# Initialize toe for each basin (density, lat)
toe1_a = np.ma.masked_all((levN, latN))
toe1_p = np.ma.masked_all((levN, latN))
toe1_i = np.ma.masked_all((levN, latN))
toe2_a = np.ma.masked_all((levN, latN))
toe2_p = np.ma.masked_all((levN, latN))
toe2_i = np.ma.masked_all((levN, latN))
# Initialize output variable
varToE1 = np.ma.masked_all(
(basinN, levN, latN)) # (>1std) (basin,density,latitude)
varToE2 = np.ma.masked_all(
(basinN, levN, latN)) # (>2std) (basin,density,latitude)
# Compute ToE as last date when diff 1%CO2 - PiControl is larger than mult * stddev
toe1_a = np.reshape(findToE(varsignal_a, stdvarpiC_a, 1), (levN, latN))
toe1_p = np.reshape(findToE(varsignal_p, stdvarpiC_p, 1), (levN, latN))
toe1_i = np.reshape(findToE(varsignal_i, stdvarpiC_i, 1), (levN, latN))
toe2_a = np.reshape(findToE(varsignal_a, stdvarpiC_a, multStd),
(levN, latN))
toe2_p = np.reshape(findToE(varsignal_p, stdvarpiC_p, multStd),
(levN, latN))
toe2_i = np.reshape(findToE(varsignal_i, stdvarpiC_i, multStd),
(levN, latN))
# Save in output variable
varToE1[1, :, :] = toe1_a
varToE1[2, :, :] = toe1_p
varToE1[3, :, :] = toe1_i
varToE2[1, :, :] = toe2_a
varToE2[2, :, :] = toe2_p
varsignal_i[:, j] = averageDom(
varCO2[:, 3, :, :] - meanvarpiC[3, :, :], 3, domain['Indian'],
lat, density)
if method_noise == 'average_std':
varnoise_i[j] = averageDom(varstd[3, :, :], 2,
domain['Indian'], lat, density)
else:
varnoise_i[j] = np.ma.std(averageDom(varpiC[:, 3, :, :], 3,
domain['Indian'], lat,
density),
axis=0)
# Compute ToE of averaged domain
if domain['Atlantic'] != None and np.ma.is_masked(
varnoise_a[j]) == False:
toe1_a[j] = findToE(varsignal_a[:, j], varnoise_a[j], 1)
toe2_a[j] = findToE(varsignal_a[:, j], varnoise_a[j], multStd)
print(toe1_a[j], toe2_a[j])
if domain['Pacific'] != None and np.ma.is_masked(
varnoise_p[j]) == False:
toe1_p[j] = findToE(varsignal_p[:, j], varnoise_p[j], 1)
toe2_p[j] = findToE(varsignal_p[:, j], varnoise_p[j], multStd)
if domain['Indian'] != None and np.ma.is_masked(
varnoise_i[j]) == False:
toe1_i[j] = findToE(varsignal_i[:, j], varnoise_i[j], 1)
toe2_i[j] = findToE(varsignal_i[:, j], varnoise_i[j], multStd)
# Take out runs where the signal is of opposite sign than expected
if signal_domains[j] == 'fresher':
if np.ma.mean(varsignal_a[-5:, j],
axis=0) > multStd * varnoise_a[j]:
if use_piC == False:
varsignal_a[145:,:,:] = varrcp_a-meanvarhn_a
varsignal_p[145:,:,:] = varrcp_p-meanvarhn_p
varsignal_i[145:,:,:] = varrcp_i-meanvarhn_i
else:
varsignal_a[145:,:,:] = varrcp_a-meanvarpiC_a
varsignal_p[145:,:,:] = varrcp_p-meanvarpiC_p
varsignal_i[145:,:,:] = varrcp_i-meanvarpiC_i
# Reorganise i,j dims in single dimension data (speeds up loops)
varsignal_a = np.reshape(varsignal_a, (timN, levN*latN))
varsignal_p = np.reshape(varsignal_p, (timN, levN*latN))
varsignal_i = np.reshape(varsignal_i, (timN, levN*latN))
# Compute ToE as last date when diff hist+RCP - histNat is larger than mult * stddev
toe_a = np.reshape(findToE(varsignal_a, stdvarhn_a, multStd),(levN,latN))
toe_p = np.reshape(findToE(varsignal_p, stdvarhn_p, multStd),(levN,latN))
toe_i = np.reshape(findToE(varsignal_i, stdvarhn_i, multStd),(levN,latN))
# Save in output variable
varToE[k,1,:,:] = toe_a
varToE[k,2,:,:] = toe_p
varToE[k,3,:,:] = toe_i
# Save in output file
if use_piC == False:
fileName = 'cmip5.'+model['name']+'.toe_zonal_rcp_histNat.nc'
dir = '/home/ysilvy/Density_bining/Yona_analysis/data/toe_zonal/toe_rcp85_histNat/'
description = 'Time of Emergence hist+rcp8.5 vs. histNat for each member. \n' \
'The historical runs are prolonged by the 95 years of RCP8.5. ' \
'The ensemble mean historicalNat is used here for all historical runs of the model. \n' \
varnoise_a[j] = np.ma.std(averageDom(varpiC[:,1,:,:], 3, domain['Atlantic'], lat, density), axis=0)
if domain['Pacific'] != None:
varsignal_p[:,j] = averageDom(varCO2[:,2,:,:]-varpiC[:,2,:,:], 3, domain['Pacific'], lat, density)
if method_noise == 'average_std':
varnoise_p[j] = averageDom(varstd[2,:,:], 2, domain['Pacific'], lat, density)
else:
varnoise_p[j] = np.ma.std(averageDom(varpiC[:,2,:,:], 3, domain['Pacific'], lat, density), axis=0)
if domain['Indian'] != None:
varsignal_i[:,j] = averageDom(varCO2[:,3,:,:]-varpiC[:,3,:,:], 3, domain['Indian'], lat, density)
if method_noise == 'average_std':
varnoise_i[j] = averageDom(varstd[3,:,:], 2, domain['Indian'], lat, density)
else:
varnoise_i[j] = np.ma.std(averageDom(varpiC[:,3,:,:], 3, domain['Indian'], lat, density), axis=0)
# Compute ToE of averaged domain
if domain['Atlantic'] != None and np.ma.is_masked(varnoise_a[j]) == False:
toe_a[j] = findToE(varsignal_a[:,j], varnoise_a[j], multStd)
if domain['Pacific'] != None and np.ma.is_masked(varnoise_p[j]) == False:
toe_p[j] = findToE(varsignal_p[:,j], varnoise_p[j], multStd)
if domain['Indian'] != None and np.ma.is_masked(varnoise_i[j]) == False:
toe_i[j] = findToE(varsignal_i[:,j], varnoise_i[j], multStd)
varToE[1,:] = toe_a
varToE[2,:] = toe_p
varToE[3,:] = toe_i
print('')
# Save in output file
fileName = 'cmip5.'+model['name']+'.toe_1pctCO2vsPiControl_method2_'+method_noise+'.nc'
if method_noise == 'average_std':
def mmeAveMsk2D(listFiles,
years,
inDir,
outDir,
outFile,
timeInt,
mme,
timeBowl,
ToeType,
debug=True):
'''
The mmeAveMsk2D() function averages rhon/lat density bined files with differing masks
It ouputs
- the MME
- a percentage of non-masked bins
- the sign agreement of period2-period1 differences
- ToE per run and for MME
Author: Eric Guilyardi : [email protected]
Created on Tue Nov 25 13:56:20 CET 2014
Inputs:
-------
- listFiles(str) - the list of files to be averaged
- years(t1,t2) - years for slice read
- inDir[](str) - input directory where files are stored (add histnat as inDir[1] for ToE)
- outDir(str) - output directory
- outFile(str) - output file
- timeInt(2xindices) - indices of init period to compare with (e.g. [1,20])
- mme(bool) - multi-model mean (will read in single model ensemble stats)
- timeBowl - either time 'mean' or time 'max' bowl used to mask out bowl
- ToeType(str) - ToE type ('F': none, 'histnat')
-> requires running first mm+mme without ToE to compute Stddev
- debug <optional> - boolean value
Notes:
-----
- EG 25 Nov 2014 - Initial function write
- EG 27 Nov 2014 - Rewrite with loop on variables
- EG 06 Dec 2014 - Added agreement on difference with init period - save as <var>Agree
- EG 07 Dec 2014 - Read bowl to remove points above bowl - save as <var>Bowl
- EG 19 Apr 2016 - ToE computation (just for 2D files)
- EG 07 Oct 2016 - add 3D file support
- EG 21 Nov 2016 - move 3D support to new function
- EG 10 jan 2017 - added timeBowl option
- TODO :
- remove loops
- add computation of ToE per model (toe 1 and toe 2) see ticket #50
- add isonhtc (see ticket #48)
'''
# CDMS initialisation - netCDF compression
comp = 1 # 0 for no compression
cdm.setNetcdfShuffleFlag(comp)
cdm.setNetcdfDeflateFlag(comp)
cdm.setNetcdfDeflateLevelFlag(comp)
cdm.setAutoBounds('on')
# Numpy initialisation
npy.set_printoptions(precision=2)
if debug:
debug = True
else:
debug = False
# File dim and grid inits
t1 = years[0]
t2 = years[1]
if t2 <= 0:
useLastYears = True
t2 = -t2
else:
useLastYears = False
t10 = t1
t20 = t2
# Bound of period average to remove
peri1 = timeInt[0]
peri2 = timeInt[1]
fi = cdm.open(inDir[0] + '/' + listFiles[0])
isond0 = fi['isondepth']
# Create variable handle
# Get grid objects
axesList = isond0.getAxisList()
sigmaGrd = isond0.getLevel()
latN = isond0.shape[3]
levN = isond0.shape[2]
basN = isond0.shape[1]
varsig = 'ptopsigma'
# Declare and open files for writing
if os.path.isfile(outDir + '/' + outFile):
os.remove(outDir + '/' + outFile)
outFile_f = cdm.open(outDir + '/' + outFile, 'w')
# Testing mme with less models
#listFiles=listFiles[0:4]
#timN = isond0.shape[0]
timN = t2 - t1
runN = len(listFiles)
print ' Number of members:', len(listFiles)
valmask = isond0.missing_value[0]
varList = [
'isondepth', 'isonpers', 'isonso', 'isonthetao', 'isonthick', 'isonvol'
]
varFill = [0., 0., valmask, valmask, 0., 0.]
# init arrays (2D rho/lat)
percent = npy.ma.ones([runN, timN, basN, levN, latN], dtype='float32') * 0.
#minbowl = npy.ma.ones([basN,latN], dtype='float32')*1000.
varbowl = npy.ma.ones([runN, timN, basN, latN], dtype='float32') * 1.
#varList = ['isondepth']
#print ' !!! ### Testing one variable ###'
#varList = ['isonthetao']
# init time axis
time = cdm.createAxis(npy.float32(range(timN)))
time.id = 'time'
time.units = 'years since 1861'
time.designateTime()
# init ensemble axis
ensembleAxis = cdm.createAxis(npy.float32(range(runN)))
ensembleAxis.id = 'members'
ensembleAxis.units = 'N'
# loop on variables
for iv, var in enumerate(varList):
# Array inits (2D rho/lat 3D rho/lat/lon)
#shapeR = [basN,levN,latN]
isonvar = npy.ma.ones([runN, timN, basN, levN, latN],
dtype='float32') * valmask
print('isonvar shape: ', isonvar.shape)
vardiff, varbowl2D = [
npy.ma.ones([runN, timN, basN, levN, latN], dtype='float32')
for _ in range(2)
]
varstd, varToE1, varToE2 = [
npy.ma.ones([runN, basN, levN, latN], dtype='float32') * valmask
for _ in range(3)
]
varones = npy.ma.ones([runN, timN, basN, levN, latN],
dtype='float32') * 1.
print ' Variable ', iv, var
# loop over files to fill up array
for i, file in enumerate(listFiles):
ft = cdm.open(inDir[0] + '/' + file)
model = file.split('.')[1]
timeax = ft.getAxis('time')
file1d = replace(inDir[0] + '/' + file, '2D', '1D')
if os.path.isfile(file1d):
f1d = cdm.open(file1d)
else:
print 'ERROR:', file1d, 'missing (if mme, run 1D first)'
sys.exit(1)
tmax = timeax.shape[0]
if i == 0:
tmax0 = tmax
#adapt [t1,t2] time bounds to piControl last NN years
if useLastYears:
t1 = tmax - t20
t2 = tmax
else:
if tmax != tmax0:
print 'wrong time axis: exiting...'
return
# read array
# loop over time/density for memory management
for it in range(timN):
t1r = t1 + it
t2r = t1r + 1
isonRead = ft(var, time=slice(t1r, t2r))
if varFill[iv] != valmask:
isonvar[i, it, ...] = isonRead.filled(varFill[iv])
else:
isonvar[i, it, ...] = isonRead
# compute percentage of non-masked points accros MME
if iv == 0:
maskvar = mv.masked_values(isonRead.data, valmask).mask
percent[i, ...] = npy.float32(npy.equal(maskvar, 0))
if mme:
# if mme then just accumulate Bowl, Agree fields
varst = var + 'Agree'
vardiff[i, ...] = ft(varst, time=slice(t1, t2))
varb = var + 'Bowl'
varbowl2D[i, ...] = ft(varb, time=slice(t1, t2))
else:
# Compute difference with average of first initN years
varinit = cdu.averager(isonvar[i, peri1:peri2, ...], axis=0)
for t in range(timN):
vardiff[i, t, ...] = isonvar[i, t, ...] - varinit
vardiff[i, ...].mask = isonvar[i, ...].mask
# Read bowl and truncate 2D field above bowl
if iv == 0:
bowlRead = f1d(varsig, time=slice(t1, t2))
varbowl[i, ...] = bowlRead
# Compute Stddev
varstd[i, ...] = npy.ma.std(isonvar[i, ...], axis=0)
# Compute ToE
if ToeType == 'histnat':
# Read mean and Std dev from histnat
if i == 0:
filehn = glob.glob(inDir[1] + '/cmip5.' + model +
'.*zon2D*')[0]
#filehn = replace(outFile,'historical','historicalNat')
fthn = cdm.open(filehn)
varmeanhn = fthn(var)
varst = var + 'Std'
varmaxstd = fthn(varst)
toemult = 1.
signal = npy.reshape(isonvar[i, ...] - varmeanhn,
(timN, basN * levN * latN))
noise = npy.reshape(varmaxstd, (basN * levN * latN))
varToE1[i,
...] = npy.reshape(findToE(signal, noise, toemult),
(basN, levN, latN))
toemult = 2.
varToE2[i,
...] = npy.reshape(findToE(signal, noise, toemult),
(basN, levN, latN))
ft.close()
f1d.close()
# <-- end of loop on files
# Compute percentage of bin presence
# Only keep points where percent > 50%
if iv == 0:
percenta = (cdu.averager(percent, axis=0)) * 100.
percenta = mv.masked_less(percenta, 50)
percentw = cdm.createVariable(
percenta,
axes=[time, axesList[1], axesList[2], axesList[3]],
id='isonpercent')
percentw._FillValue = valmask
percentw.long_name = 'percentage of MME bin'
percentw.units = '%'
outFile_f.write(percentw.astype('float32'))
# Sign of difference
if mme:
vardiffsgSum = cdu.averager(vardiff, axis=0)
vardiffsgSum = cdm.createVariable(
vardiffsgSum,
axes=[time, axesList[1], axesList[2], axesList[3]],
id='foo')
vardiffsgSum = maskVal(vardiffsgSum, valmask)
vardiffsgSum.mask = percentw.mask
else:
vardiffsg = npy.copysign(varones, vardiff)
# average signs
vardiffsgSum = cdu.averager(vardiffsg, axis=0)
vardiffsgSum = mv.masked_greater(vardiffsgSum, 10000.)
vardiffsgSum.mask = percentw.mask
vardiffsgSum._FillValue = valmask
# average variable accross members
isonVarAve = cdu.averager(isonvar, axis=0)
isonVarAve = cdm.createVariable(
isonVarAve,
axes=[time, axesList[1], axesList[2], axesList[3]],
id='foo')
# mask
if varFill[iv] == valmask:
isonVarAve = maskVal(isonVarAve, valmask)
isonVarAve.mask = percentw.mask
# Only keep points with rhon > bowl-delta_rho
delta_rho = 0.
if mme: # start from average of <var>Agree
isonVarBowl = cdu.averager(varbowl2D, axis=0)
isonVarBowl = cdm.createVariable(
isonVarBowl,
axes=[time, axesList[1], axesList[2], axesList[3]],
id='foo')
isonVarBowl = maskVal(isonVarBowl, valmask)
isonVarBowl.mask = percentw.mask
# Compute intermodel stddev
isonVarStd = statistics.std(varbowl2D, axis=0)
isonVarStd = cdm.createVariable(
isonVarStd,
axes=[time, axesList[1], axesList[2], axesList[3]],
id='foo')
isonVarStd = maskVal(isonVarStd, valmask)
isonVarStd.mask = percentw.mask
if iv == 0:
# Read mulitmodel sigma on bowl and average in time
file1d = replace(outDir + '/' + outFile, '2D', '1D')
if os.path.isfile(file1d):
f1d = cdm.open(file1d)
else:
print 'ERROR:', file1d, 'missing (if mme, run 1D first)'
sys.exit(1)
bowlRead = f1d(varsig, time=slice(t1, t2))
f1d.close()
siglimit = cdu.averager(bowlRead, axis=0) - delta_rho
# TODO: remove loop by building global array with 1/0
for il in range(latN):
for ib in range(basN):
#if ib == 2:
# print il, siglimit[ib,il]
if siglimit[ib, il] < valmask / 1000.:
# if mme bowl density defined, mask above bowl
index = (npy.argwhere(sigmaGrd[:] >= siglimit[ib, il]))
isonVarBowl[:, ib, 0:index[0], il].mask = True
isonVarStd[:, ib, 0:index[0], il].mask = True
vardiffsgSum[:, ib, 0:index[0], il].mask = True
else:
# mask all points
isonVarBowl[:, ib, :, il].mask = True
isonVarStd[:, ib, :, il].mask = True
vardiffsgSum[:, ib, :, il].mask = True
else:
isonVarBowl = isonVarAve * 1. # start from variable
isonVarStd = isonVarAve * 1. # start from variable
if iv == 0:
siglimit = cdu.averager(varbowl,
axis=0) # average accross members
# Average bowl in time
if timeBowl == 'mean':
siglimit = cdu.averager(siglimit, axis=0) - delta_rho
# or take largest sigma over time
else:
siglimit = npy.ma.max(siglimit, axis=0) - delta_rho
# TODO: remove loop by building global array with 1/0
for il in range(latN):
for ib in range(basN):
if siglimit[ib, il] < valmask / 1000.:
# if bowl density defined, mask above bowl
index = (npy.argwhere(sigmaGrd[:] >= siglimit[ib, il])
)[:, 0] #Add [:,0] for python Yona
#import code
#code.interact(banner='index', local=dict(locals(), **globals()))
isonVarBowl[:, ib, 0:index[0], il].mask = True
vardiffsgSum[:, ib, 0:index[0], il].mask = True
else:
# mask all points
vardiffsgSum[:, ib, :, il].mask = True
isonVarBowl = maskVal(isonVarBowl, valmask)
# Find max of Std dev of all members
isonVarStd = npy.ma.max(varstd, axis=0)
# mask
if varFill[iv] == valmask:
isonVarStd = maskVal(isonVarStd, valmask)
# Write
isonave = cdm.createVariable(
isonVarAve,
axes=[time, axesList[1], axesList[2], axesList[3]],
id=isonRead.id)
isonave.long_name = isonRead.long_name
isonave.units = isonRead.units
isonavediff = cdm.createVariable(
vardiffsgSum,
axes=[time, axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'Agree')
isonavediff.long_name = isonRead.long_name
isonavediff.units = isonRead.units
isonavebowl = cdm.createVariable(
isonVarBowl,
axes=[time, axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'Bowl')
isonavebowl.long_name = isonRead.long_name
isonavebowl.units = isonRead.units
if not mme:
isonmaxstd = cdm.createVariable(
isonVarStd,
axes=[axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'Std')
isonmaxstd.long_name = isonRead.long_name
isonmaxstd.units = isonRead.units
outFile_f.write(isonave.astype('float32'))
outFile_f.write(isonavediff.astype('float32'))
outFile_f.write(isonavebowl.astype('float32'))
if not mme:
outFile_f.write(isonmaxstd.astype('float32'))
if ToeType == 'histnat':
isontoe1 = cdm.createVariable(
varToE1,
axes=[ensembleAxis, axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'ToE1')
isontoe1.long_name = 'ToE 1 for ' + isonRead.long_name
isontoe1.units = 'Year'
isontoe2 = cdm.createVariable(
varToE2,
axes=[ensembleAxis, axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'ToE2')
isontoe2.long_name = 'ToE 2 for ' + isonRead.long_name
isontoe2.units = 'Year'
outFile_f.write(isontoe1.astype('float32'))
outFile_f.write(isontoe2.astype('float32'))
if mme:
isonvarstd = cdm.createVariable(
isonVarStd,
axes=[time, axesList[1], axesList[2], axesList[3]],
id=isonRead.id + 'ModStd')
isonvarstd.long_name = isonRead.long_name + ' intermodel std'
isonvarstd.units = isonRead.units
outFile_f.write(isonvarstd.astype('float32'))
# <--- end of loop on variables
outFile_f.close()
fi.close()
varpms = np.ma.std(varpiC_p, axis=0)
varims = np.ma.std(varpiC_i, axis=0)
# -- reorganise i,j dims in single dimension data (speeds up loops)
varCO2_a = np.reshape(varCO2_a, (timN,levN*latN))
varpiC_a = np.reshape(varpiC_a,(timN,levN*latN))
varams = np.reshape(varams, (levN*latN))
varCO2_p = np.reshape(varCO2_p, (timN,levN*latN))
varpiC_p = np.reshape(varpiC_p,(timN,levN*latN))
varpms = np.reshape(varpms, (levN*latN))
varCO2_i = np.reshape(varCO2_i, (timN,levN*latN))
varpiC_i = np.reshape(varpiC_i,(timN,levN*latN))
varims = np.reshape(varims, (levN*latN))
# -- Compute ToE as last date when diff 1pctCO2 - piControl is larger than mult * stddev
toei_a = np.reshape(findToE(varCO2_a-varpiC_a, varams, multStd),(levN,latN))
toei_p = np.reshape(findToE(varCO2_p-varpiC_p, varpms, multStd),(levN,latN))
toei_i = np.reshape(findToE(varCO2_i-varpiC_i, varims, multStd),(levN,latN))
# -- Save
toe_a[i,:,:] = toei_a
toe_p[i,:,:] = toei_p
toe_i[i,:,:] = toei_i
# -- Select domain to average for each model
domain = ToEdomain1pctCO2vsPiC(model['name'], domain_name)[0]
domain_char = ToEdomain1pctCO2vsPiC(model['name'], domain_name)[1]
# -- Average toe
if domain['Atlantic'] != None:
varToEA[i] = np.ma.around(averageDom(toe_a[i,:,:], 2, domain['Atlantic'], lat, density))
if domain['Indian'] != None:
varsignal_i[:,k,j] = averageDom(varhrcp_i-meanvarhn_i, 3, domain['Indian'], lat, density)
#varsignal_i[145:,k,j] = averageDom(varhrcp_i[145:,:,:]-meanvarhn_i, 3, domain['Indian'], lat, density)
else:
if domain['Atlantic'] != None:
varsignal_a[:,k,j] = averageDom(varhrcp_a-meanvarpiC_a, 3, domain['Atlantic'], lat, density)
if domain['Pacific'] !=None:
varsignal_p[:,k,j] = averageDom(varhrcp_p-meanvarpiC_p, 3, domain['Pacific'], lat, density)
if domain['Indian'] != None:
varsignal_i[:,k,j] = averageDom(varhrcp_i-meanvarpiC_i, 3, domain['Indian'], lat, density)
# print(' varsignal shape:', varsignal_a.shape, varsignal_p.shape, varsignal_i.shape)
# Compute ToE of averaged domain for run k
if domain['Atlantic'] != None and np.ma.is_masked(varnoise_a[j]) == False:
toe2_a[k,j] = findToE(varsignal_a[:,k,j], varnoise_a[j], multStd) + iniyear
toe1_a[k,j] = findToE(varsignal_a[:,k,j], varnoise_a[j], 1) + iniyear
# print(toe1_a[k,j], toe2_a[k,j])
if domain['Pacific'] != None and np.ma.is_masked(varnoise_p[j]) == False:
toe2_p[k,j] = findToE(varsignal_p[:,k,j], varnoise_p[j], multStd) + iniyear
toe1_p[k,j] = findToE(varsignal_p[:,k,j], varnoise_p[j], 1) + iniyear
if domain['Indian'] != None and np.ma.is_masked(varnoise_i[j]) == False:
toe2_i[k,j] = findToE(varsignal_i[:,k,j], varnoise_i[j], multStd) + iniyear
toe1_i[k,j] = findToE(varsignal_i[:,k,j], varnoise_i[j], 1) + iniyear
# Take out runs where the signal is of opposite sign than expected
if v != 'Z':
if signal_domains[j] == 'fresher':
if np.ma.mean(varsignal_a[-5:,k,j],axis=0) > 2 * varnoise_a[j]:
toe2_a[k,j] = np.ma.masked
if np.ma.mean(varsignal_a[-5:,k,j],axis=0) > 1 * varnoise_a[j]:
varsignal_a = varhrcp_a-meanvarpiC_a
varsignal_p = varhrcp_p-meanvarpiC_p
varsignal_i = varhrcp_i-meanvarpiC_i
# Save signal
varsignal_end[k,1,:,:] = np.ma.average(varsignal_a[-5:,:,:],axis=0)
varsignal_end[k,2,:,:] = np.ma.average(varsignal_p[-5:,:,:],axis=0)
varsignal_end[k,3,:,:] = np.ma.average(varsignal_i[-5:,:,:],axis=0)
# Reorganise i,j dims in single dimension data (speeds up loops)
varsignal_a = np.reshape(varsignal_a, (timN, levN*latN))
varsignal_p = np.reshape(varsignal_p, (timN, levN*latN))
varsignal_i = np.reshape(varsignal_i, (timN, levN*latN))
# Compute ToE as last date when diff hist+RCP - histNat is larger than mult * stddev
toe2_a = np.reshape(findToE(varsignal_a, varnoise_a, multStd),(levN,latN))
toe2_p = np.reshape(findToE(varsignal_p, varnoise_p, multStd),(levN,latN))
toe2_i = np.reshape(findToE(varsignal_i, varnoise_i, multStd),(levN,latN))
toe1_a = np.reshape(findToE(varsignal_a, varnoise_a, 1),(levN,latN))
toe1_p = np.reshape(findToE(varsignal_p, varnoise_p, 1),(levN,latN))
toe1_i = np.reshape(findToE(varsignal_i, varnoise_i, 1),(levN,latN))
# Save in output variable
varToE1[k,1,:,:] = toe1_a
varToE1[k,2,:,:] = toe1_p
varToE1[k,3,:,:] = toe1_i
varToE2[k,1,:,:] = toe2_a
varToE2[k,2,:,:] = toe2_p
varToE2[k,3,:,:] = toe2_i
# Mask points because when calculating ToE, masked points (e.g. bathy, no data) are set to 240 (=no emergence)
if ToE:
# reorganise i,j dims in single dimension data (speeds up loops)
tvarha = np.reshape(tvarha, (timN, levN * latN))
tvarhna = np.reshape(tvarhna, (timN, levN * latN))
varams = np.reshape(varams, (levN * latN))
tvarhp = np.reshape(tvarhp, (timN, levN * latN))
tvarhnp = np.reshape(tvarhnp, (timN, levN * latN))
varpms = np.reshape(varpms, (levN * latN))
tvarhi = np.reshape(tvarhi, (timN, levN * latN))
tvarhni = np.reshape(tvarhni, (timN, levN * latN))
varims = np.reshape(varims, (levN * latN))
# Compute ToE as last date when diff hist-histNat is larger than mult * stddev
varam = np.reshape(
findToE(tvarha - tvarhna, varams, multStd) + iniyear, (levN, latN))
varpm = np.reshape(
findToE(tvarhp - tvarhnp, varpms, multStd) + iniyear, (levN, latN))
varim = np.reshape(
findToE(tvarhi - tvarhni, varims, multStd) + iniyear, (levN, latN))
# shade ToE and contour diff hist-histNat
tmpa = vara
vara = varam + 1900
varam = tmpa
tmpp = varp
varp = varpm + 1900
varpm = tmpp
tmpi = vari
vari = varim + 1900
varim = tmpi
varsignal_p[145:,k,j] = averageDom(varrcp_p-meanvarhn_p, 3, domain['Pacific'], lat, density)
else:
varsignal_p[145:,k,j] = averageDom(varrcp_p-meanvarpiC_p, 3, domain['Pacific'], lat, density)
if domain['Indian'] != None:
varsignal_i[0:145,k,j] = averageDom(varh_i-varhn_i, 3, domain['Indian'], lat, density)
if use_piC != True:
varsignal_i[145:,k,j] = averageDom(varrcp_i-meanvarhn_i, 3, domain['Indian'], lat, density)
else:
varsignal_i[145:,k,j] = averageDom(varrcp_i-meanvarpiC_i, 3, domain['Indian'], lat, density)
print(' varsignal shape:', varsignal_a.shape, varsignal_p.shape, varsignal_i.shape)
# Compute ToE of averaged domain for run k
if use_piC != True:
if domain['Atlantic'] != None and np.ma.is_masked(varnoise_a[j]) == False:
toe_a[k,j] = findToE(varsignal_a[:,k,j], varnoise_a[j], multStd) + iniyear
if domain['Pacific'] != None and np.ma.is_masked(varnoise_p[j]) == False:
toe_p[k,j] = findToE(varsignal_p[:,k,j], varnoise_p[j], multStd) + iniyear
if domain['Indian'] != None and np.ma.is_masked(varnoise_i[j]) == False:
toe_i[k,j] = findToE(varsignal_i[:,k,j], varnoise_i[j], multStd) + iniyear
else:
if domain['Atlantic'] != None and np.ma.is_masked(varnoise_a[j]) == False \
and np.ma.is_masked(varnoise2_a[j]) == False:
toe_a[k,j] = findToE_2thresholds(varsignal_a[:,k,j], varnoise_a[j], varnoise2_a[j], 145, multStd) + iniyear
if domain['Pacific'] != None and np.ma.is_masked(varnoise_p[j]) == False \
and np.ma.is_masked(varnoise2_p[j]) == False:
toe_p[k,j] = findToE_2thresholds(varsignal_p[:,k,j], varnoise_p[j], varnoise2_p[j], 145, multStd) + iniyear
if domain['Indian'] != None and np.ma.is_masked(varnoise_i[j]) == False \
and np.ma.is_masked(varnoise2_i[j]) == False:
toe_i[k,j] = findToE_2thresholds(varsignal_i[:,k,j], varnoise_i[j], varnoise2_i[j], 145, multStd) + iniyear
varpms = np.ma.std(varpiC_p, axis=0)
varims = np.ma.std(varpiC_i, axis=0)
# -- reorganise i,j dims in single dimension data (speeds up loops)
varCO2_a = np.reshape(varCO2_a, (timN, levN * latN))
varpiC_a = np.reshape(varpiC_a, (timN, levN * latN))
varams = np.reshape(varams, (levN * latN))
varCO2_p = np.reshape(varCO2_p, (timN, levN * latN))
varpiC_p = np.reshape(varpiC_p, (timN, levN * latN))
varpms = np.reshape(varpms, (levN * latN))
varCO2_i = np.reshape(varCO2_i, (timN, levN * latN))
varpiC_i = np.reshape(varpiC_i, (timN, levN * latN))
varims = np.reshape(varims, (levN * latN))
# -- Compute ToE as last date when diff 1pctCO2 - piControl is larger than mult * stddev
toei_a = np.reshape(findToE(varCO2_a - varpiC_a, varams, multStd),
(levN, latN))
toei_p = np.reshape(findToE(varCO2_p - varpiC_p, varpms, multStd),
(levN, latN))
toei_i = np.reshape(findToE(varCO2_i - varpiC_i, varims, multStd),
(levN, latN))
# -- Save
toe_a[i, :, :] = toei_a
toe_p[i, :, :] = toei_p
toe_i[i, :, :] = toei_i
# -- Select domain to average for each model
domain = ToEdomain1pctCO2vsPiC(model['name'], domain_name)[0]
domain_char = ToEdomain1pctCO2vsPiC(model['name'], domain_name)[1]
def mmeAveMsk2D(listFiles, years, inDir, outDir, outFile, timeInt, mme, timeBowl, ToeType, debug=True):
'''
The mmeAveMsk2D() function averages rhon/lat density bined files with differing masks
It ouputs
- the MME
- a percentage of non-masked bins
- the sign agreement of period2-period1 differences
- ToE per run and for MME
Author: Eric Guilyardi : [email protected]
Created on Tue Nov 25 13:56:20 CET 2014
Inputs:
-------
- listFiles(str) - the list of files to be averaged
- years(t1,t2) - years for slice read
- inDir[](str) - input directory where files are stored (add histnat as inDir[1] for ToE)
- outDir(str) - output directory
- outFile(str) - output file
- timeInt(2xindices) - indices of init period to compare with (e.g. [1,20])
- mme(bool) - multi-model mean (will read in single model ensemble stats)
- timeBowl - either time 'mean' or time 'max' bowl used to mask out bowl
- ToeType(str) - ToE type ('F': none, 'histnat')
-> requires running first mm+mme without ToE to compute Stddev
- debug <optional> - boolean value
Notes:
-----
- EG 25 Nov 2014 - Initial function write
- EG 27 Nov 2014 - Rewrite with loop on variables
- EG 06 Dec 2014 - Added agreement on difference with init period - save as <var>Agree
- EG 07 Dec 2014 - Read bowl to remove points above bowl - save as <var>Bowl
- EG 19 Apr 2016 - ToE computation (just for 2D files)
- EG 07 Oct 2016 - add 3D file support
- EG 21 Nov 2016 - move 3D support to new function
- EG 10 jan 2017 - added timeBowl option
- TODO :
- remove loops
- add computation of ToE per model (toe 1 and toe 2) see ticket #50
- add isonhtc (see ticket #48)
'''
# CDMS initialisation - netCDF compression
comp = 1 # 0 for no compression
cdm.setNetcdfShuffleFlag(comp)
cdm.setNetcdfDeflateFlag(comp)
cdm.setNetcdfDeflateLevelFlag(comp)
cdm.setAutoBounds('on')
# Numpy initialisation
npy.set_printoptions(precision=2)
if debug:
debug = True
else:
debug = False
# File dim and grid inits
t1 = years[0]
t2 = years[1]
if t2 <= 0:
useLastYears = True
t2 = -t2
else:
useLastYears = False
t10 = t1
t20 = t2
# Bound of period average to remove
peri1 = timeInt[0]
peri2 = timeInt[1]
fi = cdm.open(inDir[0]+'/'+listFiles[0])
isond0 = fi['isondepth'] ; # Create variable handle
# Get grid objects
axesList = isond0.getAxisList()
sigmaGrd = isond0.getLevel()
latN = isond0.shape[3]
levN = isond0.shape[2]
basN = isond0.shape[1]
varsig='ptopsigma'
# Declare and open files for writing
if os.path.isfile(outDir+'/'+outFile):
os.remove(outDir+'/'+outFile)
outFile_f = cdm.open(outDir+'/'+outFile,'w')
# Testing mme with less models
#listFiles=listFiles[0:4]
#timN = isond0.shape[0]
timN = t2-t1
runN = len(listFiles)
print ' Number of members:',len(listFiles)
valmask = isond0.missing_value[0]
varList = ['isondepth','isonpers','isonso','isonthetao','isonthick','isonvol']
varFill = [0.,0.,valmask,valmask,0.,0.]
# init arrays (2D rho/lat)
percent = npy.ma.ones([runN,timN,basN,levN,latN], dtype='float32')*0.
#minbowl = npy.ma.ones([basN,latN], dtype='float32')*1000.
varbowl = npy.ma.ones([runN,timN,basN,latN], dtype='float32')*1.
#varList = ['isondepth']
#print ' !!! ### Testing one variable ###'
#varList = ['isonthetao']
# init time axis
time = cdm.createAxis(npy.float32(range(timN)))
time.id = 'time'
time.units = 'years since 1861'
time.designateTime()
# init ensemble axis
ensembleAxis = cdm.createAxis(npy.float32(range(runN)))
ensembleAxis.id = 'members'
ensembleAxis.units = 'N'
# loop on variables
for iv,var in enumerate(varList):
# Array inits (2D rho/lat 3D rho/lat/lon)
#shapeR = [basN,levN,latN]
isonvar = npy.ma.ones([runN,timN,basN,levN,latN], dtype='float32')*valmask
vardiff,varbowl2D = [npy.ma.ones(npy.ma.shape(isonvar)) for _ in range(2)]
varstd,varToE1,varToE2 = [npy.ma.ones([runN,basN,levN,latN], dtype='float32')*valmask for _ in range(3)]
varones = npy.ma.ones([runN,timN,basN,levN,latN], dtype='float32')*1.
print ' Variable ',iv, var
# loop over files to fill up array
for i,file in enumerate(listFiles):
ft = cdm.open(inDir[0]+'/'+file)
model = file.split('.')[1]
timeax = ft.getAxis('time')
file1d = replace(inDir[0]+'/'+file,'2D','1D')
if os.path.isfile(file1d):
f1d = cdm.open(file1d)
else:
print 'ERROR:',file1d,'missing (if mme, run 1D first)'
sys.exit(1)
tmax = timeax.shape[0]
if i == 0:
tmax0 = tmax
#adapt [t1,t2] time bounds to piControl last NN years
if useLastYears:
t1 = tmax-t20
t2 = tmax
else:
if tmax != tmax0:
print 'wrong time axis: exiting...'
return
# read array
# loop over time/density for memory management
for it in range(timN):
t1r = t1 + it
t2r = t1r + 1
isonRead = ft(var,time = slice(t1r,t2r))
if varFill[iv] != valmask:
isonvar[i,it,...] = isonRead.filled(varFill[iv])
else:
isonvar[i,it,...] = isonRead
# compute percentage of non-masked points accros MME
if iv == 0:
maskvar = mv.masked_values(isonRead.data,valmask).mask
percent[i,...] = npy.float32(npy.equal(maskvar,0))
if mme:
# if mme then just accumulate Bowl, Agree fields
varst = var+'Agree'
vardiff[i,...] = ft(varst,time = slice(t1,t2))
varb = var+'Bowl'
varbowl2D[i,...] = ft(varb,time = slice(t1,t2))
else:
# Compute difference with average of first initN years
varinit = cdu.averager(isonvar[i,peri1:peri2,...],axis=0)
for t in range(timN):
vardiff[i,t,...] = isonvar[i,t,...] - varinit
vardiff[i,...].mask = isonvar[i,...].mask
# Read bowl and truncate 2D field above bowl
if iv == 0:
bowlRead = f1d(varsig,time = slice(t1,t2))
varbowl[i,...] = bowlRead
# Compute Stddev
varstd[i,...] = npy.ma.std(isonvar[i,...], axis=0)
# Compute ToE
if ToeType == 'histnat':
# Read mean and Std dev from histnat
if i == 0:
filehn = glob.glob(inDir[1]+'/cmip5.'+model+'.*zon2D*')[0]
#filehn = replace(outFile,'historical','historicalNat')
fthn = cdm.open(filehn)
varmeanhn = fthn(var)
varst = var+'Std'
varmaxstd = fthn(varst)
toemult = 1.
signal = npy.reshape(isonvar[i,...]-varmeanhn,(timN,basN*levN*latN))
noise = npy.reshape(varmaxstd,(basN*levN*latN))
varToE1[i,...] = npy.reshape(findToE(signal, noise, toemult),(basN,levN,latN))
toemult = 2.
varToE2[i,...] = npy.reshape(findToE(signal, noise, toemult),(basN,levN,latN))
ft.close()
f1d.close()
# <-- end of loop on files
# Compute percentage of bin presence
# Only keep points where percent > 50%
if iv == 0:
percenta = (cdu.averager(percent,axis=0))*100.
percenta = mv.masked_less(percenta, 50)
percentw = cdm.createVariable(percenta, axes = [time,axesList[1],axesList[2],axesList[3]], id = 'isonpercent')
percentw._FillValue = valmask
percentw.long_name = 'percentage of MME bin'
percentw.units = '%'
outFile_f.write(percentw.astype('float32'))
# Sign of difference
if mme:
vardiffsgSum = cdu.averager(vardiff, axis=0)
vardiffsgSum = cdm.createVariable(vardiffsgSum , axes =[time,axesList[1],axesList[2],axesList[3]] , id = 'foo')
vardiffsgSum = maskVal(vardiffsgSum, valmask)
vardiffsgSum.mask = percentw.mask
else:
vardiffsg = npy.copysign(varones,vardiff)
# average signs
vardiffsgSum = cdu.averager(vardiffsg, axis=0)
vardiffsgSum = mv.masked_greater(vardiffsgSum, 10000.)
vardiffsgSum.mask = percentw.mask
vardiffsgSum._FillValue = valmask
# average variable accross members
isonVarAve = cdu.averager(isonvar, axis=0)
isonVarAve = cdm.createVariable(isonVarAve , axes =[time,axesList[1],axesList[2],axesList[3]] , id = 'foo')
# mask
if varFill[iv] == valmask:
isonVarAve = maskVal(isonVarAve, valmask)
isonVarAve.mask = percentw.mask
# Only keep points with rhon > bowl-delta_rho
delta_rho = 0.
if mme: # start from average of <var>Agree
isonVarBowl = cdu.averager(varbowl2D, axis=0)
isonVarBowl = cdm.createVariable(isonVarBowl , axes =[time,axesList[1],axesList[2],axesList[3]] , id = 'foo')
isonVarBowl = maskVal(isonVarBowl, valmask)
isonVarBowl.mask = percentw.mask
# Compute intermodel stddev
isonVarStd = statistics.std(varbowl2D, axis=0)
isonVarStd = cdm.createVariable(isonVarStd , axes =[time,axesList[1],axesList[2],axesList[3]] , id = 'foo')
isonVarStd = maskVal(isonVarStd, valmask)
isonVarStd.mask = percentw.mask
if iv == 0:
# Read mulitmodel sigma on bowl and average in time
file1d = replace(outDir+'/'+outFile,'2D','1D')
if os.path.isfile(file1d):
f1d = cdm.open(file1d)
else:
print 'ERROR:',file1d,'missing (if mme, run 1D first)'
sys.exit(1)
bowlRead = f1d(varsig,time = slice(t1,t2))
f1d.close()
siglimit = cdu.averager(bowlRead, axis=0) - delta_rho
# TODO: remove loop by building global array with 1/0
for il in range(latN):
for ib in range(basN):
#if ib == 2:
# print il, siglimit[ib,il]
if siglimit[ib,il] < valmask/1000.:
# if mme bowl density defined, mask above bowl
index = (npy.argwhere(sigmaGrd[:] >= siglimit[ib,il]))
isonVarBowl [:,ib,0:index[0],il].mask = True
isonVarStd [:,ib,0:index[0],il].mask = True
vardiffsgSum[:,ib,0:index[0],il].mask = True
else:
# mask all points
isonVarBowl [:,ib,:,il].mask = True
isonVarStd [:,ib,:,il].mask = True
vardiffsgSum[:,ib,:,il].mask = True
else:
isonVarBowl = isonVarAve*1. # start from variable
isonVarStd = isonVarAve*1. # start from variable
if iv == 0:
siglimit = cdu.averager(varbowl, axis=0) # average accross members
# Average bowl in time
if timeBowl == 'mean':
siglimit = cdu.averager(siglimit, axis=0) - delta_rho
# or take largest sigma over time
else:
siglimit = npy.ma.max(siglimit, axis=0) - delta_rho
# TODO: remove loop by building global array with 1/0
for il in range(latN):
for ib in range(basN):
if siglimit[ib,il] < valmask/1000.:
# if bowl density defined, mask above bowl
index = (npy.argwhere(sigmaGrd[:] >= siglimit[ib,il]))
isonVarBowl[:,ib,0:index[0],il].mask = True
vardiffsgSum[:,ib,0:index[0],il].mask = True
else:
# mask all points
vardiffsgSum[:,ib,:,il].mask = True
isonVarBowl = maskVal(isonVarBowl, valmask)
# Find max of Std dev of all members
isonVarStd = npy.ma.max(varstd, axis=0)
# mask
if varFill[iv] == valmask:
isonVarStd = maskVal(isonVarStd, valmask)
# Write
isonave = cdm.createVariable(isonVarAve, axes = [time,axesList[1],axesList[2],axesList[3]], id = isonRead.id)
isonave.long_name = isonRead.long_name
isonave.units = isonRead.units
isonavediff = cdm.createVariable(vardiffsgSum, axes = [time,axesList[1],axesList[2],axesList[3]], id = isonRead.id+'Agree')
isonavediff.long_name = isonRead.long_name
isonavediff.units = isonRead.units
isonavebowl = cdm.createVariable(isonVarBowl, axes = [time,axesList[1],axesList[2],axesList[3]], id = isonRead.id+'Bowl')
isonavebowl.long_name = isonRead.long_name
isonavebowl.units = isonRead.units
if not mme:
isonmaxstd = cdm.createVariable(isonVarStd, axes = [axesList[1],axesList[2],axesList[3]], id = isonRead.id+'Std')
isonmaxstd.long_name = isonRead.long_name
isonmaxstd.units = isonRead.units
outFile_f.write( isonave.astype('float32'))
outFile_f.write(isonavediff.astype('float32'))
outFile_f.write(isonavebowl.astype('float32'))
if not mme:
outFile_f.write( isonmaxstd.astype('float32'))
if ToeType == 'histnat':
isontoe1 = cdm.createVariable(varToE1, axes = [ensembleAxis,axesList[1],axesList[2],axesList[3]], id = isonRead.id+'ToE1')
isontoe1.long_name = 'ToE 1 for '+isonRead.long_name
isontoe1.units = 'Year'
isontoe2 = cdm.createVariable(varToE2, axes = [ensembleAxis,axesList[1],axesList[2],axesList[3]], id = isonRead.id+'ToE2')
isontoe2.long_name = 'ToE 2 for '+isonRead.long_name
isontoe2.units = 'Year'
outFile_f.write(isontoe1.astype('float32'))
outFile_f.write(isontoe2.astype('float32'))
if mme:
isonvarstd = cdm.createVariable(isonVarStd , axes =[time,axesList[1],axesList[2],axesList[3]] , id = isonRead.id+'ModStd')
isonvarstd.long_name = isonRead.long_name+' intermodel std'
isonvarstd.units = isonRead.units
outFile_f.write(isonvarstd.astype('float32'))
# <--- end of loop on variables
outFile_f.close()
fi.close()
varpms = np.ma.std(varpiC_p, axis=0) varims = np.ma.std(varpiC_i, axis=0) # -- reorganise i,j dims in single dimension data (speeds up loops) varCO2_a = np.reshape(varCO2_a, (timN, levN * latN)) varpiC_a = np.reshape(varpiC_a, (timN, levN * latN)) varams = np.reshape(varams, (levN * latN)) varCO2_p = np.reshape(varCO2_p, (timN, levN * latN)) varpiC_p = np.reshape(varpiC_p, (timN, levN * latN)) varpms = np.reshape(varpms, (levN * latN)) varCO2_i = np.reshape(varCO2_i, (timN, levN * latN)) varpiC_i = np.reshape(varpiC_i, (timN, levN * latN)) varims = np.reshape(varims, (levN * latN)) # -- Compute ToE as last date when diff 1pctCO2 - piControl is larger than mult * stddev toe_a = np.reshape(findToE(varCO2_a - varpiC_a, varams, multStd), (levN, latN)) toe_p = np.reshape(findToE(varCO2_p - varpiC_p, varpms, multStd), (levN, latN)) toe_i = np.reshape(findToE(varCO2_i - varpiC_i, varims, multStd), (levN, latN)) # -- Average bowl position bowlCO2_a = np.ma.average(bowlCO2_a, axis=0) bowlCO2_p = np.ma.average(bowlCO2_p, axis=0) bowlCO2_i = np.ma.average(bowlCO2_i, axis=0) bowlpiC_a = np.ma.average(bowlpiC_a, axis=0) bowlpiC_p = np.ma.average(bowlpiC_p, axis=0) bowlpiC_i = np.ma.average(bowlpiC_i, axis=0) # -- Mask var_mask = np.ma.getmask(np.ma.average(f2dCO2.variables[var][:], axis=0)) toe_a = np.ma.array(toe_a, mask=var_mask[1, :, :]) toe_p = np.ma.array(toe_p, mask=var_mask[2, :, :])
if use_piC == False:
varsignal_a[145:, :, :] = varrcp_a - meanvarhn_a
varsignal_p[145:, :, :] = varrcp_p - meanvarhn_p
varsignal_i[145:, :, :] = varrcp_i - meanvarhn_i
else:
varsignal_a[145:, :, :] = varrcp_a - meanvarpiC_a
varsignal_p[145:, :, :] = varrcp_p - meanvarpiC_p
varsignal_i[145:, :, :] = varrcp_i - meanvarpiC_i
# Reorganise i,j dims in single dimension data (speeds up loops)
varsignal_a = np.reshape(varsignal_a, (timN, levN * latN))
varsignal_p = np.reshape(varsignal_p, (timN, levN * latN))
varsignal_i = np.reshape(varsignal_i, (timN, levN * latN))
# Compute ToE as last date when diff hist+RCP - histNat is larger than mult * stddev
toe_a = np.reshape(findToE(varsignal_a, stdvarhn_a, multStd),
(levN, latN))
toe_p = np.reshape(findToE(varsignal_p, stdvarhn_p, multStd),
(levN, latN))
toe_i = np.reshape(findToE(varsignal_i, stdvarhn_i, multStd),
(levN, latN))
# Save in output variable
varToE[k, 1, :, :] = toe_a
varToE[k, 2, :, :] = toe_p
varToE[k, 3, :, :] = toe_i
# Save in output file
if use_piC == False:
fileName = 'cmip5.' + model[
'name'] + '.toe_zonal_rcp_histNat.nc'