Plotting and Inspecting the data

After the data is imported, it can be visualized. We have reduced the plotting to just two methods: [OnePlot]OnePlot, which puts all given DF-objects on a single figure, and [MultPlot]MultPlot, which groups DF-objects by the interaction type (or bond type) and then plot each group on an individual figure.

The MultPlot method replaces obsolete PlotAllDFs and is designed to plot several DFsets simultaneously, such as if one needs to compare RDFs sampled on different iterations.

Examples:

• Plot the set of RDFs, with one curve per plot:
  MT.MultPlot(RDFs_ref)
• Plot a list of sets of RDFs sampled at different iterations:
  MT.MultPlot(RDFs_smpl)
  in this case all the RDFs will be automatically grouped by the interaction they refer to.
• Plot together a list of DFsets (e.g. sampled RDFs) and an additional DFset (e.g. reference RDFs)
  MT.MultPlot(RDFs_smpl+[RDFs_ref])
  here [RDFs_ref] constructs a list with a single DFset in it, and + operation concatenates this list with RDFs_smpl which is list of DFsets.
• Plot a single function (RDF or potential) from the set:
  MT.OnePlot(RDFs_ref[0])

In addition to plotting of already imported data, one can make a quick inspection of the MagiC core log-file using following procedures: [Deviation]Deviation plots total deviation between the set of reference and set of sampled distribution functions at every iteration. [HeatMap]HeatMap Visualize IMC convergence by drawing a heatmap of interaction-specific RDF deviations. Procedure [AnalyzeIMCOuput]AnalyzeIMCOuput reads and plots reference and resulting DFs obtained in inverse procedure. They both require magic's output file (or list of files) as input.