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Graphical presentation of the results of fitting models to datasets.
Fitting itself is a quite complicated process, and it is crucial for routine
use to have graphical representations of the results, besides
automatically generated reports (see the
reports module) that usually
contain such graphical representations.
Typically, graphical representations of fit results will contain both, the original data and the fitted model, at least as long as datasets with 1D data are concerned. Additionally, one may be interested in plotting the residual as well.
Technically, the plotters in the FitPy package rely on the additional
properties of the
particularly the property
residual within the
Therein the residual of the fit is stored, and thus, the original data can
simply be recovered by adding the residual to the fitted model contained
in the data of the dataset.
Types of plots
Generally, at least two types of plotters can be distinguished with respect to the kind of information that should be represented graphically:
Graphical representation of both, data and fitted model
The simplest type of such a plotter displays both, data and fitted model in one axis, perhaps with the residual in a second, smaller axes underneath.
For 2D datasets, things become more complicated, but here, at least fitted model and residual can be plotted in two axes.
Graphical representation of the reliability and quality of fits
Particularly for robust fits including LHS or similar sampling methods, a graphical representation of the quality of the fit (e.g., the fitness value plotted as function of the index of the sample) is of great value.
- class fitpy.plotting.SinglePlotter1D
1D plots of single datasets, including original data and fitted model.
Convenience class taking care of 1D plots of single datasets. The type of plot can be set in its
aspecd.plotting.SinglePlotter1D.typeattribute. Allowed types are stored in the
Additionally to the functionality of the superclass, this plotter displays the (experimental) data together with the fitted model.
Properties of the plot, defining its appearance
For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see
aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.
Usually, you will have set another ASpecD-derived package as default package in your recipe for processing and analysing your data. Hence, you need to provide the package name (fitpy) in the
kindproperty, as shown in the examples.
In the simplest case, just invoke the plotter with default values:
- kind: fitpy.singleplot type: SinglePlotter1D properties: filename: output.pdf
- class fitpy.plotting.SinglePlot1DProperties
Properties of a 1D single plot, defining its appearance.
Additionally to the properties of the superclass, properties particularly for displaying both, data and fitted model exist.
Furthermore, some sensible settings for both, data and fitted model, are provided, such as labels and line colours. Of course, you can override these values manually.
Properties of the line representing the data.
“Data” here refers to the data the model has been fitted to.
For the properties that can be set this way, see the documentation of the