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    "info": {
        "author": "Samuel Kolb",
        "author_email": "samuel.kolb@me.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3"
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        "description": "# autodora [![Build Status](https://travis-ci.org/samuelkolb/autodora.svg?branch=master)](https://travis-ci.org/samuelkolb/autodora)\nautodora is a framework to help you:\n1. setup experiments\n2. running them for multiple parameters\n3. storing the results\n4. exploring the results\n\nThe aim of this package is to make these steps as easy and integrated as possible.\n\n## Installation\n\n    pip install autodora\n\nExperiments can be tracked using observers. Specialized observers may require optional packages to function that are\nnot included by default (because you might not need them).\n\n### Telegram observer\n\n    pip install autodora[telegram]\n\nIn order to use the observer you have to set the environment variables `TELEGRAM_BOT_TOKEN` and `TELEGRAM_CHAT_ID`.\n\n**Example usage**\n\n    export TELEGRAM_BOT_TOKEN=\"<your-bot-token>\"\n    export TELEGRAM_CHAT_ID=\"<your-chat-id>\"\n    pytest\n\n\n## Using autodora\nConsider the problem of computing the product of two numbers given in a string like this: `\"0.1 x 0.3\"`.\nThe heavy-lifting of this computation is performed by a function `multiply`:\n\n    def multiply(x, y):\n        return x * y\n\n### Setting up\nWe start off by describing the experiment in a file called `product_experiment.py`:\n\n    class ProductExperiment(Experiment):\n        input = Parameter(str, \"0.0x0.0\", \"The input values to be multiplied (e.g. 0.2x10)\")\n        product = Result(float, description=\"Computed product\")\n\n        @derived(cache=True)\n        def derived_x(self):\n            return float(self.get(self.input).split(\"x\")[0])\n\n        @derived(cache=True)\n        def derived_y(self):\n            return float(self.get(self.input).split(\"x\")[1])\n\n        def run_internal(self):\n            x, y = self.get(\"x\"), self.get(\"y\")\n            result = multiply(x, y)\n            self[\"product\"] = result\n\n\n    ProductExperiment.enable_cli()\n\n\n\n#### Describing parameters\n\n        ...\n        input = Parameter(str, \"0.0x0.0\", \"The input values to be multiplied (e.g. 0.2x10)\")\n        ...\n\nThe first step is to describe the parameters of the experiment, the name is taken from the variable you assign them to,\nother than that have to specify the type and optionally a default value and description of the parameter.\n\nWhile this is a powerful and easy way to set up parameters, you can also add them in the constructor:\n\n    class ProductExperiment(Experiment):\n        def __init__(self, group, storage=None, identifier=None):\n            super().__init__(group, storage=None, identifier=None)\n            self.parameters.add_parameter(\"complicated.name\", datetime, None, \"Description\")\n\n\n#### Describing results\n\n        ...\n        product = Result(float, description=\"Computed product\")\n        ...\n\nSimilar to the parameters, we specify expected results. The Result class is identical to the Parameter class in all but\nname, it only serves to indicate that you are trying to assign a result.\n\n\n#### Computing derived features\n\n        ...\n        @derived(cache=True)\n        def derived_x(self):\n            return float(self.get(self.input).split(\"x\")[0])\n\n        @derived(cache=True)\n        def derived_y(self):\n            return float(self.get(self.input).split(\"x\")[1])\n        ...\n\nDerived features are computed from other values (or complex computation chains) and can be marked for caching to avoid\ncomputing them over and over again: when the experiment is saved to storage, those features will be saved with the\nexperiment.\n\nYou can build derived features using the derived decorator, which internally builds a `Derived object` and saves it in\nthe `experiment.derived_callbacks (Dict[str, Derived])` dictionary. Again, you can do this in the constructor, too.\nIf the decorated function is called `derived_<name>`, it will be shortened to just `<name>`.\n\nDerived features can be accessed by calling `experiment[\"name\"]` or `experiment[\"\"derived.name\"]` to disambiguate\nif there are other parameters or results with the same name.\n\n\n#### Running the experiment\n\n        ...\n        def run_internal(self):\n            x, y = self.get(\"x\"), self.get(\"y\")\n            result = multiply(x, y)\n            self[\"product\"] = result\n        ...\n\nWhen `experiment.run()` is called, it internally calls the `run_internal` method, which is responsible for running the\nactual experiment. In this case, it fetches the (derived) parameters, computes the result and stores it.\n\n\n#### Enabling the command line interface\n\n    ...\n    ProductExperiment.enable_cli()\n\nThe `enable_cli` class method, not surprisingly, enables the current file to be run from command line.\nThis enables several key features:\n- Making the experiment executable by command line (for internal and external use)\n- Allowing you to manage (plot, list, ...) experiments of this type from command line\n\n### Specifying trajectories\n\nTODO\n\n\n",
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