-
Notifications
You must be signed in to change notification settings - Fork 1
/
Sharing Data.py
221 lines (181 loc) · 6.84 KB
/
Sharing Data.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 07 14:41:41 2018
@author: zckoh
"""
import numpy as np
import matplotlib.pyplot as plt
import itertools
np.set_printoptions(threshold=np.nan)
def safe_div(x,y):
if y == 0:
return 0
return x / y
#Importing True values collected from both boxes(Samples per Min)
lux_b1_true = []
lux_b2_true = []
slot_true = 1
tmp = []
for i in range(22,32):
with open("./highly correlated data/Box 1/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, slot_true)
for lines in fifthlines:
tmp.append(lines)
tmp = [w.replace('\n', '') for w in tmp]
f.close()
lux_b1_true.append([float(k) for k in tmp])
tmp = []
with open("./highly correlated data/Box 2/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, slot_true)
for lines in fifthlines:
tmp.append(lines)
tmp = [w.replace('\n', '') for w in tmp]
f.close()
lux_b2_true.append([float(i) for i in tmp])
tmp = []
#Importing sampled values from both boxes (samples per 30 min)
#store into 2 different lux_b1 & lux_b2
tmp = []
lux_b1 = []
lux_b2 = []
slot = 30
for i in range(22,32):
with open("./highly correlated data/Box 1/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, slot)
for lines in fifthlines:
tmp.append(lines)
tmp = [w.replace('\n', '') for w in tmp]
f.close()
lux_b1.append([float(k) for k in tmp])
tmp = []
with open("./highly correlated data/Box 2/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, slot)
for lines in fifthlines:
tmp.append(lines)
tmp = [w.replace('\n', '') for w in tmp]
f.close()
lux_b2.append([float(i) for i in tmp])
tmp = []
#combine & average (share all data samples both ways)
lux_average = np.array([[float(0)]*(1440/slot)]*len(lux_b1))
for x in range(0,len(lux_b1)):
for y in range(0,1440/slot):
lux_average[x][y] = (lux_b1[x][y]+lux_b2[x][y])/2
#intertwine the collected data ,Box 1(lux_b1_int) then Box 2(lux_b2_intt) ...
tmp_int = []
lux_b1_int = []
lux_b2_int = []
min_btw_slot_intertwine = 60
for i in range(22,32):
with open("./highly correlated data/Box 1/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, min_btw_slot_intertwine)
for lines in fifthlines:
tmp_int.append(lines)
tmp_int = [w.replace('\n', '') for w in tmp_int]
lux_b1_int.append([float(a) for a in tmp_int])
tmp_int = []
with open("./highly correlated data/Box 2/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 0, None, min_btw_slot_intertwine)
for lines in fifthlines:
tmp_int.append(lines)
tmp_int = [w.replace('\n', '') for w in tmp_int]
lux_b2_int.append([float(s) for s in tmp_int])
tmp_int = []
tmp_intt = []
lux_b1_intt = []
lux_b2_intt = []
for i in range(22,32):
with open("./highly correlated data/Box 1/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 30, None, min_btw_slot_intertwine)
for lines in fifthlines:
tmp_intt.append(lines)
tmp_intt = [w.replace('\n', '') for w in tmp_intt]
lux_b1_intt.append([float(b) for b in tmp_intt])
tmp_intt = []
with open("./highly correlated data/Box 2/%s-11-17.txt" %i , 'r') as f:
fifthlines = itertools.islice(f, 30, None, min_btw_slot_intertwine)
for lines in fifthlines:
tmp_intt.append(lines)
tmp_intt = [w.replace('\n', '') for w in tmp_intt]
lux_b2_intt.append([float(i) for i in tmp_intt])
tmp_intt = []
#combine (Box1,Box2,Box1,Box2)
temp = []
lux_added_together = []
for x in range(0,10):
for y in range(len(lux_b2_intt[0])):
temp.append(lux_b1_int[x][y])
temp.append(lux_b2_intt[x][y])
lux_added_together.append(temp)
temp = []
#Combine half_day + half day
def split_list(a_list):
half = len(a_list)/2
return a_list[:half], a_list[half:]
lux_b1_part1 = []
lux_b1_part2 = []
lux_b2_part1 = []
lux_b2_part2 = []
for i in range(0,10):
lux_b1_part1_day, lux_b1_part2_day = split_list(lux_b1[i])
lux_b1_part1.append(lux_b1_part1_day)
lux_b1_part2.append(lux_b1_part2_day)
lux_b2_part1_day, lux_b2_part2_day = split_list(lux_b2[i])
lux_b2_part1.append(lux_b2_part1_day)
lux_b2_part2.append(lux_b2_part2_day)
#box 1 first half, Box 2 next half
lux_half2_combined = []
for i in range(0,10):
a = np.append(lux_b1_part1[i],lux_b2_part2[i])
lux_half2_combined.append(a)
time = np.linspace(1,1440, num = 1440/slot_true)
time1 = np.linspace(1,1440, num = 1440/(slot))
time_avg = np.linspace(1,1440,num = 1440/slot)
time_int = np.linspace(1,1440,num = 1440/(min_btw_slot_intertwine/2))
time_half = np.linspace(1,1440,num = 1440/slot)
time2 = np.linspace(1,14400, num = 14400/(slot/2))
lux_last10_b1 = np.array([])
lux_last10_b2 = np.array([])
lux_last10_avg = np.array([])
for i in range(len(lux_b1)-10,len(lux_b1)):
lux_last10_b1 = np.append(lux_last10_b1,lux_b1[i])
lux_last10_b2 = np.append(lux_last10_b2,lux_b2[i])
lux_last10_avg = np.append(lux_last10_avg,lux_average[i])
index = 9
plt.figure(1)
fig, ax = plt.subplots(figsize=(20,4))
ax.plot(time,lux_b1_true[index],'r',label='Box 1(30 Actual)')
ax.plot(time,lux_b2_true[index],'r',label='Box 1(30 Actual)')
ax.plot(time_avg,lux_average[index],'g',label='Averaged')
ax.plot(time_half,lux_half2_combined[index],'r',label = 'Half')
ax.plot(time_int,lux_added_together[index],'b',label='Intertwine')
legend = ax.legend(loc='upper right', shadow=True)
frame = legend.get_frame()
frame.set_facecolor('1.0')
for label in legend.get_texts():
label.set_fontsize('medium')
for label in legend.get_lines():
label.set_linewidth(1.5) # the legend line width
plt.xlim([400,1100])
plt.xlabel('Time(Min)')
plt.ylabel('Light Intensity (klux)')
plt.title('Light intensity sampled every 30 mins %s/11/2017' % str(20+index+1))
'''
plt.figure(2)
fig, ax = plt.subplots(figsize=(15,4))
ax.plot(time2,lux_last10_b1,'r',label='Box 1')
ax.plot(time2,lux_last10_b2,'b',label='Box 2')
ax.plot(time2,lux_last10_avg,'g',label='Box 3')
plt.ylim([0,25])
legend = ax.legend(loc='upper right', shadow=True)
frame = legend.get_frame()
frame.set_facecolor('1.0')
for label in legend.get_texts():
label.set_fontsize('medium')
for label in legend.get_lines():
label.set_linewidth(1.5) # the legend line width
plt.grid()
plt.xlabel('Time(Min)')
plt.ylabel('Light Intensity (klux)')
plt.title('Light intensity sampled every 30 mins %s/11/2017' % str(20+index+1))
'''