Added missing Fig. 9, Table III and Table IV

This commit is contained in:
Claudio Maggioni (maggicl) 2021-04-27 14:18:14 +02:00
parent 89ff905aff
commit 5671678414
158 changed files with 113610 additions and 1 deletions

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.gitignore vendored
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**/.ipynb_checkpoints/
task_slowdown/?_state_changes.json.gz
**/*.gz
figure_9/*.parquet/
figure_9/?_task_count/
figure_9/?_machine_count/
table_iii/*.parquet/

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#!/bin/sh
i=$1
cut ${i}_task_count/part-00000 -d, -f2-3 | \
sort | uniq -c | sed 's/^\s*//' | tr ' ' ',' | \
awk 'BEGIN {print "count,term,task_count"} {print $0}' > ${i}_term_task_count.csv

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#!/bin/sh
i=$1
cut ${i}_machine_count/part-00000 -d, -f2-3 | \
sort | uniq -c | sed 's/^\s*//' | tr ' ' ',' | \
awk 'BEGIN {print "count,term,task_count"} {print $0}' > ${i}_term_machine_count.csv

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5,101,1-2,12
7,105,<1,43090
7,0,10-60,35696
6,25,2-4,32696
5,200,2-4,544
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6,100,60-1d,4
5,101,<1,4
5,100,2-4,4
7,118,60-1d,1640

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@ -0,0 +1,283 @@
6,101,10-60,8
7,115,1-2,3152
7,105,60-1d,5785
6,118,2-4,268
7,107,>=1d,104
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4,115,<1,8
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6,119,1-2,8444
5,115,4-10,28100
7,101,4-10,296
7,101,2-4,68
7,114,1-2,8
7,103,60-1d,88558
6,103,10-60,9460
-1,107,<1,24
5,25,4-10,4
5,119,60-1d,368
5,0,2-4,12
6,117,>=1d,65
7,200,1-2,82204
7,200,>=1d,20964
5,119,1-2,696
7,107,4-10,2588
6,0,4-10,64
7,25,1-2,120
6,119,4-10,13540
6,116,60-1d,669
7,118,4-10,6612
5,103,<1,68
7,118,<1,1266
5,0,<1,100
7,114,<1,16
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7,115,<1,2096
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7,25,60-1d,1044
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-1,115,<1,1495
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6,25,4-10,2780
6,119,>=1d,224
5,107,60-1d,148
7,114,10-60,4
5,0,>=1d,100
7,107,1-2,1024
7,117,60-1d,1500
6,25,60-1d,1700
7,118,1-2,4564
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7,103,<1,46801
6,105,1-2,652
5,119,4-10,440
7,360,<1,2
6,115,60-1d,28533
5,117,>=1d,160
6,107,<1,17896
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7,360,60-1d,116
7,0,<1,40658
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6,115,>=1d,1124
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5,117,<1,36482
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92
figure_9/figure9a-join.py Executable file
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@ -0,0 +1,92 @@
#!/usr/bin/env python3
# coding: utf-8
import json
import pandas
import findspark
import pyspark
import pyspark.sql
import sys
import gzip
import os
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import ByteType
from pyspark.sql.functions import col
if len(sys.argv) is not 4:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
sys.exit()
cluster=sys.argv[1]
findspark.init()
DIR = os.path.dirname(__file__)
NAME = cluster + "_figure9a.csv"
spark = pyspark.sql.SparkSession.builder \
.appName("figure_9a_join") \
.config("spark.driver.maxResultSize", "128g") \
.config("spark.local.dir", sys.argv[2]) \
.config("spark.driver.memory", sys.argv[3]) \
.getOrCreate()
sc = spark.sparkContext
dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz"
#dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_test.json"
df = spark.read.json(dfepath)
df = df.withColumnRenamed("collection_id", "jobid") \
.select("jobid", "collection_type", "time", "type")
try:
df["collection_type"] = df["collection_type"].cast(ByteType())
except:
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
df = df.filter(((col("collection_type").isNull()) | (col("collection_type") ==
"0")) & (col("time").isNotNull()))
jpath = DIR + "/figure-9a-task-count-" + cluster + ".parquet"
dfj = spark.read.parquet(jpath)
df = df.join(dfj, 'jobid', 'left')
MICROS = 1000000
def for_each_job(data):
jobid = data[0]
ts = data[1]
global non
ts = sorted(ts, key=lambda x: x["time"])
l = len(ts)
last_term = -1
task_count = 0
for i,t in enumerate(ts):
if t["type"] >= 4 and t["type"] <= 8:
last_term = t["type"]
task_count = t["task_count"]
return str(jobid) + "," + str(last_term) + "," + str(task_count)
def cleanup(x):
return {
"time": int(x.time),
"type": 0 if x.type is None else int(x.type),
"id": x.jobid,
"task_count": -1 if x.task_count is None else int(x.task_count)
}
df2 = df.rdd \
.map(cleanup) \
.groupBy(lambda x: x["id"]) \
.repartition(100) \
.map(for_each_job) \
.coalesce(1) \
.saveAsTextFile(cluster + "_task_count")
# vim: set ts=4 sw=4 et tw=80:

View file

@ -15,6 +15,7 @@ from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import *
from decimal import *
import os
if len(sys.argv) is not 5:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}")
@ -22,6 +23,12 @@ if len(sys.argv) is not 5:
cluster=sys.argv[1]
if os.path.exists("/home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working"):
print("already done")
sys.exit()
os.system("touch /home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working")
spark = pyspark.sql.SparkSession.builder \
.appName("figure_9a") \
.config("spark.driver.maxResultSize", "128g") \
@ -44,9 +51,10 @@ df = df.rdd.filter(lambda x: x.collection_id is not None and x.instance_index is
(x.collection_type == 0 or x.collection_type is None)) \
.map(lambda x: (x.collection_id, x.instance_index)) \
.groupBy(lambda x: x[0]) \
.map(lambda x: [x[0], len(x[1])]) \
.map(lambda x: [x[0], len(set(x[1]))]) \
.toDF(["jobid", "task_count"])
df.write.parquet("/home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + ".parquet")
os.system("rm /home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working")
# vim: set ts=4 sw=4 et tw=120:

92
figure_9/figure9c-join.py Executable file
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@ -0,0 +1,92 @@
#!/usr/bin/env python3
# coding: utf-8
import json
import pandas
import findspark
import pyspark
import pyspark.sql
import sys
import gzip
import os
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import ByteType
from pyspark.sql.functions import col
if len(sys.argv) is not 4:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
sys.exit()
cluster=sys.argv[1]
findspark.init()
DIR = os.path.dirname(__file__)
NAME = cluster + "_figure9a.csv"
spark = pyspark.sql.SparkSession.builder \
.appName("figure_9a_join") \
.config("spark.driver.maxResultSize", "128g") \
.config("spark.local.dir", sys.argv[2]) \
.config("spark.driver.memory", sys.argv[3]) \
.getOrCreate()
sc = spark.sparkContext
dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz"
#dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_test.json"
df = spark.read.json(dfepath)
df = df.withColumnRenamed("collection_id", "jobid") \
.select("jobid", "collection_type", "time", "type")
try:
df["collection_type"] = df["collection_type"].cast(ByteType())
except:
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
df = df.filter(((col("collection_type").isNull()) | (col("collection_type") ==
"0")) & (col("time").isNotNull()))
jpath = DIR + "/figure-9c-machine-count-" + cluster + ".parquet"
dfj = spark.read.parquet(jpath)
df = df.join(dfj, 'jobid', 'left')
MICROS = 1000000
def for_each_job(data):
jobid = data[0]
ts = data[1]
global non
ts = sorted(ts, key=lambda x: x["time"])
l = len(ts)
last_term = -1
machine_count = 0
for i,t in enumerate(ts):
if t["type"] >= 4 and t["type"] <= 8:
last_term = t["type"]
machine_count = t["machine_count"]
return str(jobid) + "," + str(last_term) + "," + str(machine_count)
def cleanup(x):
return {
"time": int(x.time),
"type": 0 if x.type is None else int(x.type),
"id": x.jobid,
"machine_count": -1 if x.machine_count is None else int(x.machine_count)
}
df2 = df.rdd \
.map(cleanup) \
.groupBy(lambda x: x["id"]) \
.repartition(100) \
.map(for_each_job) \
.coalesce(1) \
.saveAsTextFile(cluster + "_machine_count")
# vim: set ts=4 sw=4 et tw=80:

64
figure_9/figure9c.py Executable file
View file

@ -0,0 +1,64 @@
#!/usr/bin/env python3
# coding: utf-8
import os
import json
import pandas as pd
import findspark
findspark.init()
import pyspark
import pyspark.sql
import sys
import gzip
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import *
from decimal import *
import os
if len(sys.argv) is not 5:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}")
sys.exit()
cluster=sys.argv[1]
DIR = os.path.dirname(__file__)
NAME = "figure-9c-machine-count-" + cluster
if os.path.exists(DIR + "/" + NAME + "-working"):
print("already done")
sys.exit()
os.system("touch " + DIR + "/" + NAME + "-working")
spark = pyspark.sql.SparkSession.builder \
.appName(NAME) \
.config("spark.driver.maxResultSize", "128g") \
.config("spark.local.dir", sys.argv[2]) \
.config("spark.driver.memory", sys.argv[3]) \
.getOrCreate()
sc = spark.sparkContext
# READING INSTANCE EVENTS DATA
dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_instance_events*.json.gz"
#dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_test.json"
df = spark.read.json(dfepath)
try:
df["collection_type"] = df["collection_type"].cast(ByteType())
except:
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
df = df.rdd.filter(lambda x: x.collection_id is not None and x.machine_id is not None and
int(x.machine_id) is not -1 and int(x.machine_id) is not 0 and
(x.collection_type == 0 or x.collection_type is None)) \
.map(lambda x: (x.collection_id, int(x.machine_id))) \
.groupBy(lambda x: x[0]) \
.map(lambda x: [x[0], len(set(x[1]))]) \
.toDF(["jobid", "machine_count"])
df.write.parquet(DIR + "/" + NAME + ".parquet")
os.system("rm " + DIR + "/" + NAME + "-working")
# vim: set ts=4 sw=4 et tw=120:

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7,25,1-2,16
6,119,4-10,9676
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7,118,4-10,132
5,103,<1,224
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6,115,10-60,34008
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7,117,60-1d,4
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6,25,60-1d,300
-1,103,<1,609
-1,19,<1,10
8,103,<1,16
6,117,60-1d,224
7,103,<1,28288
5,119,4-10,4336
6,105,1-2,52
7,360,<1,2
6,115,60-1d,9732
6,107,<1,17812
8,105,<1,2
6,105,10-60,624
6,105,<1,60
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6,105,60-1d,116
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7,100,<1,12
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4,200,10-60,4
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6,200,4-10,180096
5,115,2-4,1240
5,116,10-60,60
6,0,>=1d,120
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6,117,4-10,1100
6,230,4-10,4
6,119,2-4,21156
6,116,1-2,8823
-1,25,<1,420
5,25,>=1d,32
7,105,1-2,5136
5,200,<1,5860
6,119,10-60,14740
6,230,60-1d,20
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5,116,60-1d,32
7,103,4-10,20908
5,100,10-60,20
6,116,4-10,7035
5,25,2-4,40
7,105,10-60,4672
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6,103,2-4,1500
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5,360,>=1d,136
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6,200,60-1d,160780
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7,100,4-10,12
7,118,60-1d,800

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44
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The Google 2019 Borg cluster traces analysis were conducted by using Apache
Spark and its Python 3 API (pyspark). Spark was used to execute a series of
queries to perform various sums and aggregations over the entire dataset
provided by Google.
In general, each query follows a general Map-Reduce template, where traces are
first read, parsed, filtered by performing selections, projections and computing
new derived fields. Then, the trace records are often grouped by one of their
fields, clustering related data toghether before a reduce or fold operation is
applied to each grouping.
Most input data is in JSONL format and adheres to a schema Google profided in
the form of a protobuffer specification located here:
https://github.com/google/cluster-data/blob/master/clusterdata_trace_format_v3.proto
On of the main quirks in the traces is that fields that have a "zero" value
(i.e. a value like 0 or the empty string) are often omitted in the JSON object
records. When reading the traces in Apache Spark is therefore necessary to check
for this possibility and populate those zero fields when omitted.
Most queries use only two or three fields in each trace records, while the
original records often are made of a couple of dozen fields. In order to save
memory during the query, a projection is often applied to the data by the means
of a .map() operation over the entire trace set, performed using Spark's RDD
API.
Another operation that is often necessary to perform prior to the Map-Reduce core of
each query is a record filtering process, which is often motivated by the
presence of incomplete data (i.e. records which contain fields whose values is
unknown). This filtering is performed using the .filter() operation of Spark's
RDD API.
The core of each query is often a groupBy followed by a map() operation on the
aggregated data. The groupby groups the set of all records into several subsets
of records each having something in common. Then, each of this small clusters is
reduced with a .map() operation to a single record. The motivation behind this
computation is often to analyze a time series of several different traces of
programs. This is implemented by groupBy()-ing records by program id, and then
map()-ing each program trace set by sorting by time the traces and computing the
desired property in the form of a record.
Sometimes intermediate results are saved in Spark's parquet format in order to
compute and save intermediate results beforehand.

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#!/usr/bin/env python3
# coding: utf-8
import json
import pandas
import findspark
findspark.init()
import pyspark
import pyspark.sql
import sys
import gzip
import os
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import ByteType
DIR = os.path.dirname(__file__)
if len(sys.argv) != 4:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
sys.exit()
cluster=sys.argv[1]
if os.path.exists(DIR + "/bigtable-" + cluster + "-working") or os.path.exists(DIR + "/bigtable-" + cluster + ".parquet"):
print("already launched")
sys.exit()
os.system("touch " + DIR + "/bigtable-" + cluster + "-working")
spark = pyspark.sql.SparkSession.builder \
.appName("bigtable") \
.config("spark.driver.maxResultSize", "32g") \
.config("spark.local.dir", sys.argv[2]) \
.config("spark.driver.memory", sys.argv[3]) \
.getOrCreate()
sc = spark.sparkContext
df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz")
#df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json")
try:
df["collection_type"] = df["collection_type"].cast(ByteType())
except:
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
def for_each_task(data):
rowids = data[0].split("-")
jobid = rowids[0]
taskid = rowids[1]
ts = data[1]
ts = sorted(ts, key=lambda x: x["time"])
last_term = -1
counts = [0] * 11
for i,t in enumerate(ts):
if t["type"] >= 4 and t["type"] <= 8:
last_term = t["type"]
counts[t["type"]] += 1
return (jobid, taskid, last_term, *counts)
def cleanup(x):
return {
"time": int(x.time),
"type": 0 if x.type is None else int(x.type),
"id": x.collection_id + "-" + x.instance_index,
"priority": -1 if x.priority is None else int(x.priority)
}
df = df.rdd \
.filter(lambda x: x.collection_type is None or x.collection_type == 0) \
.filter(lambda x: x.time is not None and x.instance_index is not None and x.collection_id is not None) \
.map(cleanup) \
.groupBy(lambda x: x["id"]) \
.repartition(1000) \
.map(for_each_task) \
.toDF(["jobid", "taskid", "task_term", *["count_" + str(x) for x in range(0,11)]])
df.write.parquet(DIR + "/bigtable-" + cluster + ".parquet")
os.system("rm " + DIR + "/bigtable-" + cluster + "-working")

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,58.0,27.395924919004106,5.835266629759437,0.7362657541797388,5.528681906150175,5.317656696475938,2.3495793687316646,0.21385905619823325,0.0034117154159850603,3.395995570544976,0.08957552916559747,0.8945408979149864,3.0310917944673723
6,9.0,12.405369995608641,2.1847132402154554,0.8799252043981606,2.1926863641910397,2.1075212224047664,0.01932128901162818,0.0037793286646258285,2.1534317263499836,0.008150029740766142,0.008989201952316337,0.7198900340111737,2.1269623546687253
5,108.0,50.03955645233451,10.198896275881816,0.9415567565632885,11.821643640723707,10.278011417527157,0.28777805615705365,11.061863580235867,0.002098283791764348,0.46765555933356,0.05314384652552837,0.3560170189158718,4.570892016678895
8,7.0,8.847144735439395,1.7224956469208628,2.0535606410575316,0.7519899790341494,0.6955243239401585,0.08334813972495647,0.0018211861696457127,0.3841903272804804,1.3299100955900642,1.007933264631676,0.21771614370491454,0.5986549873849544
4,2924.0,428.55068857072354,104.07974609774922,0.4735045965967858,102.01192332530044,98.2043818614248,73.69359521596917,0.7685530826862396,1.785086130405792E-4,28.76616421742349,0.8455007954133787,4.683940000105005,15.02320086944195
-1,84.0,14.818523496614894,0.9975457984866587,0.7739496216646754,0.7931800079649542,0.7857277976901633,0.0,0.0,0.0,0.0,0.0,1.394578853046595,10.073541417761849
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 58.0 27.395924919004106 5.835266629759437 0.7362657541797388 5.528681906150175 5.317656696475938 2.3495793687316646 0.21385905619823325 0.0034117154159850603 3.395995570544976 0.08957552916559747 0.8945408979149864 3.0310917944673723
3 6 9.0 12.405369995608641 2.1847132402154554 0.8799252043981606 2.1926863641910397 2.1075212224047664 0.01932128901162818 0.0037793286646258285 2.1534317263499836 0.008150029740766142 0.008989201952316337 0.7198900340111737 2.1269623546687253
4 5 108.0 50.03955645233451 10.198896275881816 0.9415567565632885 11.821643640723707 10.278011417527157 0.28777805615705365 11.061863580235867 0.002098283791764348 0.46765555933356 0.05314384652552837 0.3560170189158718 4.570892016678895
5 8 7.0 8.847144735439395 1.7224956469208628 2.0535606410575316 0.7519899790341494 0.6955243239401585 0.08334813972495647 0.0018211861696457127 0.3841903272804804 1.3299100955900642 1.007933264631676 0.21771614370491454 0.5986549873849544
6 4 2924.0 428.55068857072354 104.07974609774922 0.4735045965967858 102.01192332530044 98.2043818614248 73.69359521596917 0.7685530826862396 1.785086130405792E-4 28.76616421742349 0.8455007954133787 4.683940000105005 15.02320086944195
7 -1 84.0 14.818523496614894 0.9975457984866587 0.7739496216646754 0.7931800079649542 0.7857277976901633 0.0 0.0 0.0 0.0 0.0 1.394578853046595 10.073541417761849

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,60.0,40.9010406775462,8.96533411212207,1.1236242609569156,8.817185962634415,8.21370173898341,3.351495825915401,0.2763050332235977,0.0036563227006368043,5.5410792131219875,0.033457306614610785,1.0084090015199039,3.566791899753253
6,20.0,17.27759558548148,2.999636153710804,1.5549015403294333,2.9978729175206382,3.0121754750188137,0.02044427162564865,0.020628020320906527,2.942578848692604,0.011640483355261005,0.016277768205339063,1.025285737295677,2.6761543694063548
5,260.0,86.77241946490753,19.370860965779514,1.665039341604111,20.905317678349576,19.52377119459658,0.5180609751118421,19.65679772757762,5.599398064708044E-4,0.6753923953175034,0.08852298377924372,0.778502977596575,3.5895932853884873
8,14.0,25.69045523138833,4.756602112676056,4.94595699195171,2.7637072434607646,2.7792064889336014,0.2572308853118712,0.007419517102615694,1.9283513581488934,3.5154363682092553,2.0151534205231387,0.5090543259557344,2.212336519114688
4,1578.0,345.70555920501823,84.1745817071757,0.36389221634014707,84.05783324961037,82.74729832425261,64.81651789739428,0.24021407318675375,0.0,17.961538956940633,1.028401406545848,1.7111171219908035,8.604164251581077
-1,32.0,13.018129881628738,1.8312972351930517,1.1088478038599783,1.696303401460931,1.5939749765471007,0.0,0.0,0.0,0.0,0.0,1.1423670036154356,5.645339460952241
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 60.0 40.9010406775462 8.96533411212207 1.1236242609569156 8.817185962634415 8.21370173898341 3.351495825915401 0.2763050332235977 0.0036563227006368043 5.5410792131219875 0.033457306614610785 1.0084090015199039 3.566791899753253
3 6 20.0 17.27759558548148 2.999636153710804 1.5549015403294333 2.9978729175206382 3.0121754750188137 0.02044427162564865 0.020628020320906527 2.942578848692604 0.011640483355261005 0.016277768205339063 1.025285737295677 2.6761543694063548
4 5 260.0 86.77241946490753 19.370860965779514 1.665039341604111 20.905317678349576 19.52377119459658 0.5180609751118421 19.65679772757762 5.599398064708044E-4 0.6753923953175034 0.08852298377924372 0.778502977596575 3.5895932853884873
5 8 14.0 25.69045523138833 4.756602112676056 4.94595699195171 2.7637072434607646 2.7792064889336014 0.2572308853118712 0.007419517102615694 1.9283513581488934 3.5154363682092553 2.0151534205231387 0.5090543259557344 2.212336519114688
6 4 1578.0 345.70555920501823 84.1745817071757 0.36389221634014707 84.05783324961037 82.74729832425261 64.81651789739428 0.24021407318675375 0.0 17.961538956940633 1.028401406545848 1.7111171219908035 8.604164251581077
7 -1 32.0 13.018129881628738 1.8312972351930517 1.1088478038599783 1.696303401460931 1.5939749765471007 0.0 0.0 0.0 0.0 0.0 1.1423670036154356 5.645339460952241

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,32.0,24.230886526375954,5.060811887115921,1.573948615647822,4.942099494144457,4.633419469054492,1.5332365927048457,0.11608206045741715,0.003993994145486182,3.799111307139541,0.013669845891360154,0.5994831134666571,1.955030146607957
6,18.0,15.242627654582822,2.5080490741507164,0.8746289625250901,2.507759818412733,2.5083757392544026,0.017929086508531398,0.012701162996762964,2.470653874806301,0.006019712671565393,0.006413712748438859,1.6979836625222418,2.6321128479860385
5,156.0,187.03089386917128,37.82751435280233,0.4492334927406169,52.12291162115978,38.09865323383696,0.7728231070897311,48.44577283847411,2.0353779673727304,0.7560146539960415,0.1336869798261682,3.4452411393727793,2.9436644825000307
8,28.0,22.385445850996728,4.159124580112361,4.263784782831913,2.2310592169920644,2.239757675326009,0.41136520323295145,0.007568978921273818,1.4122013113696037,2.751353175010635,1.9986651606941164,0.764950933654086,2.1456148328517153
4,1748.0,404.10866897787474,99.20235081022125,0.6657593876597071,98.1256427235899,93.00203042224992,73.71552664381427,1.8128164926768464,1.6555001558117793E-4,22.90802235899034,0.5461981925833593,1.5693849330009348,12.560771463072609
-1,96.0,21.315165633982065,1.8056665534218799,0.39915551744778605,1.6912731410329003,1.617318902711943,0.0,0.0,0.0,0.0,0.0,11.252972675650753,4.548778843716802
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 32.0 24.230886526375954 5.060811887115921 1.573948615647822 4.942099494144457 4.633419469054492 1.5332365927048457 0.11608206045741715 0.003993994145486182 3.799111307139541 0.013669845891360154 0.5994831134666571 1.955030146607957
3 6 18.0 15.242627654582822 2.5080490741507164 0.8746289625250901 2.507759818412733 2.5083757392544026 0.017929086508531398 0.012701162996762964 2.470653874806301 0.006019712671565393 0.006413712748438859 1.6979836625222418 2.6321128479860385
4 5 156.0 187.03089386917128 37.82751435280233 0.4492334927406169 52.12291162115978 38.09865323383696 0.7728231070897311 48.44577283847411 2.0353779673727304 0.7560146539960415 0.1336869798261682 3.4452411393727793 2.9436644825000307
5 8 28.0 22.385445850996728 4.159124580112361 4.263784782831913 2.2310592169920644 2.239757675326009 0.41136520323295145 0.007568978921273818 1.4122013113696037 2.751353175010635 1.9986651606941164 0.764950933654086 2.1456148328517153
6 4 1748.0 404.10866897787474 99.20235081022125 0.6657593876597071 98.1256427235899 93.00203042224992 73.71552664381427 1.8128164926768464 1.6555001558117793E-4 22.90802235899034 0.5461981925833593 1.5693849330009348 12.560771463072609
7 -1 96.0 21.315165633982065 1.8056665534218799 0.39915551744778605 1.6912731410329003 1.617318902711943 0.0 0.0 0.0 0.0 0.0 11.252972675650753 4.548778843716802

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,32.0,29.9538731682486,6.43207232989496,1.5230748800354594,6.178519054595344,5.715246820278079,1.96013363220935,0.1505207364496276,0.00238531977905695,4.682411234693338,0.016155970205843924,0.6766512877024795,2.6167019024050626
6,18.0,23.105615155196222,3.882226655388177,0.7853315736358375,3.8814333232103873,3.8784288047713456,0.058650893015740224,0.01905116361354794,3.7890504581110585,0.009785355344056015,0.01869912861489448,2.684171827817283,4.098785971673896
5,269.0,228.0049751947969,48.29500852789435,0.76380209623428,62.52257238094564,48.935145540371266,0.4963163742648672,58.96820970835309,0.809520102476274,2.040396033998344,0.3247535403146475,1.8731219170423423,2.9761289729018197
8,20.0,17.065721040189125,3.330640353582074,6.579627916538185,0.12204748689485044,0.12741288930003083,0.014759995888580532,0.0035769349367869257,0.07928872443211019,4.636283276801316,1.9997944290266214,0.020803782505910164,0.15148525028266008
4,1478.0,323.36613014614846,77.59419388155071,0.7205634398700903,77.45358278322871,74.52967454130295,62.000510181861166,0.7002681565636141,3.7330380085486573E-4,14.057513672251707,0.627592128267186,1.8045505733324207,13.877307484119035
-1,103.0,27.86740294224059,1.5263777572007629,0.32681682436700127,1.1950660133805107,1.083569934937114,0.0,0.0,0.0,0.0,0.0,20.897217810428458,2.8383546019267443
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 32.0 29.9538731682486 6.43207232989496 1.5230748800354594 6.178519054595344 5.715246820278079 1.96013363220935 0.1505207364496276 0.00238531977905695 4.682411234693338 0.016155970205843924 0.6766512877024795 2.6167019024050626
3 6 18.0 23.105615155196222 3.882226655388177 0.7853315736358375 3.8814333232103873 3.8784288047713456 0.058650893015740224 0.01905116361354794 3.7890504581110585 0.009785355344056015 0.01869912861489448 2.684171827817283 4.098785971673896
4 5 269.0 228.0049751947969 48.29500852789435 0.76380209623428 62.52257238094564 48.935145540371266 0.4963163742648672 58.96820970835309 0.809520102476274 2.040396033998344 0.3247535403146475 1.8731219170423423 2.9761289729018197
5 8 20.0 17.065721040189125 3.330640353582074 6.579627916538185 0.12204748689485044 0.12741288930003083 0.014759995888580532 0.0035769349367869257 0.07928872443211019 4.636283276801316 1.9997944290266214 0.020803782505910164 0.15148525028266008
6 4 1478.0 323.36613014614846 77.59419388155071 0.7205634398700903 77.45358278322871 74.52967454130295 62.000510181861166 0.7002681565636141 3.7330380085486573E-4 14.057513672251707 0.627592128267186 1.8045505733324207 13.877307484119035
7 -1 103.0 27.86740294224059 1.5263777572007629 0.32681682436700127 1.1950660133805107 1.083569934937114 0.0 0.0 0.0 0.0 0.0 20.897217810428458 2.8383546019267443

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,258.0,55.877475071701696,13.063833371251633,1.1173576581695857,12.635772911133188,12.518487243606957,1.2879172287931027,0.056909481014429456,1.853925477924379E-4,12.159880185300567,0.05499691815902356,0.8632289725849989,2.1189057091404133
6,14.0,11.97680562448181,2.0308401767744173,1.8078136128055937,2.030681585756855,2.0673681759413753,0.013879349234882188,0.008434976564836575,1.9986772902241174,0.008240876917293001,0.02664068237046031,0.3272714370177521,1.6569574608742277
5,138.0,450.52693734836595,109.92164977879264,0.7346581989439133,112.89458755530184,110.18156843156844,0.45770301127443985,111.47104681033252,0.0,0.45570500927643787,0.18799058084772372,0.9267696589125161,3.295258313115456
8,14.0,11.899908172635445,1.8243036424854606,4.883073155800428,0.07131925313743495,0.14937251300887663,0.0,0.0,0.03397612488521579,3.1310070400979493,1.7921640648913377,0.001224364860728497,0.013468013468013467
4,310.0,84.64518885660571,18.720340845276752,0.8784253403976529,18.672697381806937,19.399654922642448,11.780753798699449,0.10611942606473455,8.952478615784034E-5,5.7909596243214425,0.6549551969138364,0.8367800376004102,7.804412758095889
-1,34.0,7.349165461025442,1.3465088494906061,0.3400815635192289,0.7647814974820162,0.7079290024348072,0.0,0.0,0.0,0.0,0.0,0.4800250034026808,3.709839544696103
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 258.0 55.877475071701696 13.063833371251633 1.1173576581695857 12.635772911133188 12.518487243606957 1.2879172287931027 0.056909481014429456 1.853925477924379E-4 12.159880185300567 0.05499691815902356 0.8632289725849989 2.1189057091404133
3 6 14.0 11.97680562448181 2.0308401767744173 1.8078136128055937 2.030681585756855 2.0673681759413753 0.013879349234882188 0.008434976564836575 1.9986772902241174 0.008240876917293001 0.02664068237046031 0.3272714370177521 1.6569574608742277
4 5 138.0 450.52693734836595 109.92164977879264 0.7346581989439133 112.89458755530184 110.18156843156844 0.45770301127443985 111.47104681033252 0.0 0.45570500927643787 0.18799058084772372 0.9267696589125161 3.295258313115456
5 8 14.0 11.899908172635445 1.8243036424854606 4.883073155800428 0.07131925313743495 0.14937251300887663 0.0 0.0 0.03397612488521579 3.1310070400979493 1.7921640648913377 0.001224364860728497 0.013468013468013467
6 4 310.0 84.64518885660571 18.720340845276752 0.8784253403976529 18.672697381806937 19.399654922642448 11.780753798699449 0.10611942606473455 8.952478615784034E-5 5.7909596243214425 0.6549551969138364 0.8367800376004102 7.804412758095889
7 -1 34.0 7.349165461025442 1.3465088494906061 0.3400815635192289 0.7647814974820162 0.7079290024348072 0.0 0.0 0.0 0.0 0.0 0.4800250034026808 3.709839544696103

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,162.0,45.039556726309925,9.904786908733803,1.1339116462680816,9.580871276442581,9.527044091989895,0.3840652955842721,0.09842973652060591,0.0011776835385799425,9.804287186832369,0.037782929127022656,0.9119289931796342,3.6552709780930823
6,20.0,19.899709298545638,3.136003779746111,1.363153013172663,3.132176993327313,3.1688171863715584,0.019381065809467326,0.003510430786726587,3.0078389040021283,0.09793363782817881,0.023707014910783277,1.3421624804362036,4.605024792154503
5,220.0,164.04307315635555,38.08070549296094,0.8814581396930471,41.14745755027091,38.52359955566639,0.27935208904808323,39.25740744938904,2.267008229239872E-5,1.5497948357552538,0.2039967355081499,0.7303053660084786,3.388973271972977
8,36.0,25.002218658112803,4.205987703822507,7.239160652232023,2.2070836674685914,1.4351242983159582,0.01181502272119754,9.0884790163058E-4,0.149585672280139,7.283533814488105,2.000427693130179,0.12675755145682974,0.3418337342956429
4,510.0,302.26234698981665,71.70614966448653,0.1528874920958502,71.67736440254065,69.58731844049944,23.973621374708607,0.19239408439272535,9.437790801929084E-5,45.97999660239531,0.3747888294308068,2.028705040724067,16.589026680634596
-1,24.0,7.784904898437184,1.5369704801518393,0.2055728304325001,1.2624157008440011,1.1139764971933934,0.0,0.0,0.0,0.0,0.0,0.5838953277066591,3.0820740621087914
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 162.0 45.039556726309925 9.904786908733803 1.1339116462680816 9.580871276442581 9.527044091989895 0.3840652955842721 0.09842973652060591 0.0011776835385799425 9.804287186832369 0.037782929127022656 0.9119289931796342 3.6552709780930823
3 6 20.0 19.899709298545638 3.136003779746111 1.363153013172663 3.132176993327313 3.1688171863715584 0.019381065809467326 0.003510430786726587 3.0078389040021283 0.09793363782817881 0.023707014910783277 1.3421624804362036 4.605024792154503
4 5 220.0 164.04307315635555 38.08070549296094 0.8814581396930471 41.14745755027091 38.52359955566639 0.27935208904808323 39.25740744938904 2.267008229239872E-5 1.5497948357552538 0.2039967355081499 0.7303053660084786 3.388973271972977
5 8 36.0 25.002218658112803 4.205987703822507 7.239160652232023 2.2070836674685914 1.4351242983159582 0.01181502272119754 9.0884790163058E-4 0.149585672280139 7.283533814488105 2.000427693130179 0.12675755145682974 0.3418337342956429
6 4 510.0 302.26234698981665 71.70614966448653 0.1528874920958502 71.67736440254065 69.58731844049944 23.973621374708607 0.19239408439272535 9.437790801929084E-5 45.97999660239531 0.3747888294308068 2.028705040724067 16.589026680634596
7 -1 24.0 7.784904898437184 1.5369704801518393 0.2055728304325001 1.2624157008440011 1.1139764971933934 0.0 0.0 0.0 0.0 0.0 0.5838953277066591 3.0820740621087914

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
7,641.0,130.05414273603333,32.198520675875095,0.7087585088856327,31.295298970192587,29.2657999265031,6.909204375453352,0.13507292467994678,3.287874849980418E-5,25.27576921314129,0.1311059582165447,0.7666908088528912,3.3678884954843866
6,18.0,105.24041797047958,14.036486764374327,1.6319637413475843,14.19607983670794,14.492640248151833,0.015228464125191524,0.001655183202748528,14.153774713161157,0.004879189146047221,0.1582999659867196,4.1007516899574465,42.448658174318595
5,40.0,40.12155252498581,8.696211692791879,1.8829566264107134,8.984963081189314,8.715229664186882,0.01611128987600368,8.592727844170227,0.0,0.3388833596491885,0.011310294399052415,0.7730361726305842,2.1101224996819647
8,4602.25,576.3841201716738,86.26609442060087,3.995708154506438,85.08583690987125,87.78969957081546,1.9313304721030042,0.3605150214592275,48.09442060085837,35.59656652360515,3.5343347639484977,20.30472103004292,203.42489270386267
4,2015.0,555.57474325323,137.80949771922224,0.005262353151041003,137.61768303560052,134.25940980097346,77.42905379577483,0.3031268316301827,0.0,58.299329782625314,0.6538187100226803,1.6500726281185494,7.547488596111211
-1,30.0,9.503553056585716,1.5523627228137338,0.2196434980260797,1.465797344179926,1.173968178011724,0.0,0.0,0.0,0.0,0.0,0.6631175977987798,4.428663715755473
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 641.0 130.05414273603333 32.198520675875095 0.7087585088856327 31.295298970192587 29.2657999265031 6.909204375453352 0.13507292467994678 3.287874849980418E-5 25.27576921314129 0.1311059582165447 0.7666908088528912 3.3678884954843866
3 6 18.0 105.24041797047958 14.036486764374327 1.6319637413475843 14.19607983670794 14.492640248151833 0.015228464125191524 0.001655183202748528 14.153774713161157 0.004879189146047221 0.1582999659867196 4.1007516899574465 42.448658174318595
4 5 40.0 40.12155252498581 8.696211692791879 1.8829566264107134 8.984963081189314 8.715229664186882 0.01611128987600368 8.592727844170227 0.0 0.3388833596491885 0.011310294399052415 0.7730361726305842 2.1101224996819647
5 8 4602.25 576.3841201716738 86.26609442060087 3.995708154506438 85.08583690987125 87.78969957081546 1.9313304721030042 0.3605150214592275 48.09442060085837 35.59656652360515 3.5343347639484977 20.30472103004292 203.42489270386267
6 4 2015.0 555.57474325323 137.80949771922224 0.005262353151041003 137.61768303560052 134.25940980097346 77.42905379577483 0.3031268316301827 0.0 58.299329782625314 0.6538187100226803 1.6500726281185494 7.547488596111211
7 -1 30.0 9.503553056585716 1.5523627228137338 0.2196434980260797 1.465797344179926 1.173968178011724 0.0 0.0 0.0 0.0 0.0 0.6631175977987798 4.428663715755473

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task_term,%95,mean,avg_count_0,avg_count_1,avg_count_2,avg_count_3,avg_count_4,avg_count_5,avg_count_6,avg_count_7,avg_count_8,avg_count_9,avg_count_10
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6,22.0,23.978294402298292,3.6882077719058923,1.081887468785444,3.6829913707382578,3.7429685027798287,0.023699507085570847,0.014129364260089101,3.6325294190151904,0.011111315224644062,0.028481837780308786,1.6026920369253232,6.4695958077977425
5,487.0,170.15370084864648,41.17235440365571,0.552648027001125,41.72223842660111,41.79343583760434,0.6004833534730614,37.59994166423601,0.0,2.8666472214120033,0.3438059919163298,1.5301054210592109,1.9720405016875704
8,386.4,94.66666666666667,20.88,0.3333333333333333,20.826666666666668,25.36,1.4933333333333334,2.4,0.5733333333333334,14.04,3.48,0.4266666666666667,4.8533333333333335
4,206.0,75.65806422555511,16.271750063805193,0.04424327042892102,16.219039469140807,16.8735606299449,6.7325436502574725,0.8371541383296551,0.0,7.164722484949482,0.4217448092600099,0.510081220255521,10.583224489183143
-1,18.0,8.123506043876587,1.659467322235171,0.4401439301785517,1.4493905084342065,1.3649208469083083,0.0,0.0,0.0,0.0,0.0,0.5973187475012122,2.612264688619138
1 task_term %95 mean avg_count_0 avg_count_1 avg_count_2 avg_count_3 avg_count_4 avg_count_5 avg_count_6 avg_count_7 avg_count_8 avg_count_9 avg_count_10
2 7 388.0 74.42554228920945 17.895524233498676 0.5817619993509507 17.243615488850107 17.2667966667992 0.6333384085077026 0.1696663924684502 2.3133795476427908E-4 17.17262432139971 0.06279913753825522 1.088900096658901 2.310284206182731
3 6 22.0 23.978294402298292 3.6882077719058923 1.081887468785444 3.6829913707382578 3.7429685027798287 0.023699507085570847 0.014129364260089101 3.6325294190151904 0.011111315224644062 0.028481837780308786 1.6026920369253232 6.4695958077977425
4 5 487.0 170.15370084864648 41.17235440365571 0.552648027001125 41.72223842660111 41.79343583760434 0.6004833534730614 37.59994166423601 0.0 2.8666472214120033 0.3438059919163298 1.5301054210592109 1.9720405016875704
5 8 386.4 94.66666666666667 20.88 0.3333333333333333 20.826666666666668 25.36 1.4933333333333334 2.4 0.5733333333333334 14.04 3.48 0.4266666666666667 4.8533333333333335
6 4 206.0 75.65806422555511 16.271750063805193 0.04424327042892102 16.219039469140807 16.8735606299449 6.7325436502574725 0.8371541383296551 0.0 7.164722484949482 0.4217448092600099 0.510081220255521 10.583224489183143
7 -1 18.0 8.123506043876587 1.659467322235171 0.4401439301785517 1.4493905084342065 1.3649208469083083 0.0 0.0 0.0 0.0 0.0 0.5973187475012122 2.612264688619138

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7,2.6865411576392657,0.812926270023483,2.553259402830044,2.4814356052649793,1.0454192149907342,0.07386656162820535,0.461386667518275,1.1887195858954966,0.04461011602641108,0.6498649728387728,1.5212777851493517
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8,1.0,2.0086799418718826,0.0,0.0,0.0,0.0,0.0,1.0340815364675386,0.974598405404344,0.0916499744707592,0.0
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1 7 2.6865411576392657 0.812926270023483 2.553259402830044 2.4814356052649793 1.0454192149907342 0.07386656162820535 0.461386667518275 1.1887195858954966 0.04461011602641108 0.6498649728387728 1.5212777851493517
2 6 1.0981809684231005 0.6130836760164798 1.0987790998703284 1.0937889142310522 0.0046962306439975905 0.0013414780719794978 1.0726227447148902 0.02439614982399709 9.523577904594333E-4 0.566999709922109 0.6397623828541761
3 5 3.2263807904568313 0.8149870827388062 3.121328830735432 2.968946761301456 0.694941716787136 0.6835561684129362 0.08595725931720571 1.8495867802311339 0.009730425479182963 0.48260478892287484 0.8415826679218458
4 8 1.0 2.0086799418718826 0.0 0.0 0.0 0.0 0.0 1.0340815364675386 0.974598405404344 0.0916499744707592 0.0
5 -1 84.51669763776025 0.6128597270976729 82.89136811273919 78.43768798331588 23.263951270275747 3.454473694810383 23.047597090019774 34.565607717784104 0.7077094276169171 9.177240424461292 57.698175220439836

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7,5.293960967421654,1.3693264100673077,5.224224392552472,4.946995947424621,1.6964485460623222,0.15783270537625355,0.6137475711552597,3.008677502126142,0.012091563523474123,0.7718873806519629,2.999386103718944
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5,9.082228823342376,0.9085572987732548,8.780879413066843,8.65149141074595,2.0033546676189595,1.9937652197355031,0.26658435954652787,4.944145420703646,0.03452602270416128,2.3009932553357197,3.4360679603461737
8,1.9967805062811692,4.956789344107064,0.0,0.0,0.0,0.0,0.0,2.9599457104980744,1.9968751972728995,0.15283126065273656,0.0
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-1,89.08430995718696,0.3986483257378175,88.05026088945665,81.26871318698608,34.681161241336575,0.7112415245158765,13.379532541509533,38.79418756440348,0.7804834173968188,6.769798682584763,20.037000284059943
1 7 5.293960967421654 1.3693264100673077 5.224224392552472 4.946995947424621 1.6964485460623222 0.15783270537625355 0.6137475711552597 3.008677502126142 0.012091563523474123 0.7718873806519629 2.999386103718944
2 6 2.3887342955525983 0.43569440172917784 2.387499604783982 2.396668796042666 0.022379645943933938 0.008476485365510145 2.3493043120223276 0.012729369005291584 0.0064840577044129105 1.5899558364064372 1.3143858989799115
3 5 9.082228823342376 0.9085572987732548 8.780879413066843 8.65149141074595 2.0033546676189595 1.9937652197355031 0.26658435954652787 4.944145420703646 0.03452602270416128 2.3009932553357197 3.4360679603461737
4 8 1.9967805062811692 4.956789344107064 0.0 0.0 0.0 0.0 0.0 2.9599457104980744 1.9968751972728995 0.15283126065273656 0.0
5 4 1.0 0.6666666666666666 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.6666666666666666 4.666666666666667
6 -1 89.08430995718696 0.3986483257378175 88.05026088945665 81.26871318698608 34.681161241336575 0.7112415245158765 13.379532541509533 38.79418756440348 0.7804834173968188 6.769798682584763 20.037000284059943

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