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        "author": "Jason Green",
        "author_email": "justjasongreen@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "=================\npredictive_punter\n=================\n\n\nThis project aims to apply predictive analytics to horse racing via Python.\n\n\n.. image:: https://travis-ci.org/justjasongreen/predictive_punter.svg?branch=master\n    :target: https://travis-ci.org/justjasongreen/predictive_punter\n    :alt: Build Status\n.. image:: https://coveralls.io/repos/github/justjasongreen/predictive_punter/badge.svg?branch=master\n    :target: https://coveralls.io/github/justjasongreen/predictive_punter?branch=master\n    :alt: Coverage Status\n.. image:: https://landscape.io/github/justjasongreen/predictive_punter/master/landscape.svg?style=flat\n    :target: https://landscape.io/github/justjasongreen/predictive_punter/master\n    :alt: Code Health\n\n\n************\nInstallation\n************\n\n\nPrior to using predictive_punter, the package must be installed in your current Python environment. In most cases, an automated installation via PyPI and pip will suffice, as follows::\n\n    pip install predictive_punter\n\nIf you would prefer to gain access to new (unstable) features via a pre-release version of the package, specify the 'pre' option when calling pip, as follows::\n\n    pip install --pre predictive_punter\n\nTo gain access to bleeding edge developments, the package can be installed from a source distribution. To do so, you will need to clone the git repository and execute the setup.py script from the root directory of the source tree, as follows::\n\n    git clone https://github.com/justjasongreen/predictive_punter.git\n    cd predictive_punter\n    python setup.py install\n\nIf you would prefer to install the package as a symlink to the source distribution (for development purposes), execute the setup.py script with the 'develop' option instead, as follows::\n\n    python setup.py develop\n\n\n***********\nBasic Usage\n***********\n\n\nBy installing predictive_punter, a number of command line utilities are made available in your current Python environment, as described below...\n\n\nScrape\n======\n\nThe 'scrape' command line utility can be used to populate a database with racing data scraped from the web. The syntax of the scrape command is:\n\n    scrape [-b] [-d <database_uri>] [-q] [-r <redis_uri>] [-v] date_from [date_to]\n\nThe mandatory date_from and optional date_to arguments must be in the format YYYY-MM-DD, and define the (inclusive) range of dates to scrape data for.\n\nIf the -b (or --backup-database) option is specified, all collections in the database will be cloned after each date successfully scraped. If an error occurs while scraping a date and the -b option has been specified, the collections in the database will be restored from the cloned collections before the script terminates.\n\nThe -d (or --database-uri=) option can be used to specify a URI for the target database. The target database must be a MongoDB version 2.6 or higher database. The default database URI is mongodb://localhost:27017/predictive_punter.\n\nThe -r (or --redis-uri=) option can be used to specify a URI for a redis server to be used for HTTP request caching. The default redis URI is redis://localhost:6379/predictive_punter. If a connection cannot be established with the specified redis server, the script will attempt to use the built in redislite service, or will run without HTTP request caching if the redislite service cannot be used.\n\nThe -q and -v (or --quiet and --verbose) options can be used to control the logging output generated by the scrape command. When the -q option is used, the logging level will be set to logging.WARNING. When the -v option is used, the logging level will be set to logging.DEBUG. By default, the logging level will be set to logging.INFO.\n\n\nSeed\n====\n\nThe 'seed' command line utility can be used to pre-seed query data for runners in the database. The syntax of the seed command is:\n\n    seed [-b] [-d <database_uri>] [-q] [-r <redis_uri>] [-v] date_from [date_to]\n\nThe application of the various command line options and arguments is the same as for the 'scrape' command described above.\n\n\n***********************\nDevelopment and Testing\n***********************\n\n\nThe source distribution includes a test suite based on pytest. To ensure compatibility with all supported versions of Python, it is recommended that the test suite be run via tox.\n\nTo install all development and test requirements into your current Python environment, execute the following command from the root directory of the source tree::\n\n    pip install -e .[dev,test]\n\nTo run the test suite included in the source distribution, execute the tox command from the root directory of the source tree as follows::\n\n    tox",
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