Test chart for sentiment post.ipynb

charts.ipynb

import matplotlib.pyplot as plt
import numpy as np

# Simulate a tech adoption curve
x = np.linspace(0, 10, 500)
y = np.exp(-((x - 3) ** 2)) * 2 + np.exp(-((x - 7) ** 2)) * 1.5

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))

# Plot the curve
ax.plot(x, y, label="Public Sentiment", linewidth=2)

# Annotations
ax.annotate("Initial resistance", xy=(1, 0.1), xytext=(0.5, 0.5), arrowprops=dict(arrowstyle="->", lw=1.5, color="silver"), fontsize=12, ha="center", color="silver")
ax.annotate("We love this", xy=(2, 1), xytext=(1.0, 1.2),  arrowprops=dict(arrowstyle="->", lw=1.5, color="silver"), fontsize=12, ha="center", color="silver")
ax.annotate("'It's overrated'", xy=(3.7, 1.3), xytext=(4.5, 1.5), arrowprops=dict(arrowstyle="->", lw=1.5, color="silver"), fontsize=12, ha="center", color="silver")
ax.annotate("'The worst!'", xy=(4.8, 0.25), xytext=(5.0, 0.5), arrowprops=dict(arrowstyle="->", lw=1.5, color="silver"), fontsize=12, ha="center", color="silver")
ax.annotate("'It's now a\nSupabase alternative'", xy=(6.0, 0.7), xytext=(5.2, 1.0), arrowprops=dict(arrowstyle="->", lw=1.5), fontsize=12, ha="center")
ax.annotate("'It can run Next.JS on Google Cloud'", xy=(6.5, 1.25), xytext=(6, 1.7), arrowprops=dict(arrowstyle="->", lw=1.5), fontsize=12, ha="center")

# Titles and labels
ax.set_title("Technology Sentiment Cycles Over Time", fontsize=16)
ax.set_xlabel("Time / Maturity", fontsize=12)
ax.set_ylabel("Public Sentiment", fontsize=12)
ax.grid(True)

plt.tight_layout()
plt.show()