def dotheconstraint_all(xem,
yem,
x1mem,
y1mem,
obsx,
sigxem=None,
sigyem=None,
sigx1mem=None,
sigy1mem=None,
rxyem=None,
rxy1mem=None,
seed=None,
nboots=1000,
method='OLS'):
# do the constraint with everything included.
# check that all the needed things are there for the method
if sigyem is None:
print(
"You need to specify the sigma_y's for the ensemble mean for any constraint"
)
sys.exit()
if sigx1mem is None:
print(
"You need to specify the sigma_x for 1 member for any constraint")
sys.exit()
if sigy1mem is None:
print(
"You need to specify the sigma_y for 1 member for any constraint")
sys.exit()
if ((method == "TLS") or (method == "BHM")):
if sigxem is None:
print(
"You need to specify the sigma_x for the ensemble mean for TLS or BHM"
)
sys.exit()
if ((method != "OLS") and (method != "TLS") and (method != "BHM")):
print("You have chosen an invalid method. Choose OLS, TLS or BHM")
sys.exit()
if (method == "BHM"):
if rxyem is None:
print(
"You need to specify rxy for the ensemble mean to use the BHM")
sys.exit()
if rxy1mem is None:
print("You need to specify rxy for 1 member to use the BHM")
sys.exit()
# calculate the regression coefficients using the ensemble mean
if (method == 'OLS'):
print("Constraining using OLS")
a, b = linfit.linfit_xy(xem, yem, sigma=sigyem)
if (method == 'TLS'):
print("Constraining using TLS")
a, b = linfit.tls(xem, yem, sigxem, sigyem)
if (method == 'BHM'):
print("Constraining using the BHM")
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
if (method == "BHM"):
# an array of standard deviations for the forced noise
sigforced = np.sqrt(del2[:])
# standard deviation for the internal variability noise component
sigyiv = np.sqrt((sigy1mem**2) * (1. - rxy1mem**2.))
else:
# calculate the single member residuals from the linear regression fit
# their standard deviation and the standard deviation of the noise term
# both with (sigwithiv) and without (sigforced) internal variability
eps = y1mem[:] - (a + b * x1mem[:])
sigeps = np.std(eps)
sigwithiv = np.sqrt(sigeps**2 - (b**2) * sigx1mem)
sigyiv = sigy1mem
sigforced = np.sqrt(sigwithiv**2. - sigyiv**2.)
# sampling the uncertainty on the observed predictor
# 250 values for each observational value
nobs = obsx.size
obspdf = np.zeros([nobs * 250])
obstrue = np.zeros([nobs * 250])
obssample = np.random.normal(0, sigx1mem, 250)
for iobs in range(0, obsx.size, 1):
obspdf[iobs * 250:(iobs + 1) * 250] = obsx[iobs] + obssample[:]
if (method == "BHM"):
obstrue[iobs * 250:(iobs + 1) * 250] = obsx[iobs]
# combine all the sampling
# OLS and TLS
if (method != "BHM"):
# sample the noise terms and regression coefficients
randomvals = np.random.normal(0, 1, nboots)
# forced + internal
noise_withiv = randomvals * np.array(sigwithiv)
# forced
noise_forced = randomvals * np.array(sigforced)
if (method == "OLS"):
sigxin = None
if (method == "TLS"):
sigxin = sigxem
aboots, bboots = boot.boot_regcoefs(xem, yem, sigx=sigxin, sigy=sigyem)
# first, regression coefficient uncertainty with observational predictor uncertainty
y = np.zeros([nobs * 250 * nboots])
for iboot in range(0, nboots, 1):
y[iboot * nobs * 250:(iboot + 1) * nobs *
250] = aboots[iboot] + bboots[iboot] * obspdf[:]
# now adding on the noise terms
yplusiv = np.zeros([nobs * 250 * nboots * nboots])
yforced = np.zeros([nobs * 250 * nboots * nboots])
for iboot in range(0, nboots, 1):
yplusiv[iboot * (nobs * 250 * nboots):(iboot + 1) *
(nobs * 250 * nboots)] = y[:] + noise_withiv[iboot]
yforced[iboot * (nobs * 250 * nboots):(iboot + 1) *
(nobs * 250 * nboots)] = y[:] + noise_forced[iboot]
else:
#sample the noise terms
# only do internal variability here because forced noise is
# dependend on delxdelx which is paired with alpha and beta's
randomvals = np.random.normal(0, 1, nboots * nboots)
randomvals2 = np.random.normal(0, 1, nboots * nboots)
noiseiv = np.array(sigyiv) * randomvals2[:]
yplusiv = np.zeros([nobs * 250 * nboots * nboots])
yforced = np.zeros([nobs * 250 * nboots * nboots])
for iboot in range(0, nboots, 1):
y = np.zeros([nobs * 250])
y[:] = aboots[iboot] + bboots[iboot] * obspdf[:]
noise_forced = randomvals[:] * sigforced[iboot]
yplusivt = np.zeros([nobs * 250 * nboots])
yforcedt = np.zeros([nobs * 250 * nboots])
for inoise in range(0, nboots, 1):
yforcedt[inoise*nboots:(inoise+1)*nboots]=\
y[inoise]+noise_forced[inoise*nboots:(inoise+1)*nboots]
yplusivt[inoise*nboots:(inoise+1)*nboots]=\
yforcedt[inoise*nboots:(inoise+1)*nboots]+\
noiseiv[inoise] + \
np.array(rxy1mem)*(np.array(sigy1mem)/np.array(sigx1mem))*(obspdf[:]-obstrue[:])
yforced[iboot * nobs * 250 * nboots:(iboot + 1) * nobs * 250 *
nboots] = yforcedt[:]
yplusiv[iboot * nobs * 250 * nboots:(iboot + 1) * nobs * 250 *
nboots] = yplusivt[:]
varforced = np.var(yforced)
varplusiv = np.var(yplusiv)
return varforced, varplusiv
def dotheconstraint_onlycoefs(xem,
yem,
x1mem,
y1mem,
obsx,
sigxem=None,
sigyem=None,
sigx1mem=None,
sigy1mem=None,
rxyem=None,
rxy1mem=None,
seed=None,
nboots=1000,
method='OLS'):
# performing the constraint while only considering the uncertainty in the regerssion coefficients
# check that all the needed things are there for the method
if sigyem is None:
print(
"You need to specify the sigma_y's for the ensemble mean for any constraint"
)
sys.exit()
if sigx1mem is None:
print(
"You need to specify the sigma_x for 1 member for any constraint")
sys.exit()
if sigy1mem is None:
print(
"You need to specify the sigma_y for 1 member for any constraint")
sys.exit()
if ((method == "TLS") or (method == "BHM")):
if sigxem is None:
print(
"You need to specify the sigma_x for the ensemble mean for TLS or BHM"
)
sys.exit()
if ((method != "OLS") and (method != "TLS") and (method != "BHM")):
print("You have chosen an invalid method. Choose OLS, TLS or BHM")
sys.exit()
if (method == "BHM"):
if rxyem is None:
print(
"You need to specify rxy for the ensemble mean to use the BHM")
sys.exit()
if rxy1mem is None:
print("You need to specify rxy for 1 member to use the BHM")
sys.exit()
# calculate the regression coefficients using the ensemble mean
if (method == 'OLS'):
print("Constraining using OLS")
a, b = linfit.linfit_xy(xem, yem, sigma=sigyem)
if (method == 'TLS'):
print("Constraining using TLS")
a, b = linfit.tls(xem, yem, sigxem, sigyem)
if (method == 'BHM'):
print("Constraining using the BHM")
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
a = np.mean(aboots)
b = np.mean(bboots)
# sampling the uncertainty on the observed predictor.
# 250 values for each observational value.
nobs = obsx.size
obsmean = np.mean(obsx)
if (method != "BHM"):
if (method == 'OLS'):
sigxin = None
if (method == 'TLS'):
sigxin = sigxem
aboots, bboots = boot.boot_regcoefs(xem, yem, sigx=sigxin, sigy=sigyem)
else:
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
y = np.zeros([nobs * 250])
y[:] = aboots[:] + bboots[:] * obsmean
vary = np.var(y)
return vary
def dotheconstraint_onlyiv(xem,
yem,
x1mem,
y1mem,
obsx,
sigxem=None,
sigyem=None,
sigx1mem=None,
sigy1mem=None,
rxyem=None,
rxy1mem=None,
seed=None,
nboots=1000,
method='OLS'):
# performing the constraint while only considering the internal variability on the future - past
# difference.
# check that all the needed things are there for the method
if sigyem is None:
print(
"You need to specify the sigma_y's for the ensemble mean for any constraint"
)
sys.exit()
if sigx1mem is None:
print(
"You need to specify the sigma_x for 1 member for any constraint")
sys.exit()
if sigy1mem is None:
print(
"You need to specify the sigma_y for 1 member for any constraint")
sys.exit()
if ((method == "TLS") or (method == "BHM")):
if sigxem is None:
print(
"You need to specify the sigma_x for the ensemble mean for TLS or BHM"
)
sys.exit()
if ((method != "OLS") and (method != "TLS") and (method != "BHM")):
print("You have chosen an invalid method. Choose OLS, TLS or BHM")
sys.exit()
if (method == "BHM"):
if rxyem is None:
print(
"You need to specify rxy for the ensemble mean to use the BHM")
sys.exit()
if rxy1mem is None:
print("You need to specify rxy for 1 member to use the BHM")
sys.exit()
# calculate the regression coefficients using the ensemble mean
if (method == 'OLS'):
print("Constraining using OLS")
a, b = linfit.linfit_xy(xem, yem, sigma=sigyem)
if (method == 'TLS'):
print("Constraining using TLS")
a, b = linfit.tls(xem, yem, sigxem, sigyem)
if (method == 'BHM'):
print("Constraining using the BHM")
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
a = np.mean(aboots)
b = np.mean(bboots)
# sampling the uncertainty on the observed predictor.
# 250 values for each observational value.
nobs = obsx.size
obsmean = np.mean(obsx)
if (method != "BHM"):
if (method == 'OLS'):
sigxin = None
if (method == 'TLS'):
sigxin = sigxem
aboots, bboots = boot.boot_regcoefs(xem, yem, sigx=sigxin, sigy=sigyem)
else:
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
if (method == "BHM"):
sigforced = np.sqrt(del2[:])
sigyiv = np.sqrt((sigy1mem**2) * (1. - rxy1mem**2.))
else:
eps = y1mem[:] - (a + b * x1mem[:])
sigeps = np.std(eps)
sigwithiv = np.sqrt(sigeps**2 - (b**2) * sigx1mem)
sigyiv = sigy1mem
sigforced = np.sqrt(sigwithiv**2. - sigyiv**2.)
randomvals = np.random.normal(0, 1, nboots)
if (method != "BHM"):
noise_forced = randomvals * np.array(sigyiv)
y = np.zeros([nboots])
y = a + b * obsmean + noise_forced[:]
else:
noise_forced = randomvals[:] * np.array(sigyiv)
y = np.zeros([nboots])
y = a + b * obsmean + noise_forced[:]
vary = np.var(y)
return vary
def dotheconstraint_onlyxvar(xem,
yem,
x1mem,
y1mem,
obsx,
sigxem=None,
sigyem=None,
sigx1mem=None,
sigy1mem=None,
rxyem=None,
rxy1mem=None,
seed=None,
nboots=1000,
method='OLS'):
# performing the constraint while only considering the uncertainty in the observed predictor
# check that all the needed things are there for the method
if sigyem is None:
print(
"You need to specify the sigma_y's for the ensemble mean for any constraint"
)
sys.exit()
if sigx1mem is None:
print(
"You need to specify the sigma_x for 1 member for any constraint")
sys.exit()
if sigy1mem is None:
print(
"You need to specify the sigma_y for 1 member for any constraint")
sys.exit()
if ((method == "TLS") or (method == "BHM")):
if sigxem is None:
print(
"You need to specify the sigma_x for the ensemble mean for TLS or BHM"
)
sys.exit()
if ((method != "OLS") and (method != "TLS") and (method != "BHM")):
print("You have chosen an invalid method. Choose OLS, TLS or BHM")
sys.exit()
if (method == "BHM"):
if rxyem is None:
print(
"You need to specify rxy for the ensemble mean to use the BHM")
sys.exit()
if rxy1mem is None:
print("You need to specify rxy for 1 member to use the BHM")
sys.exit()
# calculate the regression coefficients using the ensemble mean
if (method == 'OLS'):
print("Constraining using OLS")
a, b = linfit.linfit_xy(xem, yem, sigma=sigyem)
if (method == 'TLS'):
print("Constraining using TLS")
a, b = linfit.tls(xem, yem, sigxem, sigyem)
if (method == 'BHM'):
print("Constraining using the BHM")
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem,
yem,
sigxem,
sigyem,
rxyem,
iseed=3)
a = np.mean(aboots)
b = np.mean(bboots)
# sampling the uncertainty on the observed predictor.
# 250 values for each observational value.
nobs = obsx.size
obstrue = np.zeros([nobs * 250])
obspdf = np.zeros([nobs * 250])
obssample = np.random.normal(0, sigx1mem, 250)
for iobs in range(0, obsx.size, 1):
obspdf[iobs * 250:(iobs + 1) * 250] = obsx[iobs] + obssample[:]
if (method == "BHM"):
obstrue[iobs * 250:(iobs + 1) * 250] = obsx[iobs]
y = np.zeros([nobs * 250])
y[:] = a + b * obspdf[:]
vary = np.var(y)
return vary
def boot_regcoefs(a1, a2, sigx=None, sigy=None, nboots=1000):
""" Output bootstrap samples of regression coefficients
Input:
a1 = first array
a2 = second array
Optional input:
nboots = the number of bootstrap samples used to generate the ci
sigx = the standard deviation on the predictor points
sigy = the standard deviation on the predictand points
Output:
acoefs = nboots samples of the coefficient a
bcoefs = nboots samples of the coefficient b
where y = a + bx
Different regression methods are used
depending on the availability of sigx or sigy
if no sigx then ordinary least squares regression
if sigx and sigy then total least squares regression
"""
if (a1.size != a2.size):
print("The two arrays must have the same size")
sys.exit()
samplesize = a1.size
ranu = np.random.uniform(0, samplesize, nboots * samplesize)
ranu = np.floor(ranu).astype(int)
bootdat = np.zeros([samplesize, nboots])
bootdat1 = np.array(a1[ranu])
bootdat2 = np.array(a2[ranu])
bootdat1 = bootdat1.reshape([samplesize, nboots])
bootdat2 = bootdat2.reshape([samplesize, nboots])
if sigx is not None:
bootdatsigx = np.array(sigx[ranu])
bootdatsigx = bootdatsigx.reshape([samplesize, nboots])
if sigy is not None:
bootdatsigy = np.array(sigy[ranu])
bootdatsigy = bootdatsigy.reshape([samplesize, nboots])
acoef = np.zeros(nboots)
bcoef = np.zeros(nboots)
if sigx is not None:
for iboot in range(0, nboots, 1):
acoef[iboot], bcoef[iboot] = linfit.tls(bootdat1[:, iboot],
bootdat2[:, iboot],
bootdatsigx[:, iboot],
bootdatsigy[:, iboot])
else:
for iboot in range(0, nboots, 1):
acoef[iboot], bcoef[iboot] = linfit.linfit_xy(
bootdat1[:, iboot],
bootdat2[:, iboot],
sigma=bootdatsigy[:, iboot])
return acoef, bcoef
def boot_regcoef_ci(a1, a2, conf, sigx=None, sigy=None, nboots=1000):
""" Output the conf% confidence interval on regression coefficients between
two 1 dimensional arrays by bootstrapping with replacement
Input:
a1 = first array
a2 = second array
conf = the confidence interval you want e.g., 95 for 95% ci (2-sided)
Optional input:
nboots = the number of bootstrap samples used to generate the ci
sigx = the standard deviation on the predictor points
sigy = the standard deviation on the predictand points
Output:
aminci = the minimum range of the confidence interval on a
amaxci = the maximum range of the confidence interval on a
bminci = the minimum range of the confidence interval on b
bmaxci = the maximum range of the confidence interval on b
where y = a + bx
This assumes a two sided test. Different regression methods are used
depending on the availability of sigx or sigy
if no sigx then ordinary least squares regression
if sigx and sigy then total least squares regression
"""
ptilemin = (100. - conf) / 2.
ptilemax = conf + (100 - conf) / 2.
if (a1.size != a2.size):
print("The two arrays must have the same size")
sys.exit()
samplesize = a1.size
ranu = np.random.uniform(0, samplesize, nboots * samplesize)
ranu = np.floor(ranu).astype(int)
bootdat = np.zeros([samplesize, nboots])
bootdat1 = np.array(a1[ranu])
bootdat2 = np.array(a2[ranu])
bootdat1 = bootdat1.reshape([samplesize, nboots])
bootdat2 = bootdat2.reshape([samplesize, nboots])
if sigx is not None:
bootdatsigx = np.array(sigx[ranu])
bootdatsigx = bootdatsigx.reshape([samplesize, nboots])
if sigy is not None:
bootdatsigy = np.array(sigy[ranu])
bootdatsigy = bootdatsigy.reshape([samplesize, nboots])
acoef = np.zeros(nboots)
bcoef = np.zeros(nboots)
if sigx is not None:
for iboot in range(0, nboots, 1):
acoef[iboot], bcoef[iboot] = linfit.tls(bootdat1[:, iboot],
bootdat2[:, iboot],
bootdatsigx[:, iboot],
bootdatsigy[:, iboot])
else:
for iboot in range(0, nboots, 1):
acoef[iboot], bcoef[iboot] = linfit.linfit_xy(
bootdat1[:, iboot],
bootdat2[:, iboot],
sigma=bootdatsigy[:, iboot])
aminci = np.percentile(acoef, ptilemin)
amaxci = np.percentile(acoef, ptilemax)
bminci = np.percentile(bcoef, ptilemin)
bmaxci = np.percentile(bcoef, ptilemax)
arange = [aminci, amaxci]
brange = [bminci, bmaxci]
return arange, brange, acoef, bcoef
def dotheconstraint(xem, yem, x1mem, y1mem, obsx, sigxem=None, sigyem=None,sigx1mem=None,
sigy1mem=None, rxyem=None, rxy1mem=None, seed=None, nboots=1000, method='OLS',
outputsamples=False):
""" Performing the constraint
Inputs:
xem = the ensemble mean predictor
yem = the ensemble mean predictand
x1mem = a single member predictor
y1mem = a single member predictand
obsx = the observed predictors
sigxem = the standard deviation on the ensemble mean predictor
sigyem = the standard deviation on the ensemble mean predictand
sigx1mem = the standard deviation on the single member predictor
sigy1mem = the standard deviation on the single member predictand
rxyem = the ensemble mean correlation between predictor and predictand
rxy1mem = the single member correlation between predictor and predictand
seed = a random number seed which may be specified for reproducibility
nboots = the number of bootstrap samples for each part. Default 1000.
method = 'OLS', 'TLS' or 'BHM'
outputsamples = if True, the samples used to build up the constrained distribution are output.
Outputs:
datout = contains the mean, 95% and 66% confidence intervals and the fraction
of samples greater than the ensemble mean predictand for both the forced response plus
internal variability and the forced response in isolation.
"""
# check that all the needed things are there for the method
if sigyem is None:
print("You need to specify the sigma_y's for the ensemble mean for any constraint")
sys.exit()
if sigx1mem is None:
print("You need to specify the sigma_x for 1 member for any constraint")
sys.exit()
if sigy1mem is None:
print("You need to specify the sigma_y for 1 member for any constraint")
sys.exit()
if ((method == "TLS") or (method == "BHM")):
if sigxem is None:
print("You need to specify the sigma_x for the ensemble mean for TLS or BHM")
sys.exit()
if ((method != "OLS") and (method !="TLS") and (method != "BHM")):
print("You have chosen an invalid method. Choose OLS, TLS or BHM")
sys.exit()
if (method == "BHM"):
if rxyem is None:
print("You need to specify rxy for the ensemble mean to use the BHM")
sys.exit()
if rxy1mem is None:
print("You need to specify rxy for 1 member to use the BHM")
sys.exit()
# calculate the regression coefficients using the ensemble mean
if (method == 'OLS'):
print("Constraining using OLS")
a, b = linfit.linfit_xy(xem, yem, sigma=sigyem)
if (method == 'TLS'):
print("Constraining using TLS")
a, b = linfit.tls(xem, yem, sigxem, sigyem)
if (method == 'BHM'):
print("Constraining using the BHM")
aboots, bboots, del2, mux, delx2 = linfit.bhm(xem, yem, sigxem, sigyem, rxyem, iseed=3)
if (method == "BHM"):
# an array of standard deviations for the forced noise
sigforced = np.sqrt(del2[:])
# standard deviation for the internal variability noise component
sigyiv=np.sqrt( (sigy1mem**2)*(1.-rxy1mem**2.))
else:
# calculate the single member residuals from the linear regression fit
# their standard deviation and the standard deviation of the noise term
# both with (sigwithiv) and without (sigforced) internal variability
eps = y1mem[:] - (a + b*x1mem[:])
sigeps = np.std(eps)
sigwithiv = np.sqrt(sigeps**2 - (b**2)*sigx1mem)
sigyiv = sigy1mem
sigforced = np.sqrt(sigwithiv**2. - sigyiv**2.)
# sampling the uncertainty on the observed predictor
# 250 values for each observational value
nobs=obsx.size
obspdf = np.zeros([nobs*250])
obstrue = np.zeros([nobs*250])
obssample = np.random.normal(0,sigx1mem,250)
for iobs in range(0,obsx.size,1):
obspdf[iobs*250:(iobs+1)*250] = obsx[iobs] + obssample[:]
if (method == "BHM"):
obstrue[iobs*250:(iobs+1)*250] = obsx[iobs]
# combine all the sampling
# OLS and TLS
if (method != "BHM"):
# sample the noise terms and regression coefficients
randomvals = np.random.normal(0,1,nboots)
# forced + internal
noise_withiv = randomvals*np.array(sigwithiv)
# forced
noise_forced = randomvals*np.array(sigforced)
if (method == "OLS"):
sigxin=None
if (method == "TLS"):
sigxin=sigxem
aboots, bboots = boot.boot_regcoefs(xem, yem, sigx=sigxin, sigy=sigyem)
# first, regression coefficient uncertainty with observational predictor uncertainty
y = np.zeros([nobs*250*nboots])
for iboot in range(0,nboots,1):
y[iboot*nobs*250:(iboot+1)*nobs*250] = aboots[iboot] + bboots[iboot]*obspdf[:]
# now adding on the noise terms
yplusiv = np.zeros([nobs*250*nboots*nboots])
yforced = np.zeros([nobs*250*nboots*nboots])
for iboot in range(0,nboots,1):
yplusiv[iboot*(nobs*250*nboots):(iboot+1)*(nobs*250*nboots)] = y[:] + noise_withiv[iboot]
yforced[iboot*(nobs*250*nboots):(iboot+1)*(nobs*250*nboots)] = y[:] + noise_forced[iboot]
else:
#sample the noise terms
# only do internal variability here because forced noise is
# dependend on delxdelx which is paired with alpha and beta's
randomvals = np.random.normal(0,1,nboots*nboots)
randomvals2 = np.random.normal(0,1,nboots*nboots)
noiseiv = np.array(sigyiv)*randomvals2[:]
yplusiv = np.zeros([nobs*250*nboots*nboots])
yforced = np.zeros([nobs*250*nboots*nboots])
for iboot in range(0,nboots,1):
y=np.zeros([nobs*250])
y[:] = aboots[iboot] + bboots[iboot]*obspdf[:]
noise_forced = randomvals[:]*sigforced[iboot]
yplusivt = np.zeros([nobs*250*nboots])
yforcedt = np.zeros([nobs*250*nboots])
for inoise in range(0,nboots,1):
yforcedt[inoise*nboots:(inoise+1)*nboots]=\
y[inoise]+noise_forced[inoise*nboots:(inoise+1)*nboots]
yplusivt[inoise*nboots:(inoise+1)*nboots]=\
yforcedt[inoise*nboots:(inoise+1)*nboots]+\
noiseiv[inoise] + \
np.array(rxy1mem)*(np.array(sigy1mem)/np.array(sigx1mem))*(obspdf[:]-obstrue[:])
yforced[iboot*nobs*250*nboots:(iboot+1)*nobs*250*nboots]=yforcedt[:]
yplusiv[iboot*nobs*250*nboots:(iboot+1)*nobs*250*nboots]=yplusivt[:]
meanwithiv = np.mean(yplusiv)
meanforced = np.mean(yforced)
print("starting to calculate the percentiles - this could take a while")
min95withiv = np.percentile(yplusiv, 2.5)
max95withiv = np.percentile(yplusiv, 97.5)
min66withiv = np.percentile(yplusiv, 17)
max66withiv = np.percentile(yplusiv, 83)
min95forced = np.percentile(yforced, 2.5)
max95forced = np.percentile(yforced, 97.5)
min66forced = np.percentile(yforced, 17)
max66forced = np.percentile(yforced, 83)
print("calculating the percentage greater than the ensemble mean - this may also take a while")
ymean = np.mean(yem)
numberabovey = np.sum(yplusiv > np.array(ymean))
gtymean_withiv = (numberabovey/float(nobs*250*nboots*nboots))*100.
numberabovey = np.sum(yforced > np.array(ymean))
gtymean_forced = (numberabovey/float(nobs*250*nboots*nboots))*100.
datout={ "meanwithiv":meanwithiv, "meanforced":meanforced,
"min95withiv":min95withiv, "max95withiv":max95withiv,
"min66withiv":min66withiv, "max66withiv":max66withiv,
"min95forced":min95forced, "max95forced":max95forced,
"min66forced":min66forced, "max66forced":max66forced,
"gtymean_withiv":gtymean_withiv, "gtymean_forced":gtymean_forced}
if (outputsamples):
return datout, yplusiv
else:
return datout
