In [1]:

    

from neuralnilm.source import NILMTKSource


    

    




Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:

    

source = NILMTKSource(
    filename='/data/mine/vadeec/merged/ukdale.h5', 
    appliances=['television', 'washer dryer', 'fridge freezer', 'dish washer'],
    train_buildings=[1],
    validation_buildings=[1],
    window=("2013-04-01", "2013-05-01"),
    sample_period=6,
    seq_length=1500,
    n_seq_per_batch=10
    )


    

    




init good sections for building 1

In [3]:

    

appliances, mains, time_of_day = source._gen_single_example()


    

In [7]:

    

import matplotlib.pyplot as plt
ax = appliances.plot()
mains.plot(ax=ax)
plt.show()


    

In [6]:

    

plt.plot(time_of_day)
plt.show()


    

In [27]:

    

print appliances.shape, mains.shape


    

    




(1500, 4) (1500,)

In [18]:

    

mains[:1500].shape


    

    
Out[18]:





(1500,)

In [41]:

    

index = mains.index


    

In [76]:

    

import pandas as pd
SECS_PER_DAY = 60 * 60 * 24
index = index.tz_localize(None)
secs_into_day = (index.astype(int) / 1E9)[1000] % SECS_PER_DAY
secs_into_day / SECS_PER_DAY


    

    
Out[76]:





0.0025000000000000001

In [77]:

    

index[1000]


    

    
Out[77]:





Timestamp('2013-04-02 00:03:36', offset='6S')

In [78]:

    

mains.index[1000]


    

    
Out[78]:





Timestamp('2013-04-02 00:03:36+0100', tz='Europe/London', offset='6S')

In [29]:

    

elec = source.dataset.buildings[1].elec


    

In [31]:

    

elec.get_labels(appliances.columns)


    

    
Out[31]:





['Television', 'Washer dryer', 'Fridge freezer', 'Dish washer']

In [18]:

    

good_sections = elec.mains().good_sections()


    

In [19]:

    

good_sections[0].timedelta


    

    
Out[19]:





Timedelta('2 days 13:53:50.400000')

In [1]:

    

from neuralnilm.source import RealApplianceSource
import matplotlib.pyplot as plt


    

    




Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:

    

source = RealApplianceSource('/data/mine/vadeec/merged/ukdale.h5', ['fridge freezer', 'hair straighteners', 'television'],
                            max_input_power=800, max_output_power=150)


    

    




Loading activations for fridge freezer
Loading activations for hair straighteners
Loading activations for television
Done

In [11]:

    

X, y = source._gen_data()
fig, axes = plt.subplots(2, sharex=True)
axes[0].plot(X[0,:,0])
axes[1].plot(y[0,:,0])
plt.show()


    

In [ ]: