
    jS                        d Z ddlmZ ddlmZ ddlmZ ddlmZ ddl	m
Z
 ddlmZ d	Zdd
ZddZddZ	 	 d	 	 	 	 	 ddZy)uW  Find past forecasts similar to a draft ("analogs").

Structured similarity only — no embeddings: problem-type overlap, closeness of
likelihood / size / location for shared problems, time of season, and zone.
Danger is deliberately NOT part of the score, so analogs don't anchor the
forecaster; their danger distribution is shown separately.
    )annotations)Counter)date)
Connection   )fetchall)BANDS<   c                P    | s|sy| r|syt        | |z        t        | |z        z  S )Ng      ?        )len)abs     */var/www/avy-guidance/api/app/retrieval.py_jaccardr      s-    QAq1u:AE
""    c                    t        | j                         j                  |j                         j                  z
        }t        |d|z
        }t	        dd|t
        z  z
        S )Nim  r   r   )abs	timetupletm_ydayminmaxMAX_SEASON_DAYS)d1d2diffs      r   _season_closenessr      sP    r||~%%(>(>>?DtS4Z DsA..//r   c           
     *   | D ch c]  }|d   	 }}|D ch c]  }|d   	 }}dt        ||      z  }||z  }	|	r)d}
|	D ]  t        fd| D              }t        fd|D              }|
t        t        |j                  d      xs g       t        |j                  d      xs g             z  }
|j                  d      r=|j                  d      r,|
dt	        t        |d         t        |d         z
        z  z  }
|j                  d	      |j                  d	      |
dt	        t        |d	         t        |d	         z
        z  z  }
 ||
t        |	      z  z  }|d
t        ||      z  z  }|r|dz  }t        |d      S c c}w c c}w )Ntypeg      @r   c              3  4   K   | ]  }|d    k(  s|  ywr   N .0pts     r   	<genexpr>zscore.<locals>.<genexpr>,   s     816aQ8   c              3  4   K   | ]  }|d    k(  s|  ywr!   r"   r#   s     r   r'   zscore.<locals>.<genexpr>-   s     71&	QQ7r(   	locations
likelihoodg?size_maxg      ?g      ?   )
r   nextsetgetr   intfloatr   r   round)querycandqdatecdate
zone_matchr%   qtypesctypesssharedsubqcr&   s                @r   scorer@   $   s   !&'Aai'F'!%&Aai&F&hvv&&Af_F 	NA888A777A8Ck 2 8b93quu[?Q?WUW;XYYCuu\"quu\':sSQ|_!5AlO8L!LMMMuuZ ,z1B1NsSq}!5a
m8L!LMMM	N 	
S3v; .	..A	SA;% (&s
   FFNc           
        t        | d|f      }|xs
 t               }g }|D ]?  }	|	d   |v rt        ||	d   ||	d   |duxr |	d   |k(        |	d<   |j                  |	       A |j	                  d 	       |d| }
t
        D ci c]   t        t        fd
|
D                    " }}|
|t        |      dS c c}w )z?query: [{type, locations, likelihood, size_max}] for the draft.at  SELECT f.id, f.valid_date, f.season, f.guidance_era, f.source, f.zone_id, f.title,
                  f.danger_upper, f.danger_middle, f.danger_lower, left(f.bottom_line, 600) AS bottom_line,
                  coalesce(json_agg(json_build_object('type', p.problem_type, 'rank', p.rank, 'locations', p.locations,
                      'likelihood', p.likelihood, 'size_min', p.size_min, 'size_max', p.size_max) ORDER BY p.rank)
                      FILTER (WHERE p.id IS NOT NULL), '[]') AS problems
           FROM forecasts f LEFT JOIN forecast_problems p ON p.forecast_id = f.id
           WHERE f.center_id = %s GROUP BY f.ididproblems
valid_dateNzone_idr@   c                6    | d    | d   j                          fS )Nr@   rD   )	toordinal)rs    r   <lambda>zfind_analogs.<locals>.<lambda>O   s!    '
{Q|_-F-F-H,HI r   )keyc              3  @   K   | ]  }|d     |d       yw)danger_Nr"   )r$   rH   r   s     r   r'   zfind_analogs.<locals>.<genexpr>Q   s.     #bQQRPS}EUEaAsm$4#bs   )itemsdanger_distribution
candidates)	r   r/   r@   appendsortr	   dictr   r   )conn	center_idr4   rD   rE   kexclude_idsrowsscoredrH   topr   distributions              ` r   find_analogsr[   :   s     	3 

D &KF T7k!5!J-Q|_g]aNaN}fghqfrv}f}~'
a	
 KKIKJ
!*CmrshiAtG#bc#bbccsLsSQUYWW ts   %B;)r   r/   r   r/   returnr2   )r   r   r   r   r\   r2   )r4   
list[dict]r5   r]   r6   r   r7   r   r8   boolr\   r2   )N   N)rS   r   rT   r1   r4   r]   rD   r   rE   z
int | NonerU   r1   rV   zset[int] | Noner\   rR   )__doc__
__future__r   collectionsr   datetimer   psycopgr   dbr   enginer	   r   r   r   r@   r[   r"   r   r   <module>rg      s]    #     #0, os<@XX*9XEIXr   