/* 

  Simple ASCII plot in Picat.

  This program was created by Hakan Kjellerstrand, hakank@gmail.com
  See also my Picat page: http://www.hakank.org/picat/

*/

import ppl_distributions, ppl_utils.
import util.
% import ordset.

main => go.

/*
  [[I,I*2+normal_dist(0,1)] : I in 0..10],

ymax = 19.05
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ymin = -1.24175
x = [0,10]

  
  normal_dist_n(100,15,100)

ymax = 130.516
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| ***  *                *  *   
| *         *    *       *     
| * *  * *   ** *  * * *    ***
|  *  *       **        *      
|  *    *    *           *** * 
|        ** *   * ** **  ** *  
|          **  *  *  * *       
|    *        *    *   ** *    
|    *  ** *     *  * * *      
|*              *  *  *     *  
|     *   *                  * 
|            *    * * *        
|   **                         
|*      *  *  *                
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ymin = 64.7372
x = [1,100]


  poisson_dist_n(5,100)

ymax = 11
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ymin = 0
x = [1,100]

  [[normal_dist(0,1),normal_dist(0,1)] : I in 1..300],

ymax = 2.75843
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|        *     **********  *   
|        *****   ******        
|         *** *** ***  *      *
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|*     **  ** ** ** *** * **   
|         ** **** ********  *  
|     *     *********** * *    
|       *    ******** *****    
|        ************ *****    
|       * *** * *** ****  *    
|           ** *  * *  * *     
|        **** ******** *  *    
|               ***         *  
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ymin = -2.26819
x = [-3.31091,2.52605]

  student_t_dist_n(1,100)

ymax = 8.18073
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ymin = -60.6168
x = [1,100]



*/
go ?=>
  _ = random2(),
  Data = [[I,I*2+normal_dist(0,1)] : I in 0..10],
  % Data = normal_dist_n(100,15,100),
  % Data = poisson_dist_n(5,100),
  % Data = [[normal_dist(0,1),normal_dist(0,1)] : I in 1..300],
  % Data = beta_dist_n(10,10,100),
  % Data = student_t_dist_n(1,100),

  show_scatter_plot(Data,30,20),
  nl.
go => true.


/*
  A simple model

var : x
Probabilities (truncated):
152.523750107627052: 0.0018867924528302
151.428362507661632: 0.0018867924528302
141.625945737004713: 0.0018867924528302
140.154017931984441: 0.0018867924528302
.........
100.165847326595554: 0.0018867924528302
100.149752482413376: 0.0018867924528302
100.025586237857226: 0.0018867924528302
100.010134497142289: 0.0018867924528302
mean = 112.123
[len = 530,min = 100.01,mean = 112.123,median = 110.894,max = 152.524,variance = 76.064,stdev = 8.72147]
Scatter plot:

ymax = 152.524
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| **  *  *   **    *            **   *            * **  * * *
|    *    ** *           ** ****  *    **      ****         *
|   ** *  *   **    * **      **   * * *     *  * *          
|*  *     * **  * *  ** * **   * **** * * * *   *  *   *   * 
| ***********  **** ** ******   * *  *** ** **** *  * **  ** 
|**** *   ******* * ******* ** *** ** * **  ************** * 
| ***** **   **      *********** * *** ** ********* **** *** 
| **  * * ** *** ****** ** ****  ** **** *** **  ***** * ****
|  * * *** **** ********  **** **** *** ***** *  **** * **   
|****** *****  *** * *****    ****** * ** **** *** ********* 
|    ***       **  * *   * ***  ****  ** ** * ***   * * * * *
ymin = 100.01
x = [1,530]


var : x >= 130
Probabilities:
0: 0.9641509433962264
1: 0.0358490566037736
mean = 0.0358491
[len = 530,min = 0,mean = 0.0358491,median = 0.0,max = 1,variance = 0.0345639,stdev = 0.185914]
Scatter plot:

ymax = 1
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|************************************************************
ymin = 0
x = [1,530]

*/
go2 ?=>
  reset_store,
  run_model(1000,$model2,[show_probs_trunc,mean,show_simple_stats,
                           show_scatter_plot,scatter_plot_size=[60,20]
                           % show_percentiles,
                           % show_hpd_intervals,hpd_intervals=[0.94],
                           % show_histogram,
                           % min_accepted_samples=1000,show_accepted_samples=true
                          ]),
  nl,
  % show_store_lengths,nl,
  % fail,
  nl.
go2 => true.
  
model2() =>
  X = normal_dist(100,15),
  P = check1(X >= 130), % Note: 0/1 for the plot
  
  observe(X >= 100),

  if observed_ok then
    add("x",X),
    add("x >= 130",P),
  end.