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AOESpy (v1) Abdullah al Fahad George Mason University
Dependency Library:
Numpy, Scipy mpl_toolkits.basemap Matplotlib netCDF4
Function:
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
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
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
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
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
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)
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])
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)
cmap()
deregress(x,y,lon=[],lat=[])
#displays plot based on matplotlib.pyplot
#an output default figure size
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