spyrit.core.noise.Poisson.forward

Poisson.forward(z: tensor) → tensor

Corrupt measurement by Poisson noise

\[y \sim \mathcal{P}\left(\alpha z\right).\]

Args:

z (torch.tensor): Measurements \(z\) with arbitrary shape.

Returns:

torch.tensor: Noisy measurement \(y\) with the same shape as z.

Example:

Two different noisy measurement vectors

>>> import spyrit.core.noise as sn
>>> import torch
>>> noise = sn.Poisson(100)
>>> z = torch.empty(10, 4).uniform_(0, 1)
>>> y = noise(z)
>>> print(y.shape)
torch.Size([10, 4])
>>> print(f"Noiseless measurements in ({torch.min(z):.2f} , {torch.max(z):.2f})")
Noiseless measurements in (...)
>>> print(torch.all((z >= 0) & (z <= 1)))
tensor(True)
>>> print(f"Noisy measurements in ({torch.min(y):.2f} , {torch.max(y):.2f})")
Noisy measurements in (...)

>>> y = noise(z)
>>> print(f"Noisy measurements in ({torch.min(y):.2f} , {torch.max(y):.2f})")
Noisy measurements in (...)