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    "info": {
        "author": "Esben Sonne",
        "author_email": "esbensonne+code@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
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        "description": "Charcoal\n========\n\n.. image:: https://travis-ci.org/cknv/charcoal.svg?branch=master\n    :target: https://travis-ci.org/cknv/charcoal\n\nA simple library for sending `StatsD metrics <https://github.com/etsy/statsd/blob/master/docs/metric_types.md>`_ in python. It aims to provide a high level API for the user, however that also means that much of the lower level functionality found in most other StatsD clients are not exposed, frankly because I do not find that I need it. So no manually timing things and etc.\n\nInstalling\n----------\n\n.. code-block:: shell\n\n    $ pip install charcoal\n\n\nUse it like so:\n\n.. code-block:: python\n\n    import charcoal\n\n    my_client = charcoal.StatsClient(prefix, host, port)\n\nFor development, the client also provides a ``disabled`` kwarg, so you do not have change anything in the code when you want to not send stats:\n\n.. code-block:: python\n\n    my_client = charcoal.StatsClient(prefix, host, port, disabled=True)\n\nBy itself, the client does not provide much use, but it does provide easy ways to get specific sub-clients, such as timers, counters, etc.\n\nTiming\n------\n\n.. code-block:: python\n\n    timer = my_client.timer('my-timer-name').start()\n\n    this_takes_a_while()\n    timer.intermediate('first-pass')\n\n    this_takes_a_while()\n    timer.intermediate('second-pass')\n    timer.stop()\n\nWhen you have end up with a measurement from somewhere else, perhaps from an external service, you can also send that, using the ``.send`` function on the timer class, in fact this is what the higler level functions above use, behind the scenes.\n\n.. code-block:: python\n\n    timer = my_client.timer('my-timer-name')\n    timer.send('db-call', 12.5)\n\nCounting\n--------\n\n.. code-block:: python\n\n    counter = my_client.counter('my-counter-name')\n\n    counter.increment('some-value', 10)\n    counter.decrement('some-other-value', 10)\n\nThe counter can even be fed a dict like object, such as the Counter from the standard library and send the stats as a single message.\n\n.. code-block:: python\n\n    pre_counted = {\n        'a-name': 5,\n        'another-name': 10,\n    }\n\n    counter.from_mapping(pre_counted)\n\nGauges\n------\n\nFor setting the current value.\n\n.. code-block:: python\n\n    gauge = my_client.gauge('my-gauge')\n\n    gauge.set('a-name', 10)\n    gauge.update('a-name', 10)\n\nSets\n----\n\nFor counting unique events, such as unique users on a page.\n\n.. code-block:: python\n\n    visitors = my_client.set('visitors')\n    visitors.add('ids', user.id)\n\nCustom\n------\n\nIn case the server you are using supports more metric types than this library, you can send custom metrics:\n\n.. code-block:: python\n\n    metric_to_send = 'metric.name:{value}|{type_suffix}'.format(\n        value=str(value),\n        type_suffix=type_suffix,\n    )\n\n    my_client.send(metric_to_send)\n\nThe ``prefix`` given to the client when creating it, is then prepended to the metric name, encoded, and sent to the server.\n\nCurrently it can even accept multiple metrics in one go:\n\n.. code-block:: python\n\n    my_client.send(metric_to_send, other_metric_to_send)",
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        "license": "MIT",
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        "name": "charcoal",
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