Publication Impact Analysis

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats

np.random.seed(444)

# Academic data
impact_factor = np.random.uniform(1, 15, 65)
citations = 3 + 7 * impact_factor + np.random.exponential(8, 65)

slope, intercept, r_value, p_value, std_err = stats.linregress(impact_factor, citations)
x_fit = np.linspace(0, 16, 100)
y_fit = slope * x_fit + intercept

# Prediction interval
n = len(impact_factor)
se = np.sqrt(np.sum((citations - (slope * impact_factor + intercept))**2) / (n - 2))
ci = 1.96 * se

fig, ax = plt.subplots(figsize=(10, 7), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

# Subtle grid
ax.yaxis.grid(True, color='#f0f0f0', linewidth=1, zorder=1)
ax.xaxis.grid(True, color='#f0f0f0', linewidth=1, zorder=1)

ax.fill_between(x_fit, y_fit - ci, y_fit + ci, color='#4927F5', alpha=0.1, linewidth=0, zorder=2)
ax.plot(x_fit, y_fit, color='#4927F5', linewidth=2.5, zorder=3)
ax.scatter(impact_factor, citations, c='#F5276C', s=65, alpha=0.8, edgecolors='white', linewidths=0.8, zorder=4)

# Stats box
stats_text = f'β = {slope:.2f} ± {std_err:.2f}\nR² = {r_value**2:.3f}\np < 0.001'
ax.text(0.97, 0.05, stats_text, transform=ax.transAxes, fontsize=10,
        color='#555555', ha='right', va='bottom', family='monospace',
        bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='#dddddd'))

for spine in ['top', 'right']:
    ax.spines[spine].set_visible(False)
for spine in ['bottom', 'left']:
    ax.spines[spine].set_color('#cccccc')
    ax.spines[spine].set_linewidth(0.8)

ax.set_xlabel('Journal Impact Factor', fontsize=12, color='#333333', fontweight='500', labelpad=10)
ax.set_ylabel('Mean Citations per Paper', fontsize=12, color='#333333', fontweight='500', labelpad=10)
ax.set_title('Academic Impact Metrics', fontsize=15, color='#1a1a1a', fontweight='bold', pad=20, loc='left')
ax.tick_params(colors='#666666', labelsize=10, length=0)

plt.tight_layout()
plt.show()