def obs_unc(self):
if self.__obs_unc != None:
return self.__obs_unc
print "loading obs unc matrix"
if self.obs_uncfile is not None:
self.__obs_unc = mhand.uncert(self.nz_names)
self.__obs_unc.from_uncfile(self.obs_uncfile)
else:
self.__obs_unc = mhand.uncert(self.nz_names)
self.__obs_unc.from_obsweights(self.case + ".pst")
self.__diagonal_obs_unc = True
return self.__obs_unc
def obs_unc(self):
if self.__obs_unc != None:
return self.__obs_unc
print "loading obs unc matrix"
if self.obs_uncfile is not None:
self.__obs_unc = mhand.uncert(self.nz_names)
self.__obs_unc.from_uncfile(self.obs_uncfile)
else:
self.__obs_unc = mhand.uncert(self.nz_names)
self.__obs_unc.from_obsweights(self.case + ".pst")
self.__diagonal_obs_unc = True
return self.__obs_unc
def par_unc(self):
if self.__par_unc != None:
return self.__par_unc
print "loading paramter unc matrix"
self.__par_unc = mhand.uncert(self.nes_names)
if isinstance(self.par_uncfile, np.ndarray):
self.__par_unc.x = self.par_uncfile
elif self.par_uncfile is None:
self.__par_unc.x = np.diag(np.ones((self.jco.shape[1])))
elif self.par_uncfile.endswith("pst"):
self.__par_unc.from_parbounds(self.par_uncfile)
else:
self.__par_unc.from_uncfile(self.par_uncfile)
return self.__par_unc
def par_unc(self):
if self.__par_unc != None:
return self.__par_unc
print "loading paramter unc matrix"
self.__par_unc = mhand.uncert(self.nes_names)
if isinstance(self.par_uncfile, np.ndarray):
self.__par_unc.x = self.par_uncfile
elif self.par_uncfile is None:
self.__par_unc.x = np.diag(np.ones((self.jco.shape[1])))
elif self.par_uncfile.endswith("pst"):
self.__par_unc.from_parbounds(self.par_uncfile)
else:
self.__par_unc.from_uncfile(self.par_uncfile)
return self.__par_unc
args = ["misc\pp_locs.dat","0.0","misc\structure.dat","struct1","unc\cov.dat",'']
f = open(os.path.join("misc","ppcov.in"),'w')
f.write('\n'.join(args)+'\n')
f.close()
os.system("exe\ppcov.exe <misc\ppcov.in")
f = open(os.path.join("misc","par.info"),'r')
par_names = {}
for line in f:
pname = line.strip().split()[0]
group = pname.split('_')[0]
if group not in par_names.keys():
par_names[group] = []
par_names[group].append(pname)
cov = mhand.uncert([])
var_mult = {"k":1.0,"s":0.1,"sy":0.1}
cov.from_ascii(os.path.join("unc","cov.dat"))
f_unc = open(os.path.join("unc","unc.dat"),'w')
for group,names in par_names.iteritems():
#print names
cov.row_names = names
cov.col_names = names
cov.to_ascii(os.path.join("unc",group+".dat"))
f_unc.write("START COVARIANCE_MATRIX\n")
f_unc.write("FILE "+os.path.join("unc",group+".dat")+'\n')
f_unc.write("variance_multiplier {0:15.6E}\n".\
format(var_mult[group[:-2]]))
f_unc.write("END COVARIANCE_MATRIX\n\n")
f_unc.close()
''
]
f = open(os.path.join("misc", "ppcov.in"), 'w')
f.write('\n'.join(args) + '\n')
f.close()
os.system("exe\ppcov.exe <misc\ppcov.in")
f = open(os.path.join("misc", "par.info"), 'r')
par_names = {}
for line in f:
pname = line.strip().split()[0]
group = pname.split('_')[0]
if group not in par_names.keys():
par_names[group] = []
par_names[group].append(pname)
cov = mhand.uncert([])
var_mult = {"k": 1.0, "s": 0.1, "sy": 0.1}
cov.from_ascii(os.path.join("unc", "cov.dat"))
f_unc = open(os.path.join("unc", "unc.dat"), 'w')
for group, names in par_names.iteritems():
#print names
cov.row_names = names
cov.col_names = names
cov.to_ascii(os.path.join("unc", group + ".dat"))
f_unc.write("START COVARIANCE_MATRIX\n")
f_unc.write("FILE " + os.path.join("unc", group + ".dat") + '\n')
f_unc.write("variance_multiplier {0:15.6E}\n".\
format(var_mult[group[:-2]]))
f_unc.write("END COVARIANCE_MATRIX\n\n")
f_unc.close()
def main(in_filename,verbose=False):
#--load the parms from the infile
f = open(in_filename,'r')
pst_name = f.readline().strip()
jco_name = pst_name.replace('.pst','.jco')
ref_var = float(f.readline().strip())
unc_filename = f.readline().strip()
pv_list_file = f.readline().strip()
sing_file = f.readline().strip()
try:
struct_file = f.readline().strip()
except:
struct_file = None
f.close()
#pv_list_file = 'biweekly\\biweekly_scaled.list'
pv_dict = {}
pvec = mhand.matrix()
f = open(pv_list_file,'r')
for line in f:
if not line.startswith('#'):
raw = line.strip().split()
print 'loading pred vector',raw[0]
pvec.from_ascii(raw[0])
pv_dict[raw[1]] = copy.deepcopy(pvec.x)
f.close()
#sing_vals = np.loadtxt('singular_values.dat',dtype=np.int32)
sing_vals = np.loadtxt(sing_file,dtype=np.int32)
#sing_vals = [2]
print 'loading pest control file',pst_name
pst = phand.pst(pst_name)
print 'loading jco file',jco_name
jco = mhand.matrix()
jco.from_binary(jco_name)
print 'dropping zero-weight obs from jco'
drop_row_names = []
nz_weights,nz_names = [],[]
for name,weight in zip(pst.observation_data.obsnme,pst.observation_data.weight):
if weight == 0.0:
drop_row_names.append(name)
else:
nz_weights.append(weight)
nz_names.append(name)
jco.drop(drop_row_names)
print 'building obs cov matrix'
obs_unc = mhand.uncert(nz_names)
obs_unc.from_uncfile('simple_obs.unc')
print 'inverting obs cov matrix = q'
print 'and square root of q'
#q = np.linalg.inv(obs_unc.x)
#qhalf= linalg.sqrtm(q).real
u,s,vt = linalg.svd(obs_unc.x)
for i in range(u.shape[0]):
u[:,i] *= s[i]**(-0.5)
qhalf = np.dot(u,vt)
print 'build q^(1/2)X'
qX = np.dot(qhalf,jco.x)
if verbose:
np.savetxt('qX_py.mat',qX,fmt='%15.6E')
np.savetxt('qhalf_py.mat',qhalf,fmt='%15.6E')
np.savetxt('X_py.mat',jco.x,fmt='%15.6E')
jco_struct = None
spv_dict = None
if struct_file:
print 'loading structural parameter list'
spar_names,idxs = [],[]
#f = open('struct_pars.dat','r')
f = open(struct_file,'r')
for line in f:
name = line.strip()
if name in jco.col_names:
spar_names.append(name)
idxs.append(jco.col_names.index(name))
f.close()
print 'extracting omitted elements from q^(1/2)X'
jco_struct_q = qX[:,idxs]
jco_struct = jco.x[:,idxs]
qX = np.delete(qX,idxs,1)
X = np.delete(jco.x,idxs,1)
print 'forming omitted pred vectors'
spv_dict = {}
for out_name,pv in pv_dict.iteritems():
spv = np.atleast_2d(pv[idxs])
pv = np.atleast_2d(np.delete(pv,idxs)).transpose()
pv_dict[out_name] = pv
spv_dict[out_name] = spv
if 'halfscaled' in in_filename:
raise
print 'loading param uncert file for unscaled struct parameters'
unc = mhand.uncert(spar_names)
unc.from_uncfile('simple.unc')
#unc_struct = unc.x[idxs:idxs]
print unc_struct.shape
par_cov = None
struct_cov = None
if 'scaled' not in pst_name:
print 'building par cov matrix'
nspar_names = []
for name in jco.col_names:
if name not in spar_names:
nspar_names.append(name)
par_unc = mhand.uncert(nspar_names)
par_unc.from_uncfile('simple.unc')
if verbose:
np.savetxt('pycp.mat',par_unc.x,fmt='%15.6E')
print 'building structual par cov matrix'
struct_unc = mhand.uncert(spar_names)
struct_unc.from_uncfile('simple.unc')
par_cov = par_unc.x
struct_cov = struct_unc.x
pv = pvar.predvar(qX,pv_dict,sing_vals,verbose=verbose,jco_struct=jco_struct_q,\
pred_vectors_struct=spv_dict,struct_cov=struct_cov,par_cov=par_cov)
#pv = pvar.predvar(X,pv_dict,sing_vals,verbose=verbose,jco_struct=jco_struct,\
# pred_vectors_struct=spv_dict,struct_cov=struct_cov,par_cov=par_cov,obs_cov=obs_unc.x)
pv.calc()
def main(in_filename, verbose=False):
#--load the parms from the infile
f = open(in_filename, 'r')
pst_name = f.readline().strip()
jco_name = pst_name.replace('.pst', '.jco')
ref_var = float(f.readline().strip())
unc_filename = f.readline().strip()
pv_list_file = f.readline().strip()
sing_file = f.readline().strip()
try:
struct_file = f.readline().strip()
except:
struct_file = None
f.close()
#pv_list_file = 'biweekly\\biweekly_scaled.list'
pv_dict = {}
pvec = mhand.matrix()
f = open(pv_list_file, 'r')
for line in f:
if not line.startswith('#'):
raw = line.strip().split()
print 'loading pred vector', raw[0]
pvec.from_ascii(raw[0])
pv_dict[raw[1]] = copy.deepcopy(pvec.x)
f.close()
#sing_vals = np.loadtxt('singular_values.dat',dtype=np.int32)
sing_vals = np.loadtxt(sing_file, dtype=np.int32)
#sing_vals = [2]
print 'loading pest control file', pst_name
pst = phand.pst(pst_name)
print 'loading jco file', jco_name
jco = mhand.matrix()
jco.from_binary(jco_name)
print 'dropping zero-weight obs from jco'
drop_row_names = []
nz_weights, nz_names = [], []
for name, weight in zip(pst.observation_data.obsnme,
pst.observation_data.weight):
if weight == 0.0:
drop_row_names.append(name)
else:
nz_weights.append(weight)
nz_names.append(name)
jco.drop(drop_row_names)
print 'building obs cov matrix'
obs_unc = mhand.uncert(nz_names)
obs_unc.from_uncfile('simple_obs.unc')
print 'inverting obs cov matrix = q'
print 'and square root of q'
#q = np.linalg.inv(obs_unc.x)
#qhalf= linalg.sqrtm(q).real
u, s, vt = linalg.svd(obs_unc.x)
for i in range(u.shape[0]):
u[:, i] *= s[i]**(-0.5)
qhalf = np.dot(u, vt)
print 'build q^(1/2)X'
qX = np.dot(qhalf, jco.x)
if verbose:
np.savetxt('qX_py.mat', qX, fmt='%15.6E')
np.savetxt('qhalf_py.mat', qhalf, fmt='%15.6E')
np.savetxt('X_py.mat', jco.x, fmt='%15.6E')
jco_struct = None
spv_dict = None
if struct_file:
print 'loading structural parameter list'
spar_names, idxs = [], []
#f = open('struct_pars.dat','r')
f = open(struct_file, 'r')
for line in f:
name = line.strip()
if name in jco.col_names:
spar_names.append(name)
idxs.append(jco.col_names.index(name))
f.close()
print 'extracting omitted elements from q^(1/2)X'
jco_struct_q = qX[:, idxs]
jco_struct = jco.x[:, idxs]
qX = np.delete(qX, idxs, 1)
X = np.delete(jco.x, idxs, 1)
print 'forming omitted pred vectors'
spv_dict = {}
for out_name, pv in pv_dict.iteritems():
spv = np.atleast_2d(pv[idxs])
pv = np.atleast_2d(np.delete(pv, idxs)).transpose()
pv_dict[out_name] = pv
spv_dict[out_name] = spv
if 'halfscaled' in in_filename:
raise
print 'loading param uncert file for unscaled struct parameters'
unc = mhand.uncert(spar_names)
unc.from_uncfile('simple.unc')
#unc_struct = unc.x[idxs:idxs]
print unc_struct.shape
par_cov = None
struct_cov = None
if 'scaled' not in pst_name:
print 'building par cov matrix'
nspar_names = []
for name in jco.col_names:
if name not in spar_names:
nspar_names.append(name)
par_unc = mhand.uncert(nspar_names)
par_unc.from_uncfile('simple.unc')
if verbose:
np.savetxt('pycp.mat', par_unc.x, fmt='%15.6E')
print 'building structual par cov matrix'
struct_unc = mhand.uncert(spar_names)
struct_unc.from_uncfile('simple.unc')
par_cov = par_unc.x
struct_cov = struct_unc.x
pv = pvar.predvar(qX,pv_dict,sing_vals,verbose=verbose,jco_struct=jco_struct_q,\
pred_vectors_struct=spv_dict,struct_cov=struct_cov,par_cov=par_cov)
#pv = pvar.predvar(X,pv_dict,sing_vals,verbose=verbose,jco_struct=jco_struct,\
# pred_vectors_struct=spv_dict,struct_cov=struct_cov,par_cov=par_cov,obs_cov=obs_unc.x)
pv.calc()
