Modeling: Difference between revisions
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Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] | Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] that comes with its own modsim library. | ||
=Eyeopeners= | =Eyeopeners= | ||
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results[a] = functioncall(bla,bla) | results[a] = functioncall(bla,bla) | ||
</syntaxhighlight> | </syntaxhighlight> | ||
TimeSeries is a modified version (subclass) of the pandas.Series class. | TimeSeries is a modsim modified version (subclass) of the pandas.Series class. | ||
Revision as of 13:41, 15 December 2018
Mostly based on this paper that comes with its own modsim library.
Eyeopeners
Store results in a list.
for a in range(100):
funcAresults[a] = functionAcall(bla,bla)
funcBresults[a] = functionBcall(bla,bla)
ModSimPy is using Series from Pandas to store results. This adds handy functions.
- results.mean()
- Return the mean (average) for the items in results
I have not studied the pandas Series yet but this works:
from modsim import *
results = TimeSeries()
for a in range(100):
results[a] = functioncall(bla,bla)
TimeSeries is a modsim modified version (subclass) of the pandas.Series class.