def spatial_rmsc(self):
""" RMS centree pour chaque pas de temps """
from genutil import statistics
import cdms2
import numpy as np
# centered and biased RMS
self.spa_rmsc = list() #Initialise une liste
# Estimation de la std a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
minterm = np.reshape(self.model[i, :, :], (self.model.shape[-1]*self.model.shape[-2]))
ointerm = np.reshape(self.obs[i, :, :], (self.obs.shape[-1]*self.obs.shape[-2]))
#modif J.Gatti - pour MFS
if not minterm._grid_ is None:
minterm._grid_=None
if not ointerm._grid_ is None:
ointerm._grid_=None
#fin modif J.Gatti
self.spa_rmsc.append(statistics.rms(minterm, ointerm, centered=1))
# Transformation en variable cdms2
self.spa_rmsc = cdms2.createVariable(self.spa_rmsc,typecode='f',id='spa_rmsc', attributes=dict(units=self.model.units))
# Ajout de l'axe des temps correspondant a self...
self.spa_rmsc.setAxis(0, tps)
def spatial_rmsc(self):
""" RMS centree pour chaque pas de temps """
from genutil import statistics
import cdms2
import numpy as np
# centered and biased RMS
self.spa_rmsc = list() #Initialise une liste
# Estimation de la std a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
minterm = np.reshape(self.model[i, :, :],
(self.model.shape[-1] * self.model.shape[-2]))
ointerm = np.reshape(self.obs[i, :, :],
(self.obs.shape[-1] * self.obs.shape[-2]))
#modif J.Gatti - pour MFS
if not minterm._grid_ is None:
minterm._grid_ = None
if not ointerm._grid_ is None:
ointerm._grid_ = None
#fin modif J.Gatti
self.spa_rmsc.append(statistics.rms(minterm, ointerm, centered=1))
# Transformation en variable cdms2
self.spa_rmsc = cdms2.createVariable(
self.spa_rmsc,
typecode='f',
id='spa_rmsc',
attributes=dict(units=self.model.units))
# Ajout de l'axe des temps correspondant a self...
self.spa_rmsc.setAxis(0, tps)
def temporal_rms(self):
""" RMS entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de RMS uncentered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_rms = () #Initialise un tuple
self.temp_rms = statistics.rms(self.model, self.obs, axis=0)
gg = get_grid(self.model)
set_grid(self.temp_rms, gg, axes=True)
def temporal_rmsc(self):
""" RMS entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de RMS centered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_rmsc = () #Initialise un tuple
self.temp_rmsc = statistics.rms(self.model, self.obs, axis = 0, centered = 1)
gg = get_grid(self.model)
set_grid(self.temp_rmsc, gg, axes=True)
#
ncep = os.path.join(sys.prefix, 'sample_data', 'tas_mo.nc')
f = cdms2.open(ncep)
ncep1 = f('tas', time = (a1, b1, 'co'))
ncep2 = f('tas', time = (a2, b2, 'co'))
#
# Note that by using the 'co' option above we specified that the end
# point is 'open'
#
#
# Now we compute the "spatial" rms difference -
# "spatial" is specified by setting the axis='xy' option
#
rms = statistics.rms(ncep1, ncep2, axis='xy')
print 'RMS difference (spatial) between the 2 periods:'
print rms
#
# Similarly the "mean absolute difference" is computed...
#
absd = statistics.meanabsdiff(ncep1,ncep2,axis='xy')
print 'Mean absolute difference:'
print absd
#*******************************************************************************
#
# Example 2:
# In this example, we compute the spatial correlation between 2 fields
#
ncep = os.path.join(cdat_info.get_prefix(), 'sample_data', 'tas_mo.nc')
f = cdms2.open(ncep)
ncep1 = f('tas', time=(a1, b1, 'co'))
ncep2 = f('tas', time=(a2, b2, 'co'))
#
# Note that by using the 'co' option above we specified that the end
# point is 'open'
#
#
# Now we compute the "spatial" rms difference -
# "spatial" is specified by setting the axis='xy' option
#
rms = statistics.rms(ncep1, ncep2, axis='xy')
print 'RMS difference (spatial) between the 2 periods:'
print rms
#
# Similarly the "mean absolute difference" is computed...
#
absd = statistics.meanabsdiff(ncep1, ncep2, axis='xy')
print 'Mean absolute difference:'
print absd
#*******************************************************************************
#
# Example 2:
# In this example, we compute the spatial correlation between 2 fields
# - say the mean surface air temperature for the 1980-1989 period
# 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')
slope2, intercept2 = statistics.linearregression(data2, axis='t')
# set the 'id'
slope1.id = 'linear_slope'
slope2.id = 'linear_slope'
# False data
import numpy as N
# - time axis
t = N.linspace(0., 100., 200)
# - observations
obs = N.sin(t)
# - model
nm = 50
mod = N.resize(obs, (nm, len(t)))
mod += N.random.uniform(-3, 3, mod.shape)+.5
# Make stats
from genutil.statistics import rms
bias = (mod-obs).mean(axis=1)
crms = rms(mod, N.resize(obs, mod.shape), centered=1, axis=1)
stdmod = mod.std(axis=1)
stdref = obs.std()
# Plot
from vacumm.misc.plot import dtarget
dtarget(bias, crms, stdmod, stdref, colors='rms', show=False, units='m',
scatter_alpha=0.5, sizes=40, savefigs=__file__, close=True)
# Test taylor diagrom here:
TSstd1, TSstd2 = [], []
TSrms1, TSrms2 = [], []
TScor1, TScor2 = [], []
flag = 1
for idx in range(OBSsp.shape[0]):
aaa = ERAsp[idx]
bbb = NOAsp[idx]
ccc = OBSsp[idx]
std0 = stat.std(ccc, axis='yx')
std1 = stat.std(aaa, axis='yx') / std0
std2 = stat.std(bbb, axis='yx') / std0
cor1 = stat.correlation(aaa, ccc, axis='yx')
cor2 = stat.correlation(bbb, ccc, axis='yx')
rms1 = stat.rms(aaa, ccc, axis='yx', centered=0)
rms2 = stat.rms(bbb, ccc, axis='yx', centered=0)
TSstd1.append(std1)
TSstd2.append(std2)
TSrms1.append(rms1)
TSrms2.append(rms2)
TScor1.append(cor1)
TScor2.append(cor2)
if flag == 1:
sm.taylor_diagram(np.array([1.0, np.mean(TSstd1)]),
np.array([0.0, np.mean(TSrms1)]),
np.array([1.0, np.mean(TScor1)]),
markerColor='firebrick',
colOBS='black',
markerobs='x')
flag += 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 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 = 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)
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=(',', ': '))
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." sys.stdin.readline() x.clear() x.plot(rms,fill)
