def globsimScaled2Pandas(ncdf_in, station_nr):
"""
Read a scaled (or interpolated) globsim netCDF file and return all values
for one station as a Pandas data frame.
ncdf_in: full path to a globsim netCDF (by station)
station_nr: station_number, as given in the stations .csv file to identify
the station.
"""
# open file
ncf = nc.Dataset(ncdf_in, 'r')
# station mask
sm = ncf.variables['station'][:] == int(station_nr)
# list variables
varlist = [x.encode('UTF8') for x in ncf.variables.keys()]
# get and convert time
time = ncf.variables['time'][:]
t_unit = ncf.variables['time'].units
t_cal = ncf.variables['time'].calendar
time = nc.num2date(time, units=t_unit, calendar=t_cal)
# make data frame with time
df = pd.DataFrame(data=time, columns=['time'])
# add variables
for var in varlist:
if variables_skip(var):
continue
data = ncf.variables[var][:, sm]
df = pd.concat([df, pd.DataFrame(data=data, columns=[var])], axis=1)
return df
def netCDF_empty(self, ncfile_out, stations, nc_in):
# TODO: change date type from f4 to f8 for lat and lon
'''
Creates an empty station file to hold interpolated reults. The number of
stations is defined by the variable stations, variables are determined by
the variable list passed from the gridded original netCDF.
ncfile_out: full name of the file to be created
stations: station list read with common_utils.StationListRead()
variables: variables read from netCDF handle
lev: list of pressure levels, empty is [] (default)
'''
rootgrp = netcdf_base(ncfile_out, len(stations), None,
'hours since 1980-01-01 00:00:00')
station = rootgrp["station"]
latitude = rootgrp["latitude"]
longitude = rootgrp["longitude"]
height = rootgrp["height"]
# assign station characteristics
station[:] = list(stations['station_number'])
latitude[:] = list(stations['latitude_dd'])
longitude[:] = list(stations['longitude_dd'])
height[:] = list(stations['elevation_m'])
# extra treatment for pressure level files
try:
lev = nc_in.variables['level'][:]
logger.info("Creating empty 3D file (has pressure levels)")
level = rootgrp.createDimension('level', len(lev))
level = rootgrp.createVariable('level', 'i4', ('level'))
level.long_name = 'pressure_level'
level.units = 'hPa'
level[:] = lev
except Exception:
logger.info("Creating empty 2D file (without pressure levels)")
lev = []
# remove extra variables
varlist_merra = [str_encode(x) for x in nc_in.variables.keys()]
# create and assign variables based on input file
for n, var in enumerate(varlist_merra):
if variables_skip(var):
continue
logger.debug(f"Add empty variable: {var}")
# extra treatment for pressure level files
if len(lev):
tmp = rootgrp.createVariable(var, 'f4',
('time', 'level', 'station'))
else:
tmp = rootgrp.createVariable(var, 'f4', ('time', 'station'))
tmp.long_name = str_encode(
nc_in.variables[var].long_name) # for merra2
tmp.units = str_encode(nc_in.variables[var].units)
# close the file
rootgrp.close()
logger.debug(f"Created empty netcdf file {ncfile_out}")
def ERA2station(self,
ncfile_in,
ncfile_out,
points,
variables=None,
date=None):
"""
Biliner interpolation from fields on regular grid (latitude, longitude)
to individual point stations (latitude, longitude). This works for
surface and for pressure level files (all Era_Interim files). The type
of variable and file structure are determined from the input.
This function creates an empty of netCDF file to hold the interpolated
results, by calling ERAIgeneric().netCDF_empty. Then, data is
interpolated in temporal chunks and appended. The temporal chunking can
be set in the interpolation parameter file.
Args:
ncfile_in: Full path to an Era-Interim derived netCDF file. This can
contain wildcards to point to multiple files if temporal
chunking was used.
ncfile_out: Full path to the output netCDF file to write.
points: A dataframe of locations. See method StationListRead in
common_utils.py for more details.
variables: List of variable(s) to interpolate such as
['r', 't', 'u','v', 't2m', 'u10', 'v10', 'ssrd', 'strd', 'tp'].
Defaults to using all variables available.
date: Directory to specify begin and end time for the derived time
series. Defaluts to using all times available in ncfile_in.
cs: chunk size, i.e. how many time steps to interpolate at once. This
helps to manage overall memory usage (small cs is slower but less
memory intense).
"""
# read in one type of mutiple netcdf files
ncf_in = nc.MFDataset(ncfile_in, 'r', aggdim='time')
# is it a file with pressure levels?
pl = 'level' in ncf_in.dimensions.keys()
# build the output of empty netCDF file
rootgrp = new_interpolated_netcdf(
ncfile_out,
self.stations,
ncf_in,
time_units='hours since 1900-01-01 00:00:0.0')
rootgrp.source = 'ERA_Interim, interpolated bilinearly to stations'
rootgrp.close()
# open the output netCDF file, set it to be appendable ('a')
ncf_out = nc.Dataset(ncfile_out, 'a')
# get time and convert to datetime object
nctime = ncf_in.variables['time'][:]
#"hours since 1900-01-01 00:00:0.0"
t_unit = ncf_in.variables['time'].units
try:
t_cal = ncf_in.variables['time'].calendar
except AttributeError: # attribute doesn't exist
t_cal = u"gregorian" # standard
time = nc.num2date(nctime, units=t_unit, calendar=t_cal)
# detect invariant files (topography etc.)
invariant = True if len(time) == 1 else False
# restrict to date/time range if given
if date is None:
tmask = time < datetime(3000, 1, 1)
else:
tmask = (time < date['end']) * (time >= date['beg'])
# get time vector for output
time_in = nctime[tmask]
# ensure that chunk sizes cover entire period even if
# len(time_in) is not an integer multiple of cs
niter = len(time_in) // self.cs
niter += ((len(time_in) % self.cs) > 0)
# loop over chunks
for n in range(niter):
# indices (relative to index of the output file)
beg = n * self.cs
# restrict last chunk to lenght of tmask plus one (to get last time)
end = min(n * self.cs + self.cs, len(time_in)) - 1
# time to make tmask for chunk
beg_time = nc.num2date(time_in[beg], units=t_unit, calendar=t_cal)
if invariant:
# allow topography to work in same code, len(nctime) = 1
end_time = nc.num2date(nctime[0], units=t_unit, calendar=t_cal)
#end = 1
else:
end_time = nc.num2date(time_in[end],
units=t_unit,
calendar=t_cal)
#'<= end_time', would damage appending
tmask_chunk = (time <= end_time) * (time >= beg_time)
if invariant:
# allow topography to work in same code
tmask_chunk = [True]
# get the interpolated variables
dfield, variables = self.interp2D(ncfile_in,
ncf_in,
self.stations,
tmask_chunk,
variables=None,
date=None)
# append time
ncf_out.variables['time'][:] = np.append(
ncf_out.variables['time'][:], time_in[beg:end + 1])
#append variables
for i, var in enumerate(variables):
if variables_skip(var):
continue
if pl:
if ESMFnew:
# dfield has dimensions (station, variables, time, pressure levels)
ncf_out.variables[var][
beg:end +
1, :, :] = dfield.data[:, i, :, :].transpose(
(1, 2, 0))
else:
# dimension: time, level, station (pressure level files)
ncf_out.variables[var][
beg:end + 1, :, :] = dfield.data[i, :, :, :]
else:
if ESMFnew:
# dfield has dimensions (station, variables, time)
ncf_out.variables[var][
beg:end + 1, :] = dfield.data[:, i, :].transpose(
(1, 0))
else:
# dfield has dimensions time, station (2D files)
ncf_out.variables[var][beg:end +
1, :] = dfield.data[i, :, :]
#close the file
ncf_in.close()
ncf_out.close()
def new_interpolated_netcdf(ncfile_out, stations, nc_in, time_units):
"""
Creates an empty station file to hold interpolated reults. The number of
stations is defined by the variable stations, variables are determined by
the variable list passed from the gridded original netCDF.
ncfile_out: full name of the file to be created
stations: station list read with common_utils.StationListRead()
variables: variables read from netCDF handle
lev: list of pressure levels, empty is [] (default)
"""
logger.info(f"Creating new file {ncfile_out} from ")
rootgrp = netcdf_base(ncfile_out, len(stations), None, time_units, nc_in)
station = rootgrp['station']
latitude = rootgrp['latitude']
longitude = rootgrp['longitude']
height = rootgrp['height']
# assign station characteristics
station[:] = list(stations['station_number'])
latitude[:] = list(stations['latitude_dd'])
longitude[:] = list(stations['longitude_dd'])
height[:] = list(stations['elevation_m'])
# extra treatment for pressure level files
try:
lev = nc_in.variables['level'][:]
logger.info(f"Source dataset is 3D (has pressure levels)")
level = rootgrp.createDimension('level', len(lev))
level = rootgrp.createVariable('level', 'i4', ('level'))
level.long_name = 'pressure_level'
level.units = 'hPa'
level[:] = lev
except Exception:
logger.info(f"Source dataset is 2D (without pressure levels)")
lev = []
try:
num = rootgrp['number'][:]
except Exception:
num = []
# create and assign variables based on input file
for n, var in enumerate(nc_in.variables):
if variables_skip(var):
continue
# extra treatment for pressure level files
if len(num):
if len(lev):
tmp = rootgrp.createVariable(var,'f4',('time', 'number',
'level', 'station'))
else:
tmp = rootgrp.createVariable(var,'f4',('time','number',
'station'))
else:
if len(lev):
tmp = rootgrp.createVariable(var,'f4', ('time','level',
'station'))
else:
tmp = rootgrp.createVariable(var,'f4', ('time','station'))
# copy attributes
input_var = nc_in.variables[var]
for key in input_var.ncattrs():
if key in ['_FillValue']:
continue
tmp.setncattr(key, getattr(input_var, key))
logger.info(f"Created new empty variable: {str_encode(var)} [{tmp.units}]")
return rootgrp
def makeNCF(self, dsi):
variables = self.getVars(dsi)
dataLev = self.getDataLev(dsi)
self.getDimName(dataLev)
varf = np.sort(
glob.glob(path.join(self.directory, f'*{variables[0]}*')))
ncf = nc.MFDataset(varf.tolist(), aggdim='initial_time0_hours')
if dataLev == 'pl':
Levs = ncf['lv_ISBL1'][:].data
Times = ncf[self.timeName][:]
Lats = ncf[self.latName][:].data
Lons = ncf[self.lonName][:].data
file_new = self.getOutFile(ncf, dataLev)
# initialize new data file and create group
ncn = nc.Dataset(file_new, 'w', format='NETCDF4_CLASSIC')
# make dimensions
if dataLev == 'pl':
Levs = ncf[self.levName][:].data
ncn.createDimension('level', len(Levs))
levels = ncn.createVariable('level', 'i4', ('level', ))
levels.long_name = 'pressure level'
levels.units = 'mbar'
levels[:] = Levs
ncn.createDimension('time', len(Times))
ncn.createDimension('latitude', len(Lats))
ncn.createDimension('longitude', len(Lons))
# make dimension variables
times = ncn.createVariable('time', 'd', ('time', ))
latitudes = ncn.createVariable('latitude', 'f8', ('latitude', ))
longitudes = ncn.createVariable('longitude', 'f8', ('longitude', ))
times.standard_name = 'time'
times.units = ncf[self.timeName].units
times.calendar = 'standard'
latitudes.standard_name = ncf[self.latName].long_name
latitudes.units = ncf[self.latName].units
longitudes.standard_name = ncf[self.lonName].long_name
longitudes.units = ncf[self.lonName].units
ncf.close()
# assign dimensions
times[:] = Times
longitudes[:] = Lons
latitudes[:] = Lats
for vari in variables:
flist = np.sort(glob.glob(path.join(self.directory, f'*{vari}*')))
ncf = nc.MFDataset(flist.tolist(), aggdim=self.timeName)
for n, var in enumerate(ncf.variables.keys()):
if variables_skip(self.ncfVar[var]):
continue
logger.info(f"Creating variable: {var}")
if dataLev == 'pl':
vari = ncn.createVariable(self.ncfVar[var], 'f4', (
'time',
'level',
'latitude',
'longitude',
))
vari[:, :, :, :] = ncf[var][:, :, :, :]
else:
vari = ncn.createVariable(
self.ncfVar[var], 'f4',
('time', 'latitude', 'longitude'))
vari[:, :, :] = ncf[var][:, :, :]
vari.long_name = ncf[var].long_name
vari.units = ncf[var].units
ncf.close()
for f in flist:
remove(f)
ncn.close()
def MERRA2station(self,
ncfile_in,
ncfile_out,
points,
variables=None,
date=None):
"""
Given the type of variables to interpoalted from MERRA2 downloaded diretory
Create the empty of netCDF file to hold the interpolated results, by calling
self.netCDF_empty
Get the interpolated results from MERRA2station
Append all variables into the empty netCDF file
Close all files
Args:
ncfile_in: Full path to an MERRA-2 derived netCDF file. This can
contain wildcards to point to multiple files if temporal
chunking was used.
ncfile_out: Full path to the output netCDF file to write.
points: A dataframe of locations. See method StationListRead in
common_utils.py for more details.
variables: List of variable(s) to interpolate such as
['T','RH','U','V',' T2M', 'U2M', 'V2M', 'U10M', 'V10M',
'PRECTOT', 'SWGDN','SWGDNCLR','LWGDN', 'LWGDNCLR'].
Defaults to using all variables available.
date: Directory to specify begin and end time for the derived time
series. Defaluts to using all times available in ncfile_in.
"""
# read in one type of mutiple netcdf files
logger.info("Loading reanalysis data into memory")
ncf_in = nc.MFDataset(ncfile_in, 'r', aggdim='time')
# Check station bounds
self.validate_stations_extent(ncf_in)
# is it a file with pressure levels?
pl = 'level' in ncf_in.dimensions.keys()
# build the output of empty netCDF file
self.netCDF_empty(ncfile_out, self.stations, ncf_in)
# open the output netCDF file, set it to be appendable ('a')
ncf_out = nc.Dataset(ncfile_out, 'a')
# get time and convert to datetime object
nctime = ncf_in.variables['time'][:]
# "hours since 1980-01-01 00:00:00"
t_unit = "hours since 1980-01-01 00:00:00" # ncf_in.variables['time'].units
try:
t_cal = ncf_in.variables['time'].calendar
except AttributeError: # Attribute doesn't exist
t_cal = u"gregorian" # or standard
# TODO: rm time = [nc.num2date(timei, units=t_unit, calendar=t_cal) for timei in nctime]
# TODO: rm time = np.asarray(time)
# detect invariant files (topography etc.)
invariant = True if len(np.unique(nctime)) <= 2 else False
# restrict to date/time range if given
if date is None:
tmask = nctime < nc.date2num(
datetime(3000, 1, 1), units=t_unit, calendar=t_cal)
else:
beg_num = nc.date2num(date['beg'], units=t_unit, calendar=t_cal)
end_num = nc.date2num(date['end'], units=t_unit, calendar=t_cal)
tmask = (nctime < end_num) * (nctime >= beg_num)
if not any(tmask):
sys.exit(
'''\n ERROR: No downloaded data exist within date range specified by interpolation control file.
Download new data or change 'beg' / 'end' in interpolation control file'''
)
# get time indices
time_in = nctime[tmask]
# ensure that chunk sizes cover entire period even if
# len(time_in) is not an integer multiple of cs
niter = len(time_in) // self.cs
niter += ((len(time_in) % self.cs) > 0)
# loop in chunk size cs
for n in range(niter):
# indices
beg = n * self.cs
# restrict last chunk to lenght of tmask plus one (to get last time)
if invariant:
end = beg
else:
end = min(n * self.cs + self.cs, len(time_in)) - 1
# make tmask for chunk
beg_time = time_in[beg]
if invariant:
# allow topography to work in same code, len(nctime) = 1
# TODO: rm end_time = nc.num2date(nctime[0], units=t_unit, calendar=t_cal)
end_time = nctime[0]
# end = 1
else:
# TODO: rm end_time = nc.num2date(time_in[end], units=t_unit, calendar=t_cal)
end_time = time_in[end]
# !! CAN'T HAVE '<= end_time', would damage appeding
tmask_chunk = (nctime <= end_time) * (nctime >= beg_time)
if invariant:
# allow topography to work in same code
tmask_chunk = np.array([True])
# get the interpolated variables
dfield, variables = self.interp2D(ncfile_in,
ncf_in,
self.stations,
tmask_chunk,
variables=None,
date=None)
# append time
ncf_out.variables['time'][:] = np.append(
ncf_out.variables['time'][:], time_in[beg:end + 1])
# append variables
for i, var in enumerate(variables):
if variables_skip(var):
continue
# extra treatment for pressure level files
if pl:
# lev = ncf_in.variables['level'][:]
ncf_out.variables[var][
beg:end + 1, :, :] = dfield.data[:, i, :, :].transpose(
1, 2, 0)
else:
ncf_out.variables[var][beg:end +
1, :] = dfield.data[:,
i, :].transpose(
1, 0)
ncf_in.close()
ncf_out.close()
