def driver_vortexLoc(data, timeInds, pt_ll, cClose, cellsOnCell, nEdgesOnCell, latCell, lonCell, isMin):
cCloseList = []
valList = []
for tInd in timeInds:
#use theta on 2 pvu
output_data.print_xtime(data, tInd)
epv_ht, theta_trop = compare.calc_height_theta_2PVU(data, tInd)
#theta_trop = data.variables['temperature_500hPa'][tInd,:]
#radius = 1000.e3
radius = 500.e3
cClose = findLocalExtremum(pt_ll, cClose, radius, cellsOnCell, nEdgesOnCell, latCell, lonCell,
theta_trop, isMin)
pt_ll[0] = latCell[cClose]; pt_ll[1] = lonCell[cClose]
print "{0} {1} {2}".format(pt_ll[0], pt_ll[1], theta_trop[cClose])
cCloseList.append(cClose)
valList.append(theta_trop[cClose])
#estimate cyclone region properties
#tInd=0
#epv_ht, theta_trop = compare.calc_height_theta_2PVU(data, tInd)
rShoot = calc_objectRadius_shoot(cClose, cellsOnCell, nEdgesOnCell, latCell, lonCell, theta_trop, isMin)
cycloneCells = gather_regionGrow_nbrValues(cClose, cellsOnCell, nEdgesOnCell, theta_trop, isMin)
print "Radius shoot and cells in cyclone region: ", rShoot, cycloneCells
return (cCloseList, pt_ll, valList)
def test_run_hgt():
#long term mean
fname_mean = '/data01/forONR/hgt.mon.1981-2010.ltm.nc'
lev_500 = 5 #level of 500hPa
month = 8 #august
dataMean = netCDF4.Dataset(fname_mean,'r')
hgtMean = dataMean.variables['hgt'][month,lev_500,:,:] #lat,lon
#hgtMean += dataMean.variables['hgt'][month-1,lev_500,:,:]
#hgtMean += dataMean.variables['hgt'][month-2,lev_500,:,:]
#hgtMean /= 3.
latMean = dataMean.variables['lat'][:]*np.pi/180. #stored in degrees!
lonMean = dataMean.variables['lon'][:]*np.pi/180.
dataMean.close()
nlat = len(latMean)
nlon = len(lonMean)
var = np.zeros((nlat,nlon), dtype=float)
#mpas files
#fnames = []
fnames = sorted(glob.glob('/arctic1/nick/cases/vduda/x4/x4.t.output.2006-08-*'))
fnames.extend(sorted(glob.glob('/arctic1/nick/cases/vduda/x4.t.output.2006-08-15*')))
#fnames = sorted(glob.glob('/arctic1/nick/cases/vduda/x4/*.output.2006-08*'))
counter = 0
for iFile, fpath in enumerate(fnames):
data = output_data.open_netcdf_data(fpath)
nEdgesOnCell = data.variables['nEdgesOnCell'][:];
cellsOnCell = data.variables['cellsOnCell'][:]-1;
latCell = data.variables['latCell'][:];
lonCell = data.variables['lonCell'][:];
nTimes = len(data.dimensions['Time'])
#nTimes = 1 #3
nCells = len(latCell)
geop_mpas = np.zeros(nCells,dtype=float)
for iTime in xrange(nTimes):
#get average value for mesh over these times so can even anomaly different meshes together
output_data.print_xtime(data, iTime)
hgt_mpas = data.variables['height_500hPa'][iTime,:]
#average here to avoid summing to large number over many times
geop_mpas += mpas_hgt2geopotential(nCells, hgt_mpas, latCell)/nTimes
llMap = makeMap_ll2mpas(latMean, lonMean, latCell, lonCell, nEdgesOnCell, cellsOnCell)
var += calc_diff_field(hgtMean, geop_mpas, llMap, nlat, nlon)
#print var
counter = counter+1
data.close()
var /= counter
#var = hgtMean
# add wrap-around point in longitude.
var, lonMean = addcyclic(var, lonMean)
np.savez('tmp.dat',var,latMean,lonMean)
lats,lons = np.meshgrid(latMean, lonMean)
lats *= 180./np.pi; lons *= 180./np.pi
plot_anomaly_ll(lats, lons, np.transpose(var))