Summary

Summary#

The Summary tab collects everything the project knows after a fit into a single report: the project information, the sample parameters, the loaded experiments and the refinement result. The report is shown in the main window, and the sidebar exports it - as a document, or as figures.

The report#

The main window renders the report with four sections:

  • Project information: title, description and the number of loaded experiments.

  • Sample: every parameter of the current model, as Name, Value, Unit and Error. The Error column shows the uncertainty determined by the fit. Parameters that were not fitted (fixed parameters, and everything before the first fit) have no uncertainty, so their Error cell is left empty.

  • Experiments: one row per measured dataset, with its q-range, number of points and resolution function. A polarized experiment contributes one row per measured spin channel, named after the channel (pp, pm, mp, mm).

  • Refinement: calculation engine, minimizer, goodness of fit, and the number of total, free, fixed and constrained parameters.

Long experiment names are truncated in the table; hover over one to see the full name.

Export summary#

The Export summary group in the sidebar writes the report to disk.

  • Name: the file name, without extension.

  • Format: HTML or PDF. The HTML export embeds interactive plotly charts; the PDF export embeds static images of the same charts, because the PDF converter cannot run the JavaScript behind the interactive ones.

  • Location: the full output path. Use the folder icon to pick a different directory. The default location is the project home directory.

  • Save: writes the file and confirms the result in a dialog.

Export plots#

The Export plots group saves the reflectivity and SLD charts as a two-panel matplotlib figure. Both panels are drawn from the current project: the measured data with its error bars, the calculated curve for each spin channel, and the SLD profile of every model.

  • Open in matplotlib: opens the figure in an interactive matplotlib window without writing anything to disk. Useful for a quick look, and for matplotlib’s own zoom, pan and save-image tools.

  • Name: the figure file name, without extension.

  • Format: PDF, PNG, SVG or PICKLE (see below).

  • Location: the full output path, extension included. Use the folder icon to pick a different directory.

  • Width (cm) / Height (cm): the size of the saved figure. Images are written at 600 dpi.

  • Save plot: writes the figure and confirms the result in a dialog.

Saving charts as matplotlib objects#

PDF, PNG and SVG are rendered images: what you see is all you get. Choosing PICKLE instead writes the live matplotlib Figure object itself, as a .pickle file, so the chart can be reopened and reworked in any Python session:

import pickle
import matplotlib.pyplot as plt

with open('plots.pickle', 'rb') as handle:
    figure = pickle.load(handle)

plt.show()

The figure comes back attached to pyplot, with its axes, artists, scales and data intact. That makes it the format to pick when the chart is a starting point rather than a final result - for example to

  • restyle it for a paper or a talk (colors, fonts, legend, axis limits),

  • add curves from another source to the same axes,

  • read the plotted values back out of the artists, e.g. figure.axes[0].lines[0].get_xydata(),

  • and re-export it to any image format afterwards with figure.savefig(...).

The file is written with the standard library pickle module, so reading it back requires matplotlib to be installed - and, as with any pickle, only load files you trust.

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