Barron H. Henderson barronh

## tempo_geoxo_meeting.ipynb

      
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                TEMPO_GeoXO_Meeting.ipynb
              
          
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## backup.py
#!/usr/bin/env python
__doc__ = """
Overview
========

Module designed to make backups of scripts (.sh, .csh, .py), makefiles,
configuration files, and README files.

Contents
--------

## cmaq_dust.py
__doc__ = """
Offline CMAQ Python WBD approximator
====================================

github.com/USEPA/CMAQ/emis/emis/DUST_EMIS.F routines and functions converted
from Fortran to Python. All modules are replaced by inline code and direct
access to MCIP files. The original DUST_EMIS.F was developed by Foroutan et
al. (https://dx.doi.org/10.1002/2016MS0008230.

To-Do:

## pair_rsigcmaq.py
__all__ = ['pair_rsigcmaq']
__doc__ = """
pair_rsigcmaq is designed to pair RSIG data with CMAQ files.

Example:

    from pair_rsigcmaq import pair_rsigcmaq
    import pandas a pd

    outpath = pair_rsigcmaq('CMAQ_ACONC_20160101', 'O3', 'airnow.ozone')

## rundayenv.sh
#!/bin/bash

POSITIONAL_ARGS=()
PREFIX="ENV"
while [[ $# -gt 0 ]]; do
  case $1 in
    -h|--help)
      echo "Usage"
      echo "-----"
      echo "    $0 [--prefix=PREFIX] TEMPLATE_ENV STARTDAY [ENDDAY [SCRIPT]]"

## cmaq_taylor_example.py
"""
This example calculates statistics for a single day from the EPA EQUATES CMAQ simulation for 2018-06-14
and creates a taylor diagram.
"""


import pyrsig
import pandas as pd
import pyproj
import taylordiagram as td

## hrrr.py
__doc__ = """
# Overview

This example shows how to open High Resolution Rapid Refresh (HRRR) meteorology
"surfaces." These surfaces are slices in vertical space to create single level
maps of variables like temperature, wind speed components, pressure, etc. Each
surface is opened using the Zarr archive described in Amazon's opendata registry
described at https://registry.opendata.aws/noaa-hrrr-pds/.

# Examples

## lonlatarea.py
__all__ = ['getarea']


def getarea(nlat, nlon, late=None, R=6371000.):
    """
    Arguments
    ---------
    nlat : int
        Number of latitude bands
    nlon : int

## purpleair_epa.py
def purpleair_epa(pa, rh, method='extended'):
    """
    Corrects PM2.5 using the US-wide original or extended correction.

    The "original" correction was developed by Barkjohn et al. 2021[1]
    The "extended" correction is used in teh Fire and Smoke Map as described in
    the QA[2] document in Eq. 4 (implemented Oct 2021). This equation is cited
    and evaluated in Jaffe et al.(2023)[3]

    Use at your own risk and submit updates if you find issues.

## mpestats.py
def getstats(df, refkey='obs'):
    """
    Calculate typical model statistics
    [1] https://epa.gov/sites/production/files/2015-11/modelperformancestatisticsdefinitions.docx

    Example assuming you have a csv with 'obs' and 'mod' columns:

    ```
    import pandas as pd
	#!/usr/bin/env python
	__doc__ = """
	Overview
	========

	Module designed to make backups of scripts (.sh, .csh, .py), makefiles,
	configuration files, and README files.

	Contents
	--------
	__doc__ = """
	Offline CMAQ Python WBD approximator
	====================================

	github.com/USEPA/CMAQ/emis/emis/DUST_EMIS.F routines and functions converted
	from Fortran to Python. All modules are replaced by inline code and direct
	access to MCIP files. The original DUST_EMIS.F was developed by Foroutan et
	al. (https://dx.doi.org/10.1002/2016MS0008230.

	To-Do:
	__all__ = ['pair_rsigcmaq']
	__doc__ = """
	pair_rsigcmaq is designed to pair RSIG data with CMAQ files.

	Example:

	from pair_rsigcmaq import pair_rsigcmaq
	import pandas a pd

	outpath = pair_rsigcmaq('CMAQ_ACONC_20160101', 'O3', 'airnow.ozone')
	#!/bin/bash

	POSITIONAL_ARGS=()
	PREFIX="ENV"
	while [[ $# -gt 0 ]]; do
	case $1 in
	-h\|--help)
	echo "Usage"
	echo "-----"
	echo " $0 [--prefix=PREFIX] TEMPLATE_ENV STARTDAY [ENDDAY [SCRIPT]]"
	"""
	This example calculates statistics for a single day from the EPA EQUATES CMAQ simulation for 2018-06-14
	and creates a taylor diagram.
	"""


	import pyrsig
	import pandas as pd
	import pyproj
	import taylordiagram as td
	__doc__ = """
	# Overview

	This example shows how to open High Resolution Rapid Refresh (HRRR) meteorology
	"surfaces." These surfaces are slices in vertical space to create single level
	maps of variables like temperature, wind speed components, pressure, etc. Each
	surface is opened using the Zarr archive described in Amazon's opendata registry
	described at https://registry.opendata.aws/noaa-hrrr-pds/.

	# Examples
	__all__ = ['getarea']


	def getarea(nlat, nlon, late=None, R=6371000.):
	"""
	Arguments
	---------
	nlat : int
	Number of latitude bands
	nlon : int
	def purpleair_epa(pa, rh, method='extended'):
	"""
	Corrects PM2.5 using the US-wide original or extended correction.

	The "original" correction was developed by Barkjohn et al. 2021[1]
	The "extended" correction is used in teh Fire and Smoke Map as described in
	the QA[2] document in Eq. 4 (implemented Oct 2021). This equation is cited
	and evaluated in Jaffe et al.(2023)[3]

	Use at your own risk and submit updates if you find issues.
	def getstats(df, refkey='obs'):
	"""
	Calculate typical model statistics
	[1] https://epa.gov/sites/production/files/2015-11/modelperformancestatisticsdefinitions.docx

	Example assuming you have a csv with 'obs' and 'mod' columns:

	```
	import pandas as pd