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    "info": {
        "author": "Suyati Technologies",
        "author_email": "tkannippoyil@suyati.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Software Development :: Build Tools"
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        "description": "Mongodb relation finder \n=======================\n\nThis package now supporting closeness amoung python dicts\n\nThis is project is using to find relationship between mongodb documents\n\nThis will be the initial version of the project\n\n\nHow to use:\n\nInstall package with pip\n\n\n.. code-block:: console\n\n   pip install closeness\n\n\nSee the example,\n\n.. code-block::\n\n   from closeness.closeness_aggregation import ClosenessAggregation\n   from pymongo import MongoClient\n   client = MongoClient()\n   db = client.test_database\n   user_collection = db.user_collection\n   user1 = {\n       'name': 'User 1',\n       'age': 25,\n       'gender': 'male',\n       'tags': [\n           \"tag1\",\n           \"tag2\",\n           \"tag3\",\n       ],\n       'friends': [\n           {\"user_id\": \"friend1\", 'name': \"name1\"},\n           {\"user_id\": \"friend2\", 'name': \"name2\"},\n           {\"user_id\": \"friend3\", 'name': \"name3\"},\n       ]\n   }\n   user2 = {\n       'name': 'User 2',\n       'age': 25,\n       'gender': 'male',\n       'tags': [\n           \"tag1\",\n           \"tag2\",\n           \"tag3\",\n       ],\n       'friends': [\n           {\"user_id\": \"friend1\", 'name': \"name1\"},\n           {\"user_id\": \"friend2\", 'name': \"name2\"},\n           {\"user_id\": \"friend3\", 'name': \"name3\"},\n       ]\n   }\n   user3 = {\n       'name': 'User 3',\n       'age': 30,\n       'gender': 'female',\n       'tags': [\n           \"tag1\",\n       ],\n       'friends': [\n           {\"user_id\": \"friend3\", 'name': \"name3\"},\n       ]\n   }\n   user_collection.insert([user1, user2, user3])\n   query_stage = {'$match': {'name': {'$ne': user1['name']}}}\n   ARRAY_CMP_FIELDS = [\n       {\n           'field': 'tags',\n           'weight': 3\n       }\n   ]\n   ARRAY_DICT_CMP_FIELDS = [\n       {\n           'field': 'friends',\n           'unique': 'user_id',\n           'weight': .5\n       }\n   ]\n   STRING_CMP_FIELDS = [\n       {\n           'field': 'gender',\n           'weight': .5\n       }\n   ]\n   NUM_CMP_FIELDS = [\n       {\n           'field': 'age',\n           'from': -1,\n           'to': 1,\n           'weight': .3\n       }\n   ]\n   OUT_PUT_FIELDS = [\n       'name', 'age'\n   ]\n   test = ClosenessAggregation(\n       user1,\n       query_stage,\n       OUT_PUT_FIELDS,\n       limit=10,\n       ARRAY_CMP_FIELDS=ARRAY_CMP_FIELDS,\n       STRING_CMP_FIELDS=STRING_CMP_FIELDS,\n       NUM_CMP_FIELDS=NUM_CMP_FIELDS,\n       ARRAY_DICT_CMP_FIELDS=ARRAY_DICT_CMP_FIELDS,\n   )\n\n   aggregation_query = test.get_aggregation_pipeline(\n       mode=ClosenessAggregation.FUZZY\n   )\n   result = user_collection.aggregate(aggregation_query)\n   \n   # {u'ok': 1.0,\n   #  u'result': [{u'age': 25,\n   #               u'_id': ObjectId('55c894dcb67e20612cd6ddf0'),\n   #               u'weights': [{u'gender': 11.627906976744187,\n   #                             u'age': 6.9767441860465125,\n   #                             u'friends': 11.626615417599819,\n   #                             u'tags': 69.75969250559892}],\n   #               u'name': u'User 2',\n   #               u'rank': 99.99095908598945},\n   #              {u'age': 30,\n   #               u'_id': ObjectId('55c894dcb67e20612cd6ddf1'),\n   #               u'weights': [{u'gender': 0,\n   #                             u'age': 0,\n   #                             u'friends': 6.456076223518085,\n   #                             u'tags': 38.73645734110851}],\n   #               u'name': u'User 3',\n   #               u'rank': 45.1925335646266}]}\n\n\n   aggregation_query = closeness_obj.get_aggregation_pipeline(\n       mode=ClosenessAggregation.SIMPLE\n   )\n\n   result = user_collection.aggregate(aggregation_query)\n\n   # {u'ok': 1.0,\n   #  u'result': [{u'age': 25,\n   #               u'_id': ObjectId('55c894dcb67e20612cd6ddf3'),\n   #               u'weights': [{u'gender': 11.627906976744187,\n   #                             u'age': 6.9767441860465125,\n   #                             u'friends': 11.627906976744187,\n   #                             u'tags': 69.76744186046513}],\n   #               u'name': u'User 2',\n   #               u'rank': 100.00000000000001},\n   #              {u'age': 30,\n   #               u'_id': ObjectId('55c894dcb67e20612cd6ddf4'),\n   #               u'weights': [{u'gender': 0,\n   #                             u'age': 0,\n   #                             u'friends': 3.8759689922480622,\n   #                             u'tags': 23.255813953488374}],\n   #               u'name': u'User 3',\n   #               u'rank': 27.131782945736436}]}\n\n\n\n   # By using python dicts\n\n\n   users = [user2, user3]\n\n   closeness_dict_obj = ClosenessDict(\n       user1,\n       users,\n       ARRAY_CMP_FIELDS=ARRAY_CMP_FIELDS,\n       STRING_CMP_FIELDS=STRING_CMP_FIELDS,\n       NUM_CMP_FIELDS=NUM_CMP_FIELDS,\n       ARRAY_DICT_CMP_FIELDS=ARRAY_DICT_CMP_FIELDS,\n   )\n\n   result = closeness_dict_obj.execute(\n       mode=ClosenessDict.SIMPLE\n   )\n\n   self.assertEqual(\n       result[0]['closeness']['rank'],\n       100.00000000000001)\n   self.assertEqual(\n       result[1]['closeness']['rank'],\n       27.131782945736436)\n\n   # [{'name': 'User 2',\n   #   'tags': ['tag1',\n   #            'tag2',\n   #            'tag3'],\n   #   'gender': 'male',\n   #   'age': 25,\n   #   'closeness': {'weightages': {'gender': 11.627906976744187,\n   #                                'age': 6.9767441860465125,\n   #                                'friends': 11.627906976744187,\n   #                                'tags': 69.76744186046513},\n   #                 'rank': 100.00000000000001},\n   #   'friends': ['friend1',\n   #               'friend2',\n   #               'friend3']},\n   #  {'name': 'User 3',\n   #   'tags': ['tag1'],\n   #   'gender': 'female',\n   #   'age': 30,\n   #   'closeness': {'weightages': {'gender': 0.0,\n   #                                'age': 0.0,\n   #                                'friends': 3.8759689922480622,\n   #                                'tags': 23.255813953488374},\n   #                 'rank': 27.131782945736436},\n   #     'friends': ['friend3']}]\n\n\n   result = closeness_dict_obj.execute(\n       mode=ClosenessDict.FUZZY\n   )\n\n   self.assertEqual(\n       result[0]['closeness']['rank'],\n       100.00000000000001)\n   self.assertEqual(\n       result[1]['closeness']['rank'],\n       45.21963824289406)\n\n   # 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