#373 Scatterplot with multiple semantics using seaborn library

import numpy as np
import matplotlib.pyplot as plt
import os
from scipy.special import fresnel
#conda install -c anaconda seaborn
import seaborn as sns
sns.set_theme(style="whitegrid")
def main():
    # Load the example diamonds dataset
    diamonds = sns.load_dataset("diamonds")
    # Draw a scatter plot while assigning point colors and sizes to different
    # variables in the dataset
    f, ax = plt.subplots(figsize=(6.5, 6.5))
    sns.despine(f, left=True, bottom=True)
    clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
    sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="ch:r=-.2,d=.3_r",
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax)
    plt.savefig("ex373.png", dpi=200)
    plt.show()

if __name__ == '__main__':
    main()