tdc_gpx2/scripts/plottdc_dump.py

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


#parse the command line arguments
parser = argparse.ArgumentParser(description="Plot a histogram")
parser.add_argument("input_file",help="Path to the input data set with 7 columns")
args = parser.parse_args()

# Open the file and read in the data
with open(args.input_file, 'r') as f:
    lines = f.readlines()

# Extract the second column of data
data = []
for line in lines:
    values = line.strip().split()
    if len(values) >= 7 and float(values[5]) == 0:
        data.append(float(values[6]))

#determine histrange from data
hist_range = 10  * np.std(data)
hist_mean = np.mean(data)
hist_start = hist_mean - hist_range
hist_end = hist_mean +  hist_range

# Create the histogram with range
hist, bins, _ = plt.hist(data, bins=200, range=(hist_start,hist_end))

# Calculate the mean and RMS
bin_centers = 0.5 * (bins[:-1] + bins[1:])
mean = np.average(bin_centers, weights=hist)
rms = np.sqrt(np.average((bin_centers - mean)**2, weights=hist ))

# Set the x-axis label and title
plt.xlabel('time difference / seconds')
plt.title(' - ')

plt.annotate(f"Mean: {mean:.3g}\nRMS; {rms:.3g}",
             xy=(0.75, 0.85),
             xycoords='axes fraction',
             fontsize=12,
             bbox=dict(boxstyle="round,pad=0.3", edgecolor="gray", facecolor="lightgray"))

# Show the plot
plt.show()