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_create_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
ml.solve()
return ml
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'
"""
import pastas as ps
# read observations
obs = ps.read_dino('data/B58C0698001_1.csv')
# add 10 cm to the series from 2007
s = obs.series_original
s['2007':] = s['2007':]+0.1
obs.series_original = s
# Create the time series model
ml = ps.Model(obs)
# read weather data
rain = ps.read_knmi('data/neerslaggeg_HEIBLOEM-L_967-2.txt', variables='RD')
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
# 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.Exponential)
ml.add_stressmodel(sm)
# solve
ml.solve()
ml.plots.decomposition()
test the functioning of PASTAS during development.
"""
import pandas as pd
import pastas as ps
# 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,
"""
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()
""" @author: ruben """ import pastas as ps fname = '../data/B32D0136001_1.csv' obs = ps.read_dino(fname) fname = '../data/KNMI_Bilt.txt' stress = ps.read_knmi(fname, 'EV24') obs.plot() stress.plot()
"""
import pastas as ps
import pandas as pd
# read observations
obs = ps.read_dino('data/B58C0698001_1.csv')
# Create the time series model
ml = ps.Model(obs)
# read weather data
knmi = ps.read.knmi.KnmiStation.fromfile('data/neerslaggeg_HEIBLOEM-L_967-2.txt')
rain = ps.TimeSeries(knmi.data['RD'],settings='prec')
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
if True:
# also add 9 hours to the evaporation
s = evap.series_original
s.index = s.index+pd.to_timedelta(9,'h')
evap.series_original = s
# Create stress
sm = ps.StressModel2(stress=[rain, evap], rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
# set the time-offset of the model. This should be done automatically in the future.
ml._set_time_offset()
## Solve
"""
import pastas as ps
import pandas as pd
# read observations
obs = ps.read_dino('data/B58C0698001_1.csv')
# Create the time series model
ml = ps.Model(obs)
# read weather data
knmi = ps.read.knmi.KnmiStation.fromfile(
'data/neerslaggeg_HEIBLOEM-L_967-2.txt')
rain = ps.TimeSeries(knmi.data['RD'], settings='prec')
evap = ps.read_knmi('data/etmgeg_380.txt', variables='EV24')
if True:
# also add 9 hours to the evaporation
s = evap.series_original
s.index = s.index + pd.to_timedelta(9, 'h')
evap.series_original = s
# Create stress
sm = ps.StressModel2(stress=[rain, evap], rfunc=ps.Exponential,
name='recharge')
ml.add_stressmodel(sm)
# set the time-offset of the model. This should be done automatically in the future.
ml._set_time_offset()
## Solve
This test file is meant for developing purposes. Providing an easy method to test the functioning of PASTA during development. """ import pastas as ps # 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)
def test_read_knmi():
ps.read_knmi('tests/data/KNMI_Bilt.txt', "EV24")
return