pychopmarg.noise

Classes

class pychopmarg.noise.NoiseCalc(L: int, Tb: float, ts_ix: int, t: ndarray[Any, dtype[Real]], vic_pulse_resp: ndarray[Any, dtype[Real]], agg_pulse_resps: list[ndarray[Any, dtype[Real]]], f: ndarray[Any, dtype[Real]], Ht: ndarray[Any, dtype[Comp]], H21: ndarray[Any, dtype[Comp]], Hr: ndarray[Any, dtype[Comp]], Hctf: ndarray[Any, dtype[Comp]], eta0: float, Av: float, snr_tx: float, Add: float, sigma_Rj: float, eps: float = 0.001)[source]

Noise calculator for COM

L = 4

Tb = 9.412e-12

ts_ix = 0

t = array([0.])

vic_pulse_resp = array([0.])

agg_pulse_resps: list[ndarray[Any, dtype[Real]]] = []

f = array([0.])

Ht = array([0.+0.j])

H21 = array([0.+0.j])

Hr = array([0.+0.j])

Hctf = array([0.+0.j])

eta0 = 0.0

Av = 0.6

snr_tx = 25.0

Add = 0.0

sigma_Rj = 0.0

fN = 53125000000.0

nspui = 32

varX = 0.0

t_irfft = array([0.])

get_aliased_freq(f: float) → float[source]: Return alias of f (Hz).

from_irfft(x: ndarray[Any, dtype[Real]]) → ndarray[Any, dtype[Real]][source]

Interpolate irfft() output to t and subsample at fBaud.

Parameters:: x – irfft() results to be interpolated and subsampled.
Returns:: Interpolated and subsampled vector.
Raises:: IndexError – If length of input doesn’t match length of t_irfft vector.

Notes

Input vector is shifted, such that its peak occurs at 0.1 * max(t), before interpolating. This is done to:
- ensure that we don’t omit any non-causal behavior, which ends up at the end of an IFFT output vector when the peak is very near the beginning, and
- to ensure that the majority of our available time span is available for capturing reflections.
The sub-sampling phase is adjusted, so as to ensure that we catch the peak.

property Srn: ndarray[Any, dtype[Real]]

One-sided folded noise PSD at Rx sampler input, uniformly sampled over [0, PI] (rads./s norm.).

Notes

1. Re: the scaling term: 2 * self.f[-1], when combined w/ the implicit 1/N of the irfft() function, this gives df.

Sxn(agg_pulse_resp: ndarray[Any, dtype[Real]]) → ndarray[Any, dtype[Real]][source]

Crosstalk PSD at Rx FFE input.

Parameters:: agg_pulse_resp – Aggressor pulse response (V).
Returns:: One-sided crosstalk PSD at Rx FFE input, uniformly sampled over [0, PI] (rads./s norm.).

property Stn: ndarray[Any, dtype[Real]]

One-sided Tx noise PSD at Rx FFE input, uniformly sampled over [0, PI] (rads./s norm.).

Todo

Do I need to honor ts_ix?

property Sjn: ndarray[Any, dtype[Real]]: One-sided Noise PSD due to jitter at Rx FFE input, uniformly sampled over [0, PI] (rads./s norm.).

Rn() → ndarray[Any, dtype[Real]][source]: Noise autocorrelation vector at Rx FFE input.