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    "info": {
        "author": "MonaLabs",
        "author_email": "itai@monalabs.io",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "# Python Client\n\n## Installation\n\n\\$ pip install mona-client\n\n## Environment variables\n\nMona uses several environment variables you need to set in order for things to\nwork as intended:\n\nMONA_HOST - the Mona server's IP address you get from Mona.\nMONA_PORT - The port for the above address.\nMONA_USER_ID - A unique user id we provide for you.\n\n## Quick Start and Example\n\n- Install as mentioned above\n- Set environment variables as mentioned above\n- Instrument code with client as seen below\n\n```\nfrom mona_client import client\n\n# Only call once,\n# Add parameteres (host, port, ...) on this call if not already set through env vars.\nclient.init_client()\n\n...\n\nwith client.new_mona_context(ARC_class=\"MY_COOL_ALGORITHM_NAME\"):\n    ...\n    client.export({\"foo\": \"bar\"})\n    ...\n```\n\n## ARCs, sub-ARCs, KAPIs and Dimensions\n\nAll the information you send Mona is sent using the \"export()\" function. You can\nsend any json-serializable dict via this function. All the fields sent via\nexport are either considered (raw) KAPIs or (raw) dimensions.\n\nKAPI stands for Key Algorithmic Performance Indicator. These are all data\nrelevant for how your algorithm is performing. Examples could be a\nsub-component's confidence score, or the amount of results received from a\nspecific algorithm.\n\nDimensions are all metadata relevant for segmenting KAPI values. For example,\nend-user demographics, a customer/user-id or a data source id.\n\nWe call the exported data \"raw\", as sometimes we don't use this data directly,\nbut generate actual KAPIs/dimensions from it (e.g., when looking at an average\nvalue over time as a KAPI).\n\nAs you export data to Mona throughout your code, Mona aggregates it on its\nservers to a struct called an ARC. ARC stands for Algorithm Run Context. Each\nARC describes an entire algorithm run. You can create as many ARCs as you'd like\nin your code, but in general, a single run of your code should correspond to a\nsingle ARC. In order to tell Mona all exports should belong to the same ARC, you\nmust init a new context. This is usually done in one place in your code, where a\nnew run is started, using:\n\n```\nwith client.new_mona_context(arc_class=\"MY_COOL_ALGORITHM_NAME\"):\n```\n\nYou assign \"classes\" to contexts. This tells Mona which ARCs describe runs of\nthe same algorithm, thus allowing Mona to compare them, segment them and find\nrelevant insights.\n\nIf you have logically separable parts to your algorithm, you can init\nsub-contexts using:\n\n```\nwith client.new_mona_sub_context(arc_class=\"MY_COOL_SUB_COMPONENT_NAME\"):\n```\n\n## Override Export Timestamp\nWhen using the export function, an \"export timestamp\" is automatically added to\nthe exported message. In some cases, there's a need to override this value with\na given timestamp (For example, when exporting historical data in batch). When\nexporting with a given timestamp, use the export function and send the export \ntimestamp (in seconds) as a parameter. \nFor example: \n```\nclient.export({\"foo\": \"bar\"}, 1552903200.0)\n```\n\n## Concurrency\n\nMona saves the ARC's id as a special variable, which is local to the thread and\nto greenlets. This means that if you start a new thread/greenlet, by default the\nnew thread will have an empty context.\n\nThis is usually the preffered behavior, as a new thread usually means a new\nreceived request (on servers) or a completely new run of an algorithm.\n\nIf by any chance you'd like to continue with the same context on a new thread,\njust use the child class MonaThread under mona_thread.py. This class takes\ncare of transferring the full context id to the newly started thread.\n\n```\nfrom mona_client import client\nfrom mona_client.mona_thread import MonaThread\n\ndef threaded_function():\n    print(client.get_full_context_id() == main_context)\n\nwith client.new_mona_context(arc_class=\"threaded_algorithm_name\"):\n    global main_context\n    main_context = client.get_full_context_id()\n    MonaThread(target=threaded_function).start()  # Prints \"True\"\n```\n\n## Testing the client code\n\nThe client's tests are written using the pytest framework, so in order to run\nthe tests (assuming you have pytest and pytest-mock installed on your environment), you just\nneed to type \"pytest\" to your shell.\n\n## Uploading new version to PyPI\n\nThe main reference to follow to do that is on:\nhttps://packaging.python.org/tutorials/packaging-projects/\n\n- Register on PyPI with your mona email: https://pypi.org/\n- Ask itai@monalabs.io to add you as collaborator\n- If not installed, install twine: \\$ python3 -m pip install --user --upgrade twine\n- Change version number under setup.py\n- If a new dependency is required, add it under setup.py under \"install_requires\"\n- If not installed, install build tools: \\$ python3 -m pip install --user --upgrade setuptools wheel\n- Build new version: \\$ python3 setup.py sdist bdist_wheel\n- Upload new version (can change '\\*' to actual version): \\$ python -m twine upload dist/\\*\n\n## Configuration and Big Red Button\n\nIf you'd like to set up configuration for mona, you can fill a simple json\nconfiguration file. See mona_client_config.json for the default configuration\nfile used. There is currently only one configuration option, which is the \"Big\nRed Button\" - the \"disable_all\" configuration, which, when set to true,\ncompletely disables all mona activity (no more exporting and context inits).\n\nMona listens to changes on the configuartion file under the environment variable\n\"MONA_CLIENT_CONFIG_FILE\". So if you want to disable mona while running, just\nchange \"disable_all\" from \"false\" to \"true\" in your config file and mona will\nupdate automatically.\n\nIf you don't set MONA_CLIENT_CONFIG_FILE yourself, Mona will use a default\nconfiguration file located on mona_client/config/mona_client_config.json. 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