import matplotlib.pyplot as plt

import numpy as np

miss_mass_true = np.diff(true_dist_of_total_bins)[[-2,-1]]*true_dist_of_total_prob[[-2,-1]]

miss_mass_false = np.diff(false_dist_of_total_bins)[[-1]]*false_dist_of_total_prob[[-1]]

import hist_sampler

x_ = hist_sampler.histogram_sampler(true_dist_of_total_prob[:-2], true_dist_of_total_bins[:-2], 800000)

y_ = hist_sampler.histogram_sampler(false_dist_of_total_prob[:-2], false_dist_of_total_bins[:-2], 100000)

import kern_density_est

if kde_mode == "scipy_kde": true_dist_of_total_prob_smooth, true_dist_of_total_bins_smooth = kern_density_est.kern_scipy_gaus(x_, 'g', np.linspace(true_dist_of_total_bins[0], true_dist_of_total_bins[-3] + 50000, 3000), bandwidth) # can change the kernel to an expenential kern_density_est.kern_sklearn_expon(x, color, bandwidth, kernel = "exponential", **kwargs)

elif kde_mode == "sklearn_kde": true_dist_of_total_prob_smooth, true_dist_of_total_bins_smooth = kern_density_est.kern_sklearn_expon(x_, 'g', np.linspace(true_dist_of_total_bins[0], true_dist_of_total_bins[-3]+ 50000, 3000), bandwidth, kernel_ = kernel)

from scipy.stats import kde

import bisect

insert_index_1 = bisect.bisect_left(true_dist_of_total_prob_smooth-true_dist_of_total_prob[0], 0.)

true_dist_of_total_prob_smooth, true_dist_of_total_bins_smooth = true_dist_of_total_prob_smooth[0: insert_index_1], true_dist_of_total_bins_smooth[0: insert_index_1]

false_dist_of_total_prob_smooth, false_dist_of_total_bins_smooth = kern_density_est.kern_scipy_gaus(y_, 'y', np.linspace(false_dist_of_total_bins[0], false_dist_of_total_bins[-2], 2000), bandwidth)

plt.clf()

prob_true_ = true_dist_of_total_prob_smooth*(1-np.sum(miss_mass_true))

prob_false_ = false_dist_of_total_prob_smooth*(1-np.sum(miss_mass_false))

mean_diff_true = np.diff(true_dist_of_total_prob_smooth)/2.

true_smoothed_mean_probabilities = true_dist_of_total_prob_smooth[:-1] + mean_diff_true

mean_diff_false = np.diff(false_dist_of_total_prob_smooth)/2.

false_smoothed_mean_probabilities = false_dist_of_total_prob_smooth[:-1] + mean_diff_false

miss_mass_true_ = [true_dist_of_total_prob[-1]*abs(true_dist_of_total_bins[-1] - true_dist_of_total_bins_smooth[-1])]

miss_mass_false_ = [false_dist_of_total_prob[-1]*abs(false_dist_of_total_bins[-1] - false_dist_of_total_bins_smooth[-1])]

prob_true_norm = true_smoothed_mean_probabilities/np.sum(true_smoothed_mean_probabilities*np.diff(true_dist_of_total_bins_smooth))*(1.-np.sum(miss_mass_true_))

prob_false_norm = false_smoothed_mean_probabilities/np.sum(false_smoothed_mean_probabilities*np.diff(false_dist_of_total_bins_smooth))*(1.-np.sum(miss_mass_false_))

prob_true = np.r_[prob_true_norm, true_dist_of_total_prob[-1]]

bins_true = np.r_[true_dist_of_total_bins_smooth, true_dist_of_total_bins[-1]]

prob_false = np.r_[prob_false_norm, false_dist_of_total_prob[-1]]

bins_false = np.r_[false_dist_of_total_bins_smooth, false_dist_of_total_bins[-1]]