4. The modules

A module is a small functional unit which adds a feature to peak-o-mat. The available modules are shown as notebook tabs in the lower part of the main window. The only built-in module is the Fit module. All other modules consist of a python source (.py) and a XRC (.xrc) file, the latter describing the GUI of the module. See the ‘customisation’ section below for an example of how to add custom modules to peak-o-mat.

4.1. Fit module

This is the core module of peak-o-mat. It has four components:

4.1.1. Model

The model can be either a list of predefined tokens which may suffixed by an integer number or a valid python expression using arbitrary parameter names. Note that tokens always consist of uppercase letter and an optional number whereas fit parameters have to be lowercase. It is not possible to mix tokens and python expressions in a single model. See the ‘customisation’ section for details. Just type to the ‘Fit model’ text input to define the model. All available tokens are shown in the listbox on the left of the window. When you select a model token from the list its definition will be shown in the textbox on the right. Double-clicking on any item or clicking the ‘Add to model’ button will add the token to the model definition. While typing to the ‘Fit model’ field, the function parser will try to identify fit parameters and functions and it will tell you if the entered expression is vaild. Do not pay too much attention to the error messages which will pop up while typing until you have not finished the expression. Once the model is complete however, there should be no error message.

4.1.2. Parameters

In order to work on the parameters of the model a single data set has to be selected. In the parameters view the fit parameters of the current the model are shown in the data grid on the left. The parameter called ‘area’ though is not a fit parameter. It represents the value of the numerically integrated area in between the lineshape and the abscissa in case the lineshape has a finite area. If you just entered a new model all parameters are undefinied. Before doing a fit you need to provide an initial guess for every parameter, i.e. a value near the value you would expect. Whenever you change a parameter by editing the corresponding cell of the paramter grid, the model will be immediately evaluated at the x-data points of the selected data set and displayed in green together with the data. Like this it is easy to find a good initial guess since you can directly compare the evaluted model with the measured data. After having entered values for all parameters you may click on ‘Fit (short link)’ to do the fit. The button’s label says ‘short link’ since the button is a duplicate of the ‘Fit’ button on the fit panel (see below) where you can specify the fit options. Parameter picking

If the model is defined by combinations of those tokens which are built in to peak-o-mat there is an easier way to provide the initial guess than entering values by hand. The initial guess for any of the built-in tokens may be provided by placing the corresponding lineshape on the plot area. This is called ‘parameter picking’ since the parameters are picked up from the plot by using the mouse. If you click on the ‘Pick’ button just right to the parameters grid view and move the pointer to the plot area you will quickly learn how to handle the parameter picking procedure. In the status line of the window at its very bottom you can see which component of the model you are currently working on. The parameter picking procedure ends when all components have been defined, i.e. all lineshapes have been placed on the plot. In case you need to refine the guess for just one component of the model choose that component from the drop down list to the right of the ‘Pick’ button, click on ‘Pick’ and place the lineshape on the plot as before. Parameter export

Once the model has been fitted to the data the parameter grid is filled with the fitted parameters overwriting the initial guess. For further processing or visualization of the fitted values they can be exported or transfered to the data grid which is a spreadsheet like tool integrated into peak-o-mat. This is done by pressing the ‘Export’ button. A message will be shown in the status line telling that the parameters have been exported. To open the data grid choose the corresponding item from the ‘View’ menu or press CTRL-d. See the section on the data grid for more information. You may choose to only export a certain parameter of each component of the model, e.g. if you are only interested in the ‘pos’ parameter of a series of peak shaped components, choose ‘pos’ from the drop-down list and click ‘Export’ afterwards. If the checkbox ‘include errors’ is selected the parameters and their fit uncertainity are exported and displayed in separate rows of the data grid. Creating data sets from model parameters

Once a model has been fitted to a data set, the fit is shown along with the data evaluated on-the-fly at the x-values of the data. In order to create a data set from the evaluated modele enter values for the x-region and the number of data points of the new data set and click ‘Load’. The newly created data set will be added current plot. The end points of the region default to the x-range of the selected set. You have the choice to evaluate the complete model with all its components or only a single component by choosing a component from the drop-down list next to the ‘Load’ button.

4.1.3. Weights

In the weights view you can define regions on the x-axis each of which may have a distinct weight. Although talking of weights you actually specify a standard deviation. The deviation can be defined as relative to the y-data, in absolute values or as a combination of both. If you have not yet defined regions the table shows a single line and the region spans the wohle x-range of the data. Clik on ‘Place region borders’ and then click on the plot where you want to place a border between two regions. The region borders are displayed as vertical lines on the plot. In the moment you click on the plot the table is updated to reflect the new regions. As long as the ‘Place region borders’ button is pressed you may add region borders or drag existing ones. Right-click an existing border to remove it. For each region enter the desired values into the table and choose whether it is meant to bet relative to the y-data, absoulte or a combination of both. You will notice that the standard deviations are displayed on the plot area as light blue band with its center at the y-data. Click on ‘Attach to set’ to attach the current weights configuration to the currently selected data set. Only if you do that the weights will be honoured in the fit. If a weights configuration is attached to a set the display style changes to dark blue and it will always be displayed along with the set data also if you leave the weights view.

4.1.4. Fit

In the fit view you can tune the fit procedure and see the result message of the last fits. Although you may choose between least squares minimization and orthogonal distance regression (ODR), ODR is not completely supported as it is not possible to specify weights for both dependent and independent variables. Anyway, spectroscopic data hardly ever need ODR. Usually is is not necesary to modify

4.2. Data operations module

As mentioned before, manipulations of the set data can be done on a higher level than editing the x- and y-data directly.

Operations on the sets can be either an operation between the y-values of different sets (inter-set) or a transformation of either the x- or y-values of one set (intra-set). In the former case, a new set is created, in the latter case the transformation is attached to the current set and can be removed later thus restoring the original data. The sets can be referenced by the identifier ‘sX’, where X is the set number of the active plot shown in the tree view on the right side. Inter-set operations are only possible between sets of the same plot. The intra-set operations are applied to all selected sets. The axis and set identifiers (‘x’,’y’,’sX’) appearing in the expression determine wether the expression is an inter- or an intra-set operation. The special operator & will join two sets if they do not overlap.

examples of valid expressions are:

x+10              intra-set, x-axis
1/y               intra-set, y-axis
log10(y)          intra-set, y-axis
(s0+s1)/2         inter-set
s0&s1             inter-set (join two sets)

4.3. Evaluate module

This module allows you to create sets by evaluating a custom function or by drawing a spline on the plot canvas.

4.4. Set info module

The set info module gives information about the number of total and masked points of a set and the list of transformations attached to it. The transformations can be deactivated temporarily by unchecking the corresponding checkbox, removed completely and the transformations and their comments can be edited when double-clicking on the corresponding field.

4.5. Calibration module

calibrate data using spectral lines of Ne,Ar,He,Me, etc.

4.6. Ruby calibration module

Pressure calibration using the R1 luminescence