import pandas as pd import matplotlib.pyplot as plt
sport_color = { "Rowing": "green", "Track": "blue", "Cycling": "green", "Soccer": "blue", "Canoeing": "green", "Roller-skating": "green", "Swimming": "blue", "Volleyball": "blue", "Pentathlon": "blue", "Tennis": "blue", "Fencing": "gray", "Alpine skiing": "green", "Cross-country skiing": "green", "Equestrianism": "red", "Team handball": "blue", "Yachting": "red", "Roller hockey": "blue", "Water polo": "blue", "Tae kwon do": "blue", "Wrestling and judo": "red", "Bobsledding": "green", "Boxing": "blue", "Diving": "red", "Field weight events": "red", "Weightlifting": "red", }
sports = ["Rowing","Track","Cycling","Soccer","Canoeing","Roller-skating", "Swimming","Volleyball","Pentathlon","Tennis","Fencing","Alpine skiing", "Cross-country skiing","Equestrianism","Team handball","Yachting", "Roller hockey","Water polo","Tae kwon do","Wrestling and judo", "Bobsledding","Boxing","Diving","Field weight events","Weightlifting"]
lved_mean = [56.0,51.4,54.8,54.9,54.5,49.0,53.0,53.7,52.4,50.0,51.7,52.0, 54.5,50.4,51.8,51.2,53.4,54.7,50.6,52.6,55.1,52.5,49.6,55.5,53.2]
lved_sd = [3,4,5,4,3,4,4,3,4,3,5,3,4,3,4,4,3,3,4,5,2,3,3,4,3]
wt_mean = [11.3,9.8,10.4,9.9,10.5,9.0,9.3,9.4,9.2,9.1,9.2,8.9,9.6,9.0, 8.5,9.0,9.7,10.7,8.7,10.2,9.6,9.8,8.7,10.0,10.4]
wt_sd = [1.3,1.2,1.1,0.7,1.5,1.0,1.2,1.0,0.9,1.0,1.3,0.9,0.8,0.8,0.9,0.8, 0.9,0.6,1.2,0.9,0.5,1.0,1.1,0.5,0.7]
abbrev = { "Cross-country skiing": "XC Ski", "Wrestling and judo": "Wrest/Judo", "Field weight events": "Throws", "Roller-skating": "Roller", "Team handball": "Handball", "Water polo": "WaterPolo", "Alpine skiing": "Alpine", "Tae kwon do": "TKD", "Roller hockey": "R.Hockey", "Equestrianism": "Equestrian", }
df = pd.DataFrame({ "Sport": sports, "LVED_mean": lved_mean, "LVED_sd": lved_sd, "WallThickness_mean": wt_mean, "WallThickness_sd": wt_sd, }) df["color"] = df["Sport"].map(sport_color) df["label"] = df["Sport"].apply(lambda s: abbrev.get(s, s))
plt.figure(figsize=(11, 8))
for i, row in df.iterrows(): plt.errorbar(row["LVED_mean"], row["WallThickness_mean"], xerr=row["LVED_sd"], yerr=row["WallThickness_sd"], fmt='o', capsize=2, elinewidth=0.6, markersize=6, color=row["color"], ecolor=row["color"], alpha=0.8) plt.annotate(row["label"], (row["LVED_mean"], row["WallThickness_mean"]), xytext=(5,5), textcoords="offset points", fontsize=7.5, color=row["color"])
blue_dot = plt.Line2D([0],[0], marker='o', color='w', markerfacecolor='blue', markersize=8, label='High Dynamic') red_dot = plt.Line2D([0],[0], marker='o', color='w', markerfacecolor='red', markersize=8, label='High Static') green_dot = plt.Line2D([0],[0], marker='o', color='w', markerfacecolor='green', markersize=8, label='High Dynamic + Static') gray_dot = plt.Line2D([0],[0], marker='o', color='w', markerfacecolor='gray', markersize=8, label='Low Both')
plt.xlabel("LVED (mm)") plt.ylabel("Wall Thickness (mm)") plt.title("Athlete Cardiac Remodeling by Sport\n(Colour = Mitchell Classification)") plt.legend(handles=[blue_dot, red_dot, green_dot, gray_dot], fontsize=9, loc="upper left") plt.grid(True) plt.tight_layout() plt.show()