Modeling:Amps
From QuB
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Amps (AMP) re-estimates the amplitude and standard deviation of all conductance classes in the Model, using the Baum-Welch algorithm. The new amp and std values are put into the model.
AMP can produce an idealization of the data, by picking statet that maximizes Γstate,t = Forwardstate,t * Backwardstate,t. However, the Modeling:Idealize button generally idealizes as well, but much faster, and with unlimited-length data. AMP should be used on small selections of data, and its idealization should just be a quick visual check that it worked right.
AMP also produces all the same measurements and histograms as Modeling:Idealize.
You can run AMP directly from the Model window: select some Data, right-click in the background of the Model window, and choose "Grab all amps increasing/decreasing." This has the same effect as Data Source:sel, Auto-Init:up/down, don't Show Idealized, Quiet (no text) Output. No Results are shown.
Properties
| Data channel index | which A/D channel contains the data to measure, typically 0 |
|---|---|
| Data source | |
| Pre-process data | none: use raw data as displayed: use the same baseline correction and filtering as the Data display as such: apply the specified filter and baseline |
| Hist bin count | number of bins for amplitude histograms, displayed in the Results window |
| Auto-Init | initialize mean amplitudes by spacing them evenly between min(data) and max(data) |
| Open is up/down | when auto-initializing, should the open current be greater than (up) or less than (down) the closed current? |
| Show Idealized | generate an idealization in the Data window |
| Quiet Output | don't print anything in the Report window |
Results
In the textual Report window: the re-estimated amplitudes and standard deviations.
In the Results window:
Summary:
| amp | the mean amplitude of each class |
|---|---|
| sd | the standard deviation of each class |
Segments (and Select, Criteria): all the measurements of each segment/selection
Models: the model used
Histograms: An amplitude histogram for each segment/selection, overlaid with the Gaussian distribution (amp, std) of each class. You can quickly identify segments with errors by looking for histograms whose distribution curves don't match the histogram bars.
| Prev: Modeling:Idealize | Outline | Next: Modeling:Idl/Base |
