import matplotlib.pyplot as plt
import numpy as np
from scipy import stats

from statsmodels.distributions.mixture_rvs import mixture_rvs
from statsmodels.nonparametric import bandwidths
from statsmodels.nonparametric.kde import kdensityfft

np.random.seed(12345)
obs_dist = mixture_rvs(
    [0.25, 0.75],
    size=10000,
    dist=[stats.norm, stats.norm],
    kwargs=(dict(loc=-1, scale=0.5), dict(loc=1, scale=0.5)),
)
# .. obs_dist = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta],
# ..            kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5))))


f_hat, grid, bw = kdensityfft(obs_dist, kernel="gauss", bw="scott")

# Check the plot

plt.figure()
plt.hist(obs_dist, bins=50, normed=True, color="red")
plt.plot(grid, f_hat, lw=2, color="black")
plt.show()

# do some timings
# get bw first because they're not streamlined
bw = bandwidths.bw_scott(obs_dist)

# .. timeit kdensity(obs_dist, kernel="gauss", bw=bw, gridsize=2**10)
# .. timeit kdensityfft(obs_dist, kernel="gauss", bw=bw, gridsize=2**10)
