Loading multiple data files of the same format type

Processing data found across multiple files and performing analysis on them. This example will be filled in upon the creation of a new app note, but for now, it exists as a skeleton for theoretical demonstration purposes.

Skutils loaders can only open one file per “loader object”, meaning that you need to “instantiate” a new loader for every file you open, here’s an example

import skutils
import matplotlib.pyplot as plt
import numpy as np

# There will be a planned capture of real data for
DATA_FILES = []

Iterate over every file

# A list of dictionaries being built for per-channel histogramming
event_pulseheights = [{}, {}]
event_count = 0
# Iterate over the files
for file in DATA_FILES:
    loader = skutils.EventCSVLoader(file)
    # Iterate over each event in the loader
    for event in loader:
        event_count += 1
        waveform = event.wavedata()
        # Perform the per-channel histogram creation
        for i, data in enumerate(event.channel_data):
            if data.has_wave:
                wave = data.wave
                assert wave is not None
                bucket = int(np.max(wave))
                if bucket in event_pulseheights[i]:
                    event_pulseheights[i][bucket] += 1
                else:
                    event_pulseheights[i][bucket] = 1

Build the histogram

histogram_list = []
for channel in event_pulseheights:
    build_histo = [0 for _ in range(2**15)]
    for key in channel:
        build_histo[key] = channel[key]
    histogram_list.append(build_histo)

Plot all histogram elements

for i, item in enumerate(histogram_list):
    plt.plot(item, label=f"Channel data {i}")

plt.ylabel("Histogram quantity")
plt.xlabel("Value counted")
plt.show()