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        "author": "Alistair Miles",
        "author_email": "alimanfoo@googlemail.com",
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        "classifiers": [
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "============\ncsvvalidator\n============\n\nThis module provides some simple utilities for validating data contained in CSV \nfiles, or other similar data sources.\n\nThe source code for this module lives at: \n\n    https://github.com/alimanfoo/csvvalidator\n\nPlease report any bugs or feature requests via the issue tracker there.\n\nInstallation\n============\n\nThis module is registered with the Python package index, so you can do::\n\n    $ easy_install csvvalidator\n\n... or download from http://pypi.python.org/pypi/csvvalidator and\ninstall in the usual way::\n\n    $ python setup.py install\n\nIf you want the bleeding edge, clone the source code repository::\n\n    $ git clone git://github.com/alimanfoo/csvvalidator.git\n    $ cd csvvalidator\n    $ python setup.py install\n\nUsage\n=====\n\nThe `CSVValidator` class is the foundation for all validator objects that are \ncapable of validating CSV data. \n\nYou can use the CSVValidator class to dynamically construct a validator, e.g.::\n\n    import sys\n    import csv\n    from csvvalidator import *\n\n    field_names = (\n                   'study_id', \n                   'patient_id', \n                   'gender', \n                   'age_years', \n                   'age_months',\n                   'date_inclusion'\n                   )\n\n    validator = CSVValidator(field_names)\n    \n    # basic header and record length checks\n    validator.add_header_check('EX1', 'bad header')\n    validator.add_record_length_check('EX2', 'unexpected record length')\n    \n    # some simple value checks\n    validator.add_value_check('study_id', int, \n                              'EX3', 'study id must be an integer')\n    validator.add_value_check('patient_id', int, \n                              'EX4', 'patient id must be an integer')\n    validator.add_value_check('gender', enumeration('M', 'F'), \n                              'EX5', 'invalid gender')\n    validator.add_value_check('age_years', number_range_inclusive(0, 120, int), \n                              'EX6', 'invalid age in years')\n    validator.add_value_check('date_inclusion', datetime_string('%Y-%m-%d'),\n                              'EX7', 'invalid date')\n    \n    # a more complicated record check\n    def check_age_variables(r):\n        age_years = int(r['age_years'])\n        age_months = int(r['age_months'])\n        valid = (age_months >= age_years * 12 and \n                 age_months % age_years < 12)\n        if not valid:\n            raise RecordError('EX8', 'invalid age variables')\n    validator.add_record_check(check_age_variables)\n\n    # validate the data and write problems to stdout    \n    data = csv.reader('/path/to/data.csv', delimiter='\\t')\n    problems = validator.validate(data)\n    write_problems(problems, sys.stdout)\n\nFor more complex use cases you can also sub-class `CSVValidator` to define \nre-usable validator classes for specific data sources.\n\nFor a complete account of all of the functionality available from this module, \nsee the example.py and tests.py modules in the source code repository.\n\nNotes\n=====\n\nNote that the `csvvalidator` module is intended to be used in combination with \nthe standard Python `csv` module. The `csvvalidator` module **will not** \nvalidate the *syntax* of a CSV file. Rather, the `csvvalidator` module can be \nused to validate any source of row-oriented data, such as is provided by a \n`csv.reader` object.\n\nI.e., if you want to validate data from a CSV file, you have to first construct \na CSV reader using the standard Python `csv` module, specifying the appropriate \ndialect, and then pass the CSV reader as the source of data to either the \n`CSVValidator.validate` or the `CSVValidator.ivalidate` method.",
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