def lookup(x, y, z, table, mod='column'):
if mod == 'column':
table = table.T
_x, _y, _z, _fs = table[0], table[1], table[2], table[3:]
result = array([x, y, z])
for _f in _fs:
_table = array([_x, _y, _z, _f]).T
result.append(trunc(interp3d(x, y, z, _table), 14))
return result
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
:param jdays: Julian day (day of year) values.
:param time: Time of day for each observation (fraction of a day).
:param lon: Longitude of TC position.
:param lat: Latitude of TC position.
:param str ncfile: Path to netCDF file containing daily long-term mean
sea level pressure data.
:type jdays: :class:`numpy.ndarray`
:type time: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of long-term mean sea level pressure
values at the day of year and positions given.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
LOG.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data,
coords,
scale=[365., 180., 360.],
offset=[0., -90., 0.])
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
:param jdays: Julian day (day of year) values.
:param time: Time of day for each observation (fraction of a day).
:param lon: Longitude of TC position.
:param lat: Latitude of TC position.
:param str ncfile: Path to netCDF file containing daily long-term mean
sea level pressure data.
:type jdays: :class:`numpy.ndarray`
:type time: :class:`numpy.ndarray`
:type lon: :class:`numpy.ndarray`
:type lat: :class:`numpy.ndarray`
:returns: :class:`numpy.ndarray` of long-term mean sea level pressure
values at the day of year and positions given.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
logger.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data, coords, scale=[365., 180., 360.],
offset=[0., -90., 0.])
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
def ltmPressure(jdays, time, lon, lat, ncfile):
"""
Extract pressure value from a daily long-term mean SLP dataset at the
given day of year and lon,lat position
To use this function (and hence some form of daily LTM SLP data) requires
knowledge of the day of year.
"""
jtime = jdays + np.modf(time)[0]
coords = np.array([jtime, lat, lon])
logger.debug("Sampling data from MSLP data in {0}".format(ncfile))
ncobj = nctools.ncLoadFile(ncfile)
slpunits = getattr(ncobj.variables['slp'], 'units')
data = nctools.ncGetData(ncobj, 'slp')
# Get the MSLP by interpolating to the location of the TC:
penv = interp3d.interp3d(data, coords)
penv = metutils.convert(penv, slpunits, 'hPa')
del data
ncobj.close()
del ncobj
return penv
coords = ncv[var].coordinates.split()
v = nc.createVariable(var+'_z',vardtype,newdims)
v.units = ncv[var].units
v.long_name = ncv[var].long_name
v.standard_name = ncv[var].standard_name
v.missing_value = fillvalue
#v.coordinates = zaxname+' '+coords[1]+' '+coords[2]
v.coordinates = coords[2]+' '+coords[1]
# so far assume sigma coordinates
for t in range(tnum):
print(' timestep %d'%t)
h = numpy.tile(depth[t]/znum,(znum,1,1))
z = cumsum(h,axis=0)-0.5*h - numpy.tile(depth[t],(znum,1,1))
for varname in variables:
# Cython:
ncv[varname+'_z'][t] = interp3d(z.filled(1.0),ncv[varname][t].filled(fillvalue),-zlevels,fillvalue=fillvalue)
# Scipy interp:
#
#for j in range(ynum):
# for i in range(xnum):
# varz[:,j,i] = interp(-zlevels,squeeze(z[:,j,i]),squeeze(ncv[varname][t,:,j,i]))
#ncv[varname+'_z'][t]=varz
nc.sync()
nc.close()