Drawdown caused by groundwater extraction

In [1]:

    

import pandas as pd
import pastas as ps
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


    

In [ ]:

    




    

In [8]:

    

head = pd.read_csv("../data/head_wellex.csv", index_col="Date", parse_dates=True)
head = pd.read_csv("../data/head_wellex.csv", index_col="Date", parse_dates=True)
head = pd.read_csv("../data/head_wellex.csv", index_col="Date", parse_dates=True)
head = pd.read_csv("../data/head_wellex.csv", index_col="Date", parse_dates=True)


    

    
Out[8]:





DatetimeIndex(['1995-01-14', '1995-01-28', '1995-02-15', '1995-03-01',
               '1995-03-15', '1995-03-28', '1995-04-14', '1995-04-28',
               '1995-05-16', '1995-05-30',
               ...
               '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06',
               '2018-01-07', '2018-01-08', '2018-01-09', '2018-01-10',
               '2018-01-11', '2018-01-12'],
              dtype='datetime64[ns]', name='Date', length=3869, freq=None)

In [3]:

    

plt.figure(figsize=(14, 9))
ax1 = plt.axes([0.125, 0.68, 0.775, 0.2])
xticks = pd.date_range(start=pd.Timestamp('1995-01-01'), end=pd.Timestamp('2015-01-01'), freq='5AS-Jan')
mlw.oseries['1995':'2015'].plot(style='k.', label='observed', xticks=xticks, markersize=8)
plt.xlim(pd.Timestamp('1995-01-01'), pd.Timestamp('2015-01-01') )
plt.setp(ax1.get_xticklabels(), visible=False)
plt.xlabel('')
plt.ylabel('m')
ax2 = plt.axes([0.125, 0.54, 0.775, 0.1])
rainw['1995':'2015'].plot(color='C0')
#evapw['1995':'2015'].plot(color='C1')
plt.ylim(0, 0.05)
plt.xlim(pd.Timestamp('1995-01-01'), pd.Timestamp('2015-01-01') )
plt.setp(ax2.get_xticklabels(), visible=False)
plt.xlabel('')
plt.ylabel('m/d')
ax2b = plt.axes([0.125, 0.4, 0.775, 0.1])
#rainw['1995':'2015'].plot(color='C0')
evapw['1995':'2015'].plot(color='C2')
plt.ylim(0, 0.05)
plt.xlim(pd.Timestamp('1995-01-01'), pd.Timestamp('2015-01-01') )
plt.setp(ax2b.get_xticklabels(), visible=False)
plt.xlabel('')
plt.ylabel('m/d')
#plt.legend()
ax3 = plt.axes([0.125, 0.26, 0.775, 0.1])
wellq[:'2015'].plot(color='C1')
plt.xlim(pd.Timestamp('1995-01-01'), pd.Timestamp('2015-01-01') )
#plt.ylim(-2.1, -0.6)
plt.xlabel('Year')
plt.ylabel('Discharge')
#plt.legend(loc='upper left')
plt.savefig('figwellinput.eps', bbox_inches='tight')


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-e69fae12913a> in <module>
      2 ax1 = plt.axes([0.125, 0.68, 0.775, 0.2])
      3 xticks = pd.date_range(start=pd.Timestamp('1995-01-01'), end=pd.Timestamp('2015-01-01'), freq='5AS-Jan')
----> 4 mlw.oseries['1995':'2015'].plot(style='k.', label='observed', xticks=xticks, markersize=8)
      5 plt.xlim(pd.Timestamp('1995-01-01'), pd.Timestamp('2015-01-01') )
      6 plt.setp(ax1.get_xticklabels(), visible=False)

NameError: name 'mlw' is not defined





    

In [4]:

    

head


    

    
Out[4]:







  

    

      

      
1995-01-14
      
15.74
    
  
  

    

      
0
      
1995-01-28
      
15.920000
    
    

      
1
      
1995-02-15
      
16.170000
    
    

      
2
      
1995-03-01
      
16.260000
    
    

      
3
      
1995-03-15
      
16.120000
    
    

      
4
      
1995-03-28
      
16.080000
    
    

      
5
      
1995-04-14
      
15.960000
    
    

      
6
      
1995-04-28
      
15.590000
    
    

      
7
      
1995-05-16
      
15.800000
    
    

      
8
      
1995-05-30
      
15.790000
    
    

      
9
      
1995-06-15
      
15.770000
    
    

      
10
      
1995-06-29
      
15.690000
    
    

      
11
      
1995-07-15
      
15.400000
    
    

      
12
      
1995-07-28
      
14.860000
    
    

      
13
      
1995-08-15
      
15.140000
    
    

      
14
      
1995-08-29
      
15.040000
    
    

      
15
      
1995-09-15
      
15.020000
    
    

      
16
      
1995-09-29
      
14.980000
    
    

      
17
      
1995-10-14
      
14.990000
    
    

      
18
      
1995-10-28
      
14.950000
    
    

      
19
      
1995-11-15
      
14.910000
    
    

      
20
      
1995-11-29
      
14.870000
    
    

      
21
      
1995-12-15
      
14.830000
    
    

      
22
      
1995-12-29
      
14.860000
    
    

      
23
      
1996-01-16
      
14.880000
    
    

      
24
      
1996-01-30
      
14.910000
    
    

      
25
      
1996-02-15
      
14.900000
    
    

      
26
      
1996-02-29
      
14.960000
    
    

      
27
      
1996-03-15
      
14.990000
    
    

      
28
      
1996-03-30
      
14.990000
    
    

      
29
      
1996-04-17
      
15.020000
    
    

      
...
      
...
      
...
    
    

      
3838
      
2017-12-14
      
16.230000
    
    

      
3839
      
2017-12-15
      
16.306250
    
    

      
3840
      
2017-12-16
      
16.317083
    
    

      
3841
      
2017-12-17
      
16.301250
    
    

      
3842
      
2017-12-18
      
16.296667
    
    

      
3843
      
2017-12-19
      
16.315000
    
    

      
3844
      
2017-12-20
      
16.307500
    
    

      
3845
      
2017-12-21
      
16.303333
    
    

      
3846
      
2017-12-22
      
16.299167
    
    

      
3847
      
2017-12-23
      
16.294583
    
    

      
3848
      
2017-12-24
      
16.298333
    
    

      
3849
      
2017-12-25
      
16.300833
    
    

      
3850
      
2017-12-26
      
16.310000
    
    

      
3851
      
2017-12-27
      
16.313333
    
    

      
3852
      
2017-12-28
      
16.317917
    
    

      
3853
      
2017-12-29
      
16.272917
    
    

      
3854
      
2017-12-30
      
16.284167
    
    

      
3855
      
2017-12-31
      
16.303750
    
    

      
3856
      
2018-01-01
      
16.354167
    
    

      
3857
      
2018-01-02
      
16.400417
    
    

      
3858
      
2018-01-03
      
16.437917
    
    

      
3859
      
2018-01-04
      
16.517917
    
    

      
3860
      
2018-01-05
      
16.556250
    
    

      
3861
      
2018-01-06
      
16.550000
    
    

      
3862
      
2018-01-07
      
16.503333
    
    

      
3863
      
2018-01-08
      
16.442917
    
    

      
3864
      
2018-01-09
      
16.434583
    
    

      
3865
      
2018-01-10
      
16.436667
    
    

      
3866
      
2018-01-11
      
16.416250
    
    

      
3867
      
2018-01-12
      
16.390714
    
  

3868 rows × 2 columns

In [ ]: