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        "description": "# Simpl Modelservice\n\nPython Library to implement Simulations, built on Crossbar.io and Django.\n\n[![Build Status](https://travis-ci.com/simplworld/simpl-modelservice.svg?token=cyqpBgqLC1o8qUptfcpE&branch=master)](https://travis-ci.com/simplworld/simpl-modelservice)\n\n## Installation\n\n### Django 2.2\n\n```bash\n$ pip install simpl-modelservice\n```\n\n### Django 1.11\n\n```bash\n$ pip install \"simpl-modelservice<0.8.0\"\n```\n\n## Setup development environment\n\n    $ git clone git@github.com:simplworld/simpl-modelservice.git\n    $ cd simpl-modelservice\n    $ mkvirtualenv simpl-modelservice\n    $ pip install -r dev-requirements.txt\n    $ pip install -e .\n\n## Run tests\n\n    $ python runtests.py\n\n## Development versioning\n\nInstall `bumpversion`:\n\n    $ pip install bumpversion\n\nThen, to release a new version, increment the version number with:\n\n    $ bumpversion patch\n\nThen push to the repo:\n\n    $ git push && git push --tags\n\n## View current WAMP subscriptions and registrations\n\nPoint your browser to http://localhost:8080/monitor and open your javascript console\n\n## How to run a modelservice as two separate processes\n\nIt's sometimes useful to run [crossbar](https://github.com/crossbario/crossbar/) and your own model code as separate processes. By default, `run_modelservice` runs crossbar configured to kick off the sub-process `run_guest`.  You can change this by doing these 3 simple steps:\n\n1. Get a copy of the currently in use crossbar configuration by running `./manage.py run_modelservice --print-config`.  This will print the generated configuration file and then run normally.  Simply cut-n-paste the configuration which will be a large JSON blob just before the usual Crossbar log messages.\n\n2. Edit the configuration to remove the entire `{\"type\": \"guest\", ...}` stanza, saved to a file.\n\n3. Run each piece separately.  If we saved our configuration into `config.json` in the current directly this would look like:\n\n        ./manage.py run_modelservice --config=./config.json --loglevel info --settings=simpl-calc.settings\n\n    for the crossbar service and then:\n\n        HOSTNAME=localhost PORT=8080 ./manage.py run_guest --settings=simpl-calc.settings\n\n    for the modelservice itself. \n\n## Environment variables \n\n- *GUEST_LOGLEVEL* adjust guest process logging, defaults to info\n- *CROSSBAR_LOGLEVEL* adjust crossbar process logging, defaults to info\n\n## Profiling\n\n### Writing tasks\n\nThe profiler will run any method that starts with `profile_` one or more times against a different number of workers.\n\nKeep in mind that, unlike unit tests, profile tasks are not isolated.\n\n#### Measuring\n\nThe `modelservice.utils.instruments` contains classes for measuring execution times. Check the module's docstrings for details.\n\n#### Collecting results\n\nYou can collect the result of the task by calling the `.publish_stat()` method:\n\n```\n    async def profile_random(self):\n        with Timer() as timer:\n            some_value = random.random()\n        self.publish_stat('<unique stat name>', timer.elapsed, fmt='Average result was {stats.mean:.3f}')\n```\n\nThe `fmt` string will receive an `instruments.StatAggregator` instance called `stats`. \nThis object will collect the value from all workers that ran the task and will provide the following properties:\n\n* `.min`: The lowest collected value\n* `.max`: The highest collected value\n* `.total`: The sum of the collected values\n* `.count`: The number of collected values\n\nAdditionally, functions from the [`statistics` module](https://docs.python.org/3/library/statistics.html) \nare aliased as properties (ie: `.mean`, `.stdev`, etc.).\n\n### Running the profiler anonymously\n\n1. Run the `simpl-games-api` server. \n1. From a model service directory, run its server.\n1. From the same model service directory, call `profile.sh`. You can use `profile.sh -h` for a list of options.\n\nSome example anonymous profiling tasks are defined in `modelservice/profiles`. These tasks are run\nunless you use the `-m` option to specify a different module path.\n\nTo run from a model service directory when logged into an AWS instance, \ncall `aws_profile.sh`. You can use `aws_profile.sh -h` for a list of options.\n\n### Running the profiler with users\n\nYou can have workers that publish and call on WAMP as specific users. Using the `.call()` or `.publish()` methods, \nit can call or publish as the user associated with that worker. \n\nYou have the profiler spawn workers as specific users by passing a file with their emails using the `-u` option.\n\nYou will also need to use the `-m` option to specify your task's module path (e.g `my_task_module_path`).\n\nAssuming you have a file called `myusers.txt` with the following content:\n\n```text\ns1@mysim.edu\ns2@mysim.edu\ns3@mysim.edu\n```\n\nYou can then call:\n\n```bash\n$ profile.sh -m my_task_module_path -u myusers.txt\n```\n\nThis will spawn 3 workers, each one set up to `.call` and `.publish` as one of those users.\n\nTo run from a model service directory when logged into an AWS instance, call:\n ```bash\n$ aws_profile.sh -m my_task_module_path -u myusers.txt\n```\n\nBoth `profile.sh` and `aws_profile.sh` invoke the `profile` management command.\n\n\nCopyright \u00a9 2018 The Wharton School,\u00a0\u00a0The University of Pennsylvania\u00a0\n\nThis program is free software; you can redistribute it and/or\nmodify it under the terms of the GNU General Public License\nas published by the Free Software Foundation; either version 2\nof the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\n\n",
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