jitter calc

jitter_calc.py

import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm

data = []
num_refreshed_data = []

ITERATIONS = 5_000
N = 10_000
T = 100
window_size = T
frac_T_values = np.arange(0.5, 5, 0.5)  # noqa: N816

for frac_T in frac_T_values:
    dT = lambda: np.random.randint(0, int(T * frac_T) + 1, size=N)  # noqa: E731, N816

    num_refreshed_over_time = np.zeros(ITERATIONS, dtype=float)
    nodes = (5 * dT()) + T
    for i in tqdm(range(ITERATIONS), desc=f"Simulating frac_T={frac_T:.2f}", unit="iteration"):
        nodes -= 1
        refresh_mask = nodes <= 0
        num_refreshed = np.sum(refresh_mask)
        new_nodes = dT() + T
        nodes[refresh_mask] = new_nodes[refresh_mask]
        num_refreshed_over_time[i] = num_refreshed

    # Calculate the sum of the nodes refreshed over a window of time
    sliding_windows = np.lib.stride_tricks.sliding_window_view(num_refreshed_over_time, window_size)
    num_refreshed_over_time_windowed = np.sum(sliding_windows, axis=1) / N
    x = np.arange(0, num_refreshed_over_time_windowed.shape[0])

    data.append((frac_T, num_refreshed_over_time_windowed.mean()))
    num_refreshed_data.append((frac_T, x, num_refreshed_over_time_windowed))

# Create a single figure with two subplots
fig, axs = plt.subplots(2, 1, figsize=(12, 14))

# Plot num_refreshed_over_time per frac_T in the first subplot
for frac_T, x, num_refreshed in num_refreshed_data:
    axs[0].plot(x, num_refreshed, label=f"frac_T={frac_T:.2f}")

axs[0].set_xlabel("Time (Iterations)")
axs[0].set_ylabel(f"Fraction of Refreshed Nodes (Window Size: {window_size})")
axs[0].set_title("Fraction of Refreshed Nodes Over Time for Different frac_T")
axs[0].legend()
axs[0].grid(True)

# Plot the data variable in the second subplot
fracs = [frac_T for frac_T, _ in data]
means = [mean_refreshed for _, mean_refreshed in data]
rat_func = [(2 / (2 + frac_T)) for frac_T in fracs]

axs[1].plot(fracs, means, "bo-", label="Mean Fraction of Refreshed Nodes")
axs[1].plot(fracs, rat_func, "r--", label="2 / (2 + frac_T)")
axs[1].set_xlabel("frac_T")
axs[1].set_ylabel("Mean Fraction of Refreshed Nodes")
axs[1].set_title("Mean Fraction of Refreshed Nodes vs. frac_T")
axs[1].legend()
axs[1].grid(True)

plt.tight_layout()
plt.savefig("data/combined_plot.png")
plt.close()