Basic Data Manipulation

This examples demonstrates how to load and manipulate chromatogram data using the Data2D and Chromatogram classes.

First, let’s import some packages:

from mocca2 import Chromatogram, example_data
import numpy as np
from matplotlib import pyplot as plt

The data can be loaded simply using the Chromatogram constructor.

PATH_TO_SAMPLE_CHROMATOGRAM = "src/mocca2/example_data/data/examples/chrom1.arw"
PATH_TO_BLANK_CHROMATOGRAM = "src/mocca2/example_data/data/examples/blank1.arw"

# Load raw data from file
# Including the blank is optional, but makes the processing easier
chromatogram = Chromatogram(
    sample=PATH_TO_SAMPLE_CHROMATOGRAM,
    blank=PATH_TO_BLANK_CHROMATOGRAM,
)

# Alternatively, you can use example data
chromatogram = example_data.example_1()

The Data2D class provides basic functions for cropping, averaging, etc. Let’s crop data to wavelengths between 220 - 400 nm.

# Crop wavelength between 220 and 400 nm
chromatogram.extract_wavelength(220, 400, inplace=True)

The Data2D makes the plotting of chromatograms very easy:

# Plot chromatogram (averaged over wavelenghts)
fig, ax = plt.subplots(figsize=(8, 5))
chromatogram.plot(ax)
plt.show()

# Plot 2D chromatogram as heatmap
fig, ax = plt.subplots(figsize=(8, 5))
chromatogram.plot_2d(ax)
plt.show()

Let’s take a closer look on the peak around 2.3 minutes. First, we extract the relevant region.

# Extract raegion with peak
peak_region = chromatogram.extract_time(2.2, 2.35)

Now, we could for example average the absorption spectrum over the peak.

# Average spectrum
# The chromatogram data are just numpy arrays, axes are [wavelength, time]
avg_spectrum = np.mean(peak_region.data, axis=1)
plt.subplots(figsize=(8, 5))
plt.plot(peak_region.wavelength, avg_spectrum)
plt.xlabel("Wavelength [nm]")
plt.ylabel("Average absorbance [mAU]")
plt.show()

For futher details see the reference for Data2D and Chromatogram.