In [1]:
#Load Packages
import pandas as pd
import numpy as np
from IPython.display import display, HTML
low_memory = False
local_path = "/Users/michaeldowd/"
#Load the csv's
clion_lookup = pd.read_csv(local_path + "Google Drive/Dowd_Local/Data/1st_Sample/c_lion_node_lookup_sample.csv")
crashes = pd.read_csv(local_path + "Google Drive/Dowd_Local/Data/1st_Sample/crashes_sample.csv")
vehicles = pd.read_csv(local_path + "Google Drive/Dowd_Local/Data/1st_Sample/vehicle_sample.csv")
factors = pd.read_csv(local_path + "Google Drive/Dowd_Local/Data/1st_Sample/factor_sample.csv")
print "done"
/Users/michaeldowd/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2902: DtypeWarning: Columns (5,32,73) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
/Users/michaeldowd/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2902: DtypeWarning: Columns (4,19) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
done
In [2]:
display(HTML(pd.DataFrame(list(crashes.columns)).to_html()))
0
0
gid
1
case_num
2
case_yr
3
ref_mrkr
4
accd_dte
5
road_sys
6
num_of_fat
7
num_of_inj
8
reportable
9
police_dep
10
intersect_
11
muni
12
precinct
13
num_of_veh
14
accd_typ
15
locn
16
traf_cntl
17
light_cond
18
weather
19
road_char
20
road_surf_
21
collision_
22
ped_loc
23
ped_actn
24
ext_of_inj
25
regn_cnty_
26
accd_tme
27
rpt_agcy
28
dmv_accd_c
29
traf_way
30
rdway_acc_
31
err_cde
32
comm_veh_a
33
highway_in
34
intersect1
35
utm_northi
36
utm_eastin
37
geo_segmen
38
geo_node_i
39
geo_node_d
40
geo_node_1
41
geo_lcode
42
geom
43
loc
44
nodeid
45
segmentid1
46
segmentid2
47
segmentid3
48
segmentid4
49
segmentid5
50
segmentid6
51
segmentid7
52
segmentid8
53
exclude
54
accd_type_int
55
revized
56
crashid
57
boro
58
st_nm
59
alis_node
60
alis_mastr
61
x
62
y
63
st_x
64
st_y
65
wrong_pct
66
c_node
67
nddist
68
t_node
69
tnddist
70
t_seg
71
cleanname
72
final_node
73
issue
In [3]:
#How many nulls in each column (only show columns with more than zero nulls)
crash_nulls = crashes.isnull().sum()
crash_nulls = crash_nulls[crash_nulls > 0]
def getPercNull(df, master_df):
#Function for calculation percent of records that are null
percents = []
for b in df.iterrows():
try:
percents.append(b[1].Count/float(len(master_df[b[0]])))
except:
percents.append("NA")
df['perc_null'] = percents
crash_nulls = pd.DataFrame(crash_nulls, columns = ["Count"])
getPercNull(crash_nulls, crashes)
display(HTML(crash_nulls.to_html()))
print "Total Records : " , len(crashes)
Count
perc_null
ref_mrkr
31005
0.979776
police_dep
7888
0.249265
intersect_
31366
0.991183
precinct
7909
0.249929
locn
2
0.000063
ext_of_inj
8092
0.255712
accd_tme
1210
0.038237
rpt_agcy
3360
0.106178
traf_way
2114
0.066804
rdway_acc_
2114
0.066804
err_cde
10118
0.319735
intersect1
4748
0.150040
geo_node_1
13872
0.438363
nodeid
6490
0.205088
segmentid1
25155
0.794912
segmentid2
31645
1.000000
segmentid3
31645
1.000000
segmentid4
31645
1.000000
segmentid5
31645
1.000000
segmentid6
31645
1.000000
segmentid7
31645
1.000000
segmentid8
31645
1.000000
revized
31645
1.000000
st_nm
12357
0.390488
st_x
31645
1.000000
st_y
31645
1.000000
wrong_pct
31638
0.999779
c_node
31645
1.000000
nddist
31645
1.000000
t_node
6490
0.205088
tnddist
31645
1.000000
t_seg
25155
0.794912
cleanname
12357
0.390488
final_node
31645
1.000000
issue
31636
0.999716
Total Records : 31645
In [4]:
priority_cols_crashes = ['case_yr', "road_sys", "reportable", "accd_typ", "num_of_veh", "traf_cntl", "light_cond", \
"weather", "road_char","road_surf_", "collision_","ped_loc", \
"ped_actn", "ext_of_inj","regn_cnty_", "dmv_accd_c", "err_cde", \
"highway_in", "intersect1" ]
In [39]:
def summarize(column_list, master_df, include_sums = True):
"""
Cycles through the columns in the priority cols list and does a simple aggregate,
total number of cases for each column, the sum of fatalities, and the sum of injuries.
"""
print 5*">", "START", 5 * "<"
for col in column_list:
print ">"*20
print col.upper()
if include_sums:
out = master_df[[col, 'crashid', "num_of_fat", "num_of_inj"]].groupby(col)
aggout = out.agg({'crashid':{'count' : 'count' },
'num_of_fat' : {'sum' : 'sum'},
'num_of_inj' : {'sum' : 'sum'}
})
elif include_sums == "Vehicles":
out = master_df[[col, 'crashid', "num_of_fat", "num_of_inj"]].groupby(col)
aggout = out.agg({'crashid':{'count' : 'count' },
'num_of_fat' : {'sum' : lambda x: np.sum(x)}
})
else:
out = master_df[[col, 'crashid']].groupby(col)
aggout = out.agg({'crashid':{'count' : 'count' }
})
display(HTML(aggout.to_html()))
print 5*">", "END", 5 * "<"
return aggout
In [6]:
print 5*">", "Crash Data", 5 * "<"
summarize(priority_cols_crashes, crashes)
>>>>> Crash Data <<<<<
>>>>> START <<<<<
>>>>>>>>>>>>>>>>>>>>
CASE_YR
num_of_inj
crashid
num_of_fat
sum
count
sum
case_yr
2001
3852
3322
9
2002
3520
2730
8
2003
3019
2351
5
2004
2531
2087
8
2005
2300
1927
4
2006
2241
2089
4
2007
2142
2304
7
2008
2158
2313
9
2009
2268
2404
5
2010
2069
2152
8
2011
2230
2142
4
2012
1711
1816
2
2013
2007
2014
7
2014
1940
1994
6
>>>>>>>>>>>>>>>>>>>>
ROAD_SYS
num_of_inj
crashid
num_of_fat
sum
count
sum
road_sys
9
6
7
0
12
7061
7062
18
01
87
60
0
02
8
6
0
03
63
49
0
04
17865
15150
37
05
20
16
0
09
44
46
0
10
0
2
0
11
592
517
2
12
8103
8589
29
15
2
1
0
??
79
96
0
XX
58
44
0
>>>>>>>>>>>>>>>>>>>>
REPORTABLE
num_of_inj
crashid
num_of_fat
sum
count
sum
reportable
N
3
4
0
Y
33985
31641
86
>>>>>>>>>>>>>>>>>>>>
ACCD_TYP
num_of_inj
crashid
num_of_fat
sum
count
sum
accd_typ
01
25181
23023
22
02
5280
5065
46
03
2478
2442
10
04
3
3
0
06
7
7
0
08
64
62
0
10
137
161
1
11
120
112
2
12
17
15
0
13
14
11
0
14
18
18
0
15
38
40
0
16
55
56
0
17
39
43
2
18
21
29
0
19
9
11
0
20
2
4
0
21
28
23
0
22
1
2
0
23
7
9
0
24
15
21
0
25
3
3
0
26
10
11
0
27
40
38
0
30
118
134
1
31
22
20
0
32
1
6
0
40
214
234
0
XX
9
5
0
YY
3
3
0
ZZ
34
34
2
>>>>>>>>>>>>>>>>>>>>
NUM_OF_VEH
num_of_inj
crashid
num_of_fat
sum
count
sum
num_of_veh
0
0
2
0
1
8532
8406
55
2
21230
20550
22
3
3155
2056
4
4
739
461
3
5
218
118
0
6
56
30
0
7
31
11
0
8
9
4
0
9
7
3
2
10
9
3
0
12
2
1
0
>>>>>>>>>>>>>>>>>>>>
TRAF_CNTL
num_of_inj
crashid
num_of_fat
sum
count
sum
traf_cntl
01
8585
8628
17
02
19342
15726
57
03
1547
1209
4
04
50
63
0
05
25
28
0
06
61
56
0
07
34
36
0
08
0
1
0
09
4
5
0
10
2
2
0
11
12
13
0
12
72
73
0
13
10
13
0
14
8
8
0
15
23
32
0
16
7
16
0
20
143
208
4
XX
1
2
0
YY
62
60
0
ZZ
4000
5466
4
>>>>>>>>>>>>>>>>>>>>
LIGHT_COND
num_of_inj
crashid
num_of_fat
sum
count
sum
light_cond
1
19492
17366
45
2
678
572
6
3
1497
1242
0
4
8728
6861
33
5
117
110
2
X
1
2
0
Z
3475
5492
0
>>>>>>>>>>>>>>>>>>>>
WEATHER
num_of_inj
crashid
num_of_fat
sum
count
sum
weather
0
21
27
0
1
22385
19919
66
2
4006
3466
10
3
4047
3345
8
4
402
412
1
5
77
82
0
6
25
19
0
?
0
1
0
X
1
2
0
Z
3024
4372
1
>>>>>>>>>>>>>>>>>>>>
ROAD_CHAR
num_of_inj
crashid
num_of_fat
sum
count
sum
road_char
1
28540
24899
74
2
1319
1345
9
3
347
314
0
4
534
552
1
5
154
141
1
6
25
27
0
X
1
2
0
Y
0
1
0
Z
3068
4364
1
>>>>>>>>>>>>>>>>>>>>
ROAD_SURF_
num_of_inj
crashid
num_of_fat
sum
count
sum
road_surf_
0
37
45
0
1
24463
21728
72
2
5764
4754
12
3
14
17
0
4
561
612
2
5
126
138
0
6
14
15
0
X
1
2
0
Z
3008
4334
0
>>>>>>>>>>>>>>>>>>>>
COLLISION_
num_of_inj
crashid
num_of_fat
sum
count
sum
collision_
01
7350
6071
0
02
2558
3387
1
03
1348
913
2
04
4243
3014
4
05
319
387
0
06
100
126
0
07
269
186
4
08
373
437
0
09
13220
11479
73
10
588
611
1
XX
1
2
0
ZZ
3619
5032
1
>>>>>>>>>>>>>>>>>>>>
PED_LOC
num_of_inj
crashid
num_of_fat
sum
count
sum
ped_loc
1
6645
6272
45
2
1094
1055
14
?
2
3
0
X
2002
1766
4
Y
23675
22036
23
Z
570
513
0
>>>>>>>>>>>>>>>>>>>>
PED_ACTN
num_of_inj
crashid
num_of_fat
sum
count
sum
ped_actn
01
2457
2327
10
02
1071
1042
18
03
350
337
5
04
832
806
10
05
954
938
2
06
192
185
0
07
278
270
0
08
4
4
0
09
63
61
0
10
4
3
0
11
91
88
0
12
51
49
1
13
496
472
6
14
261
173
3
XX
2063
1821
4
YY
23675
22037
23
ZZ
1146
1032
4
>>>>>>>>>>>>>>>>>>>>
EXT_OF_INJ
num_of_inj
crashid
num_of_fat
sum
count
sum
ext_of_inj
A
1159
1159
0
AA
178
89
0
AAA
57
19
0
AAAA
16
4
0
AAAAA
45
8
0
AAAAC
5
1
0
AAABC
5
1
0
AAAC
4
1
0
AAACC
5
1
0
AAB
9
3
0
AABB
4
1
0
AAC
48
16
0
AACC
12
3
0
AACCC
10
2
0
AB
60
30
0
ABB
12
4
0
ABBB
4
1
0
ABBC
8
2
0
ABBCC
20
3
0
ABC
30
10
0
ABCC
8
2
0
ABCCC
17
3
0
AC
278
139
0
ACC
180
60
0
ACCB
4
1
0
ACCC
76
19
0
ACCCC
107
19
0
ACK
2
1
1
AKC
2
1
1
AXXXX
6
1
0
B
2193
2193
0
BB
174
87
0
BBB
33
11
0
BBBB
16
4
0
BBBBC
6
1
0
BBBC
12
3
0
BBBCC
21
4
0
BBC
87
29
0
BBCC
40
10
0
BBCCC
75
13
0
BBX
3
1
0
BC
482
241
0
BCC
270
90
0
BCCC
139
35
0
BCCCC
242
36
0
BXXXX
5
1
0
C
13873
13873
0
CA
6
3
0
CAA
3
1
0
CAAC
4
1
0
CABBA
6
1
0
CB
12
6
0
CBB
3
1
0
CBC
3
1
0
CBCC
4
1
0
CBX
3
1
0
CC
6569
3285
0
CCB
9
3
0
CCBB
4
1
0
CCC
3414
1138
0
CCCB
8
2
0
CCCC
1792
448
0
CCCCC
1778
302
0
CCCX
12
3
0
CCXC
4
1
0
CX
14
7
0
CXXX
4
1
0
K
2
61
59
KA
2
2
2
KABC
3
1
1
KAC
2
1
1
KBBB
3
1
1
KC
7
7
7
KCC
6
3
3
KK
0
3
6
KKC
1
1
2
KKCCC
6
1
2
X
18
18
0
XB
2
1
0
XC
2
1
0
XCC
9
3
0
XX
6
3
0
XXX
6
2
0
XXXX
4
1
0
>>>>>>>>>>>>>>>>>>>>
REGN_CNTY_
num_of_inj
crashid
num_of_fat
sum
count
sum
regn_cnty_
N1
1
1
0
N2
33970
31630
86
N3
7
6
0
N5
10
8
0
>>>>>>>>>>>>>>>>>>>>
DMV_ACCD_C
num_of_inj
crashid
num_of_fat
sum
count
sum
dmv_accd_c
1
37
83
86
2
28384
20363
0
3
5558
3313
0
4
0
7870
0
6
9
16
0
>>>>>>>>>>>>>>>>>>>>
ERR_CDE
num_of_inj
crashid
num_of_fat
sum
count
sum
err_cde
0
18807
15993
43
3
28
26
0
5
2
3
0
7
5
2
0
9
12
13
0
10
6
5
0
11
5171
5485
16
>>>>>>>>>>>>>>>>>>>>
HIGHWAY_IN
num_of_inj
crashid
num_of_fat
sum
count
sum
highway_in
N
33982
31626
86
Y
6
19
0
>>>>>>>>>>>>>>>>>>>>
INTERSECT1
num_of_inj
crashid
num_of_fat
sum
count
sum
intersect1
N
2715
2874
9
Y
26211
24023
66
>>>>> END <<<<<
In [15]:
#without using column value type enforcement - some mixed type colums emerged, code below corrected those in roadsys
crashes.road_sys.loc[crashes[crashes.road_sys == 12].index] = '12'
crashes.road_sys.loc[crashes[crashes.road_sys == 9].index] = '9'
pd.unique(crashes['road_sys'].ravel())
Out[15]:
array(['04', '12', '??', '03', 'XX', '11', '09', '02', '05', '01', '15',
'10', '9'], dtype=object)
Vehicles Data Column List
In [8]:
pd.DataFrame(vehicles.columns, columns = ["Cols"])
Out[8]:
Cols
0
case_num
1
case_yr
2
veh_seq_num
3
rgst_typ
4
body_typ
5
veh_typ
6
pre_accd_actn
7
second_event
8
veh_dirn_of_trav
9
haz_cargo_ind
10
school_bus_ind
11
comm_veh_ind
12
age
13
sex
14
occupant_num
15
rgst_wgt
16
cit_ind
17
drvr_lic_st
18
veh_lic_st
19
hazmat_plac_ind
20
tow_ind
21
crashid
22
tck_bus_clsf
In [9]:
#How many nulls in each column (only show columns with more than zero nulls)
number_of_nulls = vehicles.isnull().sum()
number_of_nulls = number_of_nulls[number_of_nulls > 0]
nulls_df = pd.DataFrame(number_of_nulls, columns = ["Count"])
getPercNull(nulls_df, vehicles)
display(HTML(nulls_df.to_html()))
print "Total Records : " , len(vehicles)
Count
perc_null
age
11723
0.176626
sex
14567
0.219475
occupant_num
1221
0.018396
rgst_wgt
31086
0.468360
cit_ind
14349
0.216191
drvr_lic_st
21012
0.316579
veh_lic_st
14218
0.214217
hazmat_plac_ind
41529
0.625701
tow_ind
4419
0.066579
Total Records : 66372
In [10]:
priority_cols_vehicles = [
'case_yr', 'rgst_typ', 'body_typ', 'veh_typ','pre_accd_actn', 'age', 'sex', 'rgst_wgt'
]
In [11]:
#Join Crashes(just injuries and Fatalities) & Vehicles
crash_short = crashes[['case_num','crashid', 'num_of_fat', 'num_of_inj']]
merge_Vehicles = pd.merge(vehicles, crash_short, on='crashid')
grouped_by_Case_merge_vehicles = merge_Vehicles.groupby('crashid')
In [12]:
test = merge_Vehicles[['crashid','num_of_fat','num_of_inj']].groupby('crashid').sum()
# test.head()
merge_Vehicles.head()
Out[12]:
case_num_x
case_yr
veh_seq_num
rgst_typ
body_typ
veh_typ
pre_accd_actn
second_event
veh_dirn_of_trav
haz_cargo_ind
...
cit_ind
drvr_lic_st
veh_lic_st
hazmat_plac_ind
tow_ind
crashid
tck_bus_clsf
case_num_y
num_of_fat
num_of_inj
0
1103203
2001
1
ZZ
32
0
01
ZZ
3
N
...
NaN
NaN
NaN
NaN
NaN
11032032001
ZZ
1103203
0
1
1
1103203
2001
2
ZZ
36
6
ZZ
ZZ
Z
N
...
NaN
NaN
NaN
NaN
NaN
11032032001
ZZ
1103203
0
1
2
1103232
2001
1
16
13
2
01
ZZ
1
N
...
NaN
NaN
NaN
NaN
NaN
11032322001
ZZ
1103232
0
0
3
1103232
2001
2
16
68
2
03
ZZ
5
N
...
NaN
NaN
NaN
NaN
NaN
11032322001
ZZ
1103232
0
0
4
1103241
2001
3
54
13
2
07
ZZ
1
N
...
NaN
NaN
NaN
NaN
NaN
11032412001
ZZ
1103241
0
1
5 rows × 26 columns
Note: There is double counting present in the sum results in tables below, as each row in the vehicles table represents a party in the accident (vehicles, ped or bike). Data is just to provide an idea of the distribution of value for different fields. Injuries/Fatalities are not mapped to specific people/vehicles. I.E. If a crash occured and we know there was a bike and a car and we know the age/sex/etc of the two people involved (driver / rider) we do not know which person was injured/killed.
In [13]:
summarize(priority_cols_vehicles, merge_Vehicles, include_sums=True)
>>>>> START <<<<<
>>>>>>>>>>>>>>>>>>>>
CASE_YR
num_of_inj
crashid
num_of_fat
sum
count
sum
case_yr
2001
8304
6943
18
2002
7715
5744
14
2003
6569
4951
11
2004
5507
4429
20
2005
5103
4068
9
2006
4969
4410
14
2007
4720
4839
30
2008
4609
4807
18
2009
4929
5007
12
2010
4449
4479
17
2011
4941
4525
7
2012
3627
3763
4
2013
4369
4237
20
2014
4189
4170
15
>>>>>>>>>>>>>>>>>>>>
RGST_TYP
num_of_inj
crashid
num_of_fat
sum
count
sum
rgst_typ
11
592
577
1
12
16
24
0
15
1
1
0
16
34899
30082
51
19
174
119
0
20
1
1
0
21
2
2
0
26
25
21
0
27
1
1
0
29
78
109
0
33
2
4
0
36
630
591
9
37
17
18
0
38
18
18
0
39
6
1
0
40
1
1
0
41
3
2
0
44
0
1
0
45
2
4
0
48
12
13
0
51
100
84
0
52
478
374
0
53
9
4
0
54
5970
4517
3
55
631
484
3
56
304
210
1
58
1
1
0
61
16
20
0
62
15
11
0
66
8
5
0
67
36
29
0
68
38
46
0
69
93
96
0
70
239
253
8
72
324
354
1
76
2027
1933
7
77
13
11
0
79
0
1
0
80
111
94
0
81
35
42
1
84
1
2
0
85
21
20
0
86
2
4
0
88
1019
898
4
XX
24125
23489
112
ZZ
1904
1800
8
>>>>>>>>>>>>>>>>>>>>
BODY_TYP
num_of_inj
crashid
num_of_fat
sum
count
sum
body_typ
8
3
3
0
9
1
1
0
10
3
2
0
11
11
6
0
12
203
194
0
13
252
238
0
14
13
14
0
15
1
1
0
19
14
15
0
22
1
1
0
30
13
14
1
31
0
1
0
32
97
128
0
35
41
41
0
36
97
88
1
37
1
1
0
39
1
1
0
41
2
2
0
44
0
1
0
58
0
2
0
60
4
5
0
61
3
6
0
63
10
10
0
64
1
1
0
66
2
3
0
67
2
3
0
68
29
25
0
90
7
7
0
93
21
20
0
96
2
2
0
08
24
25
0
09
8
9
0
10
179
192
1
11
698
665
0
12
16095
14023
35
13
28839
23827
41
14
3019
2536
4
15
7
14
0
19
749
718
16
20
1
1
0
21
0
1
0
22
5
8
0
23
0
2
0
30
833
995
3
31
729
590
0
32
4621
6280
8
33
18
18
0
34
2
1
0
35
2540
2469
11
36
6083
5343
56
37
86
76
0
38
1
1
0
39
76
69
0
41
22
22
0
43
0
1
0
44
24
30
1
45
6
8
1
46
0
5
0
50
3
2
0
51
3
4
0
52
20
23
0
53
1
1
0
56
2
1
0
57
0
1
0
58
375
451
8
59
8
4
0
60
345
338
0
61
244
363
4
62
63
86
2
63
634
635
2
64
20
19
0
65
43
43
0
66
71
72
1
67
84
78
0
68
2723
2450
6
70
193
206
3
8
657
379
0
80
1
2
0
81
0
1
0
83
25
25
0
85
12
14
0
87
1
2
0
9
110
116
0
90
1124
867
3
91
38
30
0
92
1
1
0
93
1594
1292
1
95
8
10
0
96
32
27
0
??
65
64
0
>>>>>>>>>>>>>>>>>>>>
VEH_TYP
num_of_inj
crashid
num_of_fat
sum
count
sum
veh_typ
0
7569
8677
10
1
763
733
16
2
53437
45408
89
3
2339
2739
23
4
1131
874
3
5
2581
2510
11
6
6180
5431
57
>>>>>>>>>>>>>>>>>>>>
PRE_ACCD_ACTN
num_of_inj
crashid
num_of_fat
sum
count
sum
pre_accd_actn
01
34865
27418
83
02
2444
2628
12
03
6174
5209
14
04
712
625
0
05
489
617
0
06
464
377
1
07
2815
2203
1
08
4449
3424
0
09
259
371
0
10
3642
4895
26
11
156
174
0
12
1039
1135
3
13
426
568
4
14
443
406
2
15
892
1242
0
16
42
41
0
17
91
77
0
18
26
15
0
20
995
1002
3
XX
753
761
6
YY
5190
4544
46
ZZ
7634
8640
8
>>>>>>>>>>>>>>>>>>>>
AGE
num_of_inj
crashid
num_of_fat
sum
count
sum
age
1
64
32
0
2
49
35
0
3
43
27
0
4
42
31
3
5
44
25
0
6
36
31
0
7
60
42
0
8
64
46
1
9
62
53
2
10
67
63
0
11
94
92
2
12
121
112
3
13
111
102
0
14
112
104
2
15
114
100
1
16
131
113
0
17
240
218
1
18
555
411
3
19
772
591
1
20
880
684
2
21
1094
864
0
22
1350
1080
0
23
1483
1157
4
24
1652
1281
6
25
1770
1396
1
26
1780
1440
2
27
1837
1421
3
28
1749
1416
6
29
1768
1431
3
30
1879
1499
3
31
1723
1453
2
32
1821
1523
4
33
1878
1446
1
34
1739
1414
3
35
1866
1494
5
36
1653
1396
4
37
1633
1369
6
38
1738
1384
9
39
1642
1364
2
40
1568
1304
6
41
1504
1259
3
42
1508
1288
3
43
1551
1320
5
44
1491
1286
5
45
1463
1202
2
46
1451
1207
3
47
1341
1146
2
48
1250
1080
3
49
1189
1083
4
50
1185
1031
4
51
1053
940
0
52
1113
986
2
53
1003
884
1
54
1005
920
1
55
902
798
1
56
883
755
2
57
785
694
1
58
759
670
5
59
706
613
1
60
639
562
2
61
606
522
1
62
472
437
2
63
390
402
3
64
381
357
0
65
345
324
2
66
319
311
1
67
276
263
0
68
259
263
0
69
229
220
3
70
204
183
2
71
166
164
1
72
136
135
2
73
116
135
1
74
129
129
0
75
130
131
2
76
95
96
1
77
97
99
2
78
68
67
2
79
68
66
1
80
55
57
0
81
46
49
0
82
50
55
0
83
47
45
1
84
31
38
0
85
28
27
0
86
20
28
2
87
16
15
0
88
5
4
0
89
7
9
1
90
6
5
0
91
7
7
1
92
1
2
0
93
3
3
0
95
2
3
0
96
0
1
0
97
1
1
0
98
2
2
0
99
1
1
0
100
2
1
0
101
3
2
0
102
37
25
1
103
39
34
0
104
50
36
0
105
43
33
0
106
45
44
0
107
21
26
0
108
36
24
0
>>>>>>>>>>>>>>>>>>>>
SEX
num_of_inj
crashid
num_of_fat
sum
count
sum
sex
F
17192
14472
35
M
44162
37077
126
U
28
29
0
f
24
41
0
m
138
185
0
u
1
1
0
>>>>>>>>>>>>>>>>>>>>
RGST_WGT
num_of_inj
crashid
num_of_fat
sum
count
sum
rgst_wgt
0
1
1
0
1
1
2
0
4
35
26
0
5
342
264
0
6
1
2
0
7
12
12
0
8
16
4
0
9
1
1
0
10
4
4
0
11
2
3
0
12
1
1
0
14
12
11
0
16
0
1
0
20
1
1
0
34
17
10
0
35
1
1
0
36
16
16
0
37
25
14
0
40
6
6
0
41
0
1
0
42
12
1
0
43
19
10
0
45
10
5
0
49
1
1
0
53
1
1
0
55
1
1
0
56
7
4
0
57
3
3
0
60
3
2
0
61
1
1
0
143
1
1
0
172
2
1
0
179
2
2
0
200
1
1
0
205
13
14
0
209
2
1
0
210
2
2
0
213
2
1
0
215
1
2
0
220
4
4
0
221
1
1
0
224
2
2
0
225
5
11
1
229
1
1
0
230
3
3
0
231
1
1
0
233
1
1
0
240
3
3
0
243
18
17
0
251
12
11
0
254
1
1
0
256
1
1
0
258
1
2
0
260
1
1
0
261
1
1
0
262
2
2
0
267
1
1
0
268
1
1
0
275
1
1
0
277
1
1
0
290
1
1
0
291
4
3
0
295
2
2
0
300
3
3
0
304
7
7
0
306
1
1
0
311
2
2
0
320
1
1
0
321
3
6
0
322
1
1
0
324
4
3
0
326
18
17
0
328
6
7
0
330
1
1
0
335
2
1
0
337
10
9
0
342
3
2
0
348
1
1
0
350
3
3
0
352
1
1
0
353
1
1
0
354
10
6
0
355
10
9
0
357
1
1
0
359
15
12
0
360
5
7
1
362
5
4
0
363
6
1
0
365
24
15
0
366
6
2
0
367
2
3
1
368
5
5
0
369
1
1
0
370
19
18
0
372
9
9
0
373
2
2
0
374
5
3
1
375
10
10
0
377
3
3
0
378
1
1
0
379
11
9
0
380
1
1
0
381
2
2
0
383
5
5
0
384
1
1
0
385
2
2
0
386
4
3
0
388
2
2
0
389
0
1
0
390
6
7
1
392
3
2
0
394
3
3
0
395
1
1
0
396
5
4
0
397
1
1
0
400
6
5
0
401
4
2
0
404
4
4
0
405
3
3
0
407
4
4
0
408
7
5
0
410
4
5
0
412
3
2
0
414
4
5
0
416
1
2
1
417
3
2
0
418
1
2
0
419
0
1
0
420
3
3
0
421
2
3
0
422
2
2
0
423
2
2
0
424
2
1
0
425
5
5
0
428
2
1
0
430
1
1
0
432
2
2
0
434
1
1
0
437
5
5
0
438
1
1
0
440
2
2
0
443
1
1
0
444
1
2
0
445
3
3
0
447
1
2
0
448
3
3
0
450
2
3
1
452
3
3
0
454
3
3
0
456
1
2
0
457
1
1
0
458
4
3
0
460
1
1
0
461
3
2
0
463
2
5
0
464
0
1
0
465
4
4
0
467
3
2
0
470
1
1
0
471
3
2
0
473
5
6
0
474
2
2
0
475
1
1
0
478
4
3
0
479
4
2
0
480
9
9
0
483
4
3
0
484
1
1
0
485
2
2
0
486
2
2
0
489
3
3
1
490
2
2
0
491
2
3
0
493
1
1
0
494
1
1
0
496
2
2
0
497
1
1
0
500
3
3
0
501
1
1
0
504
1
1
0
505
3
3
0
506
1
1
0
508
2
1
0
509
2
2
0
512
1
1
0
514
4
2
0
516
1
1
0
518
2
3
0
520
2
2
0
524
1
1
0
525
1
1
0
526
1
1
0
531
1
1
0
533
2
1
0
534
1
1
0
535
5
2
0
536
1
1
0
539
2
3
0
540
1
1
0
544
0
1
0
545
3
3
0
548
1
2
0
549
1
1
0
550
2
3
0
551
1
1
0
554
1
1
0
555
1
1
0
556
1
1
0
558
1
1
0
559
2
2
0
560
1
1
0
562
2
2
0
563
3
3
0
565
1
1
0
567
2
2
0
573
1
1
0
574
1
1
0
575
1
1
0
576
1
1
0
578
0
1
1
590
2
3
0
593
1
1
0
595
0
1
0
598
1
1
0
600
3
3
0
601
1
1
0
602
1
1
0
606
1
1
0
612
1
1
0
613
2
2
0
619
1
1
0
622
3
3
0
630
1
2
0
633
1
1
0
634
3
3
0
640
2
1
0
641
1
1
0
642
2
1
0
644
1
1
0
645
2
2
0
651
1
1
0
652
1
1
0
660
1
1
0
661
1
1
0
668
1
1
0
672
1
1
0
692
1
1
0
694
1
1
0
695
3
3
0
699
2
1
0
700
1
1
0
705
1
1
0
709
0
1
0
710
10
4
0
717
1
1
0
721
1
1
0
731
2
2
0
732
1
1
0
735
0
1
0
745
1
1
0
758
1
1
0
772
3
2
0
804
2
1
0
822
1
1
0
841
1
1
0
950
1
1
0
1180
1
1
0
1452
2
2
0
1642
3
2
0
1665
2
2
0
1692
2
1
0
1698
1
1
0
1700
0
1
0
1701
1
1
0
1761
2
1
0
1762
1
1
0
1816
1
1
0
1831
1
1
0
1834
1
1
0
1850
2
2
0
1852
1
1
0
1876
2
1
0
1889
8
5
0
1890
5
3
0
1894
1
1
0
1900
2
1
0
1905
2
1
0
1911
1
1
0
1915
1
1
0
1927
1
1
0
1933
1
1
0
1935
3
1
0
1938
3
2
0
1939
2
1
0
1940
1
2
0
1944
3
1
0
1949
3
2
0
1950
1
2
0
1955
2
1
0
1960
0
1
0
1962
1
1
0
1965
3
2
0
1967
4
1
0
1969
4
1
0
1970
1
1
0
1975
0
1
0
1980
2
1
0
1989
1
1
0
1994
11
6
0
1995
3
2
0
1997
0
1
0
2000
5
5
0
2002
1
1
0
2003
1
1
0
2004
4
1
0
2009
12
9
0
2010
4
3
0
2011
2
1
0
2012
4
2
0
2019
18
14
0
2020
5
3
0
2022
1
1
0
2024
9
6
0
2025
2
1
0
2030
3
2
0
2034
1
1
0
2038
3
1
0
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2
0
2803
3
1
0
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8
8
0
2805
1
6
0
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13
9
0
2808
4
3
0
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21
10
0
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13
9
0
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28
25
0
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1
2
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4
6
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9
8
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18
19
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15
14
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3
4
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12
13
0
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3
4
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12
13
1
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2
2
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106
78
0
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2
4
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57
35
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17
10
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18
15
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3
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18
16
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5
5
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63
54
0
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3
2
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3
3
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2
7
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35
32
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13
13
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5
3
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16
15
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9
12
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1
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16
9
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1
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3
2
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10
10
0
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5
3
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5
5
0
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8
6
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6
11
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2
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7
9
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8
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0
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8
8
0
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6
5
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23
12
0
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12
13
0
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6
5
0
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9
6
0
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4
4
0
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30
16
0
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9
9
0
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3
4
0
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5
5
0
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6
4
0
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123
100
0
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13
8
1
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31
27
0
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4
3
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3
3
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2
1
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27
35
0
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6
9
0
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22
11
0
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11
12
0
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17
15
0
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152
119
0
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1
1
0
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22
26
0
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20
19
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7
3
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3
5
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1
4
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7
6
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20
7
0
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30
25
0
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5
4
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53
30
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18
15
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6
4
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23
20
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3
4
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42
33
0
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9
5
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2
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71
62
0
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47
35
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7
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18
14
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18
18
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25
21
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5
7
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7
10
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1
3
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19
19
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47
34
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19
18
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9
5
0
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33
35
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11
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32
29
1
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22
20
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17
13
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8
7
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1
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7
9
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5
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0
2
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23
17
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36
28
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10
11
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20
13
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6
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21
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8
11
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53
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6
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4
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59
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6
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26
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4
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15
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10
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0
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9
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5
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2
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2
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9
6
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154
107
0
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30
21
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27
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84
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19
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53
37
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110
99
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7
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13
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3
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6
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65
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18
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43
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11
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10
10
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5
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1
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3
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65
39
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6
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13
10
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4
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89
69
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16
11
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40
25
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2
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6
11
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21
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21
24
0
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9
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186
168
0
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9
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1
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61
60
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13
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40
20
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7
5
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1
2
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15
12
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0
2
0
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7
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7
6
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5
6
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39
44
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52
37
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11
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2998
32
30
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2999
6
7
0
3000
94
77
1
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7
11
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25
20
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48
29
1
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5
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12
9
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4
8
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5
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8
14
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20
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33
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45
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52
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1
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12
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12
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37
27
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49
44
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8
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4
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12
11
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41
25
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39
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13
7
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108
90
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146
125
0
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20
15
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11
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10
14
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40
31
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3
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11
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28
21
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29
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15
12
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2
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7
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18
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37
34
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52
33
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7
7
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157
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2
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29
26
0
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25
25
0
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2
4
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142
103
2
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3
4
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23
16
1
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15
12
0
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6
3
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15
10
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4
4
0
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11
12
0
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13
10
0
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6
7
0
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53
42
0
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17
12
0
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44
47
1
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19
16
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37
27
0
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13
19
0
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30
30
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9
6
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10
13
0
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122
85
0
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6
8
0
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26
24
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116
100
0
3076
70
60
0
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8
7
0
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24
18
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3079
3
3
0
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24
23
0
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2
2
0
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6
7
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12
8
0
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7
8
0
3085
33
31
0
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92
64
0
3087
18
17
0
3088
7
5
0
3089
63
52
0
3090
24
18
0
3091
4
8
0
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3
3
0
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12
10
0
3094
5
4
0
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23
24
0
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8
9
0
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36
26
0
3098
13
16
0
3099
13
8
0
3100
58
50
0
3101
16
13
0
3102
34
31
0
3104
18
13
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3105
5
7
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3106
34
21
0
3107
7
5
0
3108
8
10
0
3109
30
35
0
3110
44
34
0
3111
20
25
0
3112
12
7
0
3113
10
6
0
3115
33
30
0
3116
20
14
0
3117
16
9
0
3118
27
28
0
3119
16
15
0
3120
94
97
0
3121
4
4
0
3122
12
10
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3123
6
5
0
3124
3
5
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3125
14
10
0
3126
33
27
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3127
4
5
0
3129
41
28
0
3130
73
64
0
3131
15
20
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3133
11
12
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3134
6
3
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3135
22
23
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3136
6
5
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3137
23
21
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3138
16
9
0
3139
65
45
0
3140
39
50
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3141
3
4
0
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25
22
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3143
1
1
0
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3
4
0
3145
6
6
0
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32
34
0
3147
9
6
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36
29
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3149
12
12
0
3150
29
21
0
3151
25
19
0
3152
1
4
0
3153
30
23
0
3154
2
4
0
3155
37
39
0
3156
0
1
0
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4
2
0
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14
10
0
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32
39
0
3160
38
27
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3161
3
4
0
3162
15
16
0
3163
6
5
0
3164
27
29
0
3165
24
26
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3166
22
24
1
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5
3
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16
14
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11
9
0
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21
10
0
3171
13
11
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3172
18
12
0
3173
37
31
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3174
25
22
0
3175
47
32
0
3176
7
7
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3177
31
26
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3178
3
4
0
3179
48
47
0
3180
23
24
0
3181
28
24
0
3182
27
25
0
3183
38
28
0
3184
67
51
1
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19
21
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44
32
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29
23
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10
14
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3189
3
4
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3190
14
12
0
3191
14
13
0
3192
4
6
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3193
44
40
0
3194
25
25
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3195
36
39
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13
10
1
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22
21
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20
15
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42
31
0
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92
76
0
3201
20
14
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44
34
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3203
30
31
0
3204
2
3
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3205
15
15
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3206
16
17
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3207
2
3
0
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121
119
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92
105
0
3210
48
29
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50
47
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22
20
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29
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13
16
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17
9
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42
36
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55
48
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18
15
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34
28
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23
30
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8
3
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10
12
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8
11
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8
6
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36
25
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8
6
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32
17
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53
47
0
3229
1
3
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33
41
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3231
33
35
0
3232
48
35
1
3233
5
2
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19
17
0
3235
5
5
0
3236
35
37
0
3237
12
11
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3238
8
7
0
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17
13
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39
26
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26
19
0
3242
6
2
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24
20
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3244
21
15
0
3245
8
8
0
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8
6
0
3247
19
17
0
3248
13
15
0
3249
10
12
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37
34
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3252
42
41
0
3253
22
20
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3254
36
41
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3255
27
27
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3256
16
14
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3257
15
12
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3258
11
9
0
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13
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0
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10
10
0
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14
11
0
3262
4
4
0
3263
11
14
0
3264
3
2
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3265
17
17
0
3266
62
52
0
3267
11
4
0
3268
8
8
0
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44
36
0
3270
13
13
0
3271
13
8
0
3272
51
29
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3273
11
7
0
3274
26
23
0
3275
44
33
0
3276
15
15
0
3277
10
8
0
3278
11
15
0
3279
9
9
0
3280
28
27
0
3281
20
18
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3282
2
2
0
3283
10
9
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3284
11
14
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3285
48
42
0
3286
9
9
0
3287
21
20
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3288
6
5
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3289
25
21
0
3290
10
10
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3291
12
10
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3292
5
8
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3293
3
1
0
3294
23
18
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3295
13
9
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3296
39
53
0
3297
2
3
0
3298
8
4
0
3299
10
9
0
3300
59
53
0
3301
5
6
0
3302
5
5
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3303
14
11
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3304
2
6
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3305
5
6
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3306
21
18
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3307
49
49
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3308
34
21
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3309
15
12
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3310
59
69
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3311
20
24
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3312
10
11
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3313
15
16
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3314
7
10
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3315
3
5
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3316
52
48
0
3317
4
3
0
3318
41
28
0
3319
22
21
0
3320
11
10
0
3322
15
11
0
3323
15
7
0
3324
2
10
0
3325
27
25
1
3326
12
11
0
3327
6
7
0
3328
2
2
0
3329
23
23
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3330
28
23
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3331
15
13
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3332
20
22
0
3333
27
24
0
3334
5
5
0
3335
51
49
0
3336
26
27
0
3337
1
1
0
3338
26
26
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3339
8
2
0
3340
68
49
0
3341
7
10
0
3342
29
25
0
3343
17
10
1
3344
14
7
0
3345
11
14
0
3346
6
6
0
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59
60
0
3348
20
22
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3349
25
32
0
3350
44
31
0
3351
66
59
0
3352
13
14
0
3353
5
10
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3354
13
16
0
3355
24
19
0
3356
7
7
0
3357
12
10
0
3358
40
50
0
3359
35
29
0
3360
44
46
0
3361
2
3
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3362
36
34
0
3363
17
11
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3364
5
7
0
3365
7
12
0
3366
16
10
0
3367
15
19
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3368
3
5
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3369
16
12
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3370
23
22
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3371
25
23
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3372
11
10
0
3373
47
59
0
3374
21
21
1
3375
20
26
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3376
10
5
0
3377
2
5
0
3378
7
8
0
3379
13
11
0
3380
15
13
0
3381
6
5
0
3382
22
14
0
3383
0
2
0
3384
6
7
0
3385
25
26
0
3386
9
7
0
3387
5
6
0
3388
7
7
0
3389
39
37
0
3390
15
11
0
3391
20
18
0
3392
3
6
0
3393
13
7
0
3394
4
4
0
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50
61
0
3396
3
6
0
3397
8
8
0
3398
6
9
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3399
7
5
0
3400
36
35
0
3401
10
7
0
3402
9
6
0
3403
7
10
0
3404
15
11
0
3405
7
8
0
3406
30
20
1
3407
4
4
0
3408
34
35
0
3409
3
7
0
3410
4
7
0
3411
26
26
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46
30
0
3414
8
4
0
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45
35
0
3416
1
2
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43
40
0
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21
29
0
3419
5
8
0
3420
15
14
0
3421
3
1
0
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9
9
0
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20
15
0
3424
15
15
0
3425
26
15
0
3426
26
31
0
3427
7
5
0
3428
34
33
0
3429
7
7
0
3430
24
17
0
3431
7
7
0
3432
25
22
0
3433
6
5
0
3434
5
5
0
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28
14
0
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8
6
0
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9
4
0
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4
6
0
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9
10
0
3440
18
22
0
3441
9
14
1
3442
12
15
0
3443
5
7
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0
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9
8
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5
6
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17
10
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3
2
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8
3
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4
7
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5
4
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0
2
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7
7
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14
12
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12
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5
4
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4
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19
17
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18
11
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2
2
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33
27
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5
5
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1
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0
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3
3
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14
11
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3
3
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29
18
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5
6
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4
5
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12
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1
2
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3
4
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22
20
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2
2
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10
13
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0
2
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6
3
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3
5
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7
6
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41
45
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9
8
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8
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9
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22
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10
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14
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4
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16
11
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1
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12
12
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2
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15
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21
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8
8
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6
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1
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16
10
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16
17
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9
6
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10
10
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2
3
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4
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33
23
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18
18
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12
12
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8
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0
2
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2
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23
12
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6
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0
1
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1
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1
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5
8
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11
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4
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18
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3
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7
3
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6
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6
3
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54
39
0
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16
11
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5
7
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13
13
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22
21
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62
43
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3
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4
4
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5
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11
12
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31
25
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3
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7
8
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6
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4
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4
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18
19
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8
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3
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31
23
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8
9
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23
19
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0
1
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4
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5
5
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4
4
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5
5
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1
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11
9
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1
2
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22
24
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4
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11
6
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11
10
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15
11
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8
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0
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0
2
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5
8
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50
48
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17
13
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4
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1
1
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1
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27
18
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0
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8
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18
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19
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6
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7
8
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2
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7
12
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2
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10
11
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2
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7
9
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17
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16
11
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4
4
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1
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32
29
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43
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3
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6
6
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9
10
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2
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8
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1
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33
27
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17
12
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2
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37
31
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4
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12
10
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2
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1
1
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25
21
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41
35
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31
25
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2
2
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13
9
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4
4
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5
5
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2
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2
2
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5
5
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3
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6
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3
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11
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6
13
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12
13
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2
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10
11
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17
17
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0
1
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4
8
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2
3
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11
14
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0
1
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7
7
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16
12
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7
5
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1
2
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0
1
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4
2
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8
11
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22
21
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0
2
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1
5
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6
8
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5
5
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1
2
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1
2
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4
5
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16
16
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3
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18
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21
18
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6
6
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1
1
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29
31
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11
13
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1
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7
4
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6
4
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2
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9
10
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20
21
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11
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2
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1
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9
12
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4
6
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9
9
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0
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5
5
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6
6
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3
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5
5
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1
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6
7
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5
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3
4
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3
2
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2
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24
21
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0
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2
5
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3
5
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1
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7
3
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3
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6
4
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21
12
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4
3
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10
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14
11
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3
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2
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1
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8
5
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5
5
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6
6
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4
3
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2
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5
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21
12
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20
14
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3
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14
15
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2
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13
14
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11
6
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27
24
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6
4
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0
1
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7
2
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10
9
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8
8
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0
1
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29
15
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8
4
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3
2
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3
2
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6
5
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9
8
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29
24
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3
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1
2
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1
2
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17
14
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1
1
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1
1
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3
4
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23
18
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40
23
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2
8
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3
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9
8
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6
5
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4
2
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13
16
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3
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3
6
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2
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7
7
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1
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3
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1
2
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7
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1
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11
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7
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6
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6
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7
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7
8
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1
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16
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12
11
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5
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10
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8
13
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15
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2
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7
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3
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2
10
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10
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6
12
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9
7
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1
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4
4
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1
1
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3
3
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14
12
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3
3
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6
7
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13
15
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2
2
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4
5
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6
4
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9
6
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1
1
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3
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5
5
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1
2
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8
6
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2
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8
10
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12
12
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8
12
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19
16
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3
1
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4
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8
10
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19
11
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9
10
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4
8
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5
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1
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8
7
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6
3
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1
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6
5
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3
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1
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12
14
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0
1
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1
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4
4
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4
4
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4
2
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4
3
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1
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4
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3
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8
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8
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7
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2
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2
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27
22
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8
8
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5
3
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4
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1
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3
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18
10
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9
5
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8
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15
11
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2
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9
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1
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9
16
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11
11
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12
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1
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11
9
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9
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13
9
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1
2
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5
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6
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7
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16
12
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1
2
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12
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2
2
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0
1
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4
1
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3
2
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1
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4
3
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1
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18
24
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1
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4
2
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22
25
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16
14
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4
4
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16
15
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18
21
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1
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0
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6
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5
4
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1
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1
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1
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2
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14
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1
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4
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3
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2
2
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2
1
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11
12
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8
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1
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2
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10
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6
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9
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8
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1
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8
6
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1
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1
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6
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1
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2
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4
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1
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2
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4
4
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3
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28
23
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0
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16
14
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6
4
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4
3
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4
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2
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0
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2
2
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20
19
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2
2
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8
8
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3
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0
1
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1
3
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11
9
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5
2
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5
6
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3
2
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1
1
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3
1
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5
6
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2
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3
2
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1
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9
10
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3
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3
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12
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3
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4
4
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5
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2
3
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1
2
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6
5
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12
16
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1
1
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1
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2
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5
6
0
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1
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7
7
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6
3
0
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4
3
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4
2
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5
6
0
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7
3
0
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3
4
0
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5
4
0
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2
1
0
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1
1
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5
4
0
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6
4
0
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1
2
0
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1
1
0
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7
2
0
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4
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0
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0
1
0
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9
4
0
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8
2
0
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1
1
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8
6
0
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4
5
0
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2
1
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3
3
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1
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4
5
0
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0
1
0
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10
10
0
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1
1
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2
2
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1
1
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1
1
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0
1
0
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2
3
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1
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1
1
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7
9
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5
5
0
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3
4
0
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2
2
0
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3
4
0
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3
3
0
4791
4
2
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101750
3
2
0
102000
1
3
0
105000
0
1
0
107000
3
4
1
117000
0
1
0
120000
7
11
0
158200
0
1
0
320715
1
1
0
>>>>> END <<<<<
In [14]:
print factors.head()
len(factors)
case_num case_yr veh_seq_num aprnt_seq_num aprnt_fctr crashid
0 31708092 2005 1 1 ZZ 317080922005
1 31722193 2005 2 2 ZZ 317221932005
2 31720835 2005 1 1 42 317208352005
3 31720835 2005 3 2 ZZ 317208352005
4 31725298 2005 1 2 YY 317252982005
Out[14]:
132744
In [27]:
vehicles['uid'] = vehicles.crashid.map(str) + "_" + vehicles.veh_seq_num.map(str)
factors['uid'] = factors.crashid.map(str) + "_" + factors.veh_seq_num.map(str)
vehicles.crashid
Out[27]:
0 11032032001
1 11032032001
2 11032322001
3 11032322001
4 11032412001
5 11032412001
6 11032412001
7 11033772001
8 11033772001
9 11033772001
10 11033772001
11 11046112001
12 11046112001
13 11047672001
14 11047672001
15 11047922001
16 11047922001
17 11048012001
18 11048012001
19 11048012001
20 11059052001
21 11059052001
22 11059432001
23 11059432001
24 11059502001
25 11059502001
26 11060692001
27 11060692001
28 11060832001
29 11060832001
...
66342 356345442014
66343 356345442014
66344 356345472014
66345 356345472014
66346 356372572014
66347 356372572014
66348 356430272014
66349 356430272014
66350 356479652014
66351 356479652014
66352 356479652014
66353 356479662014
66354 356479662014
66355 356479662014
66356 356479662014
66357 356996552014
66358 356996552014
66359 356886442014
66360 356886442014
66361 356886442014
66362 357124922014
66363 357124922014
66364 356954922014
66365 356954922014
66366 356977442014
66367 356977442014
66368 357021242014
66369 357021242014
66370 357021262014
66371 357021262014
Name: crashid, dtype: int64
In [40]:
factor_out = summarize(["aprnt_fctr"], factors, include_sums = False)
factor_out.sort('')
>>>>> START <<<<<
>>>>>>>>>>>>>>>>>>>>
APRNT_FCTR
crashid
count
aprnt_fctr
01
4
02
353
03
579
04
6192
05
719
06
29
07
2634
08
99
09
2414
10
60
11
60
12
122
13
913
14
1496
15
23
16
15
17
1364
18
1129
19
1035
20
773
21
71
22
30
23
6
24
12
25
143
26
544
27
107
28
298
29
101
31
2
32
4
33
4
34
2
40
980
41
35
42
258
43
2
44
6
45
214
46
48
47
40
48
9
49
5
50
24
51
1
60
1415
61
17
62
211
63
35
64
108
65
104
66
1018
67
6
68
63
69
397
80
82
XX
13567
YY
11893
ZZ
80869
>>>>> END <<<<<
Out[40]:
pandas.core.frame.DataFrame
In [62]:
print factor_out.columns.get_level_values(1)
test = factor_out.iloc[:, factor_out.columns.get_level_values(1)=='count']
Index([u'count'], dtype='object')
In [78]:
pd.DataFrame(test['crashid']['count']).sort_values('count')
Out[78]:
count
aprnt_fctr
51
1
31
2
43
2
34
2
33
4
32
4
01
4
49
5
67
6
44
6
23
6
48
9
24
12
16
15
61
17
15
23
50
24
06
29
22
30
63
35
41
35
47
40
46
48
11
60
10
60
68
63
21
71
80
82
08
99
29
101
65
104
27
107
64
108
12
122
25
143
62
211
45
214
42
258
28
298
02
353
69
397
26
544
03
579
05
719
20
773
13
913
40
980
66
1018
19
1035
18
1129
17
1364
60
1415
14
1496
09
2414
07
2634
04
6192
YY
11893
XX
13567
ZZ
80869
Content source: mdviz/mdviz.github.io
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