// hpstat: High-performance statistics implementations
// Copyright © 2023  Lee Yingtong Li (RunasSudo)
// 
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
// 
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
// 
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <https://www.gnu.org/licenses/>.

const Z_97_5: f64 = 1.959964;  // This is the limit of resolution for an f64
const CHI2_1DF_95: f64 = 3.8414588;

use std::fs::File;
use std::io::{self, BufReader};

use clap::{Args, ValueEnum};
use indicatif::{ParallelProgressIterator, ProgressBar, ProgressDrawTarget, ProgressStyle};
use nalgebra::{DMatrix, DVector, Matrix2xX};
use prettytable::{Table, format, row};
use rayon::prelude::*;
use serde::{Serialize, Deserialize};

use crate::csv::read_csv;
use crate::pava::monotonic_regression_pava;
use crate::term::UnconditionalTermLike;

#[derive(Args)]
pub struct TurnbullArgs {
	/// Path to CSV input file containing the observations
	#[arg()]
	input: String,
	
	/// Output format
	#[arg(long, value_enum, default_value="text")]
	output: OutputFormat,
	
	/// Maximum number of iterations to attempt
	#[arg(long, default_value="1000")]
	max_iterations: u32,
	
	/// Terminate algorithm when the absolute change in log-likelihood is less than this tolerance
	#[arg(long, default_value="0.01")]
	ll_tolerance: f64,
	
	/// Method for computing standard error or survival probabilities
	#[arg(long, value_enum, default_value="oim")]
	se_method: SEMethod,
	
	/// Threshold for dropping failure probability in --se-method oim-drop-zeros
	#[arg(long, default_value="0.0001")]
	zero_tolerance: f64,
	
	/// Desired precision of confidence limits in --se-method likelihood-ratio
	#[arg(long, default_value="0.01")]
	ci_precision: f64,
}

#[derive(ValueEnum, Clone)]
enum OutputFormat {
	Text,
	Json
}

#[derive(ValueEnum, Clone)]
pub enum SEMethod {
	None,
	OIM,
	OIMDropZeros,
	LikelihoodRatio,
}

pub fn main(args: TurnbullArgs) {
	// Read data
	let data_times = read_data(&args.input);
	
	// Fit regression
	let progress_bar = ProgressBar::with_draw_target(Some(0), ProgressDrawTarget::term_like(Box::new(UnconditionalTermLike::stderr())));
	let result = fit_turnbull(data_times, progress_bar, args.max_iterations, args.ll_tolerance, args.se_method, args.zero_tolerance, args.ci_precision);
	
	// Display output
	match args.output {
		OutputFormat::Text => {
			println!();
			println!();
			println!("LL = {:.5}", result.ll_model);
			
			let mut summary = Table::new();
			let format = format::FormatBuilder::new()
				.separators(&[format::LinePosition::Top, format::LinePosition::Title, format::LinePosition::Bottom], format::LineSeparator::new('-', '+', '+', '+'))
				.padding(2, 2)
				.build();
			summary.set_format(format);
			
			if let Some(survival_prob_se) = &result.survival_prob_se {
				// Standard errors available
				summary.set_titles(row!["Time", c->"Surv. Prob.", c->"Std Err.", H2c->"(95% CI)"]);
				summary.add_row(row![r->"0.000", r->"1.00000", "", "", ""]);
				for (i, prob) in result.survival_prob.iter().enumerate() {
					summary.add_row(row![
						r->format!("{:.3}", result.failure_intervals[i].1),
						r->format!("{:.5}", prob),
						r->format!("{:.5}", survival_prob_se[i]),
						r->format!("({:.5},", prob - Z_97_5 * survival_prob_se[i]),
						format!("{:.5})", prob + Z_97_5 * survival_prob_se[i]),
					]);
				}
				summary.add_row(row![r->format!("{:.3}", result.failure_intervals.last().unwrap().1), r->"0.00000", "", "", ""]);
			} else if let Some(survival_prob_ci) = &result.survival_prob_ci {
				// No standard errors, just print CIs
				summary.set_titles(row!["Time", c->"Surv. Prob.", H2c->"(95% CI)"]);
				summary.add_row(row![r->"0.000", r->"1.00000", "", ""]);
				for (i, prob) in result.survival_prob.iter().enumerate() {
					summary.add_row(row![
						r->format!("{:.3}", result.failure_intervals[i].1),
						r->format!("{:.5}", prob),
						r->format!("({:.5},", survival_prob_ci[i].0),
						format!("{:.5})", survival_prob_ci[i].1),
					]);
				}
				summary.add_row(row![r->format!("{:.3}", result.failure_intervals.last().unwrap().1), r->"0.00000", "", ""]);
			} else {
				// No standard errors or CIs
				summary.set_titles(row!["Time", c->"Surv. Prob."]);
				summary.add_row(row![r->"0.000", r->"1.00000"]);
				for (i, prob) in result.survival_prob.iter().enumerate() {
					summary.add_row(row![
						r->format!("{:.3}", result.failure_intervals[i].1),
						r->format!("{:.5}", prob),
					]);
				}
				summary.add_row(row![r->format!("{:.3}", result.failure_intervals.last().unwrap().1), r->"0.00000"]);
			}
			
			summary.printstd();
		}
		OutputFormat::Json => {
			println!("{}", serde_json::to_string(&result).unwrap());
		}
	}
}

pub fn read_data(path: &str) -> Matrix2xX<f64> {
	// Read CSV into memory
	let (_headers, records) = match path {
		"-" => read_csv(io::stdin().lock()),
		_ => read_csv(BufReader::new(File::open(path).expect("IO error")))
	};
	
	// Read data into matrices
	
	// Represent data_times as 2xX rather than Xx2 matrix to allow par_column_iter in code_times_as_indexes (no par_row_iter)
	// Serendipitously, from_vec fills column-by-column
	let data_times = Matrix2xX::from_vec(records);
	
	// TODO: Fail on left time > right time
	// TODO: Fail on left time < 0
	
	return data_times;
}

struct TurnbullData {
	data_time_interval_indexes: Vec<(usize, usize)>,
	
	// Cached intermediate values
	intervals: Vec<(f64, f64)>,
}

impl TurnbullData {
	fn num_obs(&self) -> usize {
		return self.data_time_interval_indexes.len();
	}
	
	fn num_intervals(&self) -> usize {
		return self.intervals.len();
	}
}

/// Constrains the survival probability at a particular time s[time_index] == survival_prob
struct Constraint {
	time_index: usize,
	survival_prob: f64,
}

pub fn fit_turnbull(data_times: Matrix2xX<f64>, progress_bar: ProgressBar, max_iterations: u32, ll_tolerance: f64, se_method: SEMethod, zero_tolerance: f64, ci_precision: f64) -> TurnbullResult {
	// ----------------------
	// Prepare for regression
	
	// Get Turnbull intervals
	let intervals = get_turnbull_intervals(&data_times);
	
	// Recode times as indexes
	let data_time_interval_indexes = code_times_as_indexes(&data_times, &intervals);
	
	// Initialise p
	// Faster to repeatedly index Vec than DVector, and we don't do any matrix arithmetic, so represent this as Vec
	let p = vec![1.0 / intervals.len() as f64; intervals.len()];
	
	let data = TurnbullData {
		data_time_interval_indexes: data_time_interval_indexes,
		intervals: intervals,
	};
	
	// ------------------------------------------
	// Apply iterative algorithm to fit estimator
	
	progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} {msg}").unwrap());
	progress_bar.set_length(u64::MAX);
	progress_bar.reset();
	progress_bar.println("Running EM-ICM algorithm to fit Turnbull estimator");
	
	let (p, ll) = fit_turnbull_estimator(&data, progress_bar.clone(), max_iterations, ll_tolerance, p, None);
	
	// Get survival probabilities (1 - cumulative failure probability), excluding at t=0 (prob=1) and t=inf (prob=0)
	let mut survival_prob: Vec<f64> = Vec::with_capacity(data.num_intervals() - 1);
	let mut acc = 1.0;
	for j in 0..(data.num_intervals() - 1) {
		acc -= p[j];
		survival_prob.push(acc);
	}
	
	// --------------------------------------------------
	// Compute standard errors for survival probabilities
	
	let mut survival_prob_se = None;
	let mut survival_prob_ci = None;
	
	match se_method {
		SEMethod::None => {}
		SEMethod::OIM => {
			survival_prob_se = Some(survival_prob_oim_se(&data, &p, zero_tolerance, false));
		}
		SEMethod::OIMDropZeros => {
			survival_prob_se = Some(survival_prob_oim_se(&data, &p, zero_tolerance, true));
		}
		SEMethod::LikelihoodRatio => {
			let s = p_to_s(&p);
			let oim_se = survival_prob_oim_se(&data, &p, zero_tolerance, true);
			
			progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} CI {pos}/{len}").unwrap());
			progress_bar.set_length(data.num_intervals() as u64 - 1);
			progress_bar.reset();
			progress_bar.println("Computing confidence intervals by likelihood ratio test");
			
			let confidence_intervals = (1..data.num_intervals()).into_par_iter()
				.map(|j| survival_prob_likelihood_ratio_ci(&data, ProgressBar::hidden(), max_iterations, ll_tolerance, ci_precision, &p, ll, &s, &oim_se, j))
				.progress_with(progress_bar.clone())
				.collect();
			
			survival_prob_ci = Some(confidence_intervals);
		}
	}
	
	return TurnbullResult {
		failure_intervals: data.intervals,
		failure_prob: p,
		survival_prob: survival_prob,
		survival_prob_se: survival_prob_se,
		survival_prob_ci: survival_prob_ci,
		ll_model: ll,
	};
}

fn get_turnbull_intervals(data_times: &Matrix2xX<f64>) -> Vec<(f64, f64)> {
	let mut all_time_points: Vec<(f64, bool)> = Vec::new();  // Vec of (time, is_left)
	all_time_points.extend(data_times.row(1).iter().map(|t| (*t, false)));  // So we have right bounds before left bounds when sorted - ensures correct behaviour since intervals are left-open
	all_time_points.extend(data_times.row(0).iter().map(|t| (*t, true)));
	all_time_points.dedup();
	all_time_points.sort_by(|(t1, _), (t2, _)| t1.partial_cmp(t2).unwrap());
	
	let mut intervals: Vec<(f64, f64)> = Vec::new();
	for i in 1..all_time_points.len() {
		if all_time_points[i - 1].1 == true && all_time_points[i].1 == false {
			intervals.push((all_time_points[i - 1].0, all_time_points[i].0));
		}
	}
	
	return intervals;
}

fn code_times_as_indexes(data_times: &Matrix2xX<f64>, intervals: &Vec<(f64, f64)>) -> Vec<(usize, usize)> {
	return data_times.par_column_iter().map(|t| {
		let tleft = t[0];
		let tright = t[1];
		
		// Left index is first interval >= observation left bound
		let left_index = intervals.iter().enumerate().find(|(_i, (ileft, _))| *ileft >= tleft).unwrap().0;
		
		// Right index is last interval <= observation right bound
		let right_index = intervals.iter().enumerate().rev().find(|(_i, (_, iright))| *iright <= tright).unwrap().0;
		
		(left_index, right_index)
	}).collect();
}

fn fit_turnbull_estimator(data: &TurnbullData, progress_bar: ProgressBar, max_iterations: u32, ll_tolerance: f64, mut p: Vec<f64>, constraint: Option<Constraint>) -> (Vec<f64>, f64) {
	// Pre-compute S, the survival probability at the start of each interval
	let mut s = p_to_s(&p);
	
	// Get likelihood for each observation
	let likelihood_obs = get_likelihood_obs(data, &s);
	let mut ll_model: f64 = likelihood_obs.iter().map(|l| l.ln()).sum();
	
	let mut iteration = 1;
	loop {
		// -------
		// EM step
		
		let p_after_em = do_em_step(data, &p, &s, &constraint);
		let s_after_em = p_to_s(&p_after_em);
		
		let likelihood_obs_after_em = get_likelihood_obs(data, &s_after_em);
		let ll_model_after_em: f64 = likelihood_obs_after_em.iter().map(|l| l.ln()).sum();
		
		p = p_after_em;
		s = s_after_em;
		
		// --------
		// ICM step
		
		let (p_new, s_new, ll_model_new) = do_icm_step(data, &p, &s, ll_tolerance, &constraint, ll_model_after_em);
		
		let ll_change = ll_model_new - ll_model;
		let converged = ll_change <= ll_tolerance;
		
		p = p_new;
		s = s_new;
		ll_model = ll_model_new;
		
		// Estimate progress bar according to either the order of magnitude of the ll_change relative to tolerance, or iteration/max_iterations
		let progress2 = (iteration as f64 / max_iterations as f64 * u64::MAX as f64) as u64;
		let progress3 = ((-ll_change.log10()).max(0.0) / -ll_tolerance.log10() * u64::MAX as f64) as u64;
		
		// Update progress bar
		progress_bar.set_position(progress_bar.position().max(progress3.max(progress2)));
		progress_bar.set_message(format!("Iteration {} (LL = {:.4}, ΔLL = {:.4})", iteration + 1, ll_model, ll_change));
		
		if converged {
			progress_bar.println(format!("Converged in {} iterations", iteration));
			break;
		}
		
		iteration += 1;
		if iteration > max_iterations {
			panic!("Exceeded --max-iterations");
		}
	}
	
	return (p, ll_model);
}

fn p_to_s(p: &Vec<f64>) -> Vec<f64> {
	let mut s = Vec::with_capacity(p.len() + 1);  // Survival probabilities
	let mut survival = 1.0;
	s.push(1.0);
	for p_j in p.iter() {
		survival -= p_j;
		s.push(survival);
	}
	return s;
}

fn s_to_lambda(s: &Vec<f64>) -> Vec<f64> {
	// S = 1 means Λ = -inf and S = 0 means Λ = inf so skip these
	let mut lambda = Vec::with_capacity(s.len() - 2);  // Cumulative hazard
	for s_j in &s[1..(s.len() - 1)] {
		lambda.push((-s_j.ln()).ln());
	}
	return lambda;
}

fn get_likelihood_obs(data: &TurnbullData, s: &Vec<f64>) -> Vec<f64> {
	return data.data_time_interval_indexes
		.par_iter()
		.map(|(idx_left, idx_right)| s[*idx_left] - s[*idx_right + 1])
		.collect();  // TODO: Return iterator directly
}

fn do_em_step(data: &TurnbullData, p: &Vec<f64>, s: &Vec<f64>, constraint: &Option<Constraint>) -> Vec<f64> {
	// Compute contributions to m
	let mut m_contrib = vec![0.0; data.num_intervals()];
	for (idx_left, idx_right) in data.data_time_interval_indexes.iter() {
		let contrib = 1.0 / (s[*idx_left] - s[*idx_right + 1]);
		
		// Adds to m for the first interval in the observation
		m_contrib[*idx_left] += contrib;
		
		// Subtracts from m for the first interval beyond the observation
		if *idx_right + 1 < data.num_intervals() {
			m_contrib[*idx_right + 1] -= contrib;
		}
	}
	
	// Compute m
	let mut m = Vec::with_capacity(data.num_intervals());
	let mut m_last = 0.0;
	for m_contrib_j in m_contrib {
		let m_next = m_last + m_contrib_j / (data.num_obs() as f64);
		m.push(m_next);
		m_last = m_next;
	}
	
	// Update p
	// p := p * m
	let mut p_new: Vec<f64> = p.par_iter().zip(m.into_par_iter()).map(|(p_j, m_j)| p_j * m_j).collect();
	
	// Constrain if required
	if let Some(c) = &constraint {
		let cur_fail_prob: f64 = p_new[0..c.time_index].iter().copied().sum();
		// Not sure why borrow checker thinks there is an unused borrow here...
		let _ = &mut p_new[0..c.time_index].iter_mut().for_each(|x| *x *= (1.0 - c.survival_prob) / cur_fail_prob);  // Desired failure probability over current failure probability
		let _ = &mut p_new[c.time_index..].iter_mut().for_each(|x| *x *= c.survival_prob / (1.0 - cur_fail_prob));
	}
	
	return p_new;
}

fn do_icm_step(data: &TurnbullData, p: &Vec<f64>, s: &Vec<f64>, ll_tolerance: f64, constraint: &Option<Constraint>, ll_model: f64) -> (Vec<f64>, Vec<f64>, f64) {
	// Compute Λ, the cumulative hazard
	// Since Λ = -inf when survival is 1, and Λ = inf when survival is 0, these are omitted
	// The entry at lambda[j] corresponds to the survival immediately before time point j + 1
	let lambda = s_to_lambda(&s);
	
	// Compute gradient and diagonal of Hessian
	let mut gradient = vec![0.0; data.num_intervals() - 1];
	let mut hessdiag = vec![0.0; data.num_intervals() - 1];
	for (idx_left, idx_right) in data.data_time_interval_indexes.iter() {
		let denom = s[*idx_left] - s[*idx_right + 1];
		
		// Add to gradient[j] when j + 1 == idx_right + 1
		// Add to hessdiag[j] when j + 1 == idx_right + 1
		if *idx_right < gradient.len() {
			let j = *idx_right;
			gradient[j] += (-lambda[j].exp() + lambda[j]).exp() / denom;
			
			let a = ((lambda[j] - lambda[j].exp()).exp() * (1.0 - lambda[j].exp())) / denom;
			let b = (2.0 * lambda[j] - 2.0 * lambda[j].exp()).exp() / denom.powi(2);
			hessdiag[j] += a - b;
		}
		
		// Subtract from gradient[j] when j + 1 == idx_left
		// Add to hessdiag[j] when j + 1 == idx_left
		if *idx_left > 0 {
			let j = *idx_left - 1;
			gradient[j] -= (-lambda[j].exp() + lambda[j]).exp() / denom;
			
			let a = ((lambda[j] - lambda[j].exp()).exp() * (1.0 - lambda[j].exp())) / denom;
			let b = (2.0 * lambda[j] - 2.0 * lambda[j].exp()).exp() / denom.powi(2);
			hessdiag[j] += -a - b;
		}
	}
	
	// Description in Anderson-Bergman (2017) is slightly misleading
	// Since we are maximising the likelihood, the second derivatives will be negative
	// And we will move in the direction of the gradient
	// So there are a few more negative signs here than suggested
	
	let weights = -DVector::from_vec(hessdiag.clone()) / 2.0;
	let gradient_over_hessdiag = DVector::from_vec(gradient.par_iter().zip(hessdiag.par_iter()).map(|(g, h)| g / h).collect());
	
	let mut s_new;
	let mut p_new;
	let mut ll_model_new: f64;
	
	// Take as large a step as possible while the log-likelihood increases
	let mut step_size_exponent: i32 = 0;
	loop {
		let step_size = 0.5_f64.powi(step_size_exponent);
		let lambda_target = -gradient_over_hessdiag.clone() * step_size + DVector::from_vec(lambda.clone());
		
		let lambda_new = monotonic_regression_pava(lambda_target, weights.clone());
		
		// Convert Λ to S to p
		s_new = Vec::with_capacity(data.num_intervals() + 1);
		p_new = Vec::with_capacity(data.num_intervals());
		
		let mut survival = 1.0;
		s_new.push(1.0);
		for lambda_j in lambda_new.iter() {
			let next_survival = (-lambda_j.exp()).exp();
			s_new.push(next_survival);
			p_new.push(survival - next_survival);
			survival = next_survival;
		}
		s_new.push(0.0);
		p_new.push(survival);
		
		let likelihood_obs_new = get_likelihood_obs(data, &s_new);
		ll_model_new = likelihood_obs_new.iter().map(|l| l.ln()).sum();
		
		// Constrain if required
		if let Some(c) = constraint {
			let cur_survival_prob = s_new[c.time_index];
			let _ = &mut p_new[0..c.time_index].iter_mut().for_each(|x| *x *= (1.0 - c.survival_prob) / (1.0 - cur_survival_prob));  // Desired failure probability over current failure probability
			let _ = &mut p_new[c.time_index..].iter_mut().for_each(|x| *x *= c.survival_prob / cur_survival_prob);
			
			s_new = p_to_s(&p_new);
			let likelihood_obs_new = get_likelihood_obs(data, &s_new);
			ll_model_new = likelihood_obs_new.iter().map(|l| l.ln()).sum();
		}
		
		if ll_model_new > ll_model {
			return (p_new, s_new, ll_model_new);
		}
		
		if ll_model - ll_model_new < ll_tolerance {
			// LL decreased but by less than ll_tolerance
			// This might happen because the EM algorithm already obtained the exact solution
			return (p.clone(), s.clone(), ll_model);
		}
		
		step_size_exponent += 1;
		
		if step_size_exponent > 10 {
			// If ICM does not increase log-likelihood, then simply return the original estimate and retry EM again
			// If neither increases the log-likelihood, then we will have have converged within fit_turnbull_estimator
			return (p.clone(), s.clone(), ll_model);
		}
	}
}

fn survival_prob_oim_se(data: &TurnbullData, p: &Vec<f64>, zero_tolerance: f64, drop_zeros: bool) -> Vec<f64> {
	let hessian = compute_hessian(&data, &p);
	
	if drop_zeros {
		// Drop rows/columns of Hessian corresponding to intervals with zero failure probability
		let nonzero_intervals: Vec<usize> = (0..(data.num_intervals() - 1)).filter(|i| p[*i] > zero_tolerance).collect();
		
		let mut hessian_nonzero: DMatrix<f64> = DMatrix::zeros(nonzero_intervals.len(), nonzero_intervals.len());
		for (nonzero_index1, orig_index1) in nonzero_intervals.iter().enumerate() {
			hessian_nonzero[(nonzero_index1, nonzero_index1)] = hessian[(*orig_index1, *orig_index1)];
			for (nonzero_index2, orig_index2) in nonzero_intervals.iter().enumerate().take(nonzero_index1) {
				hessian_nonzero[(nonzero_index1, nonzero_index2)] = hessian[(*orig_index1, *orig_index2)];
				hessian_nonzero[(nonzero_index2, nonzero_index1)] = hessian[(*orig_index2, *orig_index1)];
			}
		}
		
		let vcov = -hessian_nonzero.try_inverse().expect("Matrix not invertible");
		let survival_prob_se_nonzero = vcov.diagonal().apply_into(|x| { *x = x.sqrt(); });
		
		let mut se = vec![0.0; data.num_intervals() - 1];
		let mut nonzero_index = 0;
		for orig_index in 0..(data.num_intervals() - 1) {
			if nonzero_intervals.contains(&orig_index) {
				se[orig_index] = survival_prob_se_nonzero[nonzero_index];
				nonzero_index += 1;
			} else {
				se[orig_index] = se[orig_index - 1];
			}
		}
		return se;
	} else {
		// Compute covariance matrix as inverse of negative Hessian
		let vcov = -hessian.try_inverse().expect("Matrix not invertible");
		let se = vcov.diagonal().apply_into(|x| { *x = x.sqrt(); });
		return se.data.as_vec().clone();
	}
}

fn compute_hessian(data: &TurnbullData, p: &Vec<f64>) -> DMatrix<f64> {
	let mut hessian: DMatrix<f64> = DMatrix::zeros(data.num_intervals() - 1, data.num_intervals() - 1);
	
	for (idx_left, idx_right) in data.data_time_interval_indexes.iter() {
		// Compute 1 / (Σ_j α_{i,j} p_j)
		let mut one_over_hessian_denominator: f64 = p[*idx_left..(*idx_right + 1)].iter().sum();
		one_over_hessian_denominator = one_over_hessian_denominator.powi(-2);
		
		// The numerator of the log-likelihood is -(α_{i,h} - α_{i,h+1})(α_{i,k} - α_{i,k+1})
		// This is nonzero only when α_{i,h} ≠ α_{i,h+1} AND α_{i,k} ≠ α_{i,k+1}
		// Since each observation spans a continuous sequence of intervals, this is true only at two each of h and k at the boundaries of the observation
		// h = last interval not involving the observation, h + 1 = first interval involving the observation, etc.
		
		// if *idx_left > 0 { h1 = idx_left - 1; }
		// if *idx_right < data.num_intervals() - 1 { h2 = *idx_right; }
		
		if *idx_left > 0 {
			let h1 = idx_left - 1;
			
			// (h, k) = (h1, h1)
			// numerator is -(0 - 1)(0 - 1) = -1
			hessian[(h1, h1)] -= one_over_hessian_denominator;
		}
		
		if *idx_right < data.num_intervals() - 1 {
			let h2 = *idx_right;
			
			// (h, k) = (h2, h2)
			// numerator is -(1 - 0)(1 - 0) = -1
			hessian[(h2, h2)] -= one_over_hessian_denominator;
			
			if *idx_left > 0 {
				let h1 = idx_left - 1;
				
				// (h, k) = (h1, h2)
				// numerator is -(0 - 1)(1 - 0) = 1
				hessian[(h1, h2)] += one_over_hessian_denominator;
				
				// (h, k) = (h2, h1)
				// numerator is -(1 - 0)(0 - 1) = 1
				hessian[(h2, h1)] += one_over_hessian_denominator;
			}
		}
	}
	
	return hessian;
}

fn survival_prob_likelihood_ratio_ci(data: &TurnbullData, progress_bar: ProgressBar, max_iterations: u32, ll_tolerance: f64, ci_precision: f64, p: &Vec<f64>, ll_model: f64, s: &Vec<f64>, oim_se: &Vec<f64>, time_index: usize) -> (f64, f64) {
	// Compute lower confidence limit
	let mut ci_bound_lower = 0.0;
	let mut ci_bound_upper = s[time_index];
	let mut ci_estimate = s[time_index] - Z_97_5 * oim_se[time_index - 1];
	if ci_estimate < 0.0 {
		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
	}
	
	let mut iteration = 1;
	loop {
		// Get starting guess, constrained at time_index
		let mut p_test = p.clone();
		let cur_survival_prob = s[time_index];
		let _ = &mut p_test[0..time_index].iter_mut().for_each(|x| *x *= (1.0 - ci_estimate) / (1.0 - cur_survival_prob));  // Desired failure probability over current failure probability
		let _ = &mut p_test[time_index..].iter_mut().for_each(|x| *x *= ci_estimate / cur_survival_prob);
		
		let (_p, ll_test) = fit_turnbull_estimator(data, progress_bar.clone(), max_iterations, ll_tolerance, p_test, Some(Constraint { time_index: time_index, survival_prob: ci_estimate }));
		let lr_statistic = 2.0 * (ll_model - ll_test);
		
		if lr_statistic > CHI2_1DF_95 {
			// CI is too wide
			ci_bound_lower = ci_estimate;
		} else {
			// CI is too narrow
			ci_bound_upper = ci_estimate;
		}
		
		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
		
		if ci_bound_upper - ci_bound_lower <= ci_precision {
			// Desired precision has been reached
			break;
		}
		
		iteration += 1;
		if iteration > max_iterations {
			panic!("Exceeded --max-iterations");
		}
	}
	
	let ci_lower = ci_estimate;
	
	// Compute upper confidence limit
	ci_bound_lower = s[time_index];
	ci_bound_upper = 1.0;
	ci_estimate = s[time_index] + Z_97_5 * oim_se[time_index - 1];
	if ci_estimate > 1.0 {
		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
	}
	
	let mut iteration = 1;
	loop {
		// Get starting guess, constrained at time_index
		let mut p_test = p.clone();
		let cur_survival_prob = s[time_index];
		let _ = &mut p_test[0..time_index].iter_mut().for_each(|x| *x *= (1.0 - ci_estimate) / (1.0 - cur_survival_prob));  // Desired failure probability over current failure probability
		let _ = &mut p_test[time_index..].iter_mut().for_each(|x| *x *= ci_estimate / cur_survival_prob);
		
		let (_p, ll_test) = fit_turnbull_estimator(data, progress_bar.clone(), max_iterations, ll_tolerance, p_test, Some(Constraint { time_index: time_index, survival_prob: ci_estimate }));
		let lr_statistic = 2.0 * (ll_model - ll_test);
		
		if lr_statistic > CHI2_1DF_95 {
			// CI is too wide
			ci_bound_upper = ci_estimate;
		} else {
			// CI is too narrow
			ci_bound_lower = ci_estimate;
		}
		
		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
		
		if ci_bound_upper - ci_bound_lower <= ci_precision {
			// Desired precision has been reached
			break;
		}
		
		iteration += 1;
		if iteration > max_iterations {
			panic!("Exceeded --max-iterations");
		}
	}
	
	let ci_upper = ci_estimate;
	
	return (ci_lower, ci_upper);
}

#[derive(Serialize, Deserialize)]
pub struct TurnbullResult {
	pub failure_intervals: Vec<(f64, f64)>,
	pub failure_prob: Vec<f64>,
	pub survival_prob: Vec<f64>,
	pub survival_prob_se: Option<Vec<f64>>,
	pub survival_prob_ci: Option<Vec<(f64, f64)>>,
	pub ll_model: f64,
}