])
new_intvp = IntervalProd(new_begin, new_end)
new_grid = self.grid[indices]
return RectPartition(new_intvp, new_grid)
indices = normalized_index_expression(indices,
self.shape,
int_to_slice=True)
# Build the new partition
new_begin, new_end = [], []
for cvec, idx in zip(self.cell_boundary_vecs, indices):
# Determine the subinterval begin and end vectors. Take the
# first begin as new begin and the last end as new end.
sub_begin = cvec[:-1][idx]
sub_end = cvec[1:][idx]
new_begin.append(sub_begin[0])
new_end.append(sub_end[-1])
new_intvp = IntervalProd(new_begin, new_end)
new_grid = self.grid[indices]
return RectPartition(new_intvp, new_grid)
