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'
"""
This is an example with a step in the observations, which we add artificially.
We model this step through a StepModel.
"""
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)
"""
This test file is meant for developing purposes. Providing an easy method to
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)
""" @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 tools and real time simulation class
sys.path.append(dr + '/Tools')
from Eref_to_epot import calc_epot # Function for calculating potential evaporation from reference evaporation
sys.path.append(dr + '/Wrappers')
from rt_simulation import rt_simulation # Function for performing real-time forecasts
"""
Import data
"""
# Import groundwater data and edit
Pbcode = 'B58C0352'
filternumber = str(1)
gwl = ps.read_dino(dr + '/Data/Real time simulation example/Groundwater/' +
Pbcode + '00' + filternumber + '_1.csv')
gwl = gwl.series
gwl = gwl.to_frame()['2001-01-01 00:00:00':]
gwl.columns = ['Stand (cm t.o.v. NAP)']
for i in range(len(gwl)):
if gwl['Stand (cm t.o.v. NAP)'][i] < gwl['Stand (cm t.o.v. NAP)'].mean(
) - (3 * gwl['Stand (cm t.o.v. NAP)'].std()) or gwl[
'Stand (cm t.o.v. NAP)'][i] > gwl['Stand (cm t.o.v. NAP)'].mean(
) + (3 * gwl['Stand (cm t.o.v. NAP)'].std()):
gwl['Stand (cm t.o.v. NAP)'][i] = 'nan'
gwl = gwl.interpolate()
# Distinguish historical data and year with most recent observations
gwl1 = gwl[:len(gwl) - 360]
gwl2 = gwl[len(gwl) - 360:]
""" 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()
def test_read_dino():
ps.read_dino('tests/data/B32D0136001_1.csv')
return
# -*- coding: utf-8 -*- """ Created on Wed Mar 30 16:07:44 2016 @author: ruben """ import matplotlib.pyplot as plt import pastas as ps # # How to use it? fname = '../data/B32D0136001_1.csv' dino = ps.read_dino(fname) # plot dino.plot() plt.show()