def run_vortexLoc():
#fnames = cfsrFiles2()
#fnames = ['/arctic1/nick/cases/v1.0/x4/2week/tiedtke/x4.tiedtke.output.2006-07-24_12.00.00.nc',
# '/arctic1/nick/cases/v1.0/x4/2week/tiedtke/x4.tiedtke.output.2006-07-31_12.00.00.nc']
#fnames = ['/arctic1/nick/cases/v1.0/x4/longer/x4.kf.output.2006-08-15_00.00.00.nc']
#fnames = sorted(glob.glob('/arctic1/nick/cases/cfsr/output/x4.cfsr.output.2006-07-*'))[-1::-1] #track backwards
#fnames = ['/arctic1/nick/cases/cfsr/output/x4.cfsr.output.2006-08-15_00.00.00.nc']
fnames = sorted(glob.glob('/arctic1/nick/cases/vduda/x4/x4.t.output.2006-07-*'))
#fnames = ['/arctic1/nick/cases/vduda/x7/x7.kf.output.2006-08-07_18.00.00.nc']
#fnames = sorted(glob.glob('/arctic1/nick/cases/vduda/x4.t.output.2006-09-*'))
pt_ll = np.empty(2,dtype='float')
#pt_ll[0] = 90.*np.pi/180.; pt_ll[1] = 0.0
pt_ll[0] = 87.05*np.pi/180.; pt_ll[1] = 18.86*np.pi/180. #for 20060801 start time
#pt_ll[0] = 85.38*np.pi/180.; pt_ll[1] = 352.97*np.pi/180. #for 2006080312 start time
#pt_ll[0] = 86.1*np.pi/180.; pt_ll[1] = 137.*np.pi/180. #for 2006080718
#pt_ll[0] = 82.5*np.pi/180.; pt_ll[1] = 41.95*np.pi/180. # for 2006081500
#pt_ll[0] = 71.55*np.pi/180.; pt_ll[1] = 153.1*np.pi/180. #for 2006072412
#pt_ll[0] = 81.05*np.pi/180.; pt_ll[1] = 19.4*np.pi/180. #for 2006091900
#pt_ll[0] = 1.41; pt_ll[1] = 0.27
#give initial guess of cClose. guess is just seed to search, so can choose anything.
cClose = 71791 #131735 #x4 mesh for 20060801 start time
#cClose = 102439 #x7 mesh for 20060801 start time
c0IsMin = True;
#load in mesh data
fpath = fnames[0]
data = output_data.open_netcdf_data(fpath)
nEdgesOnCell = data.variables['nEdgesOnCell'][:];
cellsOnCell = data.variables['cellsOnCell'][:]-1;
latCell = data.variables['latCell'][:];
lonCell = data.variables['lonCell'][:];
data.close()
cellList = []
minValList = []
for iFile, fpath in enumerate(fnames):
data = output_data.open_netcdf_data(fpath)
#timeInds = xrange(0,28,4)
#timeInds = [0]
nTimes = len(data.dimensions['Time'])
#timeInds = xrange(0,nTimes,1)
timeInds = xrange(26,nTimes,1)
#timeInds = xrange(nTimes-1,-1,-1)
#search for cyclone center
cClose = conn.findOwner_horizNbrs_latLon(pt_ll, cClose, latCell, lonCell, nEdgesOnCell, cellsOnCell)
cCloseList, pt_ll, minVals = driver_vortexLoc(data, timeInds, pt_ll, cClose, cellsOnCell, nEdgesOnCell, latCell, lonCell, c0IsMin)
cClose = cCloseList[-1]
cellList.extend(cCloseList)
minValList.extend(minVals)
data.close()
print cellList
print minValList
def example(ncNameFile):
#Input file is a list of each output file on its own line.
#Paraview has a hard time animating the 0 time step .nc files. Maybe it's an issue with the MPAS reader?
#We can get around it by creating vtk files of the fields of interest named fnameN.vtk where N is an integer that indicates time.
#store the names of the files we want in order in a file
#ncNameFile = 'ncNames.txt'
fp = open(ncNameFile,'r')
#output the files to
outNameBase = 'june'
#
i = 0
for line in fp:
ncfname = line.rstrip('\n') #do we have to strip off any characters like \n?
if (i==0):
#reference field for orientation
vtkfname = outNameBase+'_ref'+'.vtk'
data = output_data.open_netcdf_data(ncfname)
#header and mesh info
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncfname)
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
fvtk.write('\nCELL_DATA '+str(nCells)+'\n')
output_data.write_vtk_staticGeoFields(f,data,nCells)
fvtk.close()
data.close()
#
vtkfname = outNameBase+str(i)+'.vtk'
data = output_data.open_netcdf_data(ncfname)
#header and mesh info
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncfname)
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
#write cell data
fvtk.write('\nCELL_DATA '+str(nCells)+'\n')
time = 0
vLevel = 18
output_data.write_vtk_cellCenterVelocity(fvtk, data, time, vLevel, nCells)
output_data.write_vtk_var_timeLevelCells(fvtk, 'pv_cell', data, vLevel, time, nCells)
i = i+1
#close files
data.close()
fvtk.close()
fp.close()
def example():
# file properties
ncfname = "/arctic1/nick/cases/cfsr/output.2006-08-07_12.00.00.nc"
# ncfname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #input file
vtkfname = "plotTest.vtk" # output file
data = output_data.open_netcdf_data(ncfname)
# open the output vtk file and write header. -------------------
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncfname)
# write nodes and cells
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
nLevels = len(data.dimensions["nVertLevels"])
# write some cell data --------------------
fvtk.write("\nCELL_DATA " + str(nCells) + "\n")
output_data.write_vtk_staticGeoFields(fvtk, data, nCells)
# time dependent stuff goes in different files
time = 0
output_data.write_vtk_pressureHeights(fvtk, data, nCells, time, nLevels, 50000) # 500mb = 50,000Pa
# write some node data
# close the .nc and vtk files
data.close()
fvtk.close()
def driver_tracers():
'''
given .nc (MPAS NETCDF) file and initial location for tracer, we want to
advect that tracer with the wind velocity through all time steps.
'''
#file properties
ncfname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #input file
data = output_data.open_netcdf_data(ncfname)
tSteps = len(data.dimensions['Time'])
vertLevels = len(data.dimensions['nVertLevels']);
r = np.empty(3);
r[0] = 50.; r[1]=50.; r[2] = 5000.;
dt = 6.*60.*60.; #can parse xtime in file as actual timestep
#loop through time steps
vel = np.empty(3);
for i in range(tSteps):
(hCell, vCell) = findOwner_coord(r, data, vertLevels);
vel[0]=data.variables['uReconstructX'][i,hCell,vCell]
vel[1]=data.variables['uReconstructY'][i,hCell,vCell]
vel[2]=data.variables['uReconstructZ'][i,hCell,vCell]
r = integrate(r,u,dt);
print r
data.close()
def driver_ncGeo2vtk(ncfname):
'''
given .nc (MPAS NETCDF) file, output the surface geography data into classic vtk format.
The MPAS NetCDF reader in Paraview loads only the "critical" netcdf variables, ie with time and vertLevels.
Rather than edit that reader, we can create a file of the vars we care about on the scvt mesh rather than the dual.
I think they have to use the dual for the volume mesh since the elements need to be of supported VTK type (eg prism, hex,...)
'''
#file properties
#ncfname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #input file
vtkfname = 'geoTest.vtk' #output file
data = output_data.open_netcdf_data(ncfname)
#open the output vtk file and write header.
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncfname)
#write nodes and cells
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
vLevels = len(data.dimensions['nVertLevels'])
nTimes = len(data.dimensions['Time'])
#write some geographic cell data
fvtk.write('\nCELL_DATA '+str(nCells)+'\n')
write_vtk_staticGeoFields(fvtk,data,nCells)
write_vtk_timeGeoFields(fvtk,data,nCells, nTimes)
#write node data (none for geog)
#close the .nc and vtk files
data.close()
fvtk.close()
def example_2pvu():
# fpath = '/arctic1/nick/cases/v1.0/x4/august/kf/v1.1/x4.kf.output.2006-08-01_00.00.00.nc'
fpath = "/arctic1/nick/cases/v1.0/x4/august/tiedtke/v1.1/x4.t.output.2006-08-08_00.00.00.nc"
# fpath = '/arctic1/nick/cases/v1.0/x4/august/kf/v1.1/x4.kf.output.2006-08-08_00.00.00.nc'
# fnames = searchFiles()
# for iFile, fpath in enumerate(fnames):
data = output_data.open_netcdf_data(fpath)
for timeInd in xrange(0, 28, 4):
# for timeInd in [0]:
# open the output vtk file and write header, nodes, and cells
vtkfname = "x4_t_2006-08-01_1day." + str(28 + timeInd) + ".vtk"
# vtkfname = 'x4_cfsr_2006-07-25_1day.'+str(iFile)+'.vtk'
fvtk = output_data.write_vtk_header_polydata(vtkfname, fpath)
# fvtk = open(vtkfname,'w'); nCells = 163842
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
# write some cell dataa
fvtk.write("\nCELL_DATA " + str(nCells) + "\n")
# calc values
# timeInd = 0
epv_ht, theta_trop = calc_height_theta_2PVU(data, timeInd)
output_data.write_levelData_float("ht_2pvu", fvtk, epv_ht, nCells)
output_data.write_levelData_float("theta_2pvu", fvtk, theta_trop, nCells)
fvtk.close()
data.close()
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))
def example():
#file properties
ana_fname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #analysis file
sim_fname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #simulation file
vtkfname = 'fieldDiff.vtk' #output file
#read in reference and simulation files
anaData = output_data.open_netcdf_data(ana_fname)
simData = output_data.open_netcdf_data(sim_fname)
#assuming same mesh between ana and sim
nTimes = len(data.dimensions['Time'])
nCells = len(data.dimensions['nCells']);
nVert = len(data.dimensions['nVertLevels'])
#I don't know how to write a 3 or 4d field into the .nc format so we're going to have
#to hold off on that.
#take differences of individual fields (3d, height levels, pressure levels,...)
#and generate output (surfaces-horiz and vertical, field statistics,...).
tSim = 0 #index into time steps of simulation corresponding to analysis
tAna = 0
fieldKeys = ['pv_cell','theta','rho']
for k in fieldKeys:
#index is [time,cell,vLevel]
varAna = data.variables[k][tAna,:,:]
varSim = data.variables[k][tSim,:,:]
diff = varSim-varAna
avg = sum(diff)/(nCells*nVert)
str = 'Variable %s has sim-ana mean= %s for simInd %s\n' %(k, avg, tSim)
print str
rms = sum(diff*diff)/(nCells*nVert) #square and mean
rms = np.sqrt(rms) #root
str = 'Variable %s has sim-ana RMS= %s for simInd %s\n' %(k, rms, tSim)
print str
def derivedSfcs(ncfname, vtkfname):
#write some derived surfaces to file
data = output_data.open_netcdf_data(ncfname)
#header info
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncfname)
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
nLevels = len(data.dimensions['nVertLevels'])
#cell data
fvtk.write('\nCELL_DATA '+str(nCells)+'\n')
#geo for reference
output_data.write_vtk_staticGeoFields(f,data,nCells)
time = 0
#500 mb
output_data.write_vtk_pressureHeights(fvtk, data, nCells, time, vLevels, 50000.)
#theta on dynamic tropopause
pv = np.empty((nCells,nLevels), dtype=float)
for hcell in range(nCells):
for l in range(nLevels):
pv[hcell,l] = vars.calc_ertelPV(data, 'theta', time, hcell, l, nLevels)
#
pvuVal = 2.
thetaVal = np.empty(nCells)
for hcell in range(nCells):
(l,dl) = output_data.calcIndexOfValue(pvuVal,pv[hcell,:], nLevels)
thetaVal[hcell] = output_data.calcValueOfIndex(l,dl,data.variables['theta'][time,hcell,:])
output_data.write_levelData_float('theta_pv', fvtk, thetaVal, nCells)
#slp
slp = np.empty(nCells)
for hcell in range(nCells):
slp[hcell] = vars.calc_slp(data, hcell, nLevels, time)
output_data.write_levelData_float('slp', fvtk, slp, nCells)
#close da files
fvtk.close()
data.close()
def example_vars():
# for every 6 hours of the IC CFSR data,
# write a vtk file with slp, 500mb heights, and theta on 2 pv surface
t0 = dt.datetime(2006, 6, 1, 0)
# tf = dt.datetime(2006,9,29,18)
tf = dt.datetime(2006, 6, 1, 13)
h6 = dt.timedelta(hours=6)
cfsrPath = "/arctic1/nick/cases/cfsr/"
vtkBase = "cfsrIC."
i = 1
t = t0
while t <= tf: # since increment t after check, don't do
# open the .nc data file for this datetime
tString = cfsr.form_cfsrTimeString(t)
ncName = "vert_sfc." + tString + ".nc" # initial condition netcdf file
ncName = cfsrPath + ncName
data = output_data.open_netcdf_data(ncName)
# open the output vtk file and write header. -------------------
vtkfname = vtkBase + str(i - 1) + ".vtk"
fvtk = output_data.write_vtk_header_polydata(vtkfname, ncName)
# write nodes and cells
nNodes = output_data.write_vtk_xyzNodes(fvtk, data)
nCells = output_data.write_vtk_polygons(fvtk, data)
nLevels = len(data.dimensions["nVertLevels"])
# write some cell data --------------------
fvtk.write("\nCELL_DATA " + str(nCells) + "\n")
timeInd = 0
#
data.close()
fvtk.close()
# increment day
t = t0 + i * h6
i = i + 1
def plotPoints_example():
fpath = '/arctic1/nick/cases/cfsr/output/x4.cfsr.output.2006-08-01_00.00.00.nc'
data = output_data.open_netcdf_data(fpath)
latCell = data.variables['latCell'][:];
lonCell = data.variables['lonCell'][:];
data.close()
plt.figure()
map = Basemap(projection='ortho',lon_0=0,lat_0=90, resolution='l')
'''
cellInds_cfsr = [56355, 28662, 11263, 131726, 84718, 84714, 46582, 105453, 114078,
25622, 37141, 6428, 50655, 160926, 144268, 156040, 98770, 42664, 137842, 24214,
151512, 2568, 96729, 11246, 131490, 19940, 163620, 119379, 136917, 104557, 36439,
150649, 21865, 30589, 119446, 71817, 10022, 131487, 84705, 156231, 84691, 5142,
151475, 79538, 28657, 79527, 162909, 151551, 160897, 119355, 151474, 114095,
151473, 41958, 131519, 131551, 131556, 3564]
cellInds_t = [56355, 14136, 62441, 105460, 62444, 131720, 84715, 84718, 46589, 58118, 103293,
119276, 119271, 38013, 131725, 20012, 131711, 96797, 1630, 20010, 79513, 79515,
151526, 131702, 41980, 71822, 144522, 137231, 14763]
cellInds_kf = [56355, 14136, 62441, 105460, 62444, 50736, 84714, 84713, 46589,
58118, 10538, 119277, 15864, 84711, 131725, 20012, 50734, 50738, 131727, 119266,
38009, 71828, 10022, 33361, 8554, 137254, 66155, 4203, 137296]
'''
cellInds_cfsr = cfsrStats.x4_cfsr_july_cells
lonp = lonCell[cellInds_cfsr]*180./np.pi
latp = latCell[cellInds_cfsr]*180./np.pi
xcfsr,ycfsr = map(lonp, latp)
#cells_guess = [40419,26537,10552,1634,7338,20030,437,2896,11920,22053,19965,19961,28617]
cells_guess = mpasStats.x4_t_0724_cells
cells_f500 = [138657, 149403, 38051, 28687, 131782, 131766, 71977, 58328, 22007, 11933, 14811, 72391, 103406]
cells_f1000 = [138657, 149403, 38051, 28687, 131782, 131766, 71977, 58328, 33409, 149420, 5194, 93157, 16019]
lonp = lonCell[cells_guess]*180./np.pi
latp = latCell[cells_guess]*180./np.pi
xcfsr_g,ycfsr_g = map(lonp, latp)
lonp = lonCell[cells_f500]*180./np.pi
latp = latCell[cells_f500]*180./np.pi
xcfsr_f500,ycfsr_f500 = map(lonp, latp)
lonp = lonCell[cells_f1000]*180./np.pi
latp = latCell[cells_f1000]*180./np.pi
xcfsr_f1000,ycfsr_f1000 = map(lonp, latp)
'''
lonp = lonCell[cellInds_t]*180./np.pi
latp = latCell[cellInds_t]*180./np.pi
xtiedtke,ytiedtke = map(lonp, latp)
lonp = lonCell[cellInds_kf]*180./np.pi
latp = latCell[cellInds_kf]*180./np.pi
xkf,ykf = map(lonp, latp)
'''
map.drawcoastlines()
map.drawmapboundary()
#map.scatter(x,y, picker=5)
#map.scatter(x,y)
map.plot(xcfsr,ycfsr,'b*--', label='back')
map.plot(xcfsr_g,ycfsr_g, 'go-', label='07-24_12') # label='user')
map.plot(xcfsr_f500,ycfsr_f500,'gs:', label='f,r500km')
map.plot(xcfsr_f1000,ycfsr_f1000,'r*-.', label='f,r1000km')
plt.legend()
'''
legend = plt.legend('back', 'user', 'forward')
for label in legend.get_texts():
label.set_fontsize('medium')
'''
#map.plot(xcfsr,ycfsr,'b*--', xtiedtke,ytiedtke, 'go-.', xkf,ykf, 'rs:') #, markersize=10)
#plt.legend('cfsr', 'tiedtke', 'kf')
plt.show()
def example_mpas_horiz_tri():
#plot triangulation on a map projection
#ncfname = '/home/nickszap/research/mpas/output.2010-10-23_00:00:00.nc' #input file
ncfname = '/arctic1/nick/cases/163842/r2614/output.163842.2006-07-08_00.00.00.nc'
data = output_data.open_netcdf_data(ncfname)
nCells = len(data.dimensions['nCells'])
nVertices = len(data.dimensions['nVertices'])
nLevels = len(data.dimensions['nVertLevels'])
lat = data.variables['latCell'][:]*180./np.pi; #in degrees
lon = data.variables['lonCell'][:]*180./np.pi;
level = 0; time=15;
var = data.variables['theta'][time,:,level];# minVar = np.amin(var); maxVar = np.amax(var);
''' #we can triangulate the map projection coords
#although the needed connectivity information is in the mesh,
#I don't feel like accumulating the edges into triangles so we'll compute a convex hull
#that we "know" will be the same as the dual of the Voronoi
xc = data.variables['xCell'][:]; yc = data.variables['yCell'][:]; zc = data.variables['zCell'][:];
coords = np.array([xc,yc,zc]).transpose()
triang = Delaunay(coords)
'''
'''
#create the triangulation data structure since I've had issues with scipy's delaunay and such.
#We can't triangulate all of the projected x,y since opposing sides of the earth will overlay each other
tris = recoverTriangles(data, nCells); print "Triangulation finished\n"
nContours = 50
#matplotlib.pyplot.tricontour
#plt.tripcolor(x, y, tris, facecolors=zfaces, edgecolors='k')
plt.tripcolor(lon, lat, tris, var)
plt.colorbar()
plt.show()
'''
#map = Basemap(projection='ortho',lon_0=-105,lat_0=40, resolution='l')
map = Basemap(projection='ortho',lon_0=-100,lat_0=60, resolution='l')
x,y = map(lon, lat)
plt.figure(1)
map.drawcoastlines()
map.drawmapboundary()
map.pcolor(x,y,var,tri=True,shading='flat',edgecolors='none',cmap=plt.cm.jet) #cmap=plt.cm.hot_r) #,vmin=100,vmax=1000)
#map.contour(x,y,var,10,tri=True,shading='flat',edgecolors='none',cmap=plt.cm.jet)
#map.contourf(x,y,var,10,tri=True,shading='flat',edgecolors='none',cmap=plt.cm.jet)
plt.colorbar()
if(0):
plt.figure(3)
map.drawcoastlines()
map.drawmapboundary()
map.contour(x,y,var,10,tri=True,shading='flat',edgecolors='none',cmap=plt.cm.jet)
plt.colorbar()
plt.show()
'''
map = Basemap(projection='ortho',lat_0=45,lon_0=-100,resolution='l')
x, y = map(lon,lat)
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
#cs = map.contour(lon,lat, 50, nContours, latlon=True, tri=True)
#cs = map.contour(x,y, var, nContours, latlon=False, tri=True, triangulation=triang)
#cs = map.pcolor(x,y, var, tri=True,triangulation=triang)
plt.colorbar(cs)
plt.title('Test plot')
plt.show()
'''
data.close()
# xyz gets errors of 1.6e-5.
if fVersion == 3:
fac = 1.0e6
xyz = xyz / fac
val = xyz[0] * xyz[1] * xyz[2]
fac = fac * fac * fac
deriv = (xyz[1] * xyz[2] / fac, xyz[0] * xyz[2] / fac, xyz[1] * xyz[0] / fac)
return (val, deriv)
#
# sinusoids: rms errors of (dval/dxyz): 0.00063592 0.00068587 0.000641 (refLen=300km)
# 8.51791496e-05 8.49776813e-05 6.93804044e-05 (refLen=1200km)
if fVersion == 4:
refLen = 60.0e3 * 20.0
scale = 2.0 * np.pi / refLen
val = np.sin(scale * xyz[0]) + np.cos(scale * xyz[1]) + 3.0 * np.sin(scale * xyz[2])
deriv = (scale * np.cos(scale * xyz[0]), -scale * np.cos(scale * xyz[1]), scale * 3.0 * np.cos(scale * xyz[2]))
return (val, deriv)
if __name__ == "__main__":
# test out the least squares
fpath = "/arctic1/nick/cases/40962/output.40962.2006-06-01_00.00.00.nc"
data = output_data.open_netcdf_data(fpath)
nLevels = len(data.dimensions["nVertLevels"])
nCells = len(data.dimensions["nCells"])
nCellsRMS = 20
nLevelsRMS = 8
testLSTSQ(data, nCellsRMS, nLevelsRMS)
def plotTracks_dist_time():
#how to show longitude is a vexing question since it wraps -180,180 or 360,0...
rEarth = 6371. #km
fpath = '/arctic1/nick/cases/cfsr/output/x4.cfsr.output.2006-08-01_00.00.00.nc'
data = output_data.open_netcdf_data(fpath)
latCell_x4 = data.variables['latCell'][:]
lonCell_x4 = data.variables['lonCell'][:]
data.close()
fpath = '/arctic1/nick/cases/vduda/x7/x7.kf.output.2006-08-07_18.00.00.nc'
data = output_data.open_netcdf_data(fpath)
latCell_x7 = data.variables['latCell'][:]
lonCell_x7 = data.variables['lonCell'][:]
data.close()
#cfsr
cells_cfsr = cfsrStats.x4_cfsr_cell
lat_cfsr = latCell_x4[cells_cfsr]
lon_cfsr = lonCell_x4[cells_cfsr]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_cfsr)
dateList_cfsr = [ tBase + dt.timedelta(hours=12*x) for x in range(0,nSteps) ]
plt.figure()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=4))
plt.gca().xaxis.set_minor_locator(mdates.DayLocator(interval=1))
#2006-08-01
cells_x = mpasStats.x4_t_cells
lat_x = latCell_x4[cells_x]
lon_x = lonCell_x4[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'b', label='x4_t')
cells_x = mpasStats.x4_kf_cells
lat_x = latCell_x4[cells_x]
lon_x = lonCell_x4[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'g', label='x4_kf')
cells_x = mpasStats.x7_t_cells
lat_x = latCell_x7[cells_x]
lon_x = lonCell_x7[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'r.-', label='x7_t')
cells_x = mpasStats.x7_kf_cells
lat_x = latCell_x7[cells_x]
lon_x = lonCell_x7[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'k--', label='x7_kf')
#x7 2006-08-07_18
cells_x = mpasStats.x7_kf_0807_cells
lat_x = latCell_x7[cells_x]
lon_x = lonCell_x7[cells_x]
tBase = dt.datetime(2006,8,7,18)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'k--')
#x7 2006-08-03_12
cells_x = mpasStats.x7_t_0803_cells
lat_x = latCell_x7[cells_x]
lon_x = lonCell_x7[cells_x]
tBase = dt.datetime(2006,8,3,12)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'r.-')
#x4_t 2006081500
cells_x = mpasStats.x4_t_0815_cells
lat_x = latCell_x4[cells_x]
lon_x = lonCell_x4[cells_x]
tBase = dt.datetime(2006,8,15,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'b')
#all cfsr ------------------------
#cells
cells_cfsr = np.array(cfsrStats.x4_cfsr_july_cells+cfsrStats.x4_cfsr_cell+cfsrStats.x4_cfsr_0919_cells)
lat_cfsr = latCell_x4[cells_cfsr]
lon_cfsr = lonCell_x4[cells_cfsr]
tBase = dt.datetime(2006,7,24,12)
nSteps = len(cells_cfsr)-len(cfsrStats.x4_cfsr_0919_cells)
dateList_c_all = [ tBase + dt.timedelta(hours=12*x) for x in range(0,nSteps) ]
tBase = dt.datetime(2006,9,19,0)
nSteps = len(cfsrStats.x4_cfsr_0919_cells)
dateList_cfsr = dateList_c_all + [ tBase + dt.timedelta(hours=12*x) for x in range(0,nSteps) ]
#x4_t 2006072412
cells_x = mpasStats.x4_t_0724_cells
lat_x = latCell_x4[cells_x]
lon_x = lonCell_x4[cells_x]
nSteps = len(cells_x)
tBase = dt.datetime(2006,7,24,12)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'b')
#x4_t_0919_vals
cells_x = mpasStats.x4_t_0919_cells
lat_x = latCell_x4[cells_x]
lon_x = lonCell_x4[cells_x]
nSteps = len(cells_x)
tBase = dt.datetime(2006,9,19,0)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
vals = calc_distSphere_multiple(rEarth, lat_x[ind_x], lon_x[ind_x], lat_cfsr[ind_cfsr], lon_cfsr[ind_cfsr])
plt.plot(dates_common,vals, 'b')
plt.ylabel('$|\Delta x|$'+', km')
plt.gcf().autofmt_xdate()
#plotOrder = ['x4_t', 'x4_kf', 'x7_t', 'x7_kf']
#plt.legend(plotOrder)
#plt.legend(loc=3) #bottom left
plt.legend()
plt.show()
def plotTracks_lat_time():
#how to show longitude is a vexing question since it wraps -180,180 or 360,0...
fpath = '/arctic1/nick/cases/cfsr/output/x4.cfsr.output.2006-08-01_00.00.00.nc'
data = output_data.open_netcdf_data(fpath)
latCell_x4 = data.variables['latCell'][:]*180./np.pi
data.close()
fpath = '/arctic1/nick/cases/vduda/x7/x7.kf.output.2006-08-07_18.00.00.nc'
data = output_data.open_netcdf_data(fpath)
latCell_x7 = data.variables['latCell'][:]*180./np.pi
data.close()
#cfsr
cells_cfsr = cfsrStats.x4_cfsr_cell
lat_cfsr = latCell_x4[cells_cfsr]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_cfsr)
dateList_cfsr = [ tBase + dt.timedelta(hours=12*x) for x in range(0,nSteps) ]
plt.figure()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=2))
plt.gca().xaxis.set_minor_locator(mdates.DayLocator(interval=1))
#2006-08-01
cells_x = mpasStats.x4_t_cells
lat_x = latCell_x4[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'b', label='x4_t')
cells_x = mpasStats.x4_kf_cells
lat_x = latCell_x4[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'g', label='x4_kf')
cells_x = mpasStats.x7_t_cells
lat_x = latCell_x7[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'r.-', label='x7_t')
cells_x = mpasStats.x7_kf_cells
lat_x = latCell_x7[cells_x]
tBase = dt.datetime(2006,8,1,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'k--', label='x7_kf')
#x7 2006-08-07_18
cells_x = mpasStats.x7_kf_0807_cells
lat_x = latCell_x7[cells_x]
tBase = dt.datetime(2006,8,7,18)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'k--')
#x7 2006-08-03_12
cells_x = mpasStats.x7_t_0803_cells
lat_x = latCell_x7[cells_x]
tBase = dt.datetime(2006,8,3,12)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'r.-')
#x4_t 2006081500
cells_x = mpasStats.x4_t_0815_cells
lat_x = latCell_x4[cells_x]
tBase = dt.datetime(2006,8,15,0)
nSteps = len(cells_x)
dateList_x = [ tBase + dt.timedelta(hours=6*x) for x in range(0,nSteps) ]
ind_cfsr, ind_x, dates_common = get_commonIndices_time(dateList_cfsr, dateList_x)
plt.plot(dates_common,lat_x[ind_x]-lat_cfsr[ind_cfsr], 'b')
plt.ylabel('$\Delta\phi_{min}$'+', ' +'$\deg$')
plt.gcf().autofmt_xdate()
#plotOrder = ['x4_t', 'x4_kf', 'x7_t', 'x7_kf']
#plt.legend(plotOrder)
plt.legend(loc=3)
plt.show()
