def nee2gpp_reichstein(dates,
nee,
t,
isday,
rg=False,
vpd=False,
undef=np.nan,
shape=False,
masked=False,
nogppnight=False):
"""
Calculate photosynthesis (GPP) and ecosystem respiration (Reco) from original
Eddy flux data, using several fits of Reco vs. temperature of nighttime data
over the season, as in Reichstein et al. (2005), in order to calculate Reco
and then GPP = Reco - NEE.
Definition
----------
def nee2gpp_reichstein(dates, nee, t, isday, undef=np.nan, shape=None, masked=False):
Input
-----
Inputs are 1D arrays that can be masked or not.
dates julian days
nee net ecosystem exchange (uptake is <0) [umol m-2 s-1]
t temperature [K]
Parameters
----------
undef undefined values in data (default: np.nan)
Input arrays will be masked at undef, keeping the original mask
shape if False then outputs are 1D arrays (default)
if True, output have the same shape as datain
if a shape tuple is given, then this tuple is used to reshape
masked if False: outputs are undef where nee and t are masked or undef (default)
if True: return masked arrays where outputs would be undef
nogppnight if True: Resp=NEE, GPP=0 at night
if False: Resp=lloyd_taylor, GPP=Resp-NEE at night (default)
Ouput
-----
GPP, Reco photosynthesis, ecosystem respiration
Restrictions
------------
None.
Literature
----------
Reichstein et al. (2005)
On the separation of net ecosystem exchange into assimilation and ecosystem
respiration: review and improved algorithm.
Global Change Biology 11, 1424-1439
Examples
--------
>>> from jams.fread import fread # from jams
>>> from jams.date2dec import date2dec # from jams
>>> dat = fread('test_nee2gpp.csv', skip=2, transpose=True)
>>> dates = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
>>> NEE = np.squeeze(dat[5,:])
>>> rg = np.squeeze(dat[6,:])
>>> tair = np.squeeze(dat[7,:])
>>> undef = -9999.
>>> isday = np.where(rg > 10., True, False)
>>> tt = np.where(tair == undef, undef, tair+273.15)
>>> # partition
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, undef=undef, method='local')
>>> print(GPP[1120:1128])
[-9.99900000e+03 -9.99900000e+03 -9.99900000e+03 4.40606871e+00
8.31942152e+00 1.06242542e+01 8.49245664e+00 1.12381973e+01]
>>> print(Reco[1120:1128])
[1.68311981 1.81012431 1.9874173 2.17108871 2.38759152 2.64372415
2.90076664 3.18592735]
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, undef=undef, method='local')
>>> print(GPP[1120:1128])
[-9.99900000e+03 -9.99900000e+03 -9.99900000e+03 4.40606871e+00
8.31942152e+00 1.06242542e+01 8.49245664e+00 1.12381973e+01]
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, undef=undef, method='Reichstein', masked=True)
>>> print(GPP[1120:1128])
[-- -- -- 4.406068706013192 8.319421516040766 10.624254150217764
8.492456637225963 11.238197347837367]
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, undef=undef, method='reichstein', shape=(np.size(NEE),1))
>>> print(GPP[1120:1128])
[[-9.99900000e+03]
[-9.99900000e+03]
[-9.99900000e+03]
[ 4.40606871e+00]
[ 8.31942152e+00]
[ 1.06242542e+01]
[ 8.49245664e+00]
[ 1.12381973e+01]]
License
-------
This file is part of the JAMS Python package, distributed under the MIT
License. The JAMS Python package originates from the former UFZ Python library,
Department of Computational Hydrosystems, Helmholtz Centre for Environmental
Research - UFZ, Leipzig, Germany.
Copyright (c) 2012-2013 Matthias Cuntz, Arndt Piayda - mc (at) macu (dot) de
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
History
-------
Written MC, Mar 2012
Modified AP, Mar 2012 - undef=np.nan
MC, Nov 2012 - individual routine
MC, Feb 2013 - ported to Python 3
"""
# Checks
# remember shape if any
if shape != False:
if shape != True:
inshape = shape
else:
inshape = nee.shape
dates = np.squeeze(dates)
nee = np.squeeze(nee)
t = np.squeeze(t)
isday = np.squeeze(isday)
if shape == False: inshape = nee.shape
# Check squeezed shape
if dates.ndim != 1:
raise ValueError(
'Error nee2gpp_reichstein: squeezed dates must be 1D array.')
if nee.ndim != 1:
raise ValueError(
'Error nee2gpp_reichstein: squeezed nee must be 1D array.')
if t.ndim != 1:
raise ValueError(
'Error nee2gpp_reichstein: squeezed t must be 1D array.')
if isday.ndim != 1:
raise ValueError(
'Error nee2gpp_reichstein: squeezed isday must be 1D array.')
ndata = dates.size
if ((nee.size != ndata) | (t.size != ndata) | (isday.size != ndata)):
raise ValueError(
'Error nee2gpp_reichstein: inputs must have the same size.')
# Transform to masked array with 1D mask
nee = np.ma.array(nee, mask=False)
t = np.ma.array(t, mask=False)
isday = np.ma.array(isday, mask=False)
# mask also undef
if np.isnan(undef):
if np.ma.any(np.isnan(nee)): nee[np.isnan(nee)] = np.ma.masked
if np.ma.any(np.isnan(t)): t[np.isnan(t)] = np.ma.masked
if np.ma.any(np.isnan(isday)): isday[np.isnan(isday)] = np.ma.masked
else:
if np.ma.any(nee == undef): nee[nee == undef] = np.ma.masked
if np.ma.any(t == undef): t[t == undef] = np.ma.masked
if np.ma.any(isday == undef): isday[isday == undef] = np.ma.masked
# Partition - Local relationship = Reichstein et al. (2005)
# Select valid nighttime
mask = isday | nee.mask | t.mask | isday.mask
ii = np.where(~mask)[0]
if (ii.size == 0):
print('Warning nee2gpp_reichstein: no valid nighttime data.')
if masked:
GPP = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
Reco = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
else:
GPP = np.ones(np.reshape(nee, inshape)) * undef
Reco = np.ones(np.reshape(nee, inshape)) * undef
return GPP, Reco
jul = dates[ii]
tt = np.ma.compressed(t[ii])
net = np.ma.compressed(nee[ii])
# 1. each 5 days, in 15 day period, fit if range of T > 5
locp = [] # local param
locs = [] # local err
dmin = np.floor(np.amin(jul)).astype(
int) # be aware that julian days starts at noon, i.e. 1.0 is 12h
dmax = np.ceil(np.amax(jul)).astype(
int
) # so the search will be from noon to noon and thus includes all nights
for i in range(dmin, dmax, 5):
iii = np.where((jul >= i) & (jul < (i + 14)))[0]
niii = iii.size
if niii > 6:
tt1 = tt[iii]
net1 = net[iii]
mm = ~mad(net1, z=4.5) # make fit more robust by removing outliers
if (np.ptp(tt[iii]) >= 5.) & (np.sum(mm) > 6):
# print(i)
#p = opt.fmin(functions.cost_lloyd_fix, [2.,200.], args=(tt1[mm], net1[mm]), disp=False) # robust params
p, temp1, temp2 = opt.fmin_tnc(functions.cost_lloyd_fix,
[2., 200.],
bounds=[[0., None], [0., None]],
args=(tt1[mm], net1[mm]),
approx_grad=True,
disp=False)
try:
p1, c = opt.curve_fit(functions.lloyd_fix,
tt1[mm],
net1[mm],
p0=p,
maxfev=10000) # params, covariance
if np.all(np.isfinite(
c)): # possible return of curvefit: c=inf
s = np.sqrt(np.diag(c))
else:
s = 10. * np.abs(p)
except:
s = 10. * np.abs(p)
locp += [p]
locs += [s]
# if ((s[1]/p[1])<0.5) & (p[1] > 0.): pdb.set_trace()
if len(locp) == 0:
raise ValueError(
'Error nee2gpp_reichstein: No local relationship found.')
print('Warning nee2gpp_reichstein: No local relationship found.')
if masked:
GPP = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
Reco = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
else:
GPP = np.ones(np.reshape(nee, inshape)) * undef
Reco = np.ones(np.reshape(nee, inshape)) * undef
return GPP, Reco
locp = np.squeeze(np.array(locp).astype(float))
locs = np.squeeze(np.array(locs).astype(float))
# 2. E0 = avg of best 3
# Reichstein et al. (2005), p. 1430, 1st paragraph.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
iii = np.where((locp[:, 1] > 0.) & (locp[:, 1] < 450.)
& (np.abs(locs[:, 1] / locp[:, 1]) < 0.5))[0]
niii = iii.size
if niii == 0:
# raise ValueError('Error nee2gpp_reichstein: No good local relationship found.')
# loosen the criteria: take the best three estimates anyway
iii = np.where((locp[:, 1] > 0.))[0]
niii = iii.size
if niii < 1:
raise ValueError('Error nee2gpp_reichstein: No E0>0 found.')
print('Warning nee2gpp_reichstein: No E0>0 found.')
if masked:
GPP = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
Reco = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
else:
GPP = np.ones(np.reshape(nee, inshape)) * undef
Reco = np.ones(np.reshape(nee, inshape)) * undef
return GPP, Reco
lp = locp[iii, :]
ls = locs[iii, :]
iis = np.argsort(ls[:, 1])
bestp = np.mean(lp[iis[0:np.minimum(3, niii)], :], axis=0)
bests = np.mean(ls[iis[0:np.minimum(3, niii)], :], axis=0)
elif niii == 1:
bestp = np.squeeze(locp[iii, :])
bests = np.squeeze(locs[iii, :])
elif niii == 2:
bestp = np.mean(locp[iii, :], axis=0)
bests = np.mean(locs[iii, :], axis=0)
# ls = locs[iii,:]
# iis = np.argsort(ls[:,1])
else:
lp = locp[iii, :]
ls = locs[iii, :]
iis = np.argsort(ls[:, 1])
bestp = np.mean(lp[iis[0:3], :], axis=0)
bests = np.mean(ls[iis[0:3], :], axis=0)
# 3. Refit Rref with fixed E0, each 4 days
refp = [] # Rref param
refii = [] # mean index of data points
E0 = bestp[1]
et = functions.lloyd_fix(tt, 1., E0)
for i in range(dmin, dmax, 4):
iii = np.where((jul >= i) & (jul < (i + 4)))[0]
niii = iii.size
if niii > 3:
# Calc directly minisation of (nee-p*et)**2
# p = np.sum(net[iii]*et[iii])/np.sum(et[iii]**2)
# p, c = opt.curve_fit(functions.lloyd_only_rref, et[iii], net[iii], p0=[2.])
#p = opt.fmin(functions.cost_lloyd_only_rref, [2.], args=(et[iii], net[iii]), disp=False)
#p = opt.fmin(functions.cost_abs, [2.], args=(functions.lloyd_only_rref_p, et[iii], net[iii]), disp=False)
p, temp1, temp2 = opt.fmin_tnc(functions.cost_abs, [2.],
bounds=[[0., None]],
args=(functions.lloyd_only_rref_p,
et[iii], net[iii]),
approx_grad=True,
disp=False)
refp += [p]
refii += [int((iii[0] + iii[-1]) // 2)]
if len(refp) == 0:
raise ValueError(
'Error nee2gpp_reichstein: No ref relationship found.')
print('Warning nee2gpp_reichstein: No ref relationship found.')
if masked:
GPP = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
Reco = np.ma.array(np.reshape(nee, inshape),
mask=np.ones(inshape, dtype=bool))
else:
GPP = np.ones(np.reshape(nee, inshape)) * undef
Reco = np.ones(np.reshape(nee, inshape)) * undef
return GPP, Reco
refp = np.squeeze(np.array(refp))
refii = np.squeeze(np.array(refii))
# 4. Interpol Rref
Rref = np.interp(dates, jul[refii], refp)
# 5. Calc Reco
Reco = np.ones(ndata) * undef
ii = np.where(~t.mask)[0]
Reco[ii] = functions.lloyd_fix(t[ii], Rref[ii], E0)
# 6. Calc GPP
GPP = np.ones(ndata) * undef
ii = np.where(~(t.mask | nee.mask))[0]
GPP[ii] = Reco[ii] - nee[ii]
# 7. Set GPP=0 at night, if wanted
if nogppnight:
mask = isday | nee.mask | t.mask | isday.mask # night
ii = np.where(~mask)[0]
Reco[ii] = nee[ii]
GPP[ii] = 0.
# and prohibit negative gpp at any time
mask = nee.mask | t.mask | (GPP > 0.)
ii = np.where(~mask)[0]
Reco[ii] -= GPP[ii]
GPP[ii] = 0.
if masked:
if np.isnan(undef):
GPP = np.ma.array(GPP, mask=np.isnan(GPP))
Reco = np.ma.array(Reco, mask=np.isnan(Reco))
else:
GPP = np.ma.array(GPP, mask=(GPP == undef))
Reco = np.ma.array(Reco, mask=(Reco == undef))
return GPP.reshape(inshape), Reco.reshape(inshape)
def nee2gpp_lasslop(dates,
nee,
t,
isday,
rg,
vpd,
undef=np.nan,
shape=False,
masked=False,
nogppnight=False):
"""
Calculate photosynthesis (GPP) and ecosystem respiration (Reco) from original
Eddy flux data, using the daytime method of Lasslop et al. (2010),
in order to calculate Reco and then GPP = Reco - NEE.
Definition
----------
def nee2gpp_lasslop(dates, nee, t, isday, rg, vpd, undef=np.nan,
shape=False, masked=False):
Input
-----
Inputs are 1D arrays that can be masked or not.
dates julian days
nee net ecosystem exchange (uptake is <0) [umol m-2 s-1]
t temperature [K]
rg global radiation, i.e. shortwave down [W m-2]
vpd vapour pressure deficit [Pa]
Parameters
----------
undef undefined values in data (default: np.nan)
Input arrays will be masked at undef, keeping the original mask
shape if False then outputs are 1D arrays;
if True, output have the same shape as datain
if a shape tuple is given, then this tuple is used to reshape
masked if False: outputs are undef where nee and t are masked or undef
if True: return masked arrays where outputs would be undef
nogppnight if True: Resp=NEE, GPP=0 at night
if False: Resp=lloyd_taylor, GPP=Resp-NEE at night (default)
Ouput
-----
GPP, Reco photosynthesis, ecosystem respiration
Restrictions
------------
None.
Literature
----------
Lasslop et al. (2010)
Separation of net ecosystem exchange into assimilation and respiration using
a light response curve approach: critical issues and global evaluation
Global Change Biology 16, 187-208
Examples
--------
>>> from jams.fread import fread # from jams
>>> from jams.date2dec import date2dec # from jams
>>> dat = fread('test_nee2gpp.csv', skip=2, transpose=True)
>>> dates = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
>>> NEE = np.squeeze(dat[5,:])
>>> rg = np.squeeze(dat[6,:])
>>> tair = np.squeeze(dat[7,:])
>>> undef = -9999.
>>> isday = np.where(rg > 10., True, False)
>>> tt = np.where(tair == undef, undef, tair+273.15)
>>> VPD = np.squeeze(dat[8,:])
>>> vpd = np.where(VPD == undef, undef, VPD*100.)
>>> # partition
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, rg, vpd, undef=undef, method='day')
>>> print(GPP[1120:1128])
[-9.99900000e+03 -9.99900000e+03 -9.99900000e+03 2.78457540e+00
6.63212545e+00 8.88902165e+00 6.74243873e+00 9.51364527e+00]
>>> print(Reco[1120:1128])
[0.28786696 0.34594516 0.43893276 0.5495954 0.70029545 0.90849165
1.15074873 1.46137527]
License
-------
This file is part of the JAMS Python package, distributed under the MIT
License. The JAMS Python package originates from the former UFZ Python library,
Department of Computational Hydrosystems, Helmholtz Centre for Environmental
Research - UFZ, Leipzig, Germany.
Copyright (c) 2012-2013 Matthias Cuntz, Arndt Piayda - mc (at) macu (dot) de
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
History
-------
Written MC, Mar 2012
Modified AP, Mar 2012 - undef=np.nan
MC, Nov 2012 - individual routine
MC, Feb 2013 - ported to Python 3
"""
# Checks
# remember shape if any
inshape = nee.shape
dates = np.squeeze(dates)
nee = np.squeeze(nee)
t = np.squeeze(t)
isday = np.squeeze(isday)
# Check squeezed shape
if dates.ndim != 1:
raise ValueError(
'Error nee2gpp_lasslop: squeezed dates must be 1D array.')
if nee.ndim != 1:
raise ValueError(
'Error nee2gpp_lasslop: squeezed nee must be 1D array.')
if t.ndim != 1:
raise ValueError('Error nee2gpp_lasslop: squeezed t must be 1D array.')
if isday.ndim != 1:
raise ValueError(
'Error nee2gpp_lasslop: squeezed isday must be 1D array.')
ndata = dates.size
if ((nee.size != ndata) | (t.size != ndata) | (isday.size != ndata)):
raise ValueError(
'Error nee2gpp_lasslop: inputs must have the same size.')
if rg.ndim != 1:
raise ValueError(
'Error nee2gpp_lasslop: squeezed rg must be 1D array.')
if vpd.ndim != 1:
raise ValueError(
'Error nee2gpp_lasslop: squeezed vpd must be 1D array.')
if ((rg.size != ndata) | (vpd.size != ndata)):
raise ValueError(
'Error nee2gpp_lasslop: lasslop inputs must have the same size as other inputs.'
)
# Transform to masked array with 1D mask
nee = np.ma.array(nee, mask=False)
t = np.ma.array(t, mask=False)
isday = np.ma.array(isday, mask=False)
rg = np.ma.array(rg, mask=False)
vpd = np.ma.array(vpd, mask=False)
# mask also undef
if np.isnan(undef):
if np.ma.any(np.isnan(nee)): nee[np.isnan(nee)] = np.ma.masked
if np.ma.any(np.isnan(t)): t[np.isnan(t)] = np.ma.masked
if np.ma.any(np.isnan(isday)): isday[np.isnan(isday)] = np.ma.masked
if np.ma.any(np.isnan(rg)): rg[np.isnan(rg)] = np.ma.masked
if np.ma.any(np.isnan(vpd)): vpd[np.isnan(vpd)] = np.ma.masked
else:
if np.ma.any(nee == undef): nee[nee == undef] = np.ma.masked
if np.ma.any(t == undef): t[t == undef] = np.ma.masked
if np.ma.any(isday == undef): isday[isday == undef] = np.ma.masked
if np.ma.any(rg == undef): rg[rg == undef] = np.ma.masked
if np.ma.any(vpd == undef): vpd[vpd == undef] = np.ma.masked
# Partition - Lasslop et al. (2010) method
do_lgpp = False
mask = nee.mask | t.mask | isday.mask | rg.mask | vpd.mask
# night
nmask = isday | mask
nii = np.squeeze(np.where(~nmask))
njul = dates[nii]
ntt = np.ma.compressed(t[nii])
nnet = np.ma.compressed(nee[nii])
aRref = np.mean(nnet)
# day
dmask = (~isday) | mask
dii = np.squeeze(np.where(~dmask))
djul = dates[dii]
dtt = np.ma.compressed(t[dii])
dnet = np.ma.compressed(nee[dii])
drg = np.ma.compressed(rg[dii])
dvpd = np.ma.compressed(vpd[dii])
# starting values for optim
aalpha = 0.01
qnet = np.sort(dnet)
nqnet = qnet.size
abeta0 = np.abs(qnet[np.floor(0.97 * nqnet).astype(int)] -
qnet[np.ceil(0.03 * nqnet).astype(int)])
ak = 0.
# out
lE0 = []
lalpha = []
if do_lgpp:
lbeta0 = []
lk = []
lRref = []
lii = []
dmin = np.floor(np.amin(dates)).astype(int)
dmax = np.ceil(np.amax(dates)).astype(int)
zaehl = -1
for i in range(dmin, dmax, 2):
good = True
# 1. Estimate E0 from nighttime data
iii = np.squeeze(np.where((njul >= i) & (njul < (i + 12))))
niii = iii.size
if niii > 3:
# p, c = opt.curve_fit(functions.lloyd_fix, ntt[iii], nnet[iii], p0=[aRref,100.])
#p = opt.fmin(functions.cost_lloyd_fix, [aRref,100.], args=(ntt[iii], nnet[iii]), disp=False)
#p = opt.fmin(functions.cost_abs, [aRref,100.], args=(functions.lloyd_fix_p, ntt[iii], nnet[iii]), disp=False)
p, temp1, temp2 = opt.fmin_tnc(functions.cost_abs, [aRref, 100.],
bounds=[[0., None], [0., None]],
args=(functions.lloyd_fix_p,
ntt[iii], nnet[iii]),
approx_grad=True,
disp=False)
E0 = np.maximum(p[1], 50.)
else:
if zaehl >= 0:
E0 = lE0[zaehl]
else:
# large gap at beginning of data set, i.e. skip the period
good = False
continue
# 2. Estimate alpha, k, beta0, Rref from daytime data
iii = np.squeeze(np.where((djul >= i) & (djul < (i + 4))))
niii = iii.size
if niii > 3:
et = functions.lloyd_fix(dtt[iii], 1., E0)
again = True
ialpha = aalpha
ibeta0 = abeta0
ik = ak
iRref = aRref
bounds = [[None, None], [None, None], [None, None], [None, None]]
while again:
again = False
p, nfeval, rc = opt.fmin_tnc(functions.cost_lasslop,
[ialpha, ibeta0, ik, iRref],
bounds=bounds,
args=(drg[iii], et, dvpd[iii],
dnet[iii]),
approx_grad=True,
disp=False)
# if parameters beyond some bounds, set params and redo the optim or skip
if ((p[0] < 0.) | (p[0] > 0.22)): # alpha
again = True
if zaehl >= 0:
bounds[0] = [lalpha[zaehl], lalpha[zaehl]]
ialpha = lalpha[zaehl]
else:
bounds[0] = [0., 0.]
ialpha = 0.
if p[1] < 0.: # beta0
bounds[1] = [0., 0.]
ibeta0 = 0.
again = True
if p[1] > 250.:
good = False
continue
if p[2] < 0.: # k
bounds[2] = [0., 0.]
ik = 0.
again = True
if p[3] < 0: # Rref
good = False
continue
if good:
lalpha = lalpha + [p[0]]
if do_lgpp:
lbeta0 = lbeta0 + [p[1]]
lk = lk + [p[2]]
lRref = lRref + [p[3]]
lii = lii + [int((iii[0] + iii[-1]) / 2)]
else:
continue
else:
continue
lE0 = lE0 + [E0]
zaehl += 1
if len(lE0) == 0:
raise ValueError('Error nee2gpp_lasslop: No day relationship found.')
lE0 = np.squeeze(np.array(lE0))
if do_lgpp:
lalpha = np.squeeze(np.array(lalpha))
lbeta0 = np.squeeze(np.array(lbeta0))
lk = np.squeeze(np.array(lk))
lRref = np.squeeze(np.array(lRref))
lii = np.squeeze(np.array(lii))
# 3. Interpol E0 and Rref
E0 = np.interp(dates, djul[lii], lE0)
Rref = np.interp(dates, djul[lii], lRref)
# 4. Calc Reco
Reco = np.ones(ndata) * undef
ii = np.squeeze(np.where(~t.mask))
Reco[ii] = functions.lloyd_fix(t[ii], Rref[ii], E0[ii])
# 5. Calc GPP from light response for check
if do_lgpp:
alpha = np.interp(dates, djul[lii], lE0)
beta0 = np.interp(dates, djul[lii], lbeta0)
k = np.interp(dates, djul[lii], lk)
et = functions.lloyd_fix(t, 1., E0)
lmask = t.mask | isday.mask | rg.mask | vpd.mask
ii = np.squeeze(np.where(~lmask))
lgpp = np.zeros(ndata)
lgpp[ii] = functions.lasslop(rg[ii], et[ii], vpd[ii], alpha[ii],
beta0[ii], k[ii], Rref[ii]) - Reco[ii]
# 6. GPP
GPP = np.ones(ndata) * undef
ii = np.squeeze(np.where(~(t.mask | nee.mask)))
GPP[ii] = Reco[ii] - nee[ii]
# 7. Set GPP=0 at night, if wanted
if nogppnight:
mask = isday | nee.mask | t.mask | isday.mask # night
ii = np.where(~mask)[0]
Reco[ii] = nee[ii]
GPP[ii] = 0.
# and prohibit negative gpp at any time
mask = nee.mask | t.mask | (GPP > 0.)
ii = np.where(~mask)[0]
Reco[ii] -= GPP[ii]
GPP[ii] = 0.
if masked:
if np.isnan(undef):
GPP = np.ma.array(GPP, mask=np.isnan(GPP))
Reco = np.ma.array(Reco, mask=np.isnan(Reco))
else:
GPP = np.ma.array(GPP, mask=(GPP == undef))
Reco = np.ma.array(Reco, mask=(Reco == undef))
if shape != False:
if shape != True:
return np.reshape(GPP, shape), np.reshape(Reco, shape)
else:
return np.reshape(GPP, inshape), np.reshape(Reco, inshape)
else:
return GPP, Reco
def nee2gpp_falge(dates,
nee,
t,
isday,
undef=np.nan,
shape=False,
masked=False):
"""
Calculate photosynthesis (GPP) and ecosystem respiration (Reco) from original
Eddy flux data, using a fit of Reco vs. temperature to all nighttime data,
in order to calculate Reco and then GPP = Reco - NEE.
Definition
----------
def nee2gpp_falge(dates, nee, t, isday, undef=np.nan, shape=False, masked=False):
Input
-----
Inputs are 1D arrays that can be masked or not.
dates julian days
nee net ecosystem exchange (uptake is <0) [umol m-2 s-1]
t temperature [K]
Parameters
----------
undef undefined values in data (default: np.nan)
Input arrays will be masked at undef, keeping the original mask
shape if False then outputs are 1D arrays;
if True, output have the same shape as datain
if a shape tuple is given, then this tuple is used to reshape
masked if False: outputs are undef where nee and t are masked or undef
if True: return masked arrays where outputs would be undef
Ouput
-----
GPP, Reco photosynthesis, ecosystem respiration
Restrictions
------------
None.
Literature
----------
Falge et al. (2001)
Gap filling strategies for defensible annual sums of net ecosystem exchange
Acricultural and Forest Meteorology 107, 43-69
Examples
--------
>>> from jams.fread import fread # from jams
>>> from jams.date2dec import date2dec # from jams
>>> dat = fread('test_nee2gpp.csv', skip=2, transpose=True)
>>> dates = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
>>> NEE = np.squeeze(dat[5,:])
>>> rg = np.squeeze(dat[6,:])
>>> tair = np.squeeze(dat[7,:])
>>> undef = -9999.
>>> isday = np.where(rg > 10., True, False)
>>> tt = np.where(tair == undef, undef, tair+273.15)
>>> # partition
>>> GPP, Reco = nee2gpp(dates, NEE, tt, isday, undef=undef, method='global')
>>> print(GPP[1120:1128])
[-9.99900000e+03 -9.99900000e+03 -9.99900000e+03 4.33166157e+00
8.18228013e+00 1.04092252e+01 8.19395317e+00 1.08427448e+01]
License
-------
This file is part of the JAMS Python package, distributed under the MIT
License. The JAMS Python package originates from the former UFZ Python library,
Department of Computational Hydrosystems, Helmholtz Centre for Environmental
Research - UFZ, Leipzig, Germany.
Copyright (c) 2012-2013 Matthias Cuntz, Arndt Piayda - mc (at) macu (dot) de
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
History
-------
Written MC, Mar 2012
Modified AP, Mar 2012 - undef=np.nan
MC, Nov 2012 - individual routine
MC, Feb 2013 - ported to Python 3
"""
# Checks
# remember shape if any
inshape = nee.shape
dates = np.squeeze(dates)
nee = np.squeeze(nee)
t = np.squeeze(t)
isday = np.squeeze(isday)
# Check squeezed shape
if dates.ndim != 1:
raise Error('Error nee2gpp_falge: squeezed dates must be 1D array.')
if nee.ndim != 1:
raise Error('Error nee2gpp_falge: squeezed nee must be 1D array.')
if t.ndim != 1:
raise Error('Error nee2gpp_falge: squeezed t must be 1D array.')
if isday.ndim != 1:
raise Error('Error nee2gpp_falge: squeezed isday must be 1D array.')
ndata = dates.size
if ((nee.size != ndata) | (t.size != ndata) | (isday.size != ndata)):
raise Error('Error nee2gpp_falge: inputs must have the same size.')
# Transform to masked array with 1D mask
nee = np.ma.array(nee, mask=False)
t = np.ma.array(t, mask=False)
isday = np.ma.array(isday, mask=False)
# mask also undef
if np.isnan(undef):
if np.ma.any(np.isnan(nee)): nee[np.isnan(nee)] = np.ma.masked
if np.ma.any(np.isnan(t)): t[np.isnan(t)] = np.ma.masked
if np.ma.any(np.isnan(isday)): isday[np.isnan(isday)] = np.ma.masked
else:
if np.ma.any(nee == undef): nee[nee == undef] = np.ma.masked
if np.ma.any(t == undef): t[t == undef] = np.ma.masked
if np.ma.any(isday == undef): isday[isday == undef] = np.ma.masked
# Partition - Global relationship as in Falge et al. (2001)
# Select valid nighttime
mask = isday | nee.mask | t.mask | isday.mask
ii = np.where(~mask)[0]
tt = np.ma.compressed(t[ii])
net = np.ma.compressed(nee[ii])
# p, c = opt.curve_fit(functions.lloyd_fix, tt, net, p0=[2.,200.]) # global parameter, global cov matrix
#p = opt.fmin(functions.cost_lloyd_fix, [2.,200.], args=(tt, net), disp=False)
p = opt.fmin(functions.cost_abs, [2., 200.],
args=(functions.lloyd_fix_p, tt, net),
disp=False)
Reco = np.ones(ndata) * undef
ii = np.where(~t.mask)[0]
Reco[ii] = functions.lloyd_fix(t[ii], p[0], p[1])
# GPP
GPP = np.ones(ndata) * undef
ii = np.where(~(t.mask | nee.mask))[0]
GPP[ii] = Reco[ii] - nee[ii]
# Return
if masked:
if np.isnan(undef):
GPP = np.ma.array(GPP, mask=np.isnan(GPP))
Reco = np.ma.array(Reco, mask=np.isnan(Reco))
else:
GPP = np.ma.array(GPP, mask=(GPP == undef))
Reco = np.ma.array(Reco, mask=(Reco == undef))
if shape != False:
if shape != True:
return np.reshape(GPP, shape), np.reshape(Reco, shape)
else:
return np.reshape(GPP, inshape), np.reshape(Reco, inshape)
else:
return GPP, Reco