def tenpar():
import os
import numpy as np
import pyemu
os.chdir(os.path.join("smoother", "10par_xsec"))
csv_files = [f for f in os.listdir('.') if f.endswith(".csv")]
[os.remove(csv_file) for csv_file in csv_files]
es = pyemu.EnsembleSmoother("10par_xsec.pst",
num_slaves=5,
use_approx=True)
lz = es.get_localizer().to_dataframe()
#the k pars upgrad of h01_04 and h01_06 are localized
upgrad_pars = [pname for pname in lz.columns if "_" in pname and\
int(pname.split('_')[1]) > 4]
lz.loc["h01_04", upgrad_pars] = 0.0
upgrad_pars = [pname for pname in lz.columns if '_' in pname and \
int(pname.split('_')[1]) > 6]
lz.loc["h01_06", upgrad_pars] = 0.0
lz = pyemu.Matrix.from_dataframe(lz).T
print(lz)
es.initialize(num_reals=20)
for it in range(20):
es.update(lambda_mults=[0.1, 1.0, 10.0]) #,localizer=lz,run_subset=20)
#es.update(lambda_mults=[1.0])
os.chdir(os.path.join("..", ".."))
def chenoliver():
import os
import numpy as np
import pyemu
os.chdir(os.path.join("smoother", "chenoliver"))
csv_files = [
f for f in os.listdir('.') if f.endswith(".csv") and "bak" not in f
]
[os.remove(csv_file) for csv_file in csv_files]
parcov = pyemu.Cov(x=np.ones((1, 1)), names=["par"], isdiagonal=True)
pst = pyemu.Pst("chenoliver.pst")
obscov = pyemu.Cov(x=np.ones((1, 1)) * 16.0,
names=["obs"],
isdiagonal=True)
es = pyemu.EnsembleSmoother(pst,
parcov=parcov,
obscov=obscov,
num_slaves=20,
use_approx=False)
es.initialize(num_reals=100)
for it in range(40):
es.update()
os.chdir(os.path.join("..", ".."))
def freyberg_smoother_test():
import os
import pyemu
pst = pyemu.Pst(os.path.join("smoother","freyberg.pst"))
#mc = pyemu.MonteCarlo(pst=pst)
#mc.draw(2)
#print(mc.parensemble)
num_reals = 5
es = pyemu.EnsembleSmoother(pst)
es.initialize(num_reals)
es.update()
def henry():
import os
import pyemu
os.chdir(os.path.join("smoother", "henry_pc"))
csv_files = [f for f in os.listdir('.') if f.endswith(".csv")]
[os.remove(csv_file) for csv_file in csv_files]
pst = pyemu.Pst(os.path.join("henry.pst"))
es = pyemu.EnsembleSmoother(pst, num_slaves=15, use_approx=True)
es.initialize(210, init_lambda=1.0)
for i in range(10):
es.update(lambda_mults=[0.2,5.0],run_subset=45)
os.chdir(os.path.join("..", ".."))
def freyberg():
import os
import pandas as pd
import pyemu
os.chdir(os.path.join("smoother","freyberg"))
if not os.path.exists("freyberg.xy"):
import flopy
ml = flopy.modflow.Modflow.load("freyberg.nam",model_ws="template",
load_only=[])
xy = pd.DataFrame([(x,y) for x,y in zip(ml.sr.xcentergrid.flatten(),ml.sr.ycentergrid.flatten())],
columns=['x','y'])
names = []
for i in range(ml.nrow):
for j in range(ml.ncol ):
names.append("hkr{0:02d}c{1:02d}".format(i,j))
xy.loc[:,"name"] = names
xy.to_csv("freyberg.xy")
else:
xy = pd.read_csv("freyberg.xy")
csv_files = [f for f in os.listdir('.') if f.endswith(".csv")]
[os.remove(csv_file) for csv_file in csv_files]
pst = pyemu.Pst(os.path.join("freyberg.pst"))
es = pyemu.EnsembleSmoother(pst,num_slaves=20,use_approx=True)
nothk_names = [pname for pname in pst.adj_par_names if "hk" not in pname]
parcov_nothk = es.parcov.get(row_names=nothk_names)
gs = pyemu.utils.geostats.read_struct_file("structure.dat")
cov = gs.covariance_matrix(xy.x,xy.y,xy.name)
import matplotlib.pyplot as plt
plt.imshow(cov.x,interpolation="nearest")
plt.show()
return
#gs.variograms[0].a=10000
#gs.variograms[0].contribution=0.01
#gs.variograms[0].anisotropy = 10.0
pp_df = pyemu.utils.gw_utils.pp_file_to_dataframe("points1.dat")
parcov_hk = gs.covariance_matrix(pp_df.x,pp_df.y,pp_df.name)
parcov_full = parcov_hk.extend(parcov_rch)
es.initialize(300,init_lambda=5000.0)
for i in range(3):
es.update(lambda_mults=[0.2,5.0],run_subset=40)
os.chdir(os.path.join("..",".."))
def ies():
os.chdir(pyemu_dir)
es = pyemu.EnsembleSmoother("pest.pst",
verbose="ies.log",
save_mats=True,
slave_dir=os.path.join("..", "template"),
num_slaves=5)
es.initialize(parensemble="par.csv",
obsensemble="obs.csv",
restart_obsensemble="restart_obs.csv")
for i in range(es.pst.control_data.noptmax):
es.update(use_approx=False)
#es.update(lambda_mults=[0.1,1.0,# 10.0],run_subset=10)
#es.update(lambda_mults=[0.1,1.0,10.0],run_subset=10)
os.chdir('..')
def ies():
pst.control_data.noptmax = 1
ies = pyemu.EnsembleSmoother(pst=pst, verbose="ies.log")
ies.initialize(parensemble="par1.csv", obsensemble="obs1.csv")
ies.update(lambda_mults=[10., 1, 0.1])
ies.update(lambda_mults=[10., 1, 0.1])