feat: statistics tests

research/statistics/plots.py

@@ -1,6 +1,9 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
 import seaborn as sns
 
-from research.statistics.utils import LETTERS
+from research.statistics.utils import LETTERS, build_letter_frequencies
 
 
 def plot_transition_matrix(ax, df_probs, title=""):
@@ -12,4 +15,36 @@ def plot_transition_matrix(ax, df_probs, title=""):
         ax=ax
     )
     ax.set_title(title, fontsize=12)
-    return hm
+    return hm
+
+
+def plot_letter_frequencies(males, females, sort_values=False, title=None):
+    # Compute frequencies
+    L_m = build_letter_frequencies(males['name']).set_index("letter")["freq"]
+    L_f = build_letter_frequencies(females['name']).set_index("letter")["freq"]
+
+    # Combine into one DataFrame
+    df_plot = pd.DataFrame({"Male": L_m, "Female": L_f}).fillna(0).reset_index()
+    df_plot.to_csv(f"../assets/{title}_letter_frequencies.csv", index=False)
+
+    # Optional sorting
+    if sort_values:
+        df_plot = df_plot.sort_values("Male", ascending=False)
+
+    # Plot side-by-side bars
+    x = np.arange(len(df_plot))
+    w = 0.4
+    fig, ax = plt.subplots(figsize=(16, 6))
+    ax.bar(x - w/2, df_plot["Male"], width=w, label="Male", color="steelblue", alpha=0.8)
+    ax.bar(x + w/2, df_plot["Female"], width=w, label="Female", color="salmon", alpha=0.8)
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(df_plot["letter"])
+    ax.set_ylabel("Frequency")
+    ax.set_xlabel("Letter")
+    ax.set_title(f"{title} - Letter Frequencies")
+    ax.legend()
+    ax.grid(axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    plt.show()