pymiediff.special.D1n_torch

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pymiediff.special.D1n_torch#

pymiediff.special.D1n_torch(n: Tensor, z: Tensor, n_add='auto', n_add_min=10, n_add_max=35, eps=1e-10, precision='double', **kwargs)#

Vectorized D(1)n logrithmic derivative via downward recurrence

Parameters:

  • n (torch.Tensor or int) – integer order(s)

  • z (torch.Tensor) – complex (or real) arguments to evalute

  • n_add (str or int) – ‘auto’ or integer extra depth for the downward recurrence. ‘auto’ picks a default based on max|z|. defaults to “auto”

  • n_add_min (int) – Minimum additional starting order. Defaults to 10.

  • n_add_max (int) – Maximum additional starting order. Defaults to 35.

  • eps (float) – minimum value for abs(z) to avoid numerical instability

  • precision (str) – “single” our “double”. defaults to “double”.

  • kwargs – other kwargs are ignored

Returns:

tensor of same shape of input z + (n_max+1,) dimension, where last dim indexes the order n=0..n_max.

Return type:

torch.Tensor

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