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spark examples

main
SoftwareObservatorium 2023-04-11 09:53:04 +02:00
parent 893b56179c
commit 2b2e11ffa9
21 changed files with 14814 additions and 0 deletions
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# ---> Eclipse
*.pydevproject
.metadata
.gradle
bin/
tmp/
*.tmp
*.bak
*.swp
*~.nib
local.properties
.settings/
.loadpath
# Eclipse Core
.project
# External tool builders
.externalToolBuilders/
# Locally stored "Eclipse launch configurations"
*.launch
# CDT-specific
.cproject
# JDT-specific (Eclipse Java Development Tools)
.classpath
# Java annotation processor (APT)
.factorypath
# PDT-specific
.buildpath
# sbteclipse plugin
.target
# TeXlipse plugin
.texlipse

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# Spark-It

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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
distributionUrl=https://repo.maven.apache.org/maven2/org/apache/maven/apache-maven/3.9.1/apache-maven-3.9.1-bin.zip
wrapperUrl=https://repo.maven.apache.org/maven2/org/apache/maven/wrapper/maven-wrapper/3.2.0/maven-wrapper-3.2.0.jar

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#!/bin/sh
# ----------------------------------------------------------------------------
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# ----------------------------------------------------------------------------
# ----------------------------------------------------------------------------
# Apache Maven Wrapper startup batch script, version 3.2.0
#
# Required ENV vars:
# ------------------
# JAVA_HOME - location of a JDK home dir
#
# Optional ENV vars
# -----------------
# MAVEN_OPTS - parameters passed to the Java VM when running Maven
# e.g. to debug Maven itself, use
# set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
# MAVEN_SKIP_RC - flag to disable loading of mavenrc files
# ----------------------------------------------------------------------------
if [ -z "$MAVEN_SKIP_RC" ] ; then
if [ -f /usr/local/etc/mavenrc ] ; then
. /usr/local/etc/mavenrc
fi
if [ -f /etc/mavenrc ] ; then
. /etc/mavenrc
fi
if [ -f "$HOME/.mavenrc" ] ; then
. "$HOME/.mavenrc"
fi
fi
# OS specific support. $var _must_ be set to either true or false.
cygwin=false;
darwin=false;
mingw=false
case "$(uname)" in
CYGWIN*) cygwin=true ;;
MINGW*) mingw=true;;
Darwin*) darwin=true
# Use /usr/libexec/java_home if available, otherwise fall back to /Library/Java/Home
# See https://developer.apple.com/library/mac/qa/qa1170/_index.html
if [ -z "$JAVA_HOME" ]; then
if [ -x "/usr/libexec/java_home" ]; then
JAVA_HOME="$(/usr/libexec/java_home)"; export JAVA_HOME
else
JAVA_HOME="/Library/Java/Home"; export JAVA_HOME
fi
fi
;;
esac
if [ -z "$JAVA_HOME" ] ; then
if [ -r /etc/gentoo-release ] ; then
JAVA_HOME=$(java-config --jre-home)
fi
fi
# For Cygwin, ensure paths are in UNIX format before anything is touched
if $cygwin ; then
[ -n "$JAVA_HOME" ] &&
JAVA_HOME=$(cygpath --unix "$JAVA_HOME")
[ -n "$CLASSPATH" ] &&
CLASSPATH=$(cygpath --path --unix "$CLASSPATH")
fi
# For Mingw, ensure paths are in UNIX format before anything is touched
if $mingw ; then
[ -n "$JAVA_HOME" ] && [ -d "$JAVA_HOME" ] &&
JAVA_HOME="$(cd "$JAVA_HOME" || (echo "cannot cd into $JAVA_HOME."; exit 1); pwd)"
fi
if [ -z "$JAVA_HOME" ]; then
javaExecutable="$(which javac)"
if [ -n "$javaExecutable" ] && ! [ "$(expr "\"$javaExecutable\"" : '\([^ ]*\)')" = "no" ]; then
# readlink(1) is not available as standard on Solaris 10.
readLink=$(which readlink)
if [ ! "$(expr "$readLink" : '\([^ ]*\)')" = "no" ]; then
if $darwin ; then
javaHome="$(dirname "\"$javaExecutable\"")"
javaExecutable="$(cd "\"$javaHome\"" && pwd -P)/javac"
else
javaExecutable="$(readlink -f "\"$javaExecutable\"")"
fi
javaHome="$(dirname "\"$javaExecutable\"")"
javaHome=$(expr "$javaHome" : '\(.*\)/bin')
JAVA_HOME="$javaHome"
export JAVA_HOME
fi
fi
fi
if [ -z "$JAVACMD" ] ; then
if [ -n "$JAVA_HOME" ] ; then
if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
# IBM's JDK on AIX uses strange locations for the executables
JAVACMD="$JAVA_HOME/jre/sh/java"
else
JAVACMD="$JAVA_HOME/bin/java"
fi
else
JAVACMD="$(\unset -f command 2>/dev/null; \command -v java)"
fi
fi
if [ ! -x "$JAVACMD" ] ; then
echo "Error: JAVA_HOME is not defined correctly." >&2
echo " We cannot execute $JAVACMD" >&2
exit 1
fi
if [ -z "$JAVA_HOME" ] ; then
echo "Warning: JAVA_HOME environment variable is not set."
fi
# traverses directory structure from process work directory to filesystem root
# first directory with .mvn subdirectory is considered project base directory
find_maven_basedir() {
if [ -z "$1" ]
then
echo "Path not specified to find_maven_basedir"
return 1
fi
basedir="$1"
wdir="$1"
while [ "$wdir" != '/' ] ; do
if [ -d "$wdir"/.mvn ] ; then
basedir=$wdir
break
fi
# workaround for JBEAP-8937 (on Solaris 10/Sparc)
if [ -d "${wdir}" ]; then
wdir=$(cd "$wdir/.." || exit 1; pwd)
fi
# end of workaround
done
printf '%s' "$(cd "$basedir" || exit 1; pwd)"
}
# concatenates all lines of a file
concat_lines() {
if [ -f "$1" ]; then
# Remove \r in case we run on Windows within Git Bash
# and check out the repository with auto CRLF management
# enabled. Otherwise, we may read lines that are delimited with
# \r\n and produce $'-Xarg\r' rather than -Xarg due to word
# splitting rules.
tr -s '\r\n' ' ' < "$1"
fi
}
log() {
if [ "$MVNW_VERBOSE" = true ]; then
printf '%s\n' "$1"
fi
}
BASE_DIR=$(find_maven_basedir "$(dirname "$0")")
if [ -z "$BASE_DIR" ]; then
exit 1;
fi
MAVEN_PROJECTBASEDIR=${MAVEN_BASEDIR:-"$BASE_DIR"}; export MAVEN_PROJECTBASEDIR
log "$MAVEN_PROJECTBASEDIR"
##########################################################################################
# Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
# This allows using the maven wrapper in projects that prohibit checking in binary data.
##########################################################################################
wrapperJarPath="$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.jar"
if [ -r "$wrapperJarPath" ]; then
log "Found $wrapperJarPath"
else
log "Couldn't find $wrapperJarPath, downloading it ..."
if [ -n "$MVNW_REPOURL" ]; then
wrapperUrl="$MVNW_REPOURL/org/apache/maven/wrapper/maven-wrapper/3.2.0/maven-wrapper-3.2.0.jar"
else
wrapperUrl="https://repo.maven.apache.org/maven2/org/apache/maven/wrapper/maven-wrapper/3.2.0/maven-wrapper-3.2.0.jar"
fi
while IFS="=" read -r key value; do
# Remove '\r' from value to allow usage on windows as IFS does not consider '\r' as a separator ( considers space, tab, new line ('\n'), and custom '=' )
safeValue=$(echo "$value" | tr -d '\r')
case "$key" in (wrapperUrl) wrapperUrl="$safeValue"; break ;;
esac
done < "$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.properties"
log "Downloading from: $wrapperUrl"
if $cygwin; then
wrapperJarPath=$(cygpath --path --windows "$wrapperJarPath")
fi
if command -v wget > /dev/null; then
log "Found wget ... using wget"
[ "$MVNW_VERBOSE" = true ] && QUIET="" || QUIET="--quiet"
if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
wget $QUIET "$wrapperUrl" -O "$wrapperJarPath" || rm -f "$wrapperJarPath"
else
wget $QUIET --http-user="$MVNW_USERNAME" --http-password="$MVNW_PASSWORD" "$wrapperUrl" -O "$wrapperJarPath" || rm -f "$wrapperJarPath"
fi
elif command -v curl > /dev/null; then
log "Found curl ... using curl"
[ "$MVNW_VERBOSE" = true ] && QUIET="" || QUIET="--silent"
if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
curl $QUIET -o "$wrapperJarPath" "$wrapperUrl" -f -L || rm -f "$wrapperJarPath"
else
curl $QUIET --user "$MVNW_USERNAME:$MVNW_PASSWORD" -o "$wrapperJarPath" "$wrapperUrl" -f -L || rm -f "$wrapperJarPath"
fi
else
log "Falling back to using Java to download"
javaSource="$MAVEN_PROJECTBASEDIR/.mvn/wrapper/MavenWrapperDownloader.java"
javaClass="$MAVEN_PROJECTBASEDIR/.mvn/wrapper/MavenWrapperDownloader.class"
# For Cygwin, switch paths to Windows format before running javac
if $cygwin; then
javaSource=$(cygpath --path --windows "$javaSource")
javaClass=$(cygpath --path --windows "$javaClass")
fi
if [ -e "$javaSource" ]; then
if [ ! -e "$javaClass" ]; then
log " - Compiling MavenWrapperDownloader.java ..."
("$JAVA_HOME/bin/javac" "$javaSource")
fi
if [ -e "$javaClass" ]; then
log " - Running MavenWrapperDownloader.java ..."
("$JAVA_HOME/bin/java" -cp .mvn/wrapper MavenWrapperDownloader "$wrapperUrl" "$wrapperJarPath") || rm -f "$wrapperJarPath"
fi
fi
fi
fi
##########################################################################################
# End of extension
##########################################################################################
# If specified, validate the SHA-256 sum of the Maven wrapper jar file
wrapperSha256Sum=""
while IFS="=" read -r key value; do
case "$key" in (wrapperSha256Sum) wrapperSha256Sum=$value; break ;;
esac
done < "$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.properties"
if [ -n "$wrapperSha256Sum" ]; then
wrapperSha256Result=false
if command -v sha256sum > /dev/null; then
if echo "$wrapperSha256Sum $wrapperJarPath" | sha256sum -c > /dev/null 2>&1; then
wrapperSha256Result=true
fi
elif command -v shasum > /dev/null; then
if echo "$wrapperSha256Sum $wrapperJarPath" | shasum -a 256 -c > /dev/null 2>&1; then
wrapperSha256Result=true
fi
else
echo "Checksum validation was requested but neither 'sha256sum' or 'shasum' are available."
echo "Please install either command, or disable validation by removing 'wrapperSha256Sum' from your maven-wrapper.properties."
exit 1
fi
if [ $wrapperSha256Result = false ]; then
echo "Error: Failed to validate Maven wrapper SHA-256, your Maven wrapper might be compromised." >&2
echo "Investigate or delete $wrapperJarPath to attempt a clean download." >&2
echo "If you updated your Maven version, you need to update the specified wrapperSha256Sum property." >&2
exit 1
fi
fi
MAVEN_OPTS="$(concat_lines "$MAVEN_PROJECTBASEDIR/.mvn/jvm.config") $MAVEN_OPTS"
# For Cygwin, switch paths to Windows format before running java
if $cygwin; then
[ -n "$JAVA_HOME" ] &&
JAVA_HOME=$(cygpath --path --windows "$JAVA_HOME")
[ -n "$CLASSPATH" ] &&
CLASSPATH=$(cygpath --path --windows "$CLASSPATH")
[ -n "$MAVEN_PROJECTBASEDIR" ] &&
MAVEN_PROJECTBASEDIR=$(cygpath --path --windows "$MAVEN_PROJECTBASEDIR")
fi
# Provide a "standardized" way to retrieve the CLI args that will
# work with both Windows and non-Windows executions.
MAVEN_CMD_LINE_ARGS="$MAVEN_CONFIG $*"
export MAVEN_CMD_LINE_ARGS
WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
# shellcheck disable=SC2086 # safe args
exec "$JAVACMD" \
$MAVEN_OPTS \
$MAVEN_DEBUG_OPTS \
-classpath "$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.jar" \
"-Dmaven.multiModuleProjectDirectory=${MAVEN_PROJECTBASEDIR}" \
${WRAPPER_LAUNCHER} $MAVEN_CONFIG "$@"

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@REM ----------------------------------------------------------------------------
@REM Licensed to the Apache Software Foundation (ASF) under one
@REM or more contributor license agreements. See the NOTICE file
@REM distributed with this work for additional information
@REM regarding copyright ownership. The ASF licenses this file
@REM to you under the Apache License, Version 2.0 (the
@REM "License"); you may not use this file except in compliance
@REM with the License. You may obtain a copy of the License at
@REM
@REM http://www.apache.org/licenses/LICENSE-2.0
@REM
@REM Unless required by applicable law or agreed to in writing,
@REM software distributed under the License is distributed on an
@REM "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
@REM KIND, either express or implied. See the License for the
@REM specific language governing permissions and limitations
@REM under the License.
@REM ----------------------------------------------------------------------------
@REM ----------------------------------------------------------------------------
@REM Apache Maven Wrapper startup batch script, version 3.2.0
@REM
@REM Required ENV vars:
@REM JAVA_HOME - location of a JDK home dir
@REM
@REM Optional ENV vars
@REM MAVEN_BATCH_ECHO - set to 'on' to enable the echoing of the batch commands
@REM MAVEN_BATCH_PAUSE - set to 'on' to wait for a keystroke before ending
@REM MAVEN_OPTS - parameters passed to the Java VM when running Maven
@REM e.g. to debug Maven itself, use
@REM set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
@REM MAVEN_SKIP_RC - flag to disable loading of mavenrc files
@REM ----------------------------------------------------------------------------
@REM Begin all REM lines with '@' in case MAVEN_BATCH_ECHO is 'on'
@echo off
@REM set title of command window
title %0
@REM enable echoing by setting MAVEN_BATCH_ECHO to 'on'
@if "%MAVEN_BATCH_ECHO%" == "on" echo %MAVEN_BATCH_ECHO%
@REM set %HOME% to equivalent of $HOME
if "%HOME%" == "" (set "HOME=%HOMEDRIVE%%HOMEPATH%")
@REM Execute a user defined script before this one
if not "%MAVEN_SKIP_RC%" == "" goto skipRcPre
@REM check for pre script, once with legacy .bat ending and once with .cmd ending
if exist "%USERPROFILE%\mavenrc_pre.bat" call "%USERPROFILE%\mavenrc_pre.bat" %*
if exist "%USERPROFILE%\mavenrc_pre.cmd" call "%USERPROFILE%\mavenrc_pre.cmd" %*
:skipRcPre
@setlocal
set ERROR_CODE=0
@REM To isolate internal variables from possible post scripts, we use another setlocal
@setlocal
@REM ==== START VALIDATION ====
if not "%JAVA_HOME%" == "" goto OkJHome
echo.
echo Error: JAVA_HOME not found in your environment. >&2
echo Please set the JAVA_HOME variable in your environment to match the >&2
echo location of your Java installation. >&2
echo.
goto error
:OkJHome
if exist "%JAVA_HOME%\bin\java.exe" goto init
echo.
echo Error: JAVA_HOME is set to an invalid directory. >&2
echo JAVA_HOME = "%JAVA_HOME%" >&2
echo Please set the JAVA_HOME variable in your environment to match the >&2
echo location of your Java installation. >&2
echo.
goto error
@REM ==== END VALIDATION ====
:init
@REM Find the project base dir, i.e. the directory that contains the folder ".mvn".
@REM Fallback to current working directory if not found.
set MAVEN_PROJECTBASEDIR=%MAVEN_BASEDIR%
IF NOT "%MAVEN_PROJECTBASEDIR%"=="" goto endDetectBaseDir
set EXEC_DIR=%CD%
set WDIR=%EXEC_DIR%
:findBaseDir
IF EXIST "%WDIR%"\.mvn goto baseDirFound
cd ..
IF "%WDIR%"=="%CD%" goto baseDirNotFound
set WDIR=%CD%
goto findBaseDir
:baseDirFound
set MAVEN_PROJECTBASEDIR=%WDIR%
cd "%EXEC_DIR%"
goto endDetectBaseDir
:baseDirNotFound
set MAVEN_PROJECTBASEDIR=%EXEC_DIR%
cd "%EXEC_DIR%"
:endDetectBaseDir
IF NOT EXIST "%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config" goto endReadAdditionalConfig
@setlocal EnableExtensions EnableDelayedExpansion
for /F "usebackq delims=" %%a in ("%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config") do set JVM_CONFIG_MAVEN_PROPS=!JVM_CONFIG_MAVEN_PROPS! %%a
@endlocal & set JVM_CONFIG_MAVEN_PROPS=%JVM_CONFIG_MAVEN_PROPS%
:endReadAdditionalConfig
SET MAVEN_JAVA_EXE="%JAVA_HOME%\bin\java.exe"
set WRAPPER_JAR="%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.jar"
set WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
set WRAPPER_URL="https://repo.maven.apache.org/maven2/org/apache/maven/wrapper/maven-wrapper/3.2.0/maven-wrapper-3.2.0.jar"
FOR /F "usebackq tokens=1,2 delims==" %%A IN ("%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.properties") DO (
IF "%%A"=="wrapperUrl" SET WRAPPER_URL=%%B
)
@REM Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
@REM This allows using the maven wrapper in projects that prohibit checking in binary data.
if exist %WRAPPER_JAR% (
if "%MVNW_VERBOSE%" == "true" (
echo Found %WRAPPER_JAR%
)
) else (
if not "%MVNW_REPOURL%" == "" (
SET WRAPPER_URL="%MVNW_REPOURL%/org/apache/maven/wrapper/maven-wrapper/3.2.0/maven-wrapper-3.2.0.jar"
)
if "%MVNW_VERBOSE%" == "true" (
echo Couldn't find %WRAPPER_JAR%, downloading it ...
echo Downloading from: %WRAPPER_URL%
)
powershell -Command "&{"^
"$webclient = new-object System.Net.WebClient;"^
"if (-not ([string]::IsNullOrEmpty('%MVNW_USERNAME%') -and [string]::IsNullOrEmpty('%MVNW_PASSWORD%'))) {"^
"$webclient.Credentials = new-object System.Net.NetworkCredential('%MVNW_USERNAME%', '%MVNW_PASSWORD%');"^
"}"^
"[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; $webclient.DownloadFile('%WRAPPER_URL%', '%WRAPPER_JAR%')"^
"}"
if "%MVNW_VERBOSE%" == "true" (
echo Finished downloading %WRAPPER_JAR%
)
)
@REM End of extension
@REM If specified, validate the SHA-256 sum of the Maven wrapper jar file
SET WRAPPER_SHA_256_SUM=""
FOR /F "usebackq tokens=1,2 delims==" %%A IN ("%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.properties") DO (
IF "%%A"=="wrapperSha256Sum" SET WRAPPER_SHA_256_SUM=%%B
)
IF NOT %WRAPPER_SHA_256_SUM%=="" (
powershell -Command "&{"^
"$hash = (Get-FileHash \"%WRAPPER_JAR%\" -Algorithm SHA256).Hash.ToLower();"^
"If('%WRAPPER_SHA_256_SUM%' -ne $hash){"^
" Write-Output 'Error: Failed to validate Maven wrapper SHA-256, your Maven wrapper might be compromised.';"^
" Write-Output 'Investigate or delete %WRAPPER_JAR% to attempt a clean download.';"^
" Write-Output 'If you updated your Maven version, you need to update the specified wrapperSha256Sum property.';"^
" exit 1;"^
"}"^
"}"
if ERRORLEVEL 1 goto error
)
@REM Provide a "standardized" way to retrieve the CLI args that will
@REM work with both Windows and non-Windows executions.
set MAVEN_CMD_LINE_ARGS=%*
%MAVEN_JAVA_EXE% ^
%JVM_CONFIG_MAVEN_PROPS% ^
%MAVEN_OPTS% ^
%MAVEN_DEBUG_OPTS% ^
-classpath %WRAPPER_JAR% ^
"-Dmaven.multiModuleProjectDirectory=%MAVEN_PROJECTBASEDIR%" ^
%WRAPPER_LAUNCHER% %MAVEN_CONFIG% %*
if ERRORLEVEL 1 goto error
goto end
:error
set ERROR_CODE=1
:end
@endlocal & set ERROR_CODE=%ERROR_CODE%
if not "%MAVEN_SKIP_RC%"=="" goto skipRcPost
@REM check for post script, once with legacy .bat ending and once with .cmd ending
if exist "%USERPROFILE%\mavenrc_post.bat" call "%USERPROFILE%\mavenrc_post.bat"
if exist "%USERPROFILE%\mavenrc_post.cmd" call "%USERPROFILE%\mavenrc_post.cmd"
:skipRcPost
@REM pause the script if MAVEN_BATCH_PAUSE is set to 'on'
if "%MAVEN_BATCH_PAUSE%"=="on" pause
if "%MAVEN_TERMINATE_CMD%"=="on" exit %ERROR_CODE%
cmd /C exit /B %ERROR_CODE%

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<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>de.hsma</groupId>
<artifactId>spark-it</artifactId>
<version>0.0.1-SNAPSHOT</version>
<properties>
<maven.compiler.target>11</maven.compiler.target>
<maven.compiler.source>11</maven.compiler.source>
<spark.version>3.3.0</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.13</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.13</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.13</artifactId>
<version>${spark.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.13</artifactId>
<version>${spark.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
</dependencies>
</project>

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Spark-It/spark-it/src/main/java/de/hsma/informatik/bdea/BasicDataset.java 100644
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package de.hsma.informatik.bdea;
import java.util.Arrays;
import java.util.List;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
public class BasicDataset {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("DSExample").master("local[4]")
.getOrCreate();
List<Integer> data = Arrays.asList(2, 3, 4, 6, 7, 8);
Dataset<Integer> ds = spark.createDataset(data, Encoders.INT());
ds.createOrReplaceTempView("numbers");
spark.sql("select * from numbers where value > 3").show();
}
}

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Spark-It/spark-it/src/main/java/de/hsma/informatik/bdea/BasicSQL.java 100644
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package de.hsma.informatik.bdea;
import static org.apache.spark.sql.functions.col;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java
public class BasicSQL {
public static void main(String[] args) {
SparkSession spark = SparkSession.builder()
.appName("Spark SQL examples").master("local[*]").getOrCreate();
Dataset<Row> data = spark.read().json("./src/main/resources/data.json");
data.show();
data.printSchema();
Dataset<Row> fnameDS = data.select("fname");
fnameDS.show();
Dataset<Row> nameDS = data.select(col("fname"), col("lname"));
nameDS.show();
Dataset<Row> genderDS = data.groupBy("gender").count();
genderDS.show();
Dataset<Row> maleGenederDS = data.filter(col("gender").eqNullSafe("Male"));
maleGenederDS.show();
Dataset<Person> peopleDS = data.as(Encoders.bean(Person.class));
peopleDS.show();
System.out.println("Select...");
data.createOrReplaceTempView("People");
Dataset<Row> sqlDF = spark.sql("SELECT * FROM People WHERE lname like 'W%'");
sqlDF.show();
spark.close();
spark.stop();
}
}

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package de.hsma.informatik.bdea;
import static org.apache.spark.sql.functions.dayofyear;
import static org.apache.spark.sql.functions.month;
import static org.apache.spark.sql.functions.unix_timestamp;
import static org.apache.spark.sql.functions.year;
import java.util.Arrays;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// vgl.: https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/8599738367597028/4332292154849829/3601578643761083/latest.html
// Daten von: https://data.sfgov.org/Public-Safety/Fire-Department-Calls-for-Service/nuek-vuh3
// https://www.youtube.com/watch?v=K14plpZgy_c
public class FriscoFire {
public static void main(String[] args) {
SparkSession spark = SparkSession.builder()
.appName("Spark SQL examples").master("local[4]").getOrCreate();
Dataset<Row> data = spark.read().format("csv").option("header", true).option("inferSchema", true)
.load("/Users/oliver/Downloads/Fire_Department_Calls_for_Service.csv");
// Mal schauen, was wir da geladen haben
data.printSchema();
data.limit(5).show();
// Spalten umbenennen (Leerzeichen und Bindestrich für spätere SQL-Abfragen entfernen)
for (String name : data.columns()) {
data = data.withColumnRenamed(name, name.replaceAll(" ", ""));
}
data = data.withColumnRenamed("Neighborhooods-AnalysisBoundaries", "Neighborhoods");
// // Spaltennamen ausgeben
Arrays.stream(data.columns()).forEach(System.out::println);
// // Anzahl der Zeilen
System.out.println(data.count());
//
// // Arten von Notfällen, do not truncate output
data.select("CallType").distinct().show(false);
// // Wie viele Anrufe welchen Typs gab es?
data.select("CallType").groupBy("CallType").count().orderBy("count").show(false);
// Datentypen anpassen: String zu Timestamp konvertieren
String fp1 = "MM/dd/yyyy";
String fp2 = "MM/dd/yyyy hh:mm:ss aa";
data = data.withColumn("CallDateTS", unix_timestamp(data.col("CallDate"), fp1).cast("timestamp")).drop("CallDate");
data = data.withColumn("WatchDateTS", unix_timestamp(data.col("WatchDate"), fp1).cast("timestamp")).drop("WatchDate");
data = data.withColumn("ReceivedDtTmTS", unix_timestamp(data.col("ReceivedDtTm"), fp2).cast("timestamp")).drop("ReceivedDtTm");
data = data.withColumn("EntryDtTmTS", unix_timestamp(data.col("EntryDtTm"), fp2).cast("timestamp")).drop("EntryDtTm");
data = data.withColumn("DispatchDtTmTS", unix_timestamp(data.col("DispatchDtTm"), fp2).cast("timestamp")).drop("DispatchDtTm");
data = data.withColumn("ResponseDtTmTS", unix_timestamp(data.col("ResponseDtTm"), fp2).cast("timestamp")).drop("ResponseDtTm");
data = data.withColumn("OnSceneDtTmTS", unix_timestamp(data.col("OnSceneDtTm"), fp2).cast("timestamp")).drop("OnSceneDtTm");
data = data.withColumn("TransportDtTmTS", unix_timestamp(data.col("TransportDtTm"), fp2).cast("timestamp")).drop("TransportDtTm");
data = data.withColumn("HospitalDtTmTS", unix_timestamp(data.col("HospitalDtTm"), fp2).cast("timestamp")).drop("HospitalDtTm");
data = data.withColumn("AvailableDtTmTS", unix_timestamp(data.col("AvailableDtTm"), fp2).cast("timestamp")).drop("AvailableDtTm");
// neues Schema anschauen
data.printSchema();
data.limit(5).show();
// welche Anrufgründe gibt es?
data.select(data.col("CallType")).distinct().orderBy(year(data.col("CallDateTS"))).show(100, false);
// welche Jahre an Daten haben wir?
data.select(year(data.col("CallDateTS"))).distinct().orderBy(data.col("CallDateTS")).show();
// zeige die Anrufe aus 2018
data.filter(year(data.col("CallDateTS")).equalTo("2018")).show();
// wie viele Anrufe gab es im Juli 2017?
long calls = data.filter(year(data.col("CallDateTS")).equalTo("2017"))
.filter(month(data.col("CallDateTS")).equalTo("07")).count();
System.out.println(calls);
// wie viele Anrufe in 7 Tagen in 2016?
data.filter(year(data.col("CallDateTS")).equalTo("2016"))
.filter(dayofyear(data.col("CallDateTS")).$greater("179"))
.groupBy(dayofyear(data.col("CallDateTS"))).count().orderBy(dayofyear(data.col("CallDateTS"))).show(7);
data.repartition(4).createOrReplaceTempView("fireServiceVIEW");
spark.sql("SELECT DISTINCT Neighborhoods FROM fireServiceVIEW LIMIT 15").show();
// Cachen kostet erst einmal deutlich mehr Zeit...
// spark.catalog().cacheTable("fireServiceVIEW");
// System.out.println("----> Count: " + spark.table("fireServiceVIEW").count());
// // mit SQL: woher kamen 2015 die meisten Anrufe?
spark.sql("SELECT Neighborhoods, count(Neighborhoods) AS Neighborhood_Count FROM fireServiceVIEW "
+ "WHERE year(CallDateTS) == 2015 GROUP BY Neighborhoods ORDER BY Neighborhood_Count DESC LIMIT 15").show();
// Joinen... zweite Tabelle: https://data.sfgov.org/Public-Safety/Fire-Incidents/wr8u-xric
Dataset<Row> incidents = spark.read().format("csv").option("header", true).option("inferSchema", true)
.load("/Users/oliver/Downloads/Fire_Incidents.csv").withColumnRenamed("Incident Number", "IncidentNumber").cache();
System.out.println(incidents.count());
incidents.show(3);
// Hier passiert der Join... kann einen Moment dauern
// s. auch: http://www.techburps.com/misc/apache-spark-dataset-joins-in-java/129
System.out.println("Joining...");
Dataset<Row> joined = data.join(incidents, data.col("IncidentNumber").equalTo(incidents.col("IncidentNumber")));
// Dataset<Row> joined = data.join(incidents, data.col("IncidentNumber").equalTo(incidents.col("IncidentNumber")), "fullouter");
joined.show(100);
System.out.println("Fin.");
}
}

70
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package de.hsma.informatik.bdea;
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// $example on$
import org.apache.spark.ml.classification.NaiveBayes;
import org.apache.spark.ml.classification.NaiveBayesModel;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$
/**
* An example for Naive Bayes Classification.
* https://spark.apache.org/docs/3.1.2/ml-classification-regression.html#naive-bayes
*/
public class JavaNaiveBayesExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaNaiveBayesExample").master("local[4]")
.getOrCreate();
// $example on$
// Load training data
Dataset<Row> dataFrame = spark.read().format("libsvm").load("src/main/resources/nb-data.txt");
// Split the data into train and test
Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
Dataset<Row> train = splits[0];
Dataset<Row> test = splits[1];
// create the trainer and set its parameters
NaiveBayes nb = new NaiveBayes();
// train the model
NaiveBayesModel model = nb.fit(train);
// Select example rows to display.
Dataset<Row> predictions = model.transform(test);
predictions.show();
// compute accuracy on the test set
MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy");
double accuracy = evaluator.evaluate(predictions);
System.out.println("Test set accuracy = " + accuracy);
// $example off$
spark.stop();
}
}

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package de.hsma.informatik.bdea;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
public class LineCount {
public static void main(String[] args) {
String pfad = "/tmp/faust.txt";
SparkConf conf = new SparkConf()
.setAppName("LineCount").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaRDD<String> text = sc.textFile(pfad);
long cnt = text.filter(line -> line.contains("Liebe")).count();
System.out.println(cnt + " Zeilen mit 'Liebe' in " + pfad);
sc.close();
}
}

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package de.hsma.informatik.bdea;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.io.Serializable;
@JsonIgnoreProperties
public class Person implements Serializable {
@JsonProperty(value = "id")
private Long id;
@JsonProperty(value = "fname")
private String fname;
@JsonProperty(value = "lname")
private String lname;
@JsonProperty(value = "email")
private String email;
@JsonProperty(value = "gender")
private String gender;
public Long getId() {
return id;
}
public void setId(Long id) {
this.id = id;
}
public String getFname() {
return fname;
}
public void setFname(String fname) {
this.fname = fname;
}
public String getLname() {
return lname;
}
public void setLname(String lname) {
this.lname = lname;
}
public String getEmail() {
return email;
}
public void setEmail(String email) {
this.email = email;
}
public String getGender() {
return gender;
}
public void setGender(String gender) {
this.gender = gender;
}
//getters and setters
}

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package de.hsma.informatik.bdea;
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
public class PiExample {
private final static int NUM_SAMPLES = 10000000;
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("Getting-Started").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(conf);
List<Integer> l = new ArrayList<>(NUM_SAMPLES);
for (int i = 0; i < NUM_SAMPLES; i++) {
l.add(i);
}
long count = sc.parallelize(l).filter(i -> {
double x = Math.random();
double y = Math.random();
return x*x + y*y <= 1;
}).count();
System.out.println("Pi is roughly " + 4.0 * count / NUM_SAMPLES);
sc.close();
}
}

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package de.hsma.informatik.bdea;
import java.util.Arrays;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
public class SimpleAdder {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("SimpleAdder").setMaster("local[2]");
JavaSparkContext sc = new JavaSparkContext(conf);
Integer[] ints = {1,2,4,5,6,7,8,9};
JavaRDD<Integer> dist = sc.parallelize(Arrays.asList(ints));
dist.collect().forEach(System.out::println);
System.out.println(dist.reduce((x,y) -> x+y));
Integer[] others = {2,3,1,0, 0, 3, 4};
JavaRDD<Integer> dist2 = sc.parallelize(Arrays.asList(others));
// JavaRDD<Integer> res = dist.intersection(dist2);
// JavaPairRDD<Integer, Integer> res = dist.join(dist2);
// res.collect().forEach(System.out::println);
dist.intersection(dist2).collect().forEach(System.out::println);
dist2 = dist2.sortBy(x->x, true, 1);
dist2.sample(true, 0.5).collect().forEach(System.out::println);
// dist2.take(2).forEach(System.out::println);
dist2.collect().forEach(System.out::println);
}
}

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package de.hsma.informatik.bdea;
import static org.apache.spark.sql.functions.callUDF;
import static org.apache.spark.sql.functions.explode;
import static org.apache.spark.sql.functions.lower;
import static org.apache.spark.sql.functions.split;
import org.apache.spark.ml.classification.NaiveBayes;
import org.apache.spark.ml.classification.NaiveBayesModel;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;
// inspired by: https://blog.codecentric.de/en/2016/06/spam-classification-using-sparks-dataframes-ml-zeppelin-part-1
public class SpamFilterExample {
public static void main(String[] args) {
SparkSession spark = SparkSession.builder()
.appName("Spark SQL examples").master("local[4]").getOrCreate();
StructType schema = new StructType()
.add("label", DataTypes.StringType)
.add("text", DataTypes.StringType);
final Dataset<Row> input = spark
.read()
.format("csv")
.option("delimiter", "\t")
.option("header", "false")
.option("mode", "PERMISSIVE")
.schema(schema)
.load("src/main/resources/SMSSpamCollection.txt")
.cache();
spark.udf().register("toUpperExample", (String s) -> s.toUpperCase(), DataTypes.StringType);
spark.udf().register("encoding", (String s) -> {
int res = 0;
if (s.equals("ham"))
res = 1;
return res;
}, DataTypes.IntegerType);
Dataset<Row> data = input.withColumn("normalizedText", lower(input.col("text")));
// data = data.withColumn("upperCase", callUDF("toUpperExample", data.col("text")));
data = data.withColumn("labelEnc", callUDF("encoding", data.col("label")));
// mal schauen, was wir da geladen haben
data.printSchema();
data.show();
// Tokenizing
// Dataset<String> tokens = data.flatMap((Row row) ->
// Arrays.stream(row.getString(2).split("\\W+"))
// .map(s -> s.trim())
// .filter(s -> s.length() > 0)
// .iterator(), Encoders.STRING());
//
// tokens.createOrReplaceTempView("quotes");
//
// spark.sql("select value from quotes order by value limit 20").show();
// spark.sql("select value, count(value) as anz from quotes group by value order by anz desc limit 20").show();
Dataset<Row> words = data.drop("text")
.withColumn("token", explode(split(data.col("normalizedText"), "\\W+")))
.drop("normalizedText");
StringIndexer indexer = new StringIndexer()
.setInputCol("token")
.setOutputCol("indexed");
Dataset<Row> indexed = indexer.fit(words).transform(words);
indexed = indexed.drop("label").withColumnRenamed("labelEnc", "label");
indexed.createOrReplaceTempView("words");
spark.sql("select * from words where label = 0").show();
indexed.show(20);
// ML-Teil, hilfreich: https://craftsmen.nl/an-introduction-to-machines-learning-with-apache-spark
String[] inputColumns = {"indexed"};
VectorAssembler assembler = new VectorAssembler().setInputCols(inputColumns).setOutputCol("features");
Dataset<Row> featureSet = assembler.transform(indexed);
Dataset<Row>[] trainingAndTestSet = featureSet.randomSplit(new double[]{0.7, 0.3}, 4242);
Dataset<Row> train = trainingAndTestSet[0];
Dataset<Row> test = trainingAndTestSet[1];
train.createOrReplaceTempView("train");
spark.sql("select label, count(label) from train group by label").show(100); // -> Datensatz ist biased: ~80% sind Ham-Worte.
NaiveBayes nb = new NaiveBayes();
nb.setLabelCol("label");
nb.setFeaturesCol("features");
NaiveBayesModel model = nb.fit(train);
// Achtung, erzeugtes Model ist biased und liefert immer 1 (= ham).
// Das kann an den unausgewogenen Trainingsdaten liegen oder auch an einem (Denk-)Fehler im Code...
// Select example rows to display.
Dataset<Row> predictions = model.transform(test);
predictions.createOrReplaceTempView("predictions");
spark.sql("select * from predictions where token = 'love'").show(100);
// spark.sql("select * from predictions where token = 'free'").show(100);
// compute accuracy on the test set
MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy");
double accuracy = evaluator.evaluate(predictions);
System.out.println("Test set accuracy = " + accuracy);
}
}

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package de.hsma.informatik.bdea;
import java.util.Arrays;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import scala.Tuple2;
public class WordCount {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("xy").setMaster("local[4]");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaRDD<String> tokens = sc.textFile("Faust.txt").flatMap(
s -> Arrays.asList(s.split("\\W+")).iterator());
JavaPairRDD<String, Integer> counts = tokens.mapToPair(
token -> new Tuple2<>(token, 1)).reduceByKey((x,y) -> x+y);
List<Tuple2<String, Integer>> results = counts.collect();
results.forEach(System.out::println);
sc.close();
}
}

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0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 443:148 455:85 456:252 457:230 458:25 467:7 468:135 469:253 470:186 471:12 483:85 484:252 485:223 494:7 495:131 496:252 497:225 498:71 511:85 512:252 513:145 521:48 522:165 523:252 524:173 539:86 540:253 541:225 548:114 549:238 550:253 551:162 567:85 568:252 569:249 570:146 571:48 572:29 573:85 574:178 575:225 576:253 577:223 578:167 579:56 595:85 596:252 597:252 598:252 599:229 600:215 601:252 602:252 603:252 604:196 605:130 623:28 624:199 625:252 626:252 627:253 628:252 629:252 630:233 631:145 652:25 653:128 654:252 655:253 656:252 657:141 658:37
1 159:124 160:253 161:255 162:63 186:96 187:244 188:251 189:253 190:62 214:127 215:251 216:251 217:253 218:62 241:68 242:236 243:251 244:211 245:31 246:8 268:60 269:228 270:251 271:251 272:94 296:155 297:253 298:253 299:189 323:20 324:253 325:251 326:235 327:66 350:32 351:205 352:253 353:251 354:126 378:104 379:251 380:253 381:184 382:15 405:80 406:240 407:251 408:193 409:23 432:32 433:253 434:253 435:253 436:159 460:151 461:251 462:251 463:251 464:39 487:48 488:221 489:251 490:251 491:172 515:234 516:251 517:251 518:196 519:12 543:253 544:251 545:251 546:89 570:159 571:255 572:253 573:253 574:31 597:48 598:228 599:253 600:247 601:140 602:8 625:64 626:251 627:253 628:220 653:64 654:251 655:253 656:220 681:24 682:193 683:253 684:220
1 125:145 126:255 127:211 128:31 152:32 153:237 154:253 155:252 156:71 180:11 181:175 182:253 183:252 184:71 209:144 210:253 211:252 212:71 236:16 237:191 238:253 239:252 240:71 264:26 265:221 266:253 267:252 268:124 269:31 293:125 294:253 295:252 296:252 297:108 322:253 323:252 324:252 325:108 350:255 351:253 352:253 353:108 378:253 379:252 380:252 381:108 406:253 407:252 408:252 409:108 434:253 435:252 436:252 437:108 462:255 463:253 464:253 465:170 490:253 491:252 492:252 493:252 494:42 518:149 519:252 520:252 521:252 522:144 546:109 547:252 548:252 549:252 550:144 575:218 576:253 577:253 578:255 579:35 603:175 604:252 605:252 606:253 607:35 631:73 632:252 633:252 634:253 635:35 659:31 660:211 661:252 662:253 663:35
1 153:5 154:63 155:197 181:20 182:254 183:230 184:24 209:20 210:254 211:254 212:48 237:20 238:254 239:255 240:48 265:20 266:254 267:254 268:57 293:20 294:254 295:254 296:108 321:16 322:239 323:254 324:143 350:178 351:254 352:143 378:178 379:254 380:143 406:178 407:254 408:162 434:178 435:254 436:240 462:113 463:254 464:240 490:83 491:254 492:245 493:31 518:79 519:254 520:246 521:38 547:214 548:254 549:150 575:144 576:241 577:8 603:144 604:240 605:2 631:144 632:254 633:82 659:230 660:247 661:40 687:168 688:209 689:31
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0 125:29 126:170 127:255 128:255 129:141 151:29 152:198 153:255 154:255 155:255 156:226 157:255 158:86 178:141 179:255 180:255 181:170 182:29 184:86 185:255 186:255 187:141 204:29 205:226 206:255 207:198 208:57 213:226 214:255 215:255 216:226 217:114 231:29 232:255 233:255 234:114 241:141 242:170 243:114 244:255 245:255 246:141 259:226 260:255 261:170 269:29 270:57 273:141 274:255 275:226 286:57 287:255 288:170 302:114 303:255 304:198 314:226 315:255 331:170 332:255 333:57 342:255 343:226 360:255 361:170 370:255 371:170 388:114 389:198 398:255 399:226 416:86 417:255 426:198 427:255 444:86 445:255 454:114 455:255 456:57 472:86 473:255 482:29 483:255 484:226 500:141 501:255 511:170 512:255 513:170 528:226 529:198 539:29 540:226 541:255 542:170 555:29 556:255 557:114 568:29 569:226 570:255 571:141 582:57 583:226 584:226 598:141 599:255 600:255 601:170 602:86 607:29 608:86 609:226 610:255 611:226 612:29 627:86 628:198 629:255 630:255 631:255 632:255 633:255 634:255 635:255 636:255 637:255 638:141 639:29 657:29 658:114 659:170 660:170 661:170 662:170 663:170 664:86
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1 126:94 127:132 154:250 155:250 156:4 182:250 183:254 184:95 210:250 211:254 212:95 238:250 239:254 240:95 266:250 267:254 268:95 294:250 295:254 296:95 322:250 323:254 324:95 350:250 351:254 352:95 378:250 379:254 380:95 405:77 406:254 407:250 408:19 433:96 434:254 435:249 461:53 462:253 463:252 464:43 490:250 491:251 492:32 517:85 518:254 519:249 545:96 546:254 547:249 573:83 574:254 575:250 576:14 602:250 603:254 604:95 630:250 631:255 632:95 658:132 659:254 660:95
1 124:32 125:253 126:31 152:32 153:251 154:149 180:32 181:251 182:188 208:32 209:251 210:188 236:32 237:251 238:228 239:59 264:32 265:253 266:253 267:95 292:28 293:236 294:251 295:114 321:127 322:251 323:251 349:127 350:251 351:251 377:48 378:232 379:251 406:223 407:253 408:159 434:221 435:251 436:158 462:142 463:251 464:158 490:64 491:251 492:242 493:55 518:64 519:251 520:253 521:161 546:64 547:253 548:255 549:221 574:16 575:181 576:253 577:220 603:79 604:253 605:236 606:63 632:213 633:251 634:126 660:96 661:251 662:126
1 129:39 130:254 131:255 132:254 133:140 157:136 158:253 159:253 160:228 161:67 184:6 185:227 186:253 187:253 188:58 211:29 212:188 213:253 214:253 215:253 216:17 239:95 240:253 241:253 242:253 243:157 244:8 266:3 267:107 268:253 269:253 270:245 271:77 294:29 295:253 296:253 297:240 298:100 322:141 323:253 324:253 325:215 349:129 350:248 351:253 352:253 353:215 377:151 378:253 379:253 380:253 381:144 405:151 406:253 407:253 408:253 409:27 431:3 432:102 433:242 434:253 435:253 436:110 437:3 459:97 460:253 461:253 462:253 463:214 464:55 487:207 488:253 489:253 490:253 491:158 515:67 516:253 517:253 518:253 519:158 543:207 544:253 545:253 546:240 547:88 571:207 572:253 573:253 574:224 598:32 599:217 600:253 601:253 602:224 626:141 627:253 628:253 629:253 630:133 654:36 655:219 656:253 657:140 658:10
0 123:59 124:55 149:71 150:192 151:254 152:250 153:147 154:17 176:123 177:247 178:253 179:254 180:253 181:253 182:196 183:79 184:176 185:175 186:175 187:124 188:48 203:87 204:247 205:247 206:176 207:95 208:102 209:117 210:243 211:237 212:192 213:232 214:253 215:253 216:245 217:152 218:6 230:23 231:229 232:253 233:138 238:219 239:58 241:95 242:118 243:80 244:230 245:254 246:196 247:30 258:120 259:254 260:205 261:8 266:114 272:38 273:255 274:254 275:155 276:5 286:156 287:253 288:92 301:61 302:235 303:253 304:102 314:224 315:253 316:78 330:117 331:253 332:196 333:18 342:254 343:253 344:78 358:9 359:211 360:253 361:73 370:254 371:253 372:78 387:175 388:253 389:155 398:194 399:254 400:101 415:79 416:254 417:155 426:112 427:253 428:211 429:9 443:73 444:251 445:200 454:41 455:241 456:253 457:87 471:25 472:240 473:253 483:147 484:253 485:227 486:47 499:94 500:253 501:200 511:5 512:193 513:253 514:230 515:76 527:175 528:253 529:155 540:31 541:219 542:254 543:255 544:126 545:18 553:14 554:149 555:254 556:244 557:45 569:21 570:158 571:254 572:253 573:226 574:162 575:118 576:96 577:20 578:20 579:73 580:118 581:224 582:253 583:247 584:85 598:30 599:155 600:253 601:253 602:253 603:253 604:254 605:253 606:253 607:253 608:253 609:254 610:247 611:84 627:5 628:27 629:117 630:206 631:244 632:229 633:213 634:213 635:213 636:176 637:117 638:32 659:45 660:23
1 128:58 129:139 156:247 157:247 158:25 183:121 184:253 185:156 186:3 211:133 212:253 213:145 238:11 239:227 240:253 241:145 266:7 267:189 268:253 269:145 294:35 295:252 296:253 297:145 322:146 323:252 324:253 325:131 350:146 351:252 352:253 353:13 378:146 379:252 380:253 381:13 406:147 407:253 408:255 409:13 434:146 435:252 436:253 437:13 462:146 463:252 464:253 465:13 490:146 491:252 492:253 493:13 517:22 518:230 519:252 520:221 521:9 545:22 546:230 547:252 548:133 574:146 575:252 576:133 602:146 603:252 604:120 630:146 631:252 658:146 659:252
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1 159:31 160:210 161:253 162:163 187:198 188:252 189:252 190:162 213:10 214:86 215:242 216:252 217:252 218:66 241:164 242:252 243:252 244:252 245:188 246:8 268:53 269:242 270:252 271:252 272:225 273:14 296:78 297:252 298:252 299:252 300:204 323:56 324:231 325:252 326:252 327:212 328:35 351:157 352:252 353:252 354:252 355:37 377:8 378:132 379:253 380:252 381:252 382:230 383:24 405:45 406:252 407:253 408:252 409:154 410:55 427:7 428:55 433:107 434:253 435:255 436:228 437:53 454:15 455:24 456:23 460:110 461:242 462:252 463:228 464:59 482:57 483:83 487:88 488:247 489:252 490:252 491:140 514:15 515:189 516:252 517:252 518:252 542:74 543:252 544:252 545:238 546:90 570:178 571:252 572:252 573:189 597:40 598:217 599:252 600:252 601:59 625:75 626:252 627:252 628:252 629:85 630:61 653:62 654:239 655:252 656:156 657:14 682:178 683:252 684:14
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1 130:24 131:150 132:233 133:38 156:14 157:89 158:253 159:254 160:254 161:71 183:78 184:203 185:254 186:254 187:254 188:232 189:77 190:54 191:8 209:12 210:155 211:240 212:254 213:223 214:76 215:254 216:254 217:254 218:254 219:68 235:3 236:101 237:216 238:254 239:227 240:122 241:26 242:110 243:254 244:254 245:254 246:184 247:100 262:46 263:222 264:254 265:254 266:179 267:48 270:181 271:254 272:254 273:146 274:6 288:2 289:145 290:248 291:254 292:182 293:111 294:4 297:3 298:250 299:254 300:206 301:3 315:6 316:144 317:254 318:254 319:171 325:125 326:254 327:252 328:80 342:6 343:142 344:254 345:179 346:95 347:4 352:61 353:246 354:254 355:150 370:64 371:254 372:177 373:14 380:124 381:254 382:246 383:32 398:108 399:97 400:15 407:24 408:226 409:254 410:116 435:177 436:255 437:254 438:5 463:196 464:254 465:99 466:1 490:3 491:199 492:254 493:79 518:129 519:254 520:254 521:23 546:178 547:254 548:192 549:8 550:3 551:43 573:11 574:198 575:254 576:128 577:66 578:130 579:225 595:137 596:202 597:106 598:84 599:84 600:84 601:112 602:254 603:254 604:254 605:254 606:212 607:151 623:172 624:254 625:254 626:254 627:254 628:254 629:254 630:254 631:254 632:254 633:162 634:75 651:12 652:106 653:177 654:254 655:254 656:254 657:235 658:135 659:100 660:17 661:2
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1 127:155 128:253 129:126 155:253 156:251 157:141 158:4 183:253 184:251 185:251 186:31 211:253 212:251 213:251 214:31 239:253 240:251 241:251 242:31 267:255 268:253 269:253 270:31 293:8 294:131 295:253 296:251 297:235 298:27 321:64 322:251 323:253 324:251 325:126 349:64 350:251 351:253 352:251 353:126 377:64 378:251 379:253 380:251 381:126 405:64 406:253 407:255 408:221 433:182 434:251 435:253 436:200 460:64 461:236 462:251 463:253 464:62 487:8 488:158 489:251 490:251 491:169 492:8 515:32 516:251 517:251 518:251 519:158 543:32 544:253 545:253 546:253 547:159 571:32 572:251 573:251 574:251 575:39 599:32 600:251 601:251 602:251 627:32 628:251 629:251 630:251 631:100 655:32 656:251 657:251 658:251
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0 154:32 155:134 156:218 157:254 158:254 159:254 160:217 161:84 176:44 177:208 178:215 179:156 180:35 181:119 182:236 183:246 184:136 185:91 186:69 187:151 188:249 189:246 190:78 203:44 204:230 205:254 206:254 207:254 208:254 209:254 210:196 211:48 216:60 217:224 218:210 219:24 231:118 232:254 233:202 234:19 235:201 236:254 237:181 238:9 245:35 246:233 247:168 259:193 260:223 261:34 263:59 264:163 265:236 266:15 274:140 275:205 276:8 286:60 287:254 288:176 293:38 302:54 303:237 304:80 314:59 315:254 316:93 331:131 332:200 342:59 343:240 344:24 359:79 360:214 370:59 371:234 387:67 388:248 389:54 398:59 399:234 416:235 417:58 426:60 427:235 443:79 444:255 445:59 454:59 455:251 456:66 471:79 472:250 473:54 482:59 483:254 484:108 499:146 500:214 510:5 511:203 512:187 513:3 526:4 527:188 528:199 539:118 540:254 541:57 554:96 555:254 556:117 567:16 568:237 569:224 570:14 581:14 582:187 583:206 584:8 596:88 597:252 598:186 599:16 608:16 609:187 610:252 611:125 625:100 626:254 627:237 628:94 629:24 635:13 636:214 637:254 638:166 653:3 654:57 655:215 656:248 657:241 658:235 659:197 660:137 661:137 662:137 663:231 664:238 665:155 666:25 684:57 685:155 686:246 687:254 688:254 689:254 690:254 691:147 692:36
1 124:102 125:252 126:252 127:41 152:102 153:250 154:250 155:202 180:102 181:250 182:250 183:232 184:91 208:102 209:250 210:250 211:212 212:29 236:102 237:252 238:252 239:254 240:150 264:102 265:250 266:250 267:252 268:149 292:102 293:250 294:250 295:252 296:149 320:102 321:250 322:250 323:252 324:231 325:80 349:152 350:252 351:254 352:252 353:100 377:151 378:250 379:252 380:250 381:100 405:151 406:250 407:252 408:250 409:100 433:151 434:250 435:252 436:250 437:100 461:123 462:243 463:254 464:252 465:100 490:202 491:252 492:250 493:100 518:80 519:252 520:250 521:190 522:30 547:252 548:250 549:250 550:49 575:255 576:252 577:252 578:252 579:214 580:31 603:171 604:250 605:250 606:250 607:252 608:190 609:40 631:20 632:160 633:250 634:250 635:252 636:250 637:100 660:20 661:170 662:250 663:212 664:49 665:20
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1 127:20 128:254 129:255 130:37 155:19 156:253 157:253 158:134 183:19 184:253 185:253 186:246 187:125 211:76 212:253 213:253 214:253 215:158 239:207 240:253 241:253 242:253 243:158 267:207 268:253 269:253 270:253 271:158 294:48 295:223 296:253 297:253 298:243 299:106 322:141 323:253 324:253 325:253 326:113 349:65 350:237 351:253 352:253 353:253 354:36 377:76 378:253 379:253 380:253 381:253 382:36 405:76 406:253 407:253 408:253 409:253 410:36 433:76 434:253 435:253 436:253 437:118 438:4 460:4 461:148 462:253 463:253 464:253 465:103 488:10 489:253 490:253 491:253 492:253 493:103 516:10 517:253 518:253 519:253 520:173 521:7 544:10 545:253 546:253 547:253 548:168 572:143 573:253 574:253 575:239 576:49 600:198 601:253 602:253 603:234 615:140 628:198 629:253 630:253 631:234 656:198 657:253 658:253 659:234
0 235:40 236:37 238:7 239:77 240:137 241:136 242:136 243:136 244:136 245:40 246:6 261:16 262:135 263:254 264:233 266:152 267:215 268:96 269:140 270:155 271:118 272:230 273:254 274:158 275:68 288:19 289:164 290:254 291:114 294:235 295:140 301:99 302:230 303:254 304:186 305:14 315:70 316:226 317:242 318:121 322:104 323:195 324:38 330:33 331:179 332:253 333:140 342:41 343:241 344:198 345:43 359:24 360:209 361:223 370:164 371:250 372:66 388:136 389:253 398:254 399:158 416:136 417:215 426:255 427:76 442:5 443:127 444:246 445:133 454:254 455:122 469:5 470:150 471:247 472:91 473:9 482:254 483:165 495:13 496:79 497:194 498:216 499:84 510:111 511:251 512:87 519:16 520:25 521:40 522:107 523:186 524:213 525:117 526:25 538:14 539:185 540:235 541:142 542:23 546:91 547:157 548:231 549:207 550:126 551:49 569:143 570:195 571:255 572:254 573:254 574:244 575:157 576:76 599:39 600:39 601:39 602:33
1 128:166 129:255 130:187 131:6 156:165 157:253 158:253 159:13 183:15 184:191 185:253 186:253 187:13 211:49 212:253 213:253 214:253 215:13 239:141 240:253 241:253 242:169 243:4 266:4 267:189 268:253 269:249 270:53 294:69 295:253 296:253 297:246 322:69 323:253 324:253 325:246 350:118 351:253 352:253 353:124 378:206 379:253 380:231 381:21 405:66 406:241 407:253 408:199 433:105 434:253 435:253 436:89 460:3 461:228 462:253 463:252 464:86 488:111 489:253 490:253 491:205 516:166 517:253 518:253 519:75 543:43 544:249 545:253 546:193 547:9 570:4 571:160 572:253 573:253 574:184 598:37 599:253 600:253 601:253 602:88 626:140 627:253 628:253 629:186 630:18 654:14 655:253 656:253 657:27
1 128:117 129:128 155:2 156:199 157:127 183:81 184:254 185:87 211:116 212:254 213:48 239:175 240:234 241:18 266:5 267:230 268:168 294:80 295:255 296:142 322:80 323:255 324:142 350:80 351:251 352:57 378:129 379:239 406:164 407:209 433:28 434:245 435:159 461:64 462:254 463:144 489:84 490:254 491:80 517:143 518:254 519:30 544:3 545:225 546:200 572:48 573:254 574:174 600:48 601:254 602:174 628:93 629:254 630:129 656:53 657:234 658:41
1 129:159 130:142 156:11 157:220 158:141 184:78 185:254 186:141 212:111 213:254 214:109 240:196 241:221 242:15 267:26 268:221 269:159 295:63 296:254 297:159 323:178 324:254 325:93 350:7 351:191 352:254 353:97 378:42 379:255 380:254 381:41 406:42 407:254 408:195 409:10 434:141 435:255 436:78 461:11 462:202 463:254 464:59 489:86 490:254 491:254 492:59 517:142 518:254 519:248 520:52 545:142 546:254 547:195 573:142 574:254 575:164 601:142 602:254 603:77 629:142 630:254 631:131 657:77 658:172 659:5
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1 129:101 130:222 131:84 157:225 158:252 159:84 184:89 185:246 186:208 187:19 212:128 213:252 214:195 239:79 240:253 241:252 242:195 267:141 268:255 269:253 270:133 294:26 295:240 296:253 297:252 298:55 322:60 323:252 324:253 325:154 326:12 349:7 350:178 351:252 352:253 353:27 377:57 378:252 379:252 380:253 381:27 405:57 406:253 407:253 408:204 409:15 433:104 434:252 435:252 436:94 460:19 461:209 462:252 463:252 488:101 489:252 490:252 491:157 516:225 517:252 518:252 519:112 544:226 545:253 546:240 547:63 572:225 573:252 574:223 600:225 601:252 602:223 628:225 629:252 630:242 631:75 656:146 657:252 658:236 659:50
0 124:41 125:254 126:254 127:157 128:34 129:34 130:218 131:255 132:206 133:34 134:18 151:53 152:238 153:252 154:252 155:252 156:252 157:252 158:252 159:252 160:252 161:252 162:162 163:26 178:66 179:220 180:252 181:252 182:252 183:209 184:153 185:223 186:252 187:252 188:252 189:252 190:252 191:98 206:166 207:252 208:252 209:252 210:252 211:141 213:85 214:230 215:252 216:252 217:252 218:252 219:98 234:166 235:252 236:252 237:252 238:252 239:141 242:73 243:102 244:252 245:252 246:252 247:98 262:166 263:252 264:252 265:252 266:191 267:30 271:5 272:97 273:252 274:252 275:220 276:51 289:123 290:245 291:252 292:252 293:202 294:14 300:56 301:252 302:252 303:252 304:65 316:18 317:154 318:252 319:252 320:241 328:56 329:252 330:252 331:252 332:65 343:21 344:146 345:252 346:252 347:252 348:241 356:56 357:252 358:252 359:252 360:65 371:67 372:252 373:252 374:252 375:252 376:241 384:56 385:252 386:252 387:252 388:65 399:67 400:252 401:252 402:252 403:252 404:116 412:56 413:252 414:252 415:252 416:65 427:67 428:252 429:252 430:252 431:252 432:20 440:56 441:252 442:252 443:252 444:65 455:67 456:252 457:252 458:252 459:87 460:4 468:56 469:252 470:252 471:124 472:11 483:67 484:252 485:252 486:252 487:54 494:19 495:236 496:245 497:252 498:252 499:98 511:67 512:252 513:252 514:252 515:97 516:5 521:39 522:219 523:252 524:252 525:252 526:252 527:98 539:67 540:252 541:252 542:252 543:252 544:102 545:89 546:89 547:89 548:89 549:203 550:252 551:252 552:252 553:252 554:209 555:64 567:67 568:252 569:252 570:252 571:252 572:252 573:252 574:252 575:252 576:252 577:252 578:252 579:252 580:226 581:130 582:68 595:67 596:252 597:252 598:252 599:252 600:252 601:252 602:252 603:252 604:252 605:252 606:252 607:239 608:77 623:17 624:65 625:163 626:252 627:252 628:252 629:252 630:252 631:252 632:252 633:252 634:96 635:59 653:17 654:176 655:252 656:252 657:252 658:252 659:155 660:32 661:32 662:6
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1 129:232 130:255 131:107 156:58 157:244 158:253 159:106 184:95 185:253 186:253 187:106 212:95 213:253 214:253 215:106 240:95 241:253 242:249 243:69 268:144 269:253 270:192 295:97 296:233 297:253 298:66 323:195 324:253 325:253 326:5 350:38 351:232 352:253 353:182 354:2 377:10 378:160 379:253 380:231 381:53 405:42 406:253 407:253 408:158 433:141 434:253 435:253 436:115 460:75 461:245 462:253 463:183 464:4 487:1 488:147 489:253 490:251 491:58 515:20 516:253 517:253 518:180 543:202 544:253 545:226 546:27 571:243 572:253 573:212 598:85 599:251 600:253 601:173 626:209 627:253 628:244 629:57 654:169 655:253 656:174
1 127:63 128:128 129:2 155:63 156:254 157:123 183:63 184:254 185:179 211:63 212:254 213:179 239:63 240:254 241:179 267:142 268:254 269:179 295:187 296:254 297:158 323:187 324:254 325:55 350:68 351:235 352:254 353:55 378:181 379:254 380:254 381:55 406:181 407:254 408:202 409:14 434:181 435:254 436:186 462:181 463:254 464:146 490:181 491:254 492:62 518:181 519:254 520:62 546:181 547:254 548:62 574:181 575:255 576:62 602:181 603:254 604:241 605:52 630:181 631:254 632:222 633:30 658:181 659:224 660:34
1 130:131 131:255 132:184 133:15 157:99 158:247 159:253 160:182 161:15 185:124 186:253 187:253 188:253 189:38 212:9 213:171 214:253 215:253 216:140 217:1 240:47 241:253 242:253 243:251 244:117 267:43 268:219 269:253 270:253 271:153 295:78 296:253 297:253 298:253 299:84 323:97 324:253 325:253 326:244 327:74 350:69 351:243 352:253 353:253 354:183 377:10 378:168 379:253 380:253 381:215 382:34 405:31 406:253 407:253 408:253 409:129 433:107 434:253 435:253 436:242 437:67 460:24 461:204 462:253 463:253 464:187 488:95 489:253 490:253 491:201 492:25 516:239 517:253 518:253 519:176 543:119 544:251 545:253 546:253 547:138 570:30 571:212 572:253 573:252 574:165 575:8 598:193 599:253 600:253 601:222 626:193 627:253 628:253 629:189 654:193 655:253 656:201 657:27
0 125:57 126:255 127:253 128:198 129:85 153:168 154:253 155:251 156:253 157:251 158:169 159:56 180:86 181:253 182:254 183:253 184:254 185:253 186:254 187:253 188:57 208:197 209:251 210:253 211:251 212:253 213:251 214:253 215:251 216:225 217:56 235:169 236:255 237:253 238:226 239:56 241:114 242:254 243:253 244:254 245:84 262:57 263:224 264:253 265:251 266:56 270:139 271:251 272:253 273:83 290:141 291:253 292:255 293:84 298:57 299:225 300:254 301:196 318:253 319:251 320:253 321:83 327:168 328:253 329:83 345:169 346:254 347:253 348:169 355:169 356:254 357:253 358:169 373:168 374:253 375:251 376:56 383:168 384:253 385:251 386:56 401:169 402:254 403:84 412:254 413:253 429:168 430:253 431:83 440:253 441:251 456:29 457:197 458:254 459:84 467:169 468:254 469:196 484:85 485:251 486:253 487:83 494:57 495:224 496:253 497:83 512:57 513:225 514:254 515:139 521:57 522:141 523:253 524:254 525:84 541:168 542:253 543:251 544:169 545:56 547:114 548:169 549:224 550:253 551:251 552:253 553:83 569:169 570:254 571:253 572:254 573:253 574:254 575:253 576:254 577:253 578:254 579:253 580:226 581:56 597:56 598:253 599:251 600:253 601:251 602:253 603:251 604:253 605:251 606:253 607:251 608:56 626:169 627:225 628:254 629:253 630:254 631:253 632:254 633:253 634:226 635:56 655:56 656:253 657:251 658:253 659:251 660:84 661:83 662:56
0 127:12 128:105 129:224 130:255 131:247 132:22 155:131 156:254 157:254 158:243 159:252 160:76 182:131 183:225 184:254 185:224 186:48 187:136 208:13 209:109 210:252 211:254 212:254 213:254 214:197 215:76 235:9 236:181 237:254 238:254 239:240 240:229 241:237 242:254 243:252 244:152 245:21 262:9 263:143 264:254 265:254 266:226 267:36 269:22 270:138 271:254 272:254 273:188 289:13 290:181 291:254 292:254 293:250 294:64 298:2 299:53 300:236 301:252 302:131 317:102 318:254 319:254 320:254 321:111 328:56 329:243 330:251 331:42 344:30 345:186 346:254 347:254 348:206 349:29 357:199 358:254 359:91 372:92 373:254 374:254 375:237 376:13 385:134 386:254 387:91 400:133 401:254 402:254 403:126 413:134 414:250 415:17 428:187 429:254 430:237 431:23 441:200 442:183 456:187 457:254 458:213 467:2 468:134 469:252 470:101 484:183 485:254 486:133 495:14 496:254 497:234 498:34 512:92 513:254 514:161 522:84 523:204 524:254 525:56 540:92 541:254 542:229 549:85 550:252 551:252 552:188 553:11 568:56 569:252 570:229 575:3 576:53 577:235 578:253 579:166 597:224 598:245 599:130 600:68 601:68 602:134 603:214 604:254 605:254 606:159 625:141 626:254 627:254 628:254 629:254 630:254 631:254 632:233 633:95 634:3 653:14 654:152 655:254 656:254 657:254 658:186 659:157 660:53
1 130:226 131:247 132:55 157:99 158:248 159:254 160:230 161:30 185:125 186:254 187:254 188:254 189:38 213:125 214:254 215:254 216:212 217:24 240:18 241:223 242:254 243:252 244:118 268:24 269:254 270:254 271:239 295:27 296:195 297:254 298:254 299:93 323:78 324:254 325:254 326:246 327:74 351:158 352:254 353:254 354:185 378:41 379:239 380:254 381:254 382:43 405:22 406:218 407:254 408:254 409:167 410:9 433:32 434:254 435:254 436:254 437:130 460:24 461:187 462:254 463:254 464:234 465:16 488:189 489:254 490:254 491:254 492:128 515:64 516:247 517:254 518:255 519:219 520:42 543:139 544:254 545:254 546:222 547:40 570:30 571:213 572:254 573:235 574:45 598:194 599:254 600:254 601:223 626:194 627:254 628:254 629:190 654:194 655:254 656:202 657:27
1 130:166 131:253 132:124 133:53 158:140 159:251 160:251 161:180 185:125 186:246 187:251 188:251 189:51 212:32 213:190 214:251 215:251 216:251 217:103 240:21 241:174 242:251 243:251 244:251 268:73 269:176 270:253 271:253 272:201 296:149 297:251 298:251 299:251 300:71 323:27 324:228 325:251 326:251 327:157 328:10 351:180 352:253 353:251 354:251 355:142 377:27 378:180 379:231 380:253 381:251 382:96 383:41 405:89 406:253 407:253 408:255 409:211 410:25 433:217 434:251 435:251 436:253 437:107 460:21 461:221 462:251 463:251 464:242 465:92 487:32 488:190 489:251 490:251 491:251 492:103 515:202 516:251 517:251 518:251 519:122 542:53 543:255 544:253 545:253 546:221 547:51 570:180 571:253 572:251 573:251 574:142 598:180 599:253 600:251 601:251 602:142 626:180 627:253 628:251 629:157 630:82 654:180 655:253 656:147 657:10
1 129:17 130:206 131:229 132:44 157:2 158:125 159:254 160:123 185:95 186:254 187:254 188:123 212:78 213:240 214:254 215:254 216:123 240:100 241:254 242:254 243:254 244:123 267:2 268:129 269:254 270:254 271:220 272:20 295:9 296:254 297:254 298:254 299:123 322:22 323:179 324:254 325:254 326:254 327:49 350:83 351:254 352:254 353:254 354:183 355:19 378:136 379:254 380:254 381:254 382:139 404:3 405:111 406:252 407:254 408:254 409:232 410:45 432:67 433:254 434:254 435:254 436:216 437:40 459:14 460:192 461:254 462:254 463:254 464:140 486:23 487:192 488:254 489:254 490:254 491:246 514:77 515:254 516:254 517:255 518:241 519:100 541:65 542:235 543:254 544:254 545:254 546:172 568:30 569:238 570:254 571:254 572:254 573:219 574:26 596:34 597:254 598:254 599:254 600:216 601:41 624:34 625:254 626:254 627:254 628:188 652:12 653:170 654:254 655:254 656:82
1 130:218 131:253 132:124 157:84 158:236 159:251 160:251 184:63 185:236 186:251 187:251 188:122 212:73 213:251 214:251 215:251 216:173 240:202 241:251 242:251 243:251 244:71 267:53 268:255 269:253 270:253 271:253 272:72 295:180 296:253 297:251 298:251 299:188 300:30 323:180 324:253 325:251 326:251 327:142 350:47 351:211 352:253 353:251 354:235 355:82 377:27 378:211 379:251 380:253 381:251 382:215 405:89 406:253 407:253 408:255 409:253 410:164 433:217 434:251 435:251 436:253 437:168 438:15 460:21 461:221 462:251 463:251 464:253 465:107 487:32 488:190 489:251 490:251 491:251 492:221 493:61 515:73 516:251 517:251 518:251 519:251 520:180 543:255 544:253 545:253 546:253 547:201 570:105 571:253 572:251 573:251 574:251 575:71 598:180 599:253 600:251 601:246 602:137 603:10 626:180 627:253 628:251 629:215 654:180 655:253 656:251 657:86
1 124:102 125:180 126:1 152:140 153:254 154:130 180:140 181:254 182:204 208:140 209:254 210:204 236:72 237:254 238:204 264:25 265:231 266:250 267:135 292:11 293:211 294:254 295:222 321:101 322:254 323:250 324:15 349:96 350:254 351:254 352:95 377:2 378:251 379:254 380:95 405:2 406:251 407:254 408:95 433:96 434:254 435:254 436:95 461:53 462:253 463:254 464:139 490:250 491:254 492:235 493:27 518:201 519:254 520:254 521:128 546:80 547:254 548:254 549:139 574:65 575:254 576:254 577:139 602:150 603:254 604:254 605:139 630:229 631:254 632:254 633:43 658:52 659:196 660:168 661:9
0 128:87 129:208 130:249 155:27 156:212 157:254 158:195 182:118 183:225 184:254 185:254 186:232 187:147 188:46 209:115 210:248 211:254 212:254 213:254 214:254 215:254 216:230 217:148 218:12 236:18 237:250 238:254 239:245 240:226 241:254 242:254 243:254 244:254 245:254 246:148 263:92 264:205 265:254 266:250 267:101 268:20 269:194 270:254 271:254 272:254 273:254 274:229 275:53 291:152 292:254 293:254 294:94 297:14 298:124 299:187 300:254 301:254 302:254 303:213 318:95 319:252 320:254 321:206 322:15 327:3 328:6 329:51 330:231 331:254 332:94 345:50 346:246 347:254 348:254 349:20 358:200 359:254 360:96 372:21 373:184 374:254 375:254 376:147 377:2 386:200 387:254 388:96 400:177 401:254 402:254 403:218 404:33 413:16 414:211 415:254 416:96 427:11 428:219 429:254 430:251 431:92 441:84 442:254 443:232 444:44 455:101 456:254 457:254 458:141 469:162 470:254 471:231 472:42 483:235 484:254 485:227 486:42 496:51 497:238 498:254 499:213 511:235 512:254 513:199 524:160 525:254 526:229 527:52 539:235 540:254 541:199 549:10 550:84 551:150 552:253 553:254 554:147 567:235 568:254 569:213 570:20 575:17 576:63 577:158 578:254 579:254 580:254 581:155 582:12 595:122 596:248 597:254 598:204 599:98 600:42 601:177 602:180 603:200 604:254 605:254 606:253 607:213 608:82 609:10 624:203 625:254 626:254 627:254 628:254 629:254 630:254 631:254 632:251 633:219 634:94 652:35 653:221 654:254 655:254 656:254 657:254 658:254 659:217 660:95
1 126:134 127:230 154:133 155:231 156:10 182:133 183:253 184:96 210:133 211:253 212:96 238:133 239:253 240:183 266:133 267:253 268:217 294:133 295:253 296:217 322:133 323:253 324:217 350:133 351:253 352:217 378:133 379:253 380:217 406:134 407:254 408:218 434:133 435:253 436:159 462:133 463:253 464:199 490:156 491:253 492:96 518:254 519:247 520:73 546:254 547:248 548:74 573:99 574:254 575:245 576:64 600:89 601:230 602:254 603:125 627:140 628:251 629:253 630:243 631:10 655:114 656:242 657:195 658:69
1 125:29 126:85 127:255 128:139 153:197 154:251 155:253 156:251 181:254 182:253 183:254 184:253 209:253 210:251 211:253 212:251 237:254 238:253 239:254 240:253 265:253 266:251 267:253 268:138 293:254 294:253 295:254 296:196 321:253 322:251 323:253 324:196 349:254 350:253 351:254 352:84 377:253 378:251 379:253 380:196 405:254 406:253 407:254 408:253 433:253 434:251 435:253 436:251 461:254 462:253 463:254 464:253 489:253 490:251 491:253 492:251 517:254 518:253 519:254 520:253 545:253 546:251 547:253 548:251 573:254 574:253 575:254 576:253 601:253 602:251 603:253 604:251 629:57 630:225 631:254 632:253 658:56 659:253 660:251
1 125:149 126:255 127:254 128:58 153:215 154:253 155:183 156:2 180:41 181:232 182:253 183:181 208:92 209:253 210:253 211:181 236:92 237:253 238:253 239:181 264:92 265:253 266:253 267:181 292:92 293:253 294:253 295:181 320:92 321:253 322:253 323:181 348:92 349:253 350:253 351:181 376:92 377:253 378:253 379:181 404:92 405:253 406:253 407:181 432:92 433:253 434:253 435:181 460:92 461:253 462:253 463:181 488:31 489:228 490:253 491:181 517:198 518:253 519:228 520:54 545:33 546:226 547:253 548:195 549:7 574:199 575:253 576:253 577:75 602:34 603:218 604:253 605:228 606:117 607:14 608:12 631:33 632:219 633:253 634:253 635:253 636:211 660:32 661:123 662:149 663:230 664:41
1 130:79 131:203 132:141 157:51 158:240 159:240 160:140 185:88 186:252 187:252 188:140 213:197 214:252 215:252 216:140 241:197 242:252 243:252 244:140 268:147 269:253 270:253 271:253 295:38 296:234 297:252 298:242 299:89 323:113 324:252 325:252 326:223 350:16 351:207 352:252 353:252 354:129 377:16 378:203 379:253 380:252 381:220 382:37 405:29 406:253 407:255 408:253 409:56 432:19 433:181 434:252 435:253 436:176 437:6 460:166 461:252 462:252 463:228 464:52 487:10 488:203 489:252 490:252 491:126 514:63 515:178 516:252 517:252 518:173 542:114 543:253 544:253 545:225 570:238 571:252 572:252 573:99 596:7 597:135 598:253 599:252 600:176 601:19 624:29 625:252 626:253 627:252 628:55 652:13 653:189 654:253 655:204 656:25
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0 127:46 128:105 129:254 130:254 131:224 132:59 133:59 134:9 155:196 156:254 157:253 158:253 159:253 160:253 161:253 162:128 182:96 183:235 184:254 185:253 186:253 187:253 188:253 189:253 190:247 191:122 208:4 209:101 210:244 211:253 212:254 213:234 214:241 215:253 216:253 217:253 218:253 219:186 220:18 236:96 237:253 238:253 239:253 240:232 241:83 242:109 243:170 244:253 245:253 246:253 247:253 248:116 264:215 265:253 266:253 267:253 268:196 271:40 272:253 273:253 274:253 275:253 276:116 290:8 291:141 292:247 293:253 294:253 295:237 296:29 299:6 300:38 301:171 302:253 303:253 304:116 317:13 318:146 319:253 320:253 321:253 322:253 323:57 329:156 330:253 331:253 332:116 345:40 346:253 347:253 348:253 349:253 350:178 351:27 357:156 358:253 359:253 360:116 372:136 373:204 374:253 375:253 376:253 377:192 378:27 385:156 386:253 387:253 388:116 399:28 400:195 401:254 402:254 403:254 404:250 405:135 412:99 413:255 414:254 415:254 416:117 427:118 428:253 429:253 430:253 431:253 432:142 439:19 440:170 441:253 442:253 443:216 444:62 454:42 455:212 456:253 457:253 458:253 459:253 460:38 466:124 467:188 468:253 469:253 470:253 471:174 482:59 483:253 484:253 485:253 486:237 487:93 488:3 491:31 492:40 493:130 494:247 495:253 496:253 497:253 498:204 499:13 510:59 511:253 512:253 513:253 514:154 518:54 519:218 520:254 521:253 522:253 523:253 524:253 525:253 526:38 538:59 539:253 540:253 541:253 542:215 543:156 544:156 545:156 546:209 547:253 548:255 549:253 550:253 551:253 552:192 553:97 554:15 566:55 567:242 568:253 569:253 570:253 571:253 572:253 573:253 574:253 575:253 576:254 577:253 578:253 579:204 580:23 595:118 596:253 597:253 598:253 599:253 600:253 601:253 602:253 603:253 604:254 605:216 606:174 607:13 623:54 624:116 625:243 626:253 627:253 628:253 629:253 630:253 631:146 632:117 633:62 653:53 654:132 655:253 656:253 657:192 658:57 659:13
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1 156:60 157:229 158:38 184:187 185:254 186:78 211:121 212:252 213:254 214:78 239:197 240:254 241:206 242:6 267:197 268:254 269:202 294:27 295:218 296:233 297:62 322:117 323:254 324:195 350:203 351:254 352:195 377:64 378:244 379:254 380:195 405:79 406:254 407:255 408:161 433:79 434:254 435:254 436:65 461:79 462:254 463:241 464:52 489:79 490:254 491:189 517:79 518:254 519:189 545:79 546:254 547:189 573:79 574:254 575:189 601:79 602:254 603:194 604:5 629:35 630:219 631:254 632:72 658:34 659:223 660:195 687:129 688:195
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0 127:45 128:254 129:254 130:254 131:148 132:24 133:9 154:43 155:254 156:252 157:252 158:252 159:252 160:252 161:121 162:13 181:58 182:237 183:254 184:252 185:252 186:252 187:252 188:252 189:252 190:68 208:69 209:224 210:252 211:254 212:252 213:252 214:252 215:252 216:252 217:252 218:135 219:17 235:75 236:216 237:252 238:252 239:254 240:231 241:168 242:252 243:252 244:252 245:252 246:252 247:45 262:77 263:212 264:252 265:252 266:252 267:242 268:93 269:32 270:114 271:177 272:252 273:252 274:252 275:158 276:12 289:75 290:212 291:252 292:252 293:252 294:252 295:231 299:116 300:252 301:252 302:252 303:252 304:21 316:69 317:216 318:252 319:252 320:252 321:252 322:252 323:62 327:116 328:252 329:252 330:252 331:252 332:21 344:93 345:252 346:252 347:252 348:252 349:252 350:62 355:21 356:158 357:252 358:252 359:252 360:21 371:64 372:239 373:252 374:252 375:252 376:252 377:252 378:21 384:139 385:252 386:252 387:252 388:21 398:5 399:87 400:254 401:254 402:254 403:254 404:237 405:41 411:11 412:150 413:254 414:254 415:254 416:22 425:5 426:85 427:252 428:252 429:252 430:252 431:222 432:55 439:116 440:252 441:252 442:252 443:214 444:18 453:24 454:252 455:252 456:252 457:252 458:252 459:91 466:26 467:153 468:252 469:252 470:252 471:45 481:24 482:252 483:252 484:252 485:252 486:252 487:91 492:18 493:93 494:151 495:252 496:252 497:252 498:184 499:28 509:24 510:252 511:252 512:252 513:252 514:252 515:164 516:116 517:116 518:116 519:117 520:141 521:252 522:252 523:252 524:252 525:252 526:68 537:24 538:252 539:252 540:252 541:252 542:252 543:252 544:252 545:252 546:252 547:254 548:252 549:252 550:252 551:252 552:252 553:163 554:31 565:9 566:121 567:252 568:252 569:252 570:252 571:252 572:252 573:252 574:252 575:254 576:252 577:252 578:252 579:178 580:91 581:33 594:13 595:119 596:252 597:252 598:252 599:252 600:252 601:252 602:252 603:254 604:252 605:252 606:184 607:37 623:13 624:121 625:252 626:252 627:252 628:252 629:252 630:252 631:254 632:214 633:45 634:28 652:8 653:21 654:21 655:169 656:252 657:252 658:41 659:22 660:18
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1 130:218 131:170 132:108 157:32 158:227 159:252 160:232 185:129 186:252 187:252 188:252 212:1 213:253 214:252 215:252 216:168 240:144 241:253 242:252 243:236 244:62 268:144 269:253 270:252 271:215 296:144 297:253 298:252 299:112 323:21 324:206 325:253 326:252 327:71 351:99 352:253 353:255 354:119 378:63 379:242 380:252 381:253 382:35 406:94 407:252 408:252 409:154 410:10 433:145 434:237 435:252 436:252 461:255 462:253 463:253 464:108 487:11 488:155 489:253 490:252 491:179 492:15 514:11 515:150 516:252 517:253 518:200 519:20 542:73 543:252 544:252 545:253 546:97 569:47 570:233 571:253 572:253 596:1 597:149 598:252 599:252 600:252 624:1 625:252 626:252 627:246 628:132 652:1 653:169 654:252 655:132
1 130:116 131:255 132:123 157:29 158:213 159:253 160:122 185:189 186:253 187:253 188:122 213:189 214:253 215:253 216:122 241:189 242:253 243:253 244:122 267:2 268:114 269:243 270:253 271:186 272:19 295:100 296:253 297:253 298:253 299:48 323:172 324:253 325:253 326:253 327:48 351:172 352:253 353:253 354:182 355:19 378:133 379:251 380:253 381:175 382:4 405:107 406:251 407:253 408:253 409:65 432:26 433:194 434:253 435:253 436:214 437:40 459:105 460:205 461:253 462:253 463:125 464:40 487:139 488:253 489:253 490:253 491:81 514:41 515:231 516:253 517:253 518:159 519:16 541:65 542:155 543:253 544:253 545:172 546:4 569:124 570:253 571:253 572:253 573:98 597:124 598:253 599:253 600:214 601:41 624:22 625:207 626:253 627:253 628:139 653:124 654:253 655:162 656:9