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        "author": "DboyLiao <qmalliao@gmail.com>",
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        "description": "# pypeup - Piping Up with Python\n\n[![Build Status](https://travis-ci.org/dboyliao/pypeup.svg?branch=master)](https://travis-ci.org/dboyliao/pypeup)\n\nThis is a simple python module to help you to build a data pipe in python.\n\n## First Glance\n\nSuppose you have a bunch of functions dealing with data of the same structure (e.g they are all `array`, `integer`, ...etc) and you want to pipe them up for complex computations, `pypeup` is here at your service.\n\nWith `pypeup`, you can write something like this:\n\n```{python}\nfrom pypeup import DataPipe\n\n# Note that these two funtion all return the same data structure and their\n# first arguments are all data.\n# In this example, the data are all of type list.\n\ndef fun1(data, x):\n    \"\"\"\n    x: <number>\n    data: <list>\n    \"\"\"\n    return [a + x for a in data]\n\ndef fun2(data, ind):\n    \"\"\"\n    ind: <integer>\n    data: <list>\n    \"\"\"\n    return data[:min(ind, len(data) - 1)]\n\nclass MyPipe(DataPipe):\n    pass\n\nmy_pipe = MyPipe([1, 2, 3, 4, 5])\nmy_pipe.register(fun1)      # Use register method to add any method you like \nmy_pipe.register(fun2)      # for your data.\n\nmy_pipe.fun1(1).fun2(3).fun2(2).fun1(3) # Pipe the function up at your wish\nmy_pipe.data                # Access the data by the `data` attribute\n# >>> [5, 6]\n```\n\nAlso, you can build up the pipe by one class declairation:\n\n```{python}\nfrom pypeup import DataPipe\nimport numpy as np\n\nclass MyPipe2(DataPipe):\n\n    def add(self, x):\n        return self.data + x\n\n    def sub(self, x):\n        return self.data - x\n\n    def mul(self, x):\n        return self.data * x\n\npipe2 = MyPipe2(np.array([1, 2, 3]))\npipe2.add(3).sub(2).mul(4)\npipe2.data\n# >>> np.array([8, 12, 16])\n```\n\nYou can use private method as normal python:\n\n```{python}\nfrom pypeup import DataPipe\nimport math\n\nclass MyPipe(DataPipe):\n\n    def fun(self, x):\n        return self._magic(x)\n\n    def _magic(self, x):\n        print \"Where the magic happens!\"\n        return self.data + math.sin(x)\n\npipe = MyPipe(0.)\nprint pipe.fun(math.pi / 2).data\n# >>> Where the magic happens!\n# >>> 1.\n```\n\nIn order to protect the data inside the pipe, any modification to the `data` which is outside of the execution context of the methods of the pipe is not allowed and an `ExecutionContextError` will be raised.\n\n```{python}\nfrom pypeup import DataPipe\n\nclass MyPipe(DataPipe):\n\n    def addOne(self):\n        return self.data + 1\n\npipe = MyPipe(10)\npipe.addOne(1)     # OK.\npipe.data = 11     # Not OK.\n```\n\nThere are some limits on the functions which can be applied to `pypeup`.\nSee [Limits](https://github.com/dboyliao/pypipe#limits) for detail.\n\n## Limits\n\nAs mentioned above, there are few limits on the functions that can be used with `pypeup`:\n\n- The current data can be access through `self.data`.\n    - `self.data` is a `property` defined in `DataPipe`, which means that if you want to overwrite it, you must be sure your implementation is OK.\n- All the functions' first argument must be `data`. (But not method, see below)\n    - It doesn't mean you have to name it as `data`, but you have to be sure that all the functions' first argument will hold the data you want to process.\n    - If the function is defined as an instance method, you only need to pass all the parameters needed to work with the data which can be access through `self.data`.\n    - If the instance method is private method (method with the name start with `_` or `__`) will work just like normal instance method.\n- All the `data` must be of the same (or compatible) data structure or type.\n    - for example, they must be all `list`, `number`, `numpy.array`...etc.\n- All the function must return the data which will be passed through the pipe.\n\n## Installation\n\n- Install through `pip`:\n    - Just run `pip install pypeup`\n- Install from source:\n    - run `git clone https://github.com/dboyliao/pypeup.git && cd pypeup`\n    - run `python setup.py install` to install the package.\n\n## Tests\n\n- If you haven't installed `nose` yet, run `pip install -r requirements.txt` first.\n- run `nosetests`",
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