In [35]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import types
import datetime
%matplotlib inline
In [41]:
# get the initial occupancy dataframe with each room in one of occupied room and occupied room2
occupancy_file = open('dataset-dred/Occupancy_data.csv','rb')
occupancy = pd.read_csv(occupancy_file, header='infer')
occupancy['Time'] = pd.to_datetime(occupancy['Time'], format="%Y-%m-%d %H:%M:%S")
occupancy = occupancy.drop_duplicates()
occupancy['Occupied Room'] = occupancy['Occupied Room'].apply(lambda x: x.split('[')[1].split(']')[0])
occupancy['Occupied Room'] = occupancy['Occupied Room'].apply(lambda x: x.split('\''))
occupancy['Occupied Room'] = occupancy['Occupied Room'].apply(lambda x: x if(len(x)>3) else x[1])
occupancy['Occupied Room2'] = occupancy['Occupied Room'].apply(lambda x: x[-2] if(isinstance(x, list)) else np.NaN)
occupancy['Occupied Room'] = occupancy['Occupied Room'].apply(lambda x: x[1] if(isinstance(x, list)) else x)
occupancy.head()
Out[41]:
Time
Occupied Room
Occupied Room2
0
2015-07-05 00:00:03
Kitchen
NaN
1
2015-07-05 00:00:07
LivingRoom
NaN
2
2015-07-05 00:00:08
StoreRoom
Room2
3
2015-07-05 00:00:09
LivingRoom
NaN
4
2015-07-05 00:00:10
LivingRoom
NaN
In [43]:
# create a new dummy DataFrame. index = each second from start of occupancy to end of occupancy.
# columns in the dataframe are the different rooms. For now all values are 0.
rooms = ['Kitchen', 'LivingRoom', 'StoreRoom', 'Room1', 'Room2']
# rooms
idx = occupancy.index
st = occupancy['Time'][idx[0]]
et = occupancy['Time'][idx[-1]]
new_idx = pd.date_range(start=st, end=et, freq='S')
room_occ = pd.DataFrame(columns=rooms, index=new_idx)
room_occ = room_occ.fillna(0)
room_occ.head()
Out[43]:
Kitchen
LivingRoom
StoreRoom
Room1
Room2
2015-07-05 00:00:03
0
0
0
0
0
2015-07-05 00:00:04
0
0
0
0
0
2015-07-05 00:00:05
0
0
0
0
0
2015-07-05 00:00:06
0
0
0
0
0
2015-07-05 00:00:07
0
0
0
0
0
In [44]:
# In the dataFrame created above, if value at a Time for a room is 1, it means that the room was occupied
# at that moment. These values are set by using occupancy dataframe.
idx = occupancy.index
k = 0
for i in idx:
timestamp, r1, r2 = occupancy[occupancy.index == i].values[0]
room_index1 = rooms.index(r1)
room_occ.set_value(timestamp, rooms[room_index1],1)
if (pd.isnull(r2) == False):
room_index2 = rooms.index(r2)
room_occ.set_value(timestamp, rooms[room_index2],1)
room_occ.head()
Out[44]:
Kitchen
LivingRoom
StoreRoom
Room1
Room2
2015-07-05 00:00:03
1
0
0
0
0
2015-07-05 00:00:04
0
0
0
0
0
2015-07-05 00:00:05
0
0
0
0
0
2015-07-05 00:00:06
0
0
0
0
0
2015-07-05 00:00:07
0
1
0
0
0
In [46]:
# Open All_data.csv, put it a DataFrame and set time as Index
alldata_file = open('dataset-dred/All_data.csv','rb')
alldata = pd.read_csv(alldata_file, header='infer', parse_dates=[1])
alldata['Time'] = alldata['Time'].str.split(pat='+').str[0]
alldata['Time'] = pd.to_datetime(alldata['Time'])
alldata = alldata.set_index('Time')
alldata['mains'] = alldata['mains'].astype(float)
power_data = alldata.resample('1S').mean()
power_data = power_data.fillna(0)
power_data
Out[46]:
mains
television
fan
fridge
laptop computer
electric heating element
oven
unknown
washing machine
microwave
toaster
sockets
cooker
Time
2015-07-05 00:00:00
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2015-07-05 00:00:08
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2015-07-05 00:00:09
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224.2
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2015-07-05 00:00:12
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2015-07-05 00:00:13
226.0
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2015-07-05 00:00:14
226.6
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2015-07-05 00:00:15
227.2
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2015-07-05 00:00:16
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2015-07-05 00:00:17
228.4
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2015-07-05 00:00:18
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2015-07-05 00:00:19
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2015-07-05 00:00:20
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2015-07-05 00:00:21
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2015-07-05 00:00:22
217.4
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29.65
98.776977
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2015-07-05 00:00:23
214.5
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29.65
98.746047
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2015-07-05 00:00:24
211.6
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98.715116
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2015-07-05 00:00:25
208.7
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2015-07-05 00:00:26
205.8
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98.653256
28.987619
2.290000
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2015-07-05 00:00:27
202.9
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29.65
98.622326
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2015-07-05 00:00:28
200.0
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2015-07-05 00:00:29
199.9
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29.65
98.560465
29.101905
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...
...
...
...
...
...
...
...
...
...
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...
...
...
2015-12-05 22:59:29
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2015-12-05 22:59:31
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2015-12-05 22:59:32
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2015-12-05 22:59:33
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2015-12-05 22:59:34
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13301999 rows × 13 columns
In [47]:
alldata = pd.merge(power_data, room_occ, left_index=True, right_index=True)
alldata
Out[47]:
mains
television
fan
fridge
laptop computer
electric heating element
oven
unknown
washing machine
microwave
toaster
sockets
cooker
Kitchen
LivingRoom
StoreRoom
Room1
Room2
2015-07-05 00:00:03
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2015-07-05 00:00:12
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2015-07-05 00:00:14
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2015-07-05 00:00:15
227.2
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2015-07-05 00:00:16
227.8
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2015-07-05 00:00:17
228.4
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2015-07-05 00:00:18
229.0
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2015-07-05 00:00:19
226.1
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2015-07-05 00:00:20
223.2
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2015-07-05 00:00:21
220.3
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2015-07-05 00:00:22
217.4
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2015-07-05 00:00:23
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2015-07-05 00:00:25
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2015-07-05 00:00:26
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2015-07-05 00:00:27
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2015-07-05 00:00:28
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2015-07-05 00:00:29
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2015-07-05 00:00:30
199.8
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2015-07-05 00:00:31
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2015-07-05 00:00:32
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2015-12-05 21:54:46
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2015-12-05 21:55:11
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2015-12-05 21:55:12
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2015-12-05 21:55:13
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2015-12-05 21:55:14
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13298113 rows × 18 columns
Content source: marioberges/F16-12-752
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