new_contents: import matplotlib.pyplot as plt
  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
  ml.solve()
  
  df = ml.uncertainty.block_response("recharge", n=1000)
  ax = ml.get_block_response("recharge").plot(color="C1")
  df.plot(color="k", linestyle="--", ax=ax)
  df = ml.uncertainty.block_response("recharge", n=1000, alpha=0.01)
  df.plot(color="gray", linestyle="--", ax=ax)
  
  
  df = ml.uncertainty.step_response("recharge", n=1000)
  ax = ml.get_step_response("recharge").plot(color="C1")
  df.plot(color="k", linestyle="--", ax=ax)
  df = ml.uncertainty.step_response("recharge", n=1000, alpha=0.01)
  df.plot(color="gray", linestyle="--", ax=ax)
subject: Create example_uncertainty.py