def temporal_corr(self):
""" Correlation entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de Correlation uncentered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_corr = () #Initialise un tuple
self.temp_corr = statistics.correlation(self.model, self.obs, axis=0)
gg = get_grid(self.model)
set_grid(self.temp_corr, gg, axes=True)
def temporal_corr(self):
""" Correlation entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de Correlation uncentered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_corr = () #Initialise un tuple
self.temp_corr = statistics.correlation(self.model, self.obs, axis = 0)
gg = get_grid(self.model)
set_grid(self.temp_corr, gg, axes=True)
# Example 2: # In this example, we compute the spatial correlation between 2 fields # - say the mean surface air temperature for the 1980-1989 period # versus each month of the 1980-1989 period. # #******************************************************************************* # # First compute the average for the 1980-1990 period (designated ncep2 above) # mncep2 = MV2.average(ncep2) # # And now correlate this pattern with each month of ncep2 # cor = statistics.correlation(ncep2, mncep2, axis='xy') print 'Correlation:' print cor #******************************************************************************* # # Example 3: # We take the correlation example above a step further and specify # that the correlation be computed with weights based on latitude # area taken into account. Note that the correlation can also be # weighted over axes of our choice. #******************************************************************************* # # To compute the latitude area weights, we take the difference of the sine of
# Example 2: # In this example, we compute the spatial correlation between 2 fields # - say the mean surface air temperature for the 1980-1989 period # versus each month of the 1980-1989 period. # #******************************************************************************* # # First compute the average for the 1980-1990 period (designated ncep2 above) # mncep2 = MV2.average(ncep2) # # And now correlate this pattern with each month of ncep2 # cor = statistics.correlation(ncep2, mncep2, axis='xy') print 'Correlation:' print cor #******************************************************************************* # # Example 3: # We take the correlation example above a step further and specify # that the correlation be computed with weights based on latitude # area taken into account. Note that the correlation can also be # weighted over axes of our choice. #******************************************************************************* # # To compute the latitude area weights, we take the difference of the sine of # the latitude bounds for each latitude point in the grid. First let us get the
# add id to data
z_diff.id = 'zonal_diff'
print 'Zonal data shape (after): ', z_diff.shape
# global differences
gl_diff = cdutil.averager(diff, axis='xy')
x = vcs.init()
x.setcolormap('default')
fill = x.getisofill('default')
x.plot(z_diff, fill)
x.clear()
x.plot(gl_diff)
cor_t = statistics.correlation(data1, data2, axis='xy')
# temporal correlation map betwen these to time-series
cor_m = statistics.correlation(data1, data2, axis='t')
# temporal rms difference between the two time series
rms = statistics.rms(data1, data2, axis='t')
x.clear()
x.plot(cor_m, fill)
x.clear()
x.plot(cor_t)
x.clear()
x.plot(rms, fill)
slope1, intercept1 = statistics.linearregression(data1, axis='t')
var.setAxis(2, lon)
solver = Eof(var, weights='area')
eofs = solver.eofs(neofs=num, eofscaling=scaling)
pc = solver.pcs(npcs=num, pcscaling=scaling)
vari = solver.varianceFraction(num)
eigv = solver.eigenvalues(num)
return eofs, pc, vari, eigv
ERAsp.setAxis(0, set_time_axis(ERAsp.shape[0]))
NOAsp.setAxis(0, set_time_axis(NOAsp.shape[0]))
OBSsp.setAxis(0, set_time_axis(OBSsp.shape[0]))
eofs1, pc1, vari1, eigv1 = get_EOFs(ERAsp, num=3, scaling=0)
eofs2, pc2, vari2, eigv2 = get_EOFs(NOAsp, num=3, scaling=0)
eofs3, pc3, vari3, eigv3 = get_EOFs(OBSsp, num=3, scaling=0)
cor11 = np.abs(stat.correlation(eofs1[0], eofs3[0], axis='yx'))
cor21 = np.abs(stat.correlation(eofs2[0], eofs3[0], axis='yx'))
cor12 = np.abs(stat.correlation(eofs1[1], eofs3[1], axis='yx'))
cor22 = np.abs(stat.correlation(eofs2[1], eofs3[1], axis='yx'))
cor13 = np.abs(stat.correlation(eofs1[2], eofs3[2], axis='yx'))
cor23 = np.abs(stat.correlation(eofs2[2], eofs3[2], axis='yx'))
print(cor11, cor21)
print(cor12, cor22)
print(cor13, cor23)
print()
print((cor21 - cor11) / cor11 * 100)
print((cor22 - cor12) / cor12 * 100)
print((cor23 - cor13) / cor13 * 100)
plt.bar(np.r_[0, 4, 8], np.r_[cor11, cor12, cor13], width=1)
plt.bar(np.r_[1, 5, 9], np.r_[cor21, cor22, cor23], width=1)
plt.ylim(0, 1)
def monsoon_wang_runner(args):
# args = P.parse_args(sys.argv[1:])
modpath = genutil.StringConstructor(args.test_data_path)
modpath.variable = args.modvar
outpathdata = args.results_dir
if isinstance(args.modnames, str):
mods = eval(args.modnames)
else:
mods = args.modnames
json_filename = args.outnamejson
if json_filename == "CMIP_MME":
json_filename = "/MPI_" + args.mip + "_" + args.experiment
# VAR IS FIXED TO BE PRECIP FOR CALCULATING MONSOON PRECIPITATION INDICES
var = args.modvar
thr = args.threshold
sig_digits = ".3f"
# Get flag for CMEC output
cmec = args.cmec
#########################################
# PMP monthly default PR obs
cdms2.axis.longitude_aliases.append("longitude_prclim_mpd")
cdms2.axis.latitude_aliases.append("latitude_prclim_mpd")
fobs = cdms2.open(args.reference_data_path)
dobs_orig = fobs(args.obsvar)
fobs.close()
obsgrid = dobs_orig.getGrid()
########################################
# FCN TO COMPUTE GLOBAL ANNUAL RANGE AND MONSOON PRECIP INDEX
annrange_obs, mpi_obs = mpd(dobs_orig)
#########################################
# SETUP WHERE TO OUTPUT RESULTING DATA (netcdf)
nout = os.path.join(outpathdata,
"_".join([args.experiment, args.mip, "wang-monsoon"]))
try:
os.makedirs(nout)
except BaseException:
pass
# SETUP WHERE TO OUTPUT RESULTS (json)
jout = outpathdata
try:
os.makedirs(nout)
except BaseException:
pass
gmods = [] # "Got" these MODS
for i, mod in enumerate(mods):
modpath.model = mod
for k in modpath.keys():
try:
val = getattr(args, k)
except Exception:
continue
if not isinstance(val, (list, tuple)):
setattr(modpath, k, val)
else:
setattr(modpath, k, val[i])
l1 = modpath()
if os.path.isfile(l1) is True:
gmods.append(mod)
if len(gmods) == 0:
raise RuntimeError("No model file found!")
#########################################
egg_pth = resources.resource_path()
globals = {}
locals = {}
exec(
compile(
open(os.path.join(egg_pth, "default_regions.py")).read(),
os.path.join(egg_pth, "default_regions.py"),
"exec",
),
globals,
locals,
)
regions_specs = locals["regions_specs"]
doms = ["AllMW", "AllM", "NAMM", "SAMM", "NAFM", "SAFM", "ASM", "AUSM"]
mpi_stats_dic = {}
for i, mod in enumerate(gmods):
modpath.model = mod
for k in modpath.keys():
try:
val = getattr(args, k)
except Exception:
continue
if not isinstance(val, (list, tuple)):
setattr(modpath, k, val)
else:
setattr(modpath, k, val[i])
modelFile = modpath()
mpi_stats_dic[mod] = {}
print(
"******************************************************************************************"
)
print(modelFile)
f = cdms2.open(modelFile)
d_orig = f(var)
annrange_mod, mpi_mod = mpd(d_orig)
annrange_mod = annrange_mod.regrid(obsgrid,
regridTool="regrid2",
regridMethod="conserve",
mkCyclic=True)
mpi_mod = mpi_mod.regrid(obsgrid,
regridTool="regrid2",
regridMethod="conserve",
mkCyclic=True)
for dom in doms:
mpi_stats_dic[mod][dom] = {}
reg_sel = regions_specs[dom]["domain"]
mpi_obs_reg = mpi_obs(reg_sel)
mpi_obs_reg_sd = float(statistics.std(mpi_obs_reg, axis="xy"))
mpi_mod_reg = mpi_mod(reg_sel)
cor = float(
statistics.correlation(mpi_mod_reg, mpi_obs_reg, axis="xy"))
rms = float(statistics.rms(mpi_mod_reg, mpi_obs_reg, axis="xy"))
rmsn = rms / mpi_obs_reg_sd
# DOMAIN SELECTED FROM GLOBAL ANNUAL RANGE FOR MODS AND OBS
annrange_mod_dom = annrange_mod(reg_sel)
annrange_obs_dom = annrange_obs(reg_sel)
# SKILL SCORES
# HIT/(HIT + MISSED + FALSE ALARMS)
hit, missed, falarm, score, hitmap, missmap, falarmmap = mpi_skill_scores(
annrange_mod_dom, annrange_obs_dom, thr)
# POPULATE DICTIONARY FOR JSON FILES
mpi_stats_dic[mod][dom] = {}
mpi_stats_dic[mod][dom]["cor"] = format(cor, sig_digits)
mpi_stats_dic[mod][dom]["rmsn"] = format(rmsn, sig_digits)
mpi_stats_dic[mod][dom]["threat_score"] = format(score, sig_digits)
# SAVE ANNRANGE AND HIT MISS AND FALSE ALARM FOR EACH MOD DOM
fm = os.path.join(nout, "_".join([mod, dom, "wang-monsoon.nc"]))
g = cdms2.open(fm, "w")
g.write(annrange_mod_dom)
g.write(hitmap, dtype=numpy.int32)
g.write(missmap, dtype=numpy.int32)
g.write(falarmmap, dtype=numpy.int32)
g.close()
f.close()
# OUTPUT METRICS TO JSON FILE
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(jout), json_filename)
disclaimer = open(os.path.join(egg_pth, "disclaimer.txt")).read()
metrics_dictionary = collections.OrderedDict()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary["REFERENCE"] = (
"The statistics in this file are based on" +
" Wang, B., Kim, HJ., Kikuchi, K. et al. " +
"Clim Dyn (2011) 37: 941. doi:10.1007/s00382-010-0877-0")
metrics_dictionary["RESULTS"] = mpi_stats_dic # collections.OrderedDict()
OUT.var = var
OUT.write(
metrics_dictionary,
json_structure=["model", "domain", "statistic"],
indent=4,
separators=(",", ": "),
)
if cmec:
print("Writing cmec file")
OUT.write_cmec(indent=4, separators=(",", ": "))
def genDailyCorrelationFiles(dailyAnomalyPath, dailyCorrelationPath, year, hour, **model):
"""
:func:`genDailyAnomalyFiles` : It should calculate daily anomaly
from the daily analysis and daily climatolgy for the current day.
i.e. daily model analysis - daily climatolgy
Finally stores it as nc files in corresponding directory path
which are passed in this function args.
Do the same procedure for the model forecast hours anomaly.
i.e. daily model forecast - daily climatolgy
Inputs : dailyAnomalyPath is the absolute path where the processed mean
anomaly nc files are going to store.
modelType is either 'a' for 'analysis' or 'f' for 'forecast'.
modelHour is the forecast hour.
climRegriadir is the absolute path where the regridded monthly
mean climatologies (w.r.t the model vertical resolution)
nc files were already stored.
climPFileName is the partial nc filename of the climatolgy.
climatologyYear is the year of the climatolgy to access it.
KWargs: modelName, modelXmlPath, modelXmlObj
modelName is the model data name which will become part of the
process nc files name.
modelPath is the absolute path of data where the model xml files
are located.
modelXmlObj is an instance of the GribXmlAccess class instance.
If we are passing modelXmlObj means, it will be optimized one
when we calls this same function for same model for different
months.
We can pass either modelXmlPath or modelXmlObj KWarg is enough.
Outputs : It should create daily anomaly for the particular variables which
are all set the clim_var option in the vars.txt file. Finally
store it as nc file formate in the proper directories structure
(modelname, 'Anomaly', year, daily and then 'analysis' or
fcst hours hierarchy).
Written By : Arulalan.T
Date : 02.01.2012
"""
modelXmlObj, modelPath = None, None
if 'modelName' in model:
modelName = model.get('modelName')
else:
raise RuntimeError("KWarg modelName must be passed")
if 'modelXmlObj' in model:
modelXmlObj = model.get('modelXmlObj')
elif 'modelXmlPath' in model:
modelPath = model.get('modelXmlPath')
else:
raise RuntimeError("you must pass atleast one KWargs of modelXmlPath \
or modelXmlPath ")
if not modelXmlObj:
xmlobj = xml_data_access.GribXmlAccess(modelPath)
else:
if isinstance(modelXmlObj, xml_data_access.GribXmlAccess):
xmlobj = modelXmlObj
else:
raise ValueError("Passed modelXmlObj instance %s is not an \
instance of GribXmlAccess class " % type(modelXmlObj))
ncAnoFileDic = {}
anomalyDirs = ['Analysis', hour]
for adir in anomalyDirs:
apath = '/'.join([dailyAnomalyPath, adir])
ncAnoFileDic[adir] = {}
# get the nc files name of mean anomaly
ncfiles = [f for f in os.listdir(apath) if f.endswith('.nc')]
# make ncfiles as dictionary with key as var name
for ncfile in ncfiles:
var = ncfile.split('_')[0]
anomalyFilePath = apath + '/' + ncfile
ncAnoFileDic[adir][var] = anomalyFilePath
# make memory free
del ncfiles
ncCorrFileDic = {}
# get the nc files name of mean anomaly
ncfiles = [f for f in os.listdir(dailyCorrelationPath) if f.endswith('.nc')]
# make ncfiles as dictionary with key as var name
for ncfile in ncfiles:
var = ncfile.split('_')[0]
correlationFilePath = dailyCorrelationPath + '/' + ncfile
ncCorrFileDic[var] = correlationFilePath
# make memory free
del ncfiles
modelvariables = xmlobj.listvariable(Type = 'a')
# get the namedtuple object from the global 'vars.txt' file
totalvars = variables.get(modelName)
for globalvar in totalvars.itervalues():
# get the model var name
mvar = globalvar.model_var
# # get the climatolgy var name
# cvar = globalvar.clim_var
# if not cvar:
# print "Climatology var name is empty string. So skipping anomaly \
# correlation process for %s model var name " % mvar
# continue
# # end of if not cvar:
if not mvar in modelvariables:
print "The variable %s is not available in the xml anl file object" % mvar
print "So skipping the anomaly correlation process \
for this variable %s which is one of the keys of the \
variables dictionary" % mvar
continue
# end of if not mvar in allvariables:
if not mvar in ncAnoFileDic['Analysis']:
print "The variable %s is not available in processed anomaly \
analysis directory. So skipping the anomaly correlation process \
for this variable %s " % (mvar, mvar)
continue
correlationLatestDate = None
# generate the nc filename
anomalyCorrFileName = mvar + '_'+ modelName + '_' + year
anomalyCorrFileName += '_daily_f' + hour + 'hr'
anomalyCorrFileName += '_anomaly_correlation.nc'
anomalyAnlFilePath = ncAnoFileDic['Analysis'].get(mvar)
try:
anoAnlFile = cdms2.open(anomalyAnlFilePath, 'r')
except:
raise ValueError("Couldnt open the anomaly analysis file '%s'.\
so can not perform the daily correlation" % anomalyAnlFilePath)
anoAnlTime = anoAnlFile[mvar].getTime().asComponentTime()
levAxis = anoAnlFile[mvar].getLevel()
if mvar in ncCorrFileDic:
anomalyCorrFilePath = ncCorrFileDic.get(mvar)
correlationLatestDate = None
try:
# open nc file in append mode
anomalyCorrFile = cdms2.open(anomalyCorrFilePath, 'a')
# get the ncfile timeAxis
fileTime = anomalyCorrFile[mvar].getTime().asComponentTime()
anomalyLatestDate = anoAnlTime[-1]
correlationLatestDate = fileTime[-1]
# Do check either this month timeAxis is already exists in
# the nc file's timeAxis or not. If exists means skip it.
if __timeCheck__:
if anomalyLatestDate in fileTime:
print "The daily anomaly is already exists in the \
file %s. So skipping var '%s' " % \
(anomalyCorrFile, mvar)
anomalyCorrFile.close()
continue
# end of if __timeCheck__:
except (AttributeError, cdms2.error.CDMSError):
# if it getting this error means, it may not written
# properly. So open nc file in write mode freshly.
print "Got Problem. nc file is correpted at last time. \
May be lost the previous days data.\
Now creating same nc file '%s' freshly & fully " \
% (anomalyCorrFilePath)
anomalyCorrFile = cdms2.open(anomalyCorrFilePath, 'w')
else:
anomalyCorrFilePath = dailyCorrelationPath + '/' + anomalyCorrFileName
# open new nc file in write mode
anomalyCorrFile = cdms2.open(anomalyCorrFilePath, 'w')
# end of if mvar in ncAnoFileDic:
anomalyHrFilePath = ncAnoFileDic[hour].get(mvar)
try:
anoHrFile = cdms2.open(anomalyHrFilePath, 'r')
except:
raise ValueError("Couldnt open the anomaly fcst %s hour file '%s'.\
so can not perform the daily correlation" % (hour, anomalyHrFilePath))
anoFirstYear = anoAnlTime[0].year
if correlationLatestDate:
nextIndex = anoAnlTime.index(correlationLatestDate) + 1
anoAnlTime = anoAnlTime[nextIndex:]
print "Writing daily anomaly correlation into %s file \n" % (anomalyCorrFileName)
for adate in anoAnlTime:
partnerDate = xmlobj.findPartners('a', adate, hour)
#print "Extracting the fcst", hour, " partner data on ", partnerDate, "of analysis data on ", adate
try:
# get the climatolgy data for this month alone
anoHrData = anoHrFile(mvar, time = partnerDate)
except:
print "Coundn't get the anomaly fcst '%s' hour data of %s var \
%s time" % (hour, mvar, partnerDate)
print "So skipping the anomaly correlation for the '%s' variable" % mvar
continue
# end of try:
try:
# get the model data for this day alone
anoAnlData = anoAnlFile(mvar, time = adate)
except:
print "Coundn't get the anomaly analysis data of %s var %s time" \
% (mvar, adate)
print "So skipping the anomaly correlation for the '%s' \
variable on '%s'" % (mvar, adate)
continue
# end of try:
print "Calculating Daily Anomaly Correlation on ", adate
# anomaly correlation
if levAxis:
corrValue = correlation(anoAnlData, anoHrData, axis = "tyx")
else:
corrValue = correlation(anoAnlData, anoHrData, axis = "tzyx")
# make memory free
del anoAnlData, anoHrData
fcststart = timobj._getDayCountOfYear(partnerDate, year=anoFirstYear)
startDayOfYear = '%s-1-1' % anoFirstYear
dailyCorrTime = timobj._generateTimeAxis([fcststart], startDayOfYear)
corrValue = corrValue.reshape((1, len(corrValue)))
corrValue = cdms2.createVariable(corrValue)
if levAxis:
corrValue.setAxisList([dailyCorrTime, levAxis])
else:
corrValue.setAxis(0, dailyCorrTime)
corrValue.id = mvar
#corrValue.comments = 'monthly mean anomaly of %s model data of %s' % (modelName, year)
anomalyCorrFile.write(corrValue)
# make memory free
del corrValue
# end of for mdate in modelTime:
anomalyCorrFile.close()
def monsoon_wang_runner(args):
# args = P.parse_args(sys.argv[1:])
modpath = args.modpath
outpathjsons = args.outpathjsons
outpathdata = args.results_dir
if isinstance(args.modnames, str):
mods = eval(args.modnames)
else:
mods = args.modnames
json_filename = args.jsonname
if json_filename == 'CMIP_MME':
json_filename = '/MPI_' + args.mip + '_' + args.experiment
if args.mip == 'CMIP5' and args.experiment == 'historical' and mods is None:
mods = [
'ACCESS1-0',
'ACCESS1-3',
'bcc-csm1-1',
'bcc-csm1-1-m',
'BNU-ESM',
'CanCM4',
'CanESM2',
'CCSM4',
'CESM1-BGC',
'CESM1-CAM5',
'CESM1-FASTCHEM',
'CESM1-WACCM',
'CMCC-CESM',
'CMCC-CM',
'CMCC-CMS',
'CNRM-CM5-2',
'CNRM-CM5',
'CSIRO-Mk3-6-0',
'FGOALS-g2',
'FIO-ESM',
'GFDL-CM2p1',
'GFDL-CM3',
'GFDL-ESM2G',
'GFDL-ESM2M',
'GISS-E2-H',
'GISS-E2-H-CC',
'GISS-E2-R',
'GISS-E2-R-CC',
'HadCM3',
'HadGEM2-AO',
'HadGEM2-CC',
'HadGEM2-ES',
'inmcm4',
'IPSL-CM5A-LR',
'IPSL-CM5A-MR',
'IPSL-CM5B-LR',
'MIROC4h',
'MIROC5',
'MIROC-ESM',
'MIROC-ESM-CHEM',
'MPI-ESM-LR',
'MPI-ESM-MR',
'MPI-ESM-P',
'MRI-CGCM3',
'MRI-ESM1',
'NorESM1-M',
'NorESM1-ME']
# VAR IS FIXED TO BE PRECIP FOR CALCULATING MONSOON PRECIPITATION INDICES
var = args.modvar
thr = args.threshold
sig_digits = '.3f'
#########################################
# PMP monthly default PR obs
cdms2.axis.longitude_aliases.append("longitude_prclim_mpd")
cdms2.axis.latitude_aliases.append("latitude_prclim_mpd")
fobs = cdms2.open(args.reference_data_path)
dobs_orig = fobs(args.obsvar)
fobs.close()
obsgrid = dobs_orig.getGrid()
########################################
# FCN TO COMPUTE GLOBAL ANNUAL RANGE AND MONSOON PRECIP INDEX
annrange_obs, mpi_obs = mpd(dobs_orig)
#########################################
# SETUP WHERE TO OUTPUT RESULTING DATA (netcdf)
nout = os.path.join(outpathdata, "_".join(
[args.experiment, args.mip, 'wang-monsoon']))
try:
os.makedirs(nout)
except BaseException:
pass
# SETUP WHERE TO OUTPUT RESULTS (json)
jout = outpathjsons
try:
os.makedirs(nout)
except BaseException:
pass
modpathall = modpath.replace('MODS', '*')
lst = glob.glob(modpathall)
# CONFIRM DATA FOR MODS IS AVAIL AND REMOVE THOSE IT IS NOT
gmods = [] # "Got" these MODS
for mod in mods:
for l in lst:
l1 = modpath.replace('MODS', mod)
if os.path.isfile(l1) is True:
if mod not in gmods:
gmods.append(mod)
if args.experiment == 'historical' and mods is None:
gmods = [
'ACCESS1-0',
'ACCESS1-3',
'bcc-csm1-1',
'bcc-csm1-1-m',
'BNU-ESM',
'CanCM4',
'CanESM2',
'CCSM4',
'CESM1-BGC',
'CESM1-CAM5',
'CESM1-FASTCHEM',
'CESM1-WACCM',
'CMCC-CESM',
'CMCC-CM',
'CMCC-CMS',
'CNRM-CM5-2',
'CNRM-CM5',
'CSIRO-Mk3-6-0',
'FGOALS-g2',
'FIO-ESM',
'GFDL-CM2p1',
'GFDL-CM3',
'GFDL-ESM2G',
'GFDL-ESM2M',
'GISS-E2-H',
'GISS-E2-H-CC',
'GISS-E2-R',
'GISS-E2-R-CC',
'HadCM3',
'HadGEM2-AO',
'HadGEM2-CC',
'HadGEM2-ES',
'inmcm4',
'IPSL-CM5A-LR',
'IPSL-CM5A-MR',
'IPSL-CM5B-LR',
'MIROC4h',
'MIROC5',
'MIROC-ESM',
'MIROC-ESM-CHEM',
'MPI-ESM-LR',
'MPI-ESM-MR',
'MPI-ESM-P',
'MRI-CGCM3',
'MRI-ESM1',
'NorESM1-M',
'NorESM1-ME']
#########################################
try:
egg_pth = pkg_resources.resource_filename(
pkg_resources.Requirement.parse("pcmdi_metrics"), "share/pmp")
except Exception:
# python 2 seems to fail when ran in home directory of source?
egg_pth = os.path.join(os.getcwd(), "share", "pmp")
globals = {}
locals = {}
exec(compile(open(os.path.join(egg_pth, "default_regions.py")).read(),
os.path.join(egg_pth, "default_regions.py"), 'exec'), globals, locals)
regions_specs = locals["regions_specs"]
doms = ['AllMW', 'AllM', 'NAMM', 'SAMM', 'NAFM', 'SAFM', 'ASM', 'AUSM']
mpi_stats_dic = {}
for mod in gmods:
modelFile = modpath.replace('MODS', mod)
mpi_stats_dic[mod] = {}
print("******************************************************************************************")
print(modelFile)
f = cdms2.open(modelFile)
d_orig = f(var)
annrange_mod, mpi_mod = mpd(d_orig)
annrange_mod = annrange_mod.regrid(obsgrid)
mpi_mod = mpi_mod.regrid(obsgrid)
for dom in doms:
mpi_stats_dic[mod][dom] = {}
reg_sel = regions_specs[dom]['domain']
mpi_obs_reg = mpi_obs(reg_sel)
mpi_obs_reg_sd = float(statistics.std(mpi_obs_reg, axis='xy'))
mpi_mod_reg = mpi_mod(reg_sel)
cor = float(
statistics.correlation(
mpi_mod_reg,
mpi_obs_reg,
axis='xy'))
rms = float(statistics.rms(mpi_mod_reg, mpi_obs_reg, axis='xy'))
rmsn = rms / mpi_obs_reg_sd
# DOMAIN SELECTED FROM GLOBAL ANNUAL RANGE FOR MODS AND OBS
annrange_mod_dom = annrange_mod(reg_sel)
annrange_obs_dom = annrange_obs(reg_sel)
# SKILL SCORES
# HIT/(HIT + MISSED + FALSE ALARMS)
hit, missed, falarm, score, hitmap, missmap, falarmmap = mpi_skill_scores(
annrange_mod_dom, annrange_obs_dom, thr)
# POPULATE DICTIONARY FOR JSON FILES
mpi_stats_dic[mod][dom] = {}
mpi_stats_dic[mod][dom]['cor'] = format(cor, sig_digits)
mpi_stats_dic[mod][dom]['rmsn'] = format(rmsn, sig_digits)
mpi_stats_dic[mod][dom]['threat_score'] = format(score, sig_digits)
# SAVE ANNRANGE AND HIT MISS AND FALSE ALARM FOR EACH MOD DOM
fm = os.path.join(nout, '_'.join([mod, dom, 'wang-monsoon.nc']))
g = cdms2.open(fm, 'w')
g.write(annrange_mod_dom)
g.write(hitmap, dtype=numpy.int32)
g.write(missmap, dtype=numpy.int32)
g.write(falarmmap, dtype=numpy.int32)
g.close()
f.close()
# OUTPUT METRICS TO JSON FILE
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(jout), json_filename)
disclaimer = open(
os.path.join(
egg_pth,
"disclaimer.txt")).read()
metrics_dictionary = collections.OrderedDict()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary["REFERENCE"] = "The statistics in this file are based on" +\
" Wang, B., Kim, HJ., Kikuchi, K. et al. " +\
"Clim Dyn (2011) 37: 941. doi:10.1007/s00382-010-0877-0"
metrics_dictionary["RESULTS"] = mpi_stats_dic # collections.OrderedDict()
OUT.var = var
OUT.write(
metrics_dictionary,
json_structure=["model", "domain", "statistic"],
indent=4,
separators=(
',',
': '))
annrange_mod, mpi_mod = mpd(d_orig)
annrange_mod = annrange_mod.regrid(obsgrid)
mpi_mod = mpi_mod.regrid(obsgrid)
for dom in doms:
mpi_stats_dic[mod][dom] = {}
reg_sel = regions_specs[dom]['domain']
mpi_obs_reg = mpi_obs(reg_sel)
mpi_obs_reg_sd = float(statistics.std(mpi_obs_reg, axis='xy'))
mpi_mod_reg = mpi_mod(reg_sel)
cor = float(statistics.correlation(mpi_mod_reg, mpi_obs_reg,
axis='xy'))
rms = float(statistics.rms(mpi_mod_reg, mpi_obs_reg, axis='xy'))
rmsn = rms / mpi_obs_reg_sd
# DOMAIN SELECTED FROM GLOBAL ANNUAL RANGE FOR MODS AND OBS
annrange_mod_dom = annrange_mod(reg_sel)
annrange_obs_dom = annrange_obs(reg_sel)
# SKILL SCORES
# HIT/(HIT + MISSED + FALSE ALARMS)
hit, missed, falarm, score, hitmap, missmap, falarmmap = mpi_skill_scores(
annrange_mod_dom, annrange_obs_dom, thr)
# POPULATE DICTIONARY FOR JSON FILES
mpi_stats_dic[mod][dom] = {}
mpi_stats_dic[mod][dom]['cor'] = format(cor, sig_digits)
def monsoon_wang_runner(args):
# args = P.parse_args(sys.argv[1:])
modpath = args.modpath
outpathjsons = args.outpathjsons
outpathdata = args.results_dir
if isinstance(args.modnames, str):
mods = eval(args.modnames)
else:
mods = args.modnames
json_filename = args.jsonname
if json_filename == 'CMIP_MME':
json_filename = '/MPI_' + args.mip + '_' + args.experiment
if args.mip == 'CMIP5' and args.experiment == 'historical' and mods is None:
mods = [
'ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1', 'bcc-csm1-1-m', 'BNU-ESM',
'CanCM4', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5',
'CESM1-FASTCHEM', 'CESM1-WACCM', 'CMCC-CESM', 'CMCC-CM',
'CMCC-CMS', 'CNRM-CM5-2', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'FGOALS-g2',
'FIO-ESM', 'GFDL-CM2p1', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',
'GISS-E2-H', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadCM3',
'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR',
'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC4h', 'MIROC5', 'MIROC-ESM',
'MIROC-ESM-CHEM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MPI-ESM-P',
'MRI-CGCM3', 'MRI-ESM1', 'NorESM1-M', 'NorESM1-ME'
]
# VAR IS FIXED TO BE PRECIP FOR CALCULATING MONSOON PRECIPITATION INDICES
var = args.modvar
thr = args.threshold
sig_digits = '.3f'
#########################################
# PMP monthly default PR obs
cdms2.axis.longitude_aliases.append("longitude_prclim_mpd")
cdms2.axis.latitude_aliases.append("latitude_prclim_mpd")
fobs = cdms2.open(args.reference_data_path)
dobs_orig = fobs(args.obsvar)
fobs.close()
obsgrid = dobs_orig.getGrid()
########################################
# FCN TO COMPUTE GLOBAL ANNUAL RANGE AND MONSOON PRECIP INDEX
annrange_obs, mpi_obs = mpd(dobs_orig)
#########################################
# SETUP WHERE TO OUTPUT RESULTING DATA (netcdf)
nout = os.path.join(outpathdata,
"_".join([args.experiment, args.mip, 'wang-monsoon']))
try:
os.makedirs(nout)
except BaseException:
pass
# SETUP WHERE TO OUTPUT RESULTS (json)
jout = outpathjsons
try:
os.makedirs(nout)
except BaseException:
pass
modpathall = modpath.replace('MODS', '*')
lst = glob.glob(modpathall)
# CONFIRM DATA FOR MODS IS AVAIL AND REMOVE THOSE IT IS NOT
gmods = [] # "Got" these MODS
for mod in mods:
for l in lst:
l1 = modpath.replace('MODS', mod)
if os.path.isfile(l1) is True:
if mod not in gmods:
gmods.append(mod)
if args.experiment == 'historical' and mods is None:
gmods = [
'ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1', 'bcc-csm1-1-m', 'BNU-ESM',
'CanCM4', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5',
'CESM1-FASTCHEM', 'CESM1-WACCM', 'CMCC-CESM', 'CMCC-CM',
'CMCC-CMS', 'CNRM-CM5-2', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'FGOALS-g2',
'FIO-ESM', 'GFDL-CM2p1', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',
'GISS-E2-H', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadCM3',
'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR',
'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC4h', 'MIROC5', 'MIROC-ESM',
'MIROC-ESM-CHEM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MPI-ESM-P',
'MRI-CGCM3', 'MRI-ESM1', 'NorESM1-M', 'NorESM1-ME'
]
#########################################
try:
egg_pth = pkg_resources.resource_filename(
pkg_resources.Requirement.parse("pcmdi_metrics"), "share/pmp")
except Exception:
# python 2 seems to fail when ran in home directory of source?
egg_pth = os.path.join(os.getcwd(), "share", "pmp")
globals = {}
locals = {}
exec(
compile(
open(os.path.join(egg_pth, "default_regions.py")).read(),
os.path.join(egg_pth, "default_regions.py"), 'exec'), globals,
locals)
regions_specs = locals["regions_specs"]
doms = ['AllMW', 'AllM', 'NAMM', 'SAMM', 'NAFM', 'SAFM', 'ASM', 'AUSM']
mpi_stats_dic = {}
for mod in gmods:
modelFile = modpath.replace('MODS', mod)
mpi_stats_dic[mod] = {}
print(
"******************************************************************************************"
)
print(modelFile)
f = cdms2.open(modelFile)
d_orig = f(var)
annrange_mod, mpi_mod = mpd(d_orig)
annrange_mod = annrange_mod.regrid(obsgrid)
mpi_mod = mpi_mod.regrid(obsgrid)
for dom in doms:
mpi_stats_dic[mod][dom] = {}
reg_sel = regions_specs[dom]['domain']
mpi_obs_reg = mpi_obs(reg_sel)
mpi_obs_reg_sd = float(statistics.std(mpi_obs_reg, axis='xy'))
mpi_mod_reg = mpi_mod(reg_sel)
cor = float(
statistics.correlation(mpi_mod_reg, mpi_obs_reg, axis='xy'))
rms = float(statistics.rms(mpi_mod_reg, mpi_obs_reg, axis='xy'))
rmsn = rms / mpi_obs_reg_sd
# DOMAIN SELECTED FROM GLOBAL ANNUAL RANGE FOR MODS AND OBS
annrange_mod_dom = annrange_mod(reg_sel)
annrange_obs_dom = annrange_obs(reg_sel)
# SKILL SCORES
# HIT/(HIT + MISSED + FALSE ALARMS)
hit, missed, falarm, score, hitmap, missmap, falarmmap = mpi_skill_scores(
annrange_mod_dom, annrange_obs_dom, thr)
# POPULATE DICTIONARY FOR JSON FILES
mpi_stats_dic[mod][dom] = {}
mpi_stats_dic[mod][dom]['cor'] = format(cor, sig_digits)
mpi_stats_dic[mod][dom]['rmsn'] = format(rmsn, sig_digits)
mpi_stats_dic[mod][dom]['threat_score'] = format(score, sig_digits)
# SAVE ANNRANGE AND HIT MISS AND FALSE ALARM FOR EACH MOD DOM
fm = os.path.join(nout, '_'.join([mod, dom, 'wang-monsoon.nc']))
g = cdms2.open(fm, 'w')
g.write(annrange_mod_dom)
g.write(hitmap, dtype=numpy.int32)
g.write(missmap, dtype=numpy.int32)
g.write(falarmmap, dtype=numpy.int32)
g.close()
f.close()
# OUTPUT METRICS TO JSON FILE
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(jout), json_filename)
disclaimer = open(os.path.join(egg_pth, "disclaimer.txt")).read()
metrics_dictionary = collections.OrderedDict()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary["REFERENCE"] = "The statistics in this file are based on" +\
" Wang, B., Kim, HJ., Kikuchi, K. et al. " +\
"Clim Dyn (2011) 37: 941. doi:10.1007/s00382-010-0877-0"
metrics_dictionary["RESULTS"] = mpi_stats_dic # collections.OrderedDict()
OUT.var = var
OUT.write(metrics_dictionary,
json_structure=["model", "domain", "statistic"],
indent=4,
separators=(',', ': '))
x.setcolormap('default')
fill=x.getisofill('default')
x.plot(z_diff,fill)
print ""
print "Press the Return key to see next plot."
sys.stdin.readline()
x.clear()
x.plot(gl_diff)
print ""
print "Press the Return key to see next plot."
sys.stdin.readline()
cor_t=statistics.correlation(data1,data2,axis='xy')
# temporal correlation map betwen these to time-series
cor_m=statistics.correlation(data1,data2,axis='t')
# temporal rms difference between the two time series
rms=statistics.rms(data1,data2,axis='t')
x.clear()
x.plot(cor_m,fill)
print ""
print "Press the Return key to see next plot."
sys.stdin.readline()
x.clear()
x.plot(cor_t)
print ""
print "Press the Return key to see next plot."
