def load(self,**kwargs):
""" Load Oscar fields for a given day"""
self._timeparams(**kwargs)
md = self.jd - pl.datestr2num('1992-10-05')
filename = os.path.join(self.datadir, "oscar_vel%i.nc" % self.yr)
if not os.path.exists(filename):
self.download(filename)
filenam2 = os.path.join(self.datadir, "oscar_vel%i.nc" % (self.yr+1))
if not os.path.exists(filenam2):
self.download(filenam2)
nc1 = netcdf_file(filename)
tvec = nc1.variables['time'][:]
t1 = int(np.nonzero((tvec<=md))[0].max())
print t1,max(tvec)
if t1<(len(tvec)-1):
nc2 = nc1
t2 = t1 +1
else:
nc2 = netcdf_file(filenam2)
t2 = 0
def readfld(ncvar):
return self.gmt.field(ncvar[t1, 0,:,:self.imt])[self.j1:self.j2,
self.i1:self.i2]
u1 = readfld(nc1.variables['u'])
v1 = readfld(nc1.variables['v'])
u2 = readfld(nc2.variables['u'])
v2 = readfld(nc2.variables['v'])
rat = float(md-tvec[t1])/float(tvec[t2]-tvec[t1])
self.u = u2*rat + u1*(1-rat)
self.v = v2*rat + v1*(1-rat)
print self.jd,md,t1,t2
def load(self, fld="nwnd", **kwargs):
"""Load field for a given julian date. Returns u,v, or nwnd(windnorm)"""
self._timeparams(**kwargs)
filename = os.path.join(self.datadir,
"uv%04i%02i%02i.nc" % (self.yr, self.mn, self.dy))
if not os.path.isfile(filename):
self.download(filename)
try:
nc = netcdf_file(filename)
except:
os.remove(filename)
self.download(filename)
try:
nc = netcdf_file(filename)
except:
filename = filename.rstrip(".nc") + "rt.nc"
if not os.path.isfile(filename):
self.download(filename)
try:
nc = netcdf_file(filename)
except TypeError:
os.remove(filename)
self.download(filename)
nc = netcdf_file(filename)
u = nc.variables['u'][:].copy()
v = nc.variables['v'][:].copy()
u[u<-999] = np.nan
v[v<-999] = np.nan
if (fld=="u") | (fld=="uvel"):
self.uvel = self.gmt.field(np.squeeze(u))
elif (fld=="v") | (fld=="vvel"):
self.vvel = self.gmt.field(np.squeeze(v))
else:
self.nwnd = self.gmt.field(np.squeeze(np.sqrt(u**2 + v**2)))
def getVarData(fn,var):
def getMembers(tfh):
names = sorted(tfh.getnames())
names.remove('.')
names.remove('./input.cfg')
names.remove('./output.log')
return [tfh.getmember(n) for n in names]
def getPassCount(fl):
fh = netcdf_file(fl)
S = fh.variables['u'].shape
fh.close()
return S[0]
tfh = tarfile.open(fn)
members = getMembers(tfh)
Nf = len(members)
Np = getPassCount(tfh.extractfile(members[0]))
data = pl.empty( (Nf,Np) )
for k in range(len(members)):
fl = tfh.extractfile(members[k])
fh = netcdf_file(fl)
data[k,:] = fh.variables[var][:,0,0]
fh.close()
tfh.close()
return data
def setup_grid(self):
"""Define lat and lon matrices for njord"""
gc = netcdf_file(self.gridfile)
self.lat = gc.variables['lat'][:].copy()
self.gmt = gmtgrid.Shift(gc.variables['lon'][:].copy())
self.lon = self.gmt.lonvec
self.llon,self.llat = np.meshgrid(self.lon,self.lat)
def __init__(self, projname, casename="", **kwargs):
super(Partsat,self).__init__(projname, casename, *kwargs)
postgresql.DB.__init__(self, projname, casename, database='partsat')
self.flddict = {'par':('L3',),'chl':('box8',)}
if projname == 'oscar':
import pysea.MODIS
self.sat = pysea.NASA.nasa(res='4km',
ijarea=(700,1700,2000,4000))
def calc_jd(ints,intstart):
return self.base_iso + float(ints)/6-1
elif projname=="casco":
self.sat = casco.Sat(res='500m')
def calc_jd(ints,intstart):
return (self.base_iso +(ints-(intstart)*10800)/150 +
intstart/8)
elif projname=="gompom":
n = netcdf_file('/Users/bror/svn/modtraj/box8_gompom.cdf')
self.gomi = n.variables['igompom'][:]
self.gomj = n.variables['jgompom'][:]
self.sati = n.variables['ibox8'][:]
self.satj = n.variables['jbox8'][:]
elif projname=="jplSCB":
from njord import mati
self.sat = mati.Cal()
elif projname=="jplNow":
from njord import mati
self.sat = mati.Cal()
def setup_grid(self):
"""Setup necessary variables for grid """
g = netcdf_file(self.gridfile)
self.llat = g.variables['lat_rho'][:].copy()
self.llon = g.variables['lon_rho'][:].copy()
self.depth = g.variables['h'][:].copy()
self.Cs_r = np.array([-0.882485522505154, -0.778777844867132,
-0.687254423585503, -0.606483342183883,
-0.535200908367393, -0.472291883107274,
-0.416772032329648, -0.367772728223386,
-0.324527359249072, -0.286359336228826,
-0.252671506867986, -0.222936813095075,
-0.196690045050832, -0.173520562714503,
-0.153065871294677, -0.135005949869352,
-0.11905824454479, -0.104973247799366,
-0.0925305948496471, -0.0815356159649889,
-0.0718162907903607, -0.0632205570267136,
-0.0556139313622304, -0.0488774054330831,
-0.042905583895255, -0.0376050354769002,
-0.0328928312128658, -0.0286952469915389,
-0.0249466101148999, -0.021588271825806,
-0.0185676897273263, -0.0158376057382559,
-0.0133553067236276, -0.0110819562325242,
-0.00898198688799211, -0.00702254392277909,
-0.00517297115481568, -0.0034043313603439,
-0.00168895354075999, 0.])
def load(self, mn=1):
""" Load mixed layer climatology for a given day"""
self.mn = mn
nc = netcdf_file(self.datadir + "/" + self.mldFile)
self.mld = gmtgrid.convert(nc.variables["mld"][self.mn - 1, self.j1 : self.j2, self.i1 : self.i2], self.gr)
self.mld[self.mld < 0] = np.nan
self.mld[self.mld > 1e4] = np.nan
def __init__(self, fname, variables=None, use_tmpfile=True):
# try to figure out what we got
#assert isinstance(fobj, file)
assert isinstance(fname, str)
if fname[:4]=='http':
# it's an OpenDAP url
self.ncf = netCDF4.Dataset(fname)
elif os.path.exists(fname):
if fnmatch(fname, '*.bz2') and use_tmpfile:
fobj = tempfile.TemporaryFile('w+b', suffix='nc')
subprocess.call(["bzcat", fname], stdout=fobj)
fobj.seek(0)
elif fnmatch(fname, '*.bz2'):
fobj = bz2.BZ2File(fname, 'rb')
else:
fobj = file(fname, 'rb')
self.ncf = netcdf_file(fobj)
#self.ncf = netCDF4.Dataset(fobj)
else:
raise IOError("Couldn't figure out how to open " + fname)
self.fname = fname
if variables is None:
variables = self.ncf.variables.keys()
for varname in variables:
self._get_and_scale_variable(varname)
def setup_grid(self):
gc = netcdf_file(self.gridfile)
print self.gridfile
self.llat = gc.variables['y'][:]
self.llon = gc.variables['x'][:]
self.depth = gc.variables['depth'][:].copy()
self.depth[self.depth<0] = np.nan
def get_data(file_id):
datafile = DataFile.objects.get(pk=file_id)
file = netcdf_file(os.path.join(settings.MEDIA_ROOT, settings.WAVE_WATCH_DIR, datafile.file.name))
variable_names_in_file = file.variables.keys()
print variable_names_in_file
# longs = [item for sublist in file.variables['longitude'][:1] for item in sublist]
# print "longs:"
# for each in longs:
# print each
# lats = file.variables['latitude'][:, 0]
# print "lats:"
# for each in lats:
# print each
all_day_height = file.variables['HTSGW_surface'][:, :, :]
all_day_lat = file.variables['latitude'][:, :]
all_day_long = file.variables['longitude'][:, :]
just_this_forecast_height = all_day_height[0][:1, :]
just_this_forecast_lat = all_day_lat[ :,0]
just_this_forecast_long= all_day_long[0][ :]
print "\n\n\nWAVE HEIGHTS "
for each in just_this_forecast_height:
print each
print "\n\n LATS:"
print just_this_forecast_lat
print "\n\n LONGS:"
print just_this_forecast_long
def load(self,fldname, **kwargs):
""" Load velocity fields for a given day"""
self._timeparams(**kwargs)
if fldname == "uv":
self.load('u',**kwargs)
self.load('v',**kwargs)
self.uv = np.sqrt(self.u[:,1:,:]**2 + self.v[:,:,1:]**2)
return
if self.opendap:
tpos = int(self.jd) - 714800
k1 = kwargs.get("k1", getattr(self, "k1", self.klev))
k2 = kwargs.get("k2", getattr(self, "k2", k1+1))
dapH = open_url(self.dapurl)
fld = dapH[fldname][tpos,k1:k2,self.j1:self.j2,self.i1:self.i2]
else:
filename = self.jd2filename(self.jd)
if not os.path.isfile(filename):
print "File missing"
url = urlparse.urljoin(self.dataurl, os.path.basename(filename))
self.retrive_file(url, filename)
with netcdf_file(filename) as nc:
fld = nc.variables[fldname][:].copy()
self.ssh = np.squeeze(nc.variables['zeta'][:])
self.zlev = ((self.depth + self.ssh)[np.newaxis,:,:] *
self.Cs_r[:,np.newaxis,np.newaxis])
fld[fld>9999] = np.nan
setattr(self, fldname, np.squeeze(fld))
def get_data(forecast_index, file_id):
datafile = DataFile.objects.get(pk=file_id)
file = netcdf_file(os.path.join(settings.MEDIA_ROOT, settings.WAVE_WATCH_DIR, datafile.file.name))
variable_names_in_file = file.variables.keys()
print variable_names_in_file
all_day_height = file.variables['HTSGW_surface'][:, :, :]
all_day_direction = file.variables['DIRPW_surface'][:,:,:]
all_day_lat = file.variables['latitude'][:, :]
all_day_long = file.variables['longitude'][:, :]
all_day_times = file.variables['time'][:]
#print "times: "
#for each in all_day_times:
#print each
basetime = datetime.datetime(1970,1,1,0,0,0)
# Check the first value of the forecast
forecast_zero = basetime + datetime.timedelta(all_day_times[0]/3600.0/24.0,0,0)
print(forecast_zero)
directions = all_day_direction[forecast_index, ::10, :]
directions_mod = 90.0 - directions + 180.0
index = directions_mod > 180
directions_mod[index] = directions_mod[index] - 360;
index = directions_mod < -180;
directions_mod[index] = directions_mod[index] + 360;
U = 10.*np.cos(np.deg2rad(directions_mod))
V = 10.*np.sin(np.deg2rad(directions_mod))
print "height:", all_day_height[:10, :10]
def read_woce_netcdf(fnm):
""" Read a CTD cast from a WOCE NetCDF file. """
def getvariable(nc, key):
return nc.variables[key].data.copy()
nc = netcdf_file(fnm)
coords = (getvariable(nc, "longitude")[0], getvariable(nc, "latitude")[0])
pres = getvariable(nc, "pressure")
sal = getvariable(nc, "salinity")
salqc = getvariable(nc, "salinity_QC")
sal[salqc!=2] = np.nan
temp = getvariable(nc, "temperature")
# tempqc = getvariable(nc, "temperature_QC")
# temp[tempqc!=2] = np.nan
oxy = getvariable(nc, "oxygen")
oxyqc = getvariable(nc, "oxygen_QC")
oxy[oxyqc!=2] = np.nan
date = getvariable(nc, "woce_date")
time = getvariable(nc, "woce_time")
return narwhal.CTDCast(pres, sal, temp, oxygen=oxy,
coords=coords,
properties={"woce_time":time, "woce_date":date})
def load(self,fldname, **kwargs):
""" Load velocity fields for a given day"""
if fldname == "uv":
self.load('u',jd=jd, yr=yr, mn=mn, dy=dy, hr=hr)
self.load('v',jd=jd, yr=yr, mn=mn, dy=dy, hr=hr)
self.uv = np.sqrt(self.u[:,1:,:]**2 + self.v[:,:,1:]**2)
return
self._timeparams(**kwargs)
tpos = np.nonzero(self.jdvec <= self.jd)[0].max()
vc = {'uvel': ['u', 'ecom.cdf', 9.155553e-05, 0],
'vvel': ['v', 'ecom.cdf', 9.155553e-05, 0],
'wvel': ['v', 'ecom.cdf', 6.103702e-08, 0],
'temp': ['temp', 'ecom.cdf', 0.0005340739, 12.5],
'salt': ['salt', 'ecom.cdf', 0.0006103702, 20],
'chlo': ['chl', 'bem_water.cdf', 0.001525925, 50],
'newp': ['np', 'bem_water.cdf', 0.001525925, 50],
'netp': ['pp', 'bem_water.cdf', 0.001525925, 50],
'tpoc': ['tpoc', 'bem_water.cdf', 0.01525925, 500],
}
try:
nc = netcdf_file(self.datadir + vc[fldname][1])
except KeyError:
raise KeyError, "%s is not included" % fldname
fld = nc.variables[vc[fldname][0]][tpos,:,
self.j1:self.j2,
self.i1:self.i2]
fld = (fld * vc[fldname][2] + vc[fldname][3]).astype(np.float32)
fld[:,self.landmask] = np.nan
self.__dict__[fldname] = fld
return tpos
def test_netcdf_wave_format(self):
print("Running Wave NetCDF Format Test: ")
#directory of where files will be saved at
destination_directory = os.path.join(settings.MEDIA_ROOT, settings.WAVE_WATCH_DIR)
print "Downloading File"
#file names might need to be created dynamically in the future if ftp site changes
file_name = "outer.nc"
#Connect to FTP site to get the file modification data
ftp = FTP('cil-www.oce.orst.edu')
ftp.login()
#retrieve the ftp modified datetime format
ftp_dtm = ftp.sendcmd('MDTM' + " /pub/outgoing/ww3data/" + file_name)
#convert ftp datetime format to a string datetime
modified_datetime = datetime.strptime(ftp_dtm[4:], "%Y%m%d%H%M%S").strftime("%Y-%m-%d")
#Create File Name and Download actual File into media folder
url = urljoin(settings.WAVE_WATCH_URL, file_name)
filename = "{0}_{1}_{2}.nc".format("OuterGrid", modified_datetime, uuid4())
urllib.urlretrieve(url=url, filename=os.path.join(destination_directory, filename))
datafile_read_object = netcdf_file(os.path.join(settings.MEDIA_ROOT, settings.WAVE_WATCH_DIR, filename))
print "Checking Variables: latitude, longitude, HTSGW_surface"
surface = datafile_read_object.variables['HTSGW_surface'][:, :, :]
long = datafile_read_object.variables['longitude'][:]
lat = datafile_read_object.variables['latitude'][:]
ftp.quit()
self.assertIsNotNone(surface)
self.assertIsNotNone(long)
self.assertIsNotNone(lat)
def _default_cache_entry_factory(self, key):
"""Called on a DataCache access __missing__() call.
Gets all (step, row, col) entries for the file indexed by key and reads all data
returning the entry for the requested key
Arguments:
key - (pixel_step, row, col) tuple
"""
# A cache miss will generate a file lookup, read and cache of the associated data.
path, _, _, _ = self._get_data_location(*key) # path of file containing our data
# Generate all the entries like the following for data in the file path
# {(step, row, col): [module_data object reference, 0-3], ... }
# First, enumerate data indices in current file.
indices = self._enumerate_all_data_indices_in_file(os.path.basename(path))
# OK, now read everything from the file
f = netcdf_file(path, 'r')
# buffer_ix, module_ix
for pixel_step, row, col, channel, buffer_ix, module_ix in indices:
self.module_data_cache[(pixel_step, row, col)] = \
[self._get_mode1_pixel_data(f, buffer_ix, module_ix), channel]
f.close()
return self.module_data_cache[key]
def get_searise(thklim = 0.0):
filename = inspect.getframeinfo(inspect.currentframe()).filename
home = os.path.dirname(os.path.abspath(filename))
direc = home + "/greenland/searise/Greenland_5km_dev1.2.nc"
data = netcdf_file(direc, mode = 'r')
vara = dict()
# retrieve data :
x = array(data.variables['x1'][:])
y = array(data.variables['y1'][:])
h = array(data.variables['usrf'][:][0])
adot = array(data.variables['smb'][:][0])
b = array(data.variables['topg'][:][0])
T = array(data.variables['surftemp'][:][0]) + 273.15
q_geo = array(data.variables['bheatflx'][:][0]) * 60 * 60 * 24 * 365
lat = array(data.variables['lat'][:][0])
lon = array(data.variables['lon'][:][0])
U_sar = array(data.variables['surfvelmag'][:][0])
dhdt = array(data.variables['dhdt'][:][0])
direc = home + "/greenland/searise/smooth_target.mat"
U_ob = loadmat(direc)['st']
H = h - b
h[H < thklim] = b[H < thklim] + thklim
H[H < thklim] = thklim
Tn = 41.83 - 6.309e-3*h - 0.7189*lat - 0.0672*lon + 273
# extents of domain :
east = max(x)
west = min(x)
north = max(y)
south = min(y)
#projection info :
proj = 'stere'
lat_0 = '90'
lat_ts = '71'
lon_0 = '-39'
names = ['H', 'S', 'adot', 'B', 'T', 'q_geo','U_sar', \
'U_ob', 'lat', 'lon', 'Tn','dhdt']
ftns = [H, h, adot, b, T, q_geo,U_sar, U_ob, lat, lon, Tn, dhdt]
vara['dataset'] = 'searise'
for n, f in zip(names, ftns):
vara[n] = {'map_data' : f,
'map_western_edge' : west,
'map_eastern_edge' : east,
'map_southern_edge' : south,
'map_northern_edge' : north,
'projection' : proj,
'standard lat' : lat_0,
'standard lon' : lon_0,
'lat true scale' : lat_ts}
return vara
def setup_grid(self):
"""Define lat and lon matrices for njord"""
gc = netcdf_file(self.gridfile)
self.lat = gc.variables["lat"][:].copy()
lon = gc.variables["lon"][:].copy()
lon[lon > 360] = lon[lon > 360] - 360
self.lon, self.gr = gmtgrid.config(lon, dim=0)
self.llon, self.llat = np.meshgrid(self.lon, self.lat)
def load(self, name):
if isinstance(name, basestring):
p = self._load_netcdf(netcdf_file(name, 'r'))
else:
p = self._load_netcdf(name)
self.profiles = SIUnits().apply(p)
return self
def get_currents_data(forecast_index, file_id):
datafile = DataFile.objects.get(pk=file_id)
data_file = netcdf_file(os.path.join(settings.MEDIA_ROOT, settings.NETCDF_STORAGE_DIR, datafile.file.name))
currents_u = data_file.variables['u'][forecast_index][39]
currents_v = data_file.variables['v'][forecast_index][39]
print "currents u:", 10.0*currents_u
print "\n\n\ncurrents v:", 10.0*currents_v
def get_period_data(forecast_index, file_id):
datafile = DataFile.objects.get(pk=file_id)
file = netcdf_file(os.path.join(settings.MEDIA_ROOT, settings.WAVE_WATCH_DIR, datafile.file.name))
variable_names_in_file = file.variables.keys()
print variable_names_in_file
all_day_period = file.variables['PERPW_surface'][forecast_index][:,:]
print "Period of waves, in seconds:", all_day_period
def dev_to_gmt(self):
"""
Should write grid to a NetCDF file
"""
values = np.random.rand(10, 20, 30)
grd = CartesianGrid3D(values)
fname = 'temp.grd'
if os.path.isfile(fname):
os.remove(fname)
# should create a NetCDF file
grd.to_gmt(fname)
nc = netcdf_file(fname, 'r')
for k in ['x_range', 'spacing', 'z', 'y_range',
'dimension', 'z_range']:
self.assertTrue(k in nc.variables)
# default should write values[:, :, 0]
# Note swapped ordering here
self.assertEqual(nc.variables['dimension'][0], values.shape[1])
self.assertEqual(nc.variables['dimension'][1], values.shape[0])
zz = np.reshape(nc.variables['z'][::].copy(),
(nc.variables['dimension'][1],
nc.variables['dimension'][0]))
for ix in range(values.shape[0]):
for iy in range(values.shape[1]):
self.assertEqual(zz[ix, iy], values[ix, iy, 0])
# should write values[:, iy, :]
iy0 = 3
grd.to_gmt(fname, iy=iy0)
nc = netcdf_file(fname, 'r')
self.assertEqual(nc.variables['dimension'][1], values.shape[0])
self.assertEqual(nc.variables['dimension'][0], values.shape[2])
zz = np.reshape(nc.variables['z'][::].copy(),
(nc.variables['dimension'][1],
nc.variables['dimension'][0]))
for ix in range(values.shape[0]):
for iy in range(values.shape[2]):
self.assertEqual(zz[ix, iy], values[ix, iy0, iy])
def setup_grid(self):
gc = netcdf_file(self.gridfile)
dlon, dlat = gc.variables["spacing"][:]
lon1, lon2 = gc.variables["x_range"][:]
lat1, lat2 = gc.variables["y_range"][:]
self.lon = np.arange(lon1, lon2, dlon)
self.lat = np.arange(lat1, lat2, dlat)
self.llon, self.llat = np.meshgrid(self.lon, self.lat)
def setup_grid(self):
"""Setup necessary variables for grid """
g = netcdf_file(self.gridfile, 'r')
self.llat = g.variables['TLAT'][:]
self.gmt = gmtgrid.Shift(g.variables['TLONG'][0,:].copy())
self.lon = self.gmt.lonvec
self.llon = g.variables['TLONG'][:].copy()
self.llon[self.llon>180] = self.llon[self.llon>180]-360
self.llon = self.gmt.field(self.llon)
def readResults(rootFile): # Read inverted profiles ff = io.netcdf_file(rootFile+'.inversion', 'r') synthProf = ff.variables['map'][:] ff.close() # Read inverted parameters ff = io.netcdf_file(rootFile+'.parameters', 'r') pars = ff.variables['map'][:] ff.close() # Read errors ff = io.netcdf_file(rootFile+'.errors', 'r') errors = ff.variables['map'][:] ff.close() return synthProf, pars, errors
def setup_grid(self):
print self.dataurl
if not os.path.exists(self.gridfile):
self.download(self.gridfile)
gc = netcdf_file(self.gridfile)
self.lat = gc.variables['latitude'][:]
self.gmt = gmtgrid.Shift(gc.variables['longitude'][:self.imt].copy())
self.lon = self.gmt.lonvec
self.llon,self.llat = np.meshgrid(self.lon,self.lat)
def setup_grid(self):
"""Setup necessary variables for grid """
gc = netcdf_file(self.datadir + '/land.nc')
self.lat = gc.variables['lat'][:]
self.gmt = gmtgrid.Shift(gc.variables['lon'][:].copy())
self.lon = self.gmt.lonvec
self.llon,self.llat = np.meshgrid(self.lon,self.lat)
self.landmask = self.gmt.field(
gc.variables['land'][:].copy()).astype(np.bool)
def setup_grid(self):
"""Setup necessary variables for grid """
if not os.path.isfile(self.gridfile):
self.download(self.gridfile, 'vvel')
g = netcdf_file(self.gridfile, 'r')
self.latvec = g.variables['lat'][:]
self.gmt = gmtgrid.Shift(g.variables['lon'][:].copy())
self.lonvec = self.gmt.lonvec
self.llon,self.llat = np.meshgrid(self.lonvec,self.latvec)
def load(self, fldname, **kwargs):
""" Load NCEP reanalysis fields for a given day"""
self._timeparams(**kwargs)
filename = '%s/%s.%04i.nc' % (self.datadir,self.pardict[fldname],self.yr)
if not os.path.exists(filename): self.download(filename)
nc = netcdf_file(filename)
fobj = nc.variables[self.pardict[fldname]]
fld = self.gmt.field(fobj.data * fobj.scale_factor + fobj.add_offset)
self.__dict__[fldname] = fld[self.yd-1,
self.j1:self.j2, self.i1:self.i2]
def setup_grid(self):
"""Setup necessary variables for grid """
if not os.path.isfile(self.gridfile):
self.download(self.gridfile, 'vvel')
g = netcdf_file(self.gridfile, 'r')
self.llat = g.variables['Latitude'][:]
self.gmt = gmtgrid.Shift(g.variables['Longitude'][1649,:].copy())
self.llon = self.gmt.field(g.variables['Longitude'][:].copy())
self.llon[self.llon>180] = self.llon[self.llon>180]-360
self.llon[self.llon<-180] = self.llon[self.llon<-180]+360
def to_netcdf_yr(spat_lc, map_idx, lat, lon, resin, final_landclasses, yr, model, out_file):
"""
Build a NetCDF file for each time step that contains the gridded fraction
of land cover for each land class.
:param spat_lc: An array of gridded data as fraction land cover (n_grids, n_landclasses)
:param map_idx: An array of cell index positions for spatially mapping the gridded data (n_grids, n_landclasses)
:param lat: An array of latitude values for mapping (n)
:param lon: An array of longitude values for mapping (n)
:param resin: The input spatial resolution in geographic degrees (float)
:param final_landclasses: An array of land classes (n_classes)
:param yr: The target time step (int)
:param model: The name of the model running (str)
:param out_file: A full path string of the output file with extension (str)
:return: A NetCDF classic file.
"""
# create NetCDF file
with sio.netcdf_file(out_file, 'w') as f:
# add scenario
f.history = 'test file'
# create dimensions
f.createDimension('lat', len(lat))
f.createDimension('lon', len(lon))
f.createDimension('pft', len(final_landclasses))
f.createDimension('nv', 2)
# create variables
lts = f.createVariable('lat', 'f4', ('lat',))
lns = f.createVariable('lon', 'f4', ('lon',))
lcs = f.createVariable('pft', 'i', ('pft',))
lc_frac = f.createVariable('landcoverfraction', 'f8', ('pft', 'lat', 'lon',))
# create metadata
lts.units = 'degrees_north'
lts.standard_name = 'latitude'
lns.units = 'degrees_east'
lns.standard_name = 'longitude'
lcs.description = 'Land cover class'
lc_frac.units = 'fraction'
lc_frac.scale_factor = 1.
lc_frac.add_offset = 0.
lc_frac.projection = 'WGS84'
lc_frac.description = 'Fraction land cover for {0} at {1} degree.'.format(yr, resin)
lc_frac.comment = 'See scale_factor (divide by 100 to get percentage, offset is zero)'
lc_frac.title = 'Downscaled land use projections at {0} degree, downscaled from {1}'.format(resin, model)
# assign data
lts[:] = lat
lns[:] = lon
lcs[:] = range(1, len(final_landclasses) + 1)
# set missing value to -1
lc_frac.missing_value = -1.
for pft in range(0, len(final_landclasses), 1):
# create land use matrix and populate with -1
pft_mat = np.zeros(shape=(len(lat), len(lon))) - 1
# extract base land use data for the target PFT
slh = spat_lc[:, pft]
# assign values to matrix
pft_mat[np.int_(map_idx[0, :]), np.int_(map_idx[1, :])] = slh
# set negative values to -1
pft_mat[pft_mat < 0] = -1
# assign to variable
lc_frac[pft, :, :] = pft_mat
return contours
# Define the bounds of the output grid
x_min = 171312
x_max = 633468
y_min = 5286950
y_max = 5904080
z_val = None
cl = [-100, -75, -50, -40, -30, -20, -15, -12, -9, -6, -3]
grid_spacing = 100
fid_out = "contours_depth_utm60s.vtk"
# Read the .grd file from Williams
with netcdf_file("grid_exclude_wgs84.grd") as data:
x = np.copy(data.variables["x"][:])
y = np.copy(data.variables["y"][:])
z = np.copy(data.variables["z"][:])
# Convert grid-like data to flat arrays
x, y = np.meshgrid(x, y)
x = x.flatten()
y = y.flatten()
z = z.flatten()
# Convert to UTM60S
x, y = lonlat_utm(x, y, -60, False)
# Create arrays for interpolation
points = np.vstack((x, y)).T
normed=(1016,1022),
norm_method='max')
if __name__ == "__main__":
if argv[1:] and np.all([path.isdir(dname) for dname in argv[1:]]):
files = {dir: glob(path.join(dir, '*.CDF')) for dir in argv[1:]}
elif argv[1:]:
files = {'Input': argv[1:]}
else:
files = glob('*/*.CDF')
files = {dir: glob(path.join(dir, '*.CDF'))
for dir in {path.dirname(file) for file in files}}
for day in files:
day_files = [netcdf_file(file) for file in files[day]]
sample_types = {file.experiment_title.decode().split('_r')[0]
for file in day_files}
if not day_files: continue
mpl.figure()
mpl.title(day)
color_list = {type: color for type, color in
zip(sample_types,
mpl.cm.Set3(np.linspace(0, 1, len(sample_types)))
)
}
for file in day_files:
type = file.experiment_title.decode().split('_r')[0]
plot_tic(file,
color=color_list[type],
zeroed=20,
continue
else:
st = changeidx[idx - 1]
fileptr.write(
'%d %d %s^%s-%s+%s=%s\n' %
(st * resolu, et * resolu, phonList[phoneBag[0][st]],
phonList[phoneBag[1][st]], phonList[phoneBag[2][st]],
phonList[phoneBag[3][st]], phonList[phoneBag[4][st]]))
fileptr.close()
##### data extraction #########
# read in the mean and variance
if dataMV is not None:
mv = io.netcdf_file(dataMV, 'r')
if inOutData == 1:
meanVec = mv.variables['inputMeans'][:].copy()
varVec = mv.variables['inputStdevs'][:].copy()
else:
meanVec = mv.variables['outputMeans'][:].copy()
varVec = mv.variables['outputStdevs'][:].copy()
mv.close()
else:
print('dataMV is not specified. Extracted data will be normalized data.\n')
#
dataList = os.listdir(dataDir)
for dataFile in dataList:
if re.search(dataPattern, dataFile):
print(dataFile)
def get_searise(thklim = 0.0):
s = "::: getting Searise data from DataFactory :::"
print_text(s, DataFactory.color)
global home
direc = home + "/greenland/searise/Greenland_5km_dev1.2.nc"
data = netcdf_file(direc, mode = 'r')
vara = dict()
needed_vars = {'topg' : 'B',
'usrf' : 'S',
'surftemp' : 'T',
'lat' : 'lat',
'lon' : 'lon',
'smb' : 'adot',
'bheatflx' : 'q_geo',
'dhdt' : 'dhdt',
'surfvelmag' : 'U_sar'}
s = " - data-fields collected : python dict key to access -"
print_text(s, DataFactory.color)
for v in data.variables:
try:
txt = '"' + needed_vars[v] + '"'
except KeyError:
txt = ''
print_text(' Searise : %-*s key : %s '%(30,v, txt), '230')
# retrieve data :
x = array(data.variables['x1'][:])
y = array(data.variables['y1'][:])
S = array(data.variables['usrf'][:][0])
adot = array(data.variables['smb'][:][0])
B = array(data.variables['topg'][:][0])
T = array(data.variables['surftemp'][:][0]) + 273.15
q_geo = array(data.variables['bheatflx'][:][0]) * 60 * 60 * 24 * 365
lat = array(data.variables['lat'][:][0])
lon = array(data.variables['lon'][:][0])
U_sar = array(data.variables['surfvelmag'][:][0])
dhdt = array(data.variables['dhdt'][:][0])
direc = home + "/greenland/searise/smooth_target.mat"
U_ob = loadmat(direc)['st']
H = S - B
S[H < thklim] = B[H < thklim] + thklim
Tn = 41.83 - 6.309e-3*S - 0.7189*lat - 0.0672*lon + 273
# extents of domain :
east = max(x)
west = min(x)
north = max(y)
south = min(y)
#projection info :
proj = 'stere'
lat_0 = '90'
lat_ts = '71'
lon_0 = '-39'
# create projection :
txt = " +proj=" + proj \
+ " +lat_0=" + lat_0 \
+ " +lat_ts=" + lat_ts \
+ " +lon_0=" + lon_0 \
+ " +k=1 +x_0=0 +y_0=0 +no_defs +a=6378137 +rf=298.257223563" \
+ " +towgs84=0.000,0.000,0.000 +to_meter=1"
p = Proj(txt)
# save the data in matlab format :
vara['pyproj_Proj'] = p
vara['map_western_edge'] = west
vara['map_eastern_edge'] = east
vara['map_southern_edge'] = south
vara['map_northern_edge'] = north
vara['nx'] = len(x)
vara['ny'] = len(y)
names = ['S', 'adot', 'B', 'T', 'q_geo','U_sar', \
'U_ob', 'lat', 'lon', 'Tn','dhdt']
ftns = [S, adot, B, T, q_geo,U_sar, U_ob, lat, lon, Tn, dhdt]
vara['dataset'] = 'Searise'
vara['continent'] = 'greenland'
for n, f in zip(names, ftns):
vara[n] = f
return vara
def get_ant_measures(res = 900):
s = "::: getting Antarctica measures data from DataFactory :::"
print_text(s, DataFactory.color)
global home
if res == 900:
direc = home + '/antarctica/measures/antarctica_ice_velocity_900m.nc'
elif res == 450:
direc = home + '/antarctica/measures/antarctica_ice_velocity_450m.nc'
else:
print "get_ant_measures() 'res' arg must be either 900 or 450"
exit(0)
data = netcdf_file(direc, mode = 'r')
vara = dict()
# retrieve data :
vx = array(data.variables['vx'][:])
vy = array(data.variables['vy'][:])
err = array(data.variables['err'][:])
mask = (vx != 0.0).astype('i')
names = ['vx', 'vy', 'v_err', 'mask']
ftns = [ vx, vy, err, mask ]
for n in names:
print_text(' Measures : %-*s key : "%s" '%(30,n,n), '230')
# extents of domain :
nx,ny = shape(vx)
dx = res
west = -2800000.0
east = west + nx*dx
north = 2800000.0
south = north - ny*dx
#projection info :
proj = 'stere'
lat_0 = '-90'
lat_ts = '-71'
lon_0 = '0'
# create projection :
txt = " +proj=" + proj \
+ " +lat_0=" + lat_0 \
+ " +lat_ts=" + lat_ts \
+ " +lon_0=" + lon_0 \
+ " +k=1 +x_0=0 +y_0=0 +no_defs +a=6378137 +rf=298.257223563" \
+ " +towgs84=0.000,0.000,0.000 +to_meter=1"
p = Proj(txt)
# save the data in matlab format :
vara['pyproj_Proj'] = p
vara['map_western_edge'] = west
vara['map_eastern_edge'] = east
vara['map_southern_edge'] = south
vara['map_northern_edge'] = north
vara['nx'] = nx
vara['ny'] = ny
# save the data in matlab format :
vara['dataset'] = 'measures'
vara['continent'] = 'antarctica'
for n, f in zip(names, ftns):
vara[n] = f[::-1, :]
return vara
def get_rignot():
s = "::: getting Greenland Rignot data from DataFactory :::"
print_text(s, DataFactory.color)
global home
direc = home + '/greenland/rignot/velocity_greenland_v4Aug2014.nc'
data = netcdf_file(direc, mode = 'r')
vara = dict()
needed_vars = {'vx' : 'vx',
'vy' : 'vy',
'err' : 'v_err'}
s = " - data-fields collected : python dict key to access -"
print_text(s, DataFactory.color)
for v in data.variables:
try:
txt = '"' + needed_vars[v] + '"'
except KeyError:
txt = ''
print_text(' Rignot : %-*s key : %s '%(30,v, txt), '230')
# retrieve data :
vx = array(data.variables['vx'][:])
vy = array(data.variables['vy'][:])
err = array(data.variables['err'][:])
mask = (vx != 0.0).astype('i')
# extents of domain :
ny,nx = shape(vx)
dx = 150
west = -638000.0
east = west + nx*dx
north = -657600.0
south = north - ny*dx
#projection info :
proj = 'stere'
lat_0 = '90'
lat_ts = '70'
lon_0 = '-45'
# create projection :
txt = " +proj=" + proj \
+ " +lat_0=" + lat_0 \
+ " +lat_ts=" + lat_ts \
+ " +lon_0=" + lon_0 \
+ " +k=1 +x_0=0 +y_0=0 +no_defs +a=6378137 +rf=298.257223563" \
+ " +towgs84=0.000,0.000,0.000 +to_meter=1"
p = Proj(txt)
# save the data in matlab format :
vara['pyproj_Proj'] = p
vara['map_western_edge'] = west
vara['map_eastern_edge'] = east
vara['map_southern_edge'] = south
vara['map_northern_edge'] = north
vara['nx'] = nx
vara['ny'] = ny
names = ['vx', 'vy', 'v_err', 'mask']
ftns = [ vx, vy, err, mask ]
print_text(' Rignot : %-*s key : "%s"'%(30,names[-1],names[-1]), '230')
# save the data in matlab format :
vara['dataset'] = 'Rignot'
vara['continent'] = 'greenland'
for n, f in zip(names, ftns):
vara[n] = f[::-1, :]
return vara
def setup_grid(self):
gc = netcdf_file(self.gridfile)
self.llat = gc.variables['y'][:].copy()
self.llon = gc.variables['x'][:].copy()
self.depth = gc.variables['depth'][:].copy()
self.depth[self.depth<0] = np.nan
def open(self): self.ncdf = sio.netcdf_file(self.filename, mode='r') self.opened = True
def manage_outputs(Data, Opti, Config, it):
# -- Report the full BasinSummary.txt files?
#if Config.repBS == 1:
# os.system('mv '+Config.PATH_EXEC+'/BasinSummary.txt '+Config.PATH_OUT+'/BasinSummary_run'+str(it+1)+'.txt')
# -- Group the output files in one across simulations,
# separating by observations points and veg type where it applies
for oname in Data.names:
if (Data.obs[oname]['type'] != 'map'
or Data.obs[oname]['type'] != 'mapTs') and (it == 0 or
Opti.begfail == 1):
# Historic time series file names
if Config.restart == 0:
Data.obs[oname][
'sim_hist'] = Config.PATH_OUT + '/' + oname + '_all.tab'
# Header of files
with open(Data.obs[oname]['sim_hist'], 'w') as f_out:
f_out.write('Sample,' +
','.join([str(i + 1)
for i in range(Config.trimL)]) + '\n')
# Reinit begfail (otherwise will never write all!)
Opti.begfail = 0
# Save current run outputs (and delete the file to relieve Maxwell...)
for oname in Data.names:
#print oname,
# Integrated variables (in BasinSummary.txt)
if Data.obs[oname]['type'] == 'Total':
idx = Data.obs[oname]['sim_pts'] - 1
tmp = np.genfromtxt(Data.obs[oname]['sim_file'],
delimiter='\t',
skip_header=1,
unpack=True)[idx] * Data.obs[oname]['conv']
# Shave off the transient part (if any)
if Config.trimB > 1:
tmp = tmp[Config.trimB - 1:Config.trimB - 1 + Config.trimL]
if len(tmp) != Config.trimL:
sys.exit("ERROR -> Problem with output trim: we've got " +
str(len(tmp)) + ' instead of ' +
str(Config.trimL))
with open(Data.obs[oname]['sim_hist'], 'a') as f_out:
f_out.write(
str(it + 1) + ',' + ','.join([str(j)
for j in list(tmp)]) + '\n')
# Time series
if Data.obs[oname]['type'] == 'Ts':
print(oname)
hskip = Data.nts + 3
idx = np.argsort(np.array(
Data.sim_order))[Data.obs[oname]['sim_pts'] - 1] + 1
tmp = np.genfromtxt(Data.obs[oname]['sim_file'],
delimiter='\t',
skip_header=hskip,
unpack=True)[idx] * Data.obs[oname]['conv']
# Shave off the transient part (if any)
if Config.trimB > 1:
tmp = tmp[Config.trimB - 1:Config.trimB - 1 + Config.trimL]
with open(Data.obs[oname]['sim_hist'], 'a') as f_out:
f_out.write(
str(it + 1) + ',' + ','.join([str(j)
for j in list(tmp)]) + '\n')
# Fixed-value (initial-value) maps ---------------------------------------------------------
if Data.obs[oname]['type'] == 'map':
# Missing vaue for PCraster to numpy conversion
MV = -9999.
f_m = Config.PATH_EXEC + '/' + Data.obs[oname]['sim_file'] + '.map'
if (len(f_m) == 0):
print("Warning: the variable " + oname +
" seems to be missing from the EcH2O outputs...")
continue
# Now that we have what we need, read the PCraster map...
var_val = pcr2numpy(readmap(f_m), MV)
# Write output NCDF file
ncFile = Config.PATH_OUT + '/' + oname + '_all.nc'
# -open nc dataset
# If first run, create file
if (it == 0):
ncFile = Config.PATH_OUT + '/' + oname + '_all.nc'
rootgrp = spio.netcdf_file(ncFile, 'w')
rootgrp.createDimension('time', 0)
var_y = pcr2numpy(ycoordinate(Config.cloneMap), MV)[:, 1]
var_x = pcr2numpy(xcoordinate(Config.cloneMap), MV)[1, :]
rootgrp.createDimension('latitude', len(var_y))
rootgrp.createDimension('longitude', len(var_x))
rootgrp.createDimension('ensemble', Config.nEns)
lat = rootgrp.createVariable('latitude', 'f4', ('latitude', ))
lat.standard_name = 'Latitude'
lat.long_name = 'Latitude cell centres'
lon = rootgrp.createVariable('longitude', 'f4',
('longitude', ))
lon.standard_name = 'Longitude'
lon.long_name = 'Longitude cell centres'
ens = rootgrp.createVariable('ensemble', 'i', ('ensemble', ))
ens.standard_name = 'Ensemble'
ens.long_name = 'Ensembles of runs'
# -assign lat and lon to variables
lat[:] = var_y
lon[:] = var_x
ens[:] = np.arange(Config.nEns) + 1
# -set netCDF attribute
rootgrp.title = 'Maps of ' + oname
rootgrp.institution = 'NRI, University of Aberdeen'
rootgrp.author = 'A. Neill'
rootgrp.history = 'Created on %s' % (datetime.now())
varStructure = ('latitude', 'longitude', 'ensemble')
ncVariable = rootgrp.createVariable(oname, 'f4', varStructure)
ncVariable.standard_name = oname
# -write to file
rootgrp.sync()
rootgrp.close()
# Write the actual values for this run
rootgrp = spio.netcdf_file(ncFile, 'a')
# - write data
ncVariable = rootgrp.variables[oname]
ncVariable[:, :, it] = var_val
# -update file and close
rootgrp.sync()
rootgrp.close()
# Time-varying maps ------------------------------------------------------------------------
if Data.obs[oname]['type'] == 'mapTs':
#print oname
# Missing vaue for PCraster to numpy conversion
MV = -9999.
lensuf = 8 - len(Data.obs[oname]['sim_file'])
#print lensuf
MapNames = []
itOK = []
for it2 in range(1, Data.lsim + 1):
# Only save files beyond the spinup/transient period (if any)
if it2 > Config.spinup and it2 >= Config.trimB and it2 < Config.trimB + Config.trimL:
suf = ''.join(list(np.repeat('0', lensuf))) + '.' + format(
it2, '03')
suf2 = format(it2, '04')
# Sometimes, an output has _ as final character, which is replaced with number for sim > 1000
if lensuf == 0 and Data.obs[oname]['sim_file'][-1] == "_":
if it2 >= 1000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '1.' + format(
it2 - 1000, '03')
if it2 >= 2000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '2.' + format(
it2 - 2000, '03')
if it2 >= 3000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '3.' + format(
it2 - 3000, '03')
if it2 >= 4000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '4.' + format(
it2 - 4000, '03')
if it2 >= 5000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '5.' + format(
it2 - 5000, '03')
if it2 >= 6000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '6.' + format(
it2 - 6000, '03')
if it2 >= 7000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '7.' + format(
it2 - 7000, '03')
if it2 >= 8000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '8.' + format(
it2 - 8000, '03')
if it2 >= 9000:
suf = ''.join(list(np.repeat(
'0', lensuf))) + '9.' + format(
it2 - 9000, '03')
else:
if it2 >= 1000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '1.' + format(
it2 - 1000, '03')
if it2 >= 2000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '2.' + format(
it2 - 2000, '03')
if it2 >= 3000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '3.' + format(
it2 - 3000, '03')
if it2 >= 4000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '4.' + format(
it2 - 4000, '03')
if it2 >= 5000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '5.' + format(
it2 - 5000, '03')
if it2 >= 6000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '6.' + format(
it2 - 6000, '03')
if it2 >= 7000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '7.' + format(
it2 - 7000, '03')
if it2 >= 8000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '8.' + format(
it2 - 8000, '03')
if it2 >= 9000:
suf = ''.join(list(np.repeat(
'0', lensuf - 1))) + '9.' + format(
it2 - 9000, '03')
# Store names and it2 index
# If the "_" is replaced when sim > 1000, need to remove and replace with suf
if lensuf == 0 and Data.obs[oname]['sim_file'][
-1] == "_" and it2 >= 1000:
file2Read = Data.obs[oname]['sim_file'][:-1]
f_m = Config.PATH_EXEC + '/' + file2Read + suf
else:
f_m = Config.PATH_EXEC + '/' + Data.obs[oname][
'sim_file'] + suf
if len(glob.glob(f_m)) == 0:
continue
else:
MapNames += [f_m]
itOK += [it2]
print(f_m)
# Time values for netCDF output
var_t = np.array([(Config.treal[x - Config.trimB] -
datetime(1901, 1, 1, 0, 0)).days for x in itOK])
if (len(var_t) == 0):
print("Warning: the variable " + oname +
" seems to be missing from the EcH2O outputs...")
continue
# Second now that we have what we need...
for it2 in range(len(itOK)):
# Read map at first time step of interest, convert to array using a missing value,
# and add an extra 3rd dimension (empty) for later appending
if (it2 == 0):
var_val = pcr2numpy(readmap(MapNames[it2]), MV)[None, ...]
# Read subsequent map, same procedure and then append
else:
var_val = np.append(var_val,
pcr2numpy(readmap(MapNames[it2]),
MV)[None, ...],
axis=0)
# Write output NCDF file
ncFile = Config.PATH_OUT + '/' + oname + '_all.nc'
# -open nc dataset
# If first run, create file
if (it == 0):
ncFile = Config.PATH_OUT + '/' + oname + '_all.nc'
rootgrp = spio.netcdf_file(ncFile, 'w')
rootgrp.createDimension('time', 0)
var_y = pcr2numpy(ycoordinate(Config.cloneMap), MV)[:, 1]
var_x = pcr2numpy(xcoordinate(Config.cloneMap), MV)[1, :]
rootgrp.createDimension('latitude', len(var_y))
rootgrp.createDimension('longitude', len(var_x))
if Config.mode == 'forward_runs':
rootgrp.createDimension('ensemble', Config.nEns)
elif Config.mode == 'calib_runs':
rootgrp.createDimension('ensemble', Opti.nit)
date_time = rootgrp.createVariable('time', 'f8', ('time', ))
date_time.standard_name = 'time'
date_time.long_name = 'Days since 1901-01-01 00:00:00.0'
date_time.units = 'Days since 1901-01-01 00:00:00.0'
date_time.calendar = 'gregorian'
lat = rootgrp.createVariable('latitude', 'f4', ('latitude', ))
lat.standard_name = 'Latitude'
lat.long_name = 'Latitude cell centres'
lon = rootgrp.createVariable('longitude', 'f4',
('longitude', ))
lon.standard_name = 'Longitude'
lon.long_name = 'Longitude cell centres'
ens = rootgrp.createVariable('ensemble', 'i', ('ensemble', ))
ens.standard_name = 'Ensemble'
ens.long_name = 'Ensembles of runs'
# -assign lat, lon and t to variables
lat[:] = var_y
lon[:] = var_x
date_time[:] = var_t
if Config.mode == 'forward_runs':
ens[:] = np.arange(Config.nEns) + 1
elif Config.mode == 'calib_runs':
ens[:] = np.arange(Opti.nit) + 1
#print 'var_x'
#print var_x
#print 'var_y'
#print var_y
#print 'var_t'
#print var_t
# -set netCDF attribute
rootgrp.title = 'Maps of ' + oname
rootgrp.institution = 'NRI, University of Aberdeen'
rootgrp.author = 'A. Neill'
rootgrp.history = 'Created on %s' % (datetime.now())
varStructure = ('time', 'latitude', 'longitude', 'ensemble')
ncVariable = rootgrp.createVariable(oname, 'f4', varStructure)
ncVariable.standard_name = oname
# -write to file
rootgrp.sync()
rootgrp.close()
# Write the actual values for this run
rootgrp = spio.netcdf_file(ncFile, 'a')
# - write data
ncVariable = rootgrp.variables[oname]
ncVariable[:, :, :, it] = var_val
# -update file and close
rootgrp.sync()
rootgrp.close()
yearinsec=3600*365*24.
#path where to store output
outputpath='/work/mh0033/m300411/DataEB/RESULTS/PAPER/Prelim2/'
################## READ IN ARCTIC OCEAN AREA (computed in AOmask.sh) ###################################
d["AOarea_ocean_mod"]=np.zeros((len(model_list)+1))
for i,mod in enumerate(model_list):
print mod,'AO area'
inputpath_time='/work/mh0033/m300411/DataEB/WORK_DATA/'
file0=glob.glob(inputpath_time+'AOarea2/Arctic_ocean_totalarea_%s.nc' %mod)
fid0=sio.netcdf_file(file0[0])
d["AOarea_ocean_mod"][i]=fid0.variables['tos'][0]
d["AOarea_ocean_mod"][len(model_list)]=np.nanmean(d["AOarea_ocean_mod"][0:len(model_list)])
###############################################################################################################
#############################################################################################################
#Define axis labels and titles for each variable
tit={}
lab={}
tit['rsds']='ISW'
lab['rsds']='Incoming SW [W/m$^2$]'
tit['rsus']='OSW'
lab['rsus']='Outgoing SW [W/m$^2$]'
def read_csv(fname):
with sio.netcdf_file(fname, "r") as f:
tt = np.array([
f.variables['x_ret'][0], f.variables['x_ret_err'][0],
f.variables['x_err_res'][0]
])
fi = np.array([
f.variables['x_ret'][1], f.variables['x_ret_err'][1],
f.variables['x_err_res'][1]
])
rl = np.array([
f.variables['x_ret'][2], f.variables['x_ret_err'][2],
f.variables['x_err_res'][2]
])
ri = np.array([
f.variables['x_ret'][3], f.variables['x_ret_err'][3],
f.variables['x_err_res'][3]
])
lwp = np.array([
f.variables['wp_ret'][0], f.variables['wp_ret_err'][0],
f.variables['wp_err_res'][0]
])
iwp = np.array([
f.variables['wp_ret'][1], f.variables['wp_ret_err'][1],
f.variables['wp_err_res'][1]
])
twp = np.array([
f.variables['wp_ret'][2], f.variables['wp_ret_err'][2],
f.variables['wp_err_res'][2]
])
ctemp = np.array(f.variables["av_ctemp"][:])
res = f.variables['residuum'][:].copy()
wn = f.variables['wavenumber'][:].copy()
rms = np.sqrt(np.mean(np.array(res)**2))
conv = f.variables['conv'][:].copy()
pwv = f.variables['pwv'][:].copy()
cloud = np.array([
np.float_(f.variables['cloud_base'][:]),
np.float_(f.variables['cloud_top'][:])
])
x_a = np.array(f.variables['x_a'][:].copy())
x_a_err = np.array(f.variables['x_a_err'][:].copy())
pos = [0.0, 0.0]
pos[0] = f.variables['lat'][0].copy()
pos[1] = f.variables['lon'][0].copy()
print("Results:")
print("Filename: {}".format(fname))
print("Average Cloud temperature (K): {}\n".format(ctemp[0]))
print("Cloud base height (m): {}".format(cloud[0]))
print("Cloud top height (m): {}\n".format(cloud[1]))
print("Converged: {}\n".format(conv))
print("DIRECT PRODUCTS")
print("Liquid Optical Depth (1): ({} +- {})".format(
np.float_(tt[0]), np.float_(tt[1])))
print("Ice Optical Depth (1): ({} +- {})".format(
np.float_(fi[0]), np.float_(fi[1])))
print("Liquid Radius (um): ({} +- {})".format(np.float_(rl[0]),
np.float_(rl[1])))
print("Ice Radius (um): ({} +- {})".format(np.float_(ri[0]),
np.float_(ri[1])))
print("Root-Mean-Squared Error (mW/sr (cm-1) m2): {}\n".format(rms))
print("DERIVED PRODUCTS")
print("Liquid Water Path (g/m2): ({} +- {})".format(
np.float_(lwp[0]), np.float_(lwp[1])))
print("Ice Water Path (g/m2): ({} +- {})".format(
np.float_(iwp[0]), np.float_(iwp[1])))
print("Total Water Path (g/m2): ({} +- {})".format(
np.float_(twp[0]), np.float_(twp[1])))
return [
tt, fi, rl, ri, lwp, iwp, twp, rms, ctemp, pwv, conv, cloud, pos, x_a,
x_a_err, wn, res
]
## Created By: Douglas Finch
## Python 2.7
## Take daily CH4 fluxes and make monthly mean netcdf
## Also regrid to 4x5
##==============================================================================
import numpy as np
import scipy.io as io
from datetime import datetime
import calendar
import datetime as dt
##==============================================================================
dirc = '/home/dfinch/Documents/CH4/emissions/regridded_emissions/'
orig_file = '%sGlobal_CH4_flux_daily_05x05_TOTAL.nc' % dirc
new_file = '%sGlobal_CH4_flux_monthly_4x5_TOTAL.nc' % dirc
open_f = io.netcdf_file(orig_file)
lat = open_f.variables['latitude'].data
lon = open_f.variables['longitude'].data
flux = open_f.variables['CH4_Flux'].data
month_grid = np.zeros[12, flux.shape[1], flux.shape[2]]
total_days = 0
for m in range(12):
month_len = calendar.monthrange(2013, m + 1)[1]
month_grid[m, :, :] = np.mean(flux[total_days:total_days +
month_len, :, :],
axis=0)
new_lat = np.arange(-90, 94, 4)
new_lat[0] = -88.
new_lat[-1] = 88
def to_netcdf_lc(spat_lc, lat, lon, resin, final_landclasses, years, step, model, out_dir):
"""
Build a NetCDF file for each land class that contains the gridded fraction
of land cover of that land class over all simulation years.
:param spat_lc: A 3D array representing fraction of land cover (lat, lon, fraction landclass)
:param lat: An array of latitude values for mapping (n)
:param lon: An array of longitude values for mapping (n)
:param resin: The input spatial resolution in geographic degrees (float)
:param final_landclasses: An array of land classes (n_classes)
:param years: A list of output years (int)
:param step: The current time step (int)
:param model: The name of the model running (str)
:param out_dir: A full path string of the output directory (str)
:return: A NetCDF classic file.
"""
temp_file_prefix = 'tmp_lc_'
out_file_prefix = 'lc_yearly_'
# just save yearly data until the final year
if step != years[-1]:
np.save('{0}/{1}{2}'.format(out_dir, temp_file_prefix, step), spat_lc)
return
# at the final year, gather data from all temporary files into one 4D array
# with dimensions (lat, lon, year, landclass)
tmp_files = ['{0}/{1}'.format(out_dir, f) for f in os.listdir(out_dir) if 'tmp_lc_' in f]
lc_yearly = [np.load(f) for f in tmp_files]
lc_yearly = np.stack(lc_yearly + [spat_lc], 2)
# set negative values to -1
lc_yearly[lc_yearly < 0] = -1
# remove temporary files
for tf in tmp_files:
os.remove(tf)
# output NetCDF file for each land class over all years
for lc_index, lc in enumerate(final_landclasses):
out_fname = '{0}/{1}{2}.nc'.format(out_dir, out_file_prefix, lc)
# create NetCDF file
with sio.netcdf_file(out_fname, 'w') as f:
# create dimensions
f.createDimension('lat', len(lat))
f.createDimension('lon', len(lon))
f.createDimension('time', len(years))
# create variables
lts = f.createVariable('lat', 'f4', ('lat',))
lns = f.createVariable('lon', 'f4', ('lon',))
times = f.createVariable('time', 'i4', ('time',))
lc_frac = f.createVariable('landcoverfraction', 'f8', ('lat', 'lon', 'time'))
# create metadata
lts.units = 'degrees_north'
lts.standard_name = 'latitude'
lns.units = 'degrees_east'
lns.standard_name = 'longitude'
times.description = 'years'
lc_frac.units = 'fraction'
lc_frac.scale_factor = 1.
lc_frac.add_offset = 0.
lc_frac.projection = 'WGS84'
lc_frac.description = 'Fraction land cover for {0} at {1} degree.'.format(lc, resin)
lc_frac.comment = 'See scale_factor (divide by 100 to get percentage, offset is zero)'
lc_frac.title = 'Downscaled land use projections at {0} degree, downscaled from {1}'.format(resin, model)
lc_frac.missing_value = -1.
# Add data to netcdf object
lts[:] = lat
lns[:] = lon
times[:] = years
lc_frac[:] = lc_yearly[:, :, :, lc_index]
def get_variables(filename):
open_file = io.netcdf_file(filename)
return open_file
def setup_grid(self):
gc = netcdf_file(self.gridfile)
def get_bedmap1(thklim = 0.0):
s = "::: getting Bedmap 1 data from DataFactory :::"
print_text(s, DataFactory.color)
global home
direc = home + '/antarctica/bedmap1/ALBMAPv1.nc'
data = netcdf_file(direc, mode = 'r')
vara = dict()
needed_vars = {'lsrf' : 'B',
'usrf' : 'S',
'temp' : 'T',
'acca' : 'acca',
'accr' : 'accr',
'ghffm' : 'ghffm',
'ghfsr' : 'ghfsr'}
s = " - data-fields collected : python dict key to access -"
print_text(s, DataFactory.color)
for v in data.variables:
try:
txt = '"' + needed_vars[v] + '"'
except KeyError:
txt = ''
print_text(' Bedmap 1 : %-*s key : %s '%(30,v, txt), '230')
# retrieve data :
x = array(data.variables['x1'][:])
y = array(data.variables['y1'][:])
b = array(data.variables['lsrf'][:])
h = array(data.variables['usrf'][:])
adota = array(data.variables['acca'][:])
adotr = array(data.variables['accr'][:])
mask = array(data.variables['mask'][:])
srfTemp = array(data.variables['temp'][:]) + 273.15
q_geo_f = array(data.variables['ghffm'][:]) * 60 * 60 * 24 * 365 / 1000
q_geo_s = array(data.variables['ghfsr'][:]) * 60 * 60 * 24 * 365 / 1000
H = h - b
h[H < thklim] = b[H < thklim] + thklim
H[H < thklim] = thklim
names = ['B','S','H','acca','accr','ghffm','ghfsr','temp']
ftns = [b, h, H, adota, adotr, q_geo_f, q_geo_s, srfTemp]
# extents of domain :
east = max(x)
west = min(x)
north = max(y)
south = min(y)
#projection info :
proj = 'stere'
lat_0 = '-90'
lat_ts = '-71'
lon_0 = '0'
# create projection :
txt = " +proj=" + proj \
+ " +lat_0=" + lat_0 \
+ " +lat_ts=" + lat_ts \
+ " +lon_0=" + lon_0 \
+ " +k=1 +x_0=0 +y_0=0 +no_defs +a=6378137 +rf=298.257223563" \
+ " +towgs84=0.000,0.000,0.000 +to_meter=1"
p = Proj(txt)
# save the data in matlab format :
vara['dataset'] = 'bedmap 1'
vara['continent'] = 'antarctica'
vara['pyproj_Proj'] = p
vara['map_western_edge'] = west
vara['map_eastern_edge'] = east
vara['map_southern_edge'] = south
vara['map_northern_edge'] = north
vara['nx'] = len(x)
vara['ny'] = len(y)
for n, f in zip(names, ftns):
vara[n] = f
return vara
def test_read_write_files():
# test round trip for example file
cwd = os.getcwd()
try:
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
with make_simple('simple.nc', 'w') as f:
pass
# read the file we just created in 'a' mode
with netcdf_file('simple.nc', 'a') as f:
check_simple(f)
# add something
f._attributes['appendRan'] = 1
# To read the NetCDF file we just created::
with netcdf_file('simple.nc') as f:
# Using mmap is the default (but not on pypy)
assert_equal(f.use_mmap, not IS_PYPY)
check_simple(f)
assert_equal(f._attributes['appendRan'], 1)
# Read it in append (and check mmap is off)
with netcdf_file('simple.nc', 'a') as f:
assert_(not f.use_mmap)
check_simple(f)
assert_equal(f._attributes['appendRan'], 1)
# Now without mmap
with netcdf_file('simple.nc', mmap=False) as f:
# Using mmap is the default
assert_(not f.use_mmap)
check_simple(f)
# To read the NetCDF file we just created, as file object, no
# mmap. When n * n_bytes(var_type) is not divisible by 4, this
# raised an error in pupynere 1.0.12 and scipy rev 5893, because
# calculated vsize was rounding up in units of 4 - see
# https://www.unidata.ucar.edu/software/netcdf/guide_toc.html
with open('simple.nc', 'rb') as fobj:
with netcdf_file(fobj) as f:
# by default, don't use mmap for file-like
assert_(not f.use_mmap)
check_simple(f)
# Read file from fileobj, with mmap
with suppress_warnings() as sup:
if IS_PYPY:
sup.filter(
RuntimeWarning,
"Cannot close a netcdf_file opened with mmap=True.*")
with open('simple.nc', 'rb') as fobj:
with netcdf_file(fobj, mmap=True) as f:
assert_(f.use_mmap)
check_simple(f)
# Again read it in append mode (adding another att)
with open('simple.nc', 'r+b') as fobj:
with netcdf_file(fobj, 'a') as f:
assert_(not f.use_mmap)
check_simple(f)
f.createDimension('app_dim', 1)
var = f.createVariable('app_var', 'i', ('app_dim', ))
var[:] = 42
# And... check that app_var made it in...
with netcdf_file('simple.nc') as f:
check_simple(f)
assert_equal(f.variables['app_var'][:], 42)
finally:
if IS_PYPY:
# windows cannot remove a dead file held by a mmap
# that has not been collected in PyPy
break_cycles()
break_cycles()
os.chdir(cwd)
shutil.rmtree(tmpdir)
def get_bamber(thklim = 0.0):
s = "::: getting Bamber data from DataFactory :::"
print_text(s, DataFactory.color)
global home
direc = home + '/greenland/bamber13/Greenland_bedrock_topography_V2.nc'
data = netcdf_file(direc, mode = 'r')
vara = dict()
needed_vars = {'BedrockElevation' : 'B',
'SurfaceElevation' : 'S',
'IceThickness' : 'H',
'BedrockError' : 'Herr',
'LandMask' : 'mask_orig'}
s = " - data-fields collected : python dict key to access -"
print_text(s, DataFactory.color)
for v in data.variables:
try:
txt = '"' + needed_vars[v] + '"'
except KeyError:
txt = ''
print_text(' Bamber : %-*s key : %s '%(30,v, txt), '230')
# retrieve data :
x = array(data.variables['projection_x_coordinate'][:])
y = array(data.variables['projection_y_coordinate'][:])
Bo = array(data.variables['BedrockElevation'][:])
S = array(data.variables['SurfaceElevation'][:])
H = array(data.variables['IceThickness'][:])
Herr = array(data.variables['BedrockError'][:])
mask_orig = array(data.variables['LandMask'][:])
# format the mask for cslvr :
mask = mask_orig.copy(True)
mask[mask == 1] = 0
mask[mask == 2] = 1
mask[mask == 3] = 0
mask[mask == 4] = 0
# generate mask for lateral boundaries :
Hc = mask.copy(True)
# calculate mask gradient, to properly mark lateral boundaries :
gradH = gradient(Hc)
L = gradH[0]**2 + gradH[1]**2
L[L > 0.0] = 1.0
L[L < 1.0] = 0.0
# mark one more level in :
Hc[L > 0.0] = 0
gradH = gradient(Hc)
L2 = gradH[0]**2 + gradH[1]**2
L2[L2 > 0.0] = 1.0
L2[L2 < 1.0] = 0.0
# combine them :
L[L2 > 0.0] = 1.0
# remove the junk data and impose thickness limit :
B = Bo.copy(True)
H[H == -9999.0] = 0.0
S[H < thklim] = B[H < thklim] + thklim
H[H < thklim] = thklim
B = S - H
# extents of domain :
east = max(x)
west = min(x)
north = max(y)
south = min(y)
#projection info :
proj = 'stere'
lat_0 = '90'
lat_ts = '71'
lon_0 = '-39'
# create projection :
txt = " +proj=" + proj \
+ " +lat_0=" + lat_0 \
+ " +lat_ts=" + lat_ts \
+ " +lon_0=" + lon_0 \
+ " +k=1 +x_0=0 +y_0=0 +no_defs +a=6378137 +rf=298.257223563" \
+ " +towgs84=0.000,0.000,0.000 +to_meter=1"
p = Proj(txt)
# save the data in matlab format :
vara['pyproj_Proj'] = p
vara['map_western_edge'] = west
vara['map_eastern_edge'] = east
vara['map_southern_edge'] = south
vara['map_northern_edge'] = north
vara['nx'] = len(x)
vara['ny'] = len(y)
names = ['B', 'Bo', 'S', 'H', 'lat_mask', 'Herr', 'mask', 'mask_orig']
ftns = [ B, Bo, S, H, L, Herr, mask, mask_orig]
# save the data in matlab format :
vara['dataset'] = 'Bamber'
vara['continent'] = 'greenland'
for n, f in zip(names, ftns):
vara[n] = f
return vara
CHAZ_Int_ENS = 40 PImodelname = 'ERA' ### CHAZ parameters uBeta = -2.5 vBeta = 1.0 survivalrate = 0.78 seedN = 1000 #annual seeding rate for random seeding ipath = '/Users/kaitlynnpugliese/Desktop/APAMHurricane/' opath = '/Users/kaitlynnpugliese/Desktop/APAMHurricane/bt_global_predictors.pik' obs_bt_path = '/Users/kaitlynnpugliese/Desktop/APAMHurricane/' Year1 = 1985 Year2 = 1985 ibtracs = '/Users/kaitlynnpugliese/Desktop/APAMHurricane/Allstorms.ibtracs_all.v03r08.nc' landmaskfile = '/Users/kaitlynnpugliese/Desktop/APAMHurricane/landmask.nc' f = netcdf_file(landmaskfile) llon = f.variables['lon'][:] llat = f.variables['lat'][:] lldmask = f.variables['landmask'][:, :] ldmask = lldmask[-12::-24, ::24] lldmask = lldmask[::-1, :] ################################################### # Preporcesses #### # ignore variavbles when run CHAZ is False #### ################################################### ################################################### # CHAZ #### # ignore variavbles when run CHAZ is False #### ################################################### runCHAZ = True
def test_read_with2dVar():
fname = pjoin(TEST_DATA_PATH, 'example_3_maskedvals.nc')
with netcdf_file(fname, maskandscale=True) as f:
vardata = f.variables['var7_2d'][:]
assert_mask_matches(vardata,
[[True, False], [False, False], [False, True]])
def test_read_withValuesNearFillValue():
# Regression test for ticket #5626
fname = pjoin(TEST_DATA_PATH, 'example_3_maskedvals.nc')
with netcdf_file(fname, maskandscale=True) as f:
vardata = f.variables['var1_fillval0'][:]
assert_mask_matches(vardata, [False, True, False])
import pandas as pd
style.use('lowink')
norm_kwargs = dict(zeroed=20, t_offset='auto', normed=(1010, 1032),
norm_method='max')
norm_kwargs2 = norm_kwargs.copy()
norm_kwargs2['normed'] = (1060, 1070)
if __name__ == "__main__":
normers = {}
allsamples = defaultdict(list)
for fname in glob('*/*.CDF'):
sample = netcdf_file(fname)
title = sample.experiment_title.decode().replace('pac', '').split('r')[0].strip('_').lstrip('_-').replace('zaza-', 'zaza')
allsamples[title].append(sample)
figure()
plot_cycler2 = (mpl.cycler(lw=[1,2,3,4,5,])
* mpl.cycler(linestyle=['-', ':', '-.', '--']))
clist = OrderedDict([
(1, 'darkblue'), # MelWT
(2, 'lightblue'), # eloF-
(5, 'red'), # Sec WT
(3, 'darkorange'), # CRISPR A
(4, 'lightsalmon'), # CRISPR B
(6, 'black'),
if event.artist not in self.polygons.polygons:
return
ind = event.ind[0]
self._emit('deselect', self.selected)
if (self.i, ind) != self.selected:
self.selected = (self.i, ind)
self._emit('select', self.selected)
else:
self.selected = None
self.fig.canvas.draw_idle()
if __name__ == '__main__':
from matplotlib.animation import FuncAnimation
ncf = netcdf_file('KTLX_20100510_22Z.nc')
data = ncf.variables['Reflectivity']
lats = ncf.variables['lat']
lons = ncf.variables['lon']
stormcells = storm_loader('polygons.shp')
fig, ax = plt.subplots(1, 1)
raddisp = RadarDisplay(ax, lats, lons)
raddisp.update_display(data[0])
fig.colorbar(raddisp.im)
polycolls = Stormcells(ax, stormcells)
linecoll = Tracks(ax)
# Turn on the first frame's polygons
polycolls.toggle_polygons(0, True)
ax.autoscale(True)
def doit():
with netcdf_file(filename, mmap=True) as f:
return f.variables['lat'][:]
def test_read_withFillValNaN():
fname = pjoin(TEST_DATA_PATH, 'example_3_maskedvals.nc')
with netcdf_file(fname, maskandscale=True) as f:
vardata = f.variables['var5_fillvalNaN'][:]
assert_mask_matches(vardata, [False, True, False])
def read_training(self):
if self.files_list['training'] is not None:
f = netcdf_file(self.basedir + os.sep +
self.files_list['training'])
def get_searise(thklim=0.0):
filename = inspect.getframeinfo(inspect.currentframe()).filename
home = os.path.dirname(os.path.abspath(filename))
direc = home + "/greenland/searise/Greenland_5km_dev1.2.nc"
data = netcdf_file(direc, mode='r')
vara = dict()
# retrieve data :
x = array(data.variables['x1'][:])
y = array(data.variables['y1'][:])
h = array(data.variables['usrf'][:][0])
adot = array(data.variables['smb'][:][0])
b = array(data.variables['topg'][:][0])
T = array(data.variables['surftemp'][:][0]) + 273.15
q_geo = array(data.variables['bheatflx'][:][0]) * 60 * 60 * 24 * 365
lat = array(data.variables['lat'][:][0])
lon = array(data.variables['lon'][:][0])
U_sar = array(data.variables['surfvelmag'][:][0])
dhdt = array(data.variables['dhdt'][:][0])
direc = home + "/greenland/searise/smooth_target.mat"
U_ob = loadmat(direc)['st']
H = h - b
h[H < thklim] = b[H < thklim] + thklim
H[H < thklim] = thklim
Tn = 41.83 - 6.309e-3 * h - 0.7189 * lat - 0.0672 * lon + 273
# extents of domain :
east = max(x)
west = min(x)
north = max(y)
south = min(y)
#projection info :
proj = 'stere'
lat_0 = '90'
lat_ts = '71'
lon_0 = '-39'
names = ['H', 'S', 'adot', 'B', 'T', 'q_geo','U_sar', \
'U_ob', 'lat', 'lon', 'Tn','dhdt']
ftns = [H, h, adot, b, T, q_geo, U_sar, U_ob, lat, lon, Tn, dhdt]
vara['dataset'] = 'searise'
for n, f in zip(names, ftns):
vara[n] = {
'map_data': f,
'map_western_edge': west,
'map_eastern_edge': east,
'map_southern_edge': south,
'map_northern_edge': north,
'projection': proj,
'standard lat': lat_0,
'standard lon': lon_0,
'lat true scale': lat_ts
}
return vara
print "Saving in:", F
np.save(data, c)
data.close()
return 0
###############################
# Main analysis loop
trop_temp = np.zeros( ( m 500 ) )
for z in range( m ):
fname = Mods[i] + "_PI_control_0_500_" + opt + ".nc"
print "Doing:", fname
f1 = si.netcdf_file(fname, 'r')
#Get latitude bounds
l1, l2, j = get_lats_lons( f1 )
data = f1.variables[ opts[i] ][:, l1:l2, :]
print "Detrend"
data = ss.detrend( data - np.mean(data, axis = 0) )
print "Take annual mean"
data = yrmn( data )
print "Take tropical mean"
trop_temp[z] = tropical_mean( data, f1.variables['lat'][:], f1.variables['lon'][:] )
f1.close()
from __future__ import absolute_import
from __future__ import print_function
import scipy
from scipy import io
import os
from ioTools import readwrite as py_rw
import numpy as np
ncFile = "/home/smg/wang/PROJ/DL/RNNJP/DATA/test_align/F009A/data.nc1"
labdir = "/home/smg/takaki/FEAT/F009/data/ver01/full"
labout = "/home/smg/wang/DATA/speech/F009A/nndata/labels/full_align/test_set"
prefix = "ATR_Ximera_F009A_"
resolu = 50000
ncData = io.netcdf_file(ncFile, 'r')
sentNm = ncData.dimensions['numSeqs']
sentNa = ncData.variables['seqTags'][:].copy()
sentTi = ncData.variables['seqLengths'][:].copy()
start = 0
for id, sentId in enumerate(sentNa):
sentId = ''.join(sentId)
labinpfile = labdir + os.path.sep + sentId + '.lab'
laboutfile = labout + os.path.sep + sentId + '.lab'
labentrys = py_rw.read_txt_list(labinpfile)
stime, etime = start, start + sentTi[id]
data = ncData.variables['inputs'][stime:etime, 0:-3].copy()
data = (data * data).sum(axis=1)
difd = np.diff(data)
indx = np.concatenate(