phlower.PhlowerTensor¶

class phlower.PhlowerTensor(tensor, dimension_tensor=None, is_time_series=False, is_voxel=False)[source]¶

Bases: IPhlowerTensor

PhlowerTensor is a wrapper of torch.Tensor with a physical dimension.

Parameters:

(torch.Tensor) (tensor) – Tensor to be wrapped.
(PhlowerDimensionTensor (dimension_tensor) – Physical dimension tensor.
optional) – Physical dimension tensor.
(bool (is_voxel) – Whether the tensor is a time series.
optional) – Whether the tensor is a time series.
(bool – Whether the tensor is a voxel.
optional) – Whether the tensor is a voxel.
tensor (torch.Tensor)
dimension_tensor (PhlowerDimensionTensor | None)
is_time_series (bool)
is_voxel (bool)

Examples

>>> tensor = torch.randn(5, 100, 3)
>>> phlower_tensor = PhlowerTensor(
...     tensor,
...     dimension_tensor={"L": 1, "T": -1},
...     is_time_series=True,
...     is_voxel=False,
... )
>>> print(phlower_tensor)

Methods

`__init__`(tensor[, dimension_tensor, ...])
`as_pattern`(pattern)	Convert the tensor to a pattern.
`backward`()	Backward the tensor.
`clone`()	Clone the tensor.
`coalesce`()	Coalesce the tensor.
`detach`()	Detach the tensor.
`from_pattern`(tensor[, dimension_tensor, pattern])
`indices`()	Returns the indices of the tensor.
`n_vertices`()	Returns the number of vertices.
`numel`()	Number of elements in the tensor.
`numpy`()
`rank`()	Returns the tensor rank.
`rearrange`(pattern, **axes_length)	Rearrange the tensor.
`reshape`(shape[, is_time_series, is_voxel])	Reshape the tensor.
`size`()	Size of the tensor.
`slice_time`(indices)	Slice the time series tensor.
`to`([device, non_blocking, dtype])	Convert the tensor to a different device or data type.
`to_numpy`()	Convert to numpy.ndarray.
`to_phlower_array`()
`to_tensor`()	Convert to torch.Tensor.
`to_vertexwise`()	Convert to vertexwise 2D tensor which has (n_vertices, -1) shape.
`transpose`(dim0, dim1)	Transpose the tensor.
`values`()	Returns the values of the tensor.

Attributes

`device`	Device of the tensor.
`dimension`	Physical dimension tensor.
`dtype`	Data type of the tensor.
`has_dimension`	Whether the tensor has a physical dimension.
`is_sparse`	Whether the tensor is a sparse tensor.
`is_time_series`	Whether the tensor is a time series.
`is_voxel`	Whether the tensor is a voxel.
`shape`	Shape of the tensor.
`shape_pattern`	Shape pattern of the tensor.

as_pattern(pattern)[source]¶

Convert the tensor to a pattern.

Returns:: Patterned tensor.
Return type:: PhlowerTensor
Parameters:: pattern (str)

backward()[source]¶

Backward the tensor.

Return type:: None

clone()[source]¶

Clone the tensor.

Returns:: Cloned tensor.
Return type:: PhlowerTensor

coalesce()[source]¶

Coalesce the tensor.

Returns:: Coalesced tensor.
Return type:: torch.Tensor

detach()[source]¶

Detach the tensor.

Returns:: Detached tensor.
Return type:: PhlowerTensor

property device: device¶

Device of the tensor.

Returns:: Device of the tensor.
Return type:: torch.device

property dimension: PhlowerDimensionTensor | None¶

Physical dimension tensor.

Returns:: Physical dimension tensor.
Return type:: PhlowerDimensionTensor | None

property dtype: dtype¶

Data type of the tensor.

Returns:: Data type of the tensor.
Return type:: torch.dtype

property has_dimension: bool¶

Whether the tensor has a physical dimension.

Returns:: Whether the tensor has a physical dimension.
Return type:: bool

indices()[source]¶

Returns the indices of the tensor.

Returns:: Indices of the tensor.
Return type:: torch.Tensor

property is_sparse: bool¶

Whether the tensor is a sparse tensor.

Returns:: Whether the tensor is a sparse tensor.
Return type:: bool

property is_time_series: bool¶

Whether the tensor is a time series.

Returns:: Whether the tensor is a time series.
Return type:: bool

property is_voxel: bool¶

Whether the tensor is a voxel.

Returns:: Whether the tensor is a voxel.
Return type:: bool

n_vertices()[source]¶

Returns the number of vertices.

Returns:: Number of vertices.
Return type:: int

numel()[source]¶

Number of elements in the tensor.

Returns:: Number of elements in the tensor.
Return type:: int

rank()[source]¶

Returns the tensor rank.

Returns:: Tensor rank.
Return type:: int

rearrange(pattern, **axes_length)[source]¶

Rearrange the tensor.

Returns:

Rearranged tensor.

Return type:

PhlowerTensor

Parameters:

pattern (str)
axes_length (dict[str, int])

reshape(shape, is_time_series=False, is_voxel=False)[source]¶

Reshape the tensor.

Returns:

Reshaped tensor.

Return type:

PhlowerTensor

Parameters:

shape (Sequence[int])
is_time_series (bool)
is_voxel (bool)

property shape: Size¶

Shape of the tensor.

Returns:: Shape of the tensor.
Return type:: torch.Size

property shape_pattern: PhlowerShapePattern¶

Shape pattern of the tensor.

Returns:: Shape pattern of the tensor.
Return type:: PhlowerShapePattern

size()[source]¶

Size of the tensor.

Returns:: Size of the tensor.
Return type:: torch.Size

slice_time(indices)[source]¶

Slice the time series tensor.

Returns:: Sliced tensor.
Return type:: PhlowerTensor
Parameters:: indices (int | slice | list[int] | ndarray | Tensor)

to(device=None, non_blocking=False, dtype=None)[source]¶

Convert the tensor to a different device or data type.

Returns:

Converted tensor.

Return type:

PhlowerTensor

Parameters:

device (str | device | None)
non_blocking (bool)
dtype (dtype | None)

to_numpy()[source]¶

Convert to numpy.ndarray.

Returns:: Numpy array.
Return type:: numpy.ndarray

to_tensor()[source]¶

Convert to torch.Tensor.

Returns:: Tensor.
Return type:: torch.Tensor

to_vertexwise()[source]¶

Convert to vertexwise 2D tensor which has (n_vertices, -1) shape.

Returns:

PhlowerTensor: Vertexwise PhlowerTensor object.
resultant_patternstr: Pattern of the resultant shape. Can be used for rearrange.

Return type:

vertexwise_tensor

transpose(dim0, dim1)[source]¶

Transpose the tensor.

Returns:

Transposed tensor.

Return type:

PhlowerTensor

Parameters:

dim0 (int)
dim1 (int)

values()[source]¶

Returns the values of the tensor.

Returns:: Values of the tensor.
Return type:: torch.Tensor