Metadata-Version: 2.1
Name: fastf1
Version: 2.1.5
Summary: Wrapper library for F1 data and telemetry API with additional data processing capabilities.
Home-page: https://github.com/theOehrly/Fast-F1
Author: Oehrly
Author-email: oehrly@mailbox.org
License: MIT
Description: =======
        Fast F1
        =======
        
        A python package for accessing F1 historical timing data and telemetry.
        
        
        General Information
        ===================
        
        **FastF1 v2.1 is now available for installation through pip.
        The old way of installing via pip + git directly from the master branch is no
        longer recommended.**
        
        It is no longer possible to download telemetry and car position data after a
        session!
        
        See `this <https://twitter.com/F1Help/status/1335939396240093185>`_ Twitter
        post for some information.
        
        This means:
        
          - It is still **possible** to load timing data, tire data, track status
            data and session status data.
        
          - It is **not possible** to load car telemetry data
            (includes position data). You need to record live timing data during
            a session for this!
        
        
        Live timing data
        ----------------
        
        **A live timing client has been added with the v2.1 release. The client can be
        used to save the live timing telemetry data stream that is available during
        sessions.**
        
        The live timing client does not parse data in real time!
        Data can only be parsed and used after a session has completed. This is a
        limitation of FastF1's api parser. For various reasons there is no
        intention of changing this.
        
        For usage see the documentation.
        
        
        Changes
        -------
        
        If you have used previous versions of FastF1, please read the changelog in the
        documentation.
        
        V2.1 introduces some new features and some breaking changes.
        The documentation is improved in general. Also, there is a new section
        discussing how to get the most accurate results from the data that is
        available. It may be worth reading if you want to make more complicated
        analyses and visualizations.
        
        Other
        -----
        
        Please report bugs if (when) you find them. Feel free to report complaints about
        unclear documentation too.
        
        Interested in contributing? There's some info at the end of this document...
        
        
        Installation
        ============
        
        It is recommended to install FastF1 using pip:
        
            pip install fastf1
        
        Note that Python 3.8 is required.
        
        Alternatively a wheel or a source distribution can be downloaded from the
        Github releases page.
        
        Usage
        =====
        
        Full documentation can be found
        `here <https://theoehrly.github.io/Fast-F1/fastf1.html>`_.
        
        Creating a simple analysis is not very difficult, especially if you are already familiar
        with pandas and numpy.
        
        Suppose that we want to analyse the race pace of Leclerc compared to 
        Hamilton from the Bahrain GP (weekend number 2) of 2019.
        
        .. code:: python
        
            import fastf1 as ff1
            from fastf1 import plotting
            from matplotlib import pyplot as plt
        
            plotting.setup_mpl()
        
            ff1.Cache.enable_cache('path/to/folder/for/cache')  # optional but recommended
        
            race = ff1.get_session(2020, 'Turkish Grand Prix', 'R')
            laps = race.load_laps()
        
            lec = laps.pick_driver('LEC')
            ham = laps.pick_driver('HAM')
        
        Once the session is loaded, and drivers are selected, you can plot the
        information.
        
        :code:`fastf1.plotting` provides some special axis formatting and data type conversion. This is required
        for generating a correct plot.
        
        It is not necessary to enable the usage of a cache but it is recommended. Simply provide
        the path to some empty folder on your system.
        
        .. code:: python
        
            fig, ax = plt.subplots()
            ax.plot(lec['LapNumber'], lec['LapTime'], color='red')
            ax.plot(ham['LapNumber'], ham['LapTime'], color='cyan')
            ax.set_title("LEC vs HAM")
            ax.set_xlabel("Lap Number")
            ax.set_ylabel("Lap Time")
            plt.show()
        
        .. image:: docs/_static/readme.svg
            :target: docs/_static/readme.svg
        
        
        Compatibility
        =============
        
        Timing data is available for the 2018, 2019 and 2020 season.
        Very basic weekend information is available for older seasons (limited to
        `Ergast web api <http://ergast.com/mrd/>`_).
        Car telemetry data is only available as a live stream during a session. This
        means that you need to record this data yourself, using the provided client, if you want
        to have access to it.
        
        
        Roadmap
        =======
        
        This is a rather loose roadmap with no fixed timeline whatsoever.
        
          - Improvements to the current plotting functionality
          - Some default plots to easily allow creating nice visualizations and interesting comparisons
          - General improvements and smaller additions to the current core functionality
          - Support for F1's own data api to get information about events, sessions, drivers and venues
        
        
        
        Contributing
        ============
        
        Contributions are welcome of course. If you are interested in contributing, open an issue for the proposed feature
        or issue you would like to work on. This way we can coordinate so that no unnecessary work is done.
        
        Working directly on the core and api code will require some time to understand. Creating nice default plots on the
        other hand does not required as deep of an understanding of the code and is therefore easier to accomplish. Pick
        whatever you like to do.
        
        
Platform: UNKNOWN
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
