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Human Evol Genetics

by: Helmer VonRueden

Human Evol Genetics BIOL 5221

Marketplace > University of Utah > Biology > BIOL 5221 > Human Evol Genetics
Helmer VonRueden
The U
GPA 3.93


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Class Notes
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This 6 page Class Notes was uploaded by Helmer VonRueden on Monday October 26, 2015. The Class Notes belongs to BIOL 5221 at University of Utah taught by Staff in Fall. Since its upload, it has received 46 views. For similar materials see /class/229970/biol-5221-university-of-utah in Biology at University of Utah.


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Date Created: 10/26/15
J usT Enough PyThon I WhaT does PyThon have To do wiTh The goal of This course A few words abouT modeling and learning how To do iT Random numbers as The life forcequot of simulaTion models Using random numbers in PyThon PyThon jargon 101 names objecTs meThods funcTions TesTing hypoTheses by modeling The MLB simulaTion AnThroBiol 5221 29 AugusT 2008 WhaT does PyThon have To do wiTh The goal of This course Goal how To Think more Than wharTo Think abouT The process of evoluTion Thinking involves developing applying TesTing Theory Theories are made539 ThaT represenT naTure simplified absTracTed For evoluTion we have an excellenT general model change in The relaTive frequencies of alleles aT geneTic loci So our specific models need To represenT 1 genes and Their alleles an 2 The processes ThaT change The frequencies of alleles muTaTion geneTic drifT naTural selecTion recombinaTion Models succeedTo The eXTenT ThaT Their represenTaTions are lifelike ie predicT facTs abouT variaTion wiThin and divergence beTween species CompuTing is essenTial 1707 To developing models andTo analyzing daTa WhaT is compuTing AT lowesT level flipping swiTchesl 01 AT higher level simple operaTions on numbers and leTTers The secreT These can be used To represenT almosT anyThingl A few words abouT modeling and learning how To do iT ArepresenTaTions of anyThing are absTracTions simplify realiTy ignore many deTails Is This responsible Why preTend we don39T know a parT of The True s39rar39y AcTually iT39s 25521777214 and our brains do iT auTomaTically BuT absTracTions are of many kinds and noT all are equally helpful And differenT ones may be good for differenT purposes Example The many ways To represenT a quotgenequot genome sequence To A quotaquot Your goal To learn The arTs of reducTive absTracTionquot for evoluTionary geneTics BuT you can39T learn iT by isTening To us Talk abouT iTl You can only learn iT by Trying To do iT Like riding a bicycle EXpecT feelings of frusTraTion and erininessl BuT don39T despair Random numbers as The life forcequot of simulaTion models EvoluTion is all abouT evem s39ThaT happen To genesin papua ans39 birThs deaThs muTaTions recombinaTions These evenTs occur wiTh pmbabY es39 ThaT we can describe and esTimaTe We can also represeanhem on The compuTer using random numbersquot Example modeling a birTh Mendel39s law of segregaTion Use a uniformly disTribuTed random number To choose The lefT parenT39s allele 0 05 1 Use anoTher To choose The righT parenT39s allele l O 05 1 PyThon jargon 101 names objecTs meThods funcTions 1 Names are born when you assign values To Them X O 2 They musT begin wiTh a leTTer or underscore and conTain only eTTers digiTs and underscores X1 and 1X are legal buT 1X and X 1 are noT 3 Upper and lower case leTTers are differenT from each oTher quotAquot is noT a 4 You can39T use The PyThon reserved wordsquot and del for is raise asserT elif from lambda reTurn break else global noT Try class eXcepT if or while conTinue eXec imporT pass yield def finally in prinT And would be legal buT why play wiTh fire 5 Names do noT have quotTypesquot buT The abjecrs39They refer To do X 0 means X refers To The inTeger value 0quot X 01 means X refers To The floaTing poinT number 01quot X Spam means X refers To The sTring of characTers Spam 6 The fundamenTal objecT Types are numbers inTegers floaTs sTrings of characTers isTs ordered arrays of oTher objecTs dicTionaries arrays of paired objecTs Tuples like isTs buT simpler and files 7 Each Type of objecT has merhads ThaT iT quotknowsquot how To perform on iTself For eXample lisTsorT sorTs a isT in some appropriaTe way 8 Fundans are like meThods buT They r39erLr39n a VaLe squX reTurns The square rooT of X random reTurns a random number Four Python programs demonstrated and discussed in lecture 5 Sept 08 sumslpy 5 Sept 08 from random import random n rawiinputquotnumber of random numbers to average quot n intn sum 00 i 0 while i lt n p random sum p i print ipsumsumfloati sum52py 5 Sept 08 from random import random dist O for i in rangelOO n rawiinputquotnumber of random numbers to average quot n intn cases max 0 min lOO grandimean 00 numicases 200 while cases lt num cases sum 00 7 i 0 while i lt n p random sum p i 1 mean sumfloatn bin intlO00mean distbin l grandimean mean if bin lt min min bin if bin gt max max bin cases 1 print i min while i lt max print quot42f 3dquot floatilO00 disti i 1 print quotnmean 53fquot grandimeanfloatnumicases onediepy 28 Aug 08 from random import random x O for i in xrange7 5 rawiinputquotnumber times to roll the die quot n intn for i in xrangen spots l int60random xspots 1 z O sumf 00 print for i in xrangel7 z xi f floatxifloatn sumf f print quot3d 8d lO8fquot ixif print quotnall 8d lO8fquot zsumf baseba11py 5 Sept 08 from random import random THERE ARE IO TEAMS IN THE LEAGUE PUT WINS IN EIRST COLUMN quotNAMESquot IN SECOND teams Oil for i in xrangelOH SHOW THE teams MATRIX AT THE BEGINNING OF THE SEASON print quotteams matrix before the start 0 the seasonquot print teams print PLAY BALLl for i in xrange09 for j in xrangeillO EACH TEAM PLAYS EACH OF THE OTHERS 18 TIMES for k in xrange18 if random lt 05 TEAM quotiquot WINS teamsiO 1 else TEAM 339 quot WINS teamsj0 l SHOW THE MATRIX AT THE END OF THE SEASON I quotNAMEquot ORDER print quotteams matrix at the end of the seasonquot print teams print continued on next page SORT THE MATRIX BY WINS teams print print print REVERSE THE MATRIX teams print print print FIRST NUMBER IN EACH ROW sort quotteams matrix sorted teams worst to best recordquot PUTTING BEST RECORD FIRST reverse quotteams matrix reversed teams best to worst record n PRINT THE STANDINGS for i in xrangelO W teamsi O L p floatW1620 GB teamsOO W p rint quotteam 2d won 3d Iost 3d 53f 2d GBquot teamsilWLpGB


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