def get3ddata(confM2R, myvar, year, month, day, timecounter):
varN=confM2R.globalvarnames.index(myvar)
# The variable splitExtract is defined in IOsubset.py and depends on the orientation
# and indatatype of grid (-180-180 or 0-360). Assumes regular grid.
if confM2R.useesmf:
filename = fc.getFilename(confM2R,year,month,confM2R.inputdatavarnames[varN])
print(filename,confM2R.inputdatavarnames[varN])
try:
cdf = Dataset(filename)
except:
print("Unable to open input file {}".format(filename))
return
if confM2R.indatatype == "SODA":
data = cdf.variables[confM2R.inputdatavarnames[varN]][0,:,:,:]
if confM2R.indatatype == "SODA3":
data = cdf.variables[confM2R.inputdatavarnames[varN]][month-1,:,:,:]
if confM2R.indatatype == "SODAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][:, :, :]
if confM2R.indatatype == "WOAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][month-1,:,:,:]
if confM2R.indatatype == "NORESM":
# For NorESM data - all data is in one big file so we need the timecounter to access correct data
myunits = cdf.variables[str(confM2R.inputdatavarnames[varN])].units
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][timecounter,:,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if confM2R.indatatype == "GLORYS":
myunits = cdf.variables[str(confM2R.inputdatavarnames[varN])].units
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][0,:,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
cdf.close()
if myvar == 'temperature' and confM2R.indatatype in ["NS8KMZ", "GLORYS", "NORESM"]:
if myunits == "degree_Kelvin" or myunits == "K":
if confM2R.indatatype in ["GLORYS"]:
data = np.where(data <= -32.767, confM2R.grdROMS.fillval, data)
data = data - 273.15
if confM2R.indatatype == "GLORYS":
data = np.where(data <= -32.767, confM2R.grdROMS.fillval, data)
data = np.ma.masked_where(data <= confM2R.grdROMS.fillval, data)
if __debug__:
print("Data range of {} just after extracting from netcdf file: {:3.3f}-{:3.3f}".format(str(confM2R.inputdatavarnames[varN]),
float(data.min()), float(data.max())))
return data
def get2ddata(confM2R, myvar, year, month, day, timecounter):
varN=confM2R.globalvarnames.index(myvar)
if confM2R.useesmf:
if not confM2R.set2DvarsToZero:
filename = fc.getFilename(confM2R,year,month,confM2R.inputdatavarnames[varN])
try:
cdf = Dataset(filename)
except:
print("Unable to open input file {}".format(filename))
return
if confM2R.indatatype == "SODA":
data = cdf.variables[confM2R.inputdatavarnames[varN]][0,:,:]
if confM2R.indatatype == "SODA3":
if myvar == 'aice':
# We only extract the first thickness concentration. Need to fix this so all 5 classes can be extracted.
# http://www.atmos.umd.edu/~ocean/index_files/soda3_readme.htm
# hi: sea ice thickness [m ice]
# mi: sea ice mass [kg/m^2]
# hs: snow thickness [m snow]
# {cn1,cn2,cn3,cn4,cn5}: sea ice concentration [0:1] in five ice thickness classes
data = cdf.variables[confM2R.inputdatavarnames[varN]][int(month-1),0,:,:]
else:
data = cdf.variables[confM2R.inputdatavarnames[varN]][int(month-1),:,:]
if confM2R.indatatype == "SODAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][:, :]
if confM2R.indatatype == "WOAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][month-1,:,:]
if (confM2R.indatatype == "NORESM" and confM2R.set2DvarsToZero is False):
# myunits = cdf.variables[str(grdROMS.varNames[varN])].units
# For NORESM data are 12 months of data stored in ice files. Use ID as month indicator to get data.
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][timecounter,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if confM2R.indatatype == "GLORYS":
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][0,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if not confM2R.set2DvarsToZero: cdf.close()
if __debug__ and not confM2R.set2DvarsToZero:
print("Data range of {} just after extracting from netcdf file: {:3.3f}-{:3.3f}".format(str(confM2R.inputdatavarnames[varN]),
float(data.min()), float(data.max())))
if confM2R.set2DvarsToZero:
return np.zeros((np.shape(confM2R.grdMODEL.lon)))
return data
def convertMODEL2ROMS(confM2R):
# First opening of input file is just for initialization of grid
filenamein = fc.getFilename(confM2R, confM2R.start_year,
confM2R.start_month, confM2R.start_day, None)
# Finalize creating the model grd object now that we know the filename for input data
confM2R.grdMODEL.opennetcdf(filenamein)
confM2R.grdMODEL.createobject(confM2R)
confM2R.grdMODEL.getdims()
# Create the ESMF weights used to do all of the horizontal interpolation
interp2D.setupESMFInterpolationWeights(confM2R)
# Now we want to subset the data to avoid storing more information than we need.
# We do this by finding the indices of maximum and minimum latitude and longitude in the matrixes
if confM2R.subsetindata:
IOsubset.findSubsetIndices(confM2R.grdMODEL,
min_lat=confM2R.subset[0],
max_lat=confM2R.subset[1],
min_lon=confM2R.subset[2],
max_lon=confM2R.subset[3])
print('==> Initializing done')
print('\n--------------------------')
print('==> Starting loop over time')
timecounter = 0
firstrun = True
for year in confM2R.years:
months = datetimeFunctions.createlistofmonths(confM2R, year)
for month in months:
days = datetimeFunctions.createlistofdays(confM2R, year, month)
print("days {}".format(days))
for day in days:
# Get the current date for given timestep
getTime(confM2R, year, month, day, timecounter)
# Each MODEL file consist only of one time step. Get the subset data selected, and
# store that time step in a new array:
if firstrun:
print(
"=> NOTE! Make sure that these two arrays are in sequential order:"
)
print("==> myvars: %s" % confM2R.inputdatavarnames)
print("==> varNames %s" % confM2R.globalvarnames)
firstrun = False
if confM2R.subsetindata:
# The first iteration we want to organize the subset indices we want to extract
# from the input data to get the interpolation correct and to function fast
IOsubset.organizeSplit(confM2R.grdMODEL,
confM2R.grdROMS)
for myvar in confM2R.globalvarnames:
if myvar in [
'temperature', 'salinity', 'uvel', 'vvel', 'O3_c',
'O3_TA', 'N1_p', 'N3_n', 'N5_s', 'O2_o'
]:
data = get3ddata(confM2R, myvar, year, month, day,
timecounter)
if myvar in [
'ssh', 'ageice', 'uice', 'vice', 'aice', 'hice',
'snow_thick'
]:
data = get2ddata(confM2R, myvar, year, month, day,
timecounter)
# Take the input data and horizontally interpolate to your grid
array1 = interp2D.dohorinterpolationregulargrid(
confM2R, data, myvar)
if myvar in [
'temperature', 'salinity', 'O3_c', 'O3_TA', 'N1_p',
'N3_n', 'N5_s', 'O2_o'
]:
STdata = verticalinterpolation(myvar, array1, array1,
confM2R.grdROMS,
confM2R.grdMODEL)
for dd in range(len(STdata[:, 0, 0])):
STdata[dd, :, :] = np.where(
confM2R.grdROMS.mask_rho == 0,
confM2R.grdROMS.fillval, STdata[dd, :, :])
STdata = np.where(
abs(STdata) > 1000, confM2R.grdROMS.fillval,
STdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar,
STdata)
if timecounter == confM2R.grdROMS.inittime and confM2R.grdROMS.write_init is True:
IOinitial.createinitfile(confM2R, timecounter,
myvar, STdata)
if myvar in [
'ssh', 'ageice', 'aice', 'hice', 'snow_thick'
]:
SSHdata = array1[0, :, :]
SSHdata = np.where(confM2R.grdROMS.mask_rho == 0,
confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(
abs(SSHdata) > 100, confM2R.grdROMS.fillval,
SSHdata)
SSHdata = np.where(
abs(SSHdata) == 0, confM2R.grdROMS.fillval,
SSHdata)
# Specific for ROMs. We set 0 where we should have fillvalue for ice otherwise ROMS blows up.
SSHdata = np.where(
abs(SSHdata) == confM2R.grdROMS.fillval, 0,
SSHdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar,
SSHdata)
if timecounter == confM2R.grdROMS.inittime:
IOinitial.createinitfile(confM2R, timecounter,
myvar, SSHdata)
# The following are special routines used to calculate the u and v velocity
# of ice based on the transport, which is divided by snow and ice thickenss
# and then multiplied by grid size in dx or dy direction (opposite of transport).
if myvar in ['uice', 'vice']:
SSHdata = array1[0, :, :]
if myvar == "uice": mymask = confM2R.grdROMS.mask_u
if myvar == "vice": mymask = confM2R.grdROMS.mask_v
SSHdata = np.where(mymask == 0,
confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(
abs(SSHdata) > 100, confM2R.grdROMS.fillval,
SSHdata)
SSHdata = np.where(
abs(SSHdata) == 0, confM2R.grdROMS.fillval,
SSHdata)
SSHdata = np.where(
abs(SSHdata) == confM2R.grdROMS.fillval, 0,
SSHdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar,
SSHdata)
if timecounter == confM2R.grdROMS.inittime:
if myvar == 'uice':
IOinitial.createinitfile(
confM2R, timecounter, myvar, SSHdata)
if myvar == 'vice':
IOinitial.createinitfile(
confM2R, timecounter, myvar, SSHdata)
if myvar == 'uvel':
array2 = array1
if myvar == 'vvel':
urot, vrot = rotate(confM2R.grdROMS, confM2R.grdMODEL,
data, array2, array1)
u, v = interpolate2uv(confM2R.grdROMS,
confM2R.grdMODEL, urot, vrot)
Udata, Vdata, UBARdata, VBARdata = verticalinterpolation(
myvar, u, v, confM2R.grdROMS, confM2R.grdMODEL)
if myvar == 'vvel':
Udata = np.where(confM2R.grdROMS.mask_u == 0,
confM2R.grdROMS.fillval, Udata)
Udata = np.where(
abs(Udata) > 1000, confM2R.grdROMS.fillval, Udata)
Vdata = np.where(confM2R.grdROMS.mask_v == 0,
confM2R.grdROMS.fillval, Vdata)
Vdata = np.where(
abs(Vdata) > 1000, confM2R.grdROMS.fillval, Vdata)
UBARdata = np.where(confM2R.grdROMS.mask_u == 0,
confM2R.grdROMS.fillval, UBARdata)
UBARdata = np.where(
abs(UBARdata) > 1000, confM2R.grdROMS.fillval,
UBARdata)
VBARdata = np.where(confM2R.grdROMS.mask_v == 0,
confM2R.grdROMS.fillval, VBARdata)
VBARdata = np.where(
abs(VBARdata) > 1000, confM2R.grdROMS.fillval,
VBARdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar,
Udata, Vdata, UBARdata, VBARdata)
if timecounter == confM2R.grdROMS.inittime:
IOinitial.createinitfile(confM2R, timecounter,
myvar, Udata, Vdata,
UBARdata, VBARdata)
timecounter += 1