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The time series can then be redirected to file, or piped to other\ncommand line programs like ``tstoolbox``.\n\nRequirements\n------------\n\n* python 3.7 or later\n\n* tstoolbox - utilities to process time-series\n\nInstallation\n------------\npip\n~~~\n.. code-block:: bash\n\n    pip install hspfbintoolbox\n\nconda\n~~~~~\n.. code-block:: bash\n\n    conda install -c conda-forge hspfbintoolbox\n\n\nUsage - Command Line\n--------------------\nJust run 'hspfbintoolbox --help' to get a list of subcommands:\n\n catalog\n          Prints out a catalog of data sets in the binary file.\n\n extract\n          Prints out data to the screen from a HSPF binary output file.\n\nFor the subcommands that output data it is printed to the screen and you can\nthen redirect to a file.\n\nUsage - API\n-----------\nYou can use all of the command line subcommands as functions.  The function\nsignature is identical to the command line subcommands.  The return is always\na PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS\nDataFrame and is supplied to the function via the 'input_ts' keyword.\n\nSimply import hspfbintoolbox::\n\n    import hspfbintoolbox\n\n    # Then you could call the functions\n    ntsd = hspfbintoolbox.extract('tests/test.hbn', 'yearly', ',905,,AGWS')\n\n    # Once you have a PANDAS DataFrame you can use that as input.\n    ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimcera%2Fhspfbintoolbox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimcera%2Fhspfbintoolbox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimcera%2Fhspfbintoolbox/lists"}