Metadata-Version: 1.1
Name: rets
Version: 0.0.4
Summary: RETS Client for Real Estate Data
Home-page: https://github.com/refindlyllc/python-rets
Author: REfindly
Author-email: info@refindly.com
License: MIT License
Description: [![Documentation Status](https://readthedocs.org/projects/python-rets/badge/?version=latest)](http://python-rets.readthedocs.io/en/latest/?badge=latest)
        [![PyPI version](https://badge.fury.io/py/rets.svg)](https://pypi.python.org/pypi/rets/)
        [![Build Status](https://travis-ci.org/refindlyllc/python-rets.svg?branch=master)](https://travis-ci.org/refindlyllc/python-rets)
        [![Coverage Status](https://coveralls.io/repos/github/refindlyllc/python-rets/badge.svg?branch=master)](https://coveralls.io/github/refindlyllc/python-rets?branch=master)
        
        RETS
        ====
        
        A python RETS client for real estate data.  Make requests to the MLS 
        server to get real estate listings, media, and metadata.
        
        [Documentation](http://python-rets.readthedocs.io/en/latest/)
        
        ###Warning
        This package is not yet stable. 
        
        ### <a name="installation"></a>Installation
        The easiest way to install is through pip.
        `pip install rets`
        
        If you need to build the package locally, it can be downloaded 
        from [github](https://github.com/refindlyllc/python-rets) and installed 
        through setuptools.
        
        ```
        git clone https://github.com/refindlyllc/python-rets.git
        cd python-rets
        python setup.py install
        ```
        
        You can now import the rets module within Python.
        
        ###Quickstart
        After [installing](#installation) the rets package locally, we can
        make requests to the MLS for data.
        
        ```
        >>> import rets
        >>> login_url = 'http://retrsprovider.com/login'
        >>> username = 'user123'
        >>> password = 'a48a*32fa$5'
        >>> rets_version = '1.7.2'
        >>> rets_client = rets.RETSClient(login_url,
                                          username,
                                          password,
                                          rets_version)
        >>> rets_client
        prettyprint of rets_client object
        >>> rets_client.metadata
        prettyprint of metadata
        >>> resources = rets_client.get_resources()
        >>> for r in resources:
        ...     print r
        ...     print r.get_classes()
         
        prettyprint of classes and resources
        
        
        # Get some homes over $400,000
        >>> residential_class = res_client.get_resource('Property).get_class('RES')
        >>> results_cursor = rets_client.search(class=residential_class,
                                                filter={'Status': 'A',
                                                        'ListPrice': {'gte': 400000}}, 
                                                limit=10)
        
        >>> for result in results_cursor:
        ...     print(result)
        
        prettyprint some results
        ```
        
        
        ###Login
        All requests to a RETS server must be authenticated. The login credential
        fields can be passed to the RETSClient object at instantiation or set through
        environment variables.
        
        ```
        rets_client = rets.RETSClient(login_url='http://somelogin.url'
                                      username='myusername',
                                      password='changeme',
                                      user-agent=NOne,
                                      user-agent_password=None,
                                      basic_auth=False
        ```
        
        Not all RETS providers require all fields. If a user-agent, user-agent_password
         or basic authentication is not required, do not set those parameters.
        
        ###Searching
        
        ##Filters
        
        ###Results Cursor
        Searches with the RETSClient return a results cursor. The cursor is an 
        iterator that yields results as you loop through the iterator. This prevents
        exceptionally large searches from consuming all of your memory by handling
        each result discretely. 
        
        ##Metadata
        discuss metadata 
        
        ##Media Objects
        discuss media objects and how they are returned
        
        ###What about LibRets?
        compare and contrast to librets. This is pure python, not c++.
        
        ###Contributing
        This RETS client has a long way to go, and keeping up with new [RESO Standards](http://www.reso.org/data-dictionary/)
        will require ongoing maintenance. Please feel free to fork this repo and make
        pull requests to master if you wish to contribute. Please ensure that all new 
        code has accompanying tests. Travis-CI will run your code through the current
        and new tests when you make a pull request.
        
        All pull requests should reference a [Github issue](https://github.com/refindlyllc/python-rets/issues). Features 
        and bugs can be discussed in the features rather than be discussed in a pull request.
        
        ##Testing
        If you wish to test the code prior to contribution 
        `nosetests --with-coverage --cover-package=rets`
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Software Development :: Libraries :: Python Modules
