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For many experiment, the analysis of the smalldata h5 files is not too computing intensive and the analysis team can benefit from the quick and iterative nature of the Jupyter notebooks framework over the more involved cube production.

These notebooks can be found in /sdf/cdsdata/grouplcls/psdmds/swxpp/toolsxpptut15/smalldata_toolsresults/example_notebooks.

It is highly recommended to be able to refer to the relevant example notebook while going through this part of the documentation.

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Make sure to adapt the alias to the detectors names in your smalldata h5 file. The object rr (created from rr = tables.File(f).root) in the workspace allows for easy tab-exploration of the content of the h5 file to figure out variable names.

Example from trXRD.ipynb

While most value often remain the same for the entire experiment, the scanvar should be updated to match the axis being scanned for each run (delay, energy, azimuth, ...).

# helper dict to find data in file
dataDict = {
    'scanvar': 'enc/lasDelay',
    'i0': 'ipm2/sum',
    'roi': 'jungfrau1M/ROI_0_area',
    'xon': 'lightStatus/xray',
    'lon': 'lightStatus/laser',
    'tt_corr': 'tt/ttCorr',
    'tt_fwhm': 'tt/FLTPOSFWHM',
    'tt_amp': 'tt/AMPL'
}

filters = {}
filters['i0'] = [200,10000]
filters['tt_amp'] = [0.02,0.2]
filters['tt_fwhm'] = [50,240]
damage = [dataDict['roi'].split('/')[0], dataDict['i0'].split('/')[0]]

print('Filter selection:')
for key,value in filters.items():
	print('\t{} : {}'.format(key, value))
hist_bins = {key: 35 for key in filters.keys()}

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