In [1]:
import sys, os
import glob as glob
import pandas as pd
from metatlas import integrity_monitor as monitor
from metatlas import metatlas_objects as metob
| name | formula | neutralmass | m+H | M-H+ |
| ABMBA-QC | C8H8BrNO2 | 228.9738 | 229.9811 | 227.9666 |
| ACA-QC | C15H10O2 | 222.0681 | 223.0754 | 221.0608 |
| d5-benzoic acid | C7HD5O2 | 127.0682 | 128.0754 | 126.0609 |
| DMP | C5H6N2O2 | 126.0429 | 127.0502 | 125.0357 |
| 13C-15N-L-phenylalanine | [!13C]9H11[!15N]O2 | 175.1062 | 176.1135 | 174.0989 |
| d4 lysine | C6H10D4N2O2 | 150.1306 | 151.1379 | 149.1234 |
| DUBA | C10H12N2O3 | 208.0848 | 209.0921 | 207.0775 |
In [2]:
# %system python /global/homes/b/bpb/repos/metatlas/metatlas/integrity_monitor.py "/global/project/projectdirs/metatlas/raw_data/"
In [3]:
files = metob.retrieve('Lcmsruns',experiment='20170616_KBL_C18Lip_MW_Spore_FRedo',username='*')
You have to provide the positive and negative istd m/z for this to run
std is the positive m/z
std_neg is the negative m/z
In [4]:
results = []
for f in files:
r = monitor.data_verify(f,std_neg=120.151,std=550.6298)
results.append(r)
In [5]:
df = pd.DataFrame(results)
In [6]:
df.to_csv('/global/homes/b/bpb/Downloads/custom_istd_report.csv')
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