# inline function call

Riko Wichmann riko.wichmann at remove-this.desy.de
Wed Jan 4 16:40:56 CET 2006

```> Do you have an actual use-case for that? I mean, do you have code that runs
> slow, but with inlined code embarrassingly faster?

Well, I guess it would not actually be embarrassingly faster. From
trying various things and actually copying the function code into the
DoMC routine, I estimate to get about 15-20% reduction in the execution
time. It ran very slow, in the beginning but after applying some other
'fastpython' techniques it's actually quite fast ....

'inlining' is mostly a matter of curiosity now :)

here is the code snipplet:

-----------------------------------------------------------------

[... cut out some stuff here ....]

# riskfunc(med, low, high):
#           risk function for costs: triangular distribution
#           implemented acoording to:
http://www.brighton-webs.co.uk/distributions/triangular.asp
def riskfunc(med, low, high):

if med != 0.0:
u = random()
try:
if u <= (med-low)/(high-low):
r = low+sqrt(u*(high-low)*(med-low))
else:
r = high - sqrt((1.0-u)*(high-low)*(high-med))

except ZeroDivisionError: # case high = low
r = med
else:
r = 0.0

return r

# doMC:
#      run the MC of the cost analysis
#
def doMC(Ntrial = 1):

from math import sqrt

start = time.time()
print 'run MC with ', Ntrial, ' trials'

# start with a defined seed for reproducability

total = 0.0

for i in range(Ntrial):

summe = 0.0
for k in range(len(Gcost)):

x = riskfunc(Gcost[k], Gdown[k], Gup[k])
summe += x

# store the value 'summe' for later usage
# ..... more code here

print "Summe   : ", summe
stop = time.time()
print 'Computing time: ', stop-start

####################################################################
####################################################################

if __name__ == '__main__':

n = 100000
doMC(n)

```

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