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
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")
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")
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")
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")