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# xmACIS2Py
**Creating xmACIS2 Summary Graphics in Python**
### Jupyter Lab Tutorials
1) [Creating 30 and 90 Day Temperature and Precipitation Summaries for Riverside, CA Municipal Airport (KRAL)](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/KRAL.ipynb)
2) [Creating Summaries With Custom Dates Nov 1st - Nov 30th 2024 for Saint Paul, AK (PASN)](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/PASN.ipynb)
### Table Of Contents
1) [plot_temperature_summary(station, product_type)](#plot_temperature_summarystation-product_type)
2) [plot_precipitation_summary(station, product_type)](#plot_precipitation_summarystation-product_type)
3) [References](#references)
#### plot_temperature_summary(station, product_type)
This function plots a graphic showing the Temperature Summary for a given station for a given time period.
Required Arguments:
1) station (String) - The identifier of the ACIS2 station.
2) product_type (String or Integer) - The type of product. 'Past 7 Days' as a string or enter 7 for the same result.
A value of 'custom' or 'Custom' will result in the user entering a custom start/stop date.
Optional Arguments:
1) start_date (String) - Default=None. Enter the start date as a string (i.e. year-month-day/2025-02-22)
2) end_date (String) - Default=None. Enter the end date as a string (i.e. year-month-day/2025-02-22)
#### plot_precipitation_summary(station, product_type)
This function plots a graphic showing the Precipitation Summary for a given station for a given time period.
Required Arguments:
1) station (String) - The identifier of the ACIS2 station.
2) product_type (String or Integer) - The type of product. 'Past 7 Days' as a string or enter 7 for the same result.
A value of 'custom' or 'Custom' will result in the user entering a custom start/stop date.
Optional Arguments:
1) start_date (String) - Default=None. Enter the start date as a string (i.e. year-month-day/2025-02-22)
2) end_date (String) - Default=None. Enter the end date as a string (i.e. year-month-day/2025-02-22)
#### References
1) xmACIS2: https://www.rcc-acis.org/docs_webservices.html
2) MetPy: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.
3) NumPy: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).
4) Pandas:
author = {The pandas development team},
title = {pandas-dev/pandas: Pandas},
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.3509134},
url = {https://doi.org/10.5281/zenodo.3509134}
}
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"description": " \r\n\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/version.svg\" /> </a>\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/latest_release_date.svg\" /> </a>\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/latest_release_relative_date.svg\" /> </a>\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/platforms.svg\" /> </a>\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/license.svg\" /> </a>\r\n<a href=\"https://anaconda.org/conda-forge/xmacis2py\"> <img src=\"https://anaconda.org/conda-forge/xmacis2py/badges/downloads.svg\" /> </a>\r\n\r\n\r\n# xmACIS2Py\r\n**Creating xmACIS2 Summary Graphics in Python**\r\n\r\n### Jupyter Lab Tutorials\r\n\r\n1) [Creating 30 and 90 Day Temperature and Precipitation Summaries for Riverside, CA Municipal Airport (KRAL)](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/KRAL.ipynb)\r\n2) [Creating Summaries With Custom Dates Nov 1st - Nov 30th 2024 for Saint Paul, AK (PASN)](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/PASN.ipynb)\r\n\r\n### Table Of Contents\r\n\r\n1) [plot_temperature_summary(station, product_type)](#plot_temperature_summarystation-product_type)\r\n2) [plot_precipitation_summary(station, product_type)](#plot_precipitation_summarystation-product_type)\r\n3) [References](#references)\r\n\r\n\r\n#### plot_temperature_summary(station, product_type)\r\n\r\nThis function plots a graphic showing the Temperature Summary for a given station for a given time period. \r\n\r\nRequired Arguments:\r\n\r\n1) station (String) - The identifier of the ACIS2 station. \r\n2) product_type (String or Integer) - The type of product. 'Past 7 Days' as a string or enter 7 for the same result. \r\n A value of 'custom' or 'Custom' will result in the user entering a custom start/stop date. \r\n\r\nOptional Arguments:\r\n1) start_date (String) - Default=None. Enter the start date as a string (i.e. year-month-day/2025-02-22)\r\n2) end_date (String) - Default=None. Enter the end date as a string (i.e. year-month-day/2025-02-22)\r\n\r\n#### plot_precipitation_summary(station, product_type)\r\n\r\nThis function plots a graphic showing the Precipitation Summary for a given station for a given time period. \r\n\r\nRequired Arguments:\r\n\r\n1) station (String) - The identifier of the ACIS2 station. \r\n2) product_type (String or Integer) - The type of product. 'Past 7 Days' as a string or enter 7 for the same result. \r\n A value of 'custom' or 'Custom' will result in the user entering a custom start/stop date. \r\n\r\nOptional Arguments:\r\n1) start_date (String) - Default=None. Enter the start date as a string (i.e. year-month-day/2025-02-22)\r\n2) end_date (String) - Default=None. Enter the end date as a string (i.e. year-month-day/2025-02-22)\r\n\r\n\r\n#### References\r\n\r\n\r\n1) xmACIS2: https://www.rcc-acis.org/docs_webservices.html \r\n\r\n2) MetPy: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.\r\n\r\n3) NumPy: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).\r\n\r\n4) Pandas:\r\n author = {The pandas development team},\r\n title = {pandas-dev/pandas: Pandas},\r\n publisher = {Zenodo},\r\n version = {latest},\r\n doi = {10.5281/zenodo.3509134},\r\n url = {https://doi.org/10.5281/zenodo.3509134}\r\n}\r\n",
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