Add a (frequency-dependent) phase to a frequency series. Allows for batching, as well as additional channels (such as detectors). Accounts for the fact that the data could be a complex frequency series or real and imaginary parts.
Convention: the phase \(\phi(f)\) is defined via \(\exp[- i \phi(f)]\).
Time translate frequency-domain data by dt. Time translation corresponds (in frequency domain) to multiplication by \[
\exp(-2 \pi i f \cdot dt).
\]
This method allows for multiple batch dimensions. For torch.Tensor data, allow for either a complex or a (real, imag) representation.
Parameters
Name
Type
Description
Default
data
array - like(numpy, torch)
Shape (B, C, N), where - B corresponds to any dimension >= 0, - C is either absent (for complex data) or has dimension >= 2 (for data represented as real and imaginary parts), and - N is either len(self) or len(self)-self.min_idx (for truncated data).