def dataworth_test():
import os
import numpy as np
from pyemu import Schur
w_dir = os.path.join("..", "verification", "Freyberg")
forecasts = ["travel_time", "sw_gw_0", "sw_gw_1"]
sc = Schur(jco=os.path.join(w_dir, "freyberg.jcb"),
forecasts=forecasts,
verbose=True)
sc.pst.observation_data.index = sc.pst.observation_data.obsnme
base_obs = sc.pst.nnz_obs_names
zw_names = [
name for name in sc.pst.zero_weight_obs_names if name not in forecasts
]
#print(sc.get_removed_obs_importance(obslist_dict={"test":zw_names}))
oname = "or00c00_0"
names = {"test": oname}
added = sc.get_added_obs_importance(base_obslist=base_obs,
obslist_dict=names,
reset_zero_weight=True)
removed = sc.get_removed_obs_importance(obslist_dict=names)
print("removed", removed)
print("added", added)
for fname in forecasts:
diff = np.abs(removed.loc["test", fname] - added.loc["base", fname])
assert diff < 0.01, "{0},{1},{2}".format(fname, removed.loc["test",
fname],
added.loc["base", fname])
names = {"test1": oname, "test2": ["or00c00_1", "or00c00_2"]}
sc.next_most_important_added_obs(forecast="travel_time",
obslist_dict=names,
base_obslist=sc.pst.nnz_obs_names)
def dataworth_test():
import os
import numpy as np
from pyemu import Schur
w_dir = os.path.join("..","..","verification","Freyberg")
forecasts = ["travel_time","sw_gw_0","sw_gw_1"]
sc = Schur(jco=os.path.join(w_dir,"freyberg.jcb"),forecasts=forecasts,verbose=True)
sc.pst.observation_data.index = sc.pst.observation_data.obsnme
base_obs = sc.pst.nnz_obs_names
zw_names = [name for name in sc.pst.zero_weight_obs_names if name not in forecasts ]
#print(sc.get_removed_obs_importance(obslist_dict={"test":zw_names}))
oname = "or00c00_0"
names = {"test":oname}
added = sc.get_added_obs_importance(base_obslist=base_obs,
obslist_dict=names,reset_zero_weight=True)
removed = sc.get_removed_obs_importance(obslist_dict=names)
print("removed",removed)
print("added",added)
for fname in forecasts:
diff = np.abs(removed.loc["test",fname] - added.loc["base",fname])
assert diff < 0.01,"{0},{1},{2}".format(fname,removed.loc["test",fname],added.loc["base",fname])
names = {"test1":oname,"test2":["or00c00_1","or00c00_2"]}
sc.next_most_important_added_obs(forecast="travel_time",obslist_dict=names,
base_obslist=sc.pst.nnz_obs_names)
def schur_test_nonpest():
import numpy as np
from pyemu import Matrix, Cov, Schur, Jco
#non-pest
pnames = ["p1","p2","p3"]
onames = ["o1","o2","o3","o4"]
npar = len(pnames)
nobs = len(onames)
j_arr = np.random.random((nobs,npar))
jco = Jco(x=j_arr,row_names=onames,col_names=pnames)
parcov = Cov(x=np.eye(npar),names=pnames)
obscov = Cov(x=np.eye(nobs),names=onames)
forecasts = "o2"
s = Schur(jco=jco,parcov=parcov,obscov=obscov,forecasts=forecasts)
print(s.get_parameter_summary())
print(s.get_forecast_summary())
#this should fail
passed = False
try:
print(s.get_par_group_contribution())
passed = True
except Exception as e:
print(str(e))
if passed:
raise Exception("should have failed")
#this should fail
passed = False
try:
print(s.get_removed_obs_group_importance())
passed = True
except Exception as e:
print(str(e))
if passed:
raise Exception("should have failed")
print(s.get_par_contribution({"group1":["p1","p3"]}))
print(s.get_added_obs_importance({"group1": ["o1", "o3"]},reset_zero_weight=0.0))
print(s.get_removed_obs_importance({"group1":["o1","o3"]}))
forecasts = Matrix(x=np.random.random((1,npar)),row_names=[forecasts],col_names=pnames)
sc = Schur(jco=jco,forecasts=forecasts.T,parcov=parcov,obscov=obscov)
ffile = os.path.join("temp","forecasts.jcb")
forecasts.to_binary(ffile)
sc = Schur(jco=jco, forecasts=ffile, parcov=parcov, obscov=obscov)
def dataworth_next_test():
import os
import numpy as np
from pyemu import Schur
w_dir = os.path.join("..", "verification", "Freyberg")
#w_dir = os.path.join("..","..","examples","freyberg")
forecasts = ["sw_gw_0", "sw_gw_1"]
sc = Schur(jco=os.path.join(w_dir, "freyberg.jcb"),
forecasts=forecasts,
verbose=True)
next_test = sc.next_most_important_added_obs(
forecast="sw_gw_0",
base_obslist=sc.pst.nnz_obs_names,
obslist_dict={"test": sc.pst.nnz_obs_names})
# the returned dataframe should only have one row since the 'base' case
# should be the same as the 'test' case
assert next_test.shape[0] == 1
obs = sc.pst.observation_data
obs.index = obs.obsnme
row_groups = obs.groupby([
lambda x: x.startswith("or"), lambda x: x in sc.pst.nnz_obs_names,
lambda x: x[:4]
]).groups
obslist_dict = {}
for key, idxs in row_groups.items():
if not key[0] or key[1]:
continue
obslist_dict[key[2]] = list(idxs)
imp_df = sc.get_added_obs_importance(base_obslist=sc.pst.nnz_obs_names,
obslist_dict=obslist_dict,
reset_zero_weight=1.0)
next_test = sc.next_most_important_added_obs(
forecast="sw_gw_0",
base_obslist=sc.pst.nnz_obs_names,
obslist_dict=obslist_dict,
reset_zero_weight=1.0,
niter=4)
print(next_test)
print(imp_df.sort_index())
assert next_test.shape[0] == 4
def dataworth_next_test():
import os
import numpy as np
from pyemu import Schur
w_dir = os.path.join("..","..","verification","Freyberg")
#w_dir = os.path.join("..","..","examples","freyberg")
forecasts = ["sw_gw_0","sw_gw_1"]
sc = Schur(jco=os.path.join(w_dir,"freyberg.jcb"),forecasts=forecasts,verbose=True)
next_test = sc.next_most_important_added_obs(forecast="sw_gw_0",
base_obslist=sc.pst.nnz_obs_names,
obslist_dict={"test":sc.pst.nnz_obs_names})
# the returned dataframe should only have one row since the 'base' case
# should be the same as the 'test' case
assert next_test.shape[0] == 1
obs = sc.pst.observation_data
obs.index = obs.obsnme
row_groups = obs.groupby([lambda x: x.startswith("or"), lambda x: x in sc.pst.nnz_obs_names,
lambda x: x[:4]]).groups
obslist_dict = {}
for key,idxs in row_groups.items():
if not key[0] or key[1]:
continue
obslist_dict[key[2]] = idxs
imp_df = sc.get_added_obs_importance(base_obslist=sc.pst.nnz_obs_names,
obslist_dict=obslist_dict,
reset_zero_weight=1.0)
next_test = sc.next_most_important_added_obs(forecast="sw_gw_0",
base_obslist=sc.pst.nnz_obs_names,
obslist_dict=obslist_dict,
reset_zero_weight=1.0,
niter=4)
print(next_test)
print(imp_df.sort_index())
assert next_test.shape[0] == 4
