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