def add_recharge(self, ml, rfunc, name="recharge", **kwargs):
"""Adds a recharge element to the time series model. The
selection of the precipitation and evaporation time series is based
on the shortest distance to the a stresses in the stresseslist.
Returns
-------
"""
key = ml.oseries.name
prec_name = self.get_nearest_stresses(key, kind="prec").iloc[0][0]
prec = self.stresses.loc[prec_name, "series"]
evap_name = self.get_nearest_stresses(key, kind="evap").iloc[0][0]
evap = self.stresses.loc[evap_name, "series"]
recharge = ps.StressModel2([prec, evap], rfunc, name=name, **kwargs)
ml.add_stressmodel(recharge)
def add_recharge(self, ml: ps.Model, rfunc=ps.Gamma) -> 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)
"""
# 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)
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 in tsdict.items():
metadata = self.conn.get_metadata("stresses", k, as_frame=False)
s = self.conn._get_dataframe_values("stresses", k, s,
metadata=metadata)
stresses.append(ps.TimeSeries(s, name=k, metadata=metadata))
# add recharge to model
rch = ps.StressModel2(stresses, rfunc, name="recharge",
metadata=[i.metadata for i in stresses],
settings=("prec", "evap"))
ml.add_stressmodel(rch)
def test_rfunc(rfunc_name):
if rfunc_name not in []:
# Import and check the observed groundwater time series
obs = ps.read_dino('tests/data/dino_gwl_data.csv')
# read weather data
rain = ps.read_knmi('tests/data/knmi_rain_data.txt', variables='RD')
evap = ps.read_knmi('tests/data/knmi_evap_data.txt', variables='EV24')
# Create the time series model
ml = ps.Model(obs, name="Test_Model")
## Create stress
rfunc = getattr(ps.rfunc, rfunc_name)
sm = ps.StressModel2(stress=[rain, evap], rfunc=rfunc, name='test_sm')
ml.add_stressmodel(sm)
# Solve the time series model
ml.solve()
def test_model():
# Import and check the observed groundwater time series
obs = ps.read_dino('tests/data/dino_gwl_data.csv')
# Create the time series model
ml = ps.Model(obs, name="Test_Model")
# read weather data
rain = ps.read_knmi('tests/data/knmi_rain_data.txt', variables='RD')
evap = ps.read_knmi('tests/data/knmi_evap_data.txt', variables='EV24')
## Create stress
sm = ps.StressModel2(stress=[rain, evap],
rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
# Solve the time series model
ml.solve()
return 'model succesfull'
# R.A. Collenteur - Artesia Water 2018
import pastas as ps
from pastas.stressmodels import WellModel
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")
IN2 = meny.IN['Evaporation']['values']
IN2.index = IN2.index.round("D")
sm = ps.StressModel2([IN, IN2], ps.Gamma, 'Recharge')
ml.add_stressmodel(sm)
stresses = [
meny.IN['Extraction 1']["values"], meny.IN['Extraction 2']["values"],
meny.IN['Extraction 3']["values"]
]
w = WellModel(stresses, ps.Theis, radius=[1, 1, 1], name="Wells")
ml.add_stressmodel(w)
ml.solve()
ml.plots.decomposition()
# Read observations
obs = ps.read_dino('data/B58C0698001_1.csv')
obs = obs.iloc[::5]
obs = obs[obs.index > pd.to_datetime('1-1-2010')]
# Create the time series model
ml = ps.Model(obs)
# Read weather data
prec = ps.read_knmi('data/neerslaggeg_HEIBLOEM-L_967-2.txt', variables='RD')
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
# Create stress
if False:
sm = ps.StressModel2(stress=[prec, evap],
rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
elif False:
sm = ps.StressModel(prec, rfunc=ps.Exponential, name='prec')
ml.add_stressmodel(sm)
sm = ps.StressModel(evap, rfunc=ps.Exponential, name='evap', up=False)
ml.add_stressmodel(sm)
else:
sm = ps.stressmodels.NoConvModel(prec,
rfunc=ps.Exponential,
name='prec_no_conv')
ml.add_stressmodel(sm)
sm = ps.stressmodels.NoConvModel(evap,
rfunc=ps.Exponential,
name='evap_no_conv',
squeeze=True)
# add 10 cm to the series from 2007
obs["2007":] = obs["2007":] + 1.5
# Create the time series model
ml = ps.Model(obs)
# read weather data
rain = pd.read_csv("data/rain_nb1.csv",
index_col=0,
parse_dates=True,
squeeze=True)
evap = pd.read_csv("data/evap_nb1.csv",
index_col=0,
parse_dates=True,
squeeze=True)
# create stress
sm = ps.StressModel2(stress=[rain, evap],
rfunc=ps.Exponential,
name="recharge")
ml.add_stressmodel(sm)
# add a stepmodel with an exponential response
sm = ps.stressmodels.StepModel("2007", "Step", rfunc=ps.One)
ml.add_stressmodel(sm)
# solve
ml.solve()
ml.plots.decomposition()
# read observations fname = 'data/B32D0136001_1.csv' obs = ps.read_dino(fname) # Create the time series model ml = ps.Model(obs) # read climate data fname = 'data/KNMI_Bilt.txt' RH = ps.read_knmi(fname, variables='RH') EV24 = ps.read_knmi(fname, variables='EV24') #rech = RH.series - EV24.series # Create stress #sm = ps.Recharge(RH, EV24, ps.Gamma, ps.Linear, name='recharge') #sm = Recharge(RH, EV24, Gamma, Combination, name='recharge') sm = ps.StressModel2([RH, EV24], ps.Gamma, name='recharge') #sm = ps.StressModel(RH, ps.Gamma, name='precip') #sm1 = ps.StressModel(EV24, ps.Gamma, name='evap') ml.add_stressmodel(sm) #ml.add_tseries(sm1) # Add noise model n = ps.NoiseModel() ml.add_noisemodel(n) # Solve ml.solve(freq="7D") ml.plot()
"""This is the short example in the docs. Also edit the docs when you change
something here.
Below is an example of a short script to simulate groundwater levels
(the csv-files with the data can be found in the examples directory on GitHub)
"""
import pandas as pd
import pastas as ps
oseries = pd.read_csv('data/head_nb1.csv',
parse_dates=['date'],
index_col='date',
squeeze=True)
rain = pd.read_csv('data/rain_nb1.csv',
parse_dates=['date'],
index_col='date',
squeeze=True)
evap = pd.read_csv('data/evap_nb1.csv',
parse_dates=['date'],
index_col='date',
squeeze=True)
ml = ps.Model(oseries)
sm = ps.StressModel2([rain, evap], ps.Gamma, name='recharge')
ml.add_stressmodel(sm)
ml.solve()
ml.plots.decomposition()
R.A. Collenteur - Artesia Water 2017
"""
import pastas as ps
# Create a simple model taken from example.py
obs = ps.read_dino('data/B58C0698001_1.csv')
rain = ps.read_knmi('data/neerslaggeg_HEIBLOEM-L_967-2.txt', variables='RD')
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
# Create a Pastas Project
mls = ps.Project(name="test_project")
mls.add_series(obs, "GWL", kind="oseries", metadata=dict())
mls.add_series(rain, name="Prec", kind="prec", metadata=dict())
mls.add_series(evap, name="Evap", kind="evap", metadata=dict())
ml = mls.add_model(oseries="GWL")
sm = ps.StressModel2(
[mls.stresses.loc["Prec", "series"], mls.stresses.loc["Evap", "series"]],
ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
n = ps.NoiseModel()
ml.add_noisemodel(n)
ml.solve(freq="D", warmup=1000, report=False)
mls.dump("test_project.pas")
