In [1]:

    

import pandas as pd
import numpy as np

from dataobject import DataObject


    

In [28]:

    

class MyObject(DataObject):

    _tables = ['data']
    _params = ['noise']
    
    defaults = {'noise':0.5}
    
    def __init__(self, npts=100, noise=0.5, **kwargs):

        t = np.arange(npts)
        f = np.random.randn(npts)*noise
        
        data = pd.DataFrame({'t':t, 'f':f})
        
        kwargs['noise'] = noise
        kwargs['data'] = data
        
        super(MyObject, self).__init__(**kwargs)
        
class AnotherObject(MyObject):
    
    _tables = ['moredata'] + MyObject._tables
    
    def __init__(self, npts=50, **kwargs):
        
        f = np.random.random(npts)
        kwargs['moredata'] = pd.DataFrame({'f':f})
        super(AnotherObject, self).__init__(**kwargs)


    

In [40]:

    

AnotherObject._tables


    

    
Out[40]:





['moredata', 'data']

In [36]:

    

d3 = AnotherObject()


    

In [18]:

    

d3.save_hdf('testd.h5')


    

In [19]:

    

d4 = AnotherObject.load_hdf('testd.h5')


    

In [24]:

    

d3.data.head()


    

    
Out[24]:






  

    

      

      
f
      
t
    
  
  

    

      
0
      
 0.309384
      
 0
    
    

      
1
      
 0.585847
      
 1
    
    

      
2
      
 0.882885
      
 2
    
    

      
3
      
-0.322994
      
 3
    
    

      
4
      
 0.269419
      
 4
    
  

In [25]:

    

d4.data.head()


    

    
Out[25]:






  

    

      

      
f
      
t
    
  
  

    

      
0
      
 0.309384
      
 0
    
    

      
1
      
 0.585847
      
 1
    
    

      
2
      
 0.882885
      
 2
    
    

      
3
      
-0.322994
      
 3
    
    

      
4
      
 0.269419
      
 4
    
  

In [3]:

    

d = MyObject()


    

In [4]:

    

d.save_hdf('testd.h5', overwrite=True)


    

In [5]:

    

d2 = MyObject.load_hdf('testd.h5')


    

In [6]:

    

d2.noise


    

    
Out[6]:





0.5

In [7]:

    

d2.data.head()


    

    
Out[7]:






  

    

      

      
f
      
t
    
  
  

    

      
0
      
-0.314054
      
 0
    
    

      
1
      
-0.392221
      
 1
    
    

      
2
      
 0.206492
      
 2
    
    

      
3
      
 0.363538
      
 3
    
    

      
4
      
-0.653195
      
 4
    
  

In [8]:

    

d.data.head()


    

    
Out[8]:






  

    

      

      
f
      
t
    
  
  

    

      
0
      
-0.314054
      
 0
    
    

      
1
      
-0.392221
      
 1
    
    

      
2
      
 0.206492
      
 2
    
    

      
3
      
 0.363538
      
 3
    
    

      
4
      
-0.653195
      
 4
    
  

In [ ]: