Hi Abe,
I just recently created a IPython Jupyter notebook for this purpose.
You can find it, here:
https://github.com/capn-freako/PyBERT/blob/master/scikit-rf/S-param%20Check.ipynb
<https://github.com/capn-freako/PyBERT/blob/master/scikit-rf/S-param%20Check.ipynb>
If the above link doesn’t work for you, you can view an HTML preview of the
notebook, here:
https://htmlpreview.github.io/?https://github.com/capn-freako/PyBERT/blob/master/scikit-rf/S-param_Check.html
<https://htmlpreview.github.io/?https://github.com/capn-freako/PyBERT/blob/master/scikit-rf/S-param_Check.html>
Cheers,
-db
On Dec 11, 2017, at 11:24 PM, Abe Riazi <riaziabe@xxxxxxxxx> wrote:
Hi David,
I am interested in a Python based tool which allows checking a *.s4p
S-parameter model for passivity, causality
and symmetry requirements.
Can you please provide me a link for downloading such a Python package or
module?
Thanks and with kind regards,
Abe
On Monday, December 11, 2017, David Banas <capn.freako@xxxxxxxxx
<mailto:capn.freako@xxxxxxxxx>> wrote:
Hi all,
I’ve just released v2.3.0 of PyBERT:
https://github.com/capn-freako/PyBERT/wiki ;
<https://github.com/capn-freako/PyBERT/wiki>
<https://github.com/capn-freako/PyBERT/wiki ;
<https://github.com/capn-freako/PyBERT/wiki>>
This release fixes the following issues:
- #32 The "front porch" of the trimmed impulse response is now fixed at 20%
of the total response length, after trimming.
- #33 *.s4p files may now be used directly, as an alternative to _PyBERT_'s
built-in channel model.
- #34 The optimizer may now be aborted by the user. And the results obtained
thus far may be used in the next simulation run.
- #35 The error in the _Pulse Response Zero Forcing_ approximation, for the
last simulation run, is now reported in the optimization (i.e. - _EQ Tune_)
tab.
Happy Holidays!
-db
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