How to use the pastas.TimeSeries function in pastas

def load_model(data):
    # Create model
    oseries = ps.TimeSeries(**data["oseries"])

    if "constant" in data.keys():
        constant = data["constant"]
    else:
        constant = False

    if "metadata" in data.keys():
        metadata = data["metadata"]
    else:
        metadata = None

    if "name" in data.keys():
        name = data["name"]
    else:
        name = None

This is the time at which precipitation is logged in dutch KNMI-stations. 

"""
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()

metadata=metadata)

    if "settings" in data.keys():
        ml.settings.update(data["settings"])
    if "file_info" in data.keys():
        ml.file_info.update(data["file_info"])

    # Add stressmodels
    for name, ts in data["stressmodels"].items():
        stressmodel = getattr(ps.stressmodels, ts["stressmodel"])
        ts.pop("stressmodel")
        if "rfunc" in ts.keys():
            ts["rfunc"] = getattr(ps.rfunc, ts["rfunc"])
        if "stress" in ts.keys():
            for i, stress in enumerate(ts["stress"]):
                ts["stress"][i] = ps.TimeSeries(**stress)
        if "prec" in ts.keys():
            ts["prec"] = ps.TimeSeries(**ts["prec"])
        if "evap" in ts.keys():
            ts["evap"] = ps.TimeSeries(**ts["evap"])
        if "temp" in ts.keys() and ts["temp"] is not None:
            ts["temp"] = ps.TimeSeries(**ts["temp"])
        stressmodel = stressmodel(**ts)
        ml.add_stressmodel(stressmodel)

    # Add transform
    if "transform" in data.keys():
        transform = getattr(ps.transform, data["transform"]["transform"])
        data["transform"].pop("transform")
        transform = transform(**data["transform"])
        ml.add_transform(transform)

# Add stressmodels
    for name, ts in data["stressmodels"].items():
        stressmodel = getattr(ps.stressmodels, ts["stressmodel"])
        ts.pop("stressmodel")
        if "rfunc" in ts.keys():
            ts["rfunc"] = getattr(ps.rfunc, ts["rfunc"])
        if "stress" in ts.keys():
            for i, stress in enumerate(ts["stress"]):
                ts["stress"][i] = ps.TimeSeries(**stress)
        if "prec" in ts.keys():
            ts["prec"] = ps.TimeSeries(**ts["prec"])
        if "evap" in ts.keys():
            ts["evap"] = ps.TimeSeries(**ts["evap"])
        if "temp" in ts.keys() and ts["temp"] is not None:
            ts["temp"] = ps.TimeSeries(**ts["temp"])
        stressmodel = stressmodel(**ts)
        ml.add_stressmodel(stressmodel)

    # Add transform
    if "transform" in data.keys():
        transform = getattr(ps.transform, data["transform"]["transform"])
        data["transform"].pop("transform")
        transform = transform(**data["transform"])
        ml.add_transform(transform)

    # Add noisemodel if present
    if "noisemodel" in data.keys():
        n = getattr(ps.noisemodels, data["noisemodel"]["type"])()
        ml.add_noisemodel(n)

    # Add fit object to the model

if "file_info" in data.keys():
        ml.file_info.update(data["file_info"])

    # Add stressmodels
    for name, ts in data["stressmodels"].items():
        stressmodel = getattr(ps.stressmodels, ts["stressmodel"])
        ts.pop("stressmodel")
        if "rfunc" in ts.keys():
            ts["rfunc"] = getattr(ps.rfunc, ts["rfunc"])
        if "stress" in ts.keys():
            for i, stress in enumerate(ts["stress"]):
                ts["stress"][i] = ps.TimeSeries(**stress)
        if "prec" in ts.keys():
            ts["prec"] = ps.TimeSeries(**ts["prec"])
        if "evap" in ts.keys():
            ts["evap"] = ps.TimeSeries(**ts["evap"])
        if "temp" in ts.keys() and ts["temp"] is not None:
            ts["temp"] = ps.TimeSeries(**ts["temp"])
        stressmodel = stressmodel(**ts)
        ml.add_stressmodel(stressmodel)

    # Add transform
    if "transform" in data.keys():
        transform = getattr(ps.transform, data["transform"]["transform"])
        data["transform"].pop("transform")
        transform = transform(**data["transform"])
        ml.add_transform(transform)

    # Add noisemodel if present
    if "noisemodel" in data.keys():
        n = getattr(ps.noisemodels, data["noisemodel"]["type"])()
        ml.add_noisemodel(n)

IN.name = 'Precipitation'
IN2 = meny.IN['Evaporation']['values']
IN2.index = IN2.index.round("D")
IN2.name = 'Evaporation'
sm = ps.StressModel2([IN, IN2], ps.Gamma, 'Recharge')
ml.add_stressmodel(sm)

#Add well extraction 1
# IN = meny.IN['Extraction 1']
# well = ps.TimeSeries(IN["values"], freq_original="M", settings="well")
# # extraction amount counts for the previous month
# sm = ps.StressModel(well, ps.Hantush, 'Extraction_1', up=False)

# Add well extraction 2
IN = meny.IN['Extraction 2']
well = ps.TimeSeries(IN["values"], freq_original="M", 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"], freq_original="M", 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
ml.add_stressmodel(sm, sm1, sm2)

# Solve
ml.solve(tmax="1995")

ax = ml.plots.decomposition(ytick_base=1.)