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Transkript

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count of individuals
Randomly sampled coenocline
100
50
0
0
19
samples
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count of individuals
Seriated coenocline
100
50
0
0
19
v Ž ´5Œ=‰Ÿ+„w œ w
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Scores along the time
30
score
y mC!F_&!
gradient
20
10
0
0
v Ž ´5Œ=‰Ÿ+„w ± w
19
time
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Gaussian model tmax= 0.20
1000
1000
800
800
600
600
400
400
200
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0
0
0
0.2
0.4
0.6
0.8
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0
0.2
Gaussian model tmax= 0.20
0.4
0.6
0.8
1
0.8
1
Gaussian model tmax= 0.20
1000
1000
800
800
600
600
400
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0
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Beta model alphamax= 0.50 gammamax= 0.50
Beta model alphamax= 1.00 gammamax= 0.50
100
100
90
80
80
70
60
60
50
40
40
30
20
20
10
0
0
0
0.2
0.4
0.6
0.8
1
0
0.2
Beta model alphamax= 2.00 gammamax= 0.50
0.4
0.6
0.8
1
0.8
1
Beta model alphamax= 5.00 gammamax= 0.50
100
100
90
90
80
80
70
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x U/a S<z
» Ú Ä‘Ò ¼ ¾ ¡Ç Ý » Ú Ò ¼ ¾ ½ :/;
x U/a <z
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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 ÄŸÒ ¼ ¾ Ç
Ä Ç
:/;š>?:\35$&:<2&$B!#:<"($&*-
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x U/a g<z
» Ú ÄŸÒ ¼ ¾ Ç ½)Ö T× » Ú Ä Ò #‚Ó ¼ ¾ Ç + Ó
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Ò
ðð
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*-\
» !Ê"Dß » Ú ð Ú ß º ßäÊ ¼ Ø
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» Ú Ä ¼ ¼ à # #p¾ Ç ½ ¼ g g ¼ g à Ä Â à Ç pR Ä Â Ç
x U/a j<z
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gS
» Ú Ä ¼ # ༠#‚¾ Ç ½Ý» Ú Ä ¼ ¼ à # #‚¾ ÇÞr » Ú Ä àf¼ ¾ Ç ½ ¼ g g ¼ g à Ä Â à Ç R r à{| àCz
Ä Â Ç
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²
‚ß @ àËá K á y e R ¿ÀXÁ c ‚ß @ à§á K á y e
c
x%UDa iz
~ ² g  ~
» Ú Ä ¼ # à # ­¼ # # #p¾ Ç ½Ã² » Ú Ä ¼ # ༠#‚¾ ÇÞr
» Ú Ä ¼ # à # # ¼ # # ‹ #p¾ Ç ½,» Ú Ä ¼ # à # 4¼ # # # ‹ #p¾ ǧr | z x U/a S<Zz
 ²
²
²
» Ú Ä ¼ # à # # # ¼ # ‹ #p¾ Ç ½,» Ú Ä # à # # ¼ # # ‹ #p¾ Ç¥r É Þ jSÀ ¿ÀXÁGÁ Â Ä Âˆj&É ÆPyÇ È
yÈ x U/Â a SASz
²
²y
» Ú Ä ¼ # à # # # # ˜¼ ‹ #p¾ Ç ½,» Ú Ä ¼ # à # # # ¼ # ‹ #p¾ ǧr | {z x U/a S<z
Â
²
²
» Ú Ä ¼ # à # # # # # ‹ ¼ ¾ Ç ½Ý» Ú Ä ¼ # à # # # ¼ # ‹ #‚¾ ÇÞr É Þ jEÀ ¿ÀXÁGÁ Â Ä ‹ ÂfjÉ ÆT~.ÇÈ
~È x U/a ÂS<Tz
~
¤O\p*->¬>:<\p35$&/: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 ôNˆ m?û+1ü~ûf)1+.‘•-1û+]ý”,-.+1ü0/1‘
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ã !#E*-'2&; /*-"97E/:/35)lm$&"(E–"(E/)NO:<2&')t*-,"%E)
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x U/a S<Uz
» Ú Ä Ò ¼ ¾ Ç + Ò ½,» Ú Ä‘ÒŽÝøÅ ¾ ¼ ¾ Ç + ğҎ݁Š¾ Ç
@$&9A) ¾ $&!u:f9A*-!#"%:/"(6˜+
Å
Ä Ò ÝøÅ ¾ Ç ½ + Ò 6_"%E'!
x U/a S<Yz
» Ú ÄŸÒ ¼ ¾ Ç ½ à eB!ÊCä
FI*->L2&)1"%)‚$&35/*-\:<9A)‚:MO*-'"<:€ ‚& / $B!|\p)1L\)1!#)1"%)1;M54f"%E)ƒ'$&, *-\>
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E/:N)K/*Ø$&;=)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/$&!ƒ!#9A:2&)Oa<FIE:<35)f*-,>)1:<!#'\p)1>?)1"/'/$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$&"™97:<*-/2&4šM5)!#:<"($&!#Ö)1;
Ä Ç Ò
M54
x U/a S<”z
» Ú Ä‘Ò ¼ ¾ ÇØ h Ò
}=E/$&!L\]$&*-\u$&!ƒ9A:2&2&)1;:ËåG d&!HÏH&Q a5%"lm:!!#'3535)1!#"%)1;ÇM54j‡%)1,%,%\)145!Cx%S<iT<iz 6:/;
$&"\p)1L\p)1!#)1/"(!9A*->L2&)1"%)š$&35/*-\:<9A)š:MO*-'"ƒ"%E)N:2&')*-,:?!#97:<2B)tL:<\:<>)1"%)1\oa%"I$&!
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Ò
» Ú Äwº ‹ đÒfÇ ¼ ¾ Ç ½ à eB!ÊCä
‰ øû+EæS(*-.1+ ü0/
Ä ôÄ “
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» 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!¼ ¾ Ç
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;=:<9A)16_lm)35)1"/"(E/)GL*-!#"()1\]$&*-\™L;r,, *-\Ë+ n
M
²
²
× × × × × ×
» Ú Ä + M ¼ ¼ #‚¾ ÇØ Ö ¾ Ö ¾ Ö ¾ Ö ¾ Ö ¾ Ö ¾ » Ú Ä ¼ # à # # # # # ‹ ¼ ¾ Ç + ‹ +€+ + +f%+à r ¼ x U/a S<iz
Ä Nô ç èOÿ
ùþ1øÿ½ø*/,-.(*-.+1ü0/
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x ;=')"(*!#$&df/)1!#"()1;t$&/"()135\]:"%$&*-!#z 6_lm)I\p)1!#*-\]"()1;t"(*!#$&>?'2B:<"($&*-Ca_ÊØ)I'!#)1;t§=ñ|a_g/a S<YI:!
:35)1/)1\:<"(*-\C*-,="%E)02&$&5)12&$BE/*-*-;Ø, '9A"($&*-©a5¤5*-\u"%E$&!IL'\L*-!#)16lm)+, )1;?$B":<M5'/;=:/97)1!
, \p*->q:'/$B, *-\]>¦;=$&!#"(\]$&M5'"($&*-3O)1)1\]:"%*-\]a-}rE)f'LL)1\u:/;2&*-lm)1\u2&$&>$&"%!ƒ*-,="%E)0'/$Bh
, *-\>¹;=$&!#"(\]$&M5'"($&*-Çx *-,r;=)1/!#$&"($&)1!#z|lm)1\)03O')1!#!#)1;X$&?"%E)>?://)1\ƒ*-,™97'"%"($&3Oh%*-,%,r"(E/)
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gT
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lm:<!C\:<"(E/)1\r"(*3O)1)1\]:"%)ƒL2&:'!#$&M52&);=)1/!#$&"($&)1!#Y6 + 6"(E/:{"%*:!#!#)1!#!C"%E)1$&\rL\*-M5:MO$B2&$&"($&)1!#6
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) ‡ )1,(, \p)145!#¯
L\]$&*-\©,%*-\©:MO';=:<9A)Oa
» Ú Ä + M ¼ ¼ #‚¾ ÇØ.º ß ï º ßN E + æ ¼ x U/a <Zz
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N:2&')1!35)1/)1\:<"(*-\1nu:š\:<;=*->¶NO:<\$&:"%)mE:<NO$&3Ø"%E)t!#:<>)mL;=,ƒ:!"(E/)t;=)1/!#$&"(4Xlm:!
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L$&/3XM5)1"%lK)1)1¥"(E/)m97'>?'2&:"($&N)tL\p*-M5:<M5$&2B$&"%4Ø;=$&!#"(\]$BMO'"($&*- :<;w"%E)t\]:/35) # Z6 S % a "
9A:KM5)I!#E*-lmt"%E:<"OM54"%:5$&/3{:G'/$&,%*-\]>\]:/;=*->Ò'>?M5)1\™$&¨"%E$&!u\]:/35)I:/;Ö/;=h
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0.001
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0.0008
0.0015
0.0006
0.001
0.0004
0.0005
0.0002
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200
400
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0
200
1600
0.006
400
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0.035
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0.005
0.025
0.004
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0.015
0.002
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400
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fì 6:/; í $&"()1\]:"%$B*-/!a-[:<\:<>)1"%)1\©!#)1"("($&/35!lK)1\)On ÆPy ½ ä 6XÉ y ½ ä f / 6
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àCz ½ ä /Yï 6Xà}| ½ ä ïï 6 z ½ ä 6 | ½ ä 6 TÆ ~ ½ ä f 6
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RA
QAP
800
DECORANA
number of hits
600
@
400
200
CA
PCoA, PCA, PO
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0
20
40
60
80
140
160
180
200
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0.9
PCoA
0.8
0.7
mean spearman
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number of species
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100
0.6
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PCA
0.5
PO
0.4
0.3
0.2
CA
0.1
0
20
40
60
80
100
120
number of species
140
160
180
200
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1000
number of hits
800
QAP
600
@
400
RA
200
DECORANA
CA, PCoA, PCA, PO
0
0
20
40
60
80
100
120
140
160
180
200
number of species
v Ž%´5Œr‰pŸ0&w ± w
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FI8 x?97*-\]\p)1!#L*-/;=)1/97)f:/:2&45!#$&!I*-,=!#:>?L2&)1!#6[FIÙ
8 xL\]$&9A$&L:2O9A*->L*-/)1/"(!
:/:2&45!#$&!#6[F*-8 xØL\$&/97$&L:2D97*-*-\;=$&:<"()1!:<:<2B4O!#$B!#6[™
‘ xXL*-2&:\*-\p;r$B/:"%$&*-©a
}=E/)GL2&*-"$&!uMO:!#)1;*-mSg<jZ<ZZ97:<2&97'2&:"%$B*-/!a
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1
RA, DECORANA
QAP
PCoA
0.9
PCA
0.8
mean spearman
PO
0.7
A
0.6
0.5
CA
0.4
0.3
0
20
v Ž ´5Œ=‰Ÿ+w º&w
40
60
80
100
120
140
160
180
200
number of species
§r,(Ö97$&)1/974G*-,;=$&,%,%)1\)1"_;r)1"()1\]>$&/$B!#"%$&9|!#)1\]$&:"($&*->?)1"(E/*-;=!=lm$&"(E"(E/)u­B\p*-*-,]¯
>*-;=)12ba+·=)1\]"($&97:<2=:d$&!+!#E/*-lK!"%E)K>)1:<X@L)1:\]>:<Ç\:<Ç97*-\]\p)12&:<"($&*-Ø97*-)1,%Öh
97$&)1"™, \p*->¦S<ZZ<Z"(\]$B:<2&!arv8[ xØñO':;=\]:"%$B9+:!#!#$&35/>)1/"|L\*-M52&)1> :<2&35*-\$&"%E>?6
¼0§rF‘JC8˜mŒ
8 xÇ;=)1"(\)1;r)1;9A*-\\)1!#L*-;=)1/97)0:/:2&45!#$&!#6-JCŒ
8 xÇ\p)19A$BL\*-97:<25:Nh
)1\:<35$&356oFI8 xK97*-\]\p)1!#L*-/;=)1/97)u:<:<2&45!#$&!|*-,-!#:<>L2&)1!#6o[FI8 xKL\$&/97$&L:2_9A*->h
L*-)1/"(!:<:<2B4O!#$B!#6-[FI*-Œ
8 xÇL\$&/97$&L:2/97*-*-\;=$&:<"()1!:/:2&45!#$&!#6-[Œ
‘ xÇL*-2B:<\‚*-\h
;=$&:<"($&*-©a}=E/)L2&*-"$&!‚MO:!#)1;t*-tSg<jZ<ZZ97:<2&97'2&:"%$B*-/!a
] & Ë
$&%
8 5M $&";=$&,(, )1\p)1/"-L:"("%)1\K$&!C*-M5"(:<$&)1;lmE/)1m)1,(Ö97$&)1/974$&!u>?)1:!#'\)1;t'!#$B/3{"%E)>?)1:
@L)1:\]>?:\]:/09A*-\\)12&:"($&*-¨97*-)1,(Ö9A$&)1" aQv8[¨$&!©:35:<$&~!#'L)1\$&*-\|"(*:2&2*-"%E)1\|>)1"%Eh
*-;=!a™ "r$&!G9A2&*-!#)12B4,%*-2&2&*-lm)1; M54šJC8ú:/;X¼§=FI‘JC8˜K8ša1}=E)1!#)>?)1"(E/*-;=!G:<97E/$&)1NO)1;
)1,%Ö97$&)19A4{*-N)1\CZ/a i<U/a}=E)14K:\)G, *-2B2&*-lm)1;?M54¨[FI*-8Ùx%:<2B>?*-!#"Z/a iUz 6_[FI8ìx Z/a ”<Uz 6_[‘
x :2&>?*-!#"ZDa ”z:/;=6_Ö/:2&2&476FI8 x :M5*-'"Z/a U<z a
ô$'
:
£mèþ.+1ÿ +.-.‘
8!>?)1"%$B*-/)1;w:MO*-NO)16="%E)tv8[–!#)1\]$B:<"($&*- E/:!$&"(!2&$&>$&"%!M5)1$&3ØM5:<!#)1;–*- :t­B!#',%h
Ö9A$B)1/"(2&4*-NO)1\]2B:<LL$&397*->?L:97"|L:"%"()1\]¯D;=$&!#L2&:45)1; M54"(E/)~:<:<2B4OH)1;X9A*-)1*-9A2B$&/)Oa
}lm*{*-,"%E)f>*-;=)12&!'!#)1;=6:<>)12&4K"(E/)+.0:'!#!#$&::<;"(E/)fMO)1"(:0>*-;=)12&697*-/"(\*-25"(E/)
L:<"("()1\]Ø*-,u"%E)m35)1/)1\:<"()1; 97*-)1/*-972&$&)mNO$&:t"%':<M52&)¨L:<\:<>)1"%)1\!aG}<*?NO$&!#':2&$&H)K"(E/)
\]:/35)~*-,Cv8[X!#)1\]$&:"%$B*-Ø'!#:<M5$&2&$B"%476D:m!#)1\$&)1!G*-,C\)197*-/!#"(\'97"($&*-/!fMO:!#)1;X*-X>?*-;=)12&!
lm$&"(EO*-lmšL:<\:<>)1"%)1\|!#)1"%"($&/35!IlK:<!‚\'©a¤O*-\uM5*-"(Eš>*-;=)12&!'!#)1;=6:0\)135'2&:\©:/;
\]:/;=*->ò!#:>?L2&$&3¥!#97E/)1>)1!Klm)1\p))1d:>?$&)1;aš82&"%E*-'35E "(E/)š\p)13O'2B:<\f!#:<>L2&$&/3
!#9AE)1>?)IlK:<!©,%:<\>*-\))1,%Ö97$&)1/"-"(E/:{"%E)I\]:/;=*->¸*-/)161$&"597*-'2&;tM5)I*-/2&4)1d<9A)1L"($&*-h
:<2B2&4¨\p)1:2&$&H)1;m'/;=)1\©\p)1:<2<9A$B\97'>?!#"(:<9A)1!a
5 765 4 ¸¹?
º_T<.?
>‰ŠŽB;[Z
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Ê¥$&"(Et"(E/)G.0:'!#!#$&:m>?*-;=)12&6"%E)1\)$B!‚:f!#$&/352&)GL:\]:>?)1"()1\%61"(E/)G>:<d<$&>?'>q"(*-2&)1\]:/97)16
Ê Ë*ÌGÍ 6lKE/$&97E ;=)1!#97\]$&M5)1!f"%E)K­o:"(/)1!#!#¯r*-,‚"(E/)K.0:'!#!#$&:<X97'\]NO)K'!#)1;–"(*?>*-;=)12™"(E/)
;
Qiq&QÇ÷ K LWsNRžˆR#ÁÃ
Ui
>
!#L)19A$&)1!#¯C;=)1!#$&"%4 :<2B*-/3¥"(E/)35\]:;=$&)1/"]aš}=E),%*-2&2&*-lm$&3–!#)1\]$B)1!{*-,L2&*-"%!¨$&2&2&'!#"(\]:"()1!
E/*-lð9AE:<35$&/3¨>?:d$&>'>¬"%*-2B)1\]:/97)$&Co')19A)1!‚"%E)G)1,%Ö97$&)1/974~*-,Dv8[t!#)1\]$B:<"($&*-Ca
1000
100 species
800
number of hits
600
@
400
3 species
200
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
max tolerance
v Ž%´5Œr‰pŸ0&w £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"%"($&3O!a1˜m'>MO)1\=*-,OE/$&"(!©*-'"O*-,5S<ZZ<Z"%\$&:<2B!©lm:!©'!#)1;m:!©:>?)1:!#'\)
*-,C)1,(Ö9A$&)19A4aI•–*-;=)12=lm:<!f.0:'!#!#$&:/6lm$&"(E `ùC"!#:<>L2&)¨!#L:97$&/3|a•–:<d<$&h
>?'>¹"%*-2&)1\:<9A_
) Ê`ËJÌÍ0NO:<\$&)1;Ø, \p*->¹Z/a Z<S~"(*šS/a™˜m'>MO)1\ƒ*-,™!#L)197$&)1!$&N*-2&NO)1;
lm:!x%9A'\N)1!‚, \p*->q"%*-L"%*¨M5*-"%"(*->?z n-SZZ<6U<Z6Z6SU<6<S<Z6i6”6j6Y6U6g<6<:<;?T/a
}=E/)GL2&*-"$&!uMO:!#)1;*-mgT<Z<ZZ!#$&>'2&:<"($&*-!a
1
100 species
0.9
Spearman rho
0.8
0.7
B
0.6
0.5
3 species
0.4
0.3
0
v Ž%´5Œr‰pŸ0&w Ï w
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
max tolerance
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
$,%
±*"!!_ IÕ`Ü"
! H
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"(\]$B:<2&!=lm$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\]:/97Ã
) Ê ËJÌÍ
NO:\]$&)1;, \p*->qZDa ZSf"(*KS/a˜m'>?M5)1\C*-,=!#L)19A$B)1!ƒ$&N*-2BN)1;ÇlK:<!‚x 97'\]NO)1!ƒ,%\*->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&:"%$B*-/!a
«! H
120
100
100 species
number of hits
80
@
60
40
20
3 species
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
max. tolerance
v Ž ´5Œ=‰Ÿ+w &w
v8[ )1,(Ö97$&)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"(*SDa™˜K'>?M5)1\*-,©!#L)197$&)1!$&N*-2BN)1;
lK:<!ƒx 97'\]NO)1!ƒ,%\*->q"(*-L?"(*KM5*-"("%*->z#nS<ZZ6UZ<6<Z6SU6SZ<6<i<6”6j6Y6U6g6:/;?T/a
}=E)+L2B*-"/$&!uM5:<!#)1;*-tgT<ZZ<Z!#$&>'2&:"%$&*-!a
1
100 species
0.9
number of hits
0.8
0.7
@
0.6
0.5
3 species
0.4
0.3
0
0.1
0.2
0.3
0.4
0.5
max. tolerance
0.6
0.7
0.8
0.9
1
Qiq&QÇ÷ K WL sNRžˆR#ÁÃ
YS
v Ž%´5Œr‰pŸ0&w Ü w v8[X)1,(Ö9A$&)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;{:!=:ƒ>)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
>
$,%
NO:<\$&)1;,%\*->qZ/a Z<Sf"%*¨SDa˜m'>M5)1\C*-,!#L)19A$&)1!ƒ$&/NO*-2&NO)1;?lm:!x%9A'\N)1!u,%\*->q"(*-L
"(*mM5*-"%"(*->?z nS<ZZ6U<Z6Z<6-SU6-S<Z6i6-”<6-j6-Y<6-U6-g6-:<;ØTDa/}=E/)0L2&*-"|$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 Ž%´5Œr‰pŸ0&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&)š!#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&'"%)12B4–9A*-\\)197"‚"%*-L*-2&*-354–lK:<!~\p)1N)1:2&)1;wM54 "(E/)š:/:2&45!#$&!aš«m*-lK)1N)1\(6©"(E/)š\)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–*-,ƒ@JC¤5!{lK$&"%E$&–"(E/)97*-)1/*-972&$&/)lm:!$&!#',%Ö97$&)1/"‚"(* L\p*-;='9A)
'/$&ñ5')+!#*-2&'"%$B*-/!"(*~"%E)Gv8[‚a
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Ê¥$&"(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!:aƒ\]$B%)1X;a1"%E )
ú
"%E$&!ƒ>:</)1\(6_"%E)+:!#45>?>)1"%\4¨*-÷ ,D"(E/)G@JC¤{lm:!u9AE:<3O)1;ƒa
;
¤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:!#$&3š'>?M5)1\‚*-,™!#L)197$&)1!I$&N*-2&NO)1;ƒar¤5*-\:97*-/!#"(:/"™'>?M5)1\‚*-,™!#L)197$&)1!#65"%E)
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,%*-\"%E))1:<\2&45h !#45>?>)1"%\$&97:<2C97:<!#)16 Ë*ÌGÍ ½
÷
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>?:d$&>'>ò)1,%Ö97$&)19A4 lm:!¨:9AE$&)1NO)1;
óN6 &IÕ`ùC"•! H ú *Ë ÌGÍ a
1000
900
100 species
800
number of hits
700
@
50 species
600
500
400
300
200
100
4
5
6
7
8
9
10 species
15 species
20 species
3
0
0
5
10
15
20
gammamax
v Ž ´5Œ=‰Ÿ+w „éw
v8[)1,(Ö9A$&)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:\]$&)1;t,%\*->ZDa SG"%*Z/a1˜K'>?M5)1\™*-,!#L)19A$&)1!C$B÷ /NO*-2&N)1;lm:!
x%9A'\N)1!‚, \p*->qú "(*-L?"(*KM5*-"%"(*->?z nS<ZZ<6UZ<6<<Z6SU<6<S<Z6i6”6j6Y6U6g6:/;?T/a}=E/)
L2B*-"D$&!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 Ž ´5Œ=‰Ÿ+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 LWsNRžˆR#ÁÃ
YT
>
"(\]$&:2&!=lm$&"(E5/*-lm97*-\]\p)197"<*-\p;=)1\/lm:!='!#)1;{:!=:ƒ>)1:<!#'\p)C*-,)1,(Ö97$&)1/974a7•–*-;=)12
lm:!0M5)1"(:<6DlK$&"%E `Ü"!#:>?L2&){!#L:<97$&3™a Ë*ÌGÍ lK:<!fZDa UK"(E/\p*-'35E/*-'"(6 Ë*ÌGÍ
NO:<\$&)1;,%\*->qZ/a S0"(*KZDa˜m'>M5)1\C*-,!#L)19A$&)1!ƒ÷ $&/NO*-2&NO)1;?lm:!x%9A'\N)1!u,%\*->qú "(*-L
"(*mM5*-"%"(*->?z nS<ZZ6U<Z6Z<6-SU6-S<Z6i6-”<6-j6-Y<6-U6-g6-:<;ØTDa/}=E/)0L2&*-"|$B!IM5:<!#)1;Ç*-
gg<gZ<ZZ!#$&>'2&:<"($&*-!a
&«!& H
90
80
70
number of hits
60
50
@
40
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LAYER
13
12
11
10b
10a
9
8b
8a
7
6
5
4
3
2
1
SPECIES / SAMPLE
-
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Acanthinula aculeata
0
0
1
0
0
3
0
20
3
51
0
0
0
0
0
Cochlodina laminata
0
1
2
5
16
5
3
6
1
32
6
4
1
0
1
Monachoides incarnata
0
6
3
4
16
26
9
26
2
122
5
10
2
1
1
Trichia unidentata
0
0
0
0
5
0
0
0
1
0
0
0
0
0
0
Vertigo pusilla
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
Alinda biplicata
0
0
2
2
1
0
0
0
0
0
0
0
0
0
0
Cepaea hortensis
0
1
1
0
0
0
9
1
0
2
0
1
0
0
0
Discus rotundatus
0
0
3
1
4
0
3
28
1
0
1
1
0
0
0
Aegopinella minor
0
1
3
1
7
3
0
6
0
24
0
2
0
0
0
Bradybaena fruticum
1
1
3
2
2
2
11
33
3
14
2
4
1
1
1
Helix pomatia
0
0
1
2
5
1
4
8
2
12
2
2
1
0
1
Clausilia pumila
0
5
1
3
8
10
2
8
2
30
0
3
1
0
1
Cecilioides acicula
0
0
0
0
0
0
0
1
0
0
0
1
1
4
16
Granaria frumentum
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
Helicella obvia
0
0
0
0
0
0
0
0
0
0
0
0
0
2
23
Helicopsis striata
0
1
3
1
1
1
1
1
0
0
0
0
0
1
1
Chondrula tridens
0
1
1
1
2
1
0
0
0
0
0
1
7
2
8
Pupilla sterri
0
11
6
16
4
3
6
0
9
3
1
4
29
2
1
Cepaea vindobonensis
0
0
0
2
1
0
0
0
2
8
2
3
1
1
1
Pupilla loessica
1
4
12
11
4
12
15
10
11
2
2
0
0
0
0
Pupilla muscorum
0
4
3
2
3
0
0
1
0
0
0
1
76
125
122
Truncatellina cylindrica
0
1
0
2
2
3
2
1
6
12
4
5
60
24
45
Vallonia costata
0
2
2
0
5
1
0
0
0
0
0
0
3
0
0
Vallonia excentrica
0
0
0
0
0
0
0
0
0
0
0
0
0
24
1
Vallonia pulchella
1
1
5
3
8
0
1
4
9
10
25
17
45
67
91
Vertigo pygmaea
0
0
0
0
1
0
5
1
1
3
9
2
30
36
45
Cochlicopa lubricella
0
0
1
0
7
1
2
0
0
0
3
4
8
45
18
Euomphalia strigella
0
1
2
1
1
1
7
3
2
0
0
0
1
0
1
Cochlicopa lubrica
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
Euconulus fulvus
0
0
0
0
0
2
0
1
1
0
1
1
0
0
0
Limacidae spp.
0
0
1
0
5
0
2
0
2
12
2
6
0
0
0
Punctum pygmaeum
0
0
4
1
6
0
3
4
0
33
7
0
0
0
0
Trichia cf. hispida
0
0
1
1
0
1
0
1
0
0
0
0
0
0
0
Vitrea contracta
0
2
7
4
32
25
2
51
4
4
5
1
1
0
0
Vitrina pellucida
0
0
0
0
2
0
1
1
4
0
0
0
0
0
0
Carychium tridentatum
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
Succinea oblonga
0
2
1
2
1
1
5
2
6
2
2
3
1
2
0
TOTAL
3
46
71
67
151
102
93
220
72
376
79
76
269
337
378
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9
8
7
6
5
4
3
2
1
0
SPECIES / SAMPLE
1
2
3
4
5
6
7
8
9
10
Acanthinula aculeata
0
1
3
7
27
124
151
176
136
64
lm 2mOj
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9
8
7
6
5
4
3
2
1
0
Acicula polita
0
0
0
0
0
6
7
2
0
0
Aegopinella pura
0
1
10
6
27
280
505
529
342
92
Clausilia cruciata
0
0
0
0
2
2
7
1
0
0
Cochlodina laminata
0
2
8
11
45
181
160
104
70
23
Discus ruderatus
0
193
51
196
190
154
86
15
17
0
Ena montana
0
0
0
0
0
0
0
6
9
3
Ena obscura
0
0
3
6
57
99
60
24
17
0
Helicodonta obvoluta
0
0
0
1
1
3
10
34
22
6
Isognomostoma isognomostoma
0
0
1
0
0
2
6
21
27
4
Macrogastra densestriata
0
0
0
0
0
0
1
2
0
0
Macrogastra plicatula
0
0
1
0
0
4
2
2
2
0
Monachoides incarnata
0
1
8
4
6
41
82
196
115
57
Orcula doliolum
0
1
2
1
3
48
46
4
1
0
Trichia unidentata bohemica
0
0
2
0
0
8
3
2
1
0
Vertigo pusilla
0
9
3
3
19
81
64
23
21
2
Vitrea diaphana
0
0
0
0
2
1
2
0
0
0
Alinda biplicata
0
0
13
1
10
135
220
240
184
269
Arianta arbustorum
0
0
0
11
19
0
1
1
1
0
Cepaea hortensis
0
0
0
1
0
6
2
9
10
1
Discus rotundatus
0
0
1
0
7
112
106
120
64
11
Limax sp.
0
0
0
0
1
7
10
12
3
0
Semilimax kotulai
0
19
5
2
0
0
0
0
0
0
Aegopinella minor
0
0
19
12
68
453
566
966
355
55
Bradybaena fruticum
0
2
4
8
39
37
21
18
14
0
Helix pomatia
0
0
0
0
0
0
1
2
6
0
Vitrea crystallina
1
0
3
0
0
0
0
0
0
0
Macrogastra ventricosa
0
0
0
0
0
1
6
5
2
0
Granaria frumentum
0
0
0
0
1
6
3
5
1
0
Helicopsis striata
0
6
4
1
0
1
0
0
0
0
Chondrula tridens
0
0
4
4
6
6
2
2
0
0
Pupilla sterri
0
0
4
2
1
1
0
0
0
0
Pupilla triplicata
0
0
0
0
0
0
1
0
0
0
Chondrina avenacea
0
0
0
0
0
0
0
2
7
104
Columella columella
0
213
23
6
0
0
0
0
0
0
Pupilla loessica
763
3
0
2
0
0
0
0
0
0
Truncatellina cylindrica
0
0
0
0
35
12
0
2
4
1
Vallonia costata
8
203
62
181
1574
1570
587
44
49
0
Vallonia excentrica
0
0
0
0
1
0
0
0
0
0
Vallonia pulchella
0
12
20
24
66
27
6
3
3
1
Vallonia tenuilabris
0
0
2
18
0
0
0
0
0
0
Vertigo pygmaea
0
0
3
1
6
0
4
4
1
0
Bulgarica nitidosa
0
0
2
0
0
25
22
12
4
2
Cochlicopa lubricella
0
23
14
21
52
96
50
4
6
0
Euomphalia strigella
0
0
2
5
30
29
17
14
16
0
Cochlicopa lubrica
1
356
89
27
3
0
0
0
0
0
Euconulus fulvus
1
35
11
10
16
24
25
18
9
7
Limacidae
0
3
2
16
12
6
11
23
13
0
@Q
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Y
7
6
5
4
3
2
1
0
Nesovitrea hammonis
2
97
33
40
109
81
23
7
3
0
Punctum pygmaeum
0
125
27
30
94
153
93
40
179
146
Trichia sericea
0
132
35
13
10
0
2
0
0
0
Vertigo cf. arctica
0
19
3
0
1
0
0
0
0
0
Vitrea contracta
0
0
0
0
3
27
49
40
25
0
Vitrina pellucida
0
5
0
2
5
6
3
0
5
3
Clausilia dubia
0
0
0
1
0
0
0
2
1
1
Helicigona lapicida
0
0
1
0
0
1
2
29
20
12
Laciniaria plicata
0
0
0
0
0
2
1
2
0
0
Vertigo alpestris
0
67
8
5
0
1
1
2
2
0
Carychium tridentatum
0
5
10
8
45
513
589
143
142
0
Columella edentula
0
5
4
2
2
6
2
1
6
0
Nesovitrea petronella
0
0
0
1
11
11
2
0
0
0
Vertigo substriata
0
0
3
8
0
1
0
0
0
0
TOTAL
776
1538
503
698
2606
4390
3620
2913
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9
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6
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SPECIES / SAMPLE
1
2
3
4
5
6
7
8
9
10
Acanthinula aculeata
0
0
0
0
1
2
4
7
2
4
Acicula polita
0
0
0
0
2
1
1
0
0
0
Aegopinella pura
0
0
0
2
9
5
8
15
4
0
Cochlodina laminata
2
1
3
6
16
28
60
45
21
13
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0
2
0
5
0
1
0
0
0
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Ena montana
0
0
1
0
0
2
2
5
2
0
Ena obscura
0
0
0
1
0
0
0
0
0
0
Helicodonta obvoluta
0
0
1
1
3
21
37
24
12
6
Isognomostoma isognomostoma
0
0
1
1
2
14
30
30
23
5
Macrogastra plicatula
0
0
0
2
3
0
0
0
0
0
Monachoides incarnata
1
2
4
11
18
22
43
62
38
16
Oxychilus depressus
0
0
0
1
0
0
0
0
0
0
Semilimax semilimax
0
0
0
0
0
1
2
4
0
3
Sphyradium doliolum
0
0
1
1
7
0
0
0
0
0
Vertigo pusilla
0
1
0
0
1
0
0
0
0
0
Alinda biplicata
0
0
4
15
57
22
19
60
87
47
Cepaea hortensis
0
0
1
1
3
5
7
12
9
1
Discus rotundatus
1
0
2
4
58
70
104
111
26
16
Limax sp.
0
0
0
7
12
8
16
13
18
0
Aegopinella minor
1
2
5
7
20
21
47
92
40
47
Bradybaena fruticum
1
1
4
12
12
12
9
12
8
0
lm 2mOj
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LAYER
9
8
7
Helix pomatia
1
0
1
Vitrea crystallina
0
0
2
Macrogastra ventricosa
0
0
0
Clausilia pumila
0
0
Urticicola umbrosa
0
Granaria frumentum
ú =Î
5
4
3
2
1
0
0
2
5
8
6
5
1
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
4
1
1
0
2
0
0
0
0
1
0
0
3
1
0
0
0
1
1
0
3
1
0
0
0
Helicopsis striata
1
6
2
2
0
0
0
0
0
0
Chondrula tridens
1
0
4
4
0
1
0
0
0
0
Pupilla sterri
0
1
1
0
0
0
0
0
0
0
Cepaea vindobonensis
0
0
0
0
0
0
0
1
0
0
Pupilla loessica
4
10
0
0
0
0
0
0
0
0
Pupilla muscorum
0
2
0
0
0
0
0
0
0
0
Truncatellina cylindrica
0
0
0
0
0
1
0
0
0
0
Vallonia costata
0
9
1
0
0
0
0
0
0
1
Vallonia pulchella
4
14
5
3
0
0
0
0
0
0
Bulgarica nitidosa
0
0
0
1
0
0
0
0
0
0
Cochlicopa lubricella
0
0
0
0
1
0
0
0
0
1
Euomphalia strigella
0
1
1
4
5
11
10
12
9
1
Cochlicopa lubrica
1
4
6
0
0
0
0
0
0
0
Euconulus fulvus
0
1
1
0
0
0
1
3
1
2
Limacidae & Agriolimacidae
1
4
2
6
4
16
23
28
15
0
Nesovitrea hammonis
0
3
1
1
0
0
0
1
1
24
Oxychilus cellarius
0
0
0
0
0
6
30
50
6
1
Punctum pygmaeum
0
0
0
0
0
0
2
6
4
5
Trichia plebeia
0
6
7
0
1
0
0
0
0
0
Vitrea contracta
0
0
0
0
3
3
0
0
0
0
Vitrina pellucida
0
0
0
1
1
0
0
0
1
0
Helicigona lapicida
0
0
0
2
3
10
16
18
15
2
Laciniaria plicata
0
0
0
1
0
1
0
2
1
0
Carychium tridentatum tridentatum
0
0
0
3
24
0
0
0
0
0
Succinea oblonga
0
2
1
0
0
0
0
0
0
0
TOTAL
19
72
63
106
274
293
482
622
351
196
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10
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7
6
5
4
3
2
1
SPECIES / SAMPLE
-
1
2
3
4
5
6
7
8
Acanthinula aculeata
0
0
1
3
17
24
61
66
11
Aegopinella pura
0
0
0
2
32
29
45
46
5
Cochlodina laminata
0
0
1
10
46
43
49
28
12
Discus ruderatus
0
1
15
9
9
0
0
0
4
Ena montana
0
0
1
0
0
0
0
0
0
Ena obscura
0
0
0
1
11
6
1
0
2
Helicodonta obvoluta
0
0
0
0
1
3
7
4
2
Isognomostoma isognomostoma
0
0
0
0
1
2
0
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Macrogastra plicatula
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sX&%1Ä+t
WY^X
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[uAt
8
7
6
5
4
3
2
1
Monachoides incarnata
0
0
1
3
28
63
107
79
27
Oxychilus cf. depressus
0
1
0
0
0
0
0
0
0
Sphyradium doliolum
0
0
0
3
12
5
3
13
1
Trichia unidentata bohemica
0
0
0
0
7
7
5
3
2
Vertigo pusilla
0
0
0
0
7
9
13
13
6
Alinda biplicata
0
0
0
16
229
261
345
258
80
Arianta arbustorum
0
1
5
0
1
0
0
1
1
Cepaea hortensis
0
0
0
0
0
1
3
4
2
Discus rotundatus
0
0
0
7
26
21
3
1
18
Limax cf. cinereoniger
0
0
0
2
6
4
2
7
7
Oxychilus glaber
0
0
0
0
1
0
0
0
0
Semilimax kotulae
0
0
5
1
1
0
0
0
1
Aegopinella minor
0
0
0
15
64
73
124
447
26
Bradybaena fruticum
0
1
9
20
23
5
3
1
8
Helix pomatia
0
0
1
1
1
1
0
2
2
Clausilia pumilla
0
0
0
0
0
0
0
0
1
Granaria frumentum
0
0
0
25
177
260
390
156
31
Helicopsis striata
4
44
77
10
3
0
0
0
2
Chondrula tridens
0
0
1
10
7
0
0
0
1
Pupilla sterri
0
7
18
7
16
11
46
110
13
Pupilla triplicata
0
0
7
4
8
5
15
35
12
Chondrina avenacea
0
0
8
46
200
136
106
60
55
Pyramidula rupestris
0
0
0
0
0
6
2
8
6
Truncatellina claustralis
0
0
0
0
0
0
1
8
2
Pupilla muscorum
0
5
13
0
0
0
0
0
0
Truncatellina cylindrica
0
0
1
3
3
6
18
41
10
Vallonia costata
0
0
23
50
56
8
22
42
16
Vallonia pulchella
0
0
1
7
78
79
106
80
16
Vertigo pygmaea
0
0
0
0
0
0
1
0
0
Bulgarica nitidosa
0
0
1
9
93
165
239
118
69
Cochlicopa lubricella
0
1
8
12
71
13
12
7
10
Euomphalia strigella
0
1
3
15
42
28
31
30
14
Cochlicopa lubrica
0
6
1
0
0
0
0
0
0
Euconulus fulvus
0
0
6
2
8
4
4
5
6
Nesovitrea hammonis
0
0
0
3
3
1
1
0
1
Oxychilus cellarius
0
0
0
0
0
0
1
0
0
Punctum pygmaeum
0
0
2
0
3
0
3
1
9
Trichia sericea
0
6
125
13
1
0
0
0
0
Vitrea contracta
0
0
0
0
2
4
5
2
0
Clausilia dubia
0
0
1
1
14
2
4
2
0
Helicigona lapicida
0
0
0
0
2
5
11
11
3
Laciniaria plicata
0
0
0
0
0
1
0
0
0
Vertigo alpestris
0
1
7
3
0
0
0
0
2
Carychium tridentatum tridentatum
0
0
0
2
8
12
8
4
0
Nesovitrea petronella
0
0
4
8
6
0
0
0
0
TOTAL
4
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346
325
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abu2den-0.02.c
Generates posterior pdf for density from abundance.
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) */
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double
double
double
double
double
double
double
double
double
double
double
){
Dm,
V,
Sigma_v,
Rl,
Rh,
Cl,
Ch,
Ol,
Oh,
G,
Sigma_g
/*
/*
/*
/*
/*
/*
/*
/*
/*
/*
/*
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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);
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/* etc... */
exit (0);
}
/* eof */
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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 */
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ahisto-0.02.c
*/
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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]);
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/* 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 */
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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;
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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);
}
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}
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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 );
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/* 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 */
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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
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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
*/
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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;
}
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/* 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);
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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 */
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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 */
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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 */
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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}
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__________________________________________________________________________*/
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 */
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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;
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FILE *seedfile, *matrix;
float ***dty;
char *filename = "AAAAAA";
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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 */
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/*
*
*
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*
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*
*
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*
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*
*/
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
/*
*
*
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*
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*/
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();
/*
*
*
*
*
*
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*
*
*
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
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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;
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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();
/*
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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
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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]);
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/* 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;
/*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
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*
*
*
*
*
*
*
*
*
*/
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: */
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}
/* 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,
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*
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*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*/
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];
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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
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*
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*
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*/
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;
/*
*
*
*
*
*
*
*
*
*
*
*
*
*
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*
*/
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;
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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: */
}
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L20:
;
}
return 0;
} /* mkbseq */
/*
*
*/
int insrtq(int *n2, int *v, int *iv, int *sizeq, int *q, int *iq)
{
static int sq, tsz;
/*
*
*
*
*
*
*
*
*
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*
*
*
*/
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
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*
*
*
*
*
*
*
*
*
*
*
*
*/
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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).
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1
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*/
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;
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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 ---*/
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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
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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 */
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*
itransp.c
*
reads integer mmatrix from stdin, and outputs its
*
transpose to stdout
*
written by Peter Cejchan, 1998/11/19
****************************************************************************/
#include <stdio.h>
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#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--- */
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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
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_________________________________________________________________________ */
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 */
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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;
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/* 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;
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*/
/* 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 */
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* 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++){
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/* 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 --- */
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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++){
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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 */
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*
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]);
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/****************************************************************************
*
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--- */
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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:
.
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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++;
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}
}
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);
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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);
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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++) {
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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 */
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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;
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while (scanf("%f", &score[i]) != EOF)
i++;
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num= i;
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/* 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 --- */
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