12.3. hplc-py

import numpy as np
import matplotlib.pyplot as plt
from molass_data import SAMPLE1
from molass.DataObjects import SecSaxsData as SSD
ssd = SSD(SAMPLE1)
x, y = ssd.xr.get_icurve().get_xy()
fig, ax = plt.subplots()
ax.plot(x, y)
plt.show()

csv_path = "hplc-py-example.csv"
np.savetxt(csv_path, np.column_stack((x/20, y)), delimiter=",", header="time,signal", comments='')

from hplc.io import load_chromatogram
from hplc.quant import Chromatogram
example = load_chromatogram(csv_path, cols=['time', 'signal'])
chrom = Chromatogram(example)
chrom.show()

[<Figure size 640x480 with 1 Axes>, <Axes: xlabel='time', ylabel='signal'>]

peaks = chrom.fit_peaks(prominence=0.05)
peaks.head()

Performing baseline correction: 100%|██████████| 49/49 [00:00<00:00, 5891.21it/s]
Deconvolving mixture: 100%|██████████| 1/1 [00:02<00:00,  2.08s/it]

	retention_time	scale	skew	amplitude	area	signal_maximum	peak_id
0	0.4	0.011160	0.143289	0.001278	0.007779	0.005471	1
0	0.7	0.012835	0.413577	0.000458	0.004088	0.003025	2
0	0.8	2.944615	9.217561	0.012458	0.249099	0.003237	3
0	4.6	0.407060	-2.034294	0.067984	1.359681	0.099460	4
0	4.6	0.784367	5.773530	0.054106	1.082119	0.050649	5

chrom.show([2, 12])

[<Figure size 640x480 with 1 Axes>,
 <Axes: xlabel='time', ylabel='signal (baseline corrected)'>]