Add skew of chi.

I'm not entirely convinced this is helpful as we do not make use of it in the work.
drvinceknight · Jan 25, 2019 · dae8847 · dae8847
1 parent b929a88
commit dae8847
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diff --git a/assets/img/sserror_in_stewart_plotkin/main.pdf b/assets/img/sserror_in_stewart_plotkin/main.pdf
diff --git a/assets/img/sserror_in_stewart_plotkin/main.py b/assets/img/sserror_in_stewart_plotkin/main.py
@@ -35,14 +35,10 @@ def main():
             sorted_indices = summary_df.sort_values(column, ascending=False).index
             data = [df[df["Player index"] == player_index][var] for player_index in sorted_indices]
 
-            if var == "chi":
-                ax.axhline(1, linestyle="--", color="black")
-                ax.scatter(X, list(map(np.mean, data)), color="black")
-            else:
-                ax.scatter(X, list(map(np.mean, data)), color="black", label="Mean")
-                ax.scatter(X, list(map(skew, data)), color="black", marker="+", label="Skew")
-                ax.axhline(0, color="black", linestyle="--")
-                ax.legend()
+            ax.scatter(X, list(map(np.mean, data)), color="black", label="Mean")
+            ax.scatter(X, list(map(skew, data)), color="black", marker="+", label="Skew")
+            ax.axhline(0, color="black", linestyle="--")
+            ax.legend()
 
             sorted_players = [players[i].name for i in sorted_indices]
             ax.set_xlabel("Strategies")