Introduction

In this notebook we demonstrate how to use previously computed spectra to create new gold-standard spectra which can be used to categorize the quality of new samples.



In [1]:

    
import PaSDqc



In [2]:

    
freq, nd, sample_list = PaSDqc.extra_tools.mk_ndarray("../data/gold_standard/")



In [3]:

    
sample_list









    Out[3]:





['1465_bad1',
 '1465_bad2',
 '1465_bad3',
 '1465_good1',
 '1465_good2',
 '1465_good3',
 '1465_good4']



In [4]:

    
labels = ['bad', 'bad', 'bad', 'good', 'good', 'good', 'good']



In [5]:

    
cat_spec = PaSDqc.extra_tools.mk_categorical_spectra(freq, nd, labels)



In [6]:

    
cat_spec.to_csv("categorical_spectra_Qiagen_heat_1x.txt", sep="\t")

These new categorical spectra could be used by supplying the command line option -c /path/to/categorical_spectra_Qiagen_heat_1x.txt to PaSDqc