# Examples¶

All these examples assume we are working on a folder organized as the following (which is not mandatory, you just have to put all your nmrML and zipped raw files in the same folder):

/
└ home/
└ metabolomics/
└ NMRML study/       # the name of the folder doesn't matter
├ Sample1.nmrML   # the name of the file must correspond to the sample name
├ Sample1.zip     # the raw files must be zipped and named as the nmrML file
├ Sample2.nmrML
├ Sample2.zip
└ ...


## Standalone program¶

Create a new MetaboLights study with existing nmrML data:

nmrml2isa -i "/home/metabolomics/NMRML study" -o "/home/metabolomics/isa" -s NMRML1


This will create a new folder and write the following ISA-Tab files:

/
└ home/
└ metabolomics/
└ isa/
├ a_NMRML1_metabolite_profiling_NMR_spectroscopy.txt
├ i_Investigation.txt
└ s_NMRML1.txt


## Python module¶

To do the exact same thing within a Python program:

from nmrml2isa.parsing import full_parse
full_parse("/home/metabolomics/NMRML study", "/home/metabolomics/isa", "NMRML1")


It is also possible only to extract metadatas: for instance, let’s say we want to separate .nmrML files within the same directory based on what the Acquisition Nucleus was for that particular NMR scan:

from nmrml2isa.nmrml import nmrMLmeta
import os

sorted_files = {}
in_dir = '/home/metabolomics/NMRML study'

for file in os.listdir(in_dir):

meta = nmrMLmeta(os.path.join(in_dir, file)).meta

if 'Acquisition Nucleus' in meta.keys():

if meta['Acquisition Nucleus']['name'] not in sorted_files.keys():
sorted_files[meta['Acquisition Nucleus']['name']] = []

sorted_files[meta['Acquisition Nucleus']['name']].append(file)


At the end of that snippet, sorted_files keys will be the different acquisition nucleuses used and the value of each key will be a list of nmrML files where that nucleus was used.