How to use the pastas.set_log_level function in pastas

from pandas import read_csv
import pastas as ps

ps.set_log_level("ERROR")


def test_create_project():
    pr = ps.Project(name="test")
    return pr


def test_project_add_oseries():
    pr = test_create_project()
    obs = read_csv("tests/data/obs.csv", index_col=0, parse_dates=True,
                   squeeze=True)
    pr.add_oseries(obs, name="heads", metadata={"x": 0.0, "y": 0})
    return pr


def test_project_add_stresses():

"""
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

ps.set_log_level("ERROR")

# read observations and create the time series model
obs = pd.read_csv("data/head_nb1.csv", index_col=0, parse_dates=True,
                  squeeze=True)

# Create the time series model
ml = ps.Model(obs, name="head")

# 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.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential,

# -*- coding: utf-8 -*-
"""
This example is meant to show the logger methods of pastas

"""

import pastas as ps

# using the default logger, we will see some infomration in the console:
obs = ps.read_dino('data/B58C0698001_1.csv')

# when we set the level to WARNING we do not see any output anymore
ps.set_log_level('WARNING')
# which is just a wrapper around
ps.utils.set_console_handler(ps.logger, level='WARNING')
obs = ps.read_dino('data/B58C0698001_1.csv')

# when we also want log-information saved to file, we add file-handlers
ps.utils.add_file_handlers(ps.logger)
obs = ps.read_dino('data/B58C0698001_1.csv')

# to get the default logger back we initialize it again
ps.utils.initialize_logger(ps.logger)

"""
This test file is meant for developing purposes, providing an easy method to
test the functioning of Pastas recharge module during development.

Author: R.A. Collenteur, University of Graz.

"""
import pastas as ps
import pandas as pd

ps.set_log_level("ERROR")

# 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')

import numpy as np
import pandas as pd

import pastas as ps

ps.set_log_level("ERROR")

# read observations and create the time series model
obs = pd.read_csv("data/head_nb1.csv", index_col=0, parse_dates=True,
                  squeeze=True)
ml = ps.Model(obs, name="groundwater head")

# read weather data and create stressmodel
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)
sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential,
                      recharge="Linear", name='recharge')
ml.add_stressmodel(sm)

# Solve

"""
This test file is meant for developing purposes, providing an easy method to
test the functioning of Pastas recharge module during development.

Author: R.A. Collenteur, University of Graz.

"""
import pandas as pd

import pastas as ps

ps.set_log_level("ERROR")

# read observations and create the time series model
obs = pd.read_csv("data/head_nb1.csv", index_col=0, parse_dates=True,
                  squeeze=True)

# Create the time series model
ml = ps.Model(obs, name="head")

# 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.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential,