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"""CarbonTracker Data Assimilation Shell (CTDAS) Copyright (C) 2017 Wouter Peters.
Users are recommended to contact the developers (wouter.peters@wur.nl) to receive
updates of the code. See also: http://www.carbontracker.eu.
This program is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software Foundation,
version 3. This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this
program. If not, see <http://www.gnu.org/licenses/>."""
#!/usr/bin/env python
# expand_fluxes.py
import sys
import os
sys.path.append('../../')
rootdir = os.getcwd().split('da/')[0]
analysisdir = os.path.join(rootdir, 'da/analysis')
from datetime import datetime, timedelta
import logging
import numpy as np
from da.tools.general import date2num, num2date
import da.tools.io4 as io
from da.analysis.tools_regions import globarea, state_to_grid
from da.tools.general import create_dirs
from da.analysis.tools_country import countryinfo # needed here
from da.analysis.tools_transcom import transcommask, ExtendedTCRegions
import netCDF4 as cdf
import da.analysis.tools_transcom as tc
import da.analysis.tools_country as ct
import da.analysis.tools_time as timetools
"""
Author: Wouter Peters (Wouter.Peters@noaa.gov)
Revision History:
File created on 21 Ocotber 2008.
"""
def proceed_dialog(txt, yes=['y', 'yes'], all=['a', 'all', 'yes-to-all']):
""" function to ask whether to proceed or not """
response = raw_input(txt)
if response.lower() in yes:
return 1
if response.lower() in all:
return 2
return 0
def save_weekly_avg_1x1_data(dacycle, statevector):
"""
Function creates a NetCDF file with output on 1x1 degree grid. It uses the flux data written by the
:class:`~da.baseclasses.obsoperator.ObsOperator.py`, and multiplies these with the mapped parameters and
variance (not covariance!) from the :class:`~da.baseclasses.statevector.StateVector`.
:param dacycle: a :class:`~da.tools.initexit.CycleControl` object
:param statevector: a :class:`~da.baseclasses.statevector.StateVector`
:rtype: None
"""
#
dirname = create_dirs(os.path.join(dacycle['dir.analysis'], 'data_flux1x1_weekly'))
#
# Some help variables
#
dectime0 = date2num(datetime(2000, 1, 1)) dt = dacycle['cyclelength']
startdate = dacycle['time.start']
enddate = dacycle['time.end']
nlag = statevector.nlag
logging.debug("DA Cycle start date is %s" % startdate.strftime('%Y-%m-%d %H:%M'))
logging.debug("DA Cycle end date is %s" % enddate.strftime('%Y-%m-%d %H:%M'))
#
# Create or open NetCDF output file
#
saveas = os.path.join(dirname, 'flux_1x1.%s.nc' % startdate.strftime('%Y-%m-%d'))
ncf = io.CT_CDF(saveas, 'write')
#
# Create dimensions and lat/lon grid
#
dimgrid = ncf.add_latlon_dim()
dimensemble = ncf.add_dim('members', statevector.nmembers)
dimdate = ncf.add_date_dim()
#
# set title and tell GMT that we are using "pixel registration"
#
setattr(ncf, 'Title', 'CarbonTracker fluxes')
setattr(ncf, 'node_offset', 1)
#
# skip dataset if already in file
#
ncfdate = date2num(startdate) - dectime0 + dt.days / 2.0
skip = ncf.has_date(ncfdate)
if skip:
logging.warning('Skipping writing of data for date %s : already present in file %s' % (startdate.strftime('%Y-%m-%d'), saveas))
else:
#
# if not, process this cycle. Start by getting flux input data from CTDAS
#
filename = os.path.join(dacycle['dir.output'], 'flux1x1_%s_%s.nc' % (startdate.strftime('%Y%m%d%H'), enddate.strftime('%Y%m%d%H')))
file = io.ct_read(filename, 'read')
bio = np.array(file.get_variable(dacycle.dasystem['background.co2.bio.flux']))
ocean = np.array(file.get_variable(dacycle.dasystem['background.co2.ocean.flux']))
fire = np.array(file.get_variable(dacycle.dasystem['background.co2.fires.flux']))
fossil = np.array(file.get_variable(dacycle.dasystem['background.co2.fossil.flux']))
#mapped_parameters = np.array(file.get_variable(dacycle.dasystem['final.param.mean.1x1']))
if dacycle.dasystem['background.co2.biosam.flux'] in file.variables.keys():
sam = True
biosam = np.array(file.get_variable(dacycle.dasystem['background.co2.biosam.flux']))
firesam = np.array(file.get_variable(dacycle.dasystem['background.co2.firesam.flux']))
else: sam = False
file.close()
if sam:
bio = bio + biosam
fire = fire + firesam
next = ncf.inq_unlimlen()[0]
# Start adding datasets from here on, both prior and posterior datasets for bio and ocn
for prior in [True, False]:
#
# Now fill the statevector with the prior values for this time step. Note that the prior value for this time step
# occurred nlag time steps ago, so we make a shift in the output directory, but only if we are more than nlag cycle away from the start date..
#
if prior:
qual_short = 'prior'
for n in range(nlag, 0, -1): priordate = startdate + n*dt - timedelta(dt.days * n)
savedir = dacycle['dir.output'].replace(startdate.strftime('%Y%m%d'), priordate.strftime('%Y%m%d'))
filename = os.path.join(savedir, 'savestate_%s.nc' % priordate.strftime('%Y%m%d'))
if os.path.exists(filename):
statevector.read_from_file(filename, qual=qual_short)
gridmean, gridensemble = statevector.state_to_grid(lag=n)
# Replace the mean statevector by all ones (assumed priors)
gridmean = statevector.vector2grid(vectordata=np.ones(statevector.nparams,))
logging.debug('Read prior dataset from file %s, sds %d: ' % (filename, n))
break
else:
qual_short = 'opt'
savedir = dacycle['dir.output']
filename = os.path.join(savedir, 'savestate_%s.nc' % startdate.strftime('%Y%m%d'))
statevector.read_from_file(filename, qual=qual_short)
gridmean, gridensemble = statevector.state_to_grid(lag=1)
logging.debug('Read posterior dataset from file %s, sds %d: ' % (filename, 1))
#
# if prior, do not multiply fluxes with parameters, otherwise do
#
print gridensemble.shape, bio.shape, gridmean.shape
biomapped = bio * gridmean
oceanmapped = ocean * gridmean
biovarmapped = bio * gridensemble
oceanvarmapped = ocean * gridensemble
#
#
# For each dataset, get the standard definitions from the module mysettings, add values, dimensions, and unlimited count, then write
#
savedict = ncf.standard_var(varname='bio_flux_' + qual_short)
savedict['values'] = biomapped.tolist()
savedict['dims'] = dimdate + dimgrid
savedict['count'] = next
ncf.add_data(savedict)
#
savedict = ncf.standard_var(varname='ocn_flux_' + qual_short)
savedict['values'] = oceanmapped.tolist()
savedict['dims'] = dimdate + dimgrid
savedict['count'] = next
ncf.add_data(savedict)
print biovarmapped.shape
savedict = ncf.standard_var(varname='bio_flux_%s_ensemble' % qual_short)
savedict['values'] = biovarmapped.tolist()
savedict['dims'] = dimdate + dimensemble + dimgrid
savedict['count'] = next
ncf.add_data(savedict)
#
savedict = ncf.standard_var(varname='ocn_flux_%s_ensemble' % qual_short)
savedict['values'] = oceanvarmapped.tolist()
savedict['dims'] = dimdate + dimensemble + dimgrid
savedict['count'] = next
ncf.add_data(savedict)
# End prior/posterior block
savedict = ncf.standard_var(varname='fire_flux_imp')
savedict['values'] = fire.tolist()
savedict['dims'] = dimdate + dimgrid
savedict['count'] = next
ncf.add_data(savedict)
#
savedict = ncf.standard_var(varname='fossil_flux_imp')
savedict['values'] = fossil.tolist()
savedict['dims'] = dimdate + dimgrid savedict['count'] = next
ncf.add_data(savedict)
area = globarea()
savedict = ncf.standard_var(varname='cell_area')
savedict['values'] = area.tolist()
savedict['dims'] = dimgrid
ncf.add_data(savedict)
#
savedict = ncf.standard_var(varname='date')
savedict['values'] = date2num(startdate) - dectime0 + dt.days / 2.0
savedict['dims'] = dimdate
savedict['count'] = next
ncf.add_data(savedict)
sys.stdout.write('.')
sys.stdout.flush()
#
# Done, close the new NetCDF file
#
ncf.close()
#
# Return the full name of the NetCDF file so it can be processed by the next routine
#
logging.info("Gridded weekly average fluxes now written")
return saveas
def save_weekly_avg_state_data(dacycle, statevector):
"""
Function creates a NetCDF file with output for all parameters. It uses the flux data written by the
:class:`~da.baseclasses.obsoperator.ObsOperator.py`, and multiplies these with the mapped parameters and
variance (not covariance!) from the :class:`~da.baseclasses.statevector.StateVector`.
:param dacycle: a :class:`~da.tools.initexit.CycleControl` object
:param statevector: a :class:`~da.baseclasses.statevector.StateVector`
:rtype: None
"""
dirname = create_dirs(os.path.join(dacycle['dir.analysis'], 'data_state_weekly'))
#
# Some help variables
#
dectime0 = date2num(datetime(2000, 1, 1))
dt = dacycle['cyclelength']
startdate = dacycle['time.start']
enddate = dacycle['time.end']
nlag = statevector.nlag
area = globarea()
vectorarea = statevector.grid2vector(griddata=area, method='sum')
logging.debug("DA Cycle start date is %s" % startdate.strftime('%Y-%m-%d %H:%M'))
logging.debug("DA Cycle end date is %s" % enddate.strftime('%Y-%m-%d %H:%M'))
#
# Create or open NetCDF output file
#
saveas = os.path.join(dirname, 'statefluxes.nc')
ncf = io.CT_CDF(saveas, 'write')
#
# Create dimensions and lat/lon grid
#
dimregs = ncf.add_dim('nparameters', statevector.nparams)
dimmembers = ncf.add_dim('nmembers', statevector.nmembers)
dimdate = ncf.add_date_dim()
#
# set title and tell GMT that we are using "pixel registration"
# setattr(ncf, 'Title', 'CarbonTracker fluxes')
setattr(ncf, 'node_offset', 1)
#
# skip dataset if already in file
#
ncfdate = date2num(startdate) - dectime0 + dt.days / 2.0
skip = ncf.has_date(ncfdate)
if skip:
logging.warning('Skipping writing of data for date %s : already present in file %s' % (startdate.strftime('%Y-%m-%d'), saveas))
else:
next = ncf.inq_unlimlen()[0]
#
# if not, process this cycle. Start by getting flux input data from CTDAS
#
filename = os.path.join(dacycle['dir.output'], 'flux1x1_%s_%s.nc' % (startdate.strftime('%Y%m%d%H'), enddate.strftime('%Y%m%d%H')))
file = io.ct_read(filename, 'read')
bio = np.array(file.get_variable(dacycle.dasystem['background.co2.bio.flux']))
ocean = np.array(file.get_variable(dacycle.dasystem['background.co2.ocean.flux']))
fire = np.array(file.get_variable(dacycle.dasystem['background.co2.fires.flux']))
fossil = np.array(file.get_variable(dacycle.dasystem['background.co2.fossil.flux']))
#mapped_parameters = np.array(file.get_variable(dacycle.dasystem['final.param.mean.1x1']))
if dacycle.dasystem['background.co2.biosam.flux'] in file.variables.keys():
sam = True
biosam = np.array(file.get_variable(dacycle.dasystem['background.co2.biosam.flux']))
firesam = np.array(file.get_variable(dacycle.dasystem['background.co2.firesam.flux']))
else: sam = False
file.close()
if sam:
bio = bio + biosam
fire = fire + firesam
next = ncf.inq_unlimlen()[0]
vectorbio = statevector.grid2vector(griddata=bio * area, method='sum')
vectorocn = statevector.grid2vector(griddata=ocean * area, method='sum')
vectorfire = statevector.grid2vector(griddata=fire * area, method='sum')
vectorfossil = statevector.grid2vector(griddata=fossil * area, method='sum')
# Start adding datasets from here on, both prior and posterior datasets for bio and ocn
for prior in [True, False]:
#
# Now fill the statevector with the prior values for this time step. Note that the prior value for this time step
# occurred nlag time steps ago, so we make a shift in the output directory, but only if we are more than nlag cycle away from the start date..
#
if prior:
qual_short = 'prior'
for n in range(nlag, 0, -1):
priordate = enddate - timedelta(dt.days * n)
priordate = startdate + n*dt - timedelta(dt.days * n)
savedir = dacycle['dir.output'].replace(startdate.strftime('%Y%m%d'), priordate.strftime('%Y%m%d'))
filename = os.path.join(savedir,'savestate_%s.nc' % priordate.strftime('%Y%m%d'))
if os.path.exists(filename):
statevector.read_from_file(filename, qual=qual_short)
# Replace the mean statevector by all ones (assumed priors)
statemean = np.ones((statevector.nparams,))
choicelag = n
logging.debug('Read prior dataset from file %s, lag %d: ' % (filename, choicelag))
break
else:
qual_short = 'opt'
savedir = dacycle['dir.output']
filename = os.path.join(savedir, 'savestate_%s.nc' % startdate.strftime('%Y%m%d'))
statevector.read_from_file(filename)
choicelag = 1
statemean = statevector.ensemble_members[choicelag - 1][0].param_values logging.debug('Read posterior dataset from file %s, lag %d: ' % (filename, choicelag))
#
# if prior, do not multiply fluxes with parameters, otherwise do
#
data = statemean * vectorbio # units of mole region-1 s-1
savedict = ncf.standard_var(varname='bio_flux_%s' % qual_short)
savedict['values'] = data
savedict['dims'] = dimdate + dimregs
savedict['count'] = next
ncf.add_data(savedict)
#
# Here comes a special provision for the posterior flux covariances: these are calculated relative to the prior flux covariance to
# ensure they are indeed smaller due to the data assimilation. If they would be calculated relative to the mean posterior flux, the
# uncertainties would shift just because the mean flux had increased or decreased, which is not what we want.
#
# The implementation is done by multiplying the ensemble with the vectorbio only, and not with the statemean values
# which are assumed 1.0 in the prior always.
#
members = statevector.ensemble_members[choicelag - 1]
deviations = np.array([mem.param_values * vectorbio for mem in members])
deviations = deviations - deviations[0, :]
savedict = ncf.standard_var(varname='bio_flux_%s_ensemble' % qual_short)
savedict['values'] = deviations.tolist()
savedict['dims'] = dimdate + dimmembers + dimregs
savedict['comment'] = "This is the matrix square root, use (M x M^T)/(nmembers-1) to make covariance"
savedict['units'] = "mol region-1 s-1"
savedict['count'] = next
ncf.add_data(savedict)
savedict = ncf.standard_var('unknown')
savedict['name'] = 'bio_flux_%s_std' % qual_short
savedict['long_name'] = 'Biosphere flux standard deviation, %s' % qual_short
savedict['values'] = deviations.std(axis=0)
savedict['dims'] = dimdate + dimregs
savedict['comment'] = "This is the standard deviation on each parameter"
savedict['units'] = "mol region-1 s-1"
savedict['count'] = next
ncf.add_data(savedict)
data = statemean * vectorocn # units of mole region-1 s-1
savedict = ncf.standard_var(varname='ocn_flux_%s' % qual_short)
savedict['values'] = data
savedict['dims'] = dimdate + dimregs
savedict['count'] = next
ncf.add_data(savedict)
#
# Here comes a special provision for the posterior flux covariances: these are calculated relative to the prior flux covariance to
# ensure they are indeed smaller due to the data assimilation. If they would be calculated relative to the mean posterior flux, the
# uncertainties would shift just because the mean flux had increased or decreased, which is not what we want.
#
# The implementation is done by multiplying the ensemble with the vectorocn only, and not with the statemean values
# which are assumed 1.0 in the prior always.
#
deviations = np.array([mem.param_values * vectorocn for mem in members])
deviations = deviations - deviations[0, :]
savedict = ncf.standard_var(varname='ocn_flux_%s_ensemble' % qual_short)
savedict['values'] = deviations.tolist()
savedict['dims'] = dimdate + dimmembers + dimregs
savedict['comment'] = "This is the matrix square root, use (M x M^T)/(nmembers-1) to make covariance"
savedict['units'] = "mol region-1 s-1" savedict['count'] = next
ncf.add_data(savedict)
savedict = ncf.standard_var('unknown')
savedict['name'] = 'ocn_flux_%s_std' % qual_short
savedict['long_name'] = 'Ocean flux standard deviation, %s' % qual_short
savedict['values'] = deviations.std(axis=0)
savedict['dims'] = dimdate + dimregs
savedict['comment'] = "This is the standard deviation on each parameter"
savedict['units'] = "mol region-1 s-1"
savedict['count'] = next
ncf.add_data(savedict)
data = vectorfire
savedict = ncf.standard_var(varname='fire_flux_imp')
savedict['values'] = data
savedict['dims'] = dimdate + dimregs
savedict['count'] = next
ncf.add_data(savedict)
data = vectorfossil
savedict = ncf.standard_var(varname='fossil_flux_imp')
savedict['values'] = data
savedict['dims'] = dimdate + dimregs
savedict['count'] = next
ncf.add_data(savedict)
savedict = ncf.standard_var(varname='date')
savedict['values'] = ncfdate
savedict['dims'] = dimdate
savedict['count'] = next
ncf.add_data(savedict)
sys.stdout.write('.')
sys.stdout.flush()
#
# Done, close the new NetCDF file
#
ncf.close()
#
# Return the full name of the NetCDF file so it can be processed by the next routine
#
logging.info("Vector weekly average fluxes now written")
return saveas
def save_weekly_avg_tc_data(dacycle, statevector):
"""
Function creates a NetCDF file with output on TransCom regions. It uses the flux input from the
function `save_weekly_avg_1x1_data` to create fluxes of length `nparameters`, which are then projected
onto TC regions using the internal methods from :class:`~da.baseclasses.statevector.StateVector`.
:param dacycle: a :class:`~da.tools.initexit.CycleControl` object
:param statevector: a :class:`~da.baseclasses.statevector.StateVector`
:rtype: None
This function only read the prior fluxes from the flux_1x1.nc files created before, because we want to convolve
these with the parameters in the statevector. This creates posterior fluxes, and the posterior covariance for the complete
statevector in units of mol/box/s which we then turn into TC fluxes and covariances.
"""
#
dirname = create_dirs(os.path.join(dacycle['dir.analysis'], 'data_tc_weekly'))
#
# Some help variables
#
dectime0 = date2num(datetime(2000, 1, 1)) dt = dacycle['cyclelength']
startdate = dacycle['time.start']
enddate = dacycle['time.end']
ncfdate = date2num(startdate) - dectime0 + dt.days / 2.0
logging.debug("DA Cycle start date is %s" % startdate.strftime('%Y-%m-%d %H:%M'))
logging.debug("DA Cycle end date is %s" % enddate.strftime('%Y-%m-%d %H:%M'))
# Write/Create NetCDF output file
#
saveas = os.path.join(dirname, 'tcfluxes.nc')
ncf = io.CT_CDF(saveas, 'write')
dimdate = ncf.add_date_dim()
dimidateformat = ncf.add_date_dim_format()
dimregs = ncf.add_region_dim(type='tc')
#
# set title and tell GMT that we are using "pixel registration"
#
setattr(ncf, 'Title', 'CarbonTracker TransCom fluxes')
setattr(ncf, 'node_offset', 1)
#
skip = ncf.has_date(ncfdate)
if skip:
logging.warning('Skipping writing of data for date %s : already present in file %s' % (startdate.strftime('%Y-%m-%d'), saveas))
else:
# Get input data
area = globarea()
infile = os.path.join(dacycle['dir.analysis'], 'data_state_weekly', 'statefluxes.nc')
if not os.path.exists(infile):
logging.error("Needed input file (%s) does not exist yet, please create file first, returning..." % infile)
return None
ncf_in = io.ct_read(infile, 'read')
# Transform data one by one
# Get the date variable, and find index corresponding to the dacycle date
try:
dates = ncf_in.variables['date'][:]
except KeyError:
logging.error("The variable date cannot be found in the requested input file (%s) " % infile)
logging.error("Please make sure you create gridded fluxes before making TC fluxes ")
raise KeyError
try:
index = dates.tolist().index(ncfdate)
except ValueError:
logging.error("The requested cycle date is not yet available in file %s " % infile)
logging.error("Please make sure you create state based fluxes before making TC fluxes")
raise ValueError
# First add the date for this cycle to the file, this grows the unlimited dimension
savedict = ncf.standard_var(varname='date')
savedict['values'] = ncfdate
savedict['dims'] = dimdate
savedict['count'] = index
ncf.add_data(savedict)
# Now convert other variables that were inside the flux_1x1 file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
data = ncf_in.get_variable(vname)[index]
if vname in ['latitude','longitude', 'date', 'idate'] or 'std' in vname:
continue
elif 'ensemble' in vname:
tcdata = []
for member in data:
tcdata.append(statevector.vector2tc(vectordata=member))
tcdata = np.array(tcdata)
try:
cov = tcdata.transpose().dot(tcdata) / (statevector.nmembers - 1)
except:
cov = np.dot(tcdata.transpose(), tcdata) / (statevector.nmembers - 1) # Huygens fix
#print vname,cov.sum()
tcdata = cov
savedict = ncf.standard_var(varname=vname.replace('ensemble', 'cov'))
savedict['units'] = '[mol/region/s]**2'
savedict['dims'] = dimdate + dimregs + dimregs
else:
tcdata = statevector.vector2tc(vectordata=data) # vector to TC
savedict = ncf.standard_var(varname=vname)
savedict['dims'] = dimdate + dimregs
savedict['units'] = 'mol/region/s'
savedict['count'] = index
savedict['values'] = tcdata
ncf.add_data(savedict)
ncf_in.close()
ncf.close()
logging.info("TransCom weekly average fluxes now written")
return saveas
def save_weekly_avg_ext_tc_data(dacycle):
""" Function SaveTCDataExt saves surface flux data to NetCDF files for extended TransCom regions
*** Inputs ***
rundat : a RunInfo object
*** Outputs ***
NetCDF file containing n-hourly global surface fluxes per TransCom region
*** Example ***
./expand_savestate project=enkf_release sd=20000101 ed=20010101 """
#
dirname = create_dirs(os.path.join(dacycle['dir.analysis'], 'data_tc_weekly'))
#
# Some help variables
#
dectime0 = date2num(datetime(2000, 1, 1))
dt = dacycle['cyclelength']
startdate = dacycle['time.start']
enddate = dacycle['time.end']
ncfdate = date2num(startdate) - dectime0 + dt.days / 2.0
logging.debug("DA Cycle start date is %s" % startdate.strftime('%Y-%m-%d %H:%M'))
logging.debug("DA Cycle end date is %s" % enddate.strftime('%Y-%m-%d %H:%M'))
# Write/Create NetCDF output file
# saveas = os.path.join(dirname, 'tc_extfluxes.nc')
ncf = io.CT_CDF(saveas, 'write')
dimdate = ncf.add_date_dim()
dimidateformat = ncf.add_date_dim_format()
dimregs = ncf.add_region_dim(type='tc_ext')
#
# set title and tell GMT that we are using "pixel registration"
#
setattr(ncf, 'Title', 'CarbonTracker TransCom fluxes')
setattr(ncf, 'node_offset', 1)
#
skip = ncf.has_date(ncfdate)
if skip:
logging.warning('Skipping writing of data for date %s : already present in file %s' % (startdate.strftime('%Y-%m-%d'), saveas))
else:
infile = os.path.join(dacycle['dir.analysis'], 'data_tc_weekly', 'tcfluxes.nc')
if not os.path.exists(infile):
logging.error("Needed input file (%s) does not exist yet, please create file first, returning..." % infile)
return None
ncf_in = io.ct_read(infile, 'read')
# Transform data one by one
# Get the date variable, and find index corresponding to the dacycle date
try:
dates = ncf_in.variables['date'][:]
except KeyError:
logging.error("The variable date cannot be found in the requested input file (%s) " % infile)
logging.error("Please make sure you create gridded fluxes before making extended TC fluxes")
raise KeyError
try:
index = dates.tolist().index(ncfdate)
except ValueError:
logging.error("The requested cycle date is not yet available in file %s " % infile)
logging.error("Please make sure you create state based fluxes before making extended TC fluxes ")
raise ValueError
# First add the date for this cycle to the file, this grows the unlimited dimension
savedict = ncf.standard_var(varname='date')
savedict['values'] = ncfdate
savedict['dims'] = dimdate
savedict['count'] = index
ncf.add_data(savedict)
# Now convert other variables that were inside the tcfluxes.nc file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
data = ncf_in.get_variable(vname)[index]
if vname == 'latitude': continue
elif vname == 'longitude': continue
elif vname == 'date': continue
elif vname == 'idate': continue
elif 'cov' in vname:
tcdata = ExtendedTCRegions(data, cov=True)
savedict = ncf.standard_var(varname=vname)
savedict['units'] = '[mol/region/s]**2'
savedict['dims'] = dimdate + dimregs + dimregs
else:
tcdata = ExtendedTCRegions(data, cov=False)
savedict = ncf.standard_var(varname=vname)
savedict['dims'] = dimdate + dimregs
savedict['units'] = 'mol/region/s'
savedict['count'] = index
savedict['values'] = tcdata
ncf.add_data(savedict)
ncf_in.close()
ncf.close()
logging.info("TransCom weekly average extended fluxes now written")
return saveas
def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
"""
Function creates a NetCDF file with output on TransCom regions. It uses the flux input from the
function `save_weekly_avg_1x1_data` to create fluxes of length `nparameters`, which are then projected
onto TC regions using the internal methods from :class:`~da.baseclasses.statevector.StateVector`.
:param dacycle: a :class:`~da.tools.initexit.CycleControl` object
:param StateVector: a :class:`~da.baseclasses.statevector.StateVector`
:rtype: None
This function only read the prior fluxes from the flux_1x1.nc files created before, because we want to convolve
these with the parameters in the statevector. This creates posterior fluxes, and the posterior covariance for the complete
statevector in units of mol/box/s which we then turn into TC fluxes and covariances.
"""
#
dirname = create_dirs(os.path.join(dacycle['dir.analysis'], 'data_%s_weekly' % region_aggregate))
#
# Some help variables
#
dectime0 = date2num(datetime(2000, 1, 1))
dt = dacycle['cyclelength']
startdate = dacycle['time.start']
enddate = dacycle['time.end']
ncfdate = date2num(startdate) - dectime0 + dt.days / 2.0
logging.debug("DA Cycle start date is %s" % startdate.strftime('%Y-%m-%d %H:%M'))
logging.debug("DA Cycle end date is %s" % enddate.strftime('%Y-%m-%d %H:%M'))
logging.debug("Aggregating 1x1 fluxes to %s totals" % region_aggregate)
# Write/Create NetCDF output file
#
saveas = os.path.join(dirname, '%s_fluxes.%s.nc' % (region_aggregate, startdate.strftime('%Y-%m-%d')))
ncf = io.CT_CDF(saveas, 'write')
dimdate = ncf.add_date_dim()
dimidateformat = ncf.add_date_dim_format()
dimgrid = ncf.add_latlon_dim() # for mask
#
# Select regions to aggregate to
#
if region_aggregate == "olson":
regionmask = tc.olson240mask
dimname = 'olson'
dimregs = ncf.add_dim(dimname, regionmask.max())
regionnames = []
for i in range(11):
for j in range(19):
regionnames.append("%s_%s" % (tc.transnams[i], tc.olsonnams[j],))
regionnames.extend(tc.oifnams) xform = False
for i, name in enumerate(regionnames):
lab = 'Aggregate_Region_%03d' % (i + 1,)
setattr(ncf, lab, name)
elif region_aggregate == "olson_extended":
regionmask = tc.olson_ext_mask
dimname = 'olson_ext'
dimregs = ncf.add_dim(dimname, regionmask.max())
xform = False
for i, name in enumerate(tc.olsonextnams):
lab = 'Aggreate_Region_%03d'%(i+1)
setattr(ncf, lab, name)
elif region_aggregate == "transcom":
regionmask = tc.transcommask
dimname = 'tc'
dimregs = ncf.add_region_dim(type='tc')
xform = False
elif region_aggregate == "transcom_extended":
regionmask = tc.transcommask
dimname = 'tc_ext'
dimregs = ncf.add_region_dim(type='tc_ext')
xform = True
elif region_aggregate == "amazon":
regfile = cdf.Dataset(os.path.join(analysisdir,'amazon_mask.nc'))
regionmask = regfile.variables['regionmask'][:]
regfile.close()
dimname = 'amazon'
dimregs = ncf.add_dim(dimname, regionmask.max())
xform = False
elif region_aggregate == "country":
xform = False
countrydict = ct.get_countrydict()
selected = ['Russia', 'Canada', 'China', 'United States', 'EU27', 'Brazil', 'Australia', 'India'] #,'G8','UNFCCC_annex1','UNFCCC_annex2']
regionmask = np.zeros((180, 360,), 'float')
for i, name in enumerate(selected):
lab = 'Country_%03d' % (i + 1,)
setattr(ncf, lab, name)
if name == 'EU27':
namelist = ct.EU27
elif name == 'EU25':
namelist = ct.EU25
elif name == 'G8':
namelist = ct.G8
elif name == 'UNFCCC_annex1':
namelist = ct.annex1
elif name == 'UNFCCC_annex2':
namelist = ct.annex2
else:
namelist = [name]
for countryname in namelist:
try:
country = countrydict[countryname]
regionmask.put(country.gridnr, i + 1)
except:
continue
dimname = 'country'
dimregs = ncf.add_dim(dimname, regionmask.max())
#
skip = ncf.has_date(ncfdate)
if skip:
logging.warning('Skipping writing of data for date %s : already present in file %s' % (startdate.strftime('%Y-%m-%d'), saveas))
else:
#
# set title and tell GMT that we are using "pixel registration"
#
setattr(ncf, 'Title', 'CTDAS Aggregated fluxes')
setattr(ncf, 'node_offset', 1)
savedict = ncf.standard_var('unknown')
savedict['name'] = 'regionmask'
savedict['comment'] = 'numerical mask used to aggregate 1x1 flux fields, each integer 0,...,N is one region aggregated'
savedict['values'] = regionmask.tolist()
savedict['units'] = '-'
savedict['dims'] = dimgrid
savedict['count'] = 0
ncf.add_data(savedict)
# Get input data from 1x1 degree flux files
area = globarea()
infile = os.path.join(dacycle['dir.analysis'], 'data_flux1x1_weekly', 'flux_1x1.%s.nc' % startdate.strftime('%Y-%m-%d'))
if not os.path.exists(infile):
logging.error("Needed input file (%s) does not exist yet, please create file first, returning..." % infile)
return None
ncf_in = io.ct_read(infile, 'read')
# Transform data one by one
# Get the date variable, and find index corresponding to the dacycle date
try:
dates = ncf_in.variables['date'][:]
except KeyError:
logging.error("The variable date cannot be found in the requested input file (%s) " % infile)
logging.error("Please make sure you create gridded fluxes before making TC fluxes ")
raise KeyError
try:
index = dates.tolist().index(ncfdate)
except ValueError:
logging.error("The requested cycle date is not yet available in file %s " % infile)
logging.error("Please make sure you create state based fluxes before making TC fluxes ")
raise ValueError
# First add the date for this cycle to the file, this grows the unlimited dimension
savedict = ncf.standard_var(varname='date')
savedict['values'] = ncfdate
savedict['dims'] = dimdate
savedict['count'] = index
ncf.add_data(savedict)
# Now convert other variables that were inside the statevector file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
if vname == 'latitude': continue
elif vname == 'longitude': continue
elif vname == 'date': continue
elif vname == 'idate': continue
elif 'std' in vname: continue
elif 'ensemble' in vname:
data = ncf_in.get_variable(vname)[index]
dimensemble = ncf.add_dim('members', data.shape[0])
regiondata = []
for member in data:
aggdata = state_to_grid(member * area, regionmask, reverse=True, mapname=region_aggregate)
regiondata.append(aggdata)
regiondata = np.array(regiondata)
try:
regioncov = regiondata.transpose().dot(regiondata) / (data.shape[0] - 1)
except:
regioncov = np.dot(regiondata.transpose(), regiondata) / (data.shape[0] - 1) # Huygens fix
if xform:
regiondata = ExtendedTCRegions(regiondata,cov=False)
regioncov = ExtendedTCRegions(regioncov,cov=True)
savedict = ncf.standard_var(varname=vname)
savedict['name'] = vname.replace('ensemble','covariance')
savedict['units'] = '[mol/region/s]^2'
savedict['dims'] = dimdate + dimregs + dimregs
savedict['count'] = index
savedict['values'] = regioncov
ncf.add_data(savedict)
savedict = ncf.standard_var(varname=vname)
savedict['name'] = vname
savedict['units'] = 'mol/region/s'
savedict['dims'] = dimdate + dimensemble + dimregs
elif 'flux' in vname:
data = ncf_in.get_variable(vname)[index]
regiondata = state_to_grid(data * area, regionmask, reverse=True, mapname=region_aggregate)
if xform:
regiondata = ExtendedTCRegions(regiondata)
savedict = ncf.standard_var(varname=vname)
savedict['dims'] = dimdate + dimregs
savedict['units'] = 'mol/region/s'
else:
data = ncf_in.get_variable(vname)[:]
regiondata = state_to_grid(data, regionmask, reverse=True, mapname=region_aggregate)
if xform:
regiondata = ExtendedTCRegions(regiondata)
savedict = ncf.standard_var(varname=vname)
savedict['dims'] = dimdate + dimregs
savedict['count'] = index
savedict['values'] = regiondata
ncf.add_data(savedict)
ncf_in.close()
ncf.close()
logging.info("%s aggregated weekly average fluxes now written" % dimname)
return saveas
def save_time_avg_data(dacycle, infile, avg='monthly'):
""" Function saves time mean surface flux data to NetCDF files
*** Inputs *** rundat : a RunInfo object
*** Outputs ***
daily NetCDF file containing 1-hourly global surface fluxes at 1x1 degree
*** Example ***
./expand_savestate project=enkf_release sd=20000101 ed=20010101 """
if 'weekly' in infile:
intime = 'weekly'
if 'monthly' in infile:
intime = 'monthly'
if 'yearly' in infile:
intime = 'yearly'
dirname, filename = os.path.split(infile)
outdir = create_dirs(os.path.join(dacycle['dir.analysis'], dirname.replace(intime, avg)))
dectime0 = date2num(datetime(2000, 1, 1))
# Create NetCDF output file
#
saveas = os.path.join(outdir, filename)
ncf = io.CT_CDF(saveas, 'create')
dimdate = ncf.add_date_dim()
#
# Open input file specified from the command line
#
if not os.path.exists(infile):
logging.error("Needed input file (%s) not found. Please create this first:" % infile)
logging.error("returning...")
return None
else:
pass
file = io.ct_read(infile, 'read')
datasets = file.variables.keys()
date = file.get_variable('date')
globatts = file.ncattrs()
for att in globatts:
attval = file.getncattr(att)
if not att in ncf.ncattrs():
ncf.setncattr(att, attval)
time = [datetime(2000, 1, 1) + timedelta(days=d) for d in date]
# loop over datasets in infile, skip idate and date as we will make new time axis for the averaged data
for sds in ['date'] + datasets:
# get original data
data = file.get_variable(sds)
varatts = file.variables[sds].ncattrs()
vardims = file.variables[sds].dimensions
#
# Depending on dims of input dataset, create dims for output dataset. Note that we add the new dimdate now.
#
for d in vardims:
if 'date' in d:
continue
if d in ncf.dimensions.keys():
pass
else:
dim = ncf.createDimension(d, size=len(file.dimensions[d]))
savedict = ncf.standard_var(sds) savedict['name'] = sds
savedict['dims'] = vardims
savedict['units'] = file.variables[sds].units
savedict['long_name'] = file.variables[sds].long_name
savedict['comment'] = file.variables[sds].comment
savedict['standard_name'] = file.variables[sds].standard_name
savedict['count'] = 0
if not 'date' in vardims:
savedict['values'] = data
ncf.add_data(savedict)
else:
if avg == 'monthly':
time_avg, data_avg = timetools.monthly_avg(time, data)
elif avg == 'seasonal':
time_avg, data_avg = timetools.season_avg(time, data)
elif avg == 'yearly':
time_avg, data_avg = timetools.yearly_avg(time, data)
elif avg == 'longterm':
time_avg, data_avg = timetools.longterm_avg(time, data)
time_avg = [time_avg]
data_avg = [data_avg]
else:
raise ValueError, 'Averaging (%s) does not exist' % avg
count = -1
for dd, data in zip(time_avg, data_avg):
count = count + 1
if sds == 'date':
savedict['values'] = date2num(dd) - dectime0
else:
savedict['values'] = data
savedict['count'] = count
ncf.add_data(savedict, silent=True)
sys.stdout.write('.')
sys.stdout.write('\n')
sys.stdout.flush()
# end NetCDF file access
file.close()
ncf.close()
logging.info("------------------- Finished time averaging---------------------------------")
return saveas
if __name__ == "__main__":
from da.tools.initexit import CycleControl
from da.carbondioxide.dasystem import CO2DaSystem
from da.carbondioxide.statevector import CO2StateVector
sys.path.append('../../')
logging.root.setLevel(logging.DEBUG)
dacycle = CycleControl(args={'rc':'../../ctdas-od-gfed2-glb6x4-obspack-full.rc'})
dasystem = CO2DaSystem('../rc/carbontracker_ct09_opfnew.rc')
dacycle.dasystem = dasystem
dacycle.setup()
dacycle.parse_times()
statevector = CO2StateVector()
statevector.setup(dacycle)
while dacycle['time.start'] < dacycle['time.finish']: save_weekly_avg_1x1_data(dacycle, statevector)
save_weekly_avg_state_data(dacycle, statevector)
save_weekly_avg_tc_data(dacycle, statevector)
save_weekly_avg_ext_tc_data(dacycle)
save_weekly_avg_agg_data(dacycle, region_aggregate='olson')
save_weekly_avg_agg_data(dacycle, region_aggregate='olson_extended')
save_weekly_avg_agg_data(dacycle, region_aggregate='transcom')
save_weekly_avg_agg_data(dacycle, region_aggregate='transcom_extended')
save_weekly_avg_agg_data(dacycle, region_aggregate='country')
save_weekly_avg_agg_data(dacycle, region_aggregate='amazon')
dacycle.advance_cycle_times()
statevector = None # free memory
sys.exit(0)