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monte_carlo_simulation

Examples and tutorials for conducting monte-carlo simulation in Python

https://github.com/theopensciencenerd/monte_carlo_simulation

Science Score: 44.0%

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    Low similarity (12.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Examples and tutorials for conducting monte-carlo simulation in Python

Basic Info
  • Host: GitHub
  • Owner: TheOpenScienceNerd
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 12.9 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License Citation

README.md

ORCID: Monks License: MIT

Monte Carlo Simulation in Python

Examples and tutorials for conducting monte-carlo simulation in Python.

License

The materials have been made available under an MIT license. The materials are as-is with no liability for the author. Please provide credit if you reuse the code in your own work.

Citation

Please feel free to use or adapt the code for your own work. But if so then a citation would be very much appreciated!

bibtex @software{opensciencenerd_montecarlo, author = {Monks, Thomas }, license = {MIT}, title = {{An introduction to monte-carlo simulation in Python}}, url = {https://github.com/TheOpenScienceNerd/replications-algorithm} }

Installation instructions

Installing dependencies

All dependencies can be found in binder/environment.yml and are pulled from conda-forge. To run the code locally, we recommend installing miniforge;

miniforge is Free and Open Source Software (FOSS) alternative to Anaconda and miniconda that uses conda-forge as the default channel for packages. It installs both conda and mamba (a drop in replacement for conda) package managers. We recommend mamba for faster resolving of dependencies and installation of packages.

navigating your terminal (or cmd prompt) to the directory containing the repo and issuing the following command:

bash mamba env create -f binder/environment.yml

Activate the mamba environment using the following command:

bash mamba activate mc

Run Jupyter-lab

bash jupyter-lab

Repo overview

. ├── binder │ └── environment.yml ├── CHANGELOG.md ├── CITATION.cff ├── LICENSE ├── 01_mc_investment_decision.ipynb ├── 02_mc_newsvendor.ipynb ├── newsvendor.py └── README.md

  • binder/environment.yml - contains the conda environment if you wish to work the models.
  • 01_mc_investment_decision.ipynb - an investment decision problem from Pidd (2004).
  • 02_mc_newsvendor.ipynb - a simple multi-period newsvendor problem monte carlo simulation
  • newsvendor.py - module containing newsvendor problem code
  • CHANGES.md - changelog with record of notable changes to project between versions.
  • CITATION.cff - citation information for the package.
  • LICENSE - details of the MIT permissive license of this work.

Owner

  • Name: TheOpenScienceNerd
  • Login: TheOpenScienceNerd
  • Kind: organization
  • Location: United Kingdom

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: An introduction to monte-carlo simulation in Python
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: 'Thomas '
    family-names: Monks
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0003-2631-4481'
repository-code: 'https://github.com/TheOpenScienceNerd/monte_carlo_simulation'
keywords:
  - monte carlo simulation
  - python
  - open science
license: MIT

GitHub Events

Total
  • Release event: 1
  • Push event: 6
  • Public event: 1
  • Pull request event: 2
  • Create event: 1
Last Year
  • Release event: 1
  • Push event: 6
  • Public event: 1
  • Pull request event: 2
  • Create event: 1

Dependencies

binder/environment.yml conda
  • jupyterlab 4.3.5.*
  • numpy 2.2.3.*
  • pandas 2.2.3.*
  • plotly 6.0.0.*
  • python 3.12.*