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In [1]:



    


%load_ext autoreload
%autoreload 2

import pandas as pd
import numpy as np

import latools as la

from IPython.display import HTML

from comparison_tools import helpers, stats_1sample, plots_1sample

%matplotlib inline

Data Collection Parameters





In [2]:



    


HTML(filename="./Parameter_Tables/iolite_data.html")



    


















    
Out[2]:







    




    
    

        Parameter
        Value
    

    
    
    

        Laser
        Instrument
        Photon M G2 System
    
    
    

        
        Energy
        1.48 J/cm3
    

    

        
        Wavelength
        193 nm
    

    

        
        Pulse Length
        nanosecond
    

    

        
        Repetition Rate
        4 Hz
    

    

        
        Spot Size
        35x35 μm square
    

    
    
    

        Interface
        Sample Cell Type
        2 Volume Helex
    

    

        
        Gas Flows
        1 L min-1 He
    

    

        
        
        8 ml min-1 N2
    

    

        
        Transfer Devices
        Squid, particle filter
    

    
    
    

        Mass Spec
        Type
        Thermo iCAP-Q Quadrupole ICPM-MS
    

    

        
        11B
        50 ms
    

    

        
        24Mg
        40 ms
    

    

        
        25Mg
        70 ms
    

    

        
        27Al
        30 ms
    

    

        
        43Ca
        80 ms
    

    

        
        55Mn
        30 ms
    

    

        
        57Fe
        20 ms
    

    

        
        63Cu
        20 ms
    

    

        
        66Zn
        20 ms
    

    

        
        88Sr
        50 ms
    

    

        
        Cycle Time
        410 ms

Import Data





In [3]:



    


# define data format description so it can be imported by latools
dataformat = {'genfromtext_args': {'delimiter': ',',
                                   'skip_header': 15},
              'column_id': {'name_row': 13,
                            'delimiter': ',',
                            'timecolumn': 0,
                            'pattern': '([0-9]{1,3}[A-z]{1,2})'},
              'meta_regex': {0: (['name', 'date'],
                                 '([A-z0-9-]+):([0-9/ :AMP]+);')}
             }



    











In [4]:



    


dat = la.analyse('raw_data/iolite_data', internal_standard='43Ca', srm_identifier='NIST610',
                 dataformat=dataformat, names='metadata_names')



    


















    






Loading Data:   5%|▍         | 11/225 [00:00<00:01, 107.76it/s]









    






Starting analysis using "UCD-AGILENT" configuration:










    






Loading Data: 100%|██████████| 225/225 [00:00<00:00, 314.09it/s]









    






  225 Data Files Loaded: 15 standards, 210 samples
  Analytes: 11B 24Mg 25Mg 27Al 43Ca 55Mn 57Fe 63Cu 66Zn 88Sr
  Internal Standard: 43Ca










    






























In [5]:



    


sample = '1308H1-1e'



    



















In [6]:



    


dat.data[sample].tplot()  # view raw data



    


















    
Out[6]:








(<Figure size 720x288 with 1 Axes>,
 <matplotlib.axes._axes.Axes at 0x7f7c41b67438>)










    
































Data Processing
















In [7]:



    


# despiking
dat.despike(noise_despiker=True, win=3)



    


















    






Despiking: 100%|██████████| 225/225 [00:00<00:00, 1375.82it/s]

























In [8]:



    


dat.data[sample].tplot()  # view despiked data



    


















    
Out[8]:








(<Figure size 720x288 with 1 Axes>,
 <matplotlib.axes._axes.Axes at 0x7f7c416760b8>)










    
































In [9]:



    


dat.autorange(on_mult=[3,.6], off_mult=[.5,3])



    


















    






AutoRange: 100%|██████████| 225/225 [00:04<00:00, 58.09it/s]










    







*****************************************
                 WARNING
*****************************************
Autorange failed for some samples:
  1308H1-13c : 58.7
  1308H1-7a  : 59.5

*** THIS IS NOT NECESSARILY A PROBLEM ***
But please check the plots below to make
sure they look OK. Failures are marked by
dashed vertical red lines.

To examine an autorange failure in more
detail, use the `autorange_plot` method
of the failing data object, e.g.:
dat.data['Sample'].autorange_plot(params)
*****************************************











    

















    
































In [10]:



    


dat.data[sample].tplot(ranges=True)  # view autorange info



    


















    
Out[10]:








(<Figure size 720x288 with 1 Axes>,
 <matplotlib.axes._axes.Axes at 0x7f7c403101d0>)










    
































In [11]:



    


dat.bkg_calc_weightedmean(weight_fwhm=1000, bkg_filter=True)



    



















In [12]:



    


fig, ax = dat.bkg_plot()



    


















    






Plotting backgrounds: 100%|██████████| 10/10 [00:15<00:00,  2.29s/it]