Revision as of 20:30, 16 December 2018

Mostly based on this paper that comes with its own modsim library.

Eyeopeners

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.

@@ Line 2: / Line 2: @@
 =Eyeopeners=
-Store results in a list.
+* Store results in a list.
 <syntaxhighlight lang=python>
 for a in range(100):
@@ Line 8: / Line 8: @@
      funcBresults[a] = functionBcall(bla,bla)
 </syntaxhighlight>
+: ModSimPy is using Series from [[Pandas]] to store results. This adds handy functions.
-ModSimPy is using Series from [[Pandas]] to store results. This adds handy functions.
+* The state of the model is stored in a Pandas Series too.
-;results.mean()
-:Return the mean (average) for the items in results
-I have not studied the pandas Series yet but this works:
-<syntaxhighlight lang=python>
-from modsim import *
-results = TimeSeries()
-for a in range(100):
-    results[a] = functioncall(bla,bla)
-</syntaxhighlight>
-TimeSeries is a modsim modified version (subclass) of the pandas.Series class.