simple plotting script for presentation

scripts/plot_gaussian.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+# Simulate normally distributed data
+data = np.random.normal(0, 1, 10000)
+
+# Set thresholds at 10% from the bottom and top
+lower_threshold = np.percentile(data, 10)
+upper_threshold = np.percentile(data, 90)
+
+# Create histogram without axis labels, ticks, title, or grid lines
+plt.figure(figsize=(10, 6))
+n, bins, patches = plt.hist(data, bins=50, edgecolor="black", alpha=0.7)
+
+# Color the bottom 10% in red and top 10% in green
+for i in range(len(patches)):
+    if bins[i] < lower_threshold:
+        patches[i].set_facecolor("red")
+    elif bins[i] > upper_threshold:
+        patches[i].set_facecolor("green")
+    else:
+        patches[i].set_facecolor("blue")
+
+# Remove the axes, ticks, gridlines, and title
+plt.gca().set_xticks([])
+plt.gca().set_yticks([])
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+# plt.gca().spines["left"].set_visible(False)
+# plt.gca().spines["bottom"].set_visible(False)
+plt.grid(False)
+plt.title("")
+
+# Show axes, x is "Impact", y is "Labs"
+plt.xlabel('"Impact"')
+plt.ylabel("Number of Labs")
+
+# Show the simplified plot
+plt.show()