?iJ dZddlZddlmZmZmZddlmZmZdZ dZ dZ d Z dZ d Zd Zd Zd ZdZdeedefdZ d(deej,deedeefdZdeej,deefdZd)deededeefdZdeej4deej4fdZdeej8deej8fdZdeej,deefdZdeej>deej>fdZ deej,deefdZ!deejDdeejDfdZ#deej,deefd Z$deejJdeejJfd!Z&deej,deefd"Z'deejPdeejPfd#Z)deej,deefd$Z*deejVdeejVfd%Z,deejZdeejZfd&Z.deeej4ej8ejZej>ejDejJejPejVfdefd'Z/y)*z.Popularity-aware scoring for last30days skill.N)ListOptionalUnion)datesschema??333333?皙? #xreturnc<||dkrytj|S)z1Safe log1p that handles None and negative values.rg)mathlog1p)rs P/home/ubuntu/.openclaw/workspace/skills/last30days-official/scripts/lib/score.py log1p_safersyAE ::a= engagementtop_comment_scorec|y|j |jyt|j}t|j}|jxsddz}t|}d|zd|zzd|zzd|zzS)uJCompute raw engagement score for Reddit item. Formula: 0.50*log1p(score) + 0.35*log1p(num_comments) + 0.05*(upvote_ratio*10) + 0.10*log1p(top_comment_score) The 10% comment quality weight rewards posts where the community engaged deeply — a highly upvoted top comment means the thread sparked real discussion. N?rffffff?皙?g?)score num_commentsr upvote_ratio)rrr commentsratiotop_cmts rcompute_reddit_engagement_rawr&"sJ$;$;$C z'' (E*112H  $ $ +r 1E*+G %<$/ )D5L 84'> IIrc|y|j |jyt|j}t|j}t|j}t|j}d|zd|zzd|zzd|zzS)zCompute raw engagement score for X item. Formula: 0.55*log1p(likes) + 0.25*log1p(reposts) + 0.15*log1p(replies) + 0.05*log1p(quotes) Nr r 333333?r)likesrepostsrrepliesquotes)rr)r*r+r,s rcompute_x_engagement_rawr-;s J$6$6$> z'' (E++,G++,G  )) *F %<$. (4'> 9D6M IIrvaluesdefaultcN|Dcgc]}|| }}|s|Dcgc]}||nd c}St|}t|}||z }|dk(r|Dcgc]}|dnd c}Sg}|D]2}||jd||z |z dz}|j|4|Scc}wcc}wcc}w)zNormalize a list of values to 0-100 scale. Args: values: Raw values (None values are preserved) default: Default value for None entries Returns: Normalized values N2rd)minmaxappend) r.r/vvalidmin_valmax_val range_valresult normalizeds rnormalize_to_100r=Ns 01!-Q 0E 0 6<=19",==%jG%jG'!IA~178AaiR'88 F & 9 MM$ w;)3s:J MM* % & M' 1=9sBB B B"itemsc |s|Sg}|D]N}d}|jr|jdj}|jt|j|Pt |}t |D]\}}t|jdz}tj|j}||t||}nt}tj||||_t |zt"|zzt$|zz} || | t&z} |j(dk(r| dz} n|j(dk(r| dz} t+dt-dt| |_|S) zCompute scores for Reddit items. Args: items: List of Reddit items Returns: Items with updated scores Nrr2 relevancerecencyrlowmed) top_commentsr r5r&rr= enumerateintrAr recency_scoredateDEFAULT_ENGAGEMENTr SubScoressubsWEIGHT_RELEVANCEWEIGHT_RECENCYWEIGHT_ENGAGEMENTUNKNOWN_ENGAGEMENT_PENALTYdate_confidencer4r3) r>eng_rawitem top_cmt_scoreeng_normalizedi rel_score rec_score eng_scoreoveralls rscore_reddit_itemsr]os}  GV    --a066M4T__mTU V&g.NU#%44,- '' 2  !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1G   5 ( qLG  ! !U * qLGCS\23 K%4N Lrc d|s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}|j"dk(r|dz}n|j"dk(r|dz}t%dt'dt ||_|Scc}w)zzCompute scores for X items. Args: items: List of X items Returns: Items with updated scores r2r@rCrDrErFr)r-rr=rHrIrArrJrKrLrrMrNrOrPrQrRrSr4r3r r>rUrTrWrXrYrZr[r\s r score_x_itemsr`sQ  FKKT'8KGK&g.NU#%44,- '' 2  !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1G   5 ( qLG  ! !U * qLGCS\23 K%4N LYLsD-c|y|j |jyt|j}t|j}t|j}d|zd|zzd|zzS)uCompute raw engagement score for YouTube item. Formula: 0.50*log1p(views) + 0.35*log1p(likes) + 0.15*log1p(comments) Views dominate on YouTube — they're the primary discovery signal. Nrrr(viewsr)rr!rrcr)r#s rcompute_youtube_engagement_rawres J$4$4$< z'' (E z'' (E*112H %<$, & 88rc |s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}t#dt%dt ||_|Scc}w)zvCompute scores for YouTube items. Uses same weight structure as Reddit/X (relevance + recency + engagement). r2r@r)rerr=rHrIrArrJrKrLrrMrNrOrPrQrRr4r3r r_s rscore_youtube_itemsrhs  KPQ4-doo>QGQ%g.NU#44,- '' 2 !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1GCS\23 144 L;RDc|y|j |jyt|j}t|j}t|j}d|zd|zzd|zzS)uCompute raw engagement score for TikTok item. Formula: 0.50*log1p(views) + 0.30*log1p(likes) + 0.20*log1p(comments) Views dominate on TikTok — they're the primary discovery signal. Nrr 皙?rbrds rcompute_tiktok_engagement_rawrl'rfrc |s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}t#dt%dt ||_|Scc}w)ztCompute scores for TikTok items. Uses same weight structure as YouTube (relevance + recency + engagement). r2r@r)rlrr=rHrIrArrJrKrLrrMrNrOrPrQrRr4r3r r_s rscore_tiktok_itemsrn:s  JOP$,T__=PGP%g.NU#44,- '' 2 !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1GCS\23 144 L;Qric|y|j |jyt|j}t|j}t|j}d|zd|zzd|zzS)uCompute raw engagement score for Instagram item. Formula: 0.50*log1p(views) + 0.30*log1p(likes) + 0.20*log1p(comments) Views dominate on Instagram Reels — they're the primary discovery signal. Nrr rkrbrds r compute_instagram_engagement_rawrpbrfrc |s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}t#dt%dt ||_|Scc}w)zvCompute scores for Instagram items. Uses same weight structure as TikTok (relevance + recency + engagement). r2r@r)rprr=rHrIrArrJrKrLrrMrNrOrPrQrRr4r3r r_s rscore_instagram_itemsrrus  MRST/@SGS%g.NU#44,- '' 2 !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1GCS\23 144 L;Tric|y|j |jyt|j}t|j}d|zd|zzS)zCompute raw engagement score for Hacker News item. Formula: 0.55*log1p(points) + 0.45*log1p(num_comments) Points are the primary signal on HN; comments indicate depth of discussion. Nr r )r r!r)rpointsr#s r!compute_hackernews_engagement_rawrusZ J$;$;$C  (( )F*112H &=4(? **rc |s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}t#dt%dt ||_|Scc}w)zzCompute scores for Hacker News items. Uses same weight structure as Reddit/X (relevance + recency + engagement). r2r@r)rurr=rHrIrArrJrKrLrrMrNrOrPrQrRr4r3r r_s rscore_hackernews_itemsrw  NSTd0ATGT%g.NU#44,- '' 2 !  (N1-.I*I$$    y ( Y & '  ) *  1:  1 1GCS\23 144 L;Uric|y|j |jytj|jxsd}tj|jxsd}d|zd|zzS)zCompute raw engagement score for Polymarket item. Formula: 0.60*log1p(volume) + 0.40*log1p(liquidity) Volume is the primary signal (money flowing); liquidity indicates market depth. Nrg333333?g?)volume liquidityrr)rrzr{s r!compute_polymarket_engagement_rawr|sm  Z%9%9%A ZZ )).Q /F ://415I &=4)+ ++rc |s|S|Dcgc]}t|j}}t|}t|D]\}}t |j dz}t j|j}||t ||}nt}tj||||_ t|zt|zzt|zz}|| |t z}t#dt%dt ||_|Scc}w)zyCompute scores for Polymarket items. Uses same weight structure as Reddit/X (relevance + recency + engagement). r2r@r)r|rr=rHrIrArrJrKrLrrMrNrOrPrQrRr4r3r r_s rscore_polymarket_itemsr~rxric |s|S|D]}t|jdz}tj|j}t j ||d|_t|zt|zz}|tz}|jdk(r |tz }n|jdk(r |tz}tdtdt||_|S)aCompute scores for WebSearch items WITHOUT engagement metrics. Uses reweighted formula: 55% relevance + 45% recency - 15pt source penalty. This ensures WebSearch items rank below comparable Reddit/X items. Date confidence adjustments: - High confidence (URL-verified date): +10 bonus - Med confidence (snippet-extracted date): no change - Low confidence (no date signals): -20 penalty Args: items: List of WebSearch items Returns: Items with updated scores r2rr@highrC)rIrArrJrKrrMrNWEBSEARCH_WEIGHT_RELEVANCEWEBSEARCH_WEIGHT_RECENCYWEBSEARCH_SOURCE_PENALTYrSWEBSEARCH_VERIFIED_BONUSWEBSEARCH_NO_DATE_PENALTYr4r3r )r>rUrYrZr\s rscore_websearch_itemsrs"   4,- '' 2 $$   ' 2 $y 0 1  ++   6 ) / /G  ! !U * 0 0GCS\23 A 4D Lrc"d}t||S)zSort items by score (descending), then date, then source priority. Args: items: List of items to sort Returns: Sorted items cR|j }|jxsd}t|jdd }t |t j rd}nt |t jrd}nt |t jrd}nvt |t jrd}nYt |t jrd}n.sort_keySs yy(L S"-.. dF-- .O fll +O f00 1O f// 0O f22 3O f33 4O f33 4OOtWb)FWT62-Fx$77r)key)sorted)r>rs r sort_itemsrJs8> %X &&r)N)r1)0__doc__rtypingrrrrrrrOrPrQrrrrrrLrRrIfloatr Engagementr&r-r=rr]rr`rerrhrlrrnrprrrrurrwr|rr~ WebSearchItemrrrrrs4 (("(3-E(,J**+J}Je_J2J&2C2C)DJRWJ&T%[5$u+B>d6#4#45>$v?P?P:Q>B9fll+9V\\0B9x9x8I8I/J9xX]9&%tF$6$67%DASAS