"""Reproduce VSCE continuous first-passage comparisons and monitoring study."""
from pathlib import Path
import json
import math
import numpy as np
from risk_math import (ou_continuous_bounds, survival_fv, mean_exit_time,
                       mean_exit_fv, laplace_exit,laplace_exit_fv,
                       simulate_nested_sampled_exit,poisson_clearance)


def run():
    comparison=[]
    for beta,T in ((2,.1),(2,1),(6.25,.03),(6.25,.2),(6.25,2),
                    (12.5,2),(12.5,10),(25,10)):
        R=math.sqrt(2*beta)
        bounds=ou_continuous_bounds(R,T,1,1)
        estimates={}
        if beta<=15:
            for cells in (200,400,800):
                numerical=survival_fv(R,[T],1,1,cells)
                value=float(numerical['exit_probability'][0])
                estimates[str(cells)]={
                    'exit_probability':value if math.isfinite(value) else None,
                    'resolved':bool(numerical['resolved'][0]),
                    'principal_eigenvalue':numerical['principal_eigenvalue'],
                    'roundoff_clip_max':numerical['roundoff_clip_max']}
        comparison.append({'beta':beta,'T':T,'kappa':1,'diffusion':1,'radius':R,
                           'bounds':bounds,'fv_estimates':estimates,
                           'fv_status':'spatial estimates, not interval-certified'
                            if beta<=15 else 'unsupported beta>15'})
    refinement=[]
    for beta in (0.5,2,6.25,12.5,15):
        R=math.sqrt(2*beta)
        exact_m=mean_exit_time(R,1,1)
        exact_l=laplace_exit(.2,R,1,1)
        row={'beta':beta,'exact_mean':exact_m,'exact_laplace_s0_2':exact_l,
             'grid_results':{}}
        for n in (80,160,320,800):
            m=mean_exit_fv(R,1,1,n)
            l=laplace_exit_fv(.2,R,1,1,n)
            row['grid_results'][str(n)]={'mean':m,'laplace_s0_2':l,
                                        'mean_relative_error':abs(m/exact_m-1),
                                        'laplace_relative_error':abs(l/exact_l-1)}
        refinement.append(row)
    monitoring=simulate_nested_sampled_exit(
        1,2,1,.08,.001,(1,5,10,50,100),trials=30000,seed=20261013)
    continuous=survival_fv(1,[2],1,.08,800)
    monitoring['continuous_pde_estimate']=float(continuous['exit_probability'][0])
    monitoring['continuous_pde_cells']=800
    radius=poisson_clearance(10,1,.04,.001)
    design={'T':10,'kappa':1,'diffusion':.04,'events':1,'error_budget':.001,
            'poisson_radius':radius,'poisson_beta':radius*radius/.08,
            'v5_direct_beta':20+math.log(1000),
            'v5_direct_radius':math.sqrt(.08*(20+math.log(1000)))}
    data={'schema':'VSCE continuous first-passage evidence 1.0',
          'scientific_scope':'conditional classical stochastic model; no exotic vacuum apparatus',
          'analytic_numeric_support':{'beta_max':650,'upper_underflow':'rounded upward to smallest positive float'},
          'finite_volume_support':{'beta_max':15,'cells_min':8,'cells_max':2000,
                                   'exit_resolution_floor':1e-11,
                                   'below_resolution':'null/unresolved; never a zero probability claim',
                                   'interval_certified':False},
          'comparison':comparison,'independent_fv_refinement':refinement,
          'nested_monitoring':monitoring,'clearance_design':design}
    return data


if __name__=='__main__':
    output=Path(__file__).resolve().parents[1]/'results/risk_results.json'
    output.write_text(json.dumps(run(),indent=2,allow_nan=False)+'\n')
    print(output)