Summary¶
If we put all the preceding steps together:
eg = la.analyse(data_folder='./latools_demo_tmp',
config='DEFAULT',
internal_standard='Ca43',
srm_identifier='STD')
eg.trace_plots()
eg.despike(expdecay_despiker=True,
noise_despiker=True)
eg.autorange(on_mult=[1.5, 0.8],
off_mult=[0.8, 1.5])
eg.bkg_calc_weightedmean(weight_fwhm=300,
n_min=10)
eg.bkg_plot()
eg.bkg_subtract()
eg.ratio()
eg.calibrate(drift_correct=False,
srms_used=['NIST610', 'NIST612', 'NIST614'])
eg.calibration_plot()
eg.filter_threshold(analyte='Al27', threshold=100e-6) # remember that all units are in mol/mol!
eg.filter_reports(analytes='Al27', filt_str='thresh')
eg.filter_on(filt='Albelow')
eg.filter_off(filt='Albelow', analyte='Mg25')
eg.make_subset(samples='Sample-1', name='set1')
eg.make_subset(samples=['Sample-2', 'Sample-3'], name='set2')
eg.filter_on(filt=0, subset='set1')
eg.filter_off(filt=0, subset='set2')
eg.sample_stats(stats=['mean', 'std'], filt=True)
stats = eg.getstats()
eg.minimal_export()
Here we processed just 3 files, but the same procedure can be applied to an entire day of analyses, and takes just a little longer.
The processing stage most likely to modify your results is filtering.
There are a number of filters available, ranging from simple concentration thresholds (filter_threshold()
, as above) to advanced multi-dimensional clustering algorithms (filter_clustering()
).
We recommend you read and understand the section on advanced_filtering before applying filters to your data.
Before You Go¶
Before you try to analyse your own data, you must configure latools to work with your particular instrument/standards. To do this, follow the Three Steps to Configuration guide.
We also highly recommend that you read through the Advanced Topics, so you understand how latools
works before you start using it.