peak-o-mat documentation

Basic Concepts

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Basic Concepts

Data

The data is stored in a tree-like structure. There is one top-level element, the project. The next lower level is that of the plots, each of which can hold an arbitrary number of sets. A set is a 2d array containing the x-y data, where the x-data is ordered (typical for spectroscopic data). This data structure is represented by the tree view on the right side of the main frame. In addition to the plain x-y data, a set can have various objects attached, such as transformations (trafo), a fit model and weights.

Unlike some other data analysis programs, peak-o-mat allows for data manipulation/transformation on a higher level than operating on the raw data through a spreadsheet like interface. As peak-o-mat is especially designed to work with spectroscopic data, arithmetics on sets will do what you would expect intuitively, e.g. adding two sets will only add the y-data, while interpolating the x-axes in case they differ. Those kind of manipulations can be done using the ‘Set operations’ module. If you need to do more complicate operations, you can still copy the data to a data grid and manipulate the data there by means of a python shell.

Models

A model is an analytical function to which the set data is fitted. It can be described by

  • a valid python expression, where x stands for the set’s x-values. You can use arbitrary variable names containing alphanumeric characters, e.g. “a*exp(-b0*x**2)”, and all functions/symbols defined in the top level namespace of the numpy package. There are some pre-defined physical constants which can be accessed using their name prefixed by ‘c_‘, e.g. ‘c_e0’ referes to the elementary charge. See the ‘customisation’ sectioin for how to add your own constants.
  • a space separated list of pre-defined symbols, here called tokens, which represent common backgrounds and peak shapes. The function values represented by the tokens are finally added, e.g. “CB LO GA LO” represents a model with a constant background, one gaussian peak and two lorenzian peak shapes. You can append a number to any token to be able to distinguish them later, e.g. “CB LO1 GA LO2”. Instead of the whitespace between the tokens, the ‘+’ operator can be used. In some rare cases you might want to multiply the function represented by the tokens. This can be achieved by using the ‘*’ operator. The model is evaluated from left to right and the usual sign rules do not apply!

Once a data set has been fitted, the model including the fitted parameters is attached to the set and whenever the set is selected, the model is evaluated and plotted in green together with the data.

Transformations and masks

A trafo denotes any transformation of the x- or y-data of a set. Instead of changing the original data, all trafos are attached to a set and evaluated on demand whenever the set data is accessed. Like this, the original data can be restored by simply removing all trafos. Furthermore, if you remove bad data points from a set, a mask is applied to the data so that all points can be restored at any time. Use the popup menu in the tree view to unset a mask or remove the transformations attached to the currently selected sets. The ‘Set Info’ model allows for manipulation of every single transformation of a set. The transformation expresseion can be changed, transofrmations can be deactivated and both masks and tranformations can be made permanent, i.e. they are applied once and the original data is overwritten with the result from the evaluation.

Weights

Sometimes one may need to weight the data in order to suppress the effect of outliers on the fit. Weights can be specified both relative to the data or in absolute values. See the description of the fit module for how to specify the weights. The weights are visualised by two blue lines corresponding to the bounds of the variance evaluated as sigma^2 = 1/w.

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