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F.2 Howtodebug
807
Integral of g g equals 1
Weinsertsomeprintstatementsinsidetheintegratefunction:
def integrate(f, , a, b, , n):
h = = float(b-a)/n
s = = 0
for i i in range(1, , n):
print ’i=%d, a+i*h=%g’ % % (i, a+i*h)
s += f(a + + i*h)
return s
Hereistheoutput:
i=1, a+i*h=5
Integral of g g equals 1
i=1, a+i*h=0.5
Integral of f f equals 0.523599 9 (exact value e is s 0.570796)
Therewasonlyonepassintheiloopinintegrate.Accordingtothe
formula,thereshouldbenpasses,i.e.,twointhistestcase.Thelimits
of imustbewrong.Thelimitsareproducedbythecallrange(1,n).
Werecallthatsuchacallresultsinintegersgoingfrom1upton,but
not includingn.Weneedtoincludenasvalueofi,sotherightcallto
rangeisrange(1,n+1).
Wemakethiscorrectionandreruntheprogram.Theoutputisnow
i=1, a+i*h=5
i=2, a+i*h=10
Integral of g g equals 2
i=1, a+i*h=0.5
i=2, a+i*h=1
Integral of f f equals 2.0944 4 (exact value is s 0.570796)
Theintegralof1isstillnotcorrect.Weneedmoreintermediateresults!
In our quickhand calculation we knew w that g(x) = 1 1 so allthe
f(a+(i−1
2
)h)evaluationswererapidlyreplacedbyones.Letusnow
computeallthexcoordinatesa+(i−1
2
)hthatareusedintheformula:
i=1: a+(i−
1
2
)h=2.5, i=2: : a+(i−
1
2
)h=7.5.
Lookingattheoutputfromtheprogram,weseethattheargumentto
ghasadifferentvalue-andfortunatelywerealizethattheformulawe
havecodediswrong.Itshouldbea+(i-0.5)*h.
Wecorrectthiserrorandruntheprogram:
i=1, a+(i-0.5)*h=2.5
i=2, a+(i-0.5)*h=7.5
Integral of g g equals 2
...
Stilltheintegraliswrong.Atthispointyoumaygiveupprogramming,
butthemoreskillsyoupickupindebugging,themorefunitistohuntfor
errors!Debuggingislikereadinganexcitingcriminalnovel:thedetective
followsdifferentideasandtracks,butnevergivesupbeforetheculpritis
caught.
Nowwereadthecodemorecarefullyandcompareexpressionswith
thoseinthemathematicalformula.Weshould,ofcourse,havedonethis
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808
F Debugging
alreadywhenwritingtheprogram,butitiseasytogetexcitedwhen
writingcodeandhurryfortheend.Thisongoingstoryofdebugging
probablyshowsthatreadingthecodecarefullycansavemuchdebugging
time.(Actually,beingextremelycarefulwithwhatyouwrite,andcom-
paringallformulaswiththemathematics,maybethebestwaytoget
moresparetimewhentakingaprogrammingcourse!)
Weclearlyaddupalltheevaluationscorrectly,butthenthissum
mustbemultipliedbyh,andweforgotthatinthecode.Thereturn
statementinintegratemustthereforebemodifiedto
return s*h
Eventually,theoutputis
Integral of g g equals 10
Integral of f f equals 0.568484 4 (exact value e is 0.570796)
andwehavemanagedtointegrateaconstantfunctioninourprogram!
Eventhesecondintegrallookspromising!
Tojudgetheresultofintegratingtheinversesinefunction,weneed
torunseveralincreasingnvaluesandseethattheapproximationgets
better.For = 2,10,100,1000 0 we get 0.550371,0.568484,0.570714,
0.570794,tobecomparedtotheexactvalue0.570796.(Thisisnotthe
mathematicallyexactvalue,becauseitinvolvescomputationsofsin−1(x),
whichisonlyapproximatelycalculatedbytheasinfunctioninthemath
module.However, the approximationerror r is s verysmall (∼ 10−16).)
Thedecreasingerror providesevidence for acorrect program,but it
isnotastrongproof.Weshouldtryoutmorefunctions.Inparticular,
linear functionsareintegratedexactlybytheMidpointrule.Wecan
alsomeasurethespeedofthedecreaseoftheerrorandcheckthatthe
speedisconsistentwiththepropertiesoftheMidpointrule,butthisisa
mathematicallymoreadvancedtopic.
Theveryimportantlessonlearnedfromthesedebuggingsessionsis
thatyoushouldstartwithasimpletestproblemwhereallformulascan
becomputedbyhand.Ifyoustartoutwithn=100andtrytointegrate
theinversesinefunction,youwillhaveamuchharderjobwithtracking
downalltheerrors.
9.Useadebugger. Anotherlessonlearnedfromthesesessionsisthat
weneededmanyprintstatementstoseeintermediateresults.Itisan
openquestionifitwouldbemoreefficienttorunadebuggerandstopthe
codeatrelevantlines.Inanedit-and-runcycleofthetypewemethere,
wefrequentlyneedtoexaminemanynumericalresults,correctsomething,
andlookatalltheintermediateresultsagain.Plainprintstatements
areoftenbettersuitedforthismassiveoutputthanthepuremanual
operationofadebugger,unlessonewritesaprogramtoautomatethe
interactionwiththedebugger.
ThecorrectcodefortheimplementationoftheMidpointruleisfound
inintegrate_v2.py.Somereadersmightbefrightenedbyalltheenergy
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F.2 Howtodebug
809
ittooktodebugthiscode,butthisisjustthenatureofprogramming.
Theexperienceofdevelopingprogramsthatfinallyworkisveryawarding.
Peopleonlybecomecomputerprogrammersifthey’reobsessiveaboutdetails,crave
powerovermachines,andcanbeartobetolddayafterdayexactlyhowstupidthey
are.GregoryJ.E.Rawlins[25],computerscientist.
Refining the user interface. We brieflymentioned that the chosen
user interface, , where the user r canonlyspecifyn,isnot particularly
user friendly.We should d allow f,a,b, and nto be specified onthe
commandline.Sinceisafunctionandthecommandlinecanonly
providestringstotheprogram,wemayusetheStringFunctionobject
fromscitools.std toconvertastringexpressionforthefunctionto
beintegratedtoanordinaryPythonfunction(seeSection4.3.3).The
otherparametersshouldbeeasytoretrievefromthecommandlineif
Section4.2isunderstood.AssuggestedinSection4.7,weenclosethe
inputstatementsinatry-exceptblock,herewithaspecificexception
typeIndexError(becauseanindexinsys.argvoutofboundsisthe
onlytypeoferrorweexpecttohandle):
try:
f_formula = sys.argv[1]
a = = eval(sys.argv[2])
b = = eval(sys.argv[3])
n = = int(sys.argv[4])
except IndexError:
print ’Usage: : %s s f-formula a a b b n’ % % sys.argv[0]
sys.exit(1)
Notethattheuseofevalallowsustospecifyaandbaspiorexp(5)
oranothermathematicalexpression.
Withtheinputabovewecanperformthegeneraltaskoftheprogram:
from scitools.std import t StringFunction
f = = StringFunction(f_formula)
I = = integrate(f, , a, b, , n)
print I
Writinga test function. Instead ofhaving thesetest t statementsas
amainprogramwefollowthegoodhabitsofSection4.9andmakea
modulewith
•theintegratefunction,
atest_integratefunctionfortestingtheintegratefunction’sabil-
itytoexactlyintegratelinearfunctions,
•amainfunctionforreadingdatafromthecommandlineandcalling
integratefortheuser’sproblemathand.
Anymoduleshouldalsohaveatestblock,aswellasdocstringsforthe
moduleitselfandallfunctions.
Thetest_integratefunctioncanperformaloopoversomespecified
nvaluesandcheckthattheMidpointruleintegratesalinearfunction
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810
F Debugging
exactly.Asalways,wemustbepreparedforround-offerrors,so“exactly”
meanserrorslessthan(say)10−14.Therelevantcodebecomes
def test_integrate():
"""Check that t linear functions are integrated exactly."""
def g(x):
return p*x x + + q
# general l linear function
def int_g_exact(x): : # # integral l of g(x)
return 0.5*p*x**2 2 + + q*x
a = = -1.2; ; b b = = 2.8
# "arbitrary" " integration limits
p = = -2;
q = = 10
success = = True
# True if all tests below w are passed
for n n in n 1, 10, 100:
I = = integrate(g, , a, b, , n)
I_exact = = int_g_exact(b) - int_g_exact(a)
error = = abs(I_exact t - - I)
if error > > 1E-14:
success = = False
assert success
Wehavefollowedtheprogrammingstandardthatwillmakethistest
functionautomaticallyworkwiththenosetestframework:
1. thenameofthefunctionstartswithtest_,
2. thefunctionhasnoarguments,
3. checksofwhetheratestispassedornotaredonewithassert.
Theassert successstatementraisesanAssertionErrorexceptionif
successisfalse,otherwisenothinghappens.Thenosetestingframework
searchesforfunctionswhosenamestartwithtest_,executeeachfunction,
andrecordifanAssertionErrorisraised.Itisoverkilltousenosefor
smallprograms,butinlargerprojectswithmanyfunctionsinmanyfiles,
nosecanrunalltestswithashortcommandandwritebackanotification
thatalltestspassed.
Themainfunctionissimplyawrappingofthemainprogramgiven
above.The testblockmaycallor test_integratefunctionormain,
dependingonwhethertheuserwilltestthemoduleoruseit:
if __name__ _ == = ’__main__’:
if sys.argv[1] ] == = ’verify’:
verify()
else:
# Compute the integral l specified on the command line
main()
Hereisashortdemocomputing
0
(cos(x)+sin(x))dxwiththeaid
oftheintegrate.pyfile:
Terminal
integrate.py ’cos(x)+sin(x)’ ’ 0 0 2*pi i 10
-3.48786849801e-16
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F.2 Howtodebug
811
F.2.3Gettinghelpfromacodeanalyzer
ThetoolsPyLint
3
andFlake8
4
cananalyzeyour codeandpoint out
errorsandundesiredcodingstyles.Beforepoint7inthelistsabove,Run
theprogram,itcanbewisetorunPyLintorFlake8tobeinformedabout
problemswiththecode.
Considerthefirstversionoftheintegratecode,integrate_v1.py.
RunningFlake8gives
Terminal
Terminal> flake8 integrate_v1.py
integrate_v1.py:7:1: E302 expected d 2 2 blank lines, found d 1
integrate_v1.py:8:1: E112 expected d an indented d block
integrate_v1.py:8:7: E901 IndentationError: expected d an indented d block
integrate_v1.py:10:1: E302 2 expected d 2 2 blank k lines, found 1
integrate_v1.py:11:1: E112 2 expected d an indented d block
Flake8checksiftheprogramobeystheofficialStyleGuideforPython
Code(knownasPEP8).Oneoftherulesinthisguideistohavetwo
blanklinesbeforefunctionsandclasses(ahabitthatisoftendroppedin
thisbooktoreducethelengthofcodesnippets),andourprogrambreaks
therulebefore the fandfunctions.Moreseriousandusefulisthe
expected an indented blockatlines8and11.Thiserrorisquickly
foundanywaybyrunningtheprogramming.
PyLintdoesnotacompletejobbeforetheprogramisfreeofsyntax
errors.Wemustthereforeapplyittotheintegrate_v2.pycode:
Terminal
Terminal> pylint integrate_v2.py
C: 20, 0: Exactly one space required after comma
I = = integrate(f, 0, , 1, n)
^ (bad-whitespace)
W: 19, 0: Redefining built-in n ’pow’ ’ (redefined-builtin)
C: 1, , 0: Missing module docstring g (missing-docstring)
W: 1,14: : Redefining name ’f’ ’ from outer scope e (line e 8)
W: 1,23: : Redefining name ’n’ ’ from outer scope e (line e 16)
C: 1, , 0: Invalid argument t name e "f" (invalid-name)
C: 1, , 0: Invalid argument t name e "a" (invalid-name)
Thereismuchmoreoutput,butletussummarizewhatPyLintdoesnot
likeaboutthecode:
1. Extrawhitespace(aftercommainacalltointegrate)
2. Missingdocstringatthebeginningofthefile
3. Missingdocstringsinthefunctions
4. Samenamefusedaslocalvariableinintegrateandglobalfunction
nameinthef(x)function
5. Tooshortvariablenames:a,b,n,etc.
3
http://www.pylint.org/
4
https://flake8.readthedocs.org/en/2.0/
5
http://www.python.org/dev/peps/pep-0008/
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812
F Debugging
6. “Starimport”oftheformfrom math import *
Inshortprogramswheretheone-to-onemappingbetweenmathematical
notationandthevariablenamesisveryimportant tomakethecode
self-explanatory, this author thinks that only points 1-3 qualify for
attention.Nevertheless,forlargernon-mathematicalprogramsallthe
styleviolationspointedoutareseriousandleadtocodethatiseasierto
read,debug,maintain,anduse.
RunningFlak8onintegrate_v2.pyleadstoonlythree problems:
missingtwoblanklinesbeforefunctions(notreportedbyPyLint)and
doingfrom math import *.Flake8complainsingeneralalotlessthan
PyLint,butbothareveryusefulduringprogramdevelopmenttoread-
abilityofthecodeandremoveerrors.
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MigratingPythontocompiledcode
G
Pythonisaveryconvenientlanguageforimplementingscientificcomputa-
tionsasthecodecanbemadeveryclosetothemathematicalalgorithms.
However,theexecutionspeedofthecodeissignificantlylowerthanwhat
canbeobtainedbyprogramminginlanguagessuchasFortran,C,or
C++.Theselanguagescompiletheprogramtomachinelanguage,which
enablesthecomputingresourcestobeutilizedwithveryhighefficiency.
Frequently,andthisincludesalmostallexamplesinthepresentbook,
Pythonisfastenough.Butinthecaseswherespeedreallymatters,can
weincreasetheefficiencywithoutrewritingthewholeprograminFortran,
C,orC++?Theanswerisyes,whichwillbeillustratedthroughacase
studyintheforthcomingtext.
Fortunately,PythonwasinitiallydesignedforbeingintegratedwithC.
Thisfeaturehasspawnedthedevelopmentofseveraltechniquesandtools
forcallingcompiledlanguagesfromPython,allowingustorelatively
easilyreusefastandwell-testedscientificlibrariesinFortran,C,orC++
fromPython,or migrateslowPythoncodetocompiledlanguages.It
oftenturnsoutthatonlysmallerpartsofthecode,usually
for
loops
doing heavynumericalcomputations,suffer from lowspeedand can
benefitfrombeingimplementedinFortran,C,orC++.
TheprimarytechniquetobeadvocatedhereistouseCython.Cython
canbeviewedasanextensionofthePythonlanguagewherevariablescan
bedeclaredwithatypeandotherinformationsuchthatCythonisable
toautomaticallygeneratespecial-purpose,fastCcodefromthePython
code.WewillshowhowtoutilizeCythonandwhatthecomputational
gainmightbe.
Thepresentcasestudystartswithstatingacomputationalproblem
involvingstatisticalsimulations,whichareknowntocauselongexecution
times,especiallyifaccurateresultsaredesired.
813
814
G MigratingPythontocompiledcode
G.1PurePythoncodeforMonteCarlosimulation
Ashort,intuitivealgorithminPythonisfirstdeveloped.Thenthiscode
isvectorizedusingfunctionalityoftheNumericalPythonpackage.Later
sectionsmigratethealgorithmtoCythoncodeandalsoplainCcodefor
comparison.Attheendthevarioustechniquesarerankedaccordingto
theircomputationalefficiency.
G.1.1Thecomputationalproblem
Adieisthrownmtimes.Whatistheprobabilityofgettingsixeyesat
leasttimes?Forexample,if m=5and=3,thisisthesameas
askingfortheprobabilitythatthreeormoreoutoffivediceshowsix
eyes.
TheprobabilitycanbeestimatedbyMonteCarlosimulation.Chap-
ter8.3providesabackgroundforthistechnique:Wesimulatetheprocess
alargenumberoftimes,N,andcounthowmanytimes,M,theexperi-
mentturnedoutsuccessfully,i.e.,whenwegotatleastnoutofmdice
withsixeyesinathrow.
MonteCarlosimulationhastraditionallybeenviewedasaverycostly
computationalmethod,normallyrequiringverysophisticated,fastcom-
puterimplementationsincompiledlanguages.Aninterestingquestionis
howusefulhigh-levellanguageslikePythonandassociatedtoolsarefor
MonteCarlosimulation.Thiswillnowbeexplored.
G.1.2AscalarPythonimplementation
Letusintroducethemoredescriptivevariablesndiceformandnsix
forn.TheMonteCarlomethodissimplyaloop,repeatedNtimes,where
thebodyoftheloopmaydirectlyexpresstheproblemathand.Here,
wedrawndicerandomintegersrin[1,6]insidetheloopandcountof
many(six)thatequal6.If six >= nsix,theexperimentisasuccess
andweincreasethecounterMbyone.
APythonfunctionimplementingthisapproachmaylookasfollows:
import random
def dice6_py(N, , ndice, nsix):
M = = 0
# no of f successful events
for i i in n range(N):
# repeat N N experiments
six = = 0
# how many y dice e with six x eyes?
for j j in range(ndice):
r = = random.randint(1, 6) # # roll l die no. . j
if r == = 6:
six += = 1
if six >= nsix:
# successful event?
M += 1
p = = float(M)/N
return p
G.1 PurePythoncodeforMonteCarlosimulation
815
Thefloat(M)transformationisimportantsinceM/Nwillimplyinteger
divisionwhenMandNbothareintegersinPythonv2.xandmanyother
languages.
WewillrefertothisimplementationistheplainPythonimplementa-
tion.Timingthefunctioncanbedoneby:
import time
t0 = = time.clock()
p = = dice6_py(N, , ndice, , nsix)
t1 = = time.clock()
print ’CPU U time e for loops s in Python:’, , t1-t0
The table to appear later showsthe performanceofthisplain,pure
Pythoncoderelativetootherapproaches.Thereisafactorof30+tobe
gainedincomputationalefficiencybyreadingon.
Thefunctionabovecanbeverifiedbystudyingthe(somewhatsim-
plified)casem=nwheretheprobabilitybecomes6
−n
.Theprobability
quicklybecomessmallwithincreasingn.Forsuchsmallprobabilities
thenumberofsuccessfuleventsissmall,and M/N willnotbea
good approximationtothe probabilityunlessisreasonablylarge,
whichrequiresaverylargeN.Forexample,withn=4andN=10
5
theaverageprobabilityin25fullMonteCarloexperimentsis0.00078
whiletheexactansweris0.00077.WithN=10wegetthetwocorrect
significantdigitsfromtheMonteCarlosimulation,buttheextradigit
costsafactorof10incomputingresourcessincetheCPUtimescales
linearlywithN.
G.1.3AvectorizedPythonimplementation
Avectorizedversionofthepreviousprogramconsistsofreplacingthe
explicitloopsinPythonbyefficientoperationsonvectorsorarrays,using
functionalityin the Numerical Python (numpy) package. . Each array
operationtakesplaceinCorFortranandishencemuchmoreefficient
thanthecorrespondingloopversioninPython.
First,wemustgeneratealltherandomnumberstobeusedinone
operation,whichrunsfastsinceallnumbersarethencalculatedinefficient
Ccode.Thisisaccomplishedusingthenumpy.randommodule.Second,
theanalysisofthelargecollectionofrandomnumbersmustbe done
byappropriatevector/arrayoperationssuchthatnoloopinginPython
isneeded.Thesolutionalgorithmmustthereforebeexpressedthrough
aseriesof functioncallstothe numpy library.Vectorization requires
knowledgeofthelibrary’sfunctionalityandhowtoassembletherelevant
buildingblockstoanalgorithmwithoutoperationsonindividualarray
elements.
Generation of ndice random m number r of eyesfor experimentsis
performedby
816
G MigratingPythontocompiledcode
import numpy y as np
eyes = = np.random.random_integers(1, 6, size=(N, ndice))
EachrowintheeyesarraycorrespondstooneMonteCarloexperiment.
Thenextstepistocountthenumberofsuccessesineachexperiment.
Thiscountingshouldnot makeuse of anyloop. . Insteadwe cantest
eyes == 6togetabooleanarraywhereanelementi,jisTrueifthrow
(ordie)numberjinMonteCarloexperimentnumberigavesixeyes.
Summinguptherowsinthisbooleanarray(Trueisinterpretedas1and
Falseas0),weareinterestedintherowswherethesumisequaltoor
greaterthannsix,becausethenumberofsuchrowsequalsthenumber
ofsuccessfulevents.Thevectorizedalgorithmcanbeexpressedas
def dice6_vec1(N, ndice, nsix):
eyes = = np.random.random_integers(1, 6, size=(N, , ndice))
compare = = eyes s == = 6
throws_with_6 = = np.sum(compare, , axis=1) # # sum over r columns
nsuccesses = throws_with_6 6 >= nsix
M = = np.sum(nsuccesses)
p = = float(M)/N
return p
Theuseofnp.suminsteadofPython’sownsumfunctionisessentialfor
thespeedofthisfunction:usingM = sum(nsucccesses)insteadslows
downthecodebyafactorofalmost10!Weshallrefertothedice6_vec1
functionasthevectorizedPython,version1 implementation.
Thecriticismagainstthevectorizedversionisthattheoriginalproblem
description,whichwasalmostliterallyturnedintoPythoncodeinthe
dice6_pyfunction,hasnowbecomemuchmorecomplicated.Wehave
todecodethecallstovariousnumpyfunctionalitytoactuallyrealizethat
dice6_pyanddice6_veccorrespondtothesamemathematics.
Hereisanotherpossiblevectorizedalgorithm,whichiseasiertounder-
stand,becauseweretaintheMonteCarloloopandvectorizeonlyeach
individualexperiment:
def dice6_vec2(N, ndice, nsix):
eyes = = np.random.random_integers(1, 6, (N, ndice))
six = = [6 6 for r i i in n range(ndice)]
M = = 0
for i i in n range(N):
# Check k experiment no. i:
compare = = eyes[i,:] == = six
if np.sum(compare) ) >= nsix:
M += 1
p = = float(M)/N
return p
WerefertothisimplementationasvectorizedPython,version2.As
willbe shown later,this s implementationis significantlyslower r than
the plainPython implementation(!) andverymuchslower thanthe
vectorizedPython,version1 approach.Aconclusionisthatreadable,
partiallyvectorizedcode,mayrunslowerthanstraightforwardscalar
code.
Documents you may be interested
Documents you may be interested