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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from torch import Tensor
from monai.config.type_definitions import NdarrayOrTensor
def root_sum_of_squares_t(x: Tensor, spatial_dim: int) -> Tensor:
"""
Compute the root sum of squares (rss) of the data (typically done for multi-coil MRI samples)
Args:
x: Input tensor
spatial_dim: dimension along which rss is applied
Returns:
rss of x along spatial_dim
Example:
.. code-block:: python
import numpy as np
x = torch.ones([2,3])
# the following line prints Tensor([1.41421356, 1.41421356, 1.41421356])
print(rss(x,spatial_dim=0))
"""
rss_x: Tensor = (x**2).sum(spatial_dim) ** 0.5
return rss_x
def root_sum_of_squares(x: NdarrayOrTensor, spatial_dim: int) -> NdarrayOrTensor:
"""
Compute the root sum of squares (rss) of the data (typically done for multi-coil MRI samples)
Args:
x: Input array/tensor
spatial_dim: dimension along which rss is applied
Returns:
rss of x along spatial_dim
Example:
.. code-block:: python
import numpy as np
x = np.ones([2,3])
# the following line prints array([1.41421356, 1.41421356, 1.41421356])
print(rss(x,spatial_dim=0))
"""
rss_x: NdarrayOrTensor = root_sum_of_squares_t(x, spatial_dim) # type: ignore
return rss_x
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