def test_update_ts_settings(request, pstore):
pstore.set_check_model_series_values(False)
o = pstore.get_oseries("oseries2")
ml = ps.Model(o.loc[:"2013"], name="ml_oseries2")
pnam = pstore.get_nearest_stresses("oseries2", kind="prec").iloc[0]
p = pstore.get_stresses(pnam)
enam = pstore.get_nearest_stresses("oseries2", kind="evap").iloc[0]
e = pstore.get_stresses(enam)
rm = ps.RechargeModel(p.loc[:"2013"], e.loc[:"2013"])
ml.add_stressmodel(rm)
tmax = p.index.intersection(e.index).max()
p2 = pstore.get_stresses("prec1")
sm = ps.StressModel(p2.loc[:"2013"], ps.Exponential, "prec")
ml.add_stressmodel(sm)
pstore.add_model(ml)
ml2 = pstore.get_models(ml.name, update_ts_settings=True)
try:
assert ml2.oseries.settings["tmax"] == o.index[-1]
assert ml2.stressmodels["recharge"].prec.settings["tmax"] == tmax
assert ml2.stressmodels["recharge"].evap.settings["tmax"] == tmax
assert ml2.stressmodels["prec"].stress[0].settings["tmax"] == \
p2.index[-1]
except AssertionError:
pstore.del_models("ml_oseries2")
pstore.set_check_model_series_values(True)
raise
return
def add_recharge(self,
ml: ps.Model,
rfunc=ps.Gamma,
recharge=ps.rch.Linear(),
recharge_name: str = "recharge") -> None:
"""Add recharge to a pastas model.
Uses closest precipitation and evaporation timeseries in database.
These are assumed to be labeled with kind = 'prec' or 'evap'.
Parameters
----------
ml : pastas.Model
pastas.Model object
rfunc : pastas.rfunc, optional
response function to use for recharge in model,
by default ps.Gamma (for different response functions, see
pastas documentation)
recharge : ps.RechargeModel
recharge model to use, default is ps.rch.Linear()
recharge_name : str
name of the RechargeModel
"""
# get nearest prec and evap stns
names = []
for var in ("prec", "evap"):
try:
name = self.get_nearest_stresses(ml.oseries.name,
kind=var).iloc[0, 0]
except AttributeError:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
if isinstance(name, float):
if np.isnan(name):
raise ValueError(f"Unable to find nearest '{var}' stress! "
"Check X and Y coordinates.")
else:
names.append(name)
if len(names) == 0:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
# get data
tsdict = self.conn.get_stresses(names)
stresses = []
for (k, s), setting in zip(tsdict.items(), ("prec", "evap")):
metadata = self.conn.get_metadata("stresses", k, as_frame=False)
stresses.append(
ps.TimeSeries(s, name=k, settings=setting, metadata=metadata))
# add recharge to model
rch = ps.RechargeModel(stresses[0],
stresses[1],
rfunc,
name=recharge_name,
recharge=recharge,
settings=("prec", "evap"),
metadata=[i.metadata for i in stresses])
ml.add_stressmodel(rch)
def test_linear():
index = pd.date_range("2000-01-01", "2000-01-10")
prec = pd.Series([1, 2] * 5, index=index)
evap = prec / 2
rm = ps.RechargeModel(prec=prec,
evap=evap,
rfunc=ps.Exponential,
recharge="Linear",
name="recharge")
return rm
def create_model():
obs = read_csv("tests/data/obs.csv", index_col=0, parse_dates=True,
squeeze=True)
rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True,
squeeze=True)
evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True,
squeeze=True)
ml = ps.Model(obs, name="Test_Model")
sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
return ml
def test_create_rechargemodel():
rain = read_csv("tests/data/rain.csv",
index_col=0,
parse_dates=True,
squeeze=True)
evap = read_csv("tests/data/evap.csv",
index_col=0,
parse_dates=True,
squeeze=True)
rm = ps.RechargeModel(prec=rain,
evap=evap,
name='recharge',
recharge="Linear")
return rm
fname = 'data/MenyanthesTest.men'
meny = ps.read.MenyData(fname)
# Create the time series model
H = meny.H['Obsevation well']
ml = ps.Model(H['values'])
# Add precipitation
IN = meny.IN['Precipitation']['values']
IN.index = IN.index.round("D")
IN.name = 'Precipitation'
IN2 = meny.IN['Evaporation']['values']
IN2.index = IN2.index.round("D")
IN2.name = 'Evaporation'
sm = ps.RechargeModel(IN, IN2, ps.Gamma, 'Recharge')
ml.add_stressmodel(sm)
# Add well extraction 2
IN = meny.IN['Extraction 2']
well = ps.TimeSeries(IN["values"], settings="well")
# extraction amount counts for the previous month
sm1 = ps.StressModel(well, ps.Hantush, 'Extraction_2', up=False)
# Add well extraction 3
IN = meny.IN['Extraction 3']
well = ps.TimeSeries(IN["values"], settings="well")
# extraction amount counts for the previous month
sm2 = ps.StressModel(well, ps.Hantush, 'Extraction_3', up=False)
# add_stressmodels also allows addings multiple stressmodels at once
index_col="Date",
parse_dates=True)
# Create the time series model
ml = ps.Model(head, name="head")
# read weather data
rain = pd.read_csv("notebooks/data_notebook_5/prec_wellex.csv",
index_col="Date",
parse_dates=True)
evap = pd.read_csv("notebooks/data_notebook_5/evap_wellex.csv",
index_col="Date",
parse_dates=True)
# Create stress
rm = ps.RechargeModel(prec=rain,
evap=evap,
rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(rm)
well = pd.read_csv("notebooks/data_notebook_5/well_wellex.csv",
index_col="Date",
parse_dates=True) / 1e6
sm = ps.StressModel(well, rfunc=ps.Exponential, name="well", up=False)
ml.add_stressmodel(sm)
# Solve
ml.solve(noise=True)
ml.plots.results()
"""
import pandas as pd
import pastas as ps
ps.set_log_level("ERROR")
# read observations and create the time series model and make meters
obs = pd.read_csv("data/B32C0639001.csv", parse_dates=['date'],
index_col='date', squeeze=True)
# Create the time series model
ml = ps.Model(obs, name="head")
# read weather data and make mm/d !
evap = ps.read_knmi("data/etmgeg_260.txt", variables="EV24").series * 1e3
rain = ps.read_knmi("data/etmgeg_260.txt", variables="RH").series * 1e3
# Initialize recharge model and create stressmodel
rch = ps.rch.FlexModel()
# rch = ps.rch.Berendrecht()
# rch = ps.rch.Linear()
sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma, recharge=rch)
ml.add_stressmodel(sm)
ml.solve(noise=True, tmin="1990")
ml.plots.results()
test the functioning of Pastas recharge module during development.
Author: R.A. Collenteur, University of Graz.
"""
import pastas as ps
# read observations
obs = ps.read_dino('data/B58C0698001_1.csv')
# Create the time series model
ml = ps.Model(obs, name="groundwater head")
# read weather data
rain = ps.read_knmi('data/neerslaggeg_HEIBLOEM-L_967-2.txt', variables='RD')
rain.multiply(1000)
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
evap.multiply(1000)
# Create stress
sm = ps.RechargeModel(prec=rain,
evap=evap,
rfunc=ps.Exponential,
recharge="Linear",
name='recharge')
ml.add_stressmodel(sm)
## Solve
ml.solve()
ml.plot()
def test_add_stressmodel():
ml = test_create_model()
sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma, name='rch')
ml.add_stressmodel(sm)
return ml
# identify min groundwater level
gw_min = hydrodata["LWPH4b"].min()
# normalize groundwater levels to min value
lwph4b = (hydrodata["LWPH4b"] - gw_min).asfreq("D")
# Create a model object by passing it the observed series
ml_lwph4b = ps.Model(lwph4b, name="LWPH4b")
## build stress models:
# recharge
precSurplus_mm = metdata["prcp_mm"] - metdata["ETo_mm"]
#sm_rech = ps.RechargeModel(metdata["prcp_mm"], metdata["ETo_mm"], rfunc=ps.Gamma, name="recharge")
sm_rech = ps.RechargeModel(metdata["prcp_mm"],
metdata["ETo_mm"],
rfunc=ps.Exponential,
recharge=ps.rch.FlexModel(),
name="recharge")
# pumping
wuse = ps.StressModel(wusedata,
rfunc=ps.Hantush,
name="well",
settings="well",
up=False)
# river stage
river = (hydrodata["stage_masl"] -
hydrodata["stage_masl"].min()).asfreq("D").fillna(0)
sm_river = ps.StressModel(river,
rfunc=ps.One,