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Transkript
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"%)1; "(*E/:N)0:{L)1::<"=H)1\*-65lmE$&2&!#"|:<!#45>?>)1"%\$&97:<2*-/)1!G"%)1;X"%*tE:<NO):{B, *-*-"(¯/:" H<)1\p*rao¼$&!#"(:<"/x ,%\*->H<)1\p*-zOL)1:<5!u:<\p)INO)1\]4~\]:\p)Oa HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 mmax= 100.00 HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 mmax= 100.00 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 -10 0 -5 0 5 10 -40 HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 mmax= 100.00 -20 0 20 40 HOF model amax= 30.00 bmax= 5.00 cmax= 60.00 dmax= 20.00 mmax= 100.00 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 -100 -50 0 50 100 0 -100 -50 0 50 100 v %´5rp w ± w }=E/)G*-\$&35$&/:2-«m¤K>*-;=)12ba_K*-"%$&97)G"%E)G9A2&'!#"()1\]$B/3K*-,/@JC¤5!:\p*-'/;"%E) H)1\*L*-$&" a }rE)1\)+:\)+"%lK*Klm:45!I*-'"*-,=E/)1\p)Oa5}=E/)fÖ\]!#"$&!"%*K"%\4m"%*¨\)1h%L:<\:<>)1"%\$&!#)"(E/)0>*-;=)12 !#*"%E:<"5"(E/)/)1l L:\]:>?)1"()1\]!rE/:NO)'/$B, *-\]>¸;=$&!#"(\]$&M5'"($&*-Kx%O!#:/)16oL)1\]!ao97*->?>a z a }rE'!#65, *-\"%E)0!#$&>L2&)1!#"|2B)1N)12OS«m¤>?*-;=)12/lK$&"%E!#$&3O2B)L:<\:<>)1"%)1\(6 6-lKE/$&97E?;=)1h Ö/)1!:097*-/!#"(:<"D;=)1/!#$&"(4KNO:<2B')16lK)+lK$&2&25$&/"(\p*-;r'97)G:0)1l¸L:\]:>?)1"()1\(6ð $ 6!#:<476<"(E/:" lm*-'2&;?!#:"($&!#, 4~"(E/)G)1ñ5':<"($&*-Cn ¾JI2K L ÁÂNM:q&Q{À2R ¾JIKÂtKÀ@U«MCÂrà L¿Àmà<g x /a S<Yz ½ ôIH Ä $ ÂfÅÇ= ôJH ÄFÅCÇ ð }=E/){*-"%E)1\+2B)1N)12&!+:\)~>?*-\p){9A*->L2&$&97:<"()1;=6MO'""%E)KL\]$B/97$&L2&){lm*-'2&;M5){"%E)K!#:>?)Oa §a 3|a 6=, *-\G2&)1NO)12gt!#*->)m*-,"(E/)KO)14ØL:\]:>?)1"()1\]!+:\){"%E)m2&*-97:<"($&*- *-,"(E/)K*-L"%$&>'> :<;"%E)+E)1$&35E/"D:"O"%E)+*-L"($&>'>wa1}rE)G*-L"%$&>'>q$&!u2&*-97:<"()1;:<" j Ä à ÂðXÇ ½ Ú ÅLKNMO }=E/)GE/)1$&35E"/:""(E/:"/L*-$&"D$&! x /a S<jz $ x /a S<z Ý ¿ ÀTÁ Ì Ý D È @*~$&"/$B!"(E/)1!#)Gñ5'*-"%$B)1/"(!"(E/:"/!#E*-'2&;?M5)G!#$&>?'2BÈ:<"()1;È $B/!#"()1:<;*-,D"(E/)G*-\]$&35$&:<2-L:\]:>?h ¼ KNMO ½ )1"%)1\!a}rE)1¥"(E/)m>*-;=)12©lm*-'2&; 45$&)12&;w*-L"($&>?:;=$&!#"(\]$&M5'"()1;w'/$&,%*-\]>2&4A6|$&/!#"()1:;w*-, 9A2B'!#"%)1\p)1;*-/)1!a 82&"(E/*-'35E "%E$&!{lm:<4 $&!~9A2B)1:<6C"%E)1\))1d$B!#"%!~:/*-"(E/)1\(6CB;=$&\]"(45¯B6CM5\p'"%), *-\p9A)tlK:<4 *-'" ao%"O$B!©:<97E/$B)1N)1;¨N$&:$B/"(\*-;='97$&/3*-,O45)1"-:<*-"%E)1\|L:\]:>?)1"()1\%6A"%E)IL*-!#$&"($&*-K*-{"(E/) 35\]:;r$B)1/"(P6 <a©82&"(E/*-'35EÇ"(E/)~>)1:<$&/3*-,©"(E/)L:\]:>?)1"()1\]! "(Q * +~\)1>:<$B/!*-M5!#9A'\p)16 "%E)G>?*-;=)12-9A:K*-lðMO)'!#)1;"(*{35)1)1\]:"%)®\)1:2&$&!#"($&97¯97*-)1/*-ð 972&$&/)1!n ¼Õ½ ¿ ÀXÁ Ú Í ?R $ æ ¿ ÀTÁ à + SÍ TR Ä Ý Ä ðîÝ Ç Ç Ä Ý Ä Â Ç Ç Extended HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 emax= 12.00 mmax= 100.00 x /a S<iz Extended HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 emax= 12.00 mmax= 100.00 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 -10 0 -5 0 5 10 -40 Extended HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 emax= 55.00 mmax= 100.00 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 -40 -20 0 20 40 0 20 40 Extended HOF model amax= 5.00 bmax= 5.00 cmax= 5.00 dmax= 5.00 emax= 55.00 mmax= 100.00 100 0 -20 0 -100 -50 0 50 100 v ´5= w º&w }rE)B)1d<"%)1;=)1;r¯-«K¤t>?*-;=)12ba/8, "()1\C:;r;=$&3m:BL*-!#$B"%$&*-:<2/L:<\:<>)1"%)1\(6 # 6<"(E/)G97*-)1/*-972&$&/)1!u2&*-*-¨*-'"/>*-\)GB\p)1:2&$&!#"($&9A¯a q Qi&QS¥¿ UÂNsrÃ0¿ ®KÃTM® 765 z65U VCWtBBCYXB;[Z » U }rE$&!$&!+:m>*-;r)12r$&"%\p*-;='9A)1; $&X"%E)KL\p)1!#)1/"©!#"(';=4aI8!08'!#"($&Xx Sij<Y:<z# %%L*-$&/"(! *-'"%6_B"(E/)1\p)$B!©/**-M5NO$&*-'!C\p)1:<!#*-~lmE4!#L)19A$&)1!\)1!#L*-!#)1!©"%*0)1/NO$&\*->?)1"%:235\]:;=$&h )1/"(!!#E/*-'2B;Ç"%:5)G"%E)0, *-\>q*-,!#45>?>?)1"(\]$B9A:25.0:'!#!#$&:<97'\]NO)1!#¯x LaTYz#a<«m)+\p)1L*-\]"(! 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'/\p)1:<!#*-:<M52&)"(*~)1dL)197""(E/*-!#)G97*-/!#)1ñ5')1/97)1!a ¯x @$&NO$&:+S<iiY<6oLaoz # S<.T %#aoJC)1L2&:9A$B/3 B;=)19A$B!#$&*-/¯5lm$&"(EB$&, )1\p)1/97)1¯B645*-'E:<NO)f:f2&*-35$&97:<2O:<LL\p*-:9AE¨"%*¨"%E)f!#'MoPR)197"D"(E/:"Dlm:! >?:;r)tM54Xs=:<45)1!x%S<jY<T z # Sj % 6|:<; y=:L2&:9A)mx%S<S< z # <U % 6|M5'"lm)1\p)m:2&>?*-!#", *-\p35*-"%h "%)1Ø,%*-\/)1:\]2B4ST<Z{45)1:<\!"%*M5)\)1;=$&!#97*-NO)1\)1;ØM5Ì 4 )1,(, \p)14O!x Si<Ti<z # jD %%g6 %:<45/)1!#6:<; *-"%E)1\]!a }rE)!#97E/)1>?)0*-,;=)1;='9A"($&NO)02&*-35$&9+$&!I"(E/'!n35$&NO)1:{97:'!#)16-lm)97:<$&/,%)1\$B"%!97*-/h !#)1ñO')19A)1!a=ÊØ)165:<!IlK)12&2/:!I*-"(E/)1\!#97$&)1"%$&!#"(!;=*m, \p)1ñ5')1/"(2&476O,%:<97)0E)1\)f"%E)\p)1N)1\!#)On 0m& FS Cm&&NnD_n!&'ÍFn"& ÎwÉS*m&!¡m&¡"&d&H "! Í&!dÎCIÏ }=E/$B!$&!u:m$&;r'97"($&N)2B*-3O$B9A6:0L2&:'!#$&M52&)$&, )1\p)1/97)Oa FI*-d¨x%S<igY<zû# T<Y=% lm*-\O)1;Ø*-'"r"(E/)ñ5':/"($&"%:"($&N)0\'2&)1!*-,|97*-/!#$&!#"()1/"r2&*-35$&97:<2\)1:h !#*-/$&3a_«m),%*-'/;t"(E/:"59A*-!#$&!#"%)19A4~97:<{*-/2B4{M5))1/!#'\p)1;m$&,O:<>*-'/"(!©*-,*-'\rM5)12&$&)1, Ti É&m! ¾JI2K L ÁÂNMbÐQ{Â=Ã=ÁTRK~ÁTR%ÀjiPU0ÂrÀKÃTR#ÁPÑ g<Z $&mNO:<\$&*-'!uL\*-L*-!#$B"%$&*-!97:<¨M5)+>:<LL)1;"(*~:<*-"%E)1\C!#)1"/*-,D\p)1:<2<L*-!#$&"($&N)+/'>M5)1\]! lmE/$B9AEt*-M5)145)1;"%E)+'!#':2-\p'2&)1!u*-,/L\p*-MO:M5$&2&$&"(4{"%E)1*-\]4|n `!Õó¡d&!DÏm&` b!F x U/a S<z » Ú ÄÒ ¼ ¾ ¡Ç Ý » Ú Ò ¼ ¾ ½ :/; x U/a <z » Ú Ä Ò #pÓ ¼ ¾ Ç ½,» Ú ÄÒ ¼ ÓÔ#p¾ ǧr » Ú Ä Ó ¼ ¾ Ç «m)1\p)16 ;=)1/*-"()1!G"%E)~L\*-L*-!#$&"($&*-Ø"(E/:" $&!G"(\')16 ;=)1/*-"()1!+"(E/)~L\p*-L*-!#$&"($&*- Ò *-"()"%E:<":2&2O"%E)0L\p*-M5:<M5$&2&h "%E:<" $&!,%Ò :<2&!#)16¼ ¯®>?)1:/!I®35$&N)1¯B6-:/;Ç®6 ¯\p)1:<Ò ;=!uB:/;=¯am Ò $&"($&)1!:<\p)097*-/;=$&"($&*-:<2D*- ¾ 6OlmE/$B9AE;r)1*-"%)1!:2&2\)12&)1NO:/"M5:<97535\*-';Ø$&/,%*-\]>?:"($&*- :<"-E:<;a1}=E)1\)$&!©/*0:<M5!#*-2&'"()IL\p*-MO:M5$&2&$&"(4:"-:<2&2<xw% )1,%,%\)145!=S<iTi0# j.%1E FI*-d+Si<gY# T<YD%E @$&NO$&:Si<iYW# ST.%E :45/)1!ISi<igW# j<j.%% zCrÊ ¤5\p*-> "(E/)0!#'>¹:/;ÇL\p*-;='9A"\p'2&)1!x §=ñ5!aDUDa S6 UDa z 6-!#)1NO)1\]:2/'!#)1,%'2D\)1!#'2B"%!97:<M5);r)1\$&NO)1;a=8>?*-3"(E/)1> 6"(E/)~>?*-!#"|'!#)1, '2D:\p) "%E)Gsr:45)1!#¯"(E/)1*-\p)1> x U/a T<z » Ú Ä Ò ¼ ËÓ #¾ Ç ½ » Ú Ä Ó ¼ » Ò #pÚ ¾ Ç§Ó r ¼» ¾ Ú ÄÒ ¼ ¾ Ç Ä Ç :/;>?:\35$&:<2&$B!#:<"($&*- @B× x U/a g<z » Ú ÄÒ ¼ ¾ Ç ½)Ö T× » Ú Ä Ò #Ó ¼ ¾ Ç + Ó }=E/)$B>?L*-\]"(:/97)0*-,rsr:45)1!#¯5"%E)1*-\)1> , *-\uL2&:'!#$&M52&)f$&/,%)1\)19A)fx $&;r'97"($&N)f2&*-35$&97z M5)19A*->)1!:LL:\)1"OlKE/)1mlm)Glm\$&"() ¼ » ÊgE!ßN©! , *-\ # :<;Q+ Ê ,%*-\ Ó n Ò ðð » Ú Ä ¼ » Êg !ß"!¼S+ ð Ê ð #¾ ÇØ » Ú Ä + ð Ê ð ¼S ¼ » ÊE!ß"! #p¾ ÇËr » Ú Ä ¼ » ÊgE!ßN!x U/¼ ¾a U<Ç z *-\ » !Ê"Dß » Ú ð Ú ß º ßäÊ ¼ Ø º ß ï º ß" E +¥Ù a eNàGÊ$ß e r » Dß » Ú ð Ú ß º ßÊ ¼ óNd!_!F x U/a Y<z FI*-\p*-2&2&:\]4{UDa T6<U/a U<6UDa Y+)1/97:<L!#'2B:<"()1!u"%E)fL\p*-9A)1!#!*-,$&, )1\p)1/97)On_$&"/\)12&:"()1!"(E/)+L\p*-MOh :<M5$&2B$&"%4 "%E:<""%E)E45L*-"%E)1!#$&!m$&!{"%\p')3O$BN)1w"(E/);=:<"(:<6"%* "%E)L\p*-M5:<M5$&2B$&"%4 "(E/:"Ilm) lm*-'2&;XE/:NO)*-M5!#)1\]NO)1;Ç"%E);=:<"(:lm)1\p)0"(E/)E/45L*-"(E/)1!#$&!G"(\')Oa}=E/$&!$B!I"%E)L*-lm)1\*-, s=:<45)1!#¯-"(E/)1*-\p)1>wa-m*-"%)f"%E:<"/"%E)0)1ñ5':<2B$&"%4K*-,r§=ñ|a-UDa T0lK:<!\)1L2&:97)1;M54mL\p*-L*-\]"($&*-h :<2B$&"%4¨$&t§=ñ|aUDa U&E<"(E/$&!$&!Ö/)+, *-\CL:\]:>?)1"()1\)1!#"($&>?:"($&*-/6!#$B/97)f"(E/)+*->?$&"("()1;?;=)1/*->h $&/:"(*-\$B!:/*-\>?:2&$&H:<"($&*-97*-/!#"(:<"%6-*-"r;=)1L)1;r$B/3m)1d<L2&$&97$&"%2B4m*-"(E/)fE/45L*-"(E/)1!#$&! x @$BN$&:S<ii<Yr# S<T.%%z#a s=:<45)1!#$&:L\p*-MO:M5$&2&$&"(497:<~"('\]~"(E/)I§=ñ|aQgDa SYI$&!#$&;=)*-'"5"%*0\)1NO)1:<2 "%E)GL\*-M5:MO$B2&$&"(4{;=$&!#"(\]$BMO'"($&*-t*-*, Ú 6_"%E)GL*-L'2&:<"($&*-t;=)1/!#$B"%4a M Ä $ô Û,+EÜøþ.+1÷?ü~ü0) °,/13-.+1ü0/ ÊØ)!#E:<2&2'!#):<M5'/;=:/97)L;=,-:!|"(E/)2&$&5)12&$&E*-*-;t, '9A"($&*-~, *-\=)1!#"($&>?:"%$B*-{*-,5;=)1/!#$B"%4 L*-!#"%)1\$&*-\IL;=,©,%\*-> §rñ|auUDa Y/aC}=E'!#6Dlm)~/)1)1; "%*;=)1\]$BN)"(E/){:<M5';r:/97)L;=,Ö\!#" a ÊØ)Glm$&2&2-'!#)G§=ñ|a_g/a S<&Y E1E/)19A)16_'!#$&3KL\p*-;r'97"O\'2&)Gx%§rñ|aoU/a z#n » Ú Ä ¼ ¼ à # #p¾ Ç ½ ¼ g g ¼ g à Ä Â à Ç pR Ä Â Ç x U/a j<z ÐQ«±&QSLuMR%¿ MUrR%Ã=ÁTMRwvcVÁTR ¿Àmà gS » Ú Ä ¼ # ༠#¾ Ç ½Ý» Ú Ä ¼ ¼ à # #¾ ÇÞr » Ú Ä àf¼ ¾ Ç ½ ¼ g g ¼ g à Ä Â à Ç R r à{| àCz Ä Â Ç Â x%UDa z ² ß @ àËá K á y e R ¿ÀXÁ c ß @ à§á K á y e c x%UDa iz ~ ² g  ~ » Ú Ä ¼ # à # ¼ # # #p¾ Ç ½Ã² » Ú Ä ¼ # ༠#¾ ÇÞr » Ú Ä ¼ # à # # ¼ # # #p¾ Ç ½,» Ú Ä ¼ # à # 4¼ # # # #p¾ ǧr | z x U/a S<Zz  ² ² ² » Ú Ä ¼ # à # # # ¼ # #p¾ Ç ½,» Ú Ä # à # # ¼ # # #p¾ Ç¥r É Þ jSÀ ¿ÀXÁGÁ Â Ä Âj&É ÆPyÇ È yÈ x U/ a SASz ² ²y » Ú Ä ¼ # à # # # # ¼ #p¾ Ç ½,» Ú Ä ¼ # à # # # ¼ # #p¾ ǧr | {z x U/a S<z  ² ² » Ú Ä ¼ # à # # # # # ¼ ¾ Ç ½Ý» Ú Ä ¼ # à # # # ¼ # #¾ ÇÞr É Þ jEÀ ¿ÀXÁGÁ Â Ä ÂfjÉ ÆT~.ÇÈ ~È x U/a ÂS<Tz ~ ¤O\p*->¬>:<\p35$&/:2&$&H:<"($&*-¨x ² §=ñ|a1U/a g<zlm)E/:NO)On ² @B× » Ú Ä ¼ # à # # # # j¼ ¾ Ç ½ Ö ¾ » Ú Ä ¼ # à # # # # # ¼ ¾ Ç + ² ² @B× @B× » Ú Ä ¼ # à # # # ¼ ¾ Ç ½ Ö ¾ Ö ¾ » Ú Ä ¼ # à # # # # ¼ ¾ Ç + +f æææ :<;r6_Ö:<2&2B4 ² ² @B× @B× @B× @*× @B× @B× » Ú Ä ¼ ¼ ¾ Ç ½ Ö ¾ Ö ¾ Ö ¾ Ö ¾ Ö ¾ Ö ¾ » Ú Ä ¼ # à # # # # # ¼ ¾ Ç + +f+ + +Y*+ à x U/a S<gz Ä ôN m?û+1ü~ûf)1+.-1û+]ý,-.+1ü0/1 7u5 65 4 â[BCüN? 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Z A.Z 9 @97:<2&)L:\]:>?)1"()1\]!:\p)"%E*-!#)K:!#!#*-9A$&:"()1; lK$&"%EX:¨!#$&H<)16*-\>?:35/$&"(';=)Oa ,ClK){\p)1:2&2&4 E/:N)K/*Ø$&;=)1::<M5*-'"u"(E/)t!#97:<2&)¨$&/NO*-2&NO)1;=6"(E/)tL\]$B*-\fL;=,>?'!#"MO)K$&/NO:<\$&:/"ulm$&"(E \)1!#L)197"/"(*{!#"%\p)1"%97E/$B/356*-\C!#E\]$&O$B/356-*-,D"(E/$&!!#9A:2&)Oa<FIE:<35)f*-,>)1:<!#'\p)1>?)1"/'/$B"%! >?'!#"D>:<5)+/*{;=$&,%,%)1\)19A)"%*{"%E)f:!#!#$&35/)1;L;=,#aÊØ)+97:<mlK\]$&"()f;=*-lmt"(E/$B!\p)1ñO'$B\)1h >?)1"/*-,D97*-/!#$&!#"()1/974K:! x U/a S<jz » Ú Ä Ò ¼ ¾ Ç + Ò ½,» Ú Ä í Ò ¼ ¾ Ç + Ä í ÒfÇ lmE)1\)_íÇ$&!G:¨L*-!#$B"%$&NO)~9A*-!#"%:/"]a©@$&9A)x+ í ½ íB+ 6O$&"97:<*-/2&4M5)!#:<"($&!#Ö)1; Ä Ç Ò M54 x U/a S<z » Ú ÄÒ ¼ ¾ ÇØ h Ò }=E/$&!L\]$&*-\u$&!9A:2&2&)1;:ËåG d&!HÏH&Q a5%"lm:!!#'3535)1!#"%)1;ÇM54j%)1,%,%\)145!Cx%S<iT<iz 6:/; $&"\p)1L\p)1!#)1/"(!9A*->L2&)1"%)$&35/*-\:<9A):MO*-'""%E)N:2&')*-,:?!#97:<2B)tL:<\:<>)1"%)1\oa%"I$&! )1ñ5'$&N:2&)1"/"(*{:+'/$B, *-\]>qL;=,, *-\"(E/)G2&*-35:\]$&"(E/>q*-, x @$&NO$&:+S<iiY@# S<T.% z n Ò » Ú Äwº ÄÒfÇ ¼ ¾ Ç ½ à eB!ÊCä øû+EæS(*-.1+ ü0/ Ä ôÄ ÊØ)G!#E/:2&2-!#"%:\]"O, \p*->¬"(E/)Gs=:<45)1!#¯<"%E)1*-\)1>6:!u35$&N)1K$&m§=ñ|a_U/a UDn » Ú Ä ¼ » Êg !ß"!¼S+ ð Ê ð #¾ ÇØ » Ú Ä + ð Ê ð ¼S ¼ » ÊE!ß"! 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H ¾JI2K L ÁÂNM Y<Z QSLÂNM®O¿ MKÀm¾IÂtKÀ2U sNRR Áà ?> "(\]$B:<2&!=lm$B"%E5/*-lK9A*-\\)197"<*-\p;=)1\lK:<!='!#)1;~:<!=:>?)1:!#'\)C*-,)1,%Ö97$&)1/974aA*-;=)12 lK:<!{.0:'!#!#$&:/6ulm$&"(ø E ÜC"0!#:<>L2&)!#L:97$&/3|a?:d$&>'>ò"%*-2B)1\]:/97à ) Ê ËJÌÍ NO:\]$&)1;, \p*->qZDa ZSf"(*KS/am'>?M5)1\C*-,=!#L)19A$B)1!$&N*-2BN)1;ÇlK:<!x 97'\]NO)1!,%\*->q"(*-L "(*tM5*-"%"(*->?z nOSZ<Z6UZ<6-Z6S<U6-SZ<6-i6-<6-j6-Y6-U<6-g6-:<;ÇT/a}=E)L2&*-"=$&!IM5:!#)1;Ç*- gT<ZZ<Z!#$B>?'2&:"%$B*-/!a «! H 120 100 100 species number of hits 80 @ 60 40 20 3 species 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 max. tolerance v ´5=+w &w v8[ )1,(Ö97$&)1/974lK$&"%EØ;=$&,(, )1\p)1/"='>?M5)1\I*-,©!#L)197$&)1!#6/,%*-\I;=$&,(, )1\p)1/"=L:<h \:<>)1"%)1\=!#)1"("%$B/35!a_K'>?M5)1\|*-,5E$&"%!C*-'"5*-,OS<ZZZI"%\$&:2&!lm:!©'!#)1;m:!©:>)1:<!#'\p) *-,C)1,(Ö97$&)1/974aC*-;=)12lm:!G.0:'!#!#$&:<6Dlm$&"(E &ð!#:>?L2&)~!#L:9A$&3|a:d$&h >'>ª"(*-2&)1\]:/97_ ) Ê Ë*ÌGÍ NO:<\$&)1;Ø, \p*->¹Z/a Z<S"(*SDaK'>?M5)1\*-,©!#L)197$&)1!$&N*-2BN)1; lK:<!x 97'\]NO)1!,%\*->q"(*-L?"(*KM5*-"("%*->z#nS<ZZ6UZ<6<Z6SU6SZ<6<i<66j6Y6U6g6:/;?T/a }=E)+L2B*-"/$&!uM5:<!#)1;*-tgT<ZZ<Z!#$&>'2&:"%$&*-!a 1 100 species 0.9 number of hits 0.8 0.7 @ 0.6 0.5 3 species 0.4 0.3 0 0.1 0.2 0.3 0.4 0.5 max. tolerance 0.6 0.7 0.8 0.9 1 Qiq&QÇ÷ K WL sNRR#Áà YS v %´5rp0&w Ü w v8[X)1,(Ö9A$&)19A4lm$B"%EØ;=$&,(, )1\p)1/"=/'>M5)1\*-,©!#L)19A$B)1!#6/, *-\;r$B,%,%)1\)1"rL:h \]:>?)1"()1\=!#)1"%"($&3O!a1@L)1:\]>:<>)1:<{\]:/97*-\]\p)12&:"%$&*-~97*-)1,%Ö97$&)1" Ë *-,OS<ZZZ "(\]$&:2&!=lm$&"(E5/*-lm97*-\]\p)197"<*-\p;=)1\/lm:!='!#)1;{:!=:>)1:<!#'\p)C*-,)1,(Ö97$&)1/974a7*-;=)12 lm:!.0:'!#!#$&:/6©lm$B"%E&q !#:>?L2B)t!#L:<97$&/3|am:d$&>'>å"(*-2&)1\]:/97)1Ê`Ë*ÌGÍ > $,% NO:<\$&)1;,%\*->qZ/a Z<Sf"%*¨SDam'>M5)1\C*-,!#L)19A$&)1!$&/NO*-2&NO)1;?lm:!x%9A'\N)1!u,%\*->q"(*-L "(*mM5*-"%"(*->?z nS<ZZ6U<Z6Z<6-SU6-S<Z6i6-<6-j6-Y<6-U6-g6-:<;ØTDa/}=E/)0L2&*-"|$B!IM5:<!#)1;Ç*- gT<Z<ZZ!#$&>'2&:<"($&*-!a Gaussian model tmax= 0.01 100 80 60 40 20 0 0 0.2 0.4 0.6 0.8 1 v %´5rp0&w É w y=*-lª)1,(Ö9A$B)1/974Ø.0:'!#!#$&:< >?*-;=)12©!#)1"("%$B/3|a¨m*-"()t"%E:<"@JC¤5!:\)¨/*-/h *-NO)1\]2&:LL$&/3{$&m!#*->)G\)135$&*-!a «m$&35E )1,%Ö97$&)1/974 lm:<!{:9AE$&)1N)1; , *-\\p)135'2&:\0!#:>?L2&)!#L:<97$&3¥!#97E/)1>?)16ulmE)1 "%E) >?)1:!#'\)lm:<!¨"%E)Ç/'>M5)1\{*-,+E/$B"%!a¥}=E/$B!m>?)1:/!m"(E/:"lm)Ç$B/!#$&!#"()1; "%E:<""(E/)?:<M5h !#*-2&'"%)12B49A*-\\)197""%*-L*-2&*-354lK:<!~\p)1N)1:2&)1;wM54 "(E/):/:2&45!#$&!a«m*-lK)1N)1\(6©"(E/)\)135'h 2&:<\G!#:>?L2&$B/3Ø!#97E/)1>?)K9A*-'2&;wE:<\p;=2&4?M5)¨)1N)1 :<LL\p*-d$B>?:"%)1; '/;=)1\G\)1:2r9A$&\p97'>?h !#"%:/97)1!a ,lm)\)12&:d?"(E/)97*-/;=$&"($&*- *-,I:MO!#*-2B'"%)12&4 9A*-\\)197""%*-L*-2&*-35476u2&$&5)$Bw"%E) 9A:!#)lmE)1¨"(E/)>?)1:{@L)1:<\>?:{97*-\]\p)12&:"%$B*-{97*-)1,(Ö9A$&)1"-lm:!'!#)1;m:!:>)1:<!#'\p)*-, )1,%Ö97$&)1/97456lK)3O)1"rñ5'$&"(){35*-*-; \)1!#'2B"%!lm$B"%EÇM5*-"(EÇ\p)13O'2B:<\:<;X\]:/;=*->¹!#:>?L2&$B/3 !#9AE)1>?)1!am}=E/)1/6:;=)197\)1:!#)1;)1,(Ö9A$&)19A4X*-979A'\\)1;wlKE/)1 Ê Ë*ÌGÍ lm:!{"(*-*X2&*-lG6C$ba )Oa 6 lmE/)1"(E/)*-NO)1\]2&:L*-,@JC¤5!{lK$&"%E$&"(E/)97*-)1/*-972&$&/)lm:!$&!#',%Ö97$&)1/""(* L\p*-;='9A) '/$&ñ5')+!#*-2&'"%$B*-/!"(*~"%E)Gv8[a 5 765 7 ì0Z A.?@B;[Z » Ê¥$&"(Ew"%E)MO)1"(:>?*-;=)12&6©"(E/)1\p)t:\)m"(lm*XL:\]:>?)1"()1\]!"(*XMO)t!#)1"(6©:<>)12&4 Ë*ÌGÍ 6©:<; ÷ ú, *-Ë*2&2&ÌG*-Í-lKa$&}/*-3t35!#)1)1"%E")1*-\%,6/!#"%$&E>?)14?'2&$&:p"($&o*-'/)1!#/6 97)KË*"(ÌGE/Í ){lm:<:!#45!>?!#)1>"/97)1*-"%\/4?!#"(*-:<,C"("E/"%){*{\ZD)1a U!#L6_:<*-; !#)~9AË*'ÌG\GÍ NN)1!:a\]$B%)1X;a1"%E ) ú "%E$&!>:</)1\(6_"%E)+:!#45>?>)1"%\4¨*-÷ ,D"(E/)G@JC¤{lm:!u9AE:<3O)1;a ; ¤O$B\]!#"(6O:~!#)1"=*-,!#$B>?'2&:"%$B*-/!lm:!9A:\]\$&)1;Ç*-'"=, *-\\p)13O'2B:<\u!#:>?L2&)!#L:9A$B/3|a=}=E) \)1!#'2&"(!~:<\p)m;=$&!#L2&:45)1; *-Ö3|a5j/a S<Z/a¨}=E/)t)1,(Ö9A$B)1/974 $B>?L\p*-N)1; \p)1>?:\]5:<M52&4ØlK$&"%E $&/97\)1:!#$&3'>?M5)1\*-,!#L)197$&)1!I$&N*-2&NO)1;ar¤5*-\:97*-/!#"(:/"'>?M5)1\*-,!#L)197$&)1!#65"%E) ¾JI2K L ÁÂNM Y< QSLÂNM®O¿ MKÀm¾IÂtKÀ2U sNRR Áà ,%*-\"%E))1:<\2&45h !#45>?>)1"%\$&97:<2C97:<!#)16 Ë*ÌGÍ ½ ÷ ?> >?:d$&>'>ò)1,%Ö97$&)19A4 lm:!¨:9AE$&)1NO)1; óN6 &IÕ`ùC"! H ú *Ë ÌGÍ a 1000 900 100 species 800 number of hits 700 @ 50 species 600 500 400 300 200 100 4 5 6 7 8 9 10 species 15 species 20 species 3 0 0 5 10 15 20 gammamax v ´5=+w éw v8[)1,(Ö9A$&)19A4{lm$&"(E;=$&,%,%)1\)1"5/'>?M5)1\C*-,!#L)197$&)1!#6, *-\C;=$&,(, )1\p)1/"5L:<h \:<>)1"%)1\=!#)1"("%$B/35!a_K'>?M5)1\|*-,5E$&"%!C*-'"5*-,OS<ZZZI"%\$&:2&!lm:!©'!#)1;m:!©:>)1:<!#'\p) *-,)1,(Ö97$&)1/974a1*-;=)12-lm:!uMO)1"(:6lK$&"%Õ E `Ü"©!#:>?L2B)+!#L:97$&/3|a Ë*ÌGÍlm:!uZDa U "(E/\p*-'35E/*-'"(6 Ë*ÌGÍINO:\]$&)1;t,%\*->ZDa SG"%*Z/a1K'>?M5)1\*-,!#L)19A$&)1!C$B÷ /NO*-2&N)1;lm:! x%9A'\N)1!, \p*->qú "(*-L?"(*KM5*-"%"(*->?z nS<ZZ<6UZ<6<<Z6SU<6<S<Z6i66j6Y6U6g6:/;?T/a}=E/) L2B*-"D$&!uM5:<!#)1;*-mgg<gZZ<Z!#$&>'2&:"%$&*-!a 1 100 species 0.9 50 Spearman rho 0.8 0.7 B 20 0.6 15 10 0.5 0.4 3 0.3 0 v ´5=+w o&w 2 4 6 8 10 12 14 16 18 20 gammamax v8[)1,(Ö97$&)1/974¨lK$&"%E;=$&,(, )1\p)1/"5/'>M5)1\*-,!#L)197$&)1!#6, *-\C;=$&,(, )1\p)1/"OL:<h \:<>)1"%)1\=!#)1"("%$B/35!a_@L)1:<\>?:{>)1:<~\:<9A*-\\)12&:"($&*-¨97*-)1,(Ö9A$&)1" Ë *-,SZ<ZZ $&% Qiq&QÇ÷ K LWsNRR#Áà YT > "(\]$&:2&!=lm$&"(E5/*-lm97*-\]\p)197"<*-\p;=)1\/lm:!='!#)1;{:!=:>)1:<!#'\p)C*-,)1,(Ö97$&)1/974a7*-;=)12 lm:!0M5)1"(:<6DlK$&"%E `Ü"!#:>?L2&){!#L:<97$&3a Ë*ÌGÍ lK:<!fZDa UK"(E/\p*-'35E/*-'"(6 Ë*ÌGÍ NO:<\$&)1;,%\*->qZ/a S0"(*KZDam'>M5)1\C*-,!#L)19A$&)1!÷ $&/NO*-2&NO)1;?lm:!x%9A'\N)1!u,%\*->qú "(*-L "(*mM5*-"%"(*->?z nS<ZZ6U<Z6Z<6-SU6-S<Z6i6-<6-j6-Y<6-U6-g6-:<;ØTDa/}=E/)0L2&*-"|$B!IM5:<!#)1;Ç*- gg<gZ<ZZ!#$&>'2&:<"($&*-!a &«!& H 90 80 70 number of hits 60 50 @ 40 30 20 10 0 0 2 4 6 8 10 12 14 16 18 20 gammamax v %´5rp0&w w v8[)1,%Ö97$&)1/974{lm$&"(Et;r$B,%,%)1\)1"'>?M5)1\C*-,D!#L)197$&)1!#6,%*-\©;=$&,%,%)1\)1"L:h \]:>?)1"()1\=!#)1"%"($&3O!a1m'>MO)1\=*-,OE/$&"(!©*-'"O*-,5S<ZZ<Z"%\$&:<2B!©lm:!©'!#)1;m:!©:>?)1:!#'\) *-,)1,%Ö97$&)1/974a0*-;=)12lm:!M5)1"%:6=lm$&"( E &!#:<>L2&)m!#L:9A$B/3|a Ë*ÌGͨlm:! Z/a U"(E/\p*-'35E/*-'"(6 Ë*ÌGÍN:\]$&)1;K, \p*->Z/a S"%*-LtZDaQK'>?M5)1\|*-,O!#L)197$&)1!©÷ $&N*-2&NO)1; ú "%*-L"%*¨M5*-"%"(*->?z n-SZZ<6U<Z6Z6SU<6<S<Z6i66j6Y6U6g<6<:<;?T/a lm:!x%9A'\N)1!, \p*->q m*-"()0"(E/:"=)1,%Ö97$&)1/974K;=)197\)1:!#)1;:<M5*-'""()1"($&>?)1!9A*->L:<\p)1;?"(*K\p)135'2&:<\C97:!#) x 45h%:<d<$&G! E_9A*->L:<\p)IÖ3|a j/a S<Zz#aQ}=E)L2&*-"D$&!uM5:<!#)1;*-mggg<ZZ<Z!#$B>?'2&:"%$B*-/!a ¾JI2K L ÁÂNM Y<g QSLÂNM®O¿ MKÀm¾IÂtKÀ2U sNRR Áà ?> 1 0.9 Spearman rho 0.8 B 0.7 0.6 0.5 0.4 0 2 4 6 8 10 12 14 16 18 20 gammamax v ´5=+w ± w v8[)1,(Ö9A$&)19A4{lm$&"(E;=$&,%,%)1\)1"5/'>?M5)1\C*-,!#L)197$&)1!#6, *-\C;=$&,(, )1\p)1/"5L:<h \:<>)1"%)1\=!#)1"("%$B/35!a_@L)1:<\>?:{>)1:<~\:<9A*-\\)12&:"($&*-¨97*-)1,(Ö9A$&)1" Ë¥*-,SZ<ZZ "(\]$B:<2&!=lm$B"%E5/*-lK9A*-\\)197"<*-\p;=)1\lK:<!='!#)1;~:<!=:>?)1:!#'\)C*-,)1,%Ö97$&)1/974aA*-;=)12 lK:<!+M5)1"%:6/lm$B"%E &ú!#:>?L2B)¨!#L:9A$B/3|a Ë*ÌGÍ lm:!fZ/a U¨"(E/\p*-'35E/*-'"(6 ËJÌÍ NO:\]$&)1;m,%\*->ðZ/a SI"%*-LK<Z/aAm'>MO)1\r*-,5!#L)19A$B)1!÷ $&N*-2&NO)1;tlm:!x%9A'\N)1!|, \p*->¸ú "(*-L "(*tM5*-"%"(*->?z nOSZ<Z6UZ<6-Z6S<U6-SZ<6-i6-<6-j6-Y6-U<6-g6-:<;ÇT/a}=E)L2&*-"=$&!IM5:!#)1;Ç*- ggg<ZZ<Z!#$B>?'2&:"%$B*-/!a $ % Beta model alphamax= 0.50 gammamax= 10.00 100 80 60 40 20 0 0 v ´5=+w ºw 0.2 0.4 0.6 0.8 1 y=*-lå)1,(Ö97$&)1/974¥M5)1"(:Ø>?*-;=)12!#)1"("%$B/3|am*-"%)"%E:<"G@JC¤5!m:\p)E)1:<NO$&2&4 :!#45>?>)1"%\$&9A:2ba Qiq&QÇ÷ K LWsNRR#Áà 5 765 z 1W Zuº»A. 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GF E I H * % P/ 2 RQ (/D @ ( @2@ X0 (@ @ A\[]) @ ( @ @ ( @2@ 2 H a ) ( C2) 0 ë5ìí=îHïðñò^ð"í/ó4ôí=ï]íFõ¡öïy÷"înøìù]ú/û=ü¿ð"ìïöìôöôýï]þ®înöõ¡öÿ !"#%$"'&( ) 0? 2 2 (KJ MN H ); S R) V @ ( @2@@ ZY0 ) %_ @ ( `2` ( @ ô«í=ïí/ü qõ¡öö?ìö /ïõ¡öòhïðþ&ì ëõ¡öïOþ óöõ îHí=ìôþ&ÿ Zõyþ ðò Fðÿ Zööì ú²ÿ {öìöîHí=ï]ð"þ&ìú qþ&ó"óþ ö /õ¡öôðÿöìï]í=ï]ð"þ&ìîHí=ï]ö í ïþú ökî9í D ôí/ïí ZîHí=ÿ 9 óö {þ&óøÿ¬ö úyï]þ {ö î9þ öõ¡öï]ù qþ&î*í=óóZõ¡í/ÿ ïþ û ,óöì Zï þ 0^ óöõ fþ&ìö qþ&îfí/ó"ó&õ öò^ðöõ c HN 9 G ( í5õ¡ïí=ìôí/înô óþ&ï]ïðì î9þ Zî9í/ÿ 8d6 $e& óþ&ï a +) ;T í=õºï ö í=õ Zî9í/ÿ ZðïõJõ¡ï]í=ìôí=î9ô?ôöù ú^úÿí ú²í kí=øõ¡õ¡ðí/ì ZîHí=ôðöìï ¯ï ö 43"57698 ;: 5<=5> < H Zî9í/ÿ õ¡í=ÿù í ïöî ú5ÿ înö í/înöôï ö²ôí/ïíyÿí=ï]î9ð kïþ \ï ö²í {þ í=õ óöõ5þ&ìï ö öî9öJõ¡ð"ÿøóí=ï]öôøõ¡ðì qînþ&ÿ ) 2 V R ö²õ¡ò^î9ð ïï í=ïôðôí=óó,þ ¿õ¡í/ÿ î9þ /úõ öòhðöõ í=ìô ò^þ&ìï]í=ÿðìí/ïðþ&ì¬îHí=ï]ö ð"õ¡øí/óð /öô¬øõ¡ðì ï í=ï ]þ&î 5õ¡ïîHí=ò^þ&ôí {öìöîHí=ï]þ&î ZöìöîHí=ïþ&îÿþ&ôöó Fþ í=ìô§ðïõºõ¡ï]í=ìôí=î9ô§ôö ð"í/ïðþ&ì ¯öî9ö ï ö?÷ þ&î ¯í î9þ&òhöõ¡õ¡öõyô«öõ¡òhîHð {öô½ðì \õ¡í=ÿ D b +V %_ f 57<agh8 6 $i& H a + înþ ZöìöîHí=ï]öô Ï «õ í/òhðì ðì Zóö 3í½òhþ&öìþ&òhóðìö ÿ Î ó"öÿöìïõï ö í=õ qþ&õ¡õ¡ðóð /í=ï]ð"þ&ìò í=ìòhö ï ö øõ¡ðì ï öõ¡ö²ôöìõ¡ðïðöõ =í Zøìôí=ìòhöõ í/î9í/ÿ¬öï]öî9õ ðí=ïðþ&ì ôöìõ¡ðï í=õ ïþ&óöîHí=ìòhöõ¡öïÅïþ înþ&ÿ ÷"ôöìû=í Zøü ï]ðì Oô«öìõ¡ðïðöõ ðï ÿí ð"ÿøÿ ðï ÿí ðÿ¬øÿ * + , @2@ BAC (ED E @ ( 0 ) 0 L O HUT WV *) @ ( @ ( @2@ V @ (@ ¯í/õòhî9öí=ïöô ]þ&óóþ ð"ì #!/bin/bash # generate data using Gaussian model, rows are species coenocline 0 1 < infile | den2abu | rm-zero-rows-cols 2 > gauss.dat # # plot raw data mat2gri < gauss.dat> plt gri p6.gri gnome-gv p6.ps # #eof ,í/ìô Q /j Æ count of individuals Î 0 ¯í/õ*ú²û û í/ïïöîHì Ï Ï ²Ï ú *ôöìõ¡ð"ï]ðöõ ú Ï û Ï/Ï²Ï Ï Ï /ú * 2 ìþ&ï\ð"ìíînö Zøóí=î 0 =ú =û Ï ú ö ú û öînö¿ïí {öì Q 21 . Q } Q ` 1 @ 1 *1 !V î9þ öî¿õ¡í/ÿ û=úJû/û²û Ï Î öyî9öí=õ¡þ&ì Ï óöõ (SD O } ` @ Q2QRQ1*Q2.*Q NNH G) í=îHð"õ¡þ&ì ú yú Jú yú Î Ï=Î²Ï ²Ï /û 21 2. Q +Q *Q 2 qþ&îJòhþ&ÿ ú^ú²ú=ûJú Ï kí=øõ¡õ¡ðí=ìðõfï í/ïZï ö¿õ¡í/ÿ ZîHí=ôðöìï ôí/ïí þ Î Î öî qöòhï]ó ö²îHí Ç]Ê 64 Ï D 2 Ä gradient 5î9ð Zðìí=ó ï ö ,Ê 0 þ&înôöî û Æ4È 400 ) +) s"t=u"v\w!x2yKzy|{2y 9 \ ; Q+*1 . *} ` @ 2Q Q 1 . } ` Q @ } ` @ 1 12Qb121b12.b1 1 } 1 ` . @ . ~. ~. .~Q .1 (D !) %F a * U ( û Î \k-lm Wn on \pSq rn È5Ì1Ä,Ê a + ï ö =ú /û ï öyò^øî öõºí=î9öJìþ&ï ]î9þ&ÿ îHí=ìôþ&ÿ ZîHðô qí Zøìôí/ìò^öõ þ&î9ôöînöôí=óþ&ì óö û Jû ²û 9 a 2T Zî9í/ôðöìïóþ&þ óð Zö²ï ðõ óí=ò^öõ«þ&ì lm 2m Wk = ÅÄ,Ç ZÇqÆÅÇqÈ5É§Ê count of individuals Í È c yÈ5ËÌÉÊ =Í 1 Î 70 0 0 64 gradient ) ~ M" ~F 9 stau"vrwxbz2y|y o 2 0 (D a Q+1 . } ` @ 2Q 21 2. } ` @ 2Q 21 2. } ` 2@ Q 1 . } ` Q @ Q WQ WQ Q2QWQ1WQ2.WQ Q } Q ` 1 @ 1 O1 W1 1QW121W1.G1 1 } 1 ` . @ . R. R. .2QR.21 H a / " H N ) 9 0 ( ) 0 9 "V % ( ëýõ¡ö Zøöìòhökþ í=ìô§õ¡í/ÿ óöô ú Î Ï=Ï5Ï ú^úú=ûkú Ï Ï Ï /ú ìð"ÿþ&ôí/ó í=îHïðñòhðí/ó&ôí=ï]í qí Zøìôí/ìò^öõ ú kú ú kú Î =ú Ï=Î5Ï 5Ï =û =û Jí/óþ&ì Ï í=ïï]öî9ì¯þ ZöìöîHí=ïöô¬ï înþ&ø öJõ¡í=ÿ ú ú û û=úkû=ûû Ï Ï 5ï ö öì èé è Zí5÷"ôöïöîHÿ¬ðìðõ¡ïðòhü {þ&î9ðï ÿ qþ&î*ï ö 5ë ð"ÿ kí=øõ¡õ¡ðí=ìÿ¬þ&ôöó û û kû û Î =ú Î ZîHí=ôðöìï qøì Zìþ \õ öòhðöõ*ôðõ¡ïîHð Zøïðþ&ìðõºõ¡ï]ð"óó *' ! +* *\M D 9 / ) * WM " + ã1ä å1ä å í óöõºí=î9ö4ð"ìï öJò^þ&î9î9öòhï{þ&î9ôöî«úû ì =û û û Ï Ï kÏ ú Ï Ï û Î Zö ]þ&î9ö1î9öí=ó=í/ìí/ó Zõ¡ðõ ðõ¡ð {ó"ö ²è ZîHí=ôðöìïí=ìí=ó Zõ¡ðõ óöÿ¬öìïöô F~ W %" = 2 ¯í/õ¿ò^í=îHî9ðöô ï ö înþ {î9í/ÿÖ÷ þ&øïøõ¡ðì §ï ö *ë í=óù Zí ôù ü /þ&ì§í Zøìôí/ìò^öõ . Î /j Æ ) 0 ( c) H 2 %V 9c %H (D O oV 9_ o" % ? qðìõ¡ï]öí=ô?þ ï]ð öó í/ìô «ô«öìõ¡ðïðöõ qþ&î9ÿí=ïÅøõ¡ðì ð 9 0 a W a 9 %? 2 % a H a V 9;T îHðþ&î¿ï]þï ö5í=ìí=ó Zõ¡ðõ Zï ö5ôí/ïí ®÷"î9í û Zí ù Zð Zü öôí=ï]í ðõ¡øí=óð =öô öînö5ï öì?î9öí=îHî9í/ì ÷ {î9ðü =í=õ Zöô?øõ¡ðì #eof 70 0 64 gradient ,Ê Ä Ç]Ê HV V öînöò^þ&ì ¬ï öînöõ¡øóïðì î9ö {ðþ&øõ¡ó 0 Æ4È öî9ï]öô?ïþí¾õ¡øðïù qìþ&ï4÷"î9í ¯û Zí ü *í=õï ö¬ô«öìõ¡ðïðöõ #!/bin/bash # # reconstruct using QAP, and write out sample order itransp < data | raw2qap-big | gqapd-p > sample-order # itransp < data > tdata cat tdata sample-order > ini rearr < ini | itransp | mat2gri > plt gri p6.gri gnome-gv p6.ps # count of individuals í Zóö \k-lm Wn on \pSq rn È5Ì1Ä,Ê ¯öî9öî9öó"í/ù ¬þ&î9ôöî öòhï]þ&î lm 2m Wk = Í È ÅÄ,Ç ZÇqÆÅÇqÈ5É§Ê c yÈ5ËÌÉÊ stau"vrwxbz2y|y D c W (W V a ö 5ë òhþ&öìþ&òhóðìö ë Î=Î ùqînöòhþ&ìõ¡ïî9øòhï]öô ) ) F 9 ) 9 0 ) 0^ ( þ&înô«öîkþ U /= ); " Zøìôí=ìòhöõ ïöîHÿð"ìðõ¡ïðòyÿí=ììöî¿þ ö {öî =Í öînö yí=îHïðñòhðí=ó*õ¡í/ÿ Zøðïö4ðÿ î9þ öî9ó óöõþ {øïZòhþ&ì Zî9í/ôðöìï«í=ìí=ó Zõ¡ðõ «øõ¡öô?ðìõ¡ïöí=ô?þ íyô«ð"õ¡ï]ðìò^ïøìðÿ¬þ&ôí/ó ðï í=ò^ïü í/ïïöîHì?öÿöî Zöô í=õÅìøÿ¬öî9þ&øõÅóþ&ò^í=ó{þ ÷"øìð"ÿþ&ôí/ó"ðï ðì ï]ðÿ¬í Zí=ìô¬ðõ ð"ï ¬ìþ&ðõ¡öü ]ÿöõ¡õ¡ðì ø ¾ï]þ5í²ôöù Oôöìõ¡ðïðöõ þ ù Oí/óï þ&ø Fï ö 9 ) í=ïï]öî9ì¼ð"õkìþ&ï*øìðÿþ&ôí=ó a {öî òhóþ&õ¡öïþøìð"ÿþ&ôí/ó"ðï ð"ì¬ï öõ¡öìõ¡öþ ö÷"ìþ&ðõ¡öü þ&øî*ôí/ïí kí=øõ¡õ¡ðí/ì í=ï]ïöîHìöÿöî {öô \ T M" \ /V \( D ao a % V ( a a E H MM 9 a 9V ( ëÖôðõ¡ïðìòhï*ü"òhþ&ÿ ²í ]þ&îHÿð"ì þ b ö {öî &ðõ4ìþ&ï«õ¡þ&ÿö 0 î9þ&òhöõ¡õ &ðì ) a *í=ï öî /ðïînö î9öõ¡öìï]õºþ&øîºð Zìþ&î9í/ìò^öyþ øöìò^ù ï öïî9øöôí/ïí ï öÿõ¡öó {öõ ¡ ã1ä å1ä *' !+7 'M èé 9é 9o \) ) 0 %V 0 c 7 V \ 9- Wa ¢ a -) £ \) ) £V 0 N (+D 0 W 9 H a % \^ a a N O )H H" 0 0 0 ( ë5õ4þ&ìó ô qõ¿þ fôöìõ¡ðï]ð"öõ {ìþ&ïô«öìõ¡ðïðöõ4ï öÿõ¡öó öõ Zòhí/ì þ&ì½ôöìõ¡ð"ï]ðöõkðõkðìï]î9ðìõ¡ð"ò^í=óó ô«öìõ¡ðï 5ë ô Jï]þ¼þ Zï]í=ðì (í=ó Zþ&îHðï ÿ þ&óö ï]ðÿ¬í =ïþ&óöîHí=ìòhöõ Zí Zðóð"õ¡ï]ðò õ¡ðÿøó"í/ïðþ&ìõþ înøìõ îHí=ï öî ðïOðõ²í înþ Jôöìõ¡ðï øõ ìþ&ï*íõ¡ðì Zøìò þ í Zöõ¡ðí=ì Zökí=ò ðö öô {í/óøöõ *þ&ì Zóöõ¡öîHð"í/ïðþ&ì þ&ï öõ¡öõyþ&ì?õ¡í=ÿ ,õ¡öî9ðí=ï]ðþ&ì \í/õðï*øõ¡öõkõ¡í=ÿ ðò ó"ðì ÷oï ö¯õ¡þ&óøïðþ&ìü ðõkþ óö¾îHí=ì Zðì ]î9þ&ÿ ôöïöîHÿ¬ðìðõ¡ïðò Zïí/ð"ìöô í=ìô¼õ öò^ðöõ²þ ù í/ìô¬õ Zö + G M a ) a V 9V (;D 0 W / ) 0 H) ) 9V / / 9V a T¤\¥o¦ @ ( @@ 2§¥o¦ @ ( @2@@ 2¨©¦ @ ( @ ¨'ªE¦ @ ( @ (D 0 I ? ) a 0 V 9V \ 9) 2M b %V H=U T/«h©G¦ @ ( `2` U«ª¬¦ ( @ ?'©¦ @ ( @ Kª¦ @ ( @ 22¤M®¦ ( @ -§"®¦ @ ( @2@ ( 5øðï]öìí=îHînþ d) ÿþ&ôöó îHí=ÿöïöîHõ þ §þ òhþ&ìñôöìòhöó"ðÿðïõ qþ&î¿í¾ÿðìðÿøÿ ]ðôöìïðò^í=ó í=ÿþ&øìïfþ ]þ&îÅí=óóõ¡í=ÿ «ðìôð {ðôøí=óõ¡í=ÿ úhú öõ öòhðöõ óöõ Jú D *) \ 2 ö í/ïöô qþ&óóþ ¯ðì í {þ&øïõ¡í/ÿ Zð {öì þ&ìþ&ÿ í/ìô øïfþ&ìïþí=óó í/õ í/î9í/ÿ¬öï]öî9õ²ï]þí/ó"óþ {þ&ó öô ó"öõ {í=õJìþôöïí/ð"óöô í/î9í/ÿ¬öï]öî9õ ô«öïí=ðóõþ&ì½óð qöí=ìô®ïí û ¯í/õ öî9ö føìòhöîHïí/ðìï ð"ì ðì öînö øõ ï ö5õ¡í=ÿ qþ&îHÿ¬í/ïðþ&ì {ð öì í=ù þ&ì¬õ¡öôðÿöìï]þ&óù ú qðôöìïðòhí/ó ï ö óö ú ]þ&îí=óóZï öyõ öòhðöõ1ðì ¯öî9ö ú {þ&ó {öô í=õ1ìþ Jú ú ) H" õ¡öîHðöõºþ óöJîHí=ì Zðì V " 9 ó"þ&ï]õ1î9ö înöõ¡öìïZõ¡ö {öîHí=ó 0 L þ&ï öõ¡öõ /í=ìô§înöóí/ïöôõ öòhðöõºô«ð"õ¡ï]î9ð Zøï]ðþ&ìõ )d. ~ qþ&øïþ =û ( Zöìöînù ]ò^þ&öìþ&ò^ó"ðìö } /j Æ inferred density Î \k-lm Wn on \pSq rn È5Ì1Ä,Ê Æ4È ,Ê Ä Ç]Ê 1000 0 0 64 reconstructed gradient ¯"°=±"²\³!´+µ¶|·2¶R¸N¹"ºM»¼a½H¾¿0À9¿/ÁÂÃÄ2źMÆ9¾+»ÇÈr¾Ç?À%¿SÉWÊËWÌÍWÁbÁ7ÁWÁ2ËÎÌ2ÏÁ2ÉÐWÁÏbÁÍÑbÁÌ Á2ÐGÁÊWÁ2ÑWÌ2ÌWÌÁGÁ2ÎWÌÊWÌ2ÉWÌÐGÌ2ËWÌÍWÌ2ÑWÌ2ÎOÉ2ÏWÉ2ÌWÉÁWÉ2ÍWÉÉGÉ2ÐWÉËWÉ2ÊWË2ÏOÉ2ÎWË2ÁWÉÑ Ë2ÌË2ÊË2ÍË2ËË2ÐË2ÑOÍÌÊÏÊÌÍËÊÐÊËOËÎÊÉÊÍÊÁNÊ2ÊOËÉÍÉÊÑÍÏÊÎÍÁOÍÊÂ Ò »ÅºMÄ2Ç!¾RÓNÀ%¼a½EÔMÕ;Â0Ñd Ë lm 2m Wk = ÅÄ,Ç ZÇqÆÅÇqÈ5É§Ê inferred density Í È c yÈ5ËÌÉÊ =Í Î ` 1000 0 0 64 reconstructed gradient ¯°a±"²r³´bµ2¶|Ö¶R¸N¹"ºM»¼½¾¿0À9¿SÌdÂ"ÃÄźMÆ9¾+»Ç!È\¾Ç?À9¿ÍÁWÉÊWËÌWÁÁÁ2ÉÎWÐÁ2ÏÁÍbÁËÑWÌ2ÏÁ2Ê Á2ÌWÁÐWÁ2ÑGÌÌWÌ2ÊWÁÎWÌ2ÁGÌËWÌ2ÉWÌ2ÍWÌÐGÌ2ÑWÌÎWÉ2ÏWÉÌGÉ2ÁWÉËWÉ2ÉWÉ2ÍOÉ2ÊWÉ2ÐWËÏWÉ2ÎWÉÑGË2Á Ë2ÌË2ÍË2ÊË2ÐË2ËË2ÑÍ2ÌÊ2ËÊ2ÏÊ2ÌÊ2ÐÊ2ÉÍ2ËÊ2ÊÍ2ÉOËNÎÊ2ÁÊ2ÎÊ2ÍÍ2ÏÊ2ÑË2ÉÍ2ÁÍ2ÊÂ Ò »ÅºMÄǾRÓNÀ9¼½NÔMÕ;ÂhÑ Êd }@ /j inferred density Æ \k-lm Wn on \pSq rn È5Ì1Ä,Ê Æ4È ,Ê Ä Ç]Ê 1000 0 0 64 reconstructed gradient ¯"°=±"²\³!´+µ¶|×2¶R¸N¹"ºM»¼a½H¾¿0À9¿/ÉÂÃÄ2źMÆ9¾+»ÇÈr¾Ç?À%¿SÉWÌ2ÏÎËWÍÊWÁÌWÁ2ÍÁ2ÉÐÁÁWÑÁ2ÏÁÌbÁÊ Á2ËGÁÐWÁ2ÑWÌ2ÌWÁÎGÌ2ÉWÌÊWÌ2ÁWÌËGÌ2ÍWÌÐWÌ2ÑWÉ2ÏOÌ2ÎWÉ2ÌWÉÍWÉ2ÁWÉÉGÉ2ËWÉÐWÉ2ÊWÉ2ÎOË2ÏWÉ2ÑWËÁ Ë2ÌË2ÊË2ÍË2ËÍ2ÌÊ2ÐOÊÏËÐÊËÊÉÊÌËÑOÍÉÍÏÊÁÊÍNË2ÎOÊÊÊÎÍËÊÑËÉÍÁOÍÊÂ Ò »ÅºMÄ2Ç!¾RÓNÀ%¼a½EÔMÕ;Â0Ñd Ê2Ø=Ñ Í lm 2m Wk = ÅÄ,Ç ZÇqÆÅÇqÈ5É§Ê inferred density Í È c yÈ5ËÌÉÊ } =Í ú 1000 0 0 64 reconstructed gradient ¯°a±"²r³´bµ2¶|Ù¶R¸N¹"ºM»¼½¾¿0À9¿SËdÂ"ÃÄźMÆ9¾+»Ç!È\¾Ç?À9¿ÌÏÍbÁWÉËWÊÌWÁÁÎWÐÁ2ÍÑWÁÏbÁÐÁ2ÉÁ2Ë Á2ÌWÁÊWÌ2ÌGÌËWÁ2ÑWÌÊWÌ2ÁGÁÎWÌ2ÉWÌ2ÐWÌÎGÌ2ÍWÉÏWÌ2ÑWÉÌGÉ2ÁWÉÍWÉ2ÉWÉ2ËOÉ2ÊWÉ2ÐWËÏWÉ2ÎWÉÑGË2Á Ë2ÌË2ÊË2ÍË2ËË2ÐÍ2ÌÊ2ÏË2ÑÊ2ÌÊ2ÐÊ2ËÊ2ÉË2ÎÊ2ÁÊ2ÍOÍNËÊ2ÊÍ2ÉÊ2ÑÍ2ÏË2ÉÊ2ÎÍ2ÁÍ2ÊÂ Ò »ÅºMÄǾRÓNÀ9¼½NÔMÕ;ÂhÑ ÊØaÑd Ð /j û Æ inferred density } \k-lm Wn on \pSq rn È5Ì1Ä,Ê Æ4È ,Ê Ä Ç]Ê 1000 0 0 64 reconstructed gradient ¯"°=±"²\³!´+µ¶|µ2¶R¸N¹"ºM»¼a½H¾¿0À9¿/ÊÂÃÄ2źMÆ9¾+»ÇÈr¾Ç?À%¿SÌ2ÏÉWÌÍWËÊWÁÎWÁÁÁ2ÏÐWÁ2ÉÁÌbÁËÁ2ÍÑ Á2ÊGÁÐWÌ2ÁWÌ2ÌWÁÑGÁ2ÎWÌÊWÌ2ÉWÌËGÌ2ÍWÌÎWÌ2ÑWÌ2ÐOÉ2ÏWÉ2ÌWÉÍWÉ2ÁWÉÉGÉ2ËWÉÊWË2ÏWÉ2ÐOÉ2ÎWÉ2ÑWËÁ Ë2ÌË2ÍË2ÊË2ËË2ÐÍ2ËOÊÏËÑÊÌÊËÊÉÊÐOÍÌÍÉÊÊÊÁNÊ2ÍOÍÏÊÎÊÑËÎËÉÍÊOÍÁÂ Ò »ÅºMÄ2Ç!¾RÓNÀ%¼a½EÔMÕ;Â0Ñd Ê2Ø=ѠΠlm 2m Wk = ÅÄ,Ç ZÇqÆÅÇqÈ5É§Ê inferred density Í È c yÈ5ËÌÉÊ } =Í =Ï 1000 0 0 64 reconstructed gradient ¯°a±"²r³´bµ2¶|ÚÛ2¶/¸N¹"ºM»¼½¾¿0À9¿+ÍdÂ?ÃÄ2źMÆ9¾G»ÇÈ\¾ÇSÀ9¿*ËÉOÍÁOÌÌ2ÏOÊÁÁÎOÁÉOÁ2ÍÐOÁÌOÑÁ2Ï Á2ËWÁÐWÁ2ÊGÁÑWÌ2ÌWÌÉWÌ2ÁGÁÎWÌ2ËWÌ2ÊWÌÎGÌ2ÍWÌÑWÌ2ÐWÉÏGÉ2ÌWÉÍWÉ2ÁWÉ2ÉOÉ2ËWÉ2ÐWÉÊWÉ2ÎWËÏGÉ2Ñ Ë2ÁbË2ÌËÍ~ËÊbËÐ~ÍÌË2ËbË2ÑÊ2ÏbÊ2ÌbÊ2ÐÊ2ÉbÊ2ËÍË~ÊÍbÊÁ~ËÎÊ2ÊbÍ2ÏÊÎ~ÍÉbÊÑ~ËÉÍ2ÁbÍ2ÊÂ Ò »ÅºMÄǾRÓNÀ9¼½NÔMÕ;ÂhÑ ÊØaÑd Ñ 2Ü Ý~Þ2ßà2áâ=Þ2ã!äã!ä\åæ]ãUçWè"éÞ+â=áá ñò À9ÕóôMÅ ò ¾ÇR»õ?¼½¾¿0¾O½¹"ºM»¼½¾¿0¾¿b¿0½H»ôMÆ9È ò ¾GÕ"¾ó¾ÇÄ2¼¾Èö¼»» ò ¼ÄÀ%óZ¼½¾À%ÇRÆ9À9÷"¾Æ9À9Ø ê ã ëâ=áIÞåì2ã;åéÞåí"éãrâ=ábî Þ2ï%â0ð ½»»ÈøÇ!ļÀ9»¿0ù;À] ¾ùU¼½¾ožÄ2¿0ôMÇ!¾»õ/¼a½H¾À9ÇbǾÆ9Ä2¼aÀ%úH¾NºMÇ» ò Ä ò À9Æ%À9¼aÀ%¾¿2¸N»ÓN¾ú¾Çaù;¼a½HÀ9¿GÀ9¿ â=2Þ ãràæ]áàãæ;áí"ã û »ÅºMôM¼aÄ2¼aÀ%»óÄ2Æ9Æ9¹E¾ü¼Ç¾Å¾Æ9¹À9ó¼¾ó¿0À9ú¾b¼Ä2¿0÷ù¼a½H»ôMÕ½c¼½¾bÆ%À9÷"¾Æ9À9½»»È-Ç!ļÀ9»¿IÄǾ+¼a½H¾ ý ºMÇ»ºM¾ÇUÄ2ó¿0ÓN¾Ç'þ¼a»G¼½¾*ºMÇ!» ò Æ9¾Åÿ»õ¿0¾ÇÀ%ļÀ9»ó? ¼/À9¿OÄ2Æ9¿0»Õ»»È¬¼a»ZºM»À9óH¼R»ôM¼/¼a½Hļ¼a½H¾ ý Õ"»»Èø½¹"ºM»¼½¾¿0À9¿0þL¿0½H»ôMÆ9È ºMÇ»Èrô û ¾EÄ Å»Ç¾ û »ÅºMÄ û ¼ û »¾ó» û Æ9À9ó¾ºMļ¼a¾Ç!óÓNÀ9¼½¬Æ9¾¿0¿ ý ó»À%¿0¾þÀ9ó ¼½¾,ôMóHÀ9Å»È\Ä2ÆSºMÄ2¼a¼¾Çó ½HÀ9¿SÀ%¿ ò ¾ û Ä2ôM¿0¾*¼a½H¾¿0¾~½H¹"ºM»¼a½H¾¿0¾¿/ÄǾ ò ôMÀ9Æ9¼M»óo¼½¾+¿0À9ÅôMÆ9Ä2¼¾ÈÈ\¾ó¿0À9¼À9¾¿0ù¼½ôM¿ó»¼ }Q /j Æ \k-lm Wn on \pSq rn È5Ì1Ä,Ê Æ4È ,Ê Ä Ç]Ê H a ) ) a S 2 '( D 0 "!$ < 6 ) V 9" B) o O) " G a M `( 1 `( @ a a ) M (`d( Q2'N 9G" )a) ) 7 2N 7N L65 ":27f ( ðìòhóøôðì 5ï öÅóþ&õ¡õþ í Zøìô«í=ìòhö øõ¡öô qþ&îyðóóøõ¡ïîHí=ïðþ&ì ï ö4÷"ìþ&ðõ¡öüð"ìñ ö &ð"ì ]öò^ïZþ \õ¡í=ÿ ÐJÑ qþ&î9ÿí=ï]ð"þ&ìôøöÅïþkí=óó=ï ö øõ ð"ïðõºïþ Zõ í=õí Jïþ ó"ðì Zþ&þ&ô ú ò^í=ìôðôí=ï]ö ¯ðï ï í/ïZþ õ¡ò öÿö ð"ò O×Õ{ÛJÚ ¬ÛÕ¾ß ¡ înþ&ò^öõ¡õ¡öõ«ï í=ï&ÿí ¯ôöìõ¡ðï þ&ìï]þ Zö²ò^þ&ìò^ó"øô«öôï í=ï fþ \ï ö²í Zþ qþ&îyí§õ¡þ&øìô {ñ {ö²õ¡ð þ&ï öõ¡öõ þ&ï öõ¡ðõ qòhþ&ÿ í=înö öînöÅð"ïðõOò^í=øõ¡öôõ¡þ&óöó 5ï ö ¯í/õ Õ Û ø b + D \ 9V a 9V ) < 6 8 $ 5 G ><=65 7: > 0 9) " D a 27 M 0 5 f 2#% " J % 2) 2 9 `2` } Q } (D +) ) ( D O) ;) a !) r) ) +) M" ( ¢ 2M 97 \ L + a M L V ` } 1 ` Q ( D a ) O) M" ) \ 0 9 2 E 0 (D 0 a ) a 0 H ) HV 9_ 9 2 0 a ) *) M" ( V 0 %)R c H£) a a" );) " ( ( 2M 0 9) ) a E a ~ \ d 6 > 6 ( ( ) R 0 a * ( J N) H ø HV H 2T+ 9 V 0 ? 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Written by Peter Cejchan Input (from stdin): Output (to stdout): Language: ISO C. Depends on: GNU Scientific Library (GSL). Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (2000/02/17). version 0.02 (2000/03/13): command-line parameter added. License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include <stdio.h> #include <stdlib.h> #include <math.h> #include <gsl_histogram.h> #include <gsl_matrix.h> #include <gsl_rng.h> #include <gsl_sf_gamma.h> /* #include "nrutil.h" */ #define PARFILE "abu2den.par" #define PLOTFILE "plotfile.dat" #define SEEDFILE "seeds" gsl_rng *r; /* random number generator */ /* __________________________________________________________________________ d2a __________________________________________________________________________*/ int d2a( double Dp, /* density (pure) */ úhú ` ú/û @ /j Y double double double double double double double double double double double ){ Dm, V, Sigma_v, Rl, Rh, Cl, Ch, Ol, Oh, G, Sigma_g /* /* /* /* /* /* /* /* /* /* /* \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u mean density for the species */ sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ its std */ double gsl_ran_gaussian (const gsl_rng * R, double SIGMA); double gsl_ran_flat (const gsl_rng * R, double A, double B); unsigned int gsl_ran_poisson (const gsl_rng * R, double MU); unsigned int gsl_ran_binomial (const gsl_rng * R, double P, unsigned int N); double m, d; int y, n; /* d= Dp + Dm*genuni(Ol, Oh)*gennor(V, Sigma_v)/(gennor(G, Sigma_g)*genuni(Rl, Rh)); */ d= (Dp + Dm*gsl_ran_flat (r,Ol,Oh))*(V+gsl_ran_gaussian (r,Sigma_v))/ ((G+gsl_ran_gaussian (r,Sigma_g))*gsl_ran_flat (r,Rl,Rh)); m = gsl_ran_flat (r,Cl,Ch); n = gsl_ran_poisson (r,d); y = gsl_ran_binomial (r, m, n); return(y); } /* __________________________________________________________________________ main __________________________________________________________________________*/ int main(int argc, char *argv[]) { gsl_histogram *h; gsl_histogram_pdf *posterior; double x, y, den, denmin, denmax, rnd; unsigned long int seed = 1, bins=100,repeats = 1, iter=100000, i, abu; FILE *plotfile, *seedfile, *parfile; double gsl_ran_flat (const int d2a( double Dp, double Dm, double V, double Sigma_v, double Rl, double Rh, double Cl, double Ch, double Ol, double Oh, double G, double Sigma_g gsl_rng * R, double A, double B); /* /* /* /* /* /* /* /* /* /* /* mean density for the species */ sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ its std */ ); double Dm, V, Sigma_v, Rl, Rh, Cl, Ch, Ol, Oh, G, /* /* /* /* /* /* /* /* /* /* mean density for the species */ sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ ú/û=ú Sigma_g; /* its std */ /* read in new seed */ if((seedfile = fopen(SEEDFILE,"r")) == NULL) seed = 1; else { fscanf(seedfile, "%ld ", &seed); fclose(seedfile); } /*do the RNG initialization*/ r = gsl_rng_alloc (gsl_rng_uni32); gsl_rng_set (r, seed); /* read in params */ if((parfile = fopen(PARFILE,"r")) == fprintf(stderr, "Model params file return(1); } fscanf(parfile, "%lg %lg %lg %lg %lg %lg &V, &Sigma_v, &Rl, &Rh, &Cl, &Ch, &Ol, &Dm, &denmin, &denmax, &bins, &iter); fclose(parfile); NULL){ is missing. Exit.\n"); %lg %lg %lg %lg %lg %lg %lg %ld %ld", &Oh, &G, &Sigma_g, /* read-in the value of abu */ scanf ("%ld", &abu); /* read-in the number of densities to be generated */ if (argc > 1) repeats = atoi(argv[1]); /* allocate histogram */ h = gsl_histogram_calloc_uniform (bins, denmin, denmax); /* open plotfile */ if((plotfile = fopen(PLOTFILE,"w")) == NULL){ fprintf(stderr, "Cannot open output file. Exit.\n"); exit(1); } /* create likelihood x Jeffreys’ prior */ gsl_histogram_reset (h); for (i = 0; i < iter; i++){ den = gsl_ran_flat (r,denmin, denmax); y = d2a(den,Dm,V,Sigma_v,Rl,Rh,Cl,Ch,Ol,Oh,G,Sigma_g); if (y==abu) /* 1/den is the Jeffreys’ prior: is it done properly? */ gsl_histogram_accumulate (h, den, 1/den); } /* print out the histogram */ gsl_histogram_fprintf (plotfile, h, "%g", "%g") ; fclose(plotfile); /* create posterior pdf */ posterior = gsl_histogram_pdf_alloc (h); /* sample it */ for (i = 0; i < repeats; i++){ rnd = gsl_rng_uniform_pos (r); x = gsl_histogram_pdf_sample (posterior, rnd); printf("%g ", x); } /* release memory */ gsl_histogram_free (h); gsl_histogram_pdf_free (posterior); gsl_rng_free (r); /j Y ú/û=û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u /* etc... */ exit (0); } /* eof */ ¡ M HMM* y æZæ1ä 1ä ²Õ è }~ adv-seeds.c Advances seeds for random number generator and writes them to file. compile: gcc adv-seeds.c -o adv-seeds -Wall Written by P. Cejchan, 1999/09/23. Compiled on Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.1 19990809 (prerelease) __________________________________________________________________________ */ #include <stdio.h> #include <limits.h> #define SEEDFILE "seeds" /* __________________________________________________________________________ main __________________________________________________________________________ int main(void) { long ij, kl, create = 0; FILE *seedfile; /* Read in old seeds */ if((seedfile = fopen(SEEDFILE,"r")) == NULL){ create = 1; } if(!create){ fscanf(seedfile, "%ld%ld", &ij, &kl); fclose(seedfile); } else{ ij = 1; kl = 0; } /* Create new seeds */ kl++; if(kl >= LONG_MAX){ ij++; kl = 1; } if(ij >= LONG_MAX){ ij = 1; } if((seedfile = fopen(SEEDFILE, "w")) == NULL) return(1); fprintf(seedfile, "%ld %ld\n", ij, kl); fflush(seedfile); fclose(seedfile); return(0); } /* eof */ y æZæ1ä 1ä * ' èÖ ^é }~ ahisto-0.02.c */ ú/û Ï Maps density onto abundance, and produces abundance histogram. written by Peter Cejchan Input (from stdin): density (double) Output (to stdout): abundances (long) Language: ISO C. Depends on: Netlib routines from ranlib.c. Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 /libc6 2.1.37/ gcc version 2.95.2 20000220 History: version 0.01 (2000/03/17); version 0.02 (2000/03/20) comman-line par; License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include #include #include #include #include #include #include <stdio.h> <stdlib.h> <math.h> <gsl_histogram.h> <gsl_matrix.h> <gsl_rng.h> <gsl_sf_gamma.h> #define SEEDFILE "seed" #define PARFILE "ahisto.par" #define PLOTFILE "plotfile.dat" gsl_rng *r; /* random number generator */ double gsl_ran_gaussian (const gsl_rng * R, double SIGMA); double gsl_ran_flat (const gsl_rng * R, double A, double B); unsigned int gsl_ran_poisson (const gsl_rng * R, double MU); unsigned int gsl_ran_binomial (const gsl_rng * R, double P, unsigned int N); /* __________________________________________________________________________ main __________________________________________________________________________ */ int main(int argc, char *argv[]) { gsl_histogram *h; double Dp, /* species’ pure density */ Dm, /* mean density for the species */ V, /* sample volume */ Sigma_v, /* its std */ Rl, /* sedim. rate, lower... */ Rh, /* ...upper limit */ Cl, /* fossiliz. chance, lower... */ Ch, /* ...upper limit */ Ol, /* contaminat. intensity, lower... */ Oh, /* ...upper limit */ G, /* length of a generation */ Sigma_g, /* its std */ m, d, abumin, abumax; long y, seed, bins, iter, i, n; int print = 0; FILE *seedfile, *parfile, *plotfile; /* read-in command-line arguments */ if (argc>1) print = atoi(argv[1]); 2Q /j Y ú/û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u /* read in params */ if((parfile = fopen(PARFILE,"r")) == NULL){ fprintf(stderr, "Model params file is missing. Exit.\n"); return(1); } fscanf(parfile, "%lg %lg %lg %lg %lg %lg %lg %lg %lg %lg %lg %lg %lg %ld %ld", &V, &Sigma_v, &Rl, &Rh, &Cl, &Ch, &Ol, &Oh, &G, &Sigma_g, &Dm, &abumin, &abumax, &bins, &iter); fclose(parfile); /* read in new seed */ if((seedfile = fopen(SEEDFILE,"r")) == NULL) seed = 0.0; else { fscanf(seedfile, "%ld ", &seed); fclose(seedfile); } /*do the RNG initialization*/ r = gsl_rng_alloc (gsl_rng_uni32); gsl_rng_set (r, seed); /* read-in the value of density */ scanf ("%lg", &Dp); /* allocate histogram */ h = gsl_histogram_calloc_uniform (bins, abumin, abumax); /* open plotfile */ if((plotfile = fopen(PLOTFILE,"w")) == NULL){ fprintf(stderr, "Cannot open output file. Exit.\n"); exit(1); } /* proper simulation of observations starts here */ for (i=0; i<iter; i++) { d= (Dp + Dm*gsl_ran_flat(r, Ol, Oh))*(V+gsl_ran_gaussian(r, Sigma_v))/ ((G+gsl_ran_gaussian(r, Sigma_g))*gsl_ran_flat(r, Rl, Rh)); m = gsl_ran_flat(r, Cl, Ch); n = gsl_ran_poisson(r, d); y = gsl_ran_binomial(r, m, n); /* y is abundance */ gsl_histogram_accumulate (h, y, 1); if (print) printf("%ld ", y); } /* print out the histogram */ gsl_histogram_fprintf (plotfile, h, "%g", "%g"); fclose(plotfile); /* release memory */ gsl_histogram_free (h); gsl_rng_free (r); /* etc... */ exit (0); } /* eof */ y æZæ1ä 1ä è I å é }~_nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn am2dt-0.03.c 21 ú/û Generates a tensor of densities (vector of density matrices) from an abundance matrix Written by Peter Cejchan Input (from stdin): number of species (=rows), number of samples (= columns), matrix of abundances (specieswise) Output (to files): prescribed number of matrices (a tensor) consisting of: number of species (=rows), number of samples (= columns), matrix of densities (specieswise); Language: ISO C. Depends on: GNU Scientific Library (GSL). Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / libc6 2.1.3-7 / gcc version 2.95.2 20000220 History: version 0.01 (2000/03/27). version 0.02 (2000/04/18): Makefile rewritten for libgsl0 v. 0.5+-1. version 0.03 (2000/05/29): Serious bugs in indexing corrected! License: GPL <http://www.gnu.org/copyleft/gpl.html> Example: ./am2dt 5 < gauss.dat __________________________________________________________________________*/ #include #include #include #include #include #include #include #define #define #define #define <stdio.h> <stdlib.h> <math.h> <gsl_histogram.h> <gsl_matrix.h> <gsl_rng.h> <gsl_sf_gamma.h> SPECPARFILE "spec.par" SAMPPARFILE "samp.par" COMPARFILE "com.par" SEEDFILE "seeds" gsl_rng *r; /* random number generator */ /* __________________________________________________________________________ d2a __________________________________________________________________________*/ int d2a( double double double double double double double double double double double double ){ Dp, Dm, V, Sigma_v, Rl, Rh, Cl, Ch, Ol, Oh, G, Sigma_g /* /* /* /* /* /* /* /* /* /* /* /* density (pure) */ mean density for the species */ sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ its std */ double gsl_ran_gaussian (const gsl_rng * R, double SIGMA); double gsl_ran_flat (const gsl_rng * R, double A, double B); unsigned int gsl_ran_poisson (const gsl_rng * R, double MU); unsigned int gsl_ran_binomial (const gsl_rng * R, double P, unsigned int N); double m, d; int y, n; 2. /j Y ú/û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u d= (Dp + Dm*gsl_ran_flat (r,Ol,Oh))*(V+gsl_ran_gaussian (r,Sigma_v))/((G+gsl_ran_gaussian (r,Sigma_ m = gsl_ran_flat (r,Cl,Ch); n = gsl_ran_poisson (r,d); y = gsl_ran_binomial (r, m, n); return(y); } /* __________________________________________________________________________ main __________________________________________________________________________*/ int main(int argc, char *argv[]) { gsl_histogram *h = NULL; gsl_histogram_pdf *posterior = NULL; double x, den, denmin, denmax, rnd, z,coeff, Dm = 0, /* mean density for the species */ V = 0, /* sample volume */ Sigma_v = 0, /* its std */ Rl = 0, /* sedim. rate, lower... */ Rh = 0, /* ...upper limit */ R = 0, /* mean */ Cl = 0, /* fossiliz. chance, lower... */ Ch = 0, /* ...upper limit */ C = 0, /* mean */ Ol = 0, /* contaminat. intensity, lower... */ Oh = 0, /* ...upper limit */ G = 0, /* length of a generation */ Sigma_g = 0; /* its std */ unsigned long int seed = 1, bins=100,repeats = 1, iter=10000, i, j=0, k, abu, y, spec, samp, free; FILE *seedfile, *parfile; double gsl_ran_flat (const int d2a( double Dp, double Dm, double V, double Sigma_v, double Rl, double Rh, double Cl, double Ch, double Ol, double Oh, double G, double Sigma_g gsl_rng * R, double A, double B); /* /* /* /* /* /* /* /* /* /* /* mean density for the species */ sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ its std */ ); gsl_vector *v, *sigma_v, *rl, *rh, *cl, *ch, *ol, *oh, *g, *sigma_g; /* /* /* /* /* /* /* /* /* /* sample volume */ its std */ sedim. rate, lower... */ ...upper limit */ fossiliz. chance, lower... */ ...upper limit */ contaminat. intensity, lower... */ ...upper limit */ length of a generation */ its std */ gsl_matrix *ab; /* abundances */ /* read in new seed */ if((seedfile = fopen(SEEDFILE,"r")) == NULL) seed = 1; else { fscanf(seedfile, "%ld ", &seed); fclose(seedfile); } ú/û /*do the RNG initialization*/ r = gsl_rng_alloc (gsl_rng_uni32); gsl_rng_set (r, seed); /* read-in the number of density matrices to be generated */ if (argc > 1) repeats = atoi(argv[1]); /* read-in number of species, number of samples */ scanf ("%ld%ld", &spec, &samp); /* read-in the abundance matrix */ ab = gsl_matrix_calloc (spec, samp); for (i = 0; i < spec; i++){ for (j = 0; j < samp; j++){ scanf ("%lg", &z); gsl_matrix_set (ab, i, j, z); } } /* read-in common params */ if((parfile = fopen(COMPARFILE,"r")) == NULL){ fprintf(stderr, "Common params file is missing. Exit.\n"); exit(1); } fscanf(parfile, "%lg %ld %ld", &coeff, &bins, &iter); /* read-in sample params */ v = gsl_vector_calloc (samp); sigma_v = gsl_vector_calloc (samp); rl = gsl_vector_calloc (samp); rh = gsl_vector_calloc (samp); if((parfile = fopen(SAMPPARFILE,"r")) == NULL){ fprintf(stderr, "Sample params file is missing. Exit.\n"); exit(1); } for (i = 0; i < samp; i++){ fscanf(parfile, "%lg", &z); gsl_vector_set (v, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (sigma_v, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (rl, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (rh, i, z); } fclose(parfile); /* read in species params */ cl = gsl_vector_calloc (spec); ch = gsl_vector_calloc (spec); ol = gsl_vector_calloc (spec); oh = gsl_vector_calloc (spec); g = gsl_vector_calloc (spec); sigma_g = gsl_vector_calloc (spec); if((parfile = fopen(SPECPARFILE,"r")) == NULL){ fprintf(stderr, "Species params file is missing. Exit.\n"); exit(1); } for (i = 0; i < spec; i++){ fscanf(parfile, "%lg", &z); gsl_vector_set (cl, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (ch, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (ol, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (oh, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (g, i, z); fscanf(parfile, "%lg", &z); gsl_vector_set (sigma_g, i, z); } 0 ú/û } /j Y \kRQ_Q0m sX&%1Ä+t fclose(parfile); /* print out number of species, number of samples, number of repeats */ printf("%ld %ld %ld\n", spec, samp, repeats); /* repeat for every species */ for (i = 0; i < spec; i++){ /* calculate mean density using point estimator */ Dm=0; C = gsl_vector_get (cl, i); G = gsl_vector_get (g, i); for (j = 0; j < samp; j++) { abu = gsl_matrix_get (ab, i, j); V = gsl_vector_get (v, j); R = gsl_vector_get (rl, j); Dm += (abu*V*C)/(R*G); } Dm /= (double) samp; /* read-in species params */ Cl = gsl_vector_get (cl, i); Ch = gsl_vector_get (ch, i); Ol = gsl_vector_get (ol, i); Oh = gsl_vector_get (oh, i); G = gsl_vector_get (g, i); Sigma_g = gsl_vector_get (sigma_g, i); /* repeat for every sample */ for (j = 0; j < samp; j++) { /* read-in sample params */ V = gsl_vector_get (v, j); Sigma_v = gsl_vector_get (sigma_v, j); Rl = gsl_vector_get (rl, j); Rh = gsl_vector_get (rh, j); /* read-in the value of abundance */ abu = gsl_matrix_get (ab, i, j); free = 0; /* estimate denmin, denmax */ denmin = denmax = abu * (R*G)/(V*C); if (denmin == 0.0) { denmin = denmax = 0.1 * (R*G)/(V*C); } do { if (free) { gsl_histogram_free (h); gsl_histogram_pdf_free (posterior); } denmin *= coeff; denmax *= 1.0/coeff; /* allocate histogram */ h = gsl_histogram_calloc_uniform (bins, denmin, denmax); /* create likelihood x Jeffreys’ prior */ for (k = 0; k < iter; k++) { den = gsl_ran_flat (r,denmin, denmax); y = d2a(den,Dm,V,Sigma_v,Rl,Rh,Cl,Ch,Ol,Oh,G,Sigma_g); if (y==abu) { /* 1/den is the Jeffreys’ prior: is it done properly? */ gsl_histogram_accumulate (h, den, 1/den); } } /* create posterior pdf */ posterior = gsl_histogram_pdf_alloc (h); free = 1; } while (gsl_histogram_get (h, bins-1) > 1 ); q WX&%%tb\ [u ú/û ` /* sample it */ for (k = 0; k < repeats; k++){ rnd = gsl_rng_uniform_pos (r); x = gsl_histogram_pdf_sample (posterior, rnd); printf("%g ", x); } printf("\n"); } printf("\n"); } printf("\n"); /* release memory */ gsl_histogram_free (h); gsl_histogram_pdf_free (posterior); gsl_rng_free (r); gsl_vector_free (v); gsl_vector_free (sigma_v); gsl_vector_free (rl); gsl_vector_free (rh); gsl_vector_free (cl); gsl_vector_free (ch); gsl_vector_free (ol); gsl_vector_free (oh); gsl_vector_free (g); gsl_vector_free (sigma_g); gsl_matrix_free (ab); /* etc... */ exit (0); } /* _____________________________________________________________________ eof */ y æ{æ1ä 1äÄ \? ! + }~ coenocline-0.07.h Simulates data from a gaussian/beta/piecewise-linear response function with different sampling (error) distributions. Input: samples, number of species, maximum abundance. Output: sampled abundances, rowwise, rows are species, columns are samples. Command-line params: generator type (0=gauss,1=beta,2=piecewiselinear), spacing of samples (0=regular, 1=random) Common params (file): plot? (0/1), lower, upper end of the gradient, overlap; Model-specific params (file): gauss: max. tolerance beta: max. alpha, max. gamma piecewise-linear: background density; Depends on: Netlib routines from ranlib.c. Files: Reads a pair of seeds for random number generator from the file "seeds", and model parametres from the file "params". Written by P.Cejchan, initially based heavily on J.Oksanen, June 1997. Language: ISO C Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: was gaussgen.c, betagen.c , pwlgen.c v 0.01 (1999/12/02): basic functionality v 0.02: gnuplot plotting; v 0.03 (2000/01/06): no zero-filled species v 0.04 (2000/01/13): regular vs. random sampling coenocline.c v 0.05 (2000/01/14): beta, gauss, and piecewiselinear generators together; v 0.06 (2000/01/21): treating allzero species removed; sampler splitted of 2@ /j Y ú Ï \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u to a standalone program; params splitted to common and modelspecific; v 0.07 (2000/02/10): License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________ */ #include #include #include #include #include #include #include <stdio.h> <stdlib.h> <math.h> <time.h> <string.h> "nrutil.h" "qsort.h" /* cmd-line param 1: gen */ #define GAUSS 0 #define BETA 1 #define ROOF 2 /* cmd-line param 2: spacing */ #define REGULAR 0 #define RANDOM 1 #define #define #define #define #define SEEDFILE "seeds" C_PARFILE "common.par" G_PARFILE "gauss.par" B_PARFILE "beta.par" GNUPLOTFILE "plot" #define SHAPE 0.0 extern void setall (long, long); extern float ranf (); /* initialize rng */ /* uniform [0,1) */ void generate_points(float *arr, int k, float enda, float endb, int sampling); float roof(float x, float a, float e, float m, float r); float gauss(float x, float u, float t, float c); float beta(float k, float a, float b, float alpha, float gamma, float x); float ksol(float a, float b, float alpha, float gamma, float height); void betapara(float pi, float m, float tau2, float *a, float *b); /* eof */ /* coenocline-0.07.c __________________________________________________________________________*/ #include "coenocline-0.07.h" /* __________________________________________________________________________ generate_points __________________________________________________________________________ generates sampling points along the gradient written by Peter Cejchan, 1998/11/18 */ void generate_points(float *arr, int k, float enda, float endb, int spacing){ extern float ranf (); /* uniform [0,1) */ extern void ArraySort(int This[], CMPFUN fun_ptr, uint32 the_len); int i; int *aux; /* auxilliary array to hold integers to be sorted */ aux = ivector(0, k); switch (spacing){ case REGULAR: default: for (i=0; i<k; i++) { arr[i] = enda + i*(endb-enda)/(float) (k - 1); ú Ï ú /* x = (float)i/(float)(nsimu-1)*span+enda; */ } break; case RANDOM: for (i=0; i<k; i++) { aux[i] = (int) 10000*ranf(); } ArraySort(aux, cmpfun, k); for (i=0; i<k; i++) { arr[i] = aux[i]/10000.0; } break; } free_ivector(aux, 0, k); return; } /* __________________________________________________________________________ roof __________________________________________________________________________ ‘roof’ (unimodal piecewise linear) response curve of a taxon on a gradient; made up of linear segments */ float roof(float x, float a, float e, float u, float r){ /* x point on the gradient a amplitude, maximum abundance e excentricity = left/range u mean, position of max. abundance on the gradient r range of nonzero values of abundance */ float y; if (x < u) y=x*(a/(e*r))+a-u*(a/(e*r)); else if (x < (u+r-r*e)) y=x*(-a)/(r-e*r)+a-u*(-a)/(r-e*r); else y = 0; return (y); } /* __________________________________________________________________________ gauss __________________________________________________________________________ Returns the expected value from Gaussian response function y = c*exp(-0.5(x-u)^2/t^2) u optimum t tolerance c maximum x point at which the function is evaluated */ float gauss(float x, float u, float t, float c) { return c*exp(-0.5*(x-u)*(x-u)/(t*t)); } /* __________________________________________________________________________ beta __________________________________________________________________________ Returns the expected value from beta response function */ /j Y ú Ï û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u float beta(float k, float a, float b, float alpha, float gamma, float x) { float t2, t3; /* Return zero if x is not in (a,b) */ if (x <= a || x >= b) return 0; /* Otherwise evaluate the beta-function at x */ t2 = pow(x-a,alpha); t3 = pow(b-x,gamma); return k*t2*t3; } /* __________________________________________________________________________ ksol __________________________________________________________________________ The program asks the maximum height of the response function (to the benefit of the user). Function ksol returns the value of k. */ float ksol(float a, float b, float alpha, float gamma, float height) { float t1, t4, t6, t11; t1 = b-a; t4 = t1/(alpha+gamma); t6 = pow(alpha*t4, alpha); t11= pow(gamma*t4, gamma); return height/t6/t11; } /* __________________________________________________________________________ betapara __________________________________________________________________________ For Beta-Binomial sampling model: Estimates the parameters a,b of beta distribution from expected proportion (pi), binomial denominator (m), and shape parameter (tau2). Solution (hopefully correct) of Exercise 4.17 of McCullagh & Nelder 1989, helped by Moore, Appl Stat 36, 8-14; 1987. */ void betapara(float pi, float m, float tau2, float *a, float *b) { float t1,t2,t3,t4; t1 = tau2*m; t2 = t1-m-tau2+1; t3 = 1/(1+t1-tau2); t4 = t2*t3; *a = -t4*pi; *b = t4*(pi-1); } /* __________________________________________________________________________ main __________________________________________________________________________ Ranlib.c (Netlib repository) is used for random number generation. To re-compile the program, you must either get these routines or replace them with your favourite rng-routines. */ int main(int argc, char *argv[]) { float x, u=0.0, t=0.0, c, mu, enda, endb, span, centre, span2, enda2, temp, tmax, abumax, *points, alpha=0.0, gamma=0.0, alphamax, gammamax, over, a=0.0, b=0.0, e=0.0, r=0.0, k=0.0; ú Ï=Ï int nsimu, i, nspec, j, plt, /* command -line params */ gen=GAUSS, spacing=REGULAR; long seed1, seed2; FILE FILE FILE FILE *seedfile; *commonfile; *parfile; *plotfile = NULL; /* read-in command-line arguments */ if (argc>1) gen = atoi(argv[1]); if (argc>2) spacing = atoi(argv[2]); /* initialize RNG */ if((seedfile = fopen(SEEDFILE,"r")) == NULL){ fprintf(stderr, "Seed file is missing. Exit.\n"); return(1); } fscanf(seedfile, "%ld %ld", &seed1, &seed2); fclose(seedfile); setall(seed1,seed2); /* read-in common params */ if((commonfile = fopen(C_PARFILE,"r")) == NULL){ fprintf(stderr, "Common params file is missing. Exit.\n"); return(1); } /* plot? lower, upper end of the gradient, overlap */ fscanf(commonfile, "%d %f %f %f", &plt, &enda, &endb, &over); scanf ("%d", &nspec); printf("%d ", nspec); scanf ("%d", &nsimu); printf("%d\n", nsimu); scanf ("%f", &abumax); /* number of species */ /* number of samples */ /* max abundance */ /* read-in model-specific params */ switch (gen) { case GAUSS: default: if((parfile = fopen(G_PARFILE,"r")) == NULL){ fprintf(stderr, "Model params file is missing. Exit.\n"); return(1); } fscanf(parfile, "%f", &tmax); fclose(parfile); break; case BETA: if((parfile = fopen(B_PARFILE,"r")) == NULL){ fprintf(stderr, "Model params file is missing. Exit.\n"); return(1); } fscanf(parfile, "%f %f",&alphamax, &gammamax); fclose(parfile); break; case ROOF: break; } ú Ï Q /j Y \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ /* plotting */ if (gen == ROOF) plt = 0; if (plt) { /* open plotfile */ if((plotfile = fopen(GNUPLOTFILE,"w")) == NULL){ fprintf(stderr, "Cannot open output file. Exit.\n"); exit(1); } /* print header */ fprintf(plotfile, "#!/usr/bin/gnuplot -persist\n#\n#\n"); switch (gen){ case GAUSS: default: fprintf(plotfile, "f(x) = c*exp(-0.5*(x-u)*(x-u)/(t*t))\n"); fprintf(plotfile, "set title \"Gaussian model tmax= %4.2f\"\n", tmax); break; case BETA: fprintf(plotfile, "pow(t,u)= exp(log(t)*u)\n"); fprintf(plotfile, "f(x) = k*pow(x-a,alpha)*pow(b-x,gamma)\n"); fprintf(plotfile, "set title \"Beta model alphamax= %4.2f gammamax= %4.2f\"\n", alphamax, gammamax); break; } fprintf(plotfile, "set xrange [%f:%f]\n", enda, endb); fprintf(plotfile, "set yrange [0:%f]\n", abumax); fprintf(plotfile, "plot "); } /* define the gradient */ if (endb < enda) { temp = enda; enda = endb; endb = temp; } span = endb-enda; centre = enda + 0.5*span; span2 = (1.0+over)*span; enda2 = centre - span2/2.0; points = vector(0, nsimu); generate_points(points, nsimu, enda, endb, spacing); for (j = 0; j < nspec; j++) { /* calculate parametres */ switch (gen){ case GAUSS: default: u = enda2 + span2*ranf(); /* t = tmax* ranf(); /* c = abumax* ranf(); /* if (plt) { fprintf(plotfile, "u=%f, t=%f, if (j < nspec - 1) fprintf(plotfile, ", "); } break; case BETA: /* species’max abundance */ c= abumax* ranf(); /* range endpoints a, b */ a = enda2 + span2*ranf(); b = enda2 + span2*ranf(); if (b < a) { temp = a; optimum (on the gradient) */ tolerance */ max abundance */ c=%f, f(x) notitle", u, t, c); [u ú Ï a = b; b = temp; } /* shape parameters alpha, gamma */ alpha = alphamax* ranf(); gamma = gammamax* ranf(); k = ksol(a, b, alpha, gamma, c); if (plt) { fprintf(plotfile, "k=%f, a=%f, b=%f, alpha=%f, gamma=%f, f(x) notitle", k, a, b, alpha, gamma); if (j < nspec - 1) fprintf(plotfile, ", "); } break; case ROOF: /* species’max abundance */ c = abumax* ranf(); /* range endpoints a, b */ a = enda2 + span2*ranf(); b = enda2 + span2*ranf(); if (b < a) { temp = a; a = b; b = temp; } /* excentricity */ e = ranf(); /* range */ r = b-a; /* mean */ u = a + e*r; break; } /* proper simulation of observations starts here */ for (i=0; i<nsimu; i++) { x = points[i]; /* select generator */ switch (gen) { case GAUSS: default: mu = gauss(x, u, t, c); break; case BETA: mu = beta(k,a,b,alpha,gamma,x); break; case ROOF: mu = roof(x, c, e, u, r); break; } printf("%f ", mu); } printf ("\n"); } if (plt) fclose(plotfile); /* free array */ free_vector(points, 0, nsimu); return(0); } /* eof */ y æ{æ1ä 1ä }~ \ ^é 1 ú Ï . /j Y \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u const-0.01.c Generates m x n table of constant value. Written by Peter Cejchan Input (from stdin): m= # of rows, n= # of columns, value to be printed Output (to stdout):m x n matrix of value Language: ISO C. Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (2000/03/13). License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include <stdio.h> /* __________________________________________________________________________ main __________________________________________________________________________*/ int rows, cols, c, i, j; int main(int argc, char *argv[]) { /* read number of rows and columns */ /* rows = atoi(argv[1]); */ /* cols = atoi(argv[2]); */ scanf ("%d%d%d", &rows, &cols, &c); /* print out the table */ printf ("%d %d\n", rows, cols); for (i=0; i< rows; i++) { for (j=0; j< cols; j++) printf ("%d ", c); printf("\n"); } exit (0); } /* eof */ y æZæ1ä 1ä \* * ²é Ö 9é }~ count-hits.c Reads integer matrix from stdin, and counts rows that are monotonous. Written by Peter Cejchan. Input (from stdin): number of rows, number of columns, data matrix. The matrix is readin rowwise, i.e., columns change fastest (innermost nest); Output (to stdout): number of monotonous rows. Language: ISO C. Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / libc6 2.1.3-7 / gcc version 2.95.2 20000220 History: version 0.01 (1999/03/11). version 0.02 (2000/04/07). License: GPL <http://www.gnu.org/copyleft/gpl.html> Example: ./count-hits < order __________________________________________________________________________*/ #include <stdio.h> .0 ú Ï /* __________________________________________________________________________ main __________________________________________________________________________*/ int main() { int i, j, rows, cols, a, b, increasing, error=0, cnt=0; scanf("%d%d", &rows, &cols); for (i=0; i<rows; i++) { scanf("%d%d", &a, &b); if (a != b){ if (b > a) increasing = 1; else increasing = 0; for (j=2; j<cols; j++) { a= b; scanf("%d", &b); if ((increasing && (a > b)) || (!increasing && (a<b))) error = 1; } if (! error) cnt++; error = 0; } /* if */ } printf("hits= %d\n", cnt); return(0); } /* eof */ y æ{æ1ä 1ä ã + * ²å1è }~ den2abu-0.03.c Maps density onto abundance. written by Peter Cejchan Input (from stdin): number of samples (int), number of species (int), matrix of densities (float). Density matrix is read samplewise, i.e., species change fastest, samples slowest. Output (to stdout):number of samples, number of species, matrix of abundances. Abundance matrix is read samplewise, i.e., species change fastest, samples slowest. Language: ISO C. Depends on: Netlib routines from ranlib.c. Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (2000/01/28). version 0.02 (2000/02/10): contamination included. version 0.03 (2000/02/16): bug in abu gen corrected License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include #include #include #include #include #include <stdio.h> <stdlib.h> <math.h> <time.h> <string.h> "nrutil.h" #define SEEDFILE "seeds" #define PARFILE "den2abu.par" /* __________________________________________________________________________ genuni 2} /j Y ú Ï \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u __________________________________________________________________________*/ float genuni(float low, float high){ extern float ranf (); float range; /* uniform [0,1) */ range = high-low; return low + range * ranf(); } /* __________________________________________________________________________ main __________________________________________________________________________ Ranlib.c (Netlib repository) is used for random number generation. To re-compile the program, you must either get these routines or replace them with your favourite rng-routines. */ int main(int argc, char *argv[]) { extern extern extern extern extern void setall (long, long); float ranf (); long ignbin (long, float); long ignpoi (float); float gennor (float, float); /* /* /* /* /* initialize rng */ uniform [0,1) */ Binomial */ Poisson */ Normal */ float *Dp, /* species’ pure densities */ Dm, /* mean density for the species */ V, /* sample volume */ Sigma_v, /* its std */ Rl, /* sedim. rate, lower... */ Rh, /* ...upper limit */ Cl, /* fossiliz. chance, lower... */ Ch, /* ...upper limit */ Ol, /* contaminat. intensity, lower... */ Oh, /* ...upper limit */ G, /* length of a generation */ Sigma_g, /* its std */ m, x, d; int y, rows, cols, i, j, n; long seed1,seed2; FILE *seedfile, *parfile; /* read in params */ if((parfile = fopen(PARFILE,"r")) == NULL){ fprintf(stderr, "Model params file is missing. Exit.\n"); return(1); } fscanf(parfile, "%f %f %f %f %f %f %f %f %f %f", &V, &Sigma_v, &Rl, &Rh, &Cl, &Ch, &Ol, &Oh, &G, &Sigma_g); fclose(parfile); /* read in new seeds */ if((seedfile = fopen(SEEDFILE,"r")) == NULL){ fprintf(stderr, "Seed file is missing. Exit.\n"); return(1); } fscanf(seedfile, "%ld %ld", &seed1, &seed2); fclose(seedfile); /*do the RNG initialization*/ setall(seed1,seed2); /* read in number of rows, number of columns */ scanf ("%d%d", &rows, &cols); printf ("%d %d\n", rows, cols); /* proper simulation of observations starts here */ Dp = vector(0, cols); ú Ï for (i=0; i<rows; i++) { Dm = 0; for (j = 0; j < cols; j++){ scanf("%f", &x); Dp[j] = x; Dm += x; } ` /* samples */ /* species */ for (j = 0; j < cols; j++){ /* species */ d= (Dp[j] + Dm*genuni(Ol, Oh))*gennor(V, Sigma_v)/(gennor(G, Sigma_g)*genuni(Rl, Rh)); m = genuni(Cl, Ch); n = ignpoi(d); y = ignbin(n, m); printf("%d ", y); } printf("\n"); } free_vector(Dp, 0, cols); return(0); } /* eof */ y y æ{æ1ä 1ä æ I *é9å ² }~ dt2dm-0.01.c Generates a number of density matrices from density tensor. Written by Peter Cejchan. Input (from stdin): number of species (=rows), number of samples (= columns), number of repeats of readings of density, density tensor (rows, samples, repeats); repeats change fastest (innermost nest); Output (to files): prescribed number of matrices (see the commandline parametre)consisting of: number of species (=rows), number of samples (= columns), matrix of densities (specieswise) Language: ISO C. Depends on: GNU Scientific Library (GSL). Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / libc6 2.1.3-7 / gcc version 2.95.2 20000220 History: version 0.01 (2000/04/04). License: GPL <http://www.gnu.org/copyleft/gpl.html> Example: ./dt2dm 5 < tensor __________________________________________________________________________*/ #include <stdio.h> /* #include <stdlib.h> */ #include <string.h> #include <gsl_rng.h> #include "nrutil.h" #define SEEDFILE "seed" gsl_rng *r; /* random number generator */ /* __________________________________________________________________________ main __________________________________________________________________________*/ int main(int argc, char *argv[]) { double z; int i, j, k, spec, samp, matrices; Q @ /j Y ú unsigned long int seed = 1, FILE *seedfile, *matrix; float ***dty; char *filename = "AAAAAA"; \kRQ_Q0m sX&%1Ä+t iter=10000, rnd; /* read in new seed */ if((seedfile = fopen(SEEDFILE,"r")) == NULL) seed = 1; else { fscanf(seedfile, "%ld ", &seed); fclose(seedfile); } /*do the RNG initialization*/ r = gsl_rng_alloc (gsl_rng_uni32); gsl_rng_set (r, seed); /* read-in the number of density matrices to be generated */ if (argc > 1) matrices = atoi(argv[1]); else matrices = 1; /* readin number of species, number of samples, number of repeatings */ scanf ("%d%d%ld", &spec, &samp, &iter); /* allocate output density tensor */ dty = f3tensor(0, spec, 0, samp, 0, iter); /* read-in the density tensor */ for (i = 0; i < spec; i++){ for (j = 0; j < samp; j++){ for (k = 0; k < iter; k++){ scanf("%lg", &z); dty[i][j][k]= (float) z; } } } /* generate matrices */ for (k = 0; k < matrices; k++){ /* open new file */ filename = l64a (k+2); if((matrix = fopen(filename,"w")) == NULL){ fprintf(stderr, "Cannot open the output matrix file. Exit.\n"); exit(1); } /* print number of species, number of samples */ fprintf(matrix, "%d %d\n", spec, samp); for (i = 0; i < spec; i++){ for (j = 0; j < samp; j++){ rnd = gsl_rng_uniform_int (r,iter); fprintf(matrix, "%f ", dty[i][j][rnd]); } fprintf(matrix, "\n"); } fclose(matrix); } /* release memory */ gsl_rng_free (r); /* etc... */ exit (0); } /* eof */ q WX&%%tb\ [u Q ú =ú | y ²è| æ{æ1ä 1ä æZæ /* * * * * * * * * * * * * * * * */ A Greedy Randomized Adaptive Search Procedure (GRASP) for the Quadratic Assignment Problem (QAP) Authors: M.G.C. Resende (AT&T Bell Laboratories) [[email protected]] Y. Li (Pennsylvania State University) [[email protected]] P.M. Pardalos (University of Florida) [[email protected]] TOMS 22, 1 (Mar 1996) 104. Netlib toms/754: gqapd.f -- translated by f2c (version 19951025). Language: ANSI/ISO C. Rewritten by Petr Cejchan, 1998/02/20. #define FALSE 0 #define TRUE 1 /* * * * * * * * * * * * * * */ This file includes the following functions: gqapd srtcst stage1 stage2 savsol local mkbseq insrtq removq evalij randp - control subroutine for GRASP for QAP algorithm sorts cost stage 1 of GRASP construction phase stage 2 of GRASP construction phase saves current solution as best so far 2-exchange local search for QAP makes permutation vector b = (1,2,...,n) insert element into heap for sorting remove element from heap evaluates the cost effect of swapping i and j random number generator function /* * */ int gqapd(int *n, int *n2, int *niter, float *alpha, float *beta, int *look4, int *seed, int *f, int *d, int *a, int *b, int *srtf, int *srtif, int *srtd, int *srtid, int *srtc, int *srtic, int *indexd, int *indexf, int *cost, int *fdind, int *opta, int *bestv, int *iter) { /* System generated locals */ int i1; /* Local variables */ static int objv, i, j, k, l; extern int local(), stage1(), stage2(), savsol(), srtcst(); /* * * * * * * * * * * * gqapd: Subroutine for finding an approximate solution of a dense symmetric quadratic assignment problem. Parameters: infty - a large integer Passed input n n2 niter alpha - scalars: dimension of qap problem n * n maximum number of GRASP iterations phase 1 parameter Q /j Y ú =û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ * beta - phase 1 parameter * look4 if permutation of cost look4 or less is found gqapd * returns that permutation * * Passed input/output scalar: * seed - random number generator seed * * Passed output scalars: * bestv - cost of best assignment found * iter - number of GRASP iterations taken * * Passed input arrays: * f - flow matrix stored as a 1-dimensional array, * row by row (dim = n2). * d - distance matrix stored as a 1dimensional array, * row by row (dim = n2). * * Passed work arrays: * a - permutation vector (dim = n). * b - permutation vector (dim = n). * srtf - sorted F values * srtif - sorted F values (indices) * srtd - sorted D values * srtid - sorted D values (indices) * srtc - sorted cost values * srtic - sorted cost values (indices) * indexf - indices of facilities in unsorted cost matrix * indexd - indices of locations in unsorted cost matrix * cost - sorted cost matrix * fdind - indices of sorted cost matrix * * Passed output array: * opta - best permutation vector (dim = n). * * Local scalars and functions: * i - facility index * j - facility index * k - location index * l - location index * objv - cost of permutation */ /* Initialize cost of best assignment found to infinity. */ /* Parameter adjustments */ --opta; --b; --a; --fdind; --cost; --indexf; --indexd; --srtic; --srtc; --srtid; --srtd; --srtif; --srtf; --d; --f; /* Function Body */ *bestv = 2147483647; /* Sort the cost = f(i,j) * d(k,l) in increasing order to be used */ /* by the stage1 construction phase of GRASP. */ srtcst(n, n2, beta, &f[1], &d[1], &srtf[1], &srtif[1], &srtd[1], & srtid[1], &srtc[1], &srtic[1], &indexd[1], &indexf[1], &cost[1], & fdind[1]); /* Do GRASP iterations. */ i1 = *niter; [u Q ú for (*iter = 1; *iter <= i1; ++(*iter)) { /* Stage 1 of GRASP construction phase. */ stage1(n, n2, &i, &j, &k, &l, seed, alpha, beta, &objv, &indexd[1], &indexf[1], &fdind[1], &cost[1], &a[1], &b[1]); /* Stage 2 of GRASP construction phase. */ stage2(n, n2, &i, &j, &k, &l, seed, &objv, alpha, &f[1], &d[1], & srtc[1], &srtic[1], &a[1], &b[1]); /* Local search phase of GRASP. */ local(n, n2, &objv, &f[1], &d[1], &a[1], &b[1]); /* If cost assignment is best so far, save permutation and */ /* cost of assignment. */ if (objv < *bestv) { savsol(n, &objv, bestv, &a[1], &opta[1]); /* If cost of assignment is at least as good as reque sted, */ /* return best permutation found. */ if (*bestv <= *look4) { return 0; } } /* L10: */ } /* Adjust iteration counter for output. */ *iter = *niter; return 0; } /* gqapd */ /* * */ int srtcst(int *n, int *n2, float *beta, int *f, int *d, int *srtf, int *srtif, int *srtd, int *srtid, int *srtc, int *srtic, int *indexd, int *indexf, int *cost, int *fdind) { /* System generated locals */ int i1, i2, i3; /* Local variables */ static int dind, find, i, j, nbeta, index, sizec, sized, sizef, dv, fv; extern int removq(), insrtq(); /* * * * * * * * * * * * * * * * * * * * * * * * * * srtcst: Sorts cost = f(i,j)*d(k,l) in increasing order. Passed input scalars: n - qap dimension n2 - n * n beta - construction phase parameter Passed input arrays: f - flow matrix (row major order) d - distance matrix (row major order) Passed work arrays: srtf - sorted flow matrix (values) srtif - sorted flow matrix (indices) srtd - sorted distance matrix (values) srtid - sorted distance matrix (indices) srtc - sorted cost matrix (values) srtic - sorted cost matrix (indices) Passed output arrays: indexd - indices of locations in unsorted cost matrix indexf - indices of facilities in unsorted cost matrix cost - sorted cost matrix fdind - indices of sorted cost matrix Local scalars: index - index sizec - number of elements in cost heap sized - number of elements in distance heap sizef - number of elements in flow heap Ï Q2Q /j Y ú * * * * * * * * * * * * */ \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ dv - distance value fv - flow value dind - distance index find - flow index nbeta - number of candidates i - do loop index j - do loop index Sort D in increasing order, F in decreasing order (-F in increasing order). Keep only the (n*n-n)*beta best elements in each sorting. Initialize cardinalities of sorted sets of elements of D, F, and cost. /* Parameter adjustments */ --fdind; --cost; --indexf; --indexd; --srtic; --srtc; --srtid; --srtd; --srtif; --srtf; --d; --f; /* Function Body */ sized = 0; sizef = 0; sizec = 0; /* Insert all non-diagonal elements of D into D-priority heap */ /* and all non-diagonal elements of -F into F-priority heap. */ index = 0; i1 = *n; for (i = 1; i <= i1; ++i) { i2 = *n; for (j = 1; j <= i2; ++j) { ++index; if (i != j) { insrtq(n2, &d[index], &index, &sized, &srtd[1], &srtid[1]); i3 = -f[index]; insrtq(n2, &i3, &index, &sizef, &srtf[1], &srtif[1]); } /* L10: */ } /* L20: */ } /* Compute size of sorted sets. */ nbeta = *beta * (*n * *n - *n); /* Remove the nbeta smallest D elements from D-priority heap and */ /* the nbeta smallest -F elements from F-priority heap. */ i1 = nbeta; for (i = 1; i <= i1; ++i) { removq(n2, &dv, &dind, &sized, &srtd[1], &srtid[1]); removq(n2, &fv, &find, &sizef, &srtf[1], &srtif[1]); /* Cost is product of sorted flow and distance. */ cost[i] = -dv * fv; indexd[i] = dind; indexf[i] = find; /* Insert cost into cost priority-heap. */ insrtq(n2, &cost[i], &i, &sizec, &srtc[1], &srtic[1]); /* L30: */ } /* Remove nbeta sorted cost elements from cost priority-heap. */ i1 = nbeta; for (i = 1; i <= i1; ++i) { removq(n2, &cost[i], &fdind[i], &sizec, &srtc[1], &srtic[1]); [u Q21 ú /* L40: */ } return 0; } /* srtcst */ /* * */ int stage1(int *n, int *n2, int *i, int *j, int *k, int *l, int *seed, float *alpha, float *beta, int *objv, int *indexd, int *indexf, int *fdind, int *cost, int *a, int *b) { /* System generated locals */ int i1; /* Local variables */ static int dind, high, find; extern double randp(); static float xrand; static int ii, nselct, tmp; /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ stage1: Builds the initial 2 assignments for the GRASP construction phase (facility i to site k and facility j to site l). Passed input scalars: n - qap dimension n2 - n * n alpha - construction phase parameter beta - construction phase parameter Passed input/output scalar: seed - random number generator seed Passed output scalars: i - facility index j - facility index k - location index l - location index objv - cost of initial 2 assignments Passed input arrays: indexd - indices of locations in unsorted cost matrix indexf - indices of facilities in unsorted cost matrix fdind - indices of sorted cost matrix cost - cost of assignment Passed output arrays: a - permutation array b - permutation array Local scalars and functions: nselct - index of randomly selected element dind - distance index find - flow index high - upper bound of selection range ii - loop index tmp - temporary scalar randp - random number generator function xrand - dummy probability /* Initialize permutations. */ /* Parameter adjustments */ --b; --a; --cost; --fdind; --indexf; --indexd; /* Function Body */ i1 = *n; for (ii = 1; ii <= i1; ++ii) { a[ii] = ii; b[ii] = ii; /* L10: */ Q2. /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u } /* Select element, at random, from the best (n*n-n)*alpha cost */ /* elements. */ xrand = randp(seed); high = *alpha * *beta * (*n * *n - *n); nselct = *seed / (2147483647 / high) + 1; /* Initial assignment is facility i to location k */ /* facility j to location l. */ /* Cost of initial assignment is f(i,j) * d(k,l). */ dind = indexd[fdind[nselct]]; find = indexf[fdind[nselct]]; *i = (find - 1) / *n + 1; *j = find - (*i - 1) * *n; *k = (dind - 1) / *n + 1; *l = dind - (*k - 1) * *n; *objv = cost[nselct]; /* Make initial assignments to permutation arrays: */ /* Assign facility a[1] = *i; a[*i] = 1; b[1] = *k; b[*k] = 1; /* Assign facility i1 = *n; for (ii = 1; ii <= if (a[ii] == *j) tmp = a[2]; a[2] = *j; a[ii] = tmp; goto L30; } /* L20: */ } L30: i1 = *n; for (ii = 1; ii <= if (b[ii] == *l) tmp = b[2]; b[2] = *l; b[ii] = tmp; goto L50; } /* L40: */ } L50: return 0; } /* stage1 */ i to location k. */ j to location l. */ i1; ++ii) { { i1; ++ii) { { /* * */ int stage2(int *n, int *n2, int *i, int *j, int *k, int *l, int *seed, int float *alpha, int *f, int *d, int *srtc, int *srtic, int *a, int *b) { /* System generated locals */ int i1, i2, i3, i4; /* Local variables */ static int high, kinv, cost, linv, fdind; extern double randp(); static float xrand; static int sizec, akm1tn, blm1tn, anm1tn, bnm1tn, assign, nselct; extern int removq(), insrtq(); static int tmp; /* * * * stage2: Builds a randomized greedy permutation starting from the assignments made in stage1. Permutation is returned in array a(*). *objv, Q 0 ú * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ Passed input scalars: n - problem dimension n2 - n * n i - facility index j - location index k - facility index l - location index alpha - construction phase parameter Passed input/output scalars: seed - random number generator seed objv - cost of assignment Passed input arrays: f - flow matrix d - distance matrix Passed work arrays: srtc - sorted cost matrix (values) srtic - sorted cost matrix (indices) Passed input/output arrays: a - permutation array b - permutation array Local scalars and functions: high - upper bound of selection range assign - do loop counter of assignments cost - assignment cost sizec - number of cost elemnets in cost heap nselct - selected index tmp - temporary integer variable kinv - index of k in inverted permutation linv - index of l in inverted permutation fdind - index of f d product akm1tn - (a(k)-1)*n blm1tn - (b(l)-1)*n anm1tn - (a(n)-1)*n bnm1tn - (b(n)-1)*n randp - random number generator function xrand - probability returned by random number generator /* Main loop: Assignments 3,4,..,n-1 are made. */ /* Parameter adjustments */ --b; --a; --srtic; --srtc; --d; --f; /* Function Body */ i1 = *n - 1; for (assign = 3; assign <= i1; ++assign) { /* For all pairs not assigned yet, compute costs of all */ /* possible assignments, w.r.t. already-made assignments. */ sizec = 0; i2 = *n; for (*k = assign; *k <= i2; ++(*k)) { akm1tn = (a[*k] - 1) * *n; i3 = *n; for (*l = assign; *l <= i3; ++(*l)) { blm1tn = (b[*l] - 1) * *n; cost = 0; i4 = assign - 1; for (*i = 1; *i <= i4; ++(*i)) { /* Facility a(i) already assigned to location b(i): */ /* Cost of assigning facility a(k) to location b(l) */ /* relative to assignment of facility a(i) to */ /* location b(i). */ cost += f[akm1tn + a[*i]] * d[blm1tn + b[*i]]; /* L40: */ } /* Insert cost element into cost-priority heap for */ /* sorting. */ i4 = akm1tn + b[*l]; Q } /j Y ú \kRQ_Q0m sX&%1Ä+t insrtq(n2, &cost, &i4, &sizec, &srtc[1], &srtic[1]); /* L30: */ } /* L20: */ } /* Select assignment, at random, from the best alpha*sizec */ /* assignments. */ xrand = randp(seed); high = *alpha * sizec; nselct = *seed / (2147483647 / high) + 1; i2 = nselct; for (*i = 1; *i <= i2; ++(*i)) { removq(n2, &cost, &fdind, &sizec, &srtc[1], &srtic[1]); /* L50: */ } /* Make assignment. */ *objv += cost; kinv = (fdind - 1) / *n + 1; linv = fdind - (kinv - 1) * *n; i2 = *n; for (*i = assign; *i <= i2; ++(*i)) { if (a[*i] == kinv) { *k = *i; goto L70; } /* L60: */ } L70: i2 = *n; for (*j = assign; *j <= i2; ++(*j)) { if (b[*j] == linv) { *l = *j; goto L90; } /* L80: */ } L90: tmp = a[assign]; a[assign] = a[*k]; a[*k] = tmp; tmp = b[assign]; b[assign] = b[*l]; b[*l] = tmp; /* L10: */ } anm1tn = (a[*n] - 1) * *n; bnm1tn = (b[*n] - 1) * *n; i1 = *n - 1; for (*i = 1; *i <= i1; ++(*i)) { *objv += f[anm1tn + a[*i]] * d[bnm1tn + b[*i]]; /* L100: */ } *objv += *objv; return 0; } /* stage2 */ /* * */ int savsol(int *n, int *objv, int *bestv, int *a, int *opta) { /* System generated locals */ int i1; /* Local variables */ static int i; /* * * * * savsol: Saves current best solution. Passed input scalars: n - problem dimension objv - objective function value q WX&%%tb\ [u Q ` ú * * * * * * * * */ Passed output scalars: bestv - best objective function value so far Passed input array: a - permutation array Passed output array: opta - array of best permutation so far Local scalar: i - loop index /* Parameter adjustments */ --opta; --a; /* Function Body */ i1 = *n; for (i = 1; i <= i1; ++i) { opta[i] = a[i]; /* L10: */ } *bestv = *objv; return 0; } /* savsol */ /* * */ int local(int *n, int *n2, int *objv, int *f, int *d, int *a, int *b) { /* System generated locals */ int i1, i2; /* Local variables */ static int temp, i, j, xgain; extern int evalij(), mkbseq(); static int improv; /* * * * * * * * * * * * * * * * * * * * */ local: Local 2-exchange on permutation array a. Return improved permutation array a and objv. Passed input scalars: n - problem dimension n2 - n * n Passed input/output scalar: objv - objective function value Passed input arrays: f - flow matrix d - distance matrix Passed input/output arrays: a - permutation array b - permutation array Local scalars: i - loop index j - loop index temp - temp scalar used to swap a(i) and a(j) xgain - gain from switch improv - objective function improvement /* Make array b(*) = (1,2,3,...,n) for local search. */ /* Parameter adjustments */ --b; --a; --d; --f; /* Function Body */ mkbseq(n, &a[1], &b[1]); /* Attempt to switch all pairs in permutation array a. */ L10: improv = FALSE; i1 = *n - 1; 1 @ /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ for (i = 1; i <= i1; ++i) { i2 = *n; for (j = i + 1; j <= i2; ++j) { /* Evaluate cost difference by adopting switch of a( i) */ /* and a(j). */ evalij(n, n2, &i, &j, &xgain, &f[1], &d[1], &a[1]); /* If switch improves cost, adopt it. */ if (xgain > 0) { temp = a[i]; a[i] = a[j]; a[j] = temp; *objv -= xgain; improv = TRUE; } /* L30: */ } /* L20: */ } /* If no switch improves cost (improv=.false.), return; else repeat. */ if (improv) { goto L10; } return 0; } /* local */ /* * */ int mkbseq(int *n, int *a, int *b) { /* System generated locals */ int i1, i2; /* Local variables */ static int i, j, tmp; /* * mkbseq: Change permutation arrays a and b to make b = (1,2,...,n). * Passed input scalar: * n - QAP dimension * Passed input/output arrays: * a - permutation array * b - permutation array * Local scalars: * i - loop index * j - loop index * tmp - temporary scalar */ /* Parameter adjustments */ --b; --a; /* Function Body */ i1 = *n - 1; for (i = 1; i <= i1; ++i) { i2 = *n; for (j = i + 1; j <= i2; ++j) { if (b[j] == i) { b[j] = b[i]; b[i] = i; tmp = a[i]; a[i] = a[j]; a[j] = tmp; goto L20; } /* L10: */ } [u 1 ú =ú L20: ; } return 0; } /* mkbseq */ /* * */ int insrtq(int *n2, int *v, int *iv, int *sizeq, int *q, int *iq) { static int sq, tsz; /* * * * * * * * * * * * * * * */ insrtq: Insert an element (v,iv) into a queue (q,iq). Passed input scalars: n2 - n * n v - heap element (value) iv - heap element (index) Passed input/output scalar: sizeq - size of heap Passed input/output arrays: q - heap (value) iq - heap (index) Local scalars: sq - temporary size of heap tsz - temporary variable (sq/2) Insert element into heap. /* Parameter adjustments */ --iq; --q; /* Function Body */ ++(*sizeq); q[*sizeq] = *v; iq[*sizeq] = *iv; /* Update heap to proper order. */ sq = *sizeq; *v = q[sq]; *iv = iq[sq]; L10: tsz = sq / 2; if (tsz != 0) { if (q[tsz] > *v) { q[sq] = q[tsz]; iq[sq] = iq[tsz]; sq = tsz; goto L10; } } q[sq] = *v; iq[sq] = *iv; return 0; } /* insrtq */ /* * */ int removq(int *n2, int *v, int *iv, int *sizeq, int *q, int *iq) { static int vtmp, szqd2, j, k, ivtmp; /* * * * * * * removq: Remove smallest element (v,iv) from a priority queue (q,iq). Passed input scalar: n2 - n * n Passed input/output scalar: sizeq - size of heap 1 /j Y ú =û * * * * * * * * * * * * * */ \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ Passed output scalars: v - smallest element in heap (value) iv - smallest element in heap (index) Passed input/output arrays: q - heap (value) iq - heap (index) Local scalars: vtmp - tmp smallest element in heap (value) ivtmp - tmp smallest element in heap (index) k - heap counter j - heap counter (2*k) szqd2 - sizeq/2 Remove element from heap. /* Parameter adjustments */ --iq; --q; /* Function Body */ *v = q[1]; *iv = iq[1]; q[1] = q[*sizeq]; iq[1] = iq[*sizeq]; --(*sizeq); /* Update heap to proper order. */ k = 1; vtmp = q[k]; ivtmp = iq[k]; szqd2 = *sizeq / 2; L10: if (k <= szqd2) { j = k + k; if (j < *sizeq) { if (q[j] > q[j + 1]) { ++j; } } if (vtmp > q[j]) { q[k] = q[j]; iq[k] = iq[j]; k = j; goto L10; } } q[k] = vtmp; iq[k] = ivtmp; return 0; } /* removq */ /* * */ double randp(int *ix) { /* Initialized data */ static static static static int int int int a = b15 b16 p = 16807; = 32768; = 65536; 2147483647; /* System generated locals */ float retval; /* Local variables */ static int xalo, k, leftlo, fhi, xhi; /* * * * * randp: Portable pseudo-random number generator. Reference: L. Schrage, "A More Portable Fortran Random Number Generator", ACM Transactions on Mathematical Software, Vol. 2, No. 2, (June, 1979). [u 1 ú Ï */ xhi = *ix / b16; xalo = (*ix - xhi * b16) * a; leftlo = xalo / b16; fhi = xhi * a + leftlo; k = fhi / b15; *ix = xalo - leftlo * b16 - p + (fhi - k * b15) * b16 + k; if (*ix < 0) { *ix += p; } retval = (float) (*ix) * (float)4.656612875e-10; return retval; } /* randp */ /* * */ int evalij(int *n, int *n2, int *i, int *j, int *xgain, int *f, int *d, int *a) { /* System generated locals */ int i1; /* Local variables */ static int dtmp1, dtmp2, im1tn, jm1tn, km1tn, k, aim1tn, ajm1tn, akm1tn, ai, aj, ak; /* * * * * * * * * * * * * * * * * * * * * * * * * * * * */ evalij: Computes the gain in objective function by switching the locations of facilities i and j (i < j). Passed input scalars: n - QAP dimension n2 - n * n i - permutation array index j - permutation array index Passed output scalar: xgain - gain achieved by swapping i and j in permutation Passed input arrays: f - flow matrix d - distance matrix a - permutation vector Local scalars: k - do loop index aim1tn - (a(i)-1)*n ajm1tn - (a(j)-1)*n akm1tn - (a(k)-1)*n ai - a(i) aj - a(j) ak - a(k) im1tn - (i-1)*n jm1tn - (j-1)*n km1tn - (k-1)*n dtmp1 - reusable distance computation dtmp2 - reusable distance computation /* Parameter adjustments */ --a; --d; --f; /* Function Body */ *xgain = 0; ai = a[*i]; aj = a[*j]; aim1tn = (ai - 1) * *n; ajm1tn = (aj - 1) * *n; im1tn = (*i - 1) * *n; jm1tn = (*j - 1) * *n; km1tn = 0; i1 = *n; 12Q /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u for (k = 1; k <= i1; ++k) { if (k != *i && k != *j) { ak = a[k]; akm1tn = (ak - 1) * *n; dtmp1 = d[km1tn + *i] - d[km1tn + *j]; dtmp2 = d[im1tn + k] - d[jm1tn + k]; *xgain = *xgain + dtmp1 * (f[akm1tn + ai] - f[akm1tn + aj]) + dtmp2 * (f[aim1tn + ak] - f[ajm1tn + ak]); } km1tn += *n; /* L20: */ } dtmp1 = d[im1tn + *j] - d[jm1tn + *i]; *xgain += dtmp1 * (f[aim1tn + aj] - f[ajm1tn + ai]); return 0; } /* evalij */ /*--- end of file ---*/ y æZæ1ä 1ä æå * ' ? Ö ^é Zè }~ histogram-0.01.c Generates histogram. Written by Peter Cejchan Input (from stdin): Output (to stdout): Language: ISO C. Depends on: GNU Scientific Library (GSL). Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (2000/02/16). License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include #include #include #include #include #include <stdio.h> <stdlib.h> <math.h> <gsl_histogram.h> <gsl_matrix.h> <gsl_rng.h> #define PARFILE "abu2den.par" #define PLOTFILE "plotfile.dat" /* __________________________________________________________________________ main __________________________________________________________________________*/ int main(int argc, char *argv[]) { gsl_vector *abu; gsl_histogram *h; gsl_histogram_pdf *posterior; gsl_rng *r; double x, y, abumax, abumin, rnd; int bins, repeats = 1, rows, i; unsigned long int seed = 1; FILE *parfile, *plotfile; /* read in params */ if((parfile = fopen(PARFILE,"r")) == NULL){ fprintf(stderr, "Model params file is missing. Exit.\n"); return(1); } fscanf(parfile, "%d", &bins); 121 ú fclose(parfile); /* read-in the values, get min, max */ abumax = 0.0; abumin = MAXDOUBLE; scanf ("%d", &rows); abu = gsl_vector_alloc (rows) ; for (i = 0; i < rows; i++){ scanf ("%lg", &x); gsl_vector_set(abu, i, x); if(x < abumin) abumin = x; if(x > abumax) abumax = x; } /* allocate histogram */ h = gsl_histogram_calloc_uniform (bins, abumin, abumax); /* fill-in the histogram */ for (i = 0; i < rows; i++){ gsl_histogram_increment (h, gsl_vector_get(abu, i)); } /* open plotfile */ if((plotfile = fopen(PLOTFILE,"w")) == NULL){ fprintf(stderr, "Cannot open output file. Exit.\n"); exit(1); } /* print out the histogram */ gsl_histogram_fprintf (stdout, h, "%g", "%g") ; /* create likelihood x Jeffreys’ prior */ gsl_histogram_reset (h); for (i = 0; i < rows; i++){ y = gsl_vector_get(abu, i); gsl_histogram_accumulate (h, y, 1/y); } /* create posterior pdf */ posterior = gsl_histogram_pdf_alloc (h); /* sample it */ r = gsl_rng_alloc (gsl_rng_uni32); gsl_rng_set (r, seed); for (i = 0; i < repeats; i++){ rnd = gsl_rng_uniform_pos (r); x = gsl_histogram_pdf_sample (posterior, rnd); printf("%g ", x); } /* release memory */ gsl_histogram_pdf_free (posterior); gsl_rng_free (r); /* etc... */ exit (0); } /* eof */ y M¡ æ{æ1ä 1ä æ | Hé &è }~ * itransp.c * reads integer mmatrix from stdin, and outputs its * transpose to stdout * written by Peter Cejchan, 1998/11/19 ****************************************************************************/ #include <stdio.h> 12. /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u #define DIM 1000 void readm(int *rows, int *cols, int m[DIM][DIM]); void transpose(int rows, int cols, int m[DIM][DIM]); /**************************************************** ************************ * readm ****************************************************************************/ void readm(int *rows, int *cols, int m[DIM][DIM]) { int i, j; scanf("%d%d", rows, cols ); for (i=0; i<*rows; i++) { for (j=0; j<*cols; j++) { scanf("%d", &m[i][j]); } } } /**************************************************** ************************ * transpose ****************************************************************************/ void transpose(int rows, int cols, int m[DIM][DIM]) { int i, j; printf("%d %d\n", cols, rows ); for (i=0; i<cols; i++) { for (j=0; j<rows; j++) { printf("%d ", m[j][i]); } printf("\n"); } } /**************************************************** ************************ * main ****************************************************************************/ int main(void) { int rows=DIM, cols=DIM; int m[DIM][DIM]; readm(&rows, &cols, m); transpose(rows, cols, m); return(0); } /* ---end of file--- */ y æZæ1ä 1ä æ ? èéHå }~ mat2gri-0.01.c Converts a matrix to a single column format for Gri. Input: #rows #columns, data rowwise, on stdin. Output: to stdout Language: ANSI/ISO C. Dependences: uses Gnu Scientific Library gsl. Written by Peter Cejchan. compile: gcc mat2gri.c -o mat2gri tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (1999/12/17) _________________________________________________________________________ */ #include <stdio.h> /* _________________________________________________________________________ main 1 0 ú _________________________________________________________________________ */ int main(void) { int i, j, x, rows, cols; scanf("%d%d", &rows, &cols ); for (i = 0; i < rows; i++) { for (j = 0; j < cols; j++) { scanf("%d", &x); printf("%d\n", x); } printf("\n", x); } return 0; } /* end of file */ y + æ{æ1ä 1ä æ *i'_| é è }~ monotonic-spear-0.02.c Measures the mean monotonicity of a set of sequences. Computes the mean spearman rank correlation coefficient of given sequences (matrix rows) with monotonic (either increasing, or decreasing) sequence. The better of the two is taken. ************************ WARNING ************************* NO TIES SHOULD BE PRESENT IN THE DATA ! ********************************************************** Implementation: Here, we start with rank orders as input, so we do not calculate them. Input: #rows #columns matrix of ranks in rows, rowwise, on stdin Output: mean Spearman rank correlation coefficient (rho), on stdout Language: ANSI/ISO C. Written by Petr Cejchan 1999/07/02. compile: make -k tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 License: GPL <http://www.gnu.org/copyleft/gpl.html> Example: ./monotonic-spear < out-beta-qap _________________________________________________________________________ */ #include <stdio.h> #include <gsl_matrix.h> /* _________________________________________________________________________ main _________________________________________________________________________ */ int main(void) { int i, j, rows, cols, d, d2, sum1, sum2, sum; float rs, sum_rs, meanrho; gsl_matrix * m; /* read matrix */ scanf("%d%d", &rows, &cols ); m = gsl_matrix_alloc (rows, cols) ; gsl_matrix_fscanf (stdin, m); /* initialize */ sum_rs = 0; for(i = 0; i < rows; i++){ sum1 = 0; sum2 = 0; sum = 0; 1 } /j Y ú /* for increasing sequence */ for(j = 0; j < cols; j++){ /* subtract the ranks of each pair of data. d = gsl_matrix_get(m, i, j) - (j+1); /* square each difference. d2 = d * d; \kRQ_Q0m sX&%1Ä+t */ */ /* sum the squared differences. sum1 += d2; */ } /* for decreasing sequence */ for(j = 0; j < cols; j++){ d = gsl_matrix_get(m, i, j) - (cols - j); /* square each difference. d2 = d * d; */ /* sum the squared differences. sum2 += d2; */ } /* take the lower sum */ if(sum1 < sum2) sum = sum1; else sum = sum2; /* multiply the sum of squared differences by 6: this is 6S. */ /* r_s = 1 - {6S/(n^3-n)} */ rs = 1 - (6 * (float)sum/(float)(cols * cols * cols - cols)); sum_rs += rs; } meanrho = sum_rs / rows; printf("mean Spearman rho = %f\n", meanrho); return(0); } /* eof */ y æZæ1ä 1ä æqÄ " I7\" å }~ * ord2score.c * a filter to convert an ordering of sample numbers * into sample scores * * written by P. Cejchan, 1999/06/08 * language: ISO C */ /* e.g., sample sample sample sample */ numbers are: 1 2 3 4 5 6, ordering is: 6 3 4 2 5 1, scores are: 6 4 2 3 5 1. numbers must be subsequent integers starting from 1 #include <stdio.h> #include <stdlib.h> #define DIM 10000 int main(void) { int i, j, num, count; int x; int score[DIM]; int order[DIM]; /* read samples vector */ i= 0; while (scanf("%d", &order[i]) != EOF) i++; /* read number of samples*/ num= i; for(i=0; i< num; i++){ q WX&%%tb\ [u 1 ` ú /* count samples before this one, incl. this */ count= 0; while(order[count] != i+1) count++; score[i]= count+1; } /* write out scores */ for(i=0; i<num; i++) printf("%d ", score[i]); printf("\n "); return(0); } /* --- end of file --- */ y + æ{æ1ä 1ä æ +²è| Zèå }~ raw2qap-big.c Program to convert data input format from raw format to qap. Reads an n by m data matrix and produces distance and flow matrices. Raw data start with #rows, #columns, followed by data. Species are columns, samples are rows. Language: ISO C. Input: number of rows, number of columns, data matrix rowwise. Output: distance and flow matrix formatted for QAP input. Depends on: GNU Scientific Library (GSL). Tested on: Linux 2.2.10 / libc6 2.1.3-8 / gcc version 2.95.2-8 20000313 / libgsl0 0.5+-1 History: version 0.01 (1999/03/10). version 0.02 (2000/04/10): gsl rewrite, command-line param. License: GPL <http://www.gnu.org/copyleft/gpl.html> __________________________________________________________________________*/ #include <stdio.h> #include <stdlib.h> /* for abs */ #include <gsl/gsl_matrix.h> #define NUM 1000.0 /* divide flows by NUM/maxflow */ /* __________________________________________________________________________ main __________________________________________________________________________*/ int main(int argc, char *argv[]) { int i, j, k, rows, cols, x, maxflow = 0, num, big = 0, z; gsl_matrix *matrix; /* data matrix (int) */ /* read-in command-line params */ num = NUM; if (argc > 1) big = atoi(argv[1]); if (argc > 2) num = atoi(argv[2]); /* read-in number of species, number of samples */ scanf ("%d%d", &rows, &cols); /* read-in the matrix */ matrix = gsl_matrix_calloc (rows, cols); for (i = 0; i < rows; i++){ for (j = 0; j < cols; j++){ . @ /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u scanf ("%d", &z); gsl_matrix_set (matrix, i, j, z); } } /* print dimension */ printf("%d\n\n", rows); /* calculate distances between sequence’s equidistant nodes */ for (i = 0; i < rows; i++){ for (j = 0; j < rows; j++){ printf("%d ", abs(i - j)); } printf("\n"); } printf("\n"); if (big) { /* calculate max flow */ maxflow = 0; for (i = 0; i < rows; i++){ for (j = 0; j < rows; j++){ x = 0; for (k = 0; k < cols; k++){ /* x += matrix[i][k] * matrix[j][k]; */ x += gsl_matrix_get (matrix, i, k)*gsl_matrix_get (matrix, j, k); } if(x > maxflow) maxflow = x; } } } /* calculate flows */ for (i = 0; i < rows; i++){ for (j = 0; j < rows; j++){ x = 0; for (k = 0; k < cols; k++){ /* x += matrix[i][k] * matrix[j][k]; */ x += gsl_matrix_get (matrix, i, k)*gsl_matrix_get (matrix, j, k); } printf("%d ", big ? (num*x/maxflow): x); } printf("\n"); } printf("\n"); exit(0); } /* eof */ y æZæ1ä 1ä æ " ' è } * rearr.c * reads integer mmatrix and rearrangement vector from stdin, * and outputs rows-rearranged matrix * to stdout; * rearrangement vector uses numbers of rows starting from 1, (not 0) * shows which row will be printed next * input: rows, columns, matrix (rowwise), vector * written by Peter Cejchan, 1999/03/10 ****************************************************************************/ #include <stdio.h> #define DIM 1000 void readall(int *rows, int *cols, int m[DIM][DIM], int v[DIM]); void rearrange(int rows, int cols, int m[DIM][DIM], int v[DIM]); . ú =ú /**************************************************************************** * readall ****************************************************************************/ void readall(int *rows, int *cols, int m[DIM][DIM], int v[DIM]) { int i, j; /* read number of rows and number of columns */ scanf("%d%d", rows, cols ); /* read data matrix rowwise */ for (i=0; i<*rows; i++) { for (j=0; j<*cols; j++) { scanf("%d", &m[i][j]); } } /* read rearrangement vector */ for (i=0; i<*rows; i++) scanf("%d", &v[i]); } /**************************************************************************** * rearrange ****************************************************************************/ void rearrange(int rows, int cols, int m[DIM][DIM], int v[DIM]) { int i, j; printf("%d %d\n", rows, cols); for (i=0; i<rows; i++) { for (j=0; j<cols; j++) { printf("%d ", m[v[i]1][j]); /* numbering of rows starts from 1 !!! */ } printf("\n"); } } /**************************************************************************** * main ****************************************************************************/ int main(void) { int rows=DIM, cols=DIM; int m[DIM][DIM]; int v[DIM]; readall(&rows, &cols, m, v); rearrange(rows, cols, m, v); return(0); } /* ---end of file--- */ y æ{æ1ä 1ä æã I" _\ ' } }~ rm-zero-rows-cols-0.03.c a filter to convert the integer matrix containing rows/columns consisting solely of zeroes into the matrix without such rows/columns. Input: #rows, #columns, integer matrix (rowwise), on stdin Output: to stdout Params: 0 = replace, 1 = remove zero-filled rows & columns 2 = each entry increased by one Language: ISO C. Dependences: uses Gnu Scientific Library gsl. Written by: Peter Cejchan. compile: make -k tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 Language: ISO C History: . /j Y ú =û \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u version 0.01 (1999/12/21) rows and columns were NOT removed (to preserve the number of rows and columns throughout the computing), but were slightly modified (by inserting 1s) instead version 0.02 (2000/01/06): rows and cols are (optionally) really removed; Makefile; GSL rewrite; version 0.03 (2000/01/12): optionally, all entries are x+= 1 License: GPL <http://www.gnu.org/copyleft/gpl.html> _____________________________________________________________________ */ #include <stdio.h> #include <math.h> #include <gsl_matrix.h> #define REPLACE 0 #define REMOVE 1 #define PLUSONE 2 int main(int argc, char *argv[]) { int i, j, rows, cols, good, badrows, badcols; int badrows2, badcols2, zeros = PLUSONE; gsl_matrix *m; gsl_vector *delrow = NULL; gsl_vector *delcol = NULL; if (argc>1) { zeros = atoi(argv[1]); } else zeros = PLUSONE; /* read matrix */ scanf("%d%d", &rows, &cols); m = gsl_matrix_alloc (rows, cols) ; gsl_matrix_fscanf (stdin, m); delrow = gsl_vector_alloc (rows) ; delcol= gsl_vector_alloc (cols) ; /* mark rows for deletion */ badrows = 0; for(i=0;i<rows; i++){ good = 0; gsl_vector_set(delrow, i, 0); for(j=0; j< cols; j++){ if(gsl_matrix_get(m, i, j)!=0){ good = 1; break; } } if(!good) { gsl_vector_set(delrow, i, 1); badrows++; } } /* mark columns for deletion */ badcols = 0; for(i=0; i<cols; i++){ good = 0; gsl_vector_set(delcol, i, 0); for(j=0; j<rows; j++){ if(gsl_matrix_get(m, j, i)!=0){ good = 1; break; } } if(!good) { gsl_vector_set(delcol, i, 1); badcols++; . ú } } switch (zeros) { case REMOVE: /* print # rows # columns */ printf("%d %d\n", rows - badrows, cols - badcols); /* print data rowwise */ for(i=0;i < rows; i++){ if (! gsl_vector_get(delrow, i)){ for(j=0; j< cols; j++){ if (! gsl_vector_get(delcol, j)) { printf("%d\n", (int)gsl_matrix_get(m, i, j)); } } } printf("\n"); } break; case REPLACE: /* print # rows # columns */ /* printf("%d %d\n", rows - badrows, cols - badcols); */ printf("%d %d\n", rows, cols); /* insert 1’s into zero rows */ badrows2=0; for(i=0; i<rows; i++){ if (gsl_vector_get(delrow,i)) { gsl_matrix_set(m, i, 0, 1); badrows2++; } } /* insert 1’s into zero columns */ badcols2=0; for(i=0; i<cols; i++){ if (gsl_vector_get(delcol,i)){ gsl_matrix_set(m, 0, i, 1); badcols2++; } } /* print data rowwise */ for(i=0;i < rows; i++){ for(j=0; j< cols; j++){ printf("%d\n", (int) rint(gsl_matrix_get(m, i, j))); } printf("\n"); } if((badrows!=badrows2) || (badcols!=badcols2)){ fprintf(stderr, "ERROR !"); return(1); } break; case PLUSONE: default: /* print # rows # columns */ printf("%d %d\n", rows, cols); /* print data + 1 rowwise */ for(i=0;i < rows; i++){ for(j=0; j< cols; j++){ printf("%d\n", (int) rint(gsl_matrix_get(m, i, j)) + 1); } printf("\n"); } break; } gsl_matrix_free(m); Ï .2Q /j Y ú \kRQ_Q0m sX&%1Ä+t q WX&%%tb\ [u gsl_vector_free(delrow); gsl_vector_free(delcol); return(0); } /* eof */ y y æZæ1ä 1ä å | ,è }~ sampler-0.03.c This is a sampler from a set of (Poisson/...) distributions, with expected rates read from matrix on stdin, outputting matrix of observed values drawn from proper Poisson (or other) distributions, on stdout; written by Peter Cejchan, based on J. Oksanen, 1997. Input (from stdin): Output (to stdout): Language: ISO C. Depends on: Netlib routines from ranlib.c. Written by: Peter Cejchan <[email protected]> Compile: make -k Tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version 2.95.2 19991109 History: version 0.01 (1998/11/19). version 0.02 (2000/01/13): ranlib.c, complete rewrite; version 0.03 (2000/04/07): binomial denominator read from bdfile; License: GPL <http://www.gnu.org/copyleft/gpl.html> _____________________________________________________________________ */ #include #include #include #include #include #define #define #define #define #define <stdio.h> <stdlib.h> <math.h> <time.h> <string.h> BERNOULLI 0 BINOM 1 POISSON 2 BETABIN 3 NEGBIN 4 #define SEEDFILE "seeds" #define BDFILE "bd" #define SHAPEFILE "shapes" /* __________________________________________________________________________ betapara __________________________________________________________________________ For Beta-Binomial error model: Estimates the parameters a,b of beta distribution from expected proportion (pi), binomial denominator (m), and shape parameter (tau2). Solution (hopefully correct) of Exercise 4.17 of McCullagh & Nelder 1989, helped by Moore, Appl Stat 36, 8-14; 1987. */ void betapara(float pi, float m, float tau2, float *a, float *b) { float t1,t2,t3,t4; t1 = tau2*m; t2 = t1-m-tau2+1; t3 = 1/(1+t1-tau2); .21 ú t4 = t2*t3; *a = -t4*pi; *b = t4*(pi-1); } /* __________________________________________________________________________ main __________________________________________________________________________ Ranlib.c (Netlib repository) is used for random number generation. To re-compile the program, you must either get these routines or replace them with your favourite rng-routines. */ int main(int argc, char *argv[]) { extern extern extern extern extern extern void setall (long, long); float ranf (); long ignbin (long, float); long ignpoi (float); float genbet (float, float); float gengam (float, float); /* /* /* /* /* /* initialize rng */ uniform [0,1) */ Binomial */ Poisson */ beta distribution */ gamma */ float mu, shape, aa, bb; int rows, cols, i, resp=0, error = POISSON, long seed1,seed2,bd; FILE *shapefile=NULL, *seedfile, *bdfile; j; /* Read in new seeds */ if((seedfile = fopen(SEEDFILE,"r")) == NULL){ seed1 = 1L; seed2 = 1L; } else { fscanf(seedfile, "%ld %ld", &seed1, &seed2); fclose(seedfile); } /* read-in the distribution type */ if (argc>1) { error = atoi(argv[1]); } /*Do the RNG initialization*/ setall(seed1,seed2); /* read-in the binomial denominator, bd (integer) */ if (error == BINOM || error == BETABIN) { scanf ("%ld",&bd); } /* open file with overdispersion params for each species */ if (error == BETABIN || error == NEGBIN){ if((shapefile = fopen(SHAPEFILE,"r")) == NULL){ fprintf(stderr, "File with overdispersion params is missing. Exit.\n"); return(1); } } /* read in number of rows, number of columns */ scanf ("%d%d", &rows, &cols); printf ("%d %d\n", rows, cols); /* Proper simulation of observations starts here. Ugly ‘if (mu > 0.0)’ are needed since some (!) values of zero seem to cause FP errors in ranlib.c. Please note the fall-through in BETABIN and NEGBIN. */ for (i=0; i<rows; i++) { .2. /j Y ú \kRQ_Q0m sX&%1Ä+t for (j = 0; j < cols; j++){ scanf("%f", &mu); muorig = mu; */ if (error == BINOM || error == BETABIN) muorig *= bd; */ resp = 0; switch (error) { case BERNOULLI: if (mu >= ranf()) resp=1; else resp=0; break; case BETABIN: fscanf(shapefile, "%f", &shape); if (mu > 0.0) { betapara(mu,bd,shape,&aa,&bb); mu = genbet(aa,bb); } case BINOM: if (mu > 0.0) resp = (int) ignbin(bd,mu); else resp = 0; break; case NEGBIN: fscanf(shapefile, "%f", &shape); if (mu > 0.0) mu = gengam(1/(shape*mu),1/shape); case POISSON: default: if (mu > 0.0) resp = (int) ignpoi(mu); else resp = 0; break; } printf("%d ", resp); } printf ("\n"); /* /* } if (error == BETABIN || error == NEGBIN) fclose(shapefile); return(0); } /* end of file */ 7\" M y æZæ1ä 1ä åæ å &è }~ * * * * * * * * * * * * * * * */ score2rank.c A filter to convert scores from, e.g., DECORANA, to sample ranks, without ties. See:Maurice G. Kendall. Rank Correlation Methods. Griffin, London, 4. edition, 1970. Input: sample scores vector on stdin Output: to stdout Language: ANSI/ISO C. Written by P. Cejchan, 1999/06/08. compile: gcc score2rank.c -o score2rank tested on: Linux 2.2.10 / glibc 2.1.1 / gcc version egcs-2.91.66 (egcs-1.1.2 release) #include <stdio.h> #define DIM 1000 int main(void) { int i, j, rows, cols, count, num; int x; float score[DIM]; int rank[DIM]; /* read sample scores vector */ i= 0; q WX&%%tb\ [u . 0 ú while (scanf("%f", &score[i]) != EOF) i++; /* read number of samples*/ num= i; for(i=0; i< num; i++){ /* count scores lower than this */ count= 0; for(j=0; j< num; j++){ if(score[j]<= score[i]) count++; } rank[i]= count; } /* write out scores */ for(i=0; i<num; i++) printf("%d ", rank[i]); printf("\n"); return(0); } /* --- end of file --- */ ) ) 1 0 } ` 2 1 . 2} Q 2 0 12 ` } @ 02@ }2} @.0 o 7 0 @ % 21 @ % 01 0 021 )) a 9G } 0 } 2Q2. } 0 2 0 0 0 ` 0 2.21 0 "7 2. } Q ¢ KQ 9 0 0 0 o J @ 0 a J 0 ¢ +) ` +) 0 ¢ 9K121K. 0 0 ! a 9 a 2Q } Q } P WKQ12121K12. rK. ` \)a2Q 0 0 ` K1 . ` 0 * a } @ V .2 0 ü"înþ&þ Hü qøìòhïðþ&ì í Zøìô«í=ìòhö {û &û =û Ï ú ú Ï Ï þ&ò =ú òhþ&ÿÿ¬øìðï í=òhï òhþ&ÿ öïðïðþ&ì òhþ&ÿ þ&õ¡ð"ï]ðþ&ìí/ó"ù {í/î9ðí/ïðþ&ì Ï òhþ&ÿ û í Zøìô«í=ìòhöJõ¡öîHð"í/ïðþ&ì í/ô 0 H 0.0 .2 ` =V H 0 0 ` a ` 02Q ` 2 . 2.2 0 2K1 2 ` } ) K. 0 9 0 H 9 a 2Q } bP WQ21 rPS W W W1 21.1 1 0 M S) 0 a 0 } ` . 2} Q ` a 02@ ` @ ` 9 9V 9 0 } 12 . 21 0 0 9 0 " 0 @ Q % % H1 1 0 % ` J . ) 7K1 . ..21 ! ` 9V 0Q } HV } HV ) a } HV a ` 21 0 HV V 2 } ` HV = 0 H =û ú í/înò ¯ö òhþ&ìïí=ÿðìí/ïðþ&ì /ú í/õ¡õ¡öÿ Zóí Zöõ í/õ¡õ¡öÿ Zóö Zö í/õ¡õ¡øÿ ï]ð"þ&ì í/õ¡õ¡øÿ ï]ð"þ&ìõ ú=û ú òhøïï]ðì qøìò^ïðþ&ì =ú =ú ôðõ¡ïîHð {øïðþ&ì ò^í=õ¡öJõ¡ïøô Ï ò^þ&ö {þ&óøïðþ&ì û /ú =ú Ï ú óöõ =ú ú^ú =û Ï Zðòhí/ó&înöõ þ&ìõ¡ö²ÿ¬þ&ô«öó /ú Zðòhí/ó =ú =ú =ú Zöî Züoõ1òhþ&ìòhö ï =ú öìøÿöî9í/ïð {öJÿöï þ&ôõ 4ë û û/û Ï öòhþ&óþ Ùóó"öì ZîHí=ìò ùqí=ìôù]ò^øï Zöìöî9í/ïþ&î Ï Ï öòhþ&óþ ö ñòhðöìò Ï û ZîHí=ìò ùqí=ìôù {þ&øìô§í/ó Zþ&î9ðï ÿ ò^þ&öìþ&ò^ó"ðìö Ï ú/û ôðõ¡ïí=ìòhöyÿ¬í/ïîHð =ú ò^þ&öìþ&ò^ó"ðìö =ú Ï Ï ôðí=ò înþ&ìþ&øõºõ¡í/ÿ Zðþ&ï]ð"ò^ù]ò^þ&ÿ¬ÿøìð"ï]ðöõ Zðþ&ï]øî Zí/ïðþ&ì Ï úhú ôöïî9öìôðì û=ú öï]íyÿþ&ôöó û ôöïî9öìôöô5ò^þ&î9î9öõ þ&ìô«öìò^ö*í=ìí=ó Zõ¡ðõ ú qøìò^ïðþ&ì û ôöïöîHÿ¬ðìðõ¡ïðò¿õ¡öîHðí=ï]ð"þ&ì /û Zöï]í ]øìòhï]ð"þ&ìõ =ú/û ôöïöîHÿ¬ðìðõ¡ïðò /û Zí Zöõ¡ðí=ì¯í=ìí=ó Zõ¡ðõ =ú^ú öï]í =ú ðò ôöõ¡òhîHð ïð {öJÿþ&ôöó =ú úhú Ï Zöóóùqõ í öôù ë ôöìõ¡ðï õ¡öîHð"í/ïðþ&ì =û Zöóóùqõ í öô *ë {î9í ôöìõ¡ðï Zÿöïî Zí Zöõ¡ðí=ì /ú 4ë ôöÿ¬þ =ú^ú í Zöõ¡üï öþ&înöÿ =ú /û óí=ìöJí=ó Zþ&îHð"ï ÿ ÜÙ û/ú ë5øõ¡ï]ð"ì ò^ô ú/û òhþ&îHînöõ þ&ìôöìò^öJí=ìí=ó Zõ¡ðõ =ú^ú í/õ Zÿ¬ÿöïî Zí/ï òhþ&îHînöóí=ï]ð"þ&ì òhþ&îHînöóí=ï]ð"þ&ìò^þ&ö ñòhðöìï í/õ Zÿ¬ÿöïîHðò¿î9öõ þ&ìõ¡ö =ú í/øïöòhþ&óþ Ï òhþ&ìòhö ï =ú Zöìöî9í/ïöô§ôí/ïí úhú Ï Zóí Zö Ï û òhþ&ìïðìøøÿ qöòhï êúhú í/õ¡õ¡öÿ Ï òhþ&ìõ¡ïînøò^ïðþ&ì§ÿöï þ&ôõ Ï í/î9ï]ð"ñò^ðí=óó ú òhþ&ìõ¡ïîHí=ðìï =ú í/ó {þ&î9ðï ÿ í/ìïõ Zõ¡ïöÿõ /ú òhþ&ìñôöìò^ö²óðÿ¬ðïõ û í/ìí/ó {õ¡ð"õ í=ï]ïöîHì û ú . } ú öì ð"î9þ&ìÿöìï öì ð"î9þ&ìÿöìïí=ó qí=ò^ïþ&î öì ð"î9þ&ìÿöìïí=ó {î9í/ôðöìï öì ð"î9þ&ìÿöìïí=ó í=îHðí Zóö öì ð"î9þ&ìÿöìïí=óù ZîHí=ôðöìï ö Zøí=ï]ð"þ&ì û=û öînþ&õ¡ðþ&ì Ï û û ú û /ú û . ` Nq u\ [t a ` öîHînþ&î ú û 0 öøî Zöò^þ&øõ öøî Zöò^þ&øõºòhþ&ÿ 0 ` } 9 % \Q H U \Q ` ) ` } @ @ . 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( a + 0 Ù ( úhúhú 5X öó"ó =í d ,ò^ð"öìïðñò 5î Zí=ìð =í=ï]ðþ&ìþ íõ¡þ&ð"óÿþ&ðõ¡ïøî9ö ò í ïöî ØÙì qþ&înô Zóí=ìô í/ì?í=õ¡õ¡öÿ ZîHí=ôðöìï Zóí Zö5þ 4 p 4þ&ÿ¬ù ú ú *öí/î9ó ú/û =ú/û ú & ` úhú ( *í=ÿöìõ þ î9ôìøì *øð"î {þ&ì í/ì öî9õ¡ò ðöôöìí=îHïð Zö & P o @ d( ú=û Rä L ú=û/ú (W2 & ú=û/û (W2 $ ( 1 í=ì ë ú . & ( ú=û ú=û 0d( Zöõ¡ï]ðÿ¬ÿï Zöí=î Zöðïøì ýøìô ,ôðöyô«øînò ÿö î9öînö < öînôöì L1< L =û 9C C Ï û Ï 4 âfí=ì Zþ&îHí=ï]þ&î9ðöõÅðì îHðìò^ð í/ó&òhþ&ÿ þ&ìöìïí=ìí=ó Zõ¡ðõ¿ð"ì ú þ&õ¡ïîHí=ìô înþ *öðì {í Zðóðï í=ìô þ&óô =ú õ¡ïí/ïðõ¡ïðòhõ ö þ&ð"õ¡ù ]þ&î9ô ìð"ù =ú Q6 qþ&înô ,òhðöìòhö ø Zóðòhí/ïðþ&ìõ ë5ì½ö í=ÿðìí/ïðþ&ì½þ Zðòhí/ó&ôí=ï]í Åõ¡þ&ÿö§þ&înôðìí=ï]ð"þ&ì 4 /ú =ú înþ {ó"öÿõ Føõ¡öþ û ^ú Q6 5n í í p $í=ò^ÿ¬ðóóí=ì öôðïðþ&ì û Ù ë î9þ & ö Zï]öì înöõ¡õ =ú ë Ó ( ú=û P öóöòhï]înþ&ìðòhí=óó õ¡ðÿøóí=ïöô¬öòhþ&óþ & I9d4 n n< 94 ð ð"í öî9õ¡ðï Q F â*ðîHïøí=óóí î9þ&òhöõ¡õ qí Zï]þ&înöì }* Ï ðö ZîHðõ¡ï ú=û Ïd( {öî Zóöðò öìôöì ö²øõ¡öyí/ìô¬ðìï]öî î9öïí=ï]ðþ&ì¯þ ,ð"ö {î9ðõ¡ï õ¡þ&ì ú=û ¯ðî Zöìôö hú Ï óð"öôînöõ¡öí=î9ò & & û *í=þ í & ú ÿöï þ&ôð Zôöî /öìóðõ¡ïöìøìôí=ìôöînöìþ í/ïö Zóöÿ ï]öî ÷ 3 $ ,ÿ¬ðï î9í/í í=ìô õ¡ðþ&ìí/ìô§ï ö ë Zöìöï]ðòyí !d6 î9þ&í=ò §ïþï ö ü -þ&þ&ÿ¬í/ì ü?öð Zí=øõ¡õ¡ðí=ìî9öõ þ&ìõ¡ö²ÿþ&ôöó /û=û ]ú Zøí/ôî9í/ïðò²í/õ¡õ¡ð {ìÿöìï /Ï =Ï hú ïöô¯í {öîHí {ð"ì Ï =ú^ú óþ ú Zðõ¡ïðò*înö Zî9öõ¡ù bk &%Mj ÝbuAÝGÛbuAV&r 5X _ ` 0 ú ( P ( ( a ) % ( : 25 ": } P ( w( J( r¢ 2 #% 8 $ 5 # > 56 } T 0 . ` }} ( ` (D ( # 5 <>!<a6!5< 8 $ >\5 ": < \: 5 fW$% >M5 ": >!<a6 " < 6 \ 2# 5 < fWf ""$ < $% > ( w ` }2} ( 9V 0K a (b 6!5 > 7" 5 : $ | @ ( w( 0" ( \V Q2QdT . 0 0 . ` 0 ( ( ( J H J( w0( %) ( H H T 9 ) 0 9 ( fW 6 $% 5 W57< 65 #%$ ><= ` T Q Q2Q2Q } ` .} 0 ( ( ( (D P ( ( ) -V ( T V $%# |K. dT 2QQ2 ` } . 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"$ " > 7 5> !:~7 8 !" < $% 5 # 8 6 $ < f >r5 ": > $%f "# 57< !: 5 "" 5 #$% 8dKV ` 1. )d3 < 6 < > $ f < 6 $%"'( `2` 1 ( 2Q Dr( ( - ( ( ( F 9_ 9V " ( 6 5 # 8 <=5< $7 > $%" T 1 .} 0 2. @ 2 `` ( & d( ú=û ïöî p & ( ú=û ü?í=ì d( ú Ï & Ù ü?í=îHïöì þ ú ë3ï öþ&î 5þ n {î9í/ôðöìï&í/ìí/ó {õ¡ð"õ Ï ú Ï hú A hú Ï ( ú Ï ú û ìð {öîHõ¡ðï ZöîHõ Ï înöìïðò^ö =ú P ðó"ÿí=ì îHð"ìòhöï]þ&ì & Ç î9í/í kí=ìô 6 4 înöõ¡õ îHðìò^öïþ&ì 6 !6 > D ú /LQ3 þ&ó"øï]ðþ&ìí=ìô¬ï öìðò öyò^þ&ìò^ö ï L;n LØF L û Zöî öîHõ¡þ&ì í=ìô *þ ó øï]ïðì kï ðì n \ôöïî9öìôöôò^þ&î9î9öõ þ&ìô«öìò^ö²í=ìí=ó Zõ¡ðõ {õ«ðì5þ&înôöî hí1òhîHðïð Zøö 6 =ú=û Ï ú & ü ( ú Ï û ü?öóôþ&ì ðÿ þ&îHïí/ìò^ö < & L =ú Ó û Ï ü ú Ï/Ïd( Ó ü & âö ü ö5ðìïöìõ¡ðï þ fòhþ&ÿ {öô®ôðõ¡ïðìò^ïðþ&ì¼í/ìôõ¡þ&ÿöð"ÿ Zöï]í=ïðþ&ì®þ ú öïðïðþ&ì öî9õ¡øõ4ðïõ óðòhí=ï]ðþ&ìõ !L ;L ²ï ö Zînöí/ï¿õ¡ÿþ p ®ÿþ&øìï]í=ðìõ ^ú â«ö ðïï]í Zöî Ó ( ú Ï û Zöïí/ïðþ&ì¬þ O p ]þ&îHìð"í ,óí=øõ¡þ&ì ú ( ú Ï - {öîHó"þ&þ ð"ï]ïí Zöî O & í=ìôÞü *í=ì¼þ ðï]ïí {öî Oï ö5õ¡ðõ Zð Zþ&øFÿþ&øìï]í=ðìõ þ&î9ö Zþ&ì¬í/ìô?ò^í=óðù û Ï hú Ï=Ï îHí=ôðöìïfí/ìí/ó Zõ¡ðõ²þ < {ö Zöï]í=ïðþ&ì L ;L /û û =û ú & Ó ü ( ú Ï ïðþ&ì & Ó ü d( ú Ï ðïï]í Zöî 3 ðïï]í Zöî ^Ç @ ú & Ó & $ !6 ü $í/òhÿð"óóí=ì ÷-þ&ìôþ&ì Zú5öôðù Ó ì dC L ü ðï]ïí {öî {öô«ð"ï]þ&î AL F L.8 í {öõ F ^í öËÓ¯í Zøö øì ðï]ïí {öîí/ìô1ü ïí/ð"ìõ êí=îHð =þ&ìí !6 > P öôðïþ&î > ü ËÇ /LB ö1Óí Zøö Ï øì ð"ï]ïí Zöî I ( ú Ï î ü C 4 ìï]înþ&ôøò^ïðþ&ì > =Ï d( ú Ï !6 =ú ë âö ðöîHð"ì ZîHí=ôðöìïí/ìí/ó Zõ¡ðõfþ þ&ó"øÿö ûköôðïðþ&ì Zöïí/ïðþ&ìþ Åþ ÷B D =ú &ï öºõ¡í=ìïíÅòhí/ïí/ó"ðìí¿ÿþ&øìù Zï öÅõ¡þ&øï õ¡óþ p ö 4 /û /û & ú ü?þ&ì d(K2 ú × í=ìô C ú /ú ü?þ&ì 3 ü ( þ !d6 ×{ööí/ìô r 96 î9ðì $ðïò >qd4 C !6 F >q 9 & ü öóó Zöî {þ&óøÿö ²þ ?v96 6 ú öìöîHí=óð =öô =û ; í=ôô«ð"ï]ð {öÿ¬þ&ô«öó"õ4ðì û ú ú ó"í/ìï«öòhþ&óù
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