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AOESpy

AOESpy (v1) Author@ Abdullah al Fahad ([email protected]) http://afahadabdullah.com

Cite as: Abdullah Al Fahad. (2022). afahadabdullah/AOESpy: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6100762

AOESpy Tutorial: https://www.afahadabdullah.com/blog/python-tutorial

Please Fill up the form to get future AOESpy and Python for climate science updates

https://docs.google.com/forms/d/e/1FAIpQLSeMGsFau4Upuy096Qsg89gIEdMJWb-LoXj2TJsypdBO5dCYyg/viewform

Dependency Library:

python 3+ Numpy Scipy mpl_toolkits.basemap Matplotlib netCDF4

Setup

from terminal:

> cd /homes/afahad/ #use your setup directory here
> wget https://github.com/afahadabdullah/AOESpy/archive/master.zip
> unzip master.zip
> cd AOESpy-master

##list the directory to see if all 3 files are present

> ls 
aoespy.py  README.md  setup.sh

Load function

##In ipython IDLE 3.0+ (Spyder, Jupyter Notebook. ...... or the good ol' ipython terminal)

wdir="/homes/afahad/"
import os
os.chdir("/homes/afahad/AOESpy-master/")
import aoespy as ap
os.chdir(wdir)

.
..
...

Functions

Read netcdf data: rnc

data=rnc(var,file)

inputs:	var= string variable name that needs to be read 
		file=string file path

outputs:	data= data read from the file

Linear Trend: ltrend

vart, varp=ltrend(var,lon,lat,time,sig=False)

inputs:	var= variable as 3D [time,lat,lon] or 2D [time,lat*lon]
		lon=lon array
		lat=lat array
		time=time array
		sig= alpha significance value (e.g. 0.05, 0.1), if given the output data
		will have nan values in insignificant points (default False)

outputs:	#vart= linear trend of the variable along time dimension
			#varp= P value of the trend

Mapping function: plot

plot(var,lon,lat,title='',clf=[],cl=[], cmap='coolwarm',lon1=-180,lon2=180,lat1=-90,lat2=90,bar=1,p=1,m=1)

inputs: 	var= 2D variable that will be plotted
		lon=lon

lat=lat title='title' clf= array of filled contoured levels cl= array of contoured levels (optional) cmap= string of name of colorbar (default coolwarm, for list: matplotlib colorbars) lon1= start of lon (default -180) lon2= end of lon (default 180) lat1= start of lat (default -90) lat2= end of lat (default 90) bar= 1 (default) to plot colorbar; or 0 doesn't plot colorbar in figure p= 1 (default) plots parallel line, or 0 doesn't plot m= 1 (default) plots meridioinal line, or 0 doesn't plot

3D seasonal decompose from monthly time dimensions to annual, DJF, MAM, JJA, SON: season

 ann, djf,mam,jja,son= season(data,lon,lat,time)

inputs:	data=3D data [time lat lon]
		lon=lon
		lat=lat
		time=time

outputs:	ann= annual mean
		djf= DJF mean
		mam= MAM mean
		jja= JJA mean
		son= SON mean

1D seasonal decompose from monthly time dinemsions to annual, DJF, MAM, JJA, SON: season1d

ann, djf,mam,jja,son= season1d(data,time)

inputs:	data=1D data [time]
		time=time

outputs:	ann= annual mean
		djf= DJF mean
		mam= MAM mean
		jja= JJA mean
		son= SON mean

interpolates data in desired grid: interp

data_interp=interp(var, lon, lat, new_lons, new_lats,time=arange(1))

inputs:	var= input variable 2D or 3D (includes time dimension)
		lon= lon of the variable
		lat= lat of the variable
		new_lons= new lon that grid needs to be shifted to
		new_lats= new lat that grid needs to be shifted to
		time= (optional)

outputs:	data_interp= intrepolated data to new grids

write variables in netcdf output. This function can write upto 2 variables in one file and required dimensions: wnc

 wnc(x,y,data_out1,var1='data1',data_out2=array([1]),var2='data2',t=array([1]),e=array([1]),file='output')

inputs:	x=lon
	           	y=lat
     	data_out1=first variable to write in file
           	var1='data1' ; first varible name assigned in the file
           	data_out2=second variable to write in file; (optional)

var2='data2'; first varible name assigned in the file (if second variable is given to write) t= time dimension array e= ensemble dimension array (can be used as vertical level) file= string of output file name (dont need to add .nc)

convert vertical pressure levels to geometric height: p2h

 H = p2h(T,slp,P)

    #equation from Hypsometric
    # H= z2-z1= R*T/g * ln(P0/P)
    # H= Height
    # g=9.81 m/s2
    # R=287.04 J K-1 kg-1

inputs:	T = air temperature one array (Kelvin)

SLP = sea level pressure one array (hPa) P = Pressure level that needs to be converted in to height (hPa)

outputs:   	H = Height (meters)

#example:       #h=zeros(ta.shape)

                # for i in range(len(lev)):
                #     for j in range(len(lat)):
                #         for k in range(len(lon)):
                #             h[i,j,k]=p2h(T[i,j,k],slp[j,k],P[i])

static stability (buyoncy frequency N2): N2

 N2, pn = N2(ta,slp,plev,lon,lat)

inputs:   	 ta = air temperature (Kelvin) (3D [time, lat, lon])
		slp= sea level pressure (hPa) (2D [lat, lon])
		plev= pressure level (hPa) (1D vertical array)

outputs:   	N2= static stability (s^-2)

pn= new pressure level (hPa)

customized colormap

 cmap()

This function takes two time series (x 1D, y 3D) and output gives y removing the x signal

deregress(x,y,lon=[],lat=[])

Other handly functions

d()

#displays plot based on matplotlib.pyplot

f()

  	 #an output default figure size

shiftgrid

dataout, newlon= shiftgrid(lon0, datain, lonsin, start=True, cyclic=360.0)

inputs: 	lon0=starting longitude for shifted grid (ending longitude if start=False)
		datain= original data with longitude the right-most dimension.
		lonsin= original longitudes
		start= if True, lon0 represents the starting longitude of the new         
               			grid. if False, lon0 is the ending longitude. Default True.
		cyclic=	width of periodic domain (default 360)

outputs:	dataout= shifted input data
		newlon= new shifted longitude array