def convert_to_netcdf(datadir):
"""
Convert any files we find datadir to netCDF
"""
# Change directory
os.chdir( datadir )
# Look for any MMOUT and NCOUT files
files = glob.glob('NCOUT_DOMAIN1_[0-9][0-9][0-9]')
files = files + glob.glob('MMOUTP_DOMAIN1_[0-9][0-9][0-9]')
files.sort()
# Loop over the files
for file in files:
# Skip the initial output files, we don't care about these
if file == "NCOUT_DOMAIN1_000" or file == "MMOUTP_DOMAIN1_000":
continue
if os.path.isfile( file +".nc"):
continue
# Figure out how many timesteps there are.
mm5 = mm5_class.mm5(file)
cmd = "archiver %s 0 %s" % (file, mm5.tsteps)
print "Converting %s to NetCDF %s tsteps" % (file, mm5.tsteps)
si,so = os.popen4( cmd )
a = so.read() # Necessary to keep things blocking?
if not os.path.isfile( file+".nc" ):
print "FAIL!", file
print a
sys.exit()
# Now we corrupt the grid, shift 30 degrees west
nc = netCDF3.Dataset( file+".nc", 'a')
nc.variables['coarse_cenlon'][:] = 138.
nc.variables['stdlon'][:] = 138.
nc.close()
def process_NCOUT(datadir):
# Change directory
os.chdir( datadir )
# Look for any MMOUT and NCOUT files
files = glob.glob('NCOUT_DOMAIN1_[0-9][0-9][0-9][0-9]')
files.sort()
# Loop over the files
for file in files:
# Skip the initial output files, we don't care about these
if file == "NCOUT_DOMAIN1_000":
continue
mm5 = mm5_class.mm5(file)
cmd = "archiver %s 0 %s" % (file, mm5.tsteps)
print "Converting %s to NetCDF %s tsteps" % (file, mm5.tsteps)
si,so = os.popen4( cmd )
a = so.read() # Necessary to keep things blocking?
def extract_times(mm5file):
"""
Function to return an array of mx.DateTime instances for the timestamps
in this MM5 file
"""
mm5 = mm5_class.mm5(mm5file)
# Sample for first timestamp with variable u
# Requires a modification to mm5_class.py to read header
tstr = mm5.get_field_list()['u']['time'][:13]
ts0 = mx.DateTime.strptime(tstr, "%Y-%m-%d_%H")
interval = mm5.timeincrement
ts1 = ts0 + mx.DateTime.RelativeDateTime(
seconds=(interval * (mm5.tsteps-1) ))
taxis = []
now = ts0
while (now <= ts1):
taxis.append( now )
now += mx.DateTime.RelativeDateTime(seconds=interval)
del(mm5)
return taxis
def extract_times(mm5file):
"""
Function to return an array of mx.DateTime instances for the timestamps
in this MM5 file
"""
mm5 = mm5_class.mm5(mm5file)
# Sample for first timestamp with variable u
# Requires a modification to mm5_class.py to read header
tstr = mm5.get_field_list()['u']['time'][:13]
ts0 = mx.DateTime.strptime(tstr, "%Y-%m-%d_%H")
interval = mm5.timeincrement
ts1 = ts0 + mx.DateTime.RelativeDateTime(
seconds=(interval * (mm5.tsteps-1) ))
taxis = []
now = ts0
while (now <= ts1):
taxis.append( now )
now += mx.DateTime.RelativeDateTime(seconds=interval)
del(mm5)
return taxis
def convert_to_netcdf_step():
"""
Conversion of MM5 Format files to NetCDF
"""
# Change directory
os.chdir( DATADIR )
# Look for any MMOUTP and NCOUT files
files = glob.glob("NCOUT_DOMAIN1_??")
files = files + glob.glob("MMOUTP_DOMAIN1_??")
files.sort()
# Loop over the files
for file in files:
if file == "NCOUT_DOMAIN1_00" or file == "MMOUTP_DOMAIN1_00":
continue
# Figure out how many timesteps there are.
mm5 = mm5_class.mm5(file)
cmd = "archiver %s 0 %s" % (file, mm5.tsteps)
print "Converting %s to NetCDF %s tsteps" % (file, mm5.tsteps)
si,so = os.popen4( cmd )
a = so.read() # Necessary to keep things blocking?
if not os.path.isfile( file+".nc" ):
print "FAIL!", file
print a
sys.exit()
lat[:] = nc2.variables['latitcrs'][15:-15,15:-15]
lon[:] = nc2.variables['longicrs'][15:-15,15:-15] + 360.0
xc[:] = numpy.arange(15,139) * nc2.variables['grid_ds'][:] * 1000.0
yc[:] = numpy.arange(15,114) * nc2.variables['grid_ds'][:] * 1000.0
p.standard_parallel = [nc2.variables['stdlat_2'][:], nc2.variables['stdlat_1'][:]]
p.longitude_of_central_meridian = nc2.variables['coarse_cenlon'][:]
p.latitude_of_projection_origin = nc2.variables['coarse_cenlat'][:]
lookup = {}
table = open('VEGPARM.TBL', 'r').readlines()
for line in table[3:30]:
tokens = line.split(',')
lookup[ int(tokens[0])] = float(tokens[4])
mm5 = mm5_class.mm5('MMINPUT_DOMAIN1.196801')
data = mm5.get_field('land_use', 0)['values']
data = numpy.array(data[0])
lookup2 = {0: 0, 1: 0.1, 2: 0.4, 3: 1.0, 4: 2.0}
shp = numpy.shape(data)
sm = numpy.zeros( shp, 'f')
for i in range(shp[0]):
for j in range(shp[1]):
# 1.8 meter soil depth x SMC
sm[i,j] = lookup2[ lookup[ data[i,j] ] ]
v[:] = sm[15:-16,15:-16]
nc2.close()
def process_MMOUT(datadir):
os.chdir(datadir)
# Figure out a list of MMOUT files
files = glob.glob("MMOUT_DOMAIN1_????")
files.sort()
# Move us to interpb
for file in files:
# We don't wish to convert this file, it is just along for the ride
if file == "MMOUT_DOMAIN1_000":
continue
os.chdir("/mnt/howard/narccap/INTERPB")
# Figure out time axis
taxis = lib.extract_times(datadir+file)
# Setup variable substitution values
vars = {}
vars['mm5file'] = datadir+file
vars['syear'] = taxis[0].year
vars['smonth'] = taxis[0].month
vars['sday'] = taxis[0].day
vars['shour'] = taxis[0].hour
if taxis[0].day == 21:
taxis[-1] = taxis[0] + mx.DateTime.RelativeDateTime(months=1,day=1)
vars['eyear'] = taxis[-1].year
vars['emonth'] = taxis[-1].month
vars['eday'] = taxis[-1].day
vars['ehour'] = taxis[-1].hour
# Edit the namelist.input for interb
data = open('namelist.tpl', 'r').read()
out = open('namelist.input', 'w')
out.write( data % vars )
out.close()
# Run interb for each file
print "Running INTERPB for %s [%s - %s]" % (file,
taxis[0].strftime("%Y-%m-%d %H"),
taxis[-1].strftime("%Y-%m-%d %H"))
os.system("./interpb >& interpb.log")
# Move output file to right location
os.rename("MMOUTP_DOMAIN1", datadir + file.replace("UT", "UTP"))
# Cleanup
os.system("rm -rf FILE_*")
# Gzip
os.chdir( datadir )
#os.system("gzip %s" % (file,))
# Convert to NetCDF
file = file.replace("UT", "UTP")
mm5 = mm5_class.mm5(file)
cmd = "archiver %s 0 %s" % (file, mm5.tsteps)
print "Converting %s to NetCDF %s tsteps" % (file, mm5.tsteps)
si,so = os.popen4( cmd )
a = so.read() # Necessary to keep things blocking?
# Remove MMOUTP files
if file[:6] == "MMOUTP":
os.remove(file)
import pygrib
import Ngl
import random
import sys
# two levels, lat_98 , lon_98
grbs = pygrib.open('flx.ft06.2046010100.grb')
print grbs[6]['values']
print grbs[7]['values']
lats, lons = grbs[6].latlons()
lats = lats[:,0]
lons = lons[0,:]
# Our final values
emm5 = mm5_class.mm5('MMOUT_DOMAIN1_46')
edata = numpy.ravel(emm5.get_field('soil_m_1',0)["values"])
edata4 = numpy.ravel(emm5.get_field('soil_m_4',0)["values"])
elats = numpy.ravel(emm5.get_field('latitcrs',0)["values"])
elons = numpy.ravel(emm5.get_field('longicrs',0)["values"])
for i in range(len(elats)):
elats[i] += (random.random() * 0.01)
newdata = Ngl.natgrid(elons, elats, edata, lons, lats)
grbs[6]['values'] = newdata
newdata = Ngl.natgrid(elons, elats, edata4, lons, lats)
grbs[7]['values'] = newdata
o = open('flx.ft06.2046010100.grb-new', 'wb')
grbs.rewind()
for grb in grbs:
import Nio
import pygrib
import Ngl
import random
import sys
# two levels, lat_98 , lon_98
grbs = pygrib.open('flx.ft06.2046010100.grb')
print grbs[6]['values']
print grbs[7]['values']
lats, lons = grbs[6].latlons()
lats = lats[:, 0]
lons = lons[0, :]
# Our final values
emm5 = mm5_class.mm5('MMOUT_DOMAIN1_46')
edata = numpy.ravel(emm5.get_field('soil_m_1', 0)["values"])
edata4 = numpy.ravel(emm5.get_field('soil_m_4', 0)["values"])
elats = numpy.ravel(emm5.get_field('latitcrs', 0)["values"])
elons = numpy.ravel(emm5.get_field('longicrs', 0)["values"])
for i in range(len(elats)):
elats[i] += (random.random() * 0.01)
newdata = Ngl.natgrid(elons, elats, edata, lons, lats)
grbs[6]['values'] = newdata
newdata = Ngl.natgrid(elons, elats, edata4, lons, lats)
grbs[7]['values'] = newdata
o = open('flx.ft06.2046010100.grb-new', 'wb')
grbs.rewind()
for grb in grbs:
# Generate a land/sea mask for the MRED domain
import netCDF3
import mm5_class
import mx.DateTime
mm5 = mm5_class.mm5('TERRAIN_DOMAIN1')
land = mm5.get_field('landmask', 0)
# 1,143,208
data = land['values']
lats = mm5.get_field('latitdot',0)['values']
lons = mm5.get_field('longidot',0)['values']
nc = netCDF3.Dataset('LANDSEA_IMM5.nc', 'w')
nc.institution = "Iowa State University, Ames, IA, USA"
nc.source = "MM5 (2009): atmosphere: MM5v3.6.3 non-hydrostatic, split-explicit; sea ice: Noah; land: Noah"
nc.project_id = "MRED"
nc.table_id = "Table 2"
nc.realization = 1
nc.forcing_data = "CFS01"
# Optional
nc.Conventions = 'CF-1.0'
nc.contact = "Daryl Herzmann, [email protected], 515-294-5978"
nc.history = "%s Generated" % (mx.DateTime.now().strftime("%d %B %Y"),)
nc.comment = "Runs processed on derecho@ISU, output processed on mred@ISU"
nc.title = "ISU MM5 model output prepared for MRED using CFS input"
nc.createDimension('y', 143)
nc.createDimension('x', 208)
import mm5_class
import mx.DateTime
import os
for i in range(1000):
fp = '../testdata/MMOUT_DOMAIN1_%03i' % (i,)
if not os.path.isfile( fp ):
continue
mm5 = mm5_class.mm5( fp )
tdict = mm5.reftime
ts = mx.DateTime.DateTime(tdict['year'], tdict['month'],
tdict['day'], tdict['hour'])
incsec = mm5.timeincrement
steps = mm5.tsteps
endminute = mm5.mm5_header[('bhr', 1, 12)][1]
tsend = ts + mx.DateTime.RelativeDateTime(minutes=endminute)
print fp, tsend, endminute
del( mm5 )