Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

Columbus

Columbus is a python package to simulate a series of uncertain events, such as a company's business plan.

Let's take a look at the following code snippets from example 1. Suppose you want to estimate the number of ice creams you will sell on a certain day to people passing by. You can create variables using datatypes representing Normal and Skewed distributions, as well as Scalar numbers. Don't mind the sim for now.

# usage of a normal distribution
chancePassengerBuysIceCream = Normal(sim, 0.05, 0.04)

# usage of a skewed distribution, right is the direction of the tail
numberOfPassingPeoplePerHour = Skewed(sim, 300, SKEWNESS.SMALL, 'right')

# usage of a scalar
numberOfHoursSelling = Scalar(sim, 8)

You can read more about the different datatypes below. The code snippet below illustrates operations on variables such as mul() and floor().

iceCreamsSoldPerHour = chancePassengerBuysIceCream.mul(numberOfPassingPeoplePerHour)
# only keep samples that are > 0
iceCreamsSoldPerHourPositive = iceCreamsSoldPerHour.floor(0)
iceCreamsSoldTotal = iceCreamsSoldPerHourPositive.mul(numberOfHoursSelling)

Other operations such as adding and subtracting are also possible. You can read more about the operations below. The only thing left to explain is the sim variable used above. It is used to set the simulation level of detail and can be used to plot variables.

# start of simulation
sim = Simulator(int(1e4), 2e6) # simulator parameter specifies how fine grained the simulation will be, 1e4 is a reasonable value. 2e6 is the max memory consumption in [kb]
sim.start()

# intermediate calculations...

# plot the result
sim.plot(iceCreamsSoldTotal,'Number of sold icecreams','a label')

# end of simulation
sim.stop()
sim.report() # prints the number of calculations performed and the elapsed time

The plot looks like this:

Check out the complete script for example 1 here. You can also find more examples there.

Contents

Installation, Dependencies and Usage

To install Columbus, download the source and run the python installation script. For unix systems:

sudo python setup.py install

Once this is done, it can be used in any script on your system. Columbus needs the following preinstalled modules to function properly:

  • matplotlib
  • numpy

To use the Columbus library in a script, use the following imports:

from columbus.simulator import FINESSE,STD,SKEWNESS,Simulator
from columbus.datatypes import Scalar, Normal, Skewed

Data types

Columbus has several data types. All these data types are subject to the same operations, as described below

Scalar

A scalar is a fixed scalar number. It can be initialised as following: Scalar(simulatorObject, scalarValue)

Normal

Simulates a normal distribution. Initialisation: Normal(simulatorObject, mean, sigma)

Skewed distribution

Simulates a skewed distribution. Initialisation: Skewed(simulatorObject, mean, skewness, tailDirection). For the skewness parameter, predefined values from the SKEWNESS object can be used: SKEWNESS.SMALL, SKEWNESS.MEDIUM or SKEWNESS.LARGE. `tailDirection' is a string equal to left or right, indicating if the tail is to the left or to the right of the mean.

Uniform

Implementation of a uniform distribution. Initialisation: Uniform(simulatorObject, low, high). The parameters low and high are respectively the minimum and maximum size of the sampling domain.

Operations

Data items supports the following basic arithmetic operations:

  • .add(dataItem)
  • .sub(dataItem)
  • .mul(dataItem)
  • .div(dataItem)

DataItems can also be modified to remove all samples above or below a certain value:

  • .floor(value) removes all values below value
  • .ceil(value) removes all values above value

DataItems can also be inverted (invert()) or rounded to the nearest int (toInt()).

Note: All operations have no effect on the dataItem they were performed on, they return the result (the functions are pure).

Plotting

Columbus can plot all above mentioned data types. The syntax is as following:

sim.plot(vars,title,labels)

with

  • vars either one variable or a list of variables
  • title optional the title of the plot
  • labels optional either one string or a list of strings

Calculating key figures

Columbus offers multiple descriptive statistics

  • mean()
  • median()
  • percentageSmallerThan(value)
  • percentageLargerThan(value)

About

A python package to simulate uncertain events

Resources

Stars

Watchers

Forks

Packages

No packages published

Languages