A Formula 1 simulation in python.
Developed after first concept introduced by reddit user whatthefat. He developed it in Matlab (download here), but a number of people wanted to see it in something a bit more powerful and available to others.
I've had good luck in building a thin object-oriented interface as a thin wrapper around lower level data structures. To the user, this makes generating a simulation more intuitive, and abstracts the design from specific implementations. So, we build the simulation up using a number of reusable building blocks, then use that information to build the internal data structures for simulation. At the moment, the plan would be to use Pandas/Numpy/Numba for the internal calculations. I have been looking for an excuse to use Blaze (a higher level abstraction) as well, so that could be another option.
- docs: the folder where sphinx documentation generation will be performed
- tests: the folder where pytest-compatible tests will be collected
- formulapy.core: the fundamental and generic classes used to define specific models
- formulapy.models: holds a package for each season of models. This provides a user to build models for "what-if" scenarios, or to predict a given race's performance
- formulapy.models.s2014: the first package to hold the current year's models
- formulapy/data: will be the place to put any sql, nosql, or csv files
The use of modular components should allow incorporation of higher fidelity models over time as desired, without changes to the rest of the simulation. We can also take a high fidelity model, run it offline, then use something like a neural network (see ffnet) to surrogate the higher fidelity model.
It is important in simulations to represent the uncertainty in estimates. Therefor, instead of specifying a discrete value for a parameter, we should help the user represent the distribution around that value. The result should be not be a probability of success for each driver, but a distribution around a probability of success for each driver.
We might want to run the simulation far in advance, then update the statistics when given new information (Bayesian). So, given information on qualification, or even real time information of how the race is unfolding, we should be able to reduce the uncertainty of the prediction as we get more data.
The primary data source will be the Ergast API. This library interfaces with the REST API primarily, to avoid each user having to stand up a local mysql database, which is the format that the Ergast data can be downloaded in. The approach will aim to make it modular so that the database could be switched in, instead of the rest api.
Rest API ToDo: * Generic Query Capability * Series -> Seasons * Series -> Drivers * Series -> Constructors
I'll plan on using CSVs to store supplemental data since there isn't massive amounts of F1 data that I'm aware of, and ease of use for contributions.
The goal will be to utilize tools/libraries that provide anyone the ability to contribute with no cost. The only thing I have identified that can cost money is PyCharm, but there is a free community version available. Spyder and IPython Notebook should be completely adequate.
- Base: Anaconda or Wakiri (web-based)
- IDE: PyCharm, IPython Notebook, Spyder (IPython and Spyder come with Anaconda)
- Data Structures: Pandas/Numpy
- Statistics: SciPy
- Testing: PyTest
- Docs: Sphinx, sphinx_rtd_theme
- Visualization: Seaborn (stats), Bokeh (interactive charts), Matplotlib (standard plots)