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Version 1 smart fitter in IIRrational library. Uses SVD method with high order over fitting, then switches to nonlinear fits with heuristics to remove poles and zeros down to a reasonable system order.
## `1` initial
### `1.1` initial_direct
......@@ -13,8 +12,7 @@ Version 1 smart fitter in IIRrational library. Uses SVD method with high order o
initial guess without SVD technique
![Fitttt](plot-1-1.png)
![](plot-1-1.png)
### `1.2` initial_poles
......@@ -22,8 +20,7 @@ initial guess without SVD technique
Performs the SVD for a rough initial guess
![Fitttt](plot-1-2.png)
![](plot-1-2.png)
### `1.3` initial_zeros
......@@ -31,270 +28,85 @@ Performs the SVD for a rough initial guess
Performs the SVD for a rough initial guess
![](plot-1-3.png)
![Fitttt](plot-1-3.png)
### `1.4` choose direct
### `1.4` choose poles
> `if`
Chose the direct (non-SVD) fitter as it had the smallest residual of 2.94e+02
Chose the Poles SVD fitter as it had the smaller residual of 4.87e+02 vs. 2.35e+03 for the zeros
### `1.5` seq_iter_1
### `1.5` seq_iter_3
> `IIRrational.fit_poles, IIRrational.fit_zeros`
First iterations, enforcing mindelay and stabilized poles residual of 1.46e+13
First iterations, enforcing stabilized poles residual of 1.91e+02
![](plot-1-5.png)
![Fitttt](plot-1-5.png)
### `1.6` seq_iter_2
### `1.6` seq_iter_4
> `IIRrational.fit_poles, IIRrational.fit_zeros`
First iterations, enforcing stabilized poles residual of 2.81e+02
![Fitttt](plot-1-6.png)
### `1.7` seq_iter_3
> `IIRrational.fit_poles, IIRrational.fit_zeros`
First iterations, enforcing stabilized poles residual of 4.56e+02
![Fitttt](plot-1-7.png)
### `1.8` seq_iter_4
> `IIRrational.fit_poles, IIRrational.fit_zeros`
First iterations, enforcing stabilized poles residual of 5.51e+02
First iterations, enforcing stabilized poles residual of 1.89e+02
![Fitttt](plot-1-8.png)
![](plot-1-6.png)
### `1.9` Final
### `1.7` Final
> `SVD_method`
Uses SVD to create initial guess of fit for data, followed by several iterative fits
create initial guess of fit for data, followed by several iterative fits
(see reference ???).
* It is a linear method, finding global optimum (nonlocal). This makes it get stuck if systematics are bad. To prevent this,
it requires gratuitous overfitting to reliably get good fits.
* It requires a nyquist frequency that is very low, near the last data point. This can cause artifacts due to phasing discontinuity near the nyquist.
* The provided nyquist frequency is shifted up at the end, removing the real poles/zeros that are typically due to phasing discontinuity
![Fitttt](plot-1-9.png)
![](plot-1-7.png)
## `2` optimize nonlinear
> `MultiReprFilter.optimize`
Optimizes using nonlinear parameterizations,
ing nonlinear parameterizations,
TODO: describe as used
![](plot-2.png)
![Fitttt](plot-2.png)
## `3` order_reduce 1
### `3.1` duals_ID_1
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-1.png)
### `3.2` duals_ID_2
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-2.png)
### `3.3` duals_ID_3
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-3.png)
### `3.4` duals_ID_4
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-4.png)
### `3.5` duals_ID_5
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-5.png)
### `3.6` duals_ID_6
## `3` optimize nonlinear minBW
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-6.png)
### `3.7` duals_ID_7
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-7.png)
### `3.8` duals_ID_8
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-8.png)
### `3.9` duals_ID_9
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-9.png)
### `3.10` duals_ID_10
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-10.png)
### `3.11` duals_ID_11
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-11.png)
### `3.12` duals_ID_12
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-12.png)
### `3.13` duals_ID_13
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-13.png)
### `3.14` duals_ID_14
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-14.png)
### `3.15` duals_ID_15
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-15.png)
### `3.16` duals_ID_16
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-16.png)
### `3.17` duals_ID_17
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-17.png)
### `3.18` duals_ID_18
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![Fitttt](plot-3-18.png)
## `4` flip mindelay
ing nonlinear parameterizations,
TODO: describe as used
## `5` remove_weakroots
![](plot-3.png)
## `6` order_reduce 2
## `4` order_reduce 1
### `6.1` duals_ID_1
### `4.1` duals_ID_1
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![](plot-4-1.png)
![Fitttt](plot-6-1.png)
### `6.2` duals_ID_2
### `4.2` duals_ID_2
> `MultiReprFilter.optimize`
ID Pairs for order reduction
![](plot-4-2.png)
![Fitttt](plot-6-2.png)
### `6.3` duals_ID_3
## `5` final
> `MultiReprFilter.optimize`
ID Pairs for order reduction
ing nonlinear parameterizations,
TODO: describe as used
![Fitttt](plot-6-3.png)
![](plot-5.png)