Plot random projectile impact angles

By David Minton and Dennise Valadez

This example demonstrates how to generate and visualize random impact angles for projectiles using cratermaker.

import matplotlib.pyplot as plt
import numpy as np

from cratermaker import Projectile

# Sample data generation
proj = Projectile(mean_velocity=5000, density=3000)
size = 10000
angles = np.array([proj.new_projectile().angle for _ in range(size)])

# Number of bins
bins = 50
observed_counts, bins_ang = np.histogram(angles, bins=bins, range=(0.0, 90.0))

# Expected distribution
uniform_dist = np.linspace(0, 1, size)
transformed_angles = np.rad2deg(np.arcsin(np.sqrt(uniform_dist)))
expected_counts, _ = np.histogram(transformed_angles, bins=bins, range=(0.0, 90.0))

# Plotting distribution
fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(
    bins_ang[:-1],
    observed_counts,
    width=np.diff(bins_ang),
    align="edge",
    label="Observed",
    alpha=0.5,
)
ax.plot(bins_ang[:-1], expected_counts, label="Expected", color="red")
ax.set_xlabel("Impact Angle (deg)")
ax.set_ylabel("Count")
ax.legend()
ax.set_title("Impact Angle Distribution")
plt.tight_layout()
plt.show()