def schur_test():
import os
import numpy as np
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..", "verification", "henry")
forecasts = ["pd_ten", "c_obs10_2"]
pst = Pst(os.path.join(w_dir, "pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_uncfile(os.path.join("temp", "pest.unc"), covmat_file=None)
cov2 = Cov.from_uncfile(os.path.join("temp", "pest.unc"))
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
parcov=cov2)
print(sc.prior_forecast)
print(sc.posterior_forecast)
print(sc.get_par_group_contribution())
df = sc.get_par_group_contribution(include_prior_results=True)
levels = list(df.columns.levels[1])
assert "prior" in levels, levels
assert "post" in levels, levels
print(sc.get_parameter_summary(include_map=True))
print(sc.get_forecast_summary(include_map=True))
print(sc.get_removed_obs_importance(reset_zero_weight=True))
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
sigma_range=6.0)
cov = Cov.from_parameter_data(pst, sigma_range=6.0)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
sigma_range=6.0,
scale_offset=False)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
pst.parameter_data.loc[:, "offset"] = 100.0
cov = Cov.from_parameter_data(pst)
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
pst=pst,
forecasts=forecasts,
sigma_range=6.0,
scale_offset=False)
assert np.abs((sc.parcov.x - cov.x).sum()) != 0.0
cov = Cov.from_parameter_data(pst, scale_offset=False, sigma_range=6.0)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
def gaussian_draw_test():
import os
import numpy as np
from pyemu import MonteCarlo,Cov,ParameterEnsemble
from datetime import datetime
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
num_reals = 100
start = datetime.now()
mc.draw(num_reals=num_reals,how="gaussian")
print(mc.parensemble.head())
print(datetime.now() - start)
vals = mc.pst.parameter_data.parval1.values
cov = Cov.from_parameter_data(mc.pst)
start = datetime.now()
val_array = np.random.multivariate_normal(vals, cov.as_2d,num_reals)
print(datetime.now() - start)
start = datetime.now()
pe = ParameterEnsemble.from_gaussian_draw(mc.pst,cov,num_reals=num_reals)
pet = pe._transform(inplace=False)
pe = pet._back_transform(inplace=False)
print(datetime.now() - start)
print(mc.parensemble.head())
print(pe.head())
def mc_test():
import os
import numpy as np
from pyemu import MonteCarlo, Cov
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
out_dir = os.path.join("mc")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
#write testing
mc = MonteCarlo(jco=jco,verbose=True,sigma_range=6)
cov = Cov.from_parameter_data(mc.pst,sigma_range=6)
assert np.abs((mc.parcov.x - cov.x).sum()) == 0.0
mc.draw(10,obs=True)
mc.write_psts(os.path.join("temp","real_"))
mc.parensemble.to_parfiles(os.path.join("mc","real_"))
mc = MonteCarlo(jco=jco,verbose=True)
mc.draw(10,obs=True)
print("prior ensemble variance:",
np.var(mc.parensemble.loc[:,"mult1"]))
projected_en = mc.project_parensemble(inplace=False)
print("projected ensemble variance:",
np.var(projected_en.loc[:,"mult1"]))
import pyemu
sc = pyemu.Schur(jco=jco)
mc = MonteCarlo(pst=pst,parcov=sc.posterior_parameter,verbose=True)
mc.draw(10)
print("posterior ensemble variance:",
np.var(mc.parensemble.loc[:,"mult1"]))
def schur_test():
import os
import numpy as np
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..","verification","henry")
forecasts = ["pd_ten","c_obs10_2"]
pst = Pst(os.path.join(w_dir,"pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_uncfile(os.path.join("temp","pest.unc"),covmat_file=None)
cov2 = Cov.from_uncfile(os.path.join("temp","pest.unc"))
sc = Schur(jco=os.path.join(w_dir,"pest.jcb"),
forecasts=forecasts,
parcov=cov2)
print(sc.prior_forecast)
print(sc.posterior_forecast)
print(sc.get_par_group_contribution())
print(sc.get_parameter_summary(include_map=True))
print(sc.get_forecast_summary(include_map=True))
print(sc.get_removed_obs_importance(reset_zero_weight=True))
sc = Schur(jco=os.path.join(w_dir,"pest.jcb"),
forecasts=forecasts,
sigma_range=6.0)
cov = Cov.from_parameter_data(pst,sigma_range=6.0)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
sigma_range=6.0,scale_offset=False)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
pst.parameter_data.loc[:,"offset"] = 100.0
cov = Cov.from_parameter_data(pst)
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
pst=pst,
forecasts=forecasts,
sigma_range=6.0, scale_offset=False)
assert np.abs((sc.parcov.x - cov.x).sum()) != 0.0
cov = Cov.from_parameter_data(pst,scale_offset=False,sigma_range=6.0)
assert np.abs((sc.parcov.x - cov.x).sum()) == 0.0
def la_test_io():
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..", "verification", "henry")
forecasts = ["pd_ten", "c_obs10_2"]
pst = Pst(os.path.join(w_dir, "pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_binary(os.path.join("temp", "pest.bin.cov"))
cov.to_ascii(os.path.join("temp", "pest.txt.cov"))
sc_bin = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
parcov=os.path.join("temp", "pest.bin.cov"))
sc_ascii = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
parcov=os.path.join("temp", "pest.txt.cov"))
def la_test_io():
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..","verification","henry")
forecasts = ["pd_ten","c_obs10_2"]
pst = Pst(os.path.join(w_dir,"pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_binary(os.path.join("temp","pest.bin.cov"))
cov.to_ascii(os.path.join("temp","pest.txt.cov"))
sc_bin = Schur(jco=os.path.join(w_dir,"pest.jcb"),
forecasts=forecasts,
parcov=os.path.join("temp","pest.bin.cov"))
sc_ascii = Schur(jco=os.path.join(w_dir,"pest.jcb"),
forecasts=forecasts,
parcov=os.path.join("temp","pest.txt.cov"))
def schur_test():
import os
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..", "verification", "henry")
forecasts = ["pd_ten", "c_obs10_2"]
pst = Pst(os.path.join(w_dir, "pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_uncfile(os.path.join("temp", "pest.unc"), covmat_file=None)
cov2 = Cov.from_uncfile(os.path.join("temp", "pest.unc"))
sc = Schur(jco=os.path.join(w_dir, "pest.jcb"),
forecasts=forecasts,
parcov=cov2)
print(sc.prior_forecast)
print(sc.posterior_forecast)
print(sc.get_par_group_contribution())
print(sc.get_removed_obs_group_importance())
def schur_test():
import os
from pyemu import Schur, Cov, Pst
w_dir = os.path.join("..","..","verification","henry")
forecasts = ["pd_ten","c_obs10_2"]
pst = Pst(os.path.join(w_dir,"pest.pst"))
cov = Cov.from_parameter_data(pst)
cov.to_uncfile(os.path.join("temp","pest.unc"),covmat_file=None)
cov2 = Cov.from_uncfile(os.path.join("temp","pest.unc"))
sc = Schur(jco=os.path.join(w_dir,"pest.jcb"),
forecasts=forecasts,
parcov=cov2)
print(sc.prior_forecast)
print(sc.posterior_forecast)
print(sc.get_par_group_contribution())
print(sc.get_removed_obs_group_importance())
def from_dataframe_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo,Ensemble,ParameterEnsemble,Pst, Cov
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
names = ["par_{0}".format(_) for _ in range(10)]
df = pd.DataFrame(np.random.random((10,mc.pst.npar)),columns=mc.pst.par_names)
mc.parensemble = ParameterEnsemble.from_dataframe(df=df,pst=mc.pst)
print(mc.parensemble.shape)
mc.project_parensemble()
mc.parensemble.to_csv(os.path.join("temp","test.csv"))
pstc = Pst(pst)
par = pstc.parameter_data
par.sort_values(by="parnme",ascending=False,inplace=True)
cov = Cov.from_parameter_data(pstc)
pe = ParameterEnsemble.from_gaussian_draw(pst=mc.pst,cov=cov)