def initialize(self, num_reals):
'''
(re)initialize the process
'''
self.num_reals = int(num_reals)
self.parensemble = ParameterEnsemble(self.pst)
self.parensemble.draw(cov=self.parcov, num_reals=num_reals)
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov, num_reals=num_reals)
self.obsensemble = self.obsensemble_0.copy()
if self.parcov.isdiagonal:
self.half_parcov_diag = self.parcov.inv.sqrt
else:
self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
names=self.parcov.col_names,
isdiagonal=True).inv.sqrt
#if self.obscov.isdiagonal:
#self.half_obscov_inv = self.obscov.inv.sqrt
# else:
# self.half_obscov_diag = Cov(x=np.diag(self.obscov.x),
# names=self.obscov.col_names,
# isdiagonal=True)
self.delta_par_prior = self._calc_delta_par()
self.__initialized = True
def __init__(self, **kwargs):
warnings.warn("pyemu.MonteCarlo class is deprecated. "+\
"Please use the ensemble classes directly",PyemuWarning)
super(MonteCarlo, self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
def draw(self,
num_reals=1,
par_file=None,
obs=False,
enforce_bounds=False,
cov=None,
how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations
Parameters:
----------
num_reals (int): number of realization to generate
par_file (str): parameter file to use as mean values
obs (bool): add a realization of measurement noise to obs
enforce_bounds (bool): enforce parameter bounds in control file
how (str): type of distribution. Must be in ["gaussian","uniform"]
Returns:
None
Raises:
None
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian", "uniform"]
if cov is not None:
assert isinstance(cov, Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
self.log("generating {0:d} parameter realizations".format(num_reals))
self.parensemble.draw(cov,
num_reals=num_reals,
how=how,
enforce_bounds=enforce_bounds)
#if enforce_bounds:
# self.parensemble.enforce()
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log(
"generating {0:d} observation realizations".format(num_reals))
self.obsensemble.draw(self.obscov, num_reals=num_reals)
self.log(
"generating {0:d} observation realizations".format(num_reals))
def initialize(self,num_reals):
'''
(re)initialize the process
'''
self.num_reals = int(num_reals)
self.parensemble = ParameterEnsemble(self.pst)
self.parensemble.draw(cov=self.parcov,num_reals=num_reals)
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
self.obsensemble = self.obsensemble_0.copy()
if self.parcov.isdiagonal:
self.half_parcov_diag = self.parcov.inv.sqrt
else:
self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
names=self.parcov.col_names,
isdiagonal=True).inv.sqrt
#if self.obscov.isdiagonal:
#self.half_obscov_inv = self.obscov.inv.sqrt
# else:
# self.half_obscov_diag = Cov(x=np.diag(self.obscov.x),
# names=self.obscov.col_names,
# isdiagonal=True)
self.delta_par_prior = self._calc_delta_par()
self.__initialized = True
def draw(self, num_reals=1, par_file = None, obs=False,
enforce_bounds=False,cov=None, how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations
Parameters:
----------
num_reals (int): number of realization to generate
par_file (str): parameter file to use as mean values
obs (bool): add a realization of measurement noise to obs
enforce_bounds (bool): enforce parameter bounds in control file
how (str): type of distribution. Must be in ["gaussian","uniform"]
Returns:
None
Raises:
None
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian","uniform"]
if cov is not None:
assert isinstance(cov,Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
self.log("generating {0:d} parameter realizations".format(num_reals))
self.parensemble.draw(cov,num_reals=num_reals, how=how)
if enforce_bounds:
self.parensemble.enforce()
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log("generating {0:d} observation realizations".format(num_reals))
self.obsensemble.draw(self.obscov,num_reals=num_reals)
self.log("generating {0:d} observation realizations".format(num_reals))
def initialize(
self,
num_reals=1,
init_lambda=None,
enforce_bounds="reset",
parensemble=None,
obsensemble=None,
restart_obsensemble=None,
):
"""Initialize the iES process. Depending on arguments, draws or loads
initial parameter observations ensembles and runs the initial parameter
ensemble
Parameters
----------
num_reals : int
the number of realizations to draw. Ignored if parensemble/obsensemble
are not None
init_lambda : float
the initial lambda to use. During subsequent updates, the lambda is
updated according to upgrade success
enforce_bounds : str
how to enfore parameter bound transgression. options are
reset, drop, or None
parensemble : pyemu.ParameterEnsemble or str
a parameter ensemble or filename to use as the initial
parameter ensemble. If not None, then obsenemble must not be
None
obsensemble : pyemu.ObservationEnsemble or str
an observation ensemble or filename to use as the initial
observation ensemble. If not None, then parensemble must
not be None
restart_obsensemble : pyemu.ObservationEnsemble or str
an observation ensemble or filename to use as an
evaluated observation ensemble. If not None, this will skip the initial
parameter ensemble evaluation - user beware!
Example
-------
``>>>import pyemu``
``>>>es = pyemu.EnsembleSmoother(pst="pest.pst")``
``>>>es.initialize(num_reals=100)``
"""
'''
(re)initialize the process
'''
# initialize the phi report csv
self.enforce_bounds = enforce_bounds
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if parensemble is not None and obsensemble is not None:
self.logger.log("initializing with existing ensembles")
if isinstance(parensemble, str):
self.logger.log("loading parensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find parensemble file: {0}".\
format(parensemble))
df = pd.read_csv(parensemble, index_col=0)
#df.index = [str(i) for i in df.index]
self.parensemble_0 = ParameterEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading parensemble from file")
elif isinstance(parensemble, ParameterEnsemble):
self.parensemble_0 = parensemble.copy()
else:
raise Exception("unrecognized arg type for parensemble, " +\
"should be filename or ParameterEnsemble" +\
", not {0}".format(type(parensemble)))
self.parensemble = self.parensemble_0.copy()
if isinstance(obsensemble, str):
self.logger.log("loading obsensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find obsensemble file: {0}".\
format(obsensemble))
df = pd.read_csv(obsensemble,
index_col=0).loc[:, self.pst.nnz_obs_names]
#df.index = [str(i) for i in df.index]
self.obsensemble_0 = ObservationEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading obsensemble from file")
elif isinstance(obsensemble, ObservationEnsemble):
self.obsensemble_0 = obsensemble.copy()
else:
raise Exception("unrecognized arg type for obsensemble, " +\
"should be filename or ObservationEnsemble" +\
", not {0}".format(type(obsensemble)))
assert self.parensemble_0.shape[0] == self.obsensemble_0.shape[0]
#self.num_reals = self.parensemble_0.shape[0]
num_reals = self.parensemble.shape[0]
self.logger.log("initializing with existing ensembles")
else:
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
#self.num_reals = int(num_reals)
#assert self.num_reals > 1
self.logger.log("initializing parensemble")
#self.parensemble_0 = ParameterEnsemble(self.pst)
#self.parensemble_0.draw(cov=self.parcov,num_reals=num_reals)
self.parensemble_0 = pyemu.ParameterEnsemble.from_gaussian_draw(
ParameterEnsemble(self.pst), self.parcov, num_reals=num_reals)
self.parensemble_0.enforce(enforce_bounds=enforce_bounds)
self.logger.log("initializing parensemble")
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.logger.log("initializing parensemble")
self.logger.log("initializing obsensemble")
#self.obsensemble_0 = ObservationEnsemble(self.pst)
#self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
self.obsensemble_0 = pyemu.ObservationEnsemble.from_id_gaussian_draw(
ObservationEnsemble(self.pst), num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.logger.log("initializing obsensemble")
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
self.enforce_bounds = enforce_bounds
self.phi_csv = open(self.pst.filename + ".iobj.csv", 'w')
self.phi_csv.write(
"iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_csv.write(','.join(["{0:010d}". \
format(i + 1) for i in range(num_reals)]))
self.phi_csv.write('\n')
self.phi_act_csv = open(self.pst.filename + ".iobj.actual.csv", 'w')
self.phi_act_csv.write(
"iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_act_csv.write(','.join(["{0:010d}". \
format(i + 1) for i in range(num_reals)]))
self.phi_act_csv.write('\n')
if restart_obsensemble is not None:
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
failed_runs, self.obsensemble = self._load_obs_ensemble(
restart_obsensemble)
assert self.obsensemble.shape[0] == self.obsensemble_0.shape[0]
assert list(self.obsensemble.columns) == list(
self.obsensemble_0.columns)
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
else:
# run the initial parameter ensemble
self.logger.log("evaluating initial ensembles")
failed_runs, self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
self.logger.log("evaluating initial ensembles")
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
#failed_runs_str = [str(f) for f in failed_runs]
#self.parensemble = self.parensemble.drop(failed_runs)
#self.obsensemble = self.obsensemble.drop(failed_runs)
self.parensemble.loc[failed_runs, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[failed_runs, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
if self.drop_bad_reals is not None:
drop_idx = np.argwhere(
self.current_phi_vec > self.drop_bad_reals).flatten()
run_ids = self.obsensemble.index.values
drop_idx = run_ids[drop_idx]
if len(drop_idx) == self.obsensemble.shape[0]:
raise Exception("dropped all realizations as 'bad'")
if len(drop_idx) > 0:
self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})".\
format(len(drop_idx),','.join([str(d) for d in drop_idx])))
self.parensemble.loc[drop_idx, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[drop_idx, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.phi_csv, self.current_phi_vec, 0.0)
self._phi_report(self.phi_act_csv, self.obsensemble.phi_vector.values,
0.0)
self.last_best_mean = self.current_phi_vec.mean()
self.last_best_std = self.current_phi_vec.std()
self.logger.statement("initial phi (mean, std): {0:15.6G},{1:15.6G}".\
format(self.last_best_mean,self.last_best_std))
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
# if using the approximate form of the algorithm, let
# the parameter scaling matrix be the identity matrix
# jwhite - dec 5 2016 - using the actual parcov inv
# for upgrades seems to be pushing parameters around
# too much. for now, just not using it, maybe
# better choices of lambda will tame it
self.logger.statement("current lambda:{0:15.6g}".format(
self.current_lambda))
if self.use_approx_prior:
self.logger.statement("using approximate parcov in solution")
self.half_parcov_diag = 1.0
else:
#self.logger.statement("using full parcov in solution")
# if self.parcov.isdiagonal:
# self.half_parcov_diag = self.parcov.sqrt.inv
# else:
# self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
# names=self.parcov.col_names,
# isdiagonal=True).inv.sqrt
self.half_parcov_diag = 1.0
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u, s, v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
def update(self,
lambda_mults=[1.0],
localizer=None,
run_subset=None,
use_approx=True,
calc_only=False):
"""update the iES one GLM cycle
Parameters
----------
lambda_mults : list
a list of lambda multipliers to test. Each lambda mult value will require
evaluating (a subset of) the parameter ensemble.
localizer : pyemu.Matrix
a jacobian localizing matrix
run_subset : int
the number of realizations to test for each lambda_mult value. For example,
if run_subset = 30 and num_reals=100, the first 30 realizations will be run (in
parallel) for each lambda_mult value. Then the best lambda_mult is selected and the
remaining 70 realizations for that lambda_mult value are run (in parallel).
use_approx : bool
a flag to use the MLE or MAP upgrade solution. True indicates use MLE solution
calc_only : bool
a flag to calculate the upgrade matrix only (not run the ensemble). This is mostly for
debugging and testing on travis. Default is False
Example
-------
``>>>import pyemu``
``>>>es = pyemu.EnsembleSmoother(pst="pest.pst")``
``>>>es.initialize(num_reals=100)``
``>>>es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30)``
"""
if run_subset is not None:
if run_subset >= self.obsensemble.shape[0]:
self.logger.warn("run_subset ({0}) >= num of active reals ({1})...ignoring ".\
format(run_subset,self.obsensemble.shape[0]))
run_subset = None
self.iter_num += 1
self.logger.log("iteration {0}".format(self.iter_num))
self.logger.statement("{0} active realizations".format(
self.obsensemble.shape[0]))
if self.obsensemble.shape[0] < 2:
self.logger.lraise(
"at least active 2 realizations (really like 300) are needed to update"
)
if not self.__initialized:
#raise Exception("must call initialize() before update()")
self.logger.lraise("must call initialize() before update()")
self.logger.log("calculate scaled delta obs")
scaled_delta_obs = self._calc_delta_obs(self.obsensemble)
self.logger.log("calculate scaled delta obs")
self.logger.log("calculate scaled delta par")
scaled_delta_par = self._calc_delta_par(self.parensemble)
self.logger.log("calculate scaled delta par")
self.logger.log("calculate pseudo inv comps")
u, s, v = scaled_delta_obs.pseudo_inv_components()
self.logger.log("calculate pseudo inv comps")
self.logger.log("calculate obs diff matrix")
obs_diff = self.obscov_inv_sqrt * self._get_residual_matrix(
self.obsensemble).T
self.logger.log("calculate obs diff matrix")
# here is the math part...calculate upgrade matrices
mean_lam, std_lam, paren_lam, obsen_lam = [], [], [], []
lam_vals = []
for ilam, cur_lam_mult in enumerate(lambda_mults):
parensemble_cur_lam = self.parensemble.copy()
#print(parensemble_cur_lam.isnull().values.any())
cur_lam = self.current_lambda * cur_lam_mult
lam_vals.append(cur_lam)
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
scaled_ident = Cov.identity_like(s) * (cur_lam + 1.0)
scaled_ident += s**2
scaled_ident = scaled_ident.inv
# build up this matrix as a single element so we can apply
# localization
self.logger.log("building upgrade_1 matrix")
upgrade_1 = -1.0 * (self.half_parcov_diag * scaled_delta_par) *\
v * s * scaled_ident * u.T
self.logger.log("building upgrade_1 matrix")
# apply localization
if localizer is not None:
self.logger.log("applying localization")
upgrade_1.hadamard_product(localizer)
self.logger.log("applying localization")
# apply residual information
self.logger.log("applying residuals")
upgrade_1 *= obs_diff
self.logger.log("applying residuals")
self.logger.log("processing upgrade_1")
upgrade_1 = upgrade_1.to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1 = upgrade_1.T
upgrade_1.index = [int(i) for i in upgrade_1.index]
upgrade_1.to_csv(self.pst.filename+".upgrade_1.{0:04d}.csv".\
format(self.iter_num))
if upgrade_1.isnull().values.any():
self.logger.lraise("NaNs in upgrade_1")
self.logger.log("processing upgrade_1")
#print(upgrade_1.isnull().values.any())
#print(parensemble_cur_lam.index)
#print(upgrade_1.index)
parensemble_cur_lam += upgrade_1
# parameter-based upgrade portion
if not use_approx and self.iter_num > 1:
self.logger.log("building upgrade_2 matrix")
par_diff = (self.parensemble - self.parensemble_0.loc[self.parensemble.index,:]).\
as_pyemu_matrix().T
x4 = self.Am.T * self.half_parcov_diag * par_diff
x5 = self.Am * x4
x6 = scaled_delta_par.T * x5
x7 = v * scaled_ident * v.T * x6
upgrade_2 = -1.0 * (self.half_parcov_diag * scaled_delta_par *
x7).to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2 = upgrade_2.T
upgrade_2.to_csv(self.pst.filename+".upgrade_2.{0:04d}.csv".\
format(self.iter_num))
upgrade_2.index = [int(i) for i in upgrade_2.index]
if upgrade_2.isnull().values.any():
self.logger.lraise("NaNs in upgrade_2")
parensemble_cur_lam += upgrade_2
self.logger.log("building upgrade_2 matrix")
parensemble_cur_lam.enforce(self.enforce_bounds)
# this is for testing failed runs on upgrade testing
# works with the 10par_xsec smoother test
#parensemble_cur_lam.iloc[:,:] = -1000000.0
paren_lam.append(pd.DataFrame(parensemble_cur_lam.loc[:, :]))
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
if calc_only:
return
# subset if needed
# and combine lambda par ensembles into one par ensemble for evaluation
if run_subset is not None and run_subset < self.parensemble.shape[0]:
#subset_idx = ["{0:d}".format(i) for i in np.random.randint(0,self.parensemble.shape[0]-1,run_subset)]
subset_idx = self.parensemble.iloc[:run_subset, :].index.values
self.logger.statement("subset idxs: " +
','.join([str(s) for s in subset_idx]))
paren_lam_subset = [pe.loc[subset_idx, :] for pe in paren_lam]
paren_combine = pd.concat(paren_lam_subset, ignore_index=True)
paren_lam_subset = None
else:
subset_idx = self.parensemble.index.values
paren_combine = pd.concat(paren_lam, ignore_index=True)
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
failed_runs, obsen_combine = self._calc_obs(paren_combine)
#if failed_runs is not None:
# obsen_combine.loc[failed_runs,:] = np.NaN
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
paren_combine = None
if failed_runs is not None and len(
failed_runs) == obsen_combine.shape[0]:
self.logger.lraise("all runs failed - cannot continue")
# unpack lambda obs ensembles from combined obs ensemble
nrun_per_lam = self.obsensemble.shape[0]
if run_subset is not None:
nrun_per_lam = run_subset
obsen_lam = []
for i in range(len(lam_vals)):
sidx = i * nrun_per_lam
eidx = sidx + nrun_per_lam
oe = ObservationEnsemble.from_dataframe(
df=obsen_combine.iloc[sidx:eidx, :].copy(), pst=self.pst)
oe.index = subset_idx
# check for failed runs in this set - drop failed runs from obs ensembles
if failed_runs is not None:
failed_runs_this = np.array(
[f for f in failed_runs if f >= sidx and f < eidx]) - sidx
if len(failed_runs_this) > 0:
if len(failed_runs_this) == oe.shape[0]:
self.logger.warn(
"all runs failed for lambda {0}".format(
lam_vals[i]))
else:
self.logger.warn("{0} run failed for lambda {1}".\
format(len(failed_runs_this),lam_vals[i]))
oe.iloc[failed_runs_this, :] = np.NaN
oe = oe.dropna()
# don't drop bad reals here, instead, mask bad reals in the lambda
# selection and drop later
# if self.drop_bad_reals is not None:
# assert isinstance(drop_bad_reals, float)
# drop_idx = np.argwhere(self.current_phi_vec > self.drop_bad_reals).flatten()
# run_ids = self.obsensemble.index.values
# drop_idx = run_ids[drop_idx]
# if len(drop_idx) == self.obsensemble.shape[0]:
# raise Exception("dropped all realizations as 'bad'")
# if len(drop_idx) > 0:
# self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})". \
# format(len(drop_idx), ','.join([str(d) for d in drop_idx])))
# self.parensemble.loc[drop_idx, :] = np.NaN
# self.parensemble = self.parensemble.dropna()
# self.obsensemble.loc[drop_idx, :] = np.NaN
# self.obsensemble = self.obsensemble.dropna()
#
# self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
obsen_lam.append(oe)
obsen_combine = None
# here is where we need to select out the "best" lambda par and obs
# ensembles
self.logger.statement("\n**************************")
self.logger.statement(str(datetime.now()))
self.logger.statement("total runs:{0}".format(self.total_runs))
self.logger.statement("iteration: {0}".format(self.iter_num))
self.logger.statement("current lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda,
self.last_best_mean,self.last_best_std))
phi_vecs = [self._calc_phi_vec(obsen) for obsen in obsen_lam]
if self.drop_bad_reals is not None:
for i, pv in enumerate(phi_vecs):
#for testing the drop_bad_reals functionality
#pv[[0,3,7]] = self.drop_bad_reals + 1.0
pv[pv > self.drop_bad_reals] = np.NaN
pv = pv[~np.isnan(pv)]
if len(pv) == 0:
raise Exception("all realization for lambda {0} dropped as 'bad'".\
format(lam_vals[i]))
phi_vecs[i] = pv
mean_std = [(pv.mean(), pv.std()) for pv in phi_vecs]
update_pars = False
update_lambda = False
# accept a new best if its within 10%
best_mean = self.last_best_mean * 1.1
best_std = self.last_best_std * 1.1
best_i = 0
for i, (m, s) in enumerate(mean_std):
self.logger.statement(" tested lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda * lambda_mults[i],m,s))
if m < best_mean:
update_pars = True
best_mean = m
best_i = i
if s < best_std:
update_lambda = True
best_std = s
if np.isnan(best_mean):
self.logger.lraise("best mean = NaN")
if np.isnan(best_std):
self.logger.lraise("best std = NaN")
if not update_pars:
self.current_lambda *= max(lambda_mults) * 10.0
self.current_lambda = min(self.current_lambda, 100000)
self.logger.statement("not accepting iteration, increased lambda:{0}".\
format(self.current_lambda))
else:
self.parensemble = ParameterEnsemble.from_dataframe(
df=paren_lam[best_i], pst=self.pst)
if run_subset is not None:
failed_runs, self.obsensemble = self._calc_obs(
self.parensemble)
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
self.parensemble.loc[failed_runs, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[failed_runs, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
#self._phi_report(self.current_phi_vec,self.current_lambda * lambda_mults[best_i])
best_mean = self.current_phi_vec.mean()
best_std = self.current_phi_vec.std()
else:
self.obsensemble = obsen_lam[best_i]
# reindex parensemble in case failed runs
self.parensemble = ParameterEnsemble.from_dataframe(
df=self.parensemble.loc[self.obsensemble.index],
pst=self.pst)
self.current_phi_vec = phi_vecs[best_i]
if self.drop_bad_reals is not None:
# for testing drop_bad_reals functionality
# self.current_phi_vec[::2] = self.drop_bad_reals + 1.0
drop_idx = np.argwhere(
self.current_phi_vec > self.drop_bad_reals).flatten()
run_ids = self.obsensemble.index.values
drop_idx = run_ids[drop_idx]
if len(drop_idx) > self.obsensemble.shape[0] - 3:
raise Exception("dropped too many realizations as 'bad'")
if len(drop_idx) > 0:
self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})". \
format(len(drop_idx), ','.join([str(d) for d in drop_idx])))
self.parensemble.loc[drop_idx, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[drop_idx, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
best_mean = self.current_phi_vec.mean()
best_std = self.current_phi_vec.std()
self._phi_report(self.phi_csv, self.current_phi_vec,
self.current_lambda * lambda_mults[best_i])
self._phi_report(self.phi_act_csv,
self.obsensemble.phi_vector.values,
self.current_lambda * lambda_mults[best_i])
self.logger.statement(" best lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda*lambda_mults[best_i],
best_mean,best_std))
self.logger.statement(" actual mean phi: {0:15.6G}".format(
float(self.current_actual_phi.mean())))
self.last_best_mean = best_mean
self.last_best_std = best_std
if update_lambda:
# be aggressive
self.current_lambda *= (lambda_mults[best_i] * 0.75)
# but don't let lambda get too small
self.current_lambda = max(self.current_lambda, 0.00001)
self.logger.statement("updating lambda: {0:15.6G}".\
format(self.current_lambda ))
self.logger.statement("**************************\n")
self.parensemble.to_csv(self.pst.filename+self.paren_prefix.\
format(self.iter_num))
self.obsensemble.to_csv(self.pst.filename+self.obsen_prefix.\
format(self.iter_num))
if self.raw_sweep_out is not None:
self.raw_sweep_out.to_csv(self.pst.filename+"_raw{0}".\
format(self.iter_num))
self.logger.log("iteration {0}".format(self.iter_num))
def initialize(self,
num_reals=1,
init_lambda=None,
enforce_bounds="reset",
parensemble=None,
obsensemble=None,
restart_obsensemble=None):
'''
(re)initialize the process
'''
# initialize the phi report csv
self.enforce_bounds = enforce_bounds
self.phi_csv = open(self.pst.filename + ".iobj.csv", 'w')
self.phi_csv.write(
"iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_csv.write(','.join(["{0:010d}".\
format(i+1) for i in range(num_reals)]))
self.phi_csv.write('\n')
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if parensemble is not None and obsensemble is not None:
self.logger.log("initializing with existing ensembles")
if isinstance(parensemble, str):
self.logger.log("loading parensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find parensemble file: {0}".\
format(parensemble))
df = pd.read_csv(parensemble, index_col=0)
#df.index = [str(i) for i in df.index]
self.parensemble_0 = ParameterEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading parensemble from file")
elif isinstance(parensemble, ParameterEnsemble):
self.parensemble_0 = parensemble.copy()
else:
raise Exception("unrecognized arg type for parensemble, " +\
"should be filename or ParameterEnsemble" +\
", not {0}".format(type(parensemble)))
self.parensemble = self.parensemble_0.copy()
if isinstance(obsensemble, str):
self.logger.log("loading obsensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find obsensemble file: {0}".\
format(obsensemble))
df = pd.read_csv(obsensemble,
index_col=0).loc[:, self.pst.nnz_obs_names]
#df.index = [str(i) for i in df.index]
self.obsensemble_0 = ObservationEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading obsensemble from file")
elif isinstance(obsensemble, ObservationEnsemble):
self.obsensemble_0 = obsensemble.copy()
else:
raise Exception("unrecognized arg type for obsensemble, " +\
"should be filename or ObservationEnsemble" +\
", not {0}".format(type(obsensemble)))
assert self.parensemble_0.shape[0] == self.obsensemble_0.shape[0]
#self.num_reals = self.parensemble_0.shape[0]
self.logger.log("initializing with existing ensembles")
else:
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
#self.num_reals = int(num_reals)
#assert self.num_reals > 1
self.logger.log("initializing parensemble")
self.parensemble_0 = ParameterEnsemble(self.pst)
self.parensemble_0.draw(cov=self.parcov, num_reals=num_reals)
self.parensemble_0.enforce(enforce_bounds=enforce_bounds)
self.logger.log("initializing parensemble")
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.logger.log("initializing parensemble")
self.logger.log("initializing obsensemble")
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov, num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.logger.log("initializing obsensemble")
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
self.enforce_bounds = enforce_bounds
if restart_obsensemble is not None:
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
failed_runs, self.obsensemble = self._load_obs_ensemble(
restart_obsensemble)
assert self.obsensemble.shape[0] == self.obsensemble_0.shape[0]
assert list(self.obsensemble.columns) == list(
self.obsensemble_0.columns)
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
else:
# run the initial parameter ensemble
self.logger.log("evaluating initial ensembles")
failed_runs, self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
self.logger.log("evaluating initial ensembles")
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
#failed_runs_str = [str(f) for f in failed_runs]
self.parensemble = self.parensemble.drop(failed_runs)
self.obsensemble = self.obsensemble.drop(failed_runs)
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec, 0.0)
self.last_best_mean = self.current_phi_vec.mean()
self.last_best_std = self.current_phi_vec.std()
self.logger.statement("initial phi (mean, std): {0:15.6G},{1:15.6G}".\
format(self.last_best_mean,self.last_best_std))
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
# if using the approximate form of the algorithm, let
# the parameter scaling matrix be the identity matrix
# jwhite - dec 5 2016 - using the actual parcov inv
# for upgrades seems to be pushing parameters around
# too much. for now, just not using it, maybe
# better choices of lambda will tame it
self.logger.statement("current lambda:{0:15.6g}".format(
self.current_lambda))
if self.use_approx_prior:
self.logger.statement("using approximate parcov in solution")
self.half_parcov_diag = 1.0
else:
#self.logger.statement("using full parcov in solution")
# if self.parcov.isdiagonal:
# self.half_parcov_diag = self.parcov.sqrt.inv
# else:
# self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
# names=self.parcov.col_names,
# isdiagonal=True).inv.sqrt
self.half_parcov_diag = 1.0
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u, s, v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
def __init__(self, **kwargs):
super(MonteCarlo, self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
def __init__(self,**kwargs):
super(MonteCarlo,self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
class EnsembleSmoother():
def __init__(self, pst, parcov=None, obscov=None):
assert isinstance(pst, Pst)
self.pst = pst
if parcov is not None:
assert isinstance(parcov, Cov)
else:
parcov = Cov.from_parameter_data(self.pst)
if obscov is not None:
assert isinstance(obscov, Cov)
else:
obscov = Cov.from_observation_data(pst)
self.parcov = parcov
self.obscov = obscov
self.__initialized = False
self.num_reals = 0
self.half_parcov_diag = None
self.half_obscov_diag = None
self.delta_par_prior = None
self.iter_num = 0
def initialize(self, num_reals):
'''
(re)initialize the process
'''
self.num_reals = int(num_reals)
self.parensemble = ParameterEnsemble(self.pst)
self.parensemble.draw(cov=self.parcov, num_reals=num_reals)
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov, num_reals=num_reals)
self.obsensemble = self.obsensemble_0.copy()
if self.parcov.isdiagonal:
self.half_parcov_diag = self.parcov.inv.sqrt
else:
self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
names=self.parcov.col_names,
isdiagonal=True).inv.sqrt
#if self.obscov.isdiagonal:
#self.half_obscov_inv = self.obscov.inv.sqrt
# else:
# self.half_obscov_diag = Cov(x=np.diag(self.obscov.x),
# names=self.obscov.col_names,
# isdiagonal=True)
self.delta_par_prior = self._calc_delta_par()
self.__initialized = True
def _calc_delta_par(self):
'''
calc the scaled parameter ensemble differences from the mean
'''
mean = np.array(self.parensemble.mean(axis=0))
delta = self.parensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i, :] -= mean
#delta = Matrix(x=(self.half_parcov_diag * delta.transpose()).x,
# row_names=self.parensemble.columns)
delta = self.half_parcov_diag * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_delta_obs(self):
'''
calc the scaled observation ensemble differences from the mean
'''
mean = np.array(self.obsensemble.mean(axis=0))
delta = self.obsensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i, :] -= mean
delta = self.obscov.inv.sqrt * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_obs(self):
'''
propagate the ensemble forward...
'''
self.parensemble.to_csv(os.path.join("smoother", "sweep_in.csv"))
os.chdir("smoother")
print(os.listdir('.'))
os.system("sweep freyberg.pst")
os.chdir('..')
obs = ObservationEnsemble.from_csv(os.path.join(\
"smoother",'sweep_out.csv'))
obs.columns = [item.lower() for item in obs.columns]
self.obsensemble = ObservationEnsemble.from_dataframe(
df=obs.loc[:, self.obscov.row_names], pst=self.pst)
#todo: modifiy sweep to be interactive...
return
@property
def current_lambda(self):
return 10.0
def update(self):
if not self.__initialized:
raise Exception("must call initialize() before update()")
self._calc_obs()
delta_obs = self._calc_delta_obs()
u, s, v = delta_obs.pseudo_inv_components()
#print(v)
#print(s)
#print(v)
diff = self.obsensemble.as_pyemu_matrix(
) - self.obsensemble_0.as_pyemu_matrix()
#print(diff)
x1 = u.T * self.obscov.inv.sqrt * diff.T
x1.autoalign = False
#print(x1)
x2 = (Cov.identity_like(s) + s**2).inv * x1
#print(x2)
x3 = v * s * x2
#print(x3)
upgrade_1 = (self.half_parcov_diag * self._calc_delta_par() *
x3).to_dataframe()
upgrade_1.index.name = "parnme"
print(upgrade_1)
self.parensemble += upgrade_1.T
print(self.parensemble)
if self.iter_num > 0:
raise NotImplementedError()
print(upgrade_1.shape)
class EnsembleSmoother():
def __init__(self,pst,parcov=None,obscov=None,num_slaves=0,use_approx=True,
restart_iter=0,submit_file=None):
self.num_slaves = int(num_slaves)
self.submit_file = submit_file
self.use_approx = bool(use_approx)
self.paren_prefix = ".parensemble.{0:04d}.csv"
self.obsen_prefix = ".obsensemble.{0:04d}.csv"
if isinstance(pst,str):
pst = Pst(pst)
assert isinstance(pst,Pst)
self.pst = pst
self.sweep_in_csv = pst.pestpp_options.get("sweep_parameter_csv_file","sweep_in.csv")
self.sweep_out_csv = pst.pestpp_options.get("sweep_output_csv_file","sweep_out.csv")
if parcov is not None:
assert isinstance(parcov,Cov)
else:
parcov = Cov.from_parameter_data(self.pst)
if obscov is not None:
assert isinstance(obscov,Cov)
else:
obscov = Cov.from_observation_data(pst)
self.parcov = parcov
self.obscov = obscov
self.restart = False
if restart_iter > 0:
self.restart_iter = restart_iter
paren = self.pst.filename+self.paren_prefix.format(restart_iter)
assert os.path.exists(paren),\
"could not find restart par ensemble {0}".format(paren)
obsen0 = self.pst.filename+self.obsen_prefix.format(0)
assert os.path.exists(obsen0),\
"could not find restart obs ensemble 0 {0}".format(obsen0)
obsen = self.pst.filename+self.obsen_prefix.format(restart_iter)
assert os.path.exists(obsen),\
"could not find restart obs ensemble {0}".format(obsen)
self.restart = True
self.__initialized = False
self.num_reals = 0
self.half_parcov_diag = None
self.half_obscov_diag = None
self.delta_par_prior = None
self.iter_num = 0
def initialize(self,num_reals,init_lambda=None):
'''
(re)initialize the process
'''
assert num_reals > 1
# initialize the phi report csv
self.phi_csv = open(self.pst.filename+".iobj.csv",'w')
self.phi_csv.write("iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_csv.write(','.join(["{0:010d}".\
format(i+1) for i in range(num_reals)]))
self.phi_csv.write('\n')
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if self.restart:
print("restarting...ignoring num_reals")
raise NotImplementedError()
df = pd.read_csv(self.pst.filename+self.paren_prefix.format(self.restart_iter))
self.parensemble_0 = ParameterEnsemble.from_dataframe(df=df,pst=self.pst)
self.parensemble = self.parensemble_0.copy()
df = pd.read_csv(self.pst.filename+self.obsen_prefix.format(0))
self.obsensemble_0 = ObservationEnsemble.from_dataframe(df=df.loc[:,self.pst.nnz_obs_names],
pst=self.pst)
# this matrix gets used a lot, so only calc once
self.obs0_matrix = self.obsensemble_0.as_pyemu_matrix()
df = pd.read_csv(self.pst.filename+self.obsen_prefix.format(self.restart_iter))
self.obsensemble = ObservationEnsemble.from_dataframe(df=df.loc[:,self.pst.nnz_obs_names],
pst=self.pst)
assert self.parensemble.shape[0] == self.obsensemble.shape[0]
self.num_reals = self.parensemble.shape[0]
else:
self.num_reals = int(num_reals)
self.parensemble_0 = ParameterEnsemble(self.pst)
self.parensemble_0.draw(cov=self.parcov,num_reals=num_reals)
self.parensemble_0.enforce()
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
# run the initial parameter ensemble
self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec,0.0)
self.last_best_mean = self.current_phi_vec.mean()
self.last_best_std = self.current_phi_vec.std()
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
# if using the approximate form of the algorithm, let
# the parameter scaling matrix be the identity matrix
# jwhite - dec 5 2016 - using the actual parcov inv
# for upgrades seems to be pushing parameters around
# too much. for now, just not using it, maybe
# better choices of lambda will tame it
if self.use_approx:
self.half_parcov_diag = 1.0
else:
# if self.parcov.isdiagonal:
# self.half_parcov_diag = self.parcov.sqrt.inv
# else:
# self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
# names=self.parcov.col_names,
# isdiagonal=True).inv.sqrt
self.half_parcov_diag = 1.0
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u,s,v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
def get_localizer(self):
onames = self.pst.nnz_obs_names
pnames = self.pst.adj_par_names
localizer = Matrix(x=np.ones((len(onames),len(pnames))),row_names=onames,col_names=pnames)
return localizer
def _calc_delta_par(self,parensemble):
'''
calc the scaled parameter ensemble differences from the mean
'''
return self._calc_delta(parensemble, self.half_parcov_diag)
def _calc_delta_obs(self,obsensemble):
'''
calc the scaled observation ensemble differences from the mean
'''
return self._calc_delta(obsensemble.nonzero, self.obscov.inv.sqrt)
def _calc_delta(self,ensemble,scaling_matrix):
'''
calc the scaled ensemble differences from the mean
'''
mean = np.array(ensemble.mean(axis=0))
delta = ensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i,:] -= mean
delta = scaling_matrix * delta.T
delta *= (1.0 / np.sqrt(float(self.num_reals - 1.0)))
return delta
def _calc_obs(self,parensemble):
if self.submit_file is None:
self._calc_obs_local(parensemble)
else:
self._calc_obs_condor(parensemble)
def _calc_obs_condor(self,parensemble):
parensemble.to_csv(self.sweep_in_csv)
os.system("condor_rm -all")
port = 4004
def master():
os.system("sweep {0} /h :{1} >nul".format(self.pst.filename,port))
master_thread = threading.Thread(target=master)
master_thread.start()
time.sleep(1.5) #just some time for the master to get up and running to take slaves
pyemu.utils.start_slaves("template","sweep",self.pst.filename,
self.num_slaves,slave_root='.',port=port)
os.system("condor_submit {0}".format(self.submit_file))
master_thread.join()
def _calc_obs_local(self,parensemble):
'''
propagate the ensemble forward using sweep.
'''
parensemble.to_csv(self.sweep_in_csv)
if self.num_slaves > 0:
port = 4004
def master():
os.system("sweep {0} /h :{1} >nul".format(self.pst.filename,port))
master_thread = threading.Thread(target=master)
master_thread.start()
time.sleep(1.5) #just some time for the master to get up and running to take slaves
pyemu.utils.start_slaves("template","sweep",self.pst.filename,
self.num_slaves,slave_root='.',port=port)
master_thread.join()
else:
os.system("sweep {0}".format(self.pst.filename))
obs = pd.read_csv(self.sweep_out_csv)
obs.columns = [item.lower() for item in obs.columns]
self.total_runs += obs.shape[0]
return ObservationEnsemble.from_dataframe(df=obs.loc[:,self.obscov.row_names],
pst=self.pst)
def _calc_phi_vec(self,obsensemble):
obs_diff = self._get_residual_matrix(obsensemble)
phi_vec = np.diagonal((obs_diff * self.obscov_inv_sqrt.get(row_names=obs_diff.col_names,
col_names=obs_diff.col_names) * obs_diff.T).x)
return phi_vec
def _phi_report(self,phi_vec,cur_lam):
assert phi_vec.shape[0] == self.num_reals
self.phi_csv.write("{0},{1},{2},{3},{4},{5},{6}".format(self.iter_num,
self.total_runs,
cur_lam,
phi_vec.min(),
phi_vec.max(),
phi_vec.mean(),
np.median(phi_vec),
phi_vec.std()))
self.phi_csv.write(",".join(["{0:20.8}".format(phi) for phi in phi_vec]))
self.phi_csv.write("\n")
self.phi_csv.flush()
def _get_residual_matrix(self, obsensemble):
obs_matrix = obsensemble.nonzero.as_pyemu_matrix()
return obs_matrix - self.obs0_matrix.get(col_names=obs_matrix.col_names,row_names=obs_matrix.row_names)
def update(self,lambda_mults=[1.0],localizer=None,run_subset=None):
self.iter_num += 1
if not self.__initialized:
raise Exception("must call initialize() before update()")
scaled_delta_obs = self._calc_delta_obs(self.obsensemble)
scaled_delta_par = self._calc_delta_par(self.parensemble)
u,s,v = scaled_delta_obs.pseudo_inv_components()
obs_diff = self._get_residual_matrix(self.obsensemble)
if run_subset is not None:
subset_idx = ["{0:d}".format(i) for i in np.random.randint(0,self.num_reals-1,run_subset)]
print("subset idxs: " + ','.join(subset_idx))
mean_lam,std_lam,paren_lam,obsen_lam = [],[],[],[]
for ilam,cur_lam_mult in enumerate(lambda_mults):
parensemble_cur_lam = self.parensemble.copy()
cur_lam = self.current_lambda * cur_lam_mult
scaled_ident = Cov.identity_like(s) * (cur_lam+1.0)
scaled_ident += s**2
scaled_ident = scaled_ident.inv
# build up this matrix as a single element so we can apply
# localization
upgrade_1 = -1.0 * (self.half_parcov_diag * scaled_delta_par) *\
v * s * scaled_ident * u.T
# apply localization
#print(cur_lam,upgrade_1)
if localizer is not None:
upgrade_1.hadamard_product(localizer)
# apply residual information
upgrade_1 *= (self.obscov_inv_sqrt * obs_diff.T)
upgrade_1 = upgrade_1.to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1 = upgrade_1.T
upgrade_1.to_csv(self.pst.filename+".upgrade_1.{0:04d}.csv".\
format(self.iter_num))
parensemble_cur_lam += upgrade_1
# parameter-based upgrade portion
if not self.use_approx and self.iter_num > 1:
par_diff = (self.parensemble - self.parensemble_0).\
as_pyemu_matrix().T
x4 = self.Am.T * self.half_parcov_diag * par_diff
x5 = self.Am * x4
x6 = scaled_delta_par.T * x5
x7 = v * scaled_ident * v.T * x6
upgrade_2 = -1.0 * (self.half_parcov_diag *
scaled_delta_par * x7).to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2.T.to_csv(self.pst.filename+".upgrade_2.{0:04d}.csv".\
format(self.iter_num))
parensemble_cur_lam += upgrade_2.T
parensemble_cur_lam.enforce()
paren_lam.append(parensemble_cur_lam)
if run_subset is not None:
#phi_series = pd.Series(data=self.current_phi_vec)
#phi_series.sort_values(inplace=True,ascending=False)
#subset_idx = ["{0:d}".format(i) for i in phi_series.index.values[:run_subset]]
parensemble_subset = parensemble_cur_lam.loc[subset_idx,:]
obsensemble_cur_lam = self._calc_obs(parensemble_subset)
else:
obsensemble_cur_lam = self._calc_obs(parensemble_cur_lam)
#print(obsensemble_cur_lam.head())
obsen_lam.append(obsensemble_cur_lam)
# here is where we need to select out the "best" lambda par and obs
# ensembles
print("\n**************************")
print(str(datetime.now()))
print("total runs:{0}".format(self.total_runs))
print("iteration: {0}".format(self.iter_num))
print("current lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda,
self.last_best_mean,self.last_best_std))
phi_vecs = [self._calc_phi_vec(obsen) for obsen in obsen_lam]
mean_std = [(pv.mean(),pv.std()) for pv in phi_vecs]
update_pars = False
update_lambda = False
# accept a new best if its within 10%
best_mean = self.last_best_mean * 1.1
best_std = self.last_best_std * 1.1
best_i = 0
for i,(m,s) in enumerate(mean_std):
print(" tested lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda * lambda_mults[i],m,s))
if m < best_mean:
update_pars = True
best_mean = m
best_i = i
if s < best_std:
update_lambda = True
best_std = s
if not update_pars:
self.current_lambda *= max(lambda_mults) * 3.0
self.current_lambda = min(self.current_lambda,100000)
print("not accepting iteration, increased lambda:{0}".\
format(self.current_lambda))
else:
self.parensemble = paren_lam[best_i]
if run_subset is not None:
self.obsensemble = self._calc_obs(self.parensemble)
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec,self.current_lambda * lambda_mults[best_i])
best_mean = self.current_phi_vec.mean()
best_std = self.current_phi_vec.std()
else:
self.obsensemble = obsen_lam[best_i]
self._phi_report(phi_vecs[best_i],self.current_lambda * lambda_mults[best_i])
self.current_phi_vec = phi_vecs[best_i]
print("\n" + " best lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda*lambda_mults[best_i],
best_mean,best_std))
self.last_best_mean = best_mean
self.last_best_std = best_std
if update_lambda:
# be aggressive - cut best lambda in half
self.current_lambda *= (lambda_mults[best_i] * 0.75)
# but don't let lambda get too small
self.current_lambda = max(self.current_lambda,0.001)
print("updating lambda: {0:15.6G}".\
format(self.current_lambda ))
print("**************************\n")
self.parensemble.to_csv(self.pst.filename+self.paren_prefix.\
format(self.iter_num))
self.obsensemble.to_csv(self.pst.filename+self.obsen_prefix.\
format(self.iter_num))
class EnsembleSmoother():
def __init__(self,
pst,
parcov=None,
obscov=None,
num_slaves=0,
use_approx_prior=True,
submit_file=None,
verbose=False,
port=4004,
slave_dir="template"):
self.logger = Logger(verbose)
if verbose is not False:
self.logger.echo = True
self.num_slaves = int(num_slaves)
if submit_file is not None:
if not os.path.exists(submit_file):
self.logger.lraise(
"submit_file {0} not found".format(submit_file))
elif num_slaves > 0:
if not os.path.exists(slave_dir):
self.logger.lraise(
"template dir {0} not found".format(slave_dir))
self.slave_dir = slave_dir
self.submit_file = submit_file
self.port = int(port)
self.use_approx_prior = bool(use_approx_prior)
self.paren_prefix = ".parensemble.{0:04d}.csv"
self.obsen_prefix = ".obsensemble.{0:04d}.csv"
if isinstance(pst, str):
pst = Pst(pst)
assert isinstance(pst, Pst)
self.pst = pst
self.sweep_in_csv = pst.pestpp_options.get("sweep_parameter_csv_file",
"sweep_in.csv")
self.sweep_out_csv = pst.pestpp_options.get("sweep_output_csv_file",
"sweep_out.csv")
if parcov is not None:
assert isinstance(parcov, Cov)
else:
parcov = Cov.from_parameter_data(self.pst)
if obscov is not None:
assert isinstance(obscov, Cov)
else:
obscov = Cov.from_observation_data(pst)
self.parcov = parcov
self.obscov = obscov
# if restart_iter > 0:
# self.restart_iter = restart_iter
# paren = self.pst.filename+self.paren_prefix.format(restart_iter)
# assert os.path.exists(paren),\
# "could not find restart par ensemble {0}".format(paren)
# obsen0 = self.pst.filename+self.obsen_prefix.format(0)
# assert os.path.exists(obsen0),\
# "could not find restart obs ensemble 0 {0}".format(obsen0)
# obsen = self.pst.filename+self.obsen_prefix.format(restart_iter)
# assert os.path.exists(obsen),\
# "could not find restart obs ensemble {0}".format(obsen)
# self.restart = True
self.__initialized = False
#self.num_reals = 0
self.half_parcov_diag = None
self.half_obscov_diag = None
self.delta_par_prior = None
self.iter_num = 0
#self.enforce_bounds = None
self.raw_sweep_out = None
@property
def current_phi(self):
return pd.DataFrame(data={"phi":self._calc_phi_vec(self.obsensemble)},\
index=self.obsensemble.index)
def initialize(self,
num_reals=1,
init_lambda=None,
enforce_bounds="reset",
parensemble=None,
obsensemble=None,
restart_obsensemble=None):
'''
(re)initialize the process
'''
# initialize the phi report csv
self.enforce_bounds = enforce_bounds
self.phi_csv = open(self.pst.filename + ".iobj.csv", 'w')
self.phi_csv.write(
"iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_csv.write(','.join(["{0:010d}".\
format(i+1) for i in range(num_reals)]))
self.phi_csv.write('\n')
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if parensemble is not None and obsensemble is not None:
self.logger.log("initializing with existing ensembles")
if isinstance(parensemble, str):
self.logger.log("loading parensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find parensemble file: {0}".\
format(parensemble))
df = pd.read_csv(parensemble, index_col=0)
#df.index = [str(i) for i in df.index]
self.parensemble_0 = ParameterEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading parensemble from file")
elif isinstance(parensemble, ParameterEnsemble):
self.parensemble_0 = parensemble.copy()
else:
raise Exception("unrecognized arg type for parensemble, " +\
"should be filename or ParameterEnsemble" +\
", not {0}".format(type(parensemble)))
self.parensemble = self.parensemble_0.copy()
if isinstance(obsensemble, str):
self.logger.log("loading obsensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find obsensemble file: {0}".\
format(obsensemble))
df = pd.read_csv(obsensemble,
index_col=0).loc[:, self.pst.nnz_obs_names]
#df.index = [str(i) for i in df.index]
self.obsensemble_0 = ObservationEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading obsensemble from file")
elif isinstance(obsensemble, ObservationEnsemble):
self.obsensemble_0 = obsensemble.copy()
else:
raise Exception("unrecognized arg type for obsensemble, " +\
"should be filename or ObservationEnsemble" +\
", not {0}".format(type(obsensemble)))
assert self.parensemble_0.shape[0] == self.obsensemble_0.shape[0]
#self.num_reals = self.parensemble_0.shape[0]
self.logger.log("initializing with existing ensembles")
else:
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
#self.num_reals = int(num_reals)
#assert self.num_reals > 1
self.logger.log("initializing parensemble")
self.parensemble_0 = ParameterEnsemble(self.pst)
self.parensemble_0.draw(cov=self.parcov, num_reals=num_reals)
self.parensemble_0.enforce(enforce_bounds=enforce_bounds)
self.logger.log("initializing parensemble")
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.logger.log("initializing parensemble")
self.logger.log("initializing obsensemble")
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov, num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.logger.log("initializing obsensemble")
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
self.enforce_bounds = enforce_bounds
if restart_obsensemble is not None:
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
failed_runs, self.obsensemble = self._load_obs_ensemble(
restart_obsensemble)
assert self.obsensemble.shape[0] == self.obsensemble_0.shape[0]
assert list(self.obsensemble.columns) == list(
self.obsensemble_0.columns)
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
else:
# run the initial parameter ensemble
self.logger.log("evaluating initial ensembles")
failed_runs, self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
self.logger.log("evaluating initial ensembles")
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
#failed_runs_str = [str(f) for f in failed_runs]
self.parensemble = self.parensemble.drop(failed_runs)
self.obsensemble = self.obsensemble.drop(failed_runs)
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec, 0.0)
self.last_best_mean = self.current_phi_vec.mean()
self.last_best_std = self.current_phi_vec.std()
self.logger.statement("initial phi (mean, std): {0:15.6G},{1:15.6G}".\
format(self.last_best_mean,self.last_best_std))
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
# if using the approximate form of the algorithm, let
# the parameter scaling matrix be the identity matrix
# jwhite - dec 5 2016 - using the actual parcov inv
# for upgrades seems to be pushing parameters around
# too much. for now, just not using it, maybe
# better choices of lambda will tame it
self.logger.statement("current lambda:{0:15.6g}".format(
self.current_lambda))
if self.use_approx_prior:
self.logger.statement("using approximate parcov in solution")
self.half_parcov_diag = 1.0
else:
#self.logger.statement("using full parcov in solution")
# if self.parcov.isdiagonal:
# self.half_parcov_diag = self.parcov.sqrt.inv
# else:
# self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
# names=self.parcov.col_names,
# isdiagonal=True).inv.sqrt
self.half_parcov_diag = 1.0
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u, s, v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
def get_localizer(self):
onames = self.pst.nnz_obs_names
pnames = self.pst.adj_par_names
localizer = Matrix(x=np.ones((len(onames), len(pnames))),
row_names=onames,
col_names=pnames)
return localizer
def _calc_delta_par(self, parensemble):
'''
calc the scaled parameter ensemble differences from the mean
'''
return self._calc_delta(parensemble, self.half_parcov_diag)
def _calc_delta_obs(self, obsensemble):
'''
calc the scaled observation ensemble differences from the mean
'''
return self._calc_delta(obsensemble.nonzero, self.obscov.inv.sqrt)
def _calc_delta(self, ensemble, scaling_matrix):
'''
calc the scaled ensemble differences from the mean
'''
mean = np.array(ensemble.mean(axis=0))
delta = ensemble.as_pyemu_matrix()
for i in range(ensemble.shape[0]):
delta.x[i, :] -= mean
delta = scaling_matrix * delta.T
delta *= (1.0 / np.sqrt(float(ensemble.shape[0] - 1.0)))
return delta
def _calc_obs(self, parensemble):
self.logger.log("removing existing sweep in/out files")
try:
os.remove(self.sweep_in_csv)
except Exception as e:
self.logger.warn(
"error removing existing sweep in file:{0}".format(str(e)))
try:
os.remove(self.sweep_out_csv)
except Exception as e:
self.logger.warn(
"error removing existing sweep out file:{0}".format(str(e)))
self.logger.log("removing existing sweep in/out files")
if parensemble.isnull().values.any():
parensemble.to_csv("_nan.csv")
self.logger.lraise(
"_calc_obs() error: NaNs in parensemble (written to '_nan.csv')"
)
if self.submit_file is None:
self._calc_obs_local(parensemble)
else:
self._calc_obs_condor(parensemble)
# make a copy of sweep out for restart purposes
# sweep_out = str(self.iter_num)+"_raw_"+self.sweep_out_csv
# if os.path.exists(sweep_out):
# os.remove(sweep_out)
# shutil.copy2(self.sweep_out_csv,sweep_out)
self.logger.log("reading sweep out csv {0}".format(self.sweep_out_csv))
failed_runs, obs = self._load_obs_ensemble(self.sweep_out_csv)
self.logger.log("reading sweep out csv {0}".format(self.sweep_out_csv))
self.total_runs += obs.shape[0]
self.logger.statement("total runs:{0}".format(self.total_runs))
return failed_runs, obs
def _load_obs_ensemble(self, filename):
if not os.path.exists(filename):
self.logger.lraise(
"obsensemble file {0} does not exists".format(filename))
obs = pd.read_csv(filename)
obs.columns = [item.lower() for item in obs.columns]
self.raw_sweep_out = obs.copy(
) # save this for later to support restart
assert "input_run_id" in obs.columns,\
"'input_run_id' col missing...need newer version of sweep"
obs.index = obs.input_run_id
failed_runs = None
if 1 in obs.failed_flag.values:
failed_runs = obs.loc[obs.failed_flag == 1].index.values
self.logger.warn("{0} runs failed (indices: {1})".\
format(len(failed_runs),','.join([str(f) for f in failed_runs])))
obs = ObservationEnsemble.from_dataframe(
df=obs.loc[:, self.obscov.row_names], pst=self.pst)
if obs.isnull().values.any():
self.logger.lraise("_calc_obs() error: NaNs in obsensemble")
return failed_runs, obs
def _get_master_thread(self):
master_stdout = "_master_stdout.dat"
master_stderr = "_master_stderr.dat"
def master():
try:
#os.system("sweep {0} /h :{1} 1>{2} 2>{3}". \
# format(self.pst.filename, self.port, master_stdout, master_stderr))
pyemu.helpers.run("sweep {0} /h :{1} 1>{2} 2>{3}". \
format(self.pst.filename, self.port, master_stdout, master_stderr))
except Exception as e:
self.logger.lraise("error starting condor master: {0}".format(
str(e)))
with open(master_stderr, 'r') as f:
err_lines = f.readlines()
if len(err_lines) > 0:
self.logger.warn("master stderr lines: {0}".format(','.join(
[l.strip() for l in err_lines])))
master_thread = threading.Thread(target=master)
master_thread.start()
time.sleep(2.0)
return master_thread
def _calc_obs_condor(self, parensemble):
self.logger.log("evaluating ensemble of size {0} with htcondor".\
format(parensemble.shape[0]))
parensemble.to_csv(self.sweep_in_csv)
master_thread = self._get_master_thread()
condor_temp_file = "_condor_submit_stdout.dat"
condor_err_file = "_condor_submit_stderr.dat"
self.logger.log("calling condor_submit with submit file {0}".format(
self.submit_file))
try:
os.system("condor_submit {0} 1>{1} 2>{2}".\
format(self.submit_file,condor_temp_file,condor_err_file))
except Exception as e:
self.logger.lraise("error in condor_submit: {0}".format(str(e)))
self.logger.log("calling condor_submit with submit file {0}".format(
self.submit_file))
time.sleep(
2.0) #some time for condor to submit the job and echo to stdout
condor_submit_string = "submitted to cluster"
with open(condor_temp_file, 'r') as f:
lines = f.readlines()
self.logger.statement("condor_submit stdout: {0}".\
format(','.join([l.strip() for l in lines])))
with open(condor_err_file, 'r') as f:
err_lines = f.readlines()
if len(err_lines) > 0:
self.logger.warn("stderr from condor_submit:{0}".\
format([l.strip() for l in err_lines]))
cluster_number = None
for line in lines:
if condor_submit_string in line.lower():
cluster_number = int(
float(line.split(condor_submit_string)[-1]))
if cluster_number is None:
self.logger.lraise("couldn't find cluster number...")
self.logger.statement("condor cluster: {0}".format(cluster_number))
master_thread.join()
self.logger.statement("condor master thread exited")
self.logger.log(
"calling condor_rm on cluster {0}".format(cluster_number))
os.system("condor_rm cluster {0}".format(cluster_number))
self.logger.log(
"calling condor_rm on cluster {0}".format(cluster_number))
self.logger.log("evaluating ensemble of size {0} with htcondor".\
format(parensemble.shape[0]))
def _calc_obs_local(self, parensemble):
'''
propagate the ensemble forward using sweep.
'''
self.logger.log("evaluating ensemble of size {0} locally with sweep".\
format(parensemble.shape[0]))
parensemble.to_csv(self.sweep_in_csv)
if self.num_slaves > 0:
master_thread = self._get_master_thread()
pyemu.utils.start_slaves(self.slave_dir,
"sweep",
self.pst.filename,
self.num_slaves,
slave_root='..',
port=self.port)
master_thread.join()
else:
os.system("sweep {0}".format(self.pst.filename))
self.logger.log("evaluating ensemble of size {0} locally with sweep".\
format(parensemble.shape[0]))
def _calc_phi_vec(self, obsensemble):
obs_diff = self._get_residual_matrix(obsensemble)
#phi_vec = np.diagonal((obs_diff * self.obscov_inv_sqrt.get(row_names=obs_diff.col_names,
# col_names=obs_diff.col_names) * obs_diff.T).x)
q = np.diagonal(
self.obscov_inv_sqrt.get(row_names=obs_diff.col_names,
col_names=obs_diff.col_names).x)
phi_vec = []
for i in range(obs_diff.shape[0]):
o = obs_diff.x[i, :]
phi_vec.append(((obs_diff.x[i, :] * q)**2).sum())
return np.array(phi_vec)
def _phi_report(self, phi_vec, cur_lam):
self.phi_csv.write("{0},{1},{2},{3},{4},{5},{6}".format(
self.iter_num, self.total_runs, cur_lam, phi_vec.min(),
phi_vec.max(), phi_vec.mean(), np.median(phi_vec), phi_vec.std()))
self.phi_csv.write(",".join(
["{0:20.8}".format(phi) for phi in phi_vec]))
self.phi_csv.write("\n")
self.phi_csv.flush()
def _get_residual_matrix(self, obsensemble):
obs_matrix = obsensemble.nonzero.as_pyemu_matrix()
return obs_matrix - self.obs0_matrix.get(
col_names=obs_matrix.col_names, row_names=obs_matrix.row_names)
def update(self,
lambda_mults=[1.0],
localizer=None,
run_subset=None,
use_approx=True):
if run_subset is not None:
if run_subset >= self.obsensemble.shape[0]:
self.logger.warn("run_subset ({0}) >= num of active reals ({1})...ignoring ".\
format(run_subset,self.obsensemble.shape[0]))
run_subset = None
self.iter_num += 1
self.logger.log("iteration {0}".format(self.iter_num))
self.logger.statement("{0} active realizations".format(
self.obsensemble.shape[0]))
if self.obsensemble.shape[0] < 2:
self.logger.lraise(
"at least active 2 realizations (really like 300) are needed to update"
)
if not self.__initialized:
#raise Exception("must call initialize() before update()")
self.logger.lraise("must call initialize() before update()")
self.logger.log("calculate scaled delta obs")
scaled_delta_obs = self._calc_delta_obs(self.obsensemble)
self.logger.log("calculate scaled delta obs")
self.logger.log("calculate scaled delta par")
scaled_delta_par = self._calc_delta_par(self.parensemble)
self.logger.log("calculate scaled delta par")
self.logger.log("calculate pseudo inv comps")
u, s, v = scaled_delta_obs.pseudo_inv_components()
self.logger.log("calculate pseudo inv comps")
self.logger.log("calculate obs diff matrix")
obs_diff = self.obscov_inv_sqrt * self._get_residual_matrix(
self.obsensemble).T
self.logger.log("calculate obs diff matrix")
# here is the math part...calculate upgrade matrices
mean_lam, std_lam, paren_lam, obsen_lam = [], [], [], []
lam_vals = []
for ilam, cur_lam_mult in enumerate(lambda_mults):
parensemble_cur_lam = self.parensemble.copy()
#print(parensemble_cur_lam.isnull().values.any())
cur_lam = self.current_lambda * cur_lam_mult
lam_vals.append(cur_lam)
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
scaled_ident = Cov.identity_like(s) * (cur_lam + 1.0)
scaled_ident += s**2
scaled_ident = scaled_ident.inv
# build up this matrix as a single element so we can apply
# localization
self.logger.log("building upgrade_1 matrix")
upgrade_1 = -1.0 * (self.half_parcov_diag * scaled_delta_par) *\
v * s * scaled_ident * u.T
self.logger.log("building upgrade_1 matrix")
# apply localization
if localizer is not None:
self.logger.log("applying localization")
upgrade_1.hadamard_product(localizer)
self.logger.log("applying localization")
# apply residual information
self.logger.log("applying residuals")
upgrade_1 *= obs_diff
self.logger.log("applying residuals")
self.logger.log("processing upgrade_1")
upgrade_1 = upgrade_1.to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1 = upgrade_1.T
upgrade_1.index = [int(i) for i in upgrade_1.index]
upgrade_1.to_csv(self.pst.filename+".upgrade_1.{0:04d}.csv".\
format(self.iter_num))
if upgrade_1.isnull().values.any():
self.logger.lraise("NaNs in upgrade_1")
self.logger.log("processing upgrade_1")
#print(upgrade_1.isnull().values.any())
#print(parensemble_cur_lam.index)
#print(upgrade_1.index)
parensemble_cur_lam += upgrade_1
# parameter-based upgrade portion
if not use_approx and self.iter_num > 1:
self.logger.log("building upgrade_2 matrix")
par_diff = (self.parensemble - self.parensemble_0.loc[self.parensemble.index,:]).\
as_pyemu_matrix().T
x4 = self.Am.T * self.half_parcov_diag * par_diff
x5 = self.Am * x4
x6 = scaled_delta_par.T * x5
x7 = v * scaled_ident * v.T * x6
upgrade_2 = -1.0 * (self.half_parcov_diag * scaled_delta_par *
x7).to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2 = upgrade_2.T
upgrade_2.to_csv(self.pst.filename+".upgrade_2.{0:04d}.csv".\
format(self.iter_num))
upgrade_2.index = [int(i) for i in upgrade_2.index]
if upgrade_2.isnull().values.any():
self.logger.lraise("NaNs in upgrade_2")
parensemble_cur_lam += upgrade_2
self.logger.log("building upgrade_2 matrix")
parensemble_cur_lam.enforce(self.enforce_bounds)
# this is for testing failed runs on upgrade testing
# works with the 10par_xsec smoother test
#parensemble_cur_lam.iloc[:,:] = -1000000.0
paren_lam.append(pd.DataFrame(parensemble_cur_lam.loc[:, :]))
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
# subset if needed
# and combine lambda par ensembles into one par ensemble for evaluation
if run_subset is not None and run_subset < self.parensemble.shape[0]:
#subset_idx = ["{0:d}".format(i) for i in np.random.randint(0,self.parensemble.shape[0]-1,run_subset)]
subset_idx = self.parensemble.iloc[:run_subset, :].index.values
self.logger.statement("subset idxs: " +
','.join([str(s) for s in subset_idx]))
paren_lam_subset = [pe.loc[subset_idx, :] for pe in paren_lam]
paren_combine = pd.concat(paren_lam_subset, ignore_index=True)
paren_lam_subset = None
else:
subset_idx = self.parensemble.index.values
paren_combine = pd.concat(paren_lam, ignore_index=True)
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
failed_runs, obsen_combine = self._calc_obs(paren_combine)
#if failed_runs is not None:
# obsen_combine.loc[failed_runs,:] = np.NaN
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
paren_combine = None
if failed_runs is not None and len(
failed_runs) == obsen_combine.shape[0]:
self.logger.lraise("all runs failed - cannot continue")
# unpack lambda obs ensembles from combined obs ensemble
nrun_per_lam = self.obsensemble.shape[0]
if run_subset is not None:
nrun_per_lam = run_subset
obsen_lam = []
for i in range(len(lam_vals)):
sidx = i * nrun_per_lam
eidx = sidx + nrun_per_lam
oe = ObservationEnsemble.from_dataframe(
df=obsen_combine.iloc[sidx:eidx, :].copy(), pst=self.pst)
oe.index = subset_idx
# check for failed runs in this set - drop failed runs from obs ensembles
if failed_runs is not None:
failed_runs_this = np.array(
[f for f in failed_runs if f >= sidx and f < eidx]) - sidx
if len(failed_runs_this) > 0:
if len(failed_runs_this) == oe.shape[0]:
self.logger.warn(
"all runs failed for lambda {0}".format(
lam_vals[i]))
else:
self.logger.warn("{0} run failed for lambda {1}".\
format(len(failed_runs_this),lam_vals[i]))
oe.iloc[failed_runs_this, :] = np.NaN
oe = oe.dropna()
obsen_lam.append(oe)
obsen_combine = None
# here is where we need to select out the "best" lambda par and obs
# ensembles
self.logger.statement("\n**************************")
self.logger.statement(str(datetime.now()))
self.logger.statement("total runs:{0}".format(self.total_runs))
self.logger.statement("iteration: {0}".format(self.iter_num))
self.logger.statement("current lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda,
self.last_best_mean,self.last_best_std))
phi_vecs = [self._calc_phi_vec(obsen) for obsen in obsen_lam]
mean_std = [(pv.mean(), pv.std()) for pv in phi_vecs]
update_pars = False
update_lambda = False
# accept a new best if its within 10%
best_mean = self.last_best_mean * 1.1
best_std = self.last_best_std * 1.1
best_i = 0
for i, (m, s) in enumerate(mean_std):
self.logger.statement(" tested lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda * lambda_mults[i],m,s))
if m < best_mean:
update_pars = True
best_mean = m
best_i = i
if s < best_std:
update_lambda = True
best_std = s
if np.isnan(best_mean):
self.logger.lraise("best mean = NaN")
if np.isnan(best_std):
self.logger.lraise("best std = NaN")
if not update_pars:
self.current_lambda *= max(lambda_mults) * 10.0
self.current_lambda = min(self.current_lambda, 100000)
self.logger.statement("not accepting iteration, increased lambda:{0}".\
format(self.current_lambda))
else:
self.parensemble = ParameterEnsemble.from_dataframe(
df=paren_lam[best_i], pst=self.pst)
if run_subset is not None:
failed_runs, self.obsensemble = self._calc_obs(
self.parensemble)
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
self.parensemble = self.parensemble.drop(failed_runs)
self.obsensemble = self.obsensemble.drop(failed_runs)
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec,
self.current_lambda * lambda_mults[best_i])
best_mean = self.current_phi_vec.mean()
best_std = self.current_phi_vec.std()
else:
self.obsensemble = obsen_lam[best_i]
# reindex parensemble in case failed runs
self.parensemble = ParameterEnsemble.from_dataframe(
df=self.parensemble.loc[self.obsensemble.index],
pst=self.pst)
self._phi_report(phi_vecs[best_i],
self.current_lambda * lambda_mults[best_i])
self.current_phi_vec = phi_vecs[best_i]
self.logger.statement(" best lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda*lambda_mults[best_i],
best_mean,best_std))
self.last_best_mean = best_mean
self.last_best_std = best_std
if update_lambda:
# be aggressive
self.current_lambda *= (lambda_mults[best_i] * 0.75)
# but don't let lambda get too small
self.current_lambda = max(self.current_lambda, 0.001)
self.logger.statement("updating lambda: {0:15.6G}".\
format(self.current_lambda ))
self.logger.statement("**************************\n")
self.parensemble.to_csv(self.pst.filename+self.paren_prefix.\
format(self.iter_num))
self.obsensemble.to_csv(self.pst.filename+self.obsen_prefix.\
format(self.iter_num))
if self.raw_sweep_out is not None:
self.raw_sweep_out.to_csv(self.pst.filename+"_raw{0}".\
format(self.iter_num))
self.logger.log("iteration {0}".format(self.iter_num))
def draw(
self,
num_reals=1,
par_file=None,
obs=False,
enforce_bounds=None,
cov=None,
how="gaussian",
):
"""draw stochastic realizations of parameters and
optionally observations, filling MonteCarlo.parensemble and
optionally MonteCarlo.obsensemble.
Parameters
----------
num_reals : int
number of realization to generate
par_file : str
parameter file to use as mean values. If None,
use MonteCarlo.pst.parameter_data.parval1.
Default is None
obs : bool
add a realization of measurement noise to observation values,
forming MonteCarlo.obsensemble.Default is False
enforce_bounds : str
enforce parameter bounds based on control file information.
options are 'reset', 'drop' or None. Default is None
how : str
type of distribution to draw from. Must be in ["gaussian","uniform"]
default is "gaussian".
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
``>>>mc.draw(1000)``
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian", "uniform"]
if cov is not None:
assert isinstance(cov, Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.log("generating {0:d} parameter realizations".format(num_reals))
if how == "gaussian":
self.parensemble = ParameterEnsemble.from_gaussian_draw(
pst=self.pst,
cov=cov,
num_reals=num_reals,
use_homegrown=True,
enforce_bounds=False,
)
elif how == "uniform":
self.parensemble = ParameterEnsemble.from_uniform_draw(
pst=self.pst, num_reals=num_reals)
else:
raise Exception(
"MonteCarlo.draw(): unrecognized 'how' arg: {0}".format(how))
# self.parensemble = ParameterEnsemble(pst=self.pst)
# self.obsensemble = ObservationEnsemble(pst=self.pst)
# self.parensemble.draw(cov,num_reals=num_reals, how=how,
# enforce_bounds=enforce_bounds)
if enforce_bounds is not None:
self.parensemble.enforce(enforce_bounds)
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log(
"generating {0:d} observation realizations".format(num_reals))
self.obsensemble = ObservationEnsemble.from_id_gaussian_draw(
pst=self.pst, num_reals=num_reals)
self.log(
"generating {0:d} observation realizations".format(num_reals))
class EnsembleSmoother():
def __init__(self,pst,parcov=None,obscov=None):
assert isinstance(pst,Pst)
self.pst = pst
if parcov is not None:
assert isinstance(parcov,Cov)
else:
parcov = Cov.from_parameter_data(self.pst)
if obscov is not None:
assert isinstance(obscov,Cov)
else:
obscov = Cov.from_observation_data(pst)
self.parcov = parcov
self.obscov = obscov
self.__initialized = False
self.num_reals = 0
self.half_parcov_diag = None
self.half_obscov_diag = None
self.delta_par_prior = None
self.iter_num = 0
def initialize(self,num_reals):
'''
(re)initialize the process
'''
self.num_reals = int(num_reals)
self.parensemble = ParameterEnsemble(self.pst)
self.parensemble.draw(cov=self.parcov,num_reals=num_reals)
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
self.obsensemble = self.obsensemble_0.copy()
if self.parcov.isdiagonal:
self.half_parcov_diag = self.parcov.inv.sqrt
else:
self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
names=self.parcov.col_names,
isdiagonal=True).inv.sqrt
#if self.obscov.isdiagonal:
#self.half_obscov_inv = self.obscov.inv.sqrt
# else:
# self.half_obscov_diag = Cov(x=np.diag(self.obscov.x),
# names=self.obscov.col_names,
# isdiagonal=True)
self.delta_par_prior = self._calc_delta_par()
self.__initialized = True
def _calc_delta_par(self):
'''
calc the scaled parameter ensemble differences from the mean
'''
mean = np.array(self.parensemble.mean(axis=0))
delta = self.parensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i,:] -= mean
#delta = Matrix(x=(self.half_parcov_diag * delta.transpose()).x,
# row_names=self.parensemble.columns)
delta = self.half_parcov_diag * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_delta_obs(self):
'''
calc the scaled observation ensemble differences from the mean
'''
mean = np.array(self.obsensemble.mean(axis=0))
delta = self.obsensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i,:] -= mean
delta = self.obscov.inv.sqrt * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_obs(self):
'''
propagate the ensemble forward...
'''
self.parensemble.to_csv(os.path.join("smoother","sweep_in.csv"))
os.chdir("smoother")
print(os.listdir('.'))
os.system("sweep freyberg.pst")
os.chdir('..')
obs = ObservationEnsemble.from_csv(os.path.join(\
"smoother",'sweep_out.csv'))
obs.columns = [item.lower() for item in obs.columns]
self.obsensemble = ObservationEnsemble.from_dataframe(df=obs.loc[:,self.obscov.row_names],pst=self.pst)
#todo: modifiy sweep to be interactive...
return
@property
def current_lambda(self):
return 10.0
def update(self):
if not self.__initialized:
raise Exception("must call initialize() before update()")
self._calc_obs()
delta_obs = self._calc_delta_obs()
u,s,v = delta_obs.pseudo_inv_components()
#print(v)
#print(s)
#print(v)
diff = self.obsensemble.as_pyemu_matrix() - self.obsensemble_0.as_pyemu_matrix()
#print(diff)
x1 = u.T * self.obscov.inv.sqrt * diff.T
x1.autoalign = False
#print(x1)
x2 = (Cov.identity_like(s) + s**2).inv * x1
#print(x2)
x3 = v * s * x2
#print(x3)
upgrade_1 = (self.half_parcov_diag * self._calc_delta_par() * x3).to_dataframe()
upgrade_1.index.name = "parnme"
print(upgrade_1)
self.parensemble += upgrade_1.T
print(self.parensemble)
if self.iter_num > 0:
raise NotImplementedError()
print(upgrade_1.shape)
class MonteCarlo(LinearAnalysis):
"""LinearAnalysis derived type for monte carlo analysis
Note: requires a pest control file, which can be
derived from a jco argument
MonteCarlo.project_parsensemble also
requires a jacobian
"""
def __init__(self,**kwargs):
super(MonteCarlo,self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
@property
def num_reals(self):
return self.parensemble.shape[0]
def get_nsing(self,epsilon=1.0e-4):
""" get the number of solution space dimensions given
a ratio between the largest and smallest singular
values
Parameters:
epsilon: ratio
Returns : integer (or None)
number of singular components above the epsilon ratio threshold
If nsing == nadj_par, then None is returned
"""
mx = self.xtqx.shape[0]
nsing = mx - np.searchsorted(
np.sort((self.xtqx.s.x / self.xtqx.s.x.max())[:,0]),epsilon)
if nsing == mx:
self.logger.warn("optimal nsing=npar")
nsing = None
return nsing
def get_null_proj(self,nsing=None):
""" get a null-space projection matrix of XTQX
Parameters:
----------
nsing: optional number of singular components to use
if none, call self.get_nsing()
Returns:
-------
Matrix instance : V2V2^T
"""
if nsing is None:
nsing = self.get_nsing()
if nsing is None:
raise Exception("nsing is None")
print("using {0} singular components".format(nsing))
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
v2_proj = (self.xtqx.v[:,nsing:] * self.xtqx.v[:,nsing:].T)
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
return v2_proj
def draw(self, num_reals=1, par_file = None, obs=False,
enforce_bounds=False,cov=None, how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations
Parameters:
----------
num_reals (int): number of realization to generate
par_file (str): parameter file to use as mean values
obs (bool): add a realization of measurement noise to obs
enforce_bounds (bool): enforce parameter bounds in control file
how (str): type of distribution. Must be in ["gaussian","uniform"]
Returns:
None
Raises:
None
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian","uniform"]
if cov is not None:
assert isinstance(cov,Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
self.log("generating {0:d} parameter realizations".format(num_reals))
self.parensemble.draw(cov,num_reals=num_reals, how=how)
if enforce_bounds:
self.parensemble.enforce()
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log("generating {0:d} observation realizations".format(num_reals))
self.obsensemble.draw(self.obscov,num_reals=num_reals)
self.log("generating {0:d} observation realizations".format(num_reals))
def project_parensemble(self,par_file=None,nsing=None,
inplace=True):
""" perform the null-space projection operations for null-space monte carlo
Parameters:
par_file: str
an optional file of parameter values to use
nsing: int
number of singular values to in forming null subspace matrix
inplace: bool
overwrite the existing parameter ensemble with the
projected values
Returns:
-------
if inplace is False, ParameterEnsemble instance, otherwise None
"""
assert self.jco is not None,"MonteCarlo.project_parensemble()" +\
"requires a jacobian attribute"
if par_file is not None:
assert os.path.exists(par_file),"monte_carlo.draw() error: par_file not found:" +\
par_file
self.parensemble.pst.parrep(par_file)
# project the ensemble
self.log("projecting parameter ensemble")
en = self.parensemble.project(self.get_null_proj(nsing),inplace=inplace,log=self.log)
self.log("projecting parameter ensemble")
return en
def write_psts(self,prefix,existing_jco=None,noptmax=None):
""" write parameter and optionally observation realizations
to pest control files
Parameters:
----------
prefix: str
pest control file prefix
existing_jco: str
filename of an existing jacobian matrix to add to the
pest++ options in the control file. This is useful for
NSMC since this jco can be used to get the first set of
parameter upgrades for free! Needs to be the path the jco
file as seen from the location where pest++ will be run
noptmax: int
value of NOPTMAX to set in new pest control files
Returns:
-------
None
"""
self.log("writing realized pest control files")
# get a copy of the pest control file
pst = self.pst.get(par_names=self.pst.par_names,obs_names=self.pst.obs_names)
if noptmax is not None:
pst.control_data.noptmax = noptmax
pst.control_data.noptmax = noptmax
if existing_jco is not None:
pst.pestpp_options["BASE_JACOBIAN"] = existing_jco
# set the indices
pst.parameter_data.index = pst.parameter_data.parnme
pst.observation_data.index = pst.observation_data.obsnme
if self.parensemble.istransformed:
par_en = self.parensemble._back_transform(inplace=False)
else:
par_en = self.parensemble
for i in range(self.num_reals):
pst_name = prefix + "{0:d}.pst".format(i)
self.log("writing realized pest control file " + pst_name)
pst.parameter_data.loc[par_en.columns,"parval1"] = par_en.iloc[i, :].T
# reset the regularization
if pst.control_data.pestmode == "regularization":
pst.zero_order_tikhonov(parbounds=True)
# add the obs noise realization if needed
if self.obsensemble.shape[0] == self.num_reals:
pst.observation_data.loc[self.obsensemble.columns,"obsval"] = \
self.obsensemble.iloc[i, :].T
# write
pst.write(pst_name)
self.log("writing realized pest control file " + pst_name)
self.log("writing realized pest control files")
def update(self,
lambda_mults=[1.0],
localizer=None,
run_subset=None,
use_approx=True,
calc_only=False):
"""update the iES one GLM cycle
Parameters
----------
lambda_mults : list
a list of lambda multipliers to test. Each lambda mult value will require
evaluating (a subset of) the parameter ensemble.
localizer : pyemu.Matrix
a jacobian localizing matrix
run_subset : int
the number of realizations to test for each lambda_mult value. For example,
if run_subset = 30 and num_reals=100, the first 30 realizations will be run (in
parallel) for each lambda_mult value. Then the best lambda_mult is selected and the
remaining 70 realizations for that lambda_mult value are run (in parallel).
use_approx : bool
a flag to use the MLE or MAP upgrade solution. True indicates use MLE solution
calc_only : bool
a flag to calculate the upgrade matrix only (not run the ensemble). This is mostly for
debugging and testing on travis. Default is False
Example
-------
``>>>import pyemu``
``>>>es = pyemu.EnsembleSmoother(pst="pest.pst")``
``>>>es.initialize(num_reals=100)``
``>>>es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30)``
"""
#if not self.parensemble.istransformed:
# self.parensemble._transform(inplace=False)
if run_subset is not None:
if run_subset >= self.obsensemble.shape[0]:
self.logger.warn("run_subset ({0}) >= num of active reals ({1})...ignoring ".\
format(run_subset,self.obsensemble.shape[0]))
run_subset = None
self.iter_num += 1
mat_prefix = self.pst.filename.replace('.pst', '') + ".{0}".format(
self.iter_num)
self.logger.log("iteration {0}".format(self.iter_num))
self.logger.statement("{0} active realizations".format(
self.obsensemble.shape[0]))
if self.obsensemble.shape[0] < 2:
self.logger.lraise(
"at least active 2 realizations (really like 300) are needed to update"
)
if not self._initialized:
#raise Exception("must call initialize() before update()")
self.logger.lraise("must call initialize() before update()")
self.logger.log("calculate scaled delta obs")
scaled_delta_obs = self._calc_delta_obs(self.obsensemble)
self.logger.log("calculate scaled delta obs")
self.logger.log("calculate scaled delta par")
scaled_delta_par = self._calc_delta_par(self.parensemble)
self.logger.log("calculate scaled delta par")
self.logger.log("calculate pseudo inv comps")
u, s, v = scaled_delta_obs.pseudo_inv_components(
eigthresh=self.pst.svd_data.eigthresh)
s.col_names = s.row_names
self.logger.log("calculate pseudo inv comps")
self.logger.log("calculate obs diff matrix")
#obs_diff = self.obscov_inv_sqrt * self._get_residual_obs_matrix(self.obsensemble).T
obs_diff = self.obscov_inv_sqrt * self.phi.get_residual_obs_matrix(
self.obsensemble).T
self.logger.log("calculate obs diff matrix")
if self.save_mats:
np.savetxt(mat_prefix + ".obs_diff.dat",
scaled_delta_obs.x,
fmt="%15.6e")
np.savetxt(mat_prefix + ".par_diff.dat",
scaled_delta_par.x,
fmt="%15.6e")
np.savetxt(mat_prefix + ".u.dat", u.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".s.dat", s.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".v.dat", v.x, fmt="%15.6e")
# here is the math part...calculate upgrade matrices
mean_lam, std_lam, paren_lam, obsen_lam = [], [], [], []
lam_vals = []
for ilam, cur_lam_mult in enumerate(lambda_mults):
parensemble_cur_lam = self.parensemble.copy()
#print(parensemble_cur_lam.isnull().values.any())
cur_lam = self.current_lambda * cur_lam_mult
lam_vals.append(cur_lam)
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
scaled_ident = Cov.identity_like(s) * (cur_lam + 1.0)
scaled_ident += s**2
scaled_ident = scaled_ident.inv
# build up this matrix as a single element so we can apply
# localization
self.logger.log("building upgrade_1 matrix")
upgrade_1 = -1.0 * (self.parcov_inv_sqrt * scaled_delta_par) *\
v * s * scaled_ident * u.T
if self.save_mats:
np.savetxt(mat_prefix + ".ivec.dat".format(self.iter_num),
scaled_ident.x,
fmt="%15.6e")
self.logger.log("building upgrade_1 matrix")
# apply localization
if localizer is not None:
self.logger.log("applying localization")
upgrade_1.hadamard_product(localizer)
self.logger.log("applying localization")
# apply residual information
self.logger.log("applying residuals")
upgrade_1 *= obs_diff
self.logger.log("applying residuals")
self.logger.log("processing upgrade_1")
if self.save_mats:
np.savetxt(mat_prefix + ".upgrade_1.dat",
upgrade_1.T.x,
fmt="%15.6e")
upgrade_1 = upgrade_1.to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1 = upgrade_1.T
upgrade_1.index = [int(i) for i in upgrade_1.index]
upgrade_1.to_csv(self.pst.filename+".upgrade_1.{0:04d}.csv".\
format(self.iter_num))
if upgrade_1.isnull().values.any():
self.logger.lraise("NaNs in upgrade_1")
self.logger.log("processing upgrade_1")
#print(upgrade_1.isnull().values.any())
#print(parensemble_cur_lam.index)
#print(upgrade_1.index)
parensemble_cur_lam += upgrade_1
# parameter-based upgrade portion
if not use_approx and self.iter_num > 1:
#if True:
self.logger.log("building upgrade_2 matrix")
par_diff = (self.parensemble - self.parensemble_0.loc[self.parensemble.index,:]).\
as_pyemu_matrix().T
x4 = self.Am.T * self.parcov_inv_sqrt * par_diff
x5 = self.Am * x4
x6 = scaled_delta_par.T * x5
x7 = v * scaled_ident * v.T * x6
ug2_mat = -1.0 * (self.parcov_inv_sqrt * scaled_delta_par * x7)
upgrade_2 = ug2_mat.to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2 = upgrade_2.T
upgrade_2.to_csv(self.pst.filename+".upgrade_2.{0:04d}.csv".\
format(self.iter_num))
upgrade_2.index = [int(i) for i in upgrade_2.index]
if self.save_mats:
np.savetxt(mat_prefix + ".scaled_par_resid.dat",
par_diff.x,
fmt="%15.6e")
np.savetxt(mat_prefix + ".x4.dat", x4.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".x5.dat", x5.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".x6.dat", x6.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".x7.dat", x7.x, fmt="%15.6e")
np.savetxt(mat_prefix + ".upgrade_2.dat",
ug2_mat.T.x,
fmt="%15.6e")
if upgrade_2.isnull().values.any():
self.logger.lraise("NaNs in upgrade_2")
parensemble_cur_lam += upgrade_2
self.logger.log("building upgrade_2 matrix")
self.logger.log("enforcing bounds")
parensemble_cur_lam.enforce(self.enforce_bounds)
self.logger.log("enforcing bounds")
self.logger.log("filling fixed parameters")
#fill in fixed pars with initial values
fi = parensemble_cur_lam.fixed_indexer
li = parensemble_cur_lam.log_indexer
log_values = self.pst.parameter_data.loc[:, "parval1"].copy()
log_values.loc[li] = log_values.loc[li].apply(np.log10)
fixed_vals = log_values.loc[fi]
for fname, fval in zip(fixed_vals.index, fixed_vals.values):
# if fname not in df.columns:
# continue
# print(fname)
parensemble_cur_lam.loc[:, fname] = fval
self.logger.log("filling fixed parameters")
# this is for testing failed runs on upgrade testing
# works with the 10par_xsec smoother test
#parensemble_cur_lam.iloc[:,:] = -1000000.0
# some hackery - we lose track of the transform flag here, but just
# know it is transformed. Need to create dataframe here because
# pd.concat doesn't like par ensembles later
paren_lam.append(pd.DataFrame(parensemble_cur_lam.loc[:, :]))
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
if calc_only:
return
# subset if needed
# and combine lambda par ensembles into one par ensemble for evaluation
if run_subset is not None and run_subset < self.parensemble.shape[0]:
#subset_idx = ["{0:d}".format(i) for i in np.random.randint(0,self.parensemble.shape[0]-1,run_subset)]
subset_idx = self.parensemble.iloc[:run_subset, :].index.values
self.logger.statement("subset idxs: " +
','.join([str(s) for s in subset_idx]))
# more tracking of transformed - just know it! Creating dataframes...
paren_lam_subset = [pe.loc[subset_idx, :] for pe in paren_lam]
paren_combine = pd.concat(paren_lam_subset, ignore_index=True)
paren_lam_subset = None
else:
subset_idx = self.parensemble.index.values
paren_combine = pd.concat(paren_lam, ignore_index=True)
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
# back to par ensemble and know it is transformed
paren_combine = ParameterEnsemble.from_dataframe(df=paren_combine,
pst=self.pst,
istransformed=True)
failed_runs, obsen_combine = self._calc_obs(paren_combine)
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
paren_combine = None
if failed_runs is not None and len(
failed_runs) == obsen_combine.shape[0]:
self.logger.lraise("all runs failed - cannot continue")
# unpack lambda obs ensembles from combined obs ensemble
nrun_per_lam = self.obsensemble.shape[0]
if run_subset is not None:
nrun_per_lam = run_subset
obsen_lam = []
for i in range(len(lam_vals)):
sidx = i * nrun_per_lam
eidx = sidx + nrun_per_lam
oe = ObservationEnsemble.from_dataframe(
df=obsen_combine.iloc[sidx:eidx, :].copy(), pst=self.pst)
oe.index = subset_idx
# check for failed runs in this set - drop failed runs from obs ensembles
if failed_runs is not None:
failed_runs_this = np.array(
[f for f in failed_runs if f >= sidx and f < eidx]) - sidx
if len(failed_runs_this) > 0:
if len(failed_runs_this) == oe.shape[0]:
self.logger.warn(
"all runs failed for lambda {0}".format(
lam_vals[i]))
else:
self.logger.warn("{0} run failed for lambda {1}".\
format(len(failed_runs_this),lam_vals[i]))
oe.iloc[failed_runs_this, :] = np.NaN
oe = oe.dropna()
paren_lam[i].iloc[failed_runs_this, :] = np.NaN
paren_lam[i] = ParameterEnsemble.from_dataframe(
df=paren_lam[i].dropna(), pst=self.pst)
paren_lam[i].__instransformed = True
# don't drop bad reals here, instead, mask bad reals in the lambda
# selection and drop later
# if self.drop_bad_reals is not None:
# assert isinstance(drop_bad_reals, float)
# drop_idx = np.argwhere(self.current_phi_vec > self.drop_bad_reals).flatten()
# run_ids = self.obsensemble.index.values
# drop_idx = run_ids[drop_idx]
# if len(drop_idx) == self.obsensemble.shape[0]:
# raise Exception("dropped all realizations as 'bad'")
# if len(drop_idx) > 0:
# self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})". \
# format(len(drop_idx), ','.join([str(d) for d in drop_idx])))
# self.parensemble.loc[drop_idx, :] = np.NaN
# self.parensemble = self.parensemble.dropna()
# self.obsensemble.loc[drop_idx, :] = np.NaN
# self.obsensemble = self.obsensemble.dropna()
#
# self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
obsen_lam.append(oe)
obsen_combine = None
# here is where we need to select out the "best" lambda par and obs
# ensembles
#phi_vecs = [self._calc_phi_vec(obsen) for obsen in obsen_lam]
#phi_vecs_reg = [self._calc_regul_phi_vec(paren) for paren in paren_lam]
#if self.regul_factor > 0.0:
# for i,(pv,prv) in enumerate(zip(phi_vecs,phi_vecs_reg)):
# phi_vecs[i] = pv + (prv * self.regul_factor)
self.logger.log("calc lambda phi vectors")
phi_vecs = [
self.phi.get_meas_and_regul_phi(oe, pe.loc[oe.index, :])
for oe, pe in zip(obsen_lam, paren_lam)
]
self.logger.log("calc lambda phi vectors")
if self.drop_bad_reals is not None:
for i, (meas_pv, regul_pv) in enumerate(phi_vecs):
#for testing the drop_bad_reals functionality
#pv[[0,3,7]] = self.drop_bad_reals + 1.0
regul_pv = regul_pv.copy()
regul_pv[meas_pv > self.drop_bad_reals] = np.NaN
regul_pv = regul_pv[~np.isnan(regul_pv)]
meas_pv[meas_pv > self.drop_bad_reals] = np.NaN
meas_pv = meas_pv[~np.isnan(meas_pv)]
if len(meas_pv) == 0:
#raise Exception("all realization for lambda {0} dropped as 'bad'".\
# format(lam_vals[i]))
self.logger.warn(
"all realizations for lambda {0} marked as 'bad'")
meas_pv = np.zeros_like(obsen_lam[0].shape[0]) + 1.0e+30
regul_pv = np.zeros_like(obsen_lam[0].shape[0]) + 1.0e+30
phi_vecs[i] = (meas_pv, regul_pv)
mean_std_meas = [(pv[0].mean(), pv[0].std()) for pv in phi_vecs]
mean_std_regul = [(pv[1].mean(), pv[1].std()) for pv in phi_vecs]
update_pars = False
update_lambda = False
self.logger.statement("**************************")
# self.logger.statement(str(datetime.now()))
self.logger.statement("lambda testing summary")
self.logger.statement("total runs:{0}".format(self.total_runs))
self.logger.statement("iteration: {0}".format(self.iter_num))
self.logger.statement("current lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}". \
format(self.current_lambda,
self.last_best_mean, self.last_best_std))
# accept a new best if its within 10%
best_mean = self.last_best_mean * 1.1
best_std = self.last_best_std * 1.1
best_i = 0
for i, ((mm, ms),
(rm, rs)) in enumerate(zip(mean_std_meas, mean_std_regul)):
self.logger.statement(
" tested lambda:{0:15.6G}, meas mean:{1:15.6G}, meas std:{2:15.6G}"
.format(self.current_lambda * lambda_mults[i], mm, ms))
self.logger.statement("{0:30s}regul mean:{1:15.6G}, regul std:{2:15.6G}".\
format(' ',rm,rs))
m = mm + (self.regul_factor * rm)
s = ms + (self.regul_factor * rs)
if m < best_mean:
update_pars = True
best_mean = m
best_i = i
if s < best_std:
update_lambda = True
best_std = s
if np.isnan(best_mean):
self.logger.lraise("best mean = NaN")
if np.isnan(best_std):
self.logger.lraise("best std = NaN")
if not update_pars:
self.current_lambda *= max(lambda_mults) * 10.0
self.current_lambda = min(self.current_lambda, 100000)
self.logger.statement("not accepting iteration, increased lambda:{0}".\
format(self.current_lambda))
else:
#more transformation status hard coding - ugly
self.parensemble = ParameterEnsemble.from_dataframe(
df=paren_lam[best_i], pst=self.pst, istransformed=True)
if run_subset is not None:
failed_runs, self.obsensemble = self._calc_obs(
self.parensemble)
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
self.parensemble.loc[failed_runs, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[failed_runs, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.phi.update()
best_mean = self.phi.comp_phi.mean()
best_std = self.phi.comp_phi.std()
else:
self.obsensemble = obsen_lam[best_i]
# reindex parensemble in case failed runs
self.parensemble = ParameterEnsemble.from_dataframe(
df=self.parensemble.loc[self.obsensemble.index],
pst=self.pst,
istransformed=self.parensemble.istransformed)
self.phi.update()
if self.drop_bad_reals is not None:
# for testing drop_bad_reals functionality
# self.current_phi_vec[::2] = self.drop_bad_reals + 1.0
#drop_idx = np.argwhere(self.current_phi_vec > self.drop_bad_reals).flatten()
drop_idx = np.argwhere(
self.phi.comp_phi > self.drop_bad_reals).flatten()
run_ids = self.obsensemble.index.values
drop_idx = run_ids[drop_idx]
if len(drop_idx) > self.obsensemble.shape[0] - 3:
raise Exception("dropped too many realizations as 'bad'")
if len(drop_idx) > 0:
self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})". \
format(len(drop_idx), ','.join([str(d) for d in drop_idx])))
self.parensemble.loc[drop_idx, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[drop_idx, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.phi.update()
best_mean = self.phi.comp_phi.mean()
best_std = self.phi.comp_phi.std()
self.phi.report(cur_lam=self.current_lambda * lambda_mults[best_i])
self.logger.statement(" best lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda*lambda_mults[best_i],
best_mean,best_std))
#self.logger.statement(" actual mean phi: {0:15.6G}".format(float(self.current_actual_phi.mean())))
self.last_best_mean = best_mean
self.last_best_std = best_std
if update_lambda:
# be aggressive
self.current_lambda *= (lambda_mults[best_i] * 0.75)
# but don't let lambda get too small
self.current_lambda = max(self.current_lambda, 0.00001)
self.logger.statement("updating lambda: {0:15.6G}".\
format(self.current_lambda ))
self.logger.statement("**************************\n")
self.parensemble.to_csv(self.pst.filename+self.paren_prefix.\
format(self.iter_num))
self.obsensemble.to_csv(self.pst.filename+self.obsen_prefix.\
format(self.iter_num))
if self.raw_sweep_out is not None:
self.raw_sweep_out.to_csv(self.pst.filename+"_sweepraw{0}.csv".\
format(self.iter_num))
self.logger.log("iteration {0}".format(self.iter_num))
def initialize(self,
num_reals=1,
init_lambda=None,
enforce_bounds="reset",
parensemble=None,
obsensemble=None,
restart_obsensemble=None,
regul_factor=0.0,
use_approx_prior=True,
build_empirical_prior=False):
"""Initialize the iES process. Depending on arguments, draws or loads
initial parameter observations ensembles and runs the initial parameter
ensemble
Parameters
----------
num_reals : int
the number of realizations to draw. Ignored if parensemble/obsensemble
are not None
init_lambda : float
the initial lambda to use. During subsequent updates, the lambda is
updated according to upgrade success
enforce_bounds : str
how to enfore parameter bound transgression. options are
reset, drop, or None
parensemble : pyemu.ParameterEnsemble or str
a parameter ensemble or filename to use as the initial
parameter ensemble. If not None, then obsenemble must not be
None
obsensemble : pyemu.ObservationEnsemble or str
an observation ensemble or filename to use as the initial
observation ensemble. If not None, then parensemble must
not be None
restart_obsensemble : pyemu.ObservationEnsemble or str
an observation ensemble or filename to use as an
evaluated observation ensemble. If not None, this will skip the initial
parameter ensemble evaluation - user beware!
regul_factor : float
the regularization penalty fraction of the composite objective. The
Prurist, MAP solution would be regul_factor = 1.0, yielding equal
parts measurement and regularization to the composite objective function.
Default is 0.0, which means only seek to minimize the measurement objective
function
use_approx_prior : bool
a flag to use the inverse, square root of the prior ccovariance matrix
for scaling the upgrade calculation. If True, this matrix is not used.
Default is True
build_empirical_prior : bool
flag to build the prior parameter covariance matrix from an existing parensemble.
If True and parensemble is None, an exception is raised
Example
-------
``>>>import pyemu``
``>>>es = pyemu.EnsembleSmoother(pst="pest.pst")``
``>>>es.initialize(num_reals=100)``
"""
'''
(re)initialize the process
'''
# initialize the phi report csv
self.enforce_bounds = enforce_bounds
self.regul_factor = float(regul_factor)
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if use_approx_prior:
self.logger.statement("using approximate parcov in solution")
self.parcov_inv_sqrt = 1.0
else:
self.logger.statement("using full parcov in solution")
# Chen and Oliver use a low rank approx here, but so far,
# I haven't needed it - not using enough parameters yet
self.logger.log("forming inverse sqrt parcov matrix")
self.parcov_inv_sqrt = self.parcov.inv.sqrt
self.logger.log("forming inverse sqrt parcov matrix")
if parensemble is not None and obsensemble is not None:
self.logger.log("initializing with existing ensembles")
if isinstance(parensemble, str):
self.logger.log("loading parensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find parensemble file: {0}".\
format(parensemble))
df = pd.read_csv(parensemble, index_col=0)
#df.index = [str(i) for i in df.index]
self.parensemble_0 = ParameterEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading parensemble from file")
elif isinstance(parensemble, ParameterEnsemble):
self.parensemble_0 = parensemble.copy()
else:
raise Exception("unrecognized arg type for parensemble, " +\
"should be filename or ParameterEnsemble" +\
", not {0}".format(type(parensemble)))
self.parensemble = self.parensemble_0.copy()
if isinstance(obsensemble, str):
self.logger.log("loading obsensemble from file")
if not os.path.exists(obsensemble):
self.logger.lraise("can not find obsensemble file: {0}".\
format(obsensemble))
df = pd.read_csv(obsensemble,
index_col=0).loc[:, self.pst.nnz_obs_names]
#df.index = [str(i) for i in df.index]
self.obsensemble_0 = ObservationEnsemble.from_dataframe(
df=df, pst=self.pst)
self.logger.log("loading obsensemble from file")
elif isinstance(obsensemble, ObservationEnsemble):
self.obsensemble_0 = obsensemble.copy()
else:
raise Exception("unrecognized arg type for obsensemble, " +\
"should be filename or ObservationEnsemble" +\
", not {0}".format(type(obsensemble)))
assert self.parensemble_0.shape[0] == self.obsensemble_0.shape[0]
#self.num_reals = self.parensemble_0.shape[0]
num_reals = self.parensemble.shape[0]
self.logger.log("initializing with existing ensembles")
if build_empirical_prior:
self.reset_parcov(self.parensemble.covariance_matrix())
if self.save_mats:
self.parcov.to_binary(self.pst.filename + ".empcov.jcb")
else:
if build_empirical_prior:
self.logger.lraise(
"can't use build_emprirical_prior without parensemble...")
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
self.logger.log("initializing parensemble")
self.parensemble_0 = pyemu.ParameterEnsemble.from_gaussian_draw(
self.pst, self.parcov, num_reals=num_reals)
self.parensemble_0.enforce(enforce_bounds=enforce_bounds)
self.logger.log("initializing parensemble")
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.logger.log("initializing parensemble")
self.logger.log("initializing obsensemble")
self.obsensemble_0 = pyemu.ObservationEnsemble.from_id_gaussian_draw(
self.pst, num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.logger.log("initializing obsensemble")
self.logger.log(
"initializing smoother with {0} realizations".format(
num_reals))
if use_approx_prior:
self.logger.statement("using approximate parcov in solution")
self.parcov_inv_sqrt = 1.0
else:
self.logger.statement("using full parcov in solution")
# Chen and Oliver use a low rank approx here, but so far,
# I haven't needed it - not using enough parameters yet
self.logger.log("forming inverse sqrt parcov matrix")
self.parcov_inv_sqrt = self.parcov.inv.sqrt
self.logger.log("forming inverse sqrt parcov matrix")
# self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
# self.par0_matrix = self.parensemble_0.as_pyemu_matrix()
self.enforce_bounds = enforce_bounds
if restart_obsensemble is not None:
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
failed_runs, self.obsensemble = self._load_obs_ensemble(
restart_obsensemble)
assert self.obsensemble.shape[0] == self.obsensemble_0.shape[0]
assert list(self.obsensemble.columns) == list(
self.obsensemble_0.columns)
self.logger.log(
"loading restart_obsensemble {0}".format(restart_obsensemble))
else:
# run the initial parameter ensemble
self.logger.log("evaluating initial ensembles")
failed_runs, self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
if self.raw_sweep_out is not None:
self.raw_sweep_out.to_csv(self.pst.filename + "_sweepraw0.csv")
self.logger.log("evaluating initial ensembles")
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
#failed_runs_str = [str(f) for f in failed_runs]
#self.parensemble = self.parensemble.drop(failed_runs)
#self.obsensemble = self.obsensemble.drop(failed_runs)
self.parensemble.loc[failed_runs, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[failed_runs, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
if not self.parensemble.istransformed:
self.parensemble._transform(inplace=True)
if not self.parensemble_0.istransformed:
self.parensemble_0._transform(inplace=True)
self.phi = Phi(self)
if self.drop_bad_reals is not None:
#drop_idx = np.argwhere(self.current_phi_vec > self.drop_bad_reals).flatten()
#comp_phi = self.phi.comp_phi
#drop_idx = np.argwhere(self.phi.comp_phi > self.drop_bad_reals).flatten()
#meas_phi = self.phi.meas_phi
drop_idx = np.argwhere(
self.phi.meas_phi > self.drop_bad_reals).flatten()
run_ids = self.obsensemble.index.values
drop_idx = run_ids[drop_idx]
if len(drop_idx) == self.obsensemble.shape[0]:
raise Exception("dropped all realizations as 'bad'")
if len(drop_idx) > 0:
self.logger.warn("{0} realizations dropped as 'bad' (indices :{1})".\
format(len(drop_idx),','.join([str(d) for d in drop_idx])))
self.parensemble.loc[drop_idx, :] = np.NaN
self.parensemble = self.parensemble.dropna()
self.obsensemble.loc[drop_idx, :] = np.NaN
self.obsensemble = self.obsensemble.dropna()
self.phi.update()
self.phi.report(cur_lam=0.0)
self.last_best_mean = self.phi.comp_phi.mean()
self.last_best_std = self.phi.comp_phi.std()
#self.logger.statement("initial phi (mean, std): {0:15.6G},{1:15.6G}".\
# format(self.last_best_mean,self.last_best_std))
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
self.logger.statement("current lambda:{0:15.6g}".format(
self.current_lambda))
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u, s, v = self.delta_par_prior.pseudo_inv_components(
eigthresh=self.pst.svd_data.eigthresh)
self.Am = u * s.inv
if self.save_mats:
np.savetxt(self.pst.filename.replace(".pst", '.') +
"0.prior_par_diff.dat",
self.delta_par_prior.x,
fmt="%15.6e")
np.savetxt(self.pst.filename.replace(".pst", '.') + "0.am_u.dat",
u.x,
fmt="%15.6e")
np.savetxt(self.pst.filename.replace(".pst", '.') + "0.am_v.dat",
v.x,
fmt="%15.6e")
np.savetxt(self.pst.filename.replace(".pst", '.') +
"0.am_s_inv.dat",
s.inv.as_2d,
fmt="%15.6e")
np.savetxt(self.pst.filename.replace(".pst", '.') + "0.am.dat",
self.Am.x,
fmt="%15.6e")
self._initialized = True
class MonteCarlo(LinearAnalysis):
"""LinearAnalysis derived type for monte carlo analysis
Note: requires a pest control file, which can be
derived from a jco argument
MonteCarlo.project_parsensemble also
requires a jacobian
"""
def __init__(self, **kwargs):
super(MonteCarlo, self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
@property
def num_reals(self):
return self.parensemble.shape[0]
def get_nsing(self, epsilon=1.0e-4):
""" get the number of solution space dimensions given
a ratio between the largest and smallest singular
values
Parameters:
epsilon: ratio
Returns : integer (or None)
number of singular components above the epsilon ratio threshold
If nsing == nadj_par, then None is returned
"""
mx = self.xtqx.shape[0]
nsing = mx - np.searchsorted(
np.sort((self.xtqx.s.x / self.xtqx.s.x.max())[:, 0]), epsilon)
if nsing == mx:
self.logger.warn("optimal nsing=npar")
nsing = None
return nsing
def get_null_proj(self, nsing=None):
""" get a null-space projection matrix of XTQX
Parameters:
----------
nsing: optional number of singular components to use
if none, call self.get_nsing()
Returns:
-------
Matrix instance : V2V2^T
"""
if nsing is None:
nsing = self.get_nsing()
if nsing is None:
raise Exception("nsing is None")
print("using {0} singular components".format(nsing))
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
v2_proj = (self.xtqx.v[:, nsing:] * self.xtqx.v[:, nsing:].T)
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
return v2_proj
def draw(self,
num_reals=1,
par_file=None,
obs=False,
enforce_bounds=False,
cov=None,
how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations
Parameters:
----------
num_reals (int): number of realization to generate
par_file (str): parameter file to use as mean values
obs (bool): add a realization of measurement noise to obs
enforce_bounds (bool): enforce parameter bounds in control file
how (str): type of distribution. Must be in ["gaussian","uniform"]
Returns:
None
Raises:
None
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian", "uniform"]
if cov is not None:
assert isinstance(cov, Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
self.log("generating {0:d} parameter realizations".format(num_reals))
self.parensemble.draw(cov, num_reals=num_reals, how=how)
if enforce_bounds:
self.parensemble.enforce()
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log(
"generating {0:d} observation realizations".format(num_reals))
self.obsensemble.draw(self.obscov, num_reals=num_reals)
self.log(
"generating {0:d} observation realizations".format(num_reals))
def project_parensemble(self, par_file=None, nsing=None, inplace=True):
""" perform the null-space projection operations for null-space monte carlo
Parameters:
par_file: str
an optional file of parameter values to use
nsing: int
number of singular values to in forming null subspace matrix
inplace: bool
overwrite the existing parameter ensemble with the
projected values
Returns:
-------
if inplace is False, ParameterEnsemble instance, otherwise None
"""
assert self.jco is not None,"MonteCarlo.project_parensemble()" +\
"requires a jacobian attribute"
if par_file is not None:
assert os.path.exists(par_file),"monte_carlo.draw() error: par_file not found:" +\
par_file
self.parensemble.pst.parrep(par_file)
# project the ensemble
self.log("projecting parameter ensemble")
en = self.parensemble.project(self.get_null_proj(nsing),
inplace=inplace,
log=self.log)
self.log("projecting parameter ensemble")
return en
def write_psts(self, prefix, existing_jco=None, noptmax=None):
""" write parameter and optionally observation realizations
to pest control files
Parameters:
----------
prefix: str
pest control file prefix
existing_jco: str
filename of an existing jacobian matrix to add to the
pest++ options in the control file. This is useful for
NSMC since this jco can be used to get the first set of
parameter upgrades for free! Needs to be the path the jco
file as seen from the location where pest++ will be run
noptmax: int
value of NOPTMAX to set in new pest control files
Returns:
-------
None
"""
self.log("writing realized pest control files")
# get a copy of the pest control file
pst = self.pst.get(par_names=self.pst.par_names,
obs_names=self.pst.obs_names)
if noptmax is not None:
pst.control_data.noptmax = noptmax
pst.control_data.noptmax = noptmax
if existing_jco is not None:
pst.pestpp_options["BASE_JACOBIAN"] = existing_jco
# set the indices
pst.parameter_data.index = pst.parameter_data.parnme
pst.observation_data.index = pst.observation_data.obsnme
if self.parensemble.istransformed:
par_en = self.parensemble._back_transform(inplace=False)
else:
par_en = self.parensemble
for i in range(self.num_reals):
pst_name = prefix + "{0:d}.pst".format(i)
self.log("writing realized pest control file " + pst_name)
pst.parameter_data.loc[par_en.columns,
"parval1"] = par_en.iloc[i, :].T
# reset the regularization
if pst.control_data.pestmode == "regularization":
pst.zero_order_tikhonov(parbounds=True)
# add the obs noise realization if needed
if self.obsensemble.shape[0] == self.num_reals:
pst.observation_data.loc[self.obsensemble.columns,"obsval"] = \
self.obsensemble.iloc[i, :].T
# write
pst.write(pst_name)
self.log("writing realized pest control file " + pst_name)
self.log("writing realized pest control files")
def update(self,
lambda_mults=[1.0],
localizer=None,
run_subset=None,
use_approx=True):
if run_subset is not None:
if run_subset >= self.obsensemble.shape[0]:
self.logger.warn("run_subset ({0}) >= num of active reals ({1})...ignoring ".\
format(run_subset,self.obsensemble.shape[0]))
run_subset = None
self.iter_num += 1
self.logger.log("iteration {0}".format(self.iter_num))
self.logger.statement("{0} active realizations".format(
self.obsensemble.shape[0]))
if self.obsensemble.shape[0] < 2:
self.logger.lraise(
"at least active 2 realizations (really like 300) are needed to update"
)
if not self.__initialized:
#raise Exception("must call initialize() before update()")
self.logger.lraise("must call initialize() before update()")
self.logger.log("calculate scaled delta obs")
scaled_delta_obs = self._calc_delta_obs(self.obsensemble)
self.logger.log("calculate scaled delta obs")
self.logger.log("calculate scaled delta par")
scaled_delta_par = self._calc_delta_par(self.parensemble)
self.logger.log("calculate scaled delta par")
self.logger.log("calculate pseudo inv comps")
u, s, v = scaled_delta_obs.pseudo_inv_components()
self.logger.log("calculate pseudo inv comps")
self.logger.log("calculate obs diff matrix")
obs_diff = self.obscov_inv_sqrt * self._get_residual_matrix(
self.obsensemble).T
self.logger.log("calculate obs diff matrix")
# here is the math part...calculate upgrade matrices
mean_lam, std_lam, paren_lam, obsen_lam = [], [], [], []
lam_vals = []
for ilam, cur_lam_mult in enumerate(lambda_mults):
parensemble_cur_lam = self.parensemble.copy()
#print(parensemble_cur_lam.isnull().values.any())
cur_lam = self.current_lambda * cur_lam_mult
lam_vals.append(cur_lam)
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
scaled_ident = Cov.identity_like(s) * (cur_lam + 1.0)
scaled_ident += s**2
scaled_ident = scaled_ident.inv
# build up this matrix as a single element so we can apply
# localization
self.logger.log("building upgrade_1 matrix")
upgrade_1 = -1.0 * (self.half_parcov_diag * scaled_delta_par) *\
v * s * scaled_ident * u.T
self.logger.log("building upgrade_1 matrix")
# apply localization
if localizer is not None:
self.logger.log("applying localization")
upgrade_1.hadamard_product(localizer)
self.logger.log("applying localization")
# apply residual information
self.logger.log("applying residuals")
upgrade_1 *= obs_diff
self.logger.log("applying residuals")
self.logger.log("processing upgrade_1")
upgrade_1 = upgrade_1.to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1 = upgrade_1.T
upgrade_1.index = [int(i) for i in upgrade_1.index]
upgrade_1.to_csv(self.pst.filename+".upgrade_1.{0:04d}.csv".\
format(self.iter_num))
if upgrade_1.isnull().values.any():
self.logger.lraise("NaNs in upgrade_1")
self.logger.log("processing upgrade_1")
#print(upgrade_1.isnull().values.any())
#print(parensemble_cur_lam.index)
#print(upgrade_1.index)
parensemble_cur_lam += upgrade_1
# parameter-based upgrade portion
if not use_approx and self.iter_num > 1:
self.logger.log("building upgrade_2 matrix")
par_diff = (self.parensemble - self.parensemble_0.loc[self.parensemble.index,:]).\
as_pyemu_matrix().T
x4 = self.Am.T * self.half_parcov_diag * par_diff
x5 = self.Am * x4
x6 = scaled_delta_par.T * x5
x7 = v * scaled_ident * v.T * x6
upgrade_2 = -1.0 * (self.half_parcov_diag * scaled_delta_par *
x7).to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2 = upgrade_2.T
upgrade_2.to_csv(self.pst.filename+".upgrade_2.{0:04d}.csv".\
format(self.iter_num))
upgrade_2.index = [int(i) for i in upgrade_2.index]
if upgrade_2.isnull().values.any():
self.logger.lraise("NaNs in upgrade_2")
parensemble_cur_lam += upgrade_2
self.logger.log("building upgrade_2 matrix")
parensemble_cur_lam.enforce(self.enforce_bounds)
# this is for testing failed runs on upgrade testing
# works with the 10par_xsec smoother test
#parensemble_cur_lam.iloc[:,:] = -1000000.0
paren_lam.append(pd.DataFrame(parensemble_cur_lam.loc[:, :]))
self.logger.log("calcs for lambda {0}".format(cur_lam_mult))
# subset if needed
# and combine lambda par ensembles into one par ensemble for evaluation
if run_subset is not None and run_subset < self.parensemble.shape[0]:
#subset_idx = ["{0:d}".format(i) for i in np.random.randint(0,self.parensemble.shape[0]-1,run_subset)]
subset_idx = self.parensemble.iloc[:run_subset, :].index.values
self.logger.statement("subset idxs: " +
','.join([str(s) for s in subset_idx]))
paren_lam_subset = [pe.loc[subset_idx, :] for pe in paren_lam]
paren_combine = pd.concat(paren_lam_subset, ignore_index=True)
paren_lam_subset = None
else:
subset_idx = self.parensemble.index.values
paren_combine = pd.concat(paren_lam, ignore_index=True)
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
failed_runs, obsen_combine = self._calc_obs(paren_combine)
#if failed_runs is not None:
# obsen_combine.loc[failed_runs,:] = np.NaN
self.logger.log("evaluating ensembles for lambdas : {0}".\
format(','.join(["{0:8.3E}".format(l) for l in lam_vals])))
paren_combine = None
if failed_runs is not None and len(
failed_runs) == obsen_combine.shape[0]:
self.logger.lraise("all runs failed - cannot continue")
# unpack lambda obs ensembles from combined obs ensemble
nrun_per_lam = self.obsensemble.shape[0]
if run_subset is not None:
nrun_per_lam = run_subset
obsen_lam = []
for i in range(len(lam_vals)):
sidx = i * nrun_per_lam
eidx = sidx + nrun_per_lam
oe = ObservationEnsemble.from_dataframe(
df=obsen_combine.iloc[sidx:eidx, :].copy(), pst=self.pst)
oe.index = subset_idx
# check for failed runs in this set - drop failed runs from obs ensembles
if failed_runs is not None:
failed_runs_this = np.array(
[f for f in failed_runs if f >= sidx and f < eidx]) - sidx
if len(failed_runs_this) > 0:
if len(failed_runs_this) == oe.shape[0]:
self.logger.warn(
"all runs failed for lambda {0}".format(
lam_vals[i]))
else:
self.logger.warn("{0} run failed for lambda {1}".\
format(len(failed_runs_this),lam_vals[i]))
oe.iloc[failed_runs_this, :] = np.NaN
oe = oe.dropna()
obsen_lam.append(oe)
obsen_combine = None
# here is where we need to select out the "best" lambda par and obs
# ensembles
self.logger.statement("\n**************************")
self.logger.statement(str(datetime.now()))
self.logger.statement("total runs:{0}".format(self.total_runs))
self.logger.statement("iteration: {0}".format(self.iter_num))
self.logger.statement("current lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda,
self.last_best_mean,self.last_best_std))
phi_vecs = [self._calc_phi_vec(obsen) for obsen in obsen_lam]
mean_std = [(pv.mean(), pv.std()) for pv in phi_vecs]
update_pars = False
update_lambda = False
# accept a new best if its within 10%
best_mean = self.last_best_mean * 1.1
best_std = self.last_best_std * 1.1
best_i = 0
for i, (m, s) in enumerate(mean_std):
self.logger.statement(" tested lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda * lambda_mults[i],m,s))
if m < best_mean:
update_pars = True
best_mean = m
best_i = i
if s < best_std:
update_lambda = True
best_std = s
if np.isnan(best_mean):
self.logger.lraise("best mean = NaN")
if np.isnan(best_std):
self.logger.lraise("best std = NaN")
if not update_pars:
self.current_lambda *= max(lambda_mults) * 10.0
self.current_lambda = min(self.current_lambda, 100000)
self.logger.statement("not accepting iteration, increased lambda:{0}".\
format(self.current_lambda))
else:
self.parensemble = ParameterEnsemble.from_dataframe(
df=paren_lam[best_i], pst=self.pst)
if run_subset is not None:
failed_runs, self.obsensemble = self._calc_obs(
self.parensemble)
if failed_runs is not None:
self.logger.warn("dropping failed realizations")
self.parensemble = self.parensemble.drop(failed_runs)
self.obsensemble = self.obsensemble.drop(failed_runs)
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec,
self.current_lambda * lambda_mults[best_i])
best_mean = self.current_phi_vec.mean()
best_std = self.current_phi_vec.std()
else:
self.obsensemble = obsen_lam[best_i]
# reindex parensemble in case failed runs
self.parensemble = ParameterEnsemble.from_dataframe(
df=self.parensemble.loc[self.obsensemble.index],
pst=self.pst)
self._phi_report(phi_vecs[best_i],
self.current_lambda * lambda_mults[best_i])
self.current_phi_vec = phi_vecs[best_i]
self.logger.statement(" best lambda:{0:15.6G}, mean:{1:15.6G}, std:{2:15.6G}".\
format(self.current_lambda*lambda_mults[best_i],
best_mean,best_std))
self.last_best_mean = best_mean
self.last_best_std = best_std
if update_lambda:
# be aggressive
self.current_lambda *= (lambda_mults[best_i] * 0.75)
# but don't let lambda get too small
self.current_lambda = max(self.current_lambda, 0.001)
self.logger.statement("updating lambda: {0:15.6G}".\
format(self.current_lambda ))
self.logger.statement("**************************\n")
self.parensemble.to_csv(self.pst.filename+self.paren_prefix.\
format(self.iter_num))
self.obsensemble.to_csv(self.pst.filename+self.obsen_prefix.\
format(self.iter_num))
if self.raw_sweep_out is not None:
self.raw_sweep_out.to_csv(self.pst.filename+"_raw{0}".\
format(self.iter_num))
self.logger.log("iteration {0}".format(self.iter_num))
class MonteCarlo(LinearAnalysis):
"""LinearAnalysis derived type for monte carlo analysis
Parameters
----------
**kwargs : dict
dictionary of keyword arguments. See pyemu.LinearAnalysis for
complete definitions
Attributes
----------
parensemble : pyemu.ParameterEnsemble
pyemu object derived from a pandas dataframe, the ensemble
of parameters from the PEST control file with associated
starting value and bounds. Object also exposes methods
relevant to the dataframe and parameters-- see documentation.
obsensemble : pyemu.ObservationEnsemble
pyemu object derived from a pandas dataframe, the ensemble
of observations from the PEST control file with associated
starting weights. Object also exposes methods
relevant to the dataframe and observations-- see documentation.
Returns
-------
MonteCarlo
pyEMU MonteCarlo object
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
"""
def __init__(self, **kwargs):
warnings.warn(
"pyemu.MonteCarlo class is deprecated. " +
"Please use the ensemble classes directly",
PyemuWarning,
)
super(MonteCarlo, self).__init__(**kwargs)
assert self.pst is not None, "monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
@property
def num_reals(self):
""" get the number of realizations in the parameter ensemble
Returns
-------
num_real : int
"""
return self.parensemble.shape[0]
def get_nsing(self, epsilon=1.0e-4):
""" get the number of solution space dimensions given
a ratio between the largest and smallest singular values
Parameters
----------
epsilon: float
singular value ratio
Returns
-------
nsing : float
number of singular components above the epsilon ratio threshold
Note
-----
If nsing == nadj_par, then None is returned
"""
mx = self.xtqx.shape[0]
nsing = mx - np.searchsorted(
np.sort((self.xtqx.s.x / self.xtqx.s.x.max())[:, 0]), epsilon)
if nsing == mx:
self.logger.warn("optimal nsing=npar")
nsing = None
return nsing
def get_null_proj(self, nsing=None):
""" get a null-space projection matrix of XTQX
Parameters
----------
nsing: int
optional number of singular components to use
If Nonte, then nsing is determined from
call to MonteCarlo.get_nsing()
Returns
-------
v2_proj : pyemu.Matrix
the null-space projection matrix (V2V2^T)
"""
if nsing is None:
nsing = self.get_nsing()
if nsing is None:
raise Exception("nsing is None")
print("using {0} singular components".format(nsing))
self.log(
"forming null space projection matrix with " +
"{0} of {1} singular components".format(nsing, self.jco.shape[1]))
v2_proj = self.xtqx.v[:, nsing:] * self.xtqx.v[:, nsing:].T
self.log(
"forming null space projection matrix with " +
"{0} of {1} singular components".format(nsing, self.jco.shape[1]))
return v2_proj
def draw(
self,
num_reals=1,
par_file=None,
obs=False,
enforce_bounds=None,
cov=None,
how="gaussian",
):
"""draw stochastic realizations of parameters and
optionally observations, filling MonteCarlo.parensemble and
optionally MonteCarlo.obsensemble.
Parameters
----------
num_reals : int
number of realization to generate
par_file : str
parameter file to use as mean values. If None,
use MonteCarlo.pst.parameter_data.parval1.
Default is None
obs : bool
add a realization of measurement noise to observation values,
forming MonteCarlo.obsensemble.Default is False
enforce_bounds : str
enforce parameter bounds based on control file information.
options are 'reset', 'drop' or None. Default is None
how : str
type of distribution to draw from. Must be in ["gaussian","uniform"]
default is "gaussian".
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
``>>>mc.draw(1000)``
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian", "uniform"]
if cov is not None:
assert isinstance(cov, Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.log("generating {0:d} parameter realizations".format(num_reals))
if how == "gaussian":
self.parensemble = ParameterEnsemble.from_gaussian_draw(
pst=self.pst,
cov=cov,
num_reals=num_reals,
use_homegrown=True,
enforce_bounds=False,
)
elif how == "uniform":
self.parensemble = ParameterEnsemble.from_uniform_draw(
pst=self.pst, num_reals=num_reals)
else:
raise Exception(
"MonteCarlo.draw(): unrecognized 'how' arg: {0}".format(how))
# self.parensemble = ParameterEnsemble(pst=self.pst)
# self.obsensemble = ObservationEnsemble(pst=self.pst)
# self.parensemble.draw(cov,num_reals=num_reals, how=how,
# enforce_bounds=enforce_bounds)
if enforce_bounds is not None:
self.parensemble.enforce(enforce_bounds)
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log(
"generating {0:d} observation realizations".format(num_reals))
self.obsensemble = ObservationEnsemble.from_id_gaussian_draw(
pst=self.pst, num_reals=num_reals)
self.log(
"generating {0:d} observation realizations".format(num_reals))
def project_parensemble(self,
par_file=None,
nsing=None,
inplace=True,
enforce_bounds="reset"):
""" perform the null-space projection operations for null-space monte carlo
Parameters
----------
par_file: str
an optional file of parameter values to use
nsing: int
number of singular values to in forming null subspace matrix
inplace: bool
overwrite the existing parameter ensemble with the
projected values
enforce_bounds: str
how to enforce parameter bounds. can be None, 'reset', or 'drop'.
Default is None
Returns
-------
par_en : pyemu.ParameterEnsemble
if inplace is False, otherwise None
Note
----
to use this method, the MonteCarlo instance must have been constructed
with the ``jco`` argument.
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(jco="pest.jcb")``
``>>>mc.draw(1000)``
``>>>mc.project_parensemble(par_file="final.par",nsing=100)``
"""
assert self.jco is not None, ("MonteCarlo.project_parensemble()" +
"requires a jacobian attribute")
if par_file is not None:
assert os.path.exists(par_file), (
"monte_carlo.draw() error: par_file not found:" + par_file)
self.parensemble.pst.parrep(par_file)
# project the ensemble
self.log("projecting parameter ensemble")
en = self.parensemble.project(self.get_null_proj(nsing),
inplace=inplace,
log=self.log)
self.log("projecting parameter ensemble")
return en
def write_psts(self, prefix, existing_jco=None, noptmax=None):
""" write parameter and optionally observation realizations
to a series of pest control files
Parameters
----------
prefix: str
pest control file prefix
existing_jco: str
filename of an existing jacobian matrix to add to the
pest++ options in the control file. This is useful for
NSMC since this jco can be used to get the first set of
parameter upgrades for free! Needs to be the path the jco
file as seen from the location where pest++ will be run
noptmax: int
value of NOPTMAX to set in new pest control files
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(jco="pest.jcb")``
``>>>mc.draw(1000, obs=True)``
``>>>mc.write_psts("mc_", existing_jco="pest.jcb", noptmax=1)``
"""
self.log("writing realized pest control files")
# get a copy of the pest control file
pst = self.pst.get(par_names=self.pst.par_names,
obs_names=self.pst.obs_names)
if noptmax is not None:
pst.control_data.noptmax = noptmax
pst.control_data.noptmax = noptmax
if existing_jco is not None:
pst.pestpp_options["BASE_JACOBIAN"] = existing_jco
# set the indices
pst.parameter_data.index = pst.parameter_data.parnme
pst.observation_data.index = pst.observation_data.obsnme
if self.parensemble.istransformed:
par_en = self.parensemble._back_transform(inplace=False)
else:
par_en = self.parensemble
for i in range(self.num_reals):
pst_name = prefix + "{0:d}.pst".format(i)
self.log("writing realized pest control file " + pst_name)
pst.parameter_data.loc[par_en.columns,
"parval1"] = par_en.iloc[i, :].T
# reset the regularization
# if pst.control_data.pestmode == "regularization":
# pst.zero_order_tikhonov(parbounds=True)
# zero_order_tikhonov(pst,parbounds=True)
# add the obs noise realization if needed
if self.obsensemble.shape[0] == self.num_reals:
pst.observation_data.loc[self.obsensemble.columns,
"obsval"] = self.obsensemble.iloc[
i, :].T
# write
pst.write(pst_name)
self.log("writing realized pest control file " + pst_name)
self.log("writing realized pest control files")
class MonteCarlo(LinearAnalysis):
"""LinearAnalysis derived type for monte carlo analysis
Note: requires a pest control file, which can be
derived from a jco argument
MonteCarlo.project_parsensemble also
requires a jacobian
"""
def __init__(self,**kwargs):
super(MonteCarlo,self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
@property
def num_reals(self):
return self.parensemble.shape[0]
def get_nsing(self,epsilon=1.0e-6):
""" get the number of solution space dimensions given
a machine floating point precision (epsilon)
Parameters:
epsilon: machine floating point precision
Returns : integer
number of singular components above the epsilon ratio threshold
"""
nsing = self.xtqx.shape[0] - np.searchsorted(
np.sort((self.xtqx.s.x / self.xtqx.s.x.max())[:,0]),epsilon)
return nsing
def get_null_proj(self,nsing=None):
""" get a null-space projection matrix of XTQX
Parameters:
----------
nsing: optional number of singular components to use
if none, call self.get_nsing()
Returns:
-------
Matrix instance : V2V2^T
"""
if nsing is None:
nsing = self.get_nsing()
v2_proj = (self.xtqx.v[:,nsing:] * self.xtqx.v[:,nsing:].T)
#v2_proj = (self.qhalfx.v[:,nsing:] * self.qhalfx.v[:,nsing:].T)
#self.__parcov = self.parcov.identity
return v2_proj
def draw(self, num_reals=1, par_file = None, obs=False,
enforce_bounds=False,cov=None):
"""draw stochastic realizations of parameters and
optionally observations
Parameters:
----------
num_reals (int): number of realization to generate
par_file (str): parameter file to use as mean values
obs (bool): add a realization of measurement noise to obs
enforce_bounds (bool): enforce parameter bounds in control file
Returns:
None
Raises:
None
"""
if par_file is not None:
self.pst.parrep(par_file)
if cov is not None:
assert isinstance(cov,Cov)
else:
cov = self.parcov
self.log("generating {0:d} parameter realizations".format(num_reals))
self.parensemble.draw(cov,num_reals=num_reals)
if enforce_bounds:
self.parensemble.enforce()
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log("generating {0:d} observation realizations".format(num_reals))
self.obsensemble.draw(self.obscov,num_reals=num_reals)
self.log("generating {0:d} observation realizations".format(num_reals))
def project_parensemble(self,par_file=None,nsing=None,
inplace=True):
""" perform the null-space projection operations for null-space monte carlo
Parameters:
par_file: str
an optional file of parameter values to use
nsing: int
number of singular values to in forming null subspace matrix
inplace: bool
overwrite the existing parameter ensemble with the
projected values
Returns:
-------
if inplace is False, ParameterEnsemble instance, otherwise None
"""
assert self.jco is not None,"MonteCarlo.project_parensemble()" +\
"requires a jacobian attribute"
if par_file is not None:
assert os.path.exists(par_file),"monte_carlo.draw() error: par_file not found:" +\
par_file
self.parensemble.pst.parrep(par_file)
# project the ensemble
self.log("projecting parameter ensemble")
en = self.parensemble.project(self.get_null_proj(nsing),inplace=inplace,log=self.log)
self.log("projecting parameter ensemble")
return en
def write_psts(self,prefix):
""" write parameter and optionally observation realizations
to pest control files
Parameters:
----------
prefix: str
pest control file prefix
Returns:
-------
None
"""
self.log("writing realized pest control files")
# get a copy of the pest control file
pst = self.pst.get(par_names=self.pst.par_names,obs_names=self.pst.obs_names)
# set the indices
pst.parameter_data.index = pst.parameter_data.parnme
pst.observation_data.index = pst.observation_data.obsnme
if self.parensemble.islog:
par_en = self.parensemble._back_transform(inplace=False)
else:
par_en = self.parensemble
for i in range(self.num_reals):
pst_name = prefix + "{0:d}.pst".format(i)
self.log("writing realized pest control file " + pst_name)
pst.parameter_data.loc[par_en.columns,"parval1"] = par_en.iloc[i, :].T
if self.obsensemble.shape[0] == self.num_reals:
pst.observation_data.loc[self.obsensemble.columns,"obsval"] = \
self.obsensemble.iloc[i, :].T
pst.write(pst_name)
self.log("writing realized pest control file " + pst_name)
self.log("writing realized pest control files")
def draw(self, num_reals=1, par_file = None, obs=False,
enforce_bounds=None, cov=None, how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations, filling MonteCarlo.parensemble and
optionally MonteCarlo.obsensemble.
Parameters
----------
num_reals : int
number of realization to generate
par_file : str
parameter file to use as mean values. If None,
use MonteCarlo.pst.parameter_data.parval1.
Default is None
obs : bool
add a realization of measurement noise to observation values,
forming MonteCarlo.obsensemble.Default is False
enforce_bounds : str
enforce parameter bounds based on control file information.
options are 'reset', 'drop' or None. Default is None
how : str
type of distribution to draw from. Must be in ["gaussian","uniform"]
default is "gaussian".
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
``>>>mc.draw(1000)``
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian","uniform"]
if cov is not None:
assert isinstance(cov,Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.log("generating {0:d} parameter realizations".format(num_reals))
if how == "gaussian":
self.parensemble = ParameterEnsemble.from_gaussian_draw(pst=self.pst,cov=cov,
num_reals=num_reals,
use_homegrown=True)
elif how == "uniform":
self.parensemble = ParameterEnsemble.from_uniform_draw(pst=self.pst,num_reals=num_reals)
else:
raise Exception("MonteCarlo.draw(): unrecognized 'how' arg: {0}".format(how))
#self.parensemble = ParameterEnsemble(pst=self.pst)
#self.obsensemble = ObservationEnsemble(pst=self.pst)
#self.parensemble.draw(cov,num_reals=num_reals, how=how,
# enforce_bounds=enforce_bounds)
if enforce_bounds is not None:
self.parensemble.enforce(enforce_bounds)
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log("generating {0:d} observation realizations".format(num_reals))
self.obsensemble = ObservationEnsemble.from_id_gaussian_draw(pst=self.pst,num_reals=num_reals)
self.log("generating {0:d} observation realizations".format(num_reals))
class MonteCarlo(LinearAnalysis):
"""LinearAnalysis derived type for monte carlo analysis
Parameters
----------
**kwargs : dict
dictionary of keyword arguments. See pyemu.LinearAnalysis for
complete definitions
Attributes
----------
parensemble : pyemu.ParameterEnsemble
obsensemble : pyemu.ObservationEnsemble
Returns
-------
MonteCarlo : MonteCarlo
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
"""
def __init__(self,**kwargs):
super(MonteCarlo,self).__init__(**kwargs)
assert self.pst is not None, \
"monte carlo requires a pest control file"
self.parensemble = ParameterEnsemble(pst=self.pst)
self.obsensemble = ObservationEnsemble(pst=self.pst)
@property
def num_reals(self):
""" get the number of realizations in the parameter ensemble
Returns
-------
num_real : int
"""
return self.parensemble.shape[0]
def get_nsing(self,epsilon=1.0e-4):
""" get the number of solution space dimensions given
a ratio between the largest and smallest singular
values
Parameters
----------
epsilon: float
singular value ratio
Returns
-------
nsing : float
number of singular components above the epsilon ratio threshold
Note
-----
If nsing == nadj_par, then None is returned
"""
mx = self.xtqx.shape[0]
nsing = mx - np.searchsorted(
np.sort((self.xtqx.s.x / self.xtqx.s.x.max())[:,0]),epsilon)
if nsing == mx:
self.logger.warn("optimal nsing=npar")
nsing = None
return nsing
def get_null_proj(self,nsing=None):
""" get a null-space projection matrix of XTQX
Parameters
----------
nsing: int
optional number of singular components to use
If Nonte, then nsing is determined from
call to MonteCarlo.get_nsing()
Returns
-------
v2_proj : pyemu.Matrix
the null-space projection matrix (V2V2^T)
"""
if nsing is None:
nsing = self.get_nsing()
if nsing is None:
raise Exception("nsing is None")
print("using {0} singular components".format(nsing))
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
v2_proj = (self.xtqx.v[:,nsing:] * self.xtqx.v[:,nsing:].T)
self.log("forming null space projection matrix with " +\
"{0} of {1} singular components".format(nsing,self.jco.shape[1]))
return v2_proj
def draw(self, num_reals=1, par_file = None, obs=False,
enforce_bounds=None, cov=None, how="gaussian"):
"""draw stochastic realizations of parameters and
optionally observations, filling MonteCarlo.parensemble and
optionally MonteCarlo.obsensemble.
Parameters
----------
num_reals : int
number of realization to generate
par_file : str
parameter file to use as mean values. If None,
use MonteCarlo.pst.parameter_data.parval1.
Default is None
obs : bool
add a realization of measurement noise to observation values,
forming MonteCarlo.obsensemble.Default is False
enforce_bounds : str
enforce parameter bounds based on control file information.
options are 'reset', 'drop' or None. Default is None
how : str
type of distribution to draw from. Must be in ["gaussian","uniform"]
default is "gaussian".
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(pst="pest.pst")``
``>>>mc.draw(1000)``
"""
if par_file is not None:
self.pst.parrep(par_file)
how = how.lower().strip()
assert how in ["gaussian","uniform"]
if cov is not None:
assert isinstance(cov,Cov)
if how == "uniform":
raise Exception("MonteCarlo.draw() error: 'how'='uniform'," +\
" 'cov' arg cannot be passed")
else:
cov = self.parcov
self.log("generating {0:d} parameter realizations".format(num_reals))
if how == "gaussian":
self.parensemble = ParameterEnsemble.from_gaussian_draw(pst=self.pst,cov=cov,
num_reals=num_reals,
use_homegrown=True)
elif how == "uniform":
self.parensemble = ParameterEnsemble.from_uniform_draw(pst=self.pst,num_reals=num_reals)
else:
raise Exception("MonteCarlo.draw(): unrecognized 'how' arg: {0}".format(how))
#self.parensemble = ParameterEnsemble(pst=self.pst)
#self.obsensemble = ObservationEnsemble(pst=self.pst)
#self.parensemble.draw(cov,num_reals=num_reals, how=how,
# enforce_bounds=enforce_bounds)
if enforce_bounds is not None:
self.parensemble.enforce(enforce_bounds)
self.log("generating {0:d} parameter realizations".format(num_reals))
if obs:
self.log("generating {0:d} observation realizations".format(num_reals))
self.obsensemble = ObservationEnsemble.from_id_gaussian_draw(pst=self.pst,num_reals=num_reals)
self.log("generating {0:d} observation realizations".format(num_reals))
def project_parensemble(self,par_file=None,nsing=None,
inplace=True,enforce_bounds='reset'):
""" perform the null-space projection operations for null-space monte carlo
Parameters
----------
par_file: str
an optional file of parameter values to use
nsing: int
number of singular values to in forming null subspace matrix
inplace: bool
overwrite the existing parameter ensemble with the
projected values
enforce_bounds: str
how to enforce parameter bounds. can be None, 'reset', or 'drop'.
Default is None
Returns
-------
par_en : pyemu.ParameterEnsemble
if inplace is False, otherwise None
Note
----
to use this method, the MonteCarlo instance must have been constructed
with the ``jco`` argument.
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(jco="pest.jcb")``
``>>>mc.draw(1000)``
``>>>mc.project_parensemble(par_file="final.par",nsing=100)``
"""
assert self.jco is not None,"MonteCarlo.project_parensemble()" +\
"requires a jacobian attribute"
if par_file is not None:
assert os.path.exists(par_file),"monte_carlo.draw() error: par_file not found:" +\
par_file
self.parensemble.pst.parrep(par_file)
# project the ensemble
self.log("projecting parameter ensemble")
en = self.parensemble.project(self.get_null_proj(nsing),inplace=inplace,log=self.log)
self.log("projecting parameter ensemble")
return en
def write_psts(self,prefix,existing_jco=None,noptmax=None):
""" write parameter and optionally observation realizations
to a series of pest control files
Parameters
----------
prefix: str
pest control file prefix
existing_jco: str
filename of an existing jacobian matrix to add to the
pest++ options in the control file. This is useful for
NSMC since this jco can be used to get the first set of
parameter upgrades for free! Needs to be the path the jco
file as seen from the location where pest++ will be run
noptmax: int
value of NOPTMAX to set in new pest control files
Example
-------
``>>>import pyemu``
``>>>mc = pyemu.MonteCarlo(jco="pest.jcb")``
``>>>mc.draw(1000, obs=True)``
``>>>mc.write_psts("mc_", existing_jco="pest.jcb", noptmax=1)``
"""
self.log("writing realized pest control files")
# get a copy of the pest control file
pst = self.pst.get(par_names=self.pst.par_names,obs_names=self.pst.obs_names)
if noptmax is not None:
pst.control_data.noptmax = noptmax
pst.control_data.noptmax = noptmax
if existing_jco is not None:
pst.pestpp_options["BASE_JACOBIAN"] = existing_jco
# set the indices
pst.parameter_data.index = pst.parameter_data.parnme
pst.observation_data.index = pst.observation_data.obsnme
if self.parensemble.istransformed:
par_en = self.parensemble._back_transform(inplace=False)
else:
par_en = self.parensemble
for i in range(self.num_reals):
pst_name = prefix + "{0:d}.pst".format(i)
self.log("writing realized pest control file " + pst_name)
pst.parameter_data.loc[par_en.columns,"parval1"] = par_en.iloc[i, :].T
# reset the regularization
#if pst.control_data.pestmode == "regularization":
#pst.zero_order_tikhonov(parbounds=True)
#zero_order_tikhonov(pst,parbounds=True)
# add the obs noise realization if needed
if self.obsensemble.shape[0] == self.num_reals:
pst.observation_data.loc[self.obsensemble.columns,"obsval"] = \
self.obsensemble.iloc[i, :].T
# write
pst.write(pst_name)
self.log("writing realized pest control file " + pst_name)
self.log("writing realized pest control files")
def initialize(self,num_reals,init_lambda=None):
'''
(re)initialize the process
'''
assert num_reals > 1
# initialize the phi report csv
self.phi_csv = open(self.pst.filename+".iobj.csv",'w')
self.phi_csv.write("iter_num,total_runs,lambda,min,max,mean,median,std,")
self.phi_csv.write(','.join(["{0:010d}".\
format(i+1) for i in range(num_reals)]))
self.phi_csv.write('\n')
self.total_runs = 0
# this matrix gets used a lot, so only calc once and store
self.obscov_inv_sqrt = self.obscov.get(self.pst.nnz_obs_names).inv.sqrt
if self.restart:
print("restarting...ignoring num_reals")
raise NotImplementedError()
df = pd.read_csv(self.pst.filename+self.paren_prefix.format(self.restart_iter))
self.parensemble_0 = ParameterEnsemble.from_dataframe(df=df,pst=self.pst)
self.parensemble = self.parensemble_0.copy()
df = pd.read_csv(self.pst.filename+self.obsen_prefix.format(0))
self.obsensemble_0 = ObservationEnsemble.from_dataframe(df=df.loc[:,self.pst.nnz_obs_names],
pst=self.pst)
# this matrix gets used a lot, so only calc once
self.obs0_matrix = self.obsensemble_0.as_pyemu_matrix()
df = pd.read_csv(self.pst.filename+self.obsen_prefix.format(self.restart_iter))
self.obsensemble = ObservationEnsemble.from_dataframe(df=df.loc[:,self.pst.nnz_obs_names],
pst=self.pst)
assert self.parensemble.shape[0] == self.obsensemble.shape[0]
self.num_reals = self.parensemble.shape[0]
else:
self.num_reals = int(num_reals)
self.parensemble_0 = ParameterEnsemble(self.pst)
self.parensemble_0.draw(cov=self.parcov,num_reals=num_reals)
self.parensemble_0.enforce()
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv(self.pst.filename +\
self.paren_prefix.format(0))
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
#self.obsensemble = self.obsensemble_0.copy()
# save the base obsensemble
self.obsensemble_0.to_csv(self.pst.filename +\
self.obsen_prefix.format(-1))
self.obs0_matrix = self.obsensemble_0.nonzero.as_pyemu_matrix()
# run the initial parameter ensemble
self.obsensemble = self._calc_obs(self.parensemble)
self.obsensemble.to_csv(self.pst.filename +\
self.obsen_prefix.format(0))
self.current_phi_vec = self._calc_phi_vec(self.obsensemble)
self._phi_report(self.current_phi_vec,0.0)
self.last_best_mean = self.current_phi_vec.mean()
self.last_best_std = self.current_phi_vec.std()
if init_lambda is not None:
self.current_lambda = float(init_lambda)
else:
#following chen and oliver
x = self.last_best_mean / (2.0 * float(self.obsensemble.shape[1]))
self.current_lambda = 10.0**(np.floor(np.log10(x)))
# if using the approximate form of the algorithm, let
# the parameter scaling matrix be the identity matrix
# jwhite - dec 5 2016 - using the actual parcov inv
# for upgrades seems to be pushing parameters around
# too much. for now, just not using it, maybe
# better choices of lambda will tame it
if self.use_approx:
self.half_parcov_diag = 1.0
else:
# if self.parcov.isdiagonal:
# self.half_parcov_diag = self.parcov.sqrt.inv
# else:
# self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
# names=self.parcov.col_names,
# isdiagonal=True).inv.sqrt
self.half_parcov_diag = 1.0
self.delta_par_prior = self._calc_delta_par(self.parensemble_0)
u,s,v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
class EnsembleSmoother():
def __init__(self,pst,parcov=None,obscov=None):
if isinstance(pst,str):
pst = Pst(pst)
assert isinstance(pst,Pst)
self.pst = pst
if parcov is not None:
assert isinstance(parcov,Cov)
else:
parcov = Cov.from_parameter_data(self.pst)
if obscov is not None:
assert isinstance(obscov,Cov)
else:
obscov = Cov.from_observation_data(pst)
self.parcov = parcov
self.obscov = obscov
self.__initialized = False
self.num_reals = 0
self.half_parcov_diag = None
self.half_obscov_diag = None
self.delta_par_prior = None
self.iter_num = 0
def initialize(self,num_reals):
'''
(re)initialize the process
'''
self.num_reals = int(num_reals)
self.parensemble_0 = ParameterEnsemble(self.pst)
self.parensemble_0.draw(cov=self.parcov,num_reals=num_reals)
self.parensemble = self.parensemble_0.copy()
self.parensemble_0.to_csv("parensemble.0.csv")
self.obsensemble_0 = ObservationEnsemble(self.pst)
self.obsensemble_0.draw(cov=self.obscov,num_reals=num_reals)
self.obsensemble = self.obsensemble_0.copy()
self.obsensemble_0.to_csv("obsensemble.0.csv")
if self.parcov.isdiagonal:
self.half_parcov_diag = self.parcov.inv.sqrt
else:
self.half_parcov_diag = Cov(x=np.diag(self.parcov.x),
names=self.parcov.col_names,
isdiagonal=True).inv.sqrt
#if self.obscov.isdiagonal:
#self.half_obscov_inv = self.obscov.inv.sqrt
# else:
# self.half_obscov_diag = Cov(x=np.diag(self.obscov.x),
# names=self.obscov.col_names,
# isdiagonal=True)
self.delta_par_prior = self._calc_delta_par()
u,s,v = self.delta_par_prior.pseudo_inv_components()
self.Am = u * s.inv
self.__initialized = True
def _calc_delta_par(self):
'''
calc the scaled parameter ensemble differences from the mean
'''
mean = np.array(self.parensemble.mean(axis=0))
delta = self.parensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i,:] -= mean
#delta = Matrix(x=(self.half_parcov_diag * delta.transpose()).x,
# row_names=self.parensemble.columns)
delta = self.half_parcov_diag * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_delta_obs(self):
'''
calc the scaled observation ensemble differences from the mean
'''
mean = np.array(self.obsensemble.mean(axis=0))
delta = self.obsensemble.as_pyemu_matrix()
for i in range(self.num_reals):
delta.x[i,:] -= mean
delta = self.obscov.inv.sqrt * delta.T
return delta * (1.0 / np.sqrt(float(self.num_reals - 1.0)))
def _calc_obs(self):
'''
propagate the ensemble forward...
'''
self.parensemble.to_csv(os.path.join("sweep_in.csv"))
#os.chdir("smoother")
print(os.listdir('.'))
os.system("sweep {0}".format(self.pst.filename))
#os.chdir('..')
obs = ObservationEnsemble.from_csv(os.path.join('sweep_out.csv'))
obs.columns = [item.lower() for item in obs.columns]
self.obsensemble = ObservationEnsemble.from_dataframe(df=obs.loc[:,self.obscov.row_names],pst=self.pst)
#todo: modifiy sweep to be interactive...
return
@property
def current_lambda(self):
return 1.0
def update(self):
if not self.__initialized:
raise Exception("must call initialize() before update()")
self._calc_obs()
self.iter_num += 1
self.obsensemble.to_csv("obsensemble.{0}.csv".format(self.iter_num))
delta_obs = self._calc_delta_obs()
u,s,v = delta_obs.pseudo_inv_components()
scaled_par_diff = self._calc_delta_par()
scaled_obs_diff = self.obsensemble.as_pyemu_matrix() -\
self.obsensemble_0.as_pyemu_matrix()
scaled_ident = (self.current_lambda*Cov.identity_like(s) + s**2).inv
x1 = u.T * self.obscov.inv.sqrt * scaled_obs_diff.T
x1.autoalign = False
x2 = scaled_ident * x1
x3 = v * s * x2
upgrade_1 = -1.0 * (self.half_parcov_diag * scaled_par_diff *\
x3).to_dataframe()
upgrade_1.index.name = "parnme"
upgrade_1.T.to_csv("upgrade_1.{0}.csv".format(self.iter_num))
self.parensemble += upgrade_1.T
if self.iter_num > 1:
par_diff = (self.parensemble - self.parensemble_0).\
as_pyemu_matrix().T
x4 = self.Am.T * self.half_parcov_diag * par_diff
x5 = self.Am * x4
x6 = scaled_par_diff.T * x5
x7 = v * scaled_ident * v.T * x6
upgrade_2 = -1.0 * (self.half_parcov_diag *
scaled_par_diff * x7).to_dataframe()
upgrade_2.index.name = "parnme"
upgrade_2.T.to_csv("upgrade_2.{0}.csv".format(self.iter_num))
self.parensemble += upgrade_2.T
self.parensemble.to_csv("parensemble.{0}.csv".format(self.iter_num))
