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Energy","sub_category":"Hydro Energy","monthly_downloads":26,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"\u003cimg align=\"right\" src=\"https://github.com/jschneidewind/pyH2A/blob/master/src/pyH2A/Other/pyH2A.svg?raw=true\"/\u003e\n\n[![Documentation Status](https://readthedocs.org/projects/pyh2a/badge/?version=latest)](https://pyh2a.readthedocs.io/en/latest/?badge=latest)\n[![DOI](https://zenodo.org/badge/332826132.svg)](https://zenodo.org/badge/latestdoi/332826132)\n\n# pyH2A\n\n- **PyPI:** https://pypi.org/project/pyH2A\n- **Documentation:** https://pyh2a.readthedocs.io\n- **Source code:** https://github.com/jschneidewind/pyH2A\n\npyH2A is an extensible Python framework for the analysis of hydrogen production cost. Its discounted cash flow module is based on the H2A Hydrogen Analysis model developed by the [U.S. Department of Energy](https://www.hydrogen.energy.gov/h2a_analysis.html)/[NREL](https://www.nrel.gov/hydrogen/h2a-production-models.html).\n\nThe basic discounted cash flow analysis functionality enables calculation of levelized cost of hydrogen (LCOH\u003csub\u003e2\u003c/sub\u003e). It can be interfaced with different `Plugins` to allow modelling of various hydrogen production technologies. Furthermore, different `Analysis` modules can be applied, allowing for detailed analysis of the discounted cash flow results.\n\nIt is a command line tool, with the input being provided using Markdown formatted plaintext files and the output being plots (and formatted PDF reports in the future).\n\nNote: pyH2A is currently under development and may undergo major changes in its design.\n\n# Installation\n\npyH2A can be installed using `pip`:\n\n```bash\npip install pyH2A\n```\n\n# Documentation\n\nDocumentation for pyH2A is available at: https://pyh2a.readthedocs.io\n\n# Dependencies\n\npyH2A uses Python \u003e=3.7 with the following libraries: `NumPy`, `SciPy`, `Pandas`, `Matplotlib` and `Click`\n\n# Use\n\npyH2A can be used from the command line:\n\n```bash\npyH2A run -i input_file -o output_directory\n```\n\nFor example, if the input file `Input.md` is in the `../Input` directory and the output directory is `../Output/Example_Output`:\n\n```bash\npyH2A run -i ../Input/Input.md -o ../Output/Example_Output\n```\n\nAlternatively, the `pyH2A` class from `pyH2A.run_pyH2A.py` can imported and used within a Python script.\n\nInput is provided using a plaintext Markdown file. 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This can help with readability of complex tables.\n\n# Example output\n\n* Cost breakdown\n\n![cost breakdown plot](https://github.com/jschneidewind/pyH2A/blob/master/Example_Output/Cost_Breakdown_Plot.png?raw=True \"Cost breakdown plot\")\n\n* Sensitivity analysis\n\n![sensitivity plot](https://github.com/jschneidewind/pyH2A/blob/master/Example_Output/Sensitivity_Box_Plot.png?raw=true \"Sensitivity plot\")\n\n* Waterfall analysis\n\n![waterfall plot](https://github.com/jschneidewind/pyH2A/blob/master/Example_Output/Waterfall_Chart.png?raw=true \"Waterfall plot\")\n\n* Monte Carlo analysis, also allowing for comparison of different production pathways\n\n![colored scatter](https://github.com/jschneidewind/pyH2A/blob/master/Example_Output/Monte_Carlo_Colored_Scatter.png?raw=true \"Colored Scatter\")\n\n![comparative distance cost relationship and histograms](https://github.com/jschneidewind/pyH2A/blob/master/Example_Output/Monte_Carlo_Combined_Plot.png?raw=true \"Comparative distance cost relationship and histograms\")\n\n# Publication data \n\nData for the accompanying publication can be found in the `data` directory.\n\n# To do\n\n* Importing plugins and analysis modules from arbitrary location\n\n* Enabling use of Default.md file in arbitrary location\n\n* Block diagram illustrating flow of program\n\n* Creation of graph showing how inputs are processed by series of plugins\n\n* Lifecycle analysis \u0026 net energy analysis?\n\n# License\n\nCopyright (c) Jacob Schneidewind\n\nAll software is licensed under a MIT license (see `LICENSE` file).\n\nShield: [![CC BY 4.0][cc-by-shield]][cc-by]\n\nAll other files and their contents are licensed under a\n[Creative Commons Attribution 4.0 International License][cc-by]. 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