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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Simulating out-of-energy occurrences for an electric car


Electric cars are getting more and more popular these days. However, even though they are cheaper to run, the range of the car somewhat limits its use to travel long distance, at least until a sufficient infrastructure is in place to recharge the car along the way.

In this recipe, we will simulate out-of-power situations for an electric car. We start by randomly placing the recharge stations along the way of the car and then simulating the recharge situations. In this recipe, we will allow the driver of the car to drive the car without fully recharging.

Getting ready

To execute this recipe, you will need SimPy and NumPy. No other prerequisites are required.

How to do it…

As in the previous recipe, we start by defining the environment and all its agents (the sim_recharge.py file):

import numpy as np
import simpy


if __name__ == '__main__':
    # what is the simulation horizon (in minutes)
    SIM_TIME = 10 * 60 * 60 # 10 hours

    # create...
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