import matplotlib.pyplot as plt
import statsmodels.api as sm
bw_ml_x = sm.nonparametric.KDEMultivariate(data=merged_variants['ccf_x'], var_type='c', bw='cv_ml')
bw_ml_y = sm.nonparametric.KDEMultivariate(data=merged_variants['ccf_y'], var_type='c', bw='cv_ml')

g = sns.jointplot(x='ccf_x', y='ccf_y', data=merged_variants, kind="kde", stat_func=None, bw=[bw_ml_x.bw, bw_ml_y.bw])
g.set_axis_labels("Presentation (CCF %)", "Relapse (CCF %)")

g.plot_joint(plt.scatter, c="w")
g.ax_joint.collections[0].set_alpha(0)

sns.plt.show()

fhat <- kde(x=as.data.frame(merged_variants[1], merged_variants[2]), H=H)
plot(fhat, display="filled.contour2", cont=seq(10,90,by=10))