Menyanthes File

Developed by Ruben Caljé

Menyanthes is timeseries analysis software used by many people in the Netherlands. In this example a Menyanthes-file with one observation-series is imported, and simulated. There are several stresses in the Menyanthes-file, among which are three groundwater extractions with a significant influence on groundwater head.



In [1]:

    
# First perform the necessary imports
import matplotlib.pyplot as plt
import pastas as ps
%matplotlib notebook

1. Importing the Menyanthes-file

Import the Menyanthes-file with observations and stresses. Then plot the observations, together with the diferent stresses in the Menyanthes file.



In [2]:

    
# how to use it?
fname = '../data/MenyanthesTest.men'
meny = ps.read.MenyData(fname)

# plot some series
f1, axarr = plt.subplots(len(meny.IN)+1, sharex=True)
oseries = meny.H['Obsevation well']["values"]
oseries.plot(ax=axarr[0])
axarr[0].set_title(meny.H['Obsevation well']["Name"])
for i, val in enumerate(meny.IN.items()):
    name, data = val
    data["values"].plot(ax=axarr[i+1])
    axarr[i+1].set_title(name)
plt.tight_layout(pad=0)
plt.show()

2. Run a model

Make a model with precipitation, evaporation and three groundwater extractions.



In [3]:

    
# Create the time series model
ml = ps.Model(oseries)

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

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

# Add well extraction 2
IN = meny.IN['Extraction 2']
# extraction amount counts for the previous month
ts = ps.StressModel(IN['values'], ps.Hantush, 'Extraction_2', up=False,
                    settings="well")
ml.add_stressmodel(ts)

# Add well extraction 3
IN = meny.IN['Extraction 3']
# extraction amount counts for the previous month
ts = ps.StressModel(IN['values'], ps.Hantush, 'Extraction_3', up=False,
                    settings="well")
ml.add_stressmodel(ts)

# Solve the model (can take around 20 seconds..)
ml.solve()









    



INFO: Cannot determine frequency of series None
INFO: Inferred frequency from time series None: freq=D 
INFO: Inferred frequency from time series None: freq=D 
INFO: Cannot determine frequency of series None
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: Cannot determine frequency of series None
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: Cannot determine frequency of series None
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: Time Series None was sampled down to freq D with method timestep_weighted_resample
INFO: There are observations between the simulation timesteps. Linear interpolation between simulated values is used.

Model Results Observations                Fit Statistics
============================    ============================
nfev     31                     EVP                    95.68
nobs     2842                   NSE                     0.96
noise    1                      Pearson R2              0.96
tmin     1960-04-29 00:00:00    RMSE                    0.19
tmax     2015-06-29 00:00:00    AIC                    23.80
freq     D                      BIC                   113.09
warmup   3650                   __                          
solver   LeastSquares           ___                         

Parameters (15 were optimized)
============================================================
                      optimal    stderr     initial vary
Recharge_A        1443.371413   ±11.69%  210.498526    1
Recharge_n           1.077166    ±3.19%    1.000000    1
Recharge_a         727.249383   ±17.50%   10.000000    1
Recharge_f          -1.736517    ±9.64%   -1.000000    1
Extraction_1_A      -0.000181   ±15.30%   -0.000178    1
Extraction_1_rho     3.003399   ±37.04%    1.000000    1
Extraction_1_cS    999.973740   ±39.90%  100.000000    1
Extraction_2_A      -0.000084    ±6.75%   -0.000086    1
Extraction_2_rho     0.444669   ±38.96%    1.000000    1
Extraction_2_cS    998.710093   ±47.29%  100.000000    1
Extraction_3_A      -0.000048    ±8.55%   -0.000170    1
Extraction_3_rho     0.145767   ±93.85%    1.000000    1
Extraction_3_cS    475.251078  ±117.84%  100.000000    1
constant_d          13.741975    ±3.16%    8.557530    1
noise_alpha         33.877511    ±7.33%   14.000000    1

Warnings
============================================================

3. Plot the decomposition

Show the decomposition of the groundwater head, by plotting the influence on groundwater head of each of the stresses.



In [4]:

    
ax = ml.plots.decomposition(ytick_base=1.)
ax[0].set_title('Observations vs simulation')
ax[0].legend()
ax[0].figure.tight_layout(pad=0)