spyrit.core.meas.FreeformLinearSplit.measure_H

FreeformLinearSplit.measure_H(x: tensor) → tensor

Simulate measurements from signal/image.

The mask is first applied to the input tensor, then the input tensor is multiplied by the measurement patterns.

Note

This method does not include the noise model.

Args:

x (torch.tensor): A tensor where the dimensions indexed by self.meas_dims match the measurement shape self.meas_shape.

Returns:

torch.tensor: A tensor of shape (*, self.M) where * denotes all the dimensions of the input tensor not included in self.meas_dims.

Example: Select one every second point on the diagonal of a batch of images

>>> from spyrit.core.meas import FreeformLinearSplit
>>> import torch
>>> images = torch.rand(17, 3, 40, 40)
>>> mask = torch.tensor([[i, i] for i in range(0,40,2)]).T
>>> H = torch.randn(13, 20)
>>> meas_op = FreeformLinearSplit(H, meas_shape=(40,40), index_mask=mask)
>>> x_masked = meas_op.measure_H(images)
>>> print(x_masked.shape)
torch.Size([17, 3, 13])