def plot_id_bar_test():
import pyemu
import matplotlib.pyplot as plt
w_dir = "la"
ev = pyemu.ErrVar(jco=os.path.join(w_dir, "pest.jcb"))
id_df = ev.get_identifiability_dataframe(singular_value=15)
pyemu.plot_utils.plot_id_bar(id_df)
def predvar1b_test():
out_files = [os.path.join(verf_dir,f) for f in os.listdir(verf_dir) if f.endswith(".out") and "ident" not in f]
pv1b_results = {}
for out_file in out_files:
pred_name = os.path.split(out_file)[-1].split('.')[0]
f = open(out_file,'r')
for _ in range(3):
f.readline()
arr = np.loadtxt(f)
pv1b_results[pred_name] = arr
pst = pyemu.Pst(ord_base+".pst")
omitted_parameters = [pname for pname in pst.parameter_data.parnme if pname.startswith("wf")]
la_ord_errvar = pyemu.ErrVar(jco=ord_base+".jco",
predictions=predictions,
omitted_parameters=omitted_parameters,
verbose=False)
df = la_ord_errvar.get_errvar_dataframe(np.arange(36))
max_idx = 13
idx = np.arange(max_idx)
for ipred,pred in enumerate(predictions):
arr = pv1b_results[pred][:max_idx,:]
first = df[("first", pred)][:max_idx]
second = df[("second", pred)][:max_idx]
third = df[("third", pred)][:max_idx]
first_diff = (np.abs(arr[:,1] - first)).sum()
second_diff = (np.abs(arr[:,2] - second)).sum()
third_diff = (np.abs(arr[:,3] - third)).sum()
print(pred,first_diff,second_diff,third_diff)
assert first_diff < 1.5
assert second_diff < 1.5
assert third_diff < 1.5
def pnulpar_test():
import os
import pyemu
ev = pyemu.ErrVar(jco=os.path.join("mc","freyberg_ord.jco"))
ev.get_null_proj(maxsing=1).to_ascii("ev_new_proj.mat")
pst = ev.pst
par_dir = os.path.join("mc","prior_par_draws")
par_files = [os.path.join(par_dir,f) for f in os.listdir(par_dir) if f.endswith('.par')]
pe = pyemu.ParameterEnsemble.from_parfiles(pst=pst,parfile_names=par_files)
real_num = [int(os.path.split(f)[-1].split('.')[0].split('_')[1]) for f in par_files]
pe._df.index = real_num
pe_proj = pe.project(ev.get_null_proj(maxsing=1), enforce_bounds='reset')
par_dir = os.path.join("mc", "proj_par_draws")
par_files = [os.path.join(par_dir, f) for f in os.listdir(par_dir) if f.endswith('.par')]
real_num = [int(os.path.split(f)[-1].split('.')[0].split('_')[1]) for f in par_files]
pe_pnul = pyemu.ParameterEnsemble.from_parfiles(pst=pst,parfile_names=par_files)
pe_pnul._df.index = real_num
pe_proj._df.sort_index(axis=1, inplace=True)
pe_proj._df.sort_index(axis=0, inplace=True)
pe_pnul._df.sort_index(axis=1, inplace=True)
pe_pnul._df.sort_index(axis=0, inplace=True)
diff = 100.0 * ((pe_proj._df - pe_pnul._df) / pe_proj._df)
assert max(diff.max()) < 1.0e-4,diff
def ident_test():
idf = pd.read_csv(os.path.join(verf_dir,"ident.out"),delim_whitespace=True,index_col="parameter")
la_ord_errvar = pyemu.ErrVar(jco=ord_base+".jco",
predictions=predictions,
verbose=False)
df = la_ord_errvar.get_identifiability_dataframe(5)
for pname in idf.index:
ival = idf.loc[pname,"identifiability"]
val = df.loc[pname,"ident"]
diff = np.abs(ival - val)
print(pname,ival,val)
assert diff < 1.0E-3,"{0}:{1}".format(pname,diff)
def freyberg_verf_test():
import os
import pyemu
import numpy as np
import pandas as pd
wdir = os.path.join("..", "verification", "Freyberg")
post_pd7 = pyemu.Cov.from_ascii(os.path.join(wdir, "post.cov"))
sc = pyemu.Schur(os.path.join(wdir, "freyberg.jcb"))
post_pyemu = sc.posterior_parameter
diff = (post_pd7 - post_pyemu).to_dataframe()
diff = (diff / sc.pst.parameter_data.parval1 * 100.0).apply(np.abs)
print(diff.max().max())
assert diff.max().max() < 10.0
pd1_file = os.path.join(wdir, "predunc1_textable.dat")
names = ["forecasts", "pd1_pr", "py_pr", "pd1_pt", "py_pt"]
pd1_df = pd.read_csv(pd1_file, sep='&', header=None, names=names)
pd1_df.index = pd1_df.forecasts
fsum = sc.get_forecast_summary()
pd1_cols = ["pd1_pr", "pd1_pt"]
py_cols = ["prior_var", "post_var"]
forecasts = ["sw_gw_0", "sw_gw_1"]
for fname in forecasts:
for pd1_col, py_col in zip(pd1_cols, py_cols):
pd1_pr = pd1_df.loc[fname, pd1_col]
py_pr = np.sqrt(fsum.loc[fname, py_col])
assert np.abs(pd1_pr - py_pr) < 1.0e-1, "{0},{1}".format(
pd1_pr, py_pr)
out_files = [os.path.join(wdir,f) for f in os.listdir(wdir)\
if ".predvar1b.out" in f and f.split('.')[0] in forecasts]
print(out_files)
pv1b_results = {}
for out_file in out_files:
pred_name = os.path.split(out_file)[-1].split('.')[0]
f = open(out_file, 'r')
for _ in range(3):
f.readline()
arr = np.loadtxt(f)
pv1b_results[pred_name] = arr
omitted_parameters = [pname for pname in sc.pst.parameter_data.parnme if\
pname.startswith("wf")]
obs_names = sc.pst.nnz_obs_names
obs_names.extend(forecasts)
jco = pyemu.Jco.from_binary(os.path.join(wdir,"freyberg.jcb")).\
get(obs_names,sc.pst.adj_par_names)
ev = pyemu.ErrVar(jco=jco,
pst=sc.pst,
forecasts=forecasts,
omitted_parameters=omitted_parameters,
verbose=False)
#print(ev.jco.shape)
df = ev.get_errvar_dataframe(np.arange(36))
#print(df)
max_idx = 12
#print(pv1b_results.keys())
for ipred, pred in enumerate(forecasts):
arr = pv1b_results[pred][:max_idx, :]
first = df[("first", pred)][:max_idx]
second = df[("second", pred)][:max_idx]
third = df[("third", pred)][:max_idx]
#print(arr[:,1])
#print(first)
diff = np.abs(arr[:, 1] - first) / arr[:, 1] * 100.0
assert diff.max() < 0.01
diff = np.abs(arr[:, 2] - second) / arr[:, 1] * 100.0
assert diff.max() < 0.01
diff = np.abs(arr[:, 3] - third) / arr[:, 1] * 100.0
assert diff.max() < 0.01