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        "author": "soraxas",
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        "description": "# Async CSV Logger\n[![PyPI](https://img.shields.io/pypi/v/asynccsv.svg)](https://pypi.python.org/pypi/asynccsv)\n\nThis module is a async csv logger that helps you log performance of your algorithm.\n\n\n## Description\n\nThis module is intented to be a csv logger which will write to file async-ly.\n\nWith the hope to have minimum performance impact on benchmarking your algorithm (i.e. I/O blocking to write to disk), this is especially useful for logging performance at each iteration/time-step. Internally, it utilise threading to async write to file.\n\nThere are two way to initialise and use the logger.\n\n\n## Install\n\n```\npip install asynccsv\n```\n\n\n### 1. Recommended way (with block)\n\n```python\nimport time\nimport datetime\nfrom asynccsv import AsyncCSVLogger\n\nwith AsyncCSVLogger('path_of_your_log.csv') as logger:\n    # csv titles\n    logger.write(['Time', 'Accuracy', 'Num of nodes'])\n\n    # do your other stuff\n    # ......\n\n    for i in range(10):\n        # perform calculation\n        # ....\n        # write results to file\n        logger.write([datetime.datetime.now().strftime(\"%S.%f\"), acc, num_nodes])\n        time.sleep(0.5)\n```\n\n\n### 2. The normal way\n\n```python\nimport time\nfrom asynccsv import AsyncCSVLogger\n\nclass MyAwesomeAlgorithm():\n\n    def __init__(self):\n        # with the 'log_timestamp' flag it will automatically log timestamp\n        self.logger = AsyncCSVLogger('path_of_your_log.csv', log_timestamp=True)\n        self.logger.write(['Time', 'Accuracy', 'Num of nodes'])\n\n    def run(self):\n        # perform calculation\n        # ...\n        logger.write([acc, num_nodes])\n        time.sleep(0.5)\n\n\nif __name__ == '__main__':\n    awesome = MyAwesomeAlgorithm()\n    for i in range(10):\n        awesome.run()\n\n    # You SHOULD run this to properly close the threading and force\n    # everything to be written to disk\n    # This is automatically done by the 'with' block in previous example\n    awesome.logger.close()\n```\n\n---\n\nWith both methods, the final content of `path_of_your_log.csv` will look something like:\n\n```csv\n\"Time\",\"Accuracy\",\"Num of nodes\"\n\"57.689359\",\"92.5\",\"11\"\n\"58.189979\",\"93.5\",\"12\"\n\"58.690520\",\"94.22\",\"13\"\n\"59.191268\",\"93.5\",\"15\"\n\"59.692062\",\"92.2\",\"17\"\n\"00.192850\",\"92.4\",\"22\"\n\"00.693661\",\"94.8\",\"26\"\n\"01.194634\",\"96.6\",\"27\"\n\"01.695368\",\"94.1\",\"30\"\n\"02.196014\",\"97.5\",\"42\"\n\n```\n\n\n",
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