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import json
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
infile = "output.jsonl"
date = "2024-03" # used in the plot
durations = []
with open(infile) as f:
for line in f:
data = json.loads(line)
l = data["left"]["finish"]
r = data["right"]["finish"]
v = data["timestamp"]
durations.append(v - max(l, r))
print(
f"Avg: {np.mean(durations)}, Median: {np.median(durations)}, Max: {np.max(durations)}"
)
# Define the new cutoff and number of bins
cutoff = 200.0 # New cutoff value
num_bins_inside_cutoff = 20 # Number of bins from 0 to cutoff
for i, n in enumerate(durations):
if n > cutoff:
durations[i] = cutoff + 0.5 * cutoff / num_bins_inside_cutoff
# Create bin edges from 0 to cutoff, with the specified number of bins
bin_edges = np.linspace(0, cutoff, num_bins_inside_cutoff + 1)
# Adjusting the overflow bin to end at 110
overflow_cap = (
cutoff + cutoff / num_bins_inside_cutoff
) # Adjust as needed based on distribution
bin_edges = np.append(bin_edges, overflow_cap)
# Create the plot with custom bins
sns.histplot(
durations, bins=bin_edges, kde=False
) # Turn off KDE for clearer bar visibility
plt.title(f'Distribution of "time to vote" {date}')
plt.xlabel("Duration (seconds)")
plt.ylabel("Frequency")
# Highlight the overflow bin
plt.axvline(x=cutoff, color="red", linestyle="--")
plt.text(
cutoff + 1, plt.ylim()[1] * 0.9, "Overflow", color="red", ha="left"
) # Adjust text alignment
# Customizing x-axis labels to hide the "110"
ax = plt.gca() # Get current axis
labels = [item.get_text() for item in ax.get_xticklabels()]
if "110" in labels:
labels[labels.index("110")] = "" # Replace "110" with an empty string
ax.set_xticklabels(labels)
# Ensure nothing is cut off in the plot
plt.tight_layout()
# Save the plot to a file with high resolution
plt.savefig(f"duration_distribution_time_to_vote_{date}.png", dpi=300)
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