In [25]:
import pandas
import numpy
In [26]:
data = pandas.read_csv("logfile2.txt")
data = data.replace(to_replace="None", value=np.nan)
In [82]:
data=data.astype(float)
In [100]:
data
Out[100]:
leftposx
leftposy
rightposx
rightposy
ballx
bally
leftvely
rightvely
ballvelx
ballvely
action
reward
framei
i
terminate
0
NaN
NaN
139
62
0
0
NaN
NaN
NaN
NaN
1
0
51
51
0
1
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
52
52
0
2
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
53
53
0
3
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
54
54
0
4
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
55
55
0
5
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
56
56
0
6
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
57
57
0
7
NaN
NaN
139
62
0
0
NaN
0
0
0
1
0
58
58
0
8
19
81
139
62
81
78
NaN
0
81
78
1
0
59
59
0
9
19
81
139
62
81
78
0
0
0
0
1
0
60
60
0
10
19
81
139
62
82
77
0
0
1
-1
1
0
61
61
0
11
19
83
139
62
83
76
2
0
1
-1
1
0
62
62
0
12
19
83
139
62
84
75
0
0
1
-1
1
0
63
63
0
13
19
85
139
62
85
74
2
0
1
-1
1
0
64
64
0
14
19
85
139
62
86
73
0
0
1
-1
1
0
65
65
0
15
19
87
139
62
87
72
2
0
1
-1
1
0
66
66
0
16
19
87
139
62
88
71
0
0
1
-1
1
0
67
67
0
17
19
89
139
62
89
70
2
0
1
-1
1
0
68
68
0
18
19
89
139
62
90
69
0
0
1
-1
7
0
69
69
0
19
19
91
139
62
91
68
2
0
1
-1
1
0
70
70
0
20
19
91
139
62
92
67
0
0
1
-1
1
0
71
71
0
21
19
93
139
62
93
66
2
0
1
-1
1
0
72
72
0
22
19
93
139
64
94
65
0
2
1
-1
1
0
73
73
0
23
19
95
139
64
95
64
2
0
1
-1
1
0
74
74
0
24
19
95
139
64
96
63
0
0
1
-1
1
0
75
75
0
25
19
97
139
64
97
62
2
0
1
-1
1
0
76
76
0
26
19
97
139
66
98
61
0
2
1
-1
1
0
77
77
0
27
19
99
139
66
99
60
2
0
1
-1
1
0
78
78
0
28
19
99
139
66
100
59
0
0
1
-1
7
0
79
79
0
29
19
101
139
66
101
58
2
0
1
-1
7
0
80
80
0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
9084
19
139
139
100
141
41
-2
0
-1
4
1
0
369
9135
0
9085
19
139
139
100
140
45
0
0
-1
4
1
0
370
9136
0
9086
19
137
139
100
139
49
-2
0
-1
4
1
0
371
9137
0
9087
19
137
139
100
138
53
0
0
-1
4
1
0
372
9138
0
9088
19
135
139
100
137
57
-2
0
-1
4
1
0
373
9139
0
9089
19
135
139
100
136
61
0
0
-1
4
3
0
374
9140
0
9090
19
133
139
100
135
65
-2
0
-1
4
1
0
375
9141
0
9091
19
133
139
96
134
69
0
-4
-1
4
1
0
376
9142
0
9092
19
131
139
96
133
73
-2
0
-1
4
1
0
377
9143
0
9093
19
131
139
96
132
77
0
0
-1
4
3
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378
9144
0
9094
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129
139
96
131
81
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-1
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1
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379
9145
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19
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130
85
0
-4
-1
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380
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127
139
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128
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382
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139
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127
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383
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0
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139
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126
101
0
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384
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9100
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125
105
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0
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9102
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121
139
88
123
113
-2
0
-1
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1
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387
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9164
0
9114 rows × 15 columns
In [ ]:
In [ ]:
In [99]:
%pylab gtk
#import pylab
test=np.array(data2['ballvelx'][:1000].values)
data2=data.dropna()
pylab.plot(data2['i'][:1000],data2['bally'][:1000],'o')
pylab.figure()
pylab.plot(data2['i'][:1000],np.sqrt(np.array(data2['ballvely'][:1000])**2 +np.array(data2['ballvelx'][:1000])**2) ,'o')
pylab.ylim([0,10])
pylab.figure()
pylab.plot(data2['i'][1000:3000],np.abs(np.array(data2['ballvely'][1000:3000])) ,'o', color='r')
pylab.ylim([0,10])
pylab.figure()
pylab.plot(data2['i'][:1000],np.abs(np.array(data2['ballvelx'][:1000])) ,'o', color='g')
pylab.ylim([0,10])
/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 22920 was not found when attempting to remove it
gobject.source_remove(self._idle_event_id)
/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 22923 was not found when attempting to remove it
gobject.source_remove(self._idle_event_id)
/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 22925 was not found when attempting to remove it
gobject.source_remove(self._idle_event_id)
/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 22927 was not found when attempting to remove it
gobject.source_remove(self._idle_event_id)
Populating the interactive namespace from numpy and matplotlib
WARNING: pylab import has clobbered these variables: ['test']
`%matplotlib` prevents importing * from pylab and numpy
Out[99]:
(0, 10)
In [56]:
print data2[data2['reward'] == 1].index.tolist()
print np.array(data2[["i"]])[1987]
print data2[data2['reward'] == -1].index.tolist()
print data[["ballx"]].max()
print data[["ballx"]].min()
print data[["bally"]].max()
print data[["bally"]].min()
[1987, 2440, 2891, 3303, 5173, 8266]
[2179]
[400, 729, 1347, 1796, 2247, 2700, 3675, 4004, 4452, 4842, 4982, 5433, 5685, 5825, 6213, 6729, 6988, 7240, 8072, 8715]
ballx 157
dtype: int64
ballx 0
dtype: int64
bally 159
dtype: int64
bally 0
dtype: int64
In [ ]:
Content source: jamesmcm/pongcode
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