Differentiable Rounding Helpers for Pulseq Zero Sequences

This module provides differentiable stand-ins for common rounding operations (ceil, floor, round) used in Pulseq sequences. They are designed to keep the optimization pipeline differentiable by passing gradients through unchanged, effectively treating these operations as the identity during backpropagation.

Design idea

• Forward pass: Behaves exactly like the corresponding PyTorch / NumPy rounding operation.
• Backward pass: Returns the incoming gradient unchanged.
• Use case: Allows differentiable optimization of otherwise discrete parameters.

Functional Wrappers

To use the wrapper functions:

from pulseqzero import round, ceil, floor

ceil(x)

Differentiable version of torch.ceil.

y = ceil(x)

floor(x)

Differentiable version of torch.floor.

y = floor(x)

round(x)

Differentiable version of torch.round.

y = round(x)