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Commit 933f10d4 authored by Woude, Auke van der's avatar Woude, Auke van der
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Remove R code; STILT ObsOp in Python

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ctdas-stilt.jb deleted 100755 → 0
View file @ f0075661
#!/bin/sh
#$ das.py
#$ co2
#$ nserial 1
#$ 06:30:00
#$ /bin/sh
echo "All output piped to file template.log"
#source /usr/local/Modules/3.2.8/init/sh
#source /opt/intel/bin/ifortvars.sh intel64
export HOST='daint'
#module load python
export icycle_in_job=999
python ctdas-stilt.py rc=ctdas-stilt.rc -v $1 >& ctdas-stilt.log &
ctdas-stilt.log deleted 100644 → 0
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Traceback (most recent call last):
File "ctdas-stilt.py", line 30, in <module>
from da.platform.cartesius import CartesiusPlatform
File "/nfs/home5/awoude/CTDAS/da/platform/cartesius.py", line 135
print 'output', output
^
SyntaxError: Missing parentheses in call to 'print'. Did you mean print('output', output)?
ctdas-stilt.py deleted 100755 → 0
View file @ f0075661
"""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
#################################################################################################
# First order of business is always to make all other python modules accessible through the path
#################################################################################################
import sys
import os
import logging
sys.path.append(os.getcwd())
#################################################################################################
# Next, import the tools needed to initialize a data assimilation cycle
#################################################################################################
from da.tools.initexit import start_logger, validate_opts_args, parse_options, CycleControl
from da.tools.pipeline import forward_pipeline, header, footer
from da.platform.cartesius import CartesiusPlatform
from da.baseclasses.dasystem import DaSystem
from da.baseclasses.statevector import StateVector
from da.carbondioxide.obspack_globalviewplus2 import ObsPackObservations
from da.carbondioxide.optimizer import CO2Optimizer
from da.baseclasses.observationoperator import ObservationOperator
#from da.analysis.expand_fluxes import save_weekly_avg_1x1_data, save_weekly_avg_state_data, save_weekly_avg_tc_data, save_weekly_avg_ext_tc_data
#from da.analysis.expand_molefractions import write_mole_fractions
#################################################################################################
# Parse and validate the command line options, start logging
#################################################################################################
start_logger()
opts, args = parse_options()
opts, args = validate_opts_args(opts, args)
#################################################################################################
# Create the Cycle Control object for this job
#################################################################################################
dacycle = CycleControl(opts, args)
platform = MaunaloaPlatform()
dasystem = DaSystem(dacycle['da.system.rc'])
obsoperator = ObservationOperator(dacycle['da.obsoperator.rc'])
samples = ObsPackObservations()
statevector = StateVector()
optimizer = CO2Optimizer()
##########################################################################################
################### ENTER THE PIPELINE WITH THE OBJECTS PASSED BY THE USER ###############
##########################################################################################
logging.info(header + "Entering Pipeline " + footer)
#ensemble_smoother_pipeline(dacycle, platform, dasystem, samples, statevector, obsoperator, optimizer)
forward_pipeline(dacycle, platform, dasystem, samples, statevector, obsoperator, optimizer)
##########################################################################################
################### All done, extra stuff can be added next, such as analysis
##########################################################################################
#logging.info(header + "Starting analysis" + footer)
#sys.exit(0)
#
#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)
#write_mole_fractions(dacycle)
#
#sys.exit(0)
ctdas-stilt.rc deleted 100644 → 0
View file @ f0075661
! 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/>.
! author: Wouter Peters
!
! This is a blueprint for an rc-file used in CTDAS. Feel free to modify it, and please go to the main webpage for further documentation.
!
! Note that rc-files have the convention that commented lines start with an exclamation mark (!), while special lines start with a hashtag (#).
!
! When running the script start_ctdas.sh, this /.rc file will be copied to your run directory, and some items will be replaced for you.
! The result will be a nearly ready-to-go rc-file for your assimilation job. The entries and their meaning are explained by the comments below.
!
!
! HISTORY:
!
! Created on August 20th, 2013 by Wouter Peters
!
!
! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS
time.start : 2016-01-10 00:00:00
time.finish : 2016-01-15 00:00:00
! Whether to restart the CTDAS system from a previous cycle, or to start the sequence fresh. Valid entries are T/F/True/False/TRUE/FALSE
time.restart : False
! The length of a cycle is given in days, such that the integer 7 denotes the typically used weekly cycle. Valid entries are integers > 1
time.cycle : 7
! The number of cycles of lag to use for a smoother version of CTDAS. CarbonTracker CO2 typically uses 5 weeks of lag. Valid entries are integers > 0
time.nlag : 3
! The directory under which the code, input, and output will be stored. This is the base directory for a run. The word
! '/' will be replaced through the start_ctdas.sh script by a user-specified folder name. DO NOT REPLACE
dir.da_run : /projects/0/ctdas/awoude
! The resources used to complete the data assimilation experiment. This depends on your computing platform.
! The number of cycles per job denotes how many cycles should be completed before starting a new process or job, this
! allows you to complete many cycles before resubmitting a job to the queue and having to wait again for resources.
! Valid entries are integers > 0
da.resources.ncycles_per_job : 1
! The ntasks specifies the number of threads to use for the MPI part of the code, if relevant. Note that the CTDAS code
! itself is not parallelized and the python code underlying CTDAS does not use multiple processors. The chosen observation
! operator though might use many processors, like TM5. Valid entries are integers > 0
da.resources.ntasks : 1
! This specifies the amount of wall-clock time to request for each job. Its value depends on your computing platform and might take
! any form appropriate for your system. Typically, HPC queueing systems allow you a certain number of hours of usage before
! your job is killed, and you are expected to finalize and submit a next job before that time. Valid entries are strings.
da.resources.ntime : 04:00:00
! The resource settings above will cause the creation of a job file in which 2 cycles will be run, and 30 threads
! are asked for a duration of 4 hours
!
! Info on the DA system used, this depends on your application of CTDAS and might refer to for instance CO2, or CH4 optimizations.
!
da.system : CarbonTracker
! The specific settings for your system are read from a separate rc-file, which points to the data directories, observations, etc
da.system.rc : da/rc/carbontracker_random.rc
! This flag should probably be moved to the da.system.rc file. It denotes which type of filtering to use in the optimizer
da.system.localization : None
! Info on the observation operator to be used, these keys help to identify the settings for the transport model in this case
da.obsoperator : STILT
!
! The TM5 transport model is controlled by an rc-file as well. The value below refers to the configuration of the TM5 model to
! be used as observation operator in this experiment.
!
da.obsoperator.home : da/stilt
da.obsoperator.rc : ${da.obsoperator.home}/stilt.rc
!
! The number of ensemble members used in the experiment. Valid entries are integers > 2
!
da.optimizer.nmembers : 10
! Finally, info on the archive task, if any. Archive tasks are run after each cycle to ensure that the results of each cycle are
! preserved, even if you run on scratch space or a temporary disk. Since an experiment can take multiple weeks to complete, moving
! your results out of the way, or backing them up, is usually a good idea. Note that the tasks are commented and need to be uncommented
! to use this feature.
! The following key identifies that two archive tasks will be executed, one called 'alldata' and the other 'resultsonly'.
!task.rsync : alldata onlyresults
! The specifics for the first task.
! 1> Which source directories to back up. Valid entry is a list of folders separated by spaces
! 2> Which destination directory to use. Valid entries are a folder name, or server and folder name in rsync format as below
! 3> Which flags to add to the rsync command
! The settings below will result in an rsync command that looks like:
!
! rsync -auv -e ssh ${dir.da_run} you@yourserver.com:/yourfolder/
!
!task.rsync.alldata.sourcedirs : ${dir.da_run}
!task.rsync.alldata.destinationdir : you@yourserver.com:/yourfolder/
!task.rsync.alldata.flags g -auv -e ssh
! Repeated for rsync task 2, note that we only back up the analysis and output dirs here
!task.rsync.onlyresults.sourcedirs : ${dir.da_run}/analysis ${dir.da_run}/output
!task.rsync.onlyresults.destinationdir : you@yourserver.com:/yourfolder/
!task.rsync.onlyresults.flags : -auv -e ssh
da/analysis/expand_fluxes.py
View file @ 933f10d4
......@@ -45,7 +45,7 @@ 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)
response = input(txt)
if response.lower() in yes:
return 1
if response.lower() in all:
......@@ -113,7 +113,7 @@ def save_weekly_avg_1x1_data(dacycle, statevector):
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():
if dacycle.dasystem['background.co2.biosam.flux'] in list(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']))
......@@ -162,7 +162,7 @@ def save_weekly_avg_1x1_data(dacycle, statevector):
#
# if prior, do not multiply fluxes with parameters, otherwise do
#
print gridensemble.shape, bio.shape, gridmean.shape
print(gridensemble.shape, bio.shape, gridmean.shape)
biomapped = bio * gridmean
oceanmapped = ocean * gridmean
biovarmapped = bio * gridensemble
......@@ -184,7 +184,7 @@ def save_weekly_avg_1x1_data(dacycle, statevector):
savedict['count'] = next
ncf.add_data(savedict)
print biovarmapped.shape
print(biovarmapped.shape)
savedict = ncf.standard_var(varname='bio_flux_%s_ensemble' % qual_short)
savedict['values'] = biovarmapped.tolist()
savedict['dims'] = dimdate + dimensemble + dimgrid
......@@ -301,7 +301,7 @@ def save_weekly_avg_state_data(dacycle, statevector):
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():
if dacycle.dasystem['background.co2.biosam.flux'] in list(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']))
......@@ -555,7 +555,7 @@ def save_weekly_avg_tc_data(dacycle, statevector):
# Now convert other variables that were inside the flux_1x1 file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
for vname, vprop in vardict.items():
data = ncf_in.get_variable(vname)[index]
......@@ -680,7 +680,7 @@ def save_weekly_avg_ext_tc_data(dacycle):
# Now convert other variables that were inside the tcfluxes.nc file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
for vname, vprop in vardict.items():
data = ncf_in.get_variable(vname)[index]
......@@ -899,7 +899,7 @@ def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
# Now convert other variables that were inside the statevector file
vardict = ncf_in.variables
for vname, vprop in vardict.iteritems():
for vname, vprop in vardict.items():
if vname == 'latitude': continue
elif vname == 'longitude': continue
elif vname == 'date': continue
......@@ -1014,7 +1014,7 @@ def save_time_avg_data(dacycle, infile, avg='monthly'):
pass
file = io.ct_read(infile, 'read')
datasets = file.variables.keys()
datasets = list(file.variables.keys())
date = file.get_variable('date')
globatts = file.ncattrs()
......@@ -1042,7 +1042,7 @@ def save_time_avg_data(dacycle, infile, avg='monthly'):
for d in vardims:
if 'date' in d:
continue
if d in ncf.dimensions.keys():
if d in list(ncf.dimensions.keys()):
pass
else:
dim = ncf.createDimension(d, size=len(file.dimensions[d]))
......@@ -1072,7 +1072,7 @@ def save_time_avg_data(dacycle, infile, avg='monthly'):
time_avg = [time_avg]
data_avg = [data_avg]
else:
raise ValueError, 'Averaging (%s) does not exist' % avg
raise ValueError('Averaging (%s) does not exist' % avg)
count = -1
for dd, data in zip(time_avg, data_avg):
......
da/analysis/expand_fluxes.py.bak 0 → 100755
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This diff is collapsed.
da/analysis/merge_ctdas_runs.py
View file @ 933f10d4
......@@ -28,7 +28,7 @@ import datetime as dt
import os
import sys
import shutil
import time_avg_fluxes as tma
from . import time_avg_fluxes as tma
basedir = '/Storage/CO2/ingrid/'
basedir2 = '/Storage/CO2/peters/'
......@@ -60,12 +60,12 @@ if __name__ == "__main__":
os.makedirs(os.path.join(targetdir,'analysis','data_%s_weekly'%nam) )
timedirs=[]
for ss,vv in sources.iteritems():
for ss,vv in sources.items():
sds,eds = ss.split(' through ')
sd = dt.datetime.strptime(sds,'%Y-%m-%d')
ed = dt.datetime.strptime(eds,'%Y-%m-%d')
timedirs.append([sd,ed,vv])
print sd,ed, vv
print(sd,ed, vv)
while dacycle['time.start'] < dacycle['time.end']:
......
da/analysis/merge_ctdas_runs.py.bak 0 → 100755
View file @ 933f10d4
"""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
# merge_ctdas_runs.py
"""
Author : peters
Revision History:
File created on 14 Jul 2014.
This scrip merges the analysis directory from multiple projects into one new folder.
It steps over existing analysis output files from weekly means, and then averages these to daily/monthy/yearly values.
"""
import datetime as dt
import os
import sys
import shutil
import time_avg_fluxes as tma
basedir = '/Storage/CO2/ingrid/'
basedir2 = '/Storage/CO2/peters/'
targetproject = 'geocarbon-ei-sibcasa-gfed4-zoom-gridded-combined-convec-20011230-20130101'
targetdir = os.path.join(basedir2,targetproject)
sources = {
'2000-01-01 through 2011-12-31': os.path.join(basedir,'carbontracker','geocarbon-ei-sibcasa-gfed4-zoom-gridded-convec-combined'),
'2012-01-01 through 2012-12-31': os.path.join(basedir2,'geocarbon-ei-sibcasa-gfed4-zoom-gridded-convec-20111231-20140101'),
}
dirs = ['flux1x1','transcom','country','olson']
dacycle = {}
dacycle['time.start'] = dt.datetime(2000,12,30)
dacycle['time.end'] = dt.datetime(2013,1,1)
dacycle['cyclelength'] = dt.timedelta(days=7)
dacycle['dir.analysis'] = os.path.join(targetdir,'analysis')
if __name__ == "__main__":
if not os.path.exists(targetdir):
os.makedirs(targetdir)
if not os.path.exists(os.path.join(targetdir,'analysis')):
os.makedirs(os.path.join(targetdir,'analysis') )
for nam in dirs:
if not os.path.exists(os.path.join(targetdir,'analysis','data_%s_weekly'%nam)):
os.makedirs(os.path.join(targetdir,'analysis','data_%s_weekly'%nam) )
timedirs=[]
for ss,vv in sources.iteritems():
sds,eds = ss.split(' through ')
sd = dt.datetime.strptime(sds,'%Y-%m-%d')
ed = dt.datetime.strptime(eds,'%Y-%m-%d')
timedirs.append([sd,ed,vv])
print sd,ed, vv
while dacycle['time.start'] < dacycle['time.end']:
# copy the weekly flux1x1 file from the original dir to the new project dir
for td in timedirs:
if dacycle['time.start'] >= td[0] and dacycle['time.start'] <= td[1]:
indir=td[2]
# Now time avg new fluxes
infile = os.path.join(indir,'analysis','data_flux1x1_weekly','flux_1x1.%s.nc'%(dacycle['time.start'].strftime('%Y-%m-%d') ) )
#print os.path.exists(infile),infile
shutil.copy(infile,infile.replace(indir,targetdir) )
tma.time_avg(dacycle,avg='flux1x1')
infile = os.path.join(indir,'analysis','data_transcom_weekly','transcom_fluxes.%s.nc'%(dacycle['time.start'].strftime('%Y-%m-%d') ) )
#print os.path.exists(infile),infile
shutil.copy(infile,infile.replace(indir,targetdir) )
tma.time_avg(dacycle,avg='transcom')
infile = os.path.join(indir,'analysis','data_olson_weekly','olson_fluxes.%s.nc'%(dacycle['time.start'].strftime('%Y-%m-%d') ) )
#print os.path.exists(infile),infile
shutil.copy(infile,infile.replace(indir,targetdir) )
tma.time_avg(dacycle,avg='olson')
infile = os.path.join(indir,'analysis','data_country_weekly','country_fluxes.%s.nc'%(dacycle['time.start'].strftime('%Y-%m-%d') ) )
#print os.path.exists(infile),infile
shutil.copy(infile,infile.replace(indir,targetdir) )
tma.time_avg(dacycle,avg='country')
dacycle['time.start'] += dacycle['cyclelength']
da/analysis/siteseries.py
View file @ 933f10d4
......@@ -40,8 +40,8 @@ import logging
import copy
from da.analysis.summarize_obs import nice_lon, nice_lat, nice_alt
from PIL import Image
import urllib2
import StringIO
import urllib.request, urllib.error, urllib.parse
import io
"""
General data needed to set up proper aces inside a figure instance
......@@ -336,7 +336,7 @@ def timehistograms_new(fig, infile, option='final'):
# Get a scaling factor for the x-axis range. Now we will include 5 standard deviations
sc = res.std()
print 'sc',sc
print('sc',sc)
# If there is too little data for a reasonable PDF, skip to the next value in the loop
if res.shape[0] < 10: continue
......@@ -435,12 +435,12 @@ def timehistograms_new(fig, infile, option='final'):
#fig.text(0.12,0.16,str1,fontsize=0.8*fontsize,color='0.75')
try:
img = urllib2.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
img = urllib.request.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
except:
logging.warning("No logo found for this program, continuing...")
return fig
im = Image.open(StringIO.StringIO(img))
im = Image.open(io.StringIO(img))
height = im.size[1]
width = im.size[0]
......@@ -674,12 +674,12 @@ def timevssite_new(fig, infile):
#fig.text(0.12, 0.16, str1, fontsize=0.8 * fontsize, color='0.75')
try:
img = urllib2.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
img = urllib.request.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
except:
logging.warning("No logo found for this program, continuing...")
return fig
im = Image.open(StringIO.StringIO(img))
im = Image.open(io.StringIO(img))
height = im.size[1]
width = im.size[0]
......@@ -933,12 +933,12 @@ def residuals_new(fig, infile, option):
#fig.text(0.12, 0.16, str1, fontsize=0.8 * fontsize, color='0.75')
try:
img = urllib2.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
img = urllib.request.urlopen('http://www.esrl.noaa.gov/gmd/webdata/ccgg/ObsPack/images/logos/'+SDSInfo['lab_1_logo']).read()
except:
logging.warning("No logo found for this program, continuing...")
return fig
im = Image.open(StringIO.StringIO(img))
im = Image.open(io.StringIO(img))
height = im.size[1]
width = im.size[0]
......
da/analysis/siteseries.py.bak 0 → 100755
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da/analysis/summarize_obs.py
View file @ 933f10d4
......@@ -97,9 +97,9 @@ def summarize_obs(analysisdir, printfmt='html'):
infiles = [os.path.join(mrdir, f) for f in mrfiles if f.endswith('.nc')]
if printfmt == 'tex':
print '\\begin{tabular*}{\\textheight}{l l l l r r r r}'
print 'Code & Name & Lat, Lon, Elev & Lab & N (flagged) & $\\sqrt{R}$ &Inn \\XS &Bias\\\\'
print '\hline\\\\ \n\multicolumn{8}{ c }{Semi-Continuous Surface Samples}\\\\[3pt] '
print('\\begin{tabular*}{\\textheight}{l l l l r r r r}')
print('Code & Name & Lat, Lon, Elev & Lab & N (flagged) & $\\sqrt{R}$ &Inn \\XS &Bias\\\\')
print('\hline\\\\ \n\multicolumn{8}{ c }{Semi-Continuous Surface Samples}\\\\[3pt] ')
fmt = '%8s & ' + ' %55s & ' + '%20s &' + '%6s &' + ' %4d (%d) & ' + ' %5.2f & ' + ' %5.2f & ' + '%+5.2f \\\\'
elif printfmt == 'html':
tablehead = \
......@@ -136,7 +136,7 @@ def summarize_obs(analysisdir, printfmt='html'):
<TD>%s</TD>\n \
</TR>\n"""
elif printfmt == 'scr':
print 'Code Site NObs flagged R Inn X2'
print('Code Site NObs flagged R Inn X2')
fmt = '%8s ' + ' %55s %s %s' + ' %4d ' + ' %4d ' + ' %5.2f ' + ' %5.2f'
table = []
......@@ -363,15 +363,15 @@ def make_map(analysisdir): #makes a map of amount of assimilated observations pe
ax.annotate(labs[i],xy=m(172,86-count),xycoords='data',fontweight='bold')
count = count + 4
fig.text(0.15,0.945,u'\u2022',fontsize=35,color='blue')
fig.text(0.15,0.945,'\u2022',fontsize=35,color='blue')
fig.text(0.16,0.95,': N<250',fontsize=24,color='blue')
fig.text(0.30,0.94,u'\u2022',fontsize=40,color='green')
fig.text(0.30,0.94,'\u2022',fontsize=40,color='green')
fig.text(0.31,0.95,': N<500',fontsize=24,color='green')
fig.text(0.45,0.94,u'\u2022',fontsize=45,color='orange')
fig.text(0.45,0.94,'\u2022',fontsize=45,color='orange')
fig.text(0.46,0.95,': N<750',fontsize=24,color='orange')
fig.text(0.60,0.939,u'\u2022',fontsize=50,color='brown')
fig.text(0.60,0.939,'\u2022',fontsize=50,color='brown')
fig.text(0.61,0.95,': N<1000',fontsize=24,color='brown')
fig.text(0.75,0.938,u'\u2022',fontsize=55,color='red')
fig.text(0.75,0.938,'\u2022',fontsize=55,color='red')
fig.text(0.765,0.95,': N>1000',fontsize=24,color='red')
ax.set_title('Assimilated observations',fontsize=24)
......
da/analysis/summarize_obs.py.bak 0 → 100755
View file @ 933f10d4
This diff is collapsed.
da/analysis/time_avg_fluxes.py
View file @ 933f10d4
......@@ -33,12 +33,12 @@ def time_avg(dacycle,avg='transcom'):
""" Function to create a set of averaged files in a folder, needed to make longer term means """
if avg not in ['transcom','transcom_extended','olson','olson_extended','country','flux1x1']:
raise IOError,'Choice of averaging invalid'
raise IOError('Choice of averaging invalid')
analysisdir = dacycle['dir.analysis']
if not os.path.exists(analysisdir):
raise IOError,'analysis dir requested (%s) does not exist, exiting...'%analysisdir
raise IOError('analysis dir requested (%s) does not exist, exiting...'%analysisdir)
daily_avg(dacycle,avg)
......@@ -68,14 +68,14 @@ def daily_avg(dacycle,avg):
""" Function to create a set of daily files in a folder, needed to make longer term means """
if avg not in ['transcom','transcom_extended','olson','olson_extended','country','flux1x1']:
raise IOError,'Choice of averaging invalid'
raise IOError('Choice of averaging invalid')
analysisdir = dacycle['dir.analysis']
weekdir = os.path.join(analysisdir , 'data_%s_weekly'%avg)
daydir = os.path.join(analysisdir , 'data_%s_daily'%avg)
if not os.path.exists(daydir):
print "Creating new output directory " + daydir
print("Creating new output directory " + daydir)
os.makedirs(daydir)
files = os.listdir(weekdir)
......@@ -88,7 +88,7 @@ def daily_avg(dacycle,avg):
dt = dacycle['cyclelength']
for k,v in fileinfo.iteritems():
for k,v in fileinfo.items():
cycle_file = os.path.join(weekdir,k)
for i in range(abs(dt.days)):
daily_file = os.path.join(daydir,'%s_fluxes.%s.nc'%(avg,(v+datetime.timedelta(days=i)).strftime('%Y-%m-%d')))
......@@ -100,7 +100,7 @@ def monthly_avg(dacycle,avg):
""" Function to average a set of files in a folder from daily to monthly means """