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 differenceFileName(t0):
# file names are like: 'diff.2006-07-10_00.nc'
s = cfsr.form_cfsrTimeString(t0)
fname = "diff." + s + ".nc"
return fname
def example_difference_vtk():
# For this, we have mpas outputs and CFSR ICs every 6 hours.
# The files are on the same horizontal and vertical mesh
t0 = dt.datetime(2006, 7, 15, 0)
mpasFile = mpasOutputName(t0)
mpasData = netCDF4.Dataset(mpasFile, "r")
# fields: config_frames_per_outfile tells us when we need to get new file
# config_output_interval time separation between values
nTimeInds = 28
h6 = dt.timedelta(hours=6)
s = cfsr.form_cfsrTimeString(t0)
diffFile = "diff." + s + ".6hr.vtk"
fvtk = output_data.write_vtk_header_polydata(diffFile, s)
# write nodes and cells
nNodes = output_data.write_vtk_xyzNodes(fvtk, mpasData)
nCells = output_data.write_vtk_polygons(fvtk, mpasData)
nLevels = len(mpasData.dimensions["nVertLevels"])
fieldKeys = [
"uReconstructX",
"uReconstructY",
"uReconstructZ",
"uReconstructZonal",
"uReconstructMeridional",
"w",
"theta",
] # ,'qv','qc','qr']
for i in xrange(nTimeInds):
# for i in xrange(4,5,1):
t = t0 + i * h6
initFile = loc_initFile(t)
initData = netCDF4.Dataset(initFile, "r")
for key in fieldKeys:
varInit = initData.variables[key][0, :, :]
varMPAS = mpasData.variables[key][i, :, :]
varMPAS -= varInit
s = "Statistics for MPAS-Init for variable {0} at timInd {1}\n".format(key, i)
m = np.mean(varMPAS)
dev = np.std(varMPAS)
maxd = np.max(np.abs(varMPAS))
s += "{0},\t{1},\t{2}\n".format(m, dev, maxd)
print s
s = ""
# diffData.variables[key][i,:,:] = varMPAS[:,:]
# histograms
varMPAS.flatten() # else each column is separate dataset
nBins = 100
plt2.hist(varMPAS, bins=nBins, normed=False)
# save instead of displaying
figName = key + ".t" + str(i)
plt2.title(figName)
# plt2.savefig(figName+'.png')
plt2.autoscale(enable=True, axis="x", tight=True)
plt2.savefig(figName + ".png")
plt2.clf()
initData.close()
mpasData.close()
