supervised_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ffbfcaa5-0116-4997-b3ec-80e493185654",
   "metadata": {},
   "source": [
    "# Supervised ML\n",
    "\n",
    "The goal of this model is to predict the ridership that occurs within the University of Chicago Lyft Program Area. We will do this by using as features the ridership counts of other Chicago community areas, as well as adding daily weather data and key weather variables that might affect ridership counts. The labels are the daily ridership counts within the program area.\n",
    "\n",
    "We will create the model that functions up until the introduction of the University Lyft program and then look at the difference between the predictions and the actual ridership as a rough estimate of the effect of the program on rideshare usage in the area. We will do this by looking at both the change when the program was initially introduced (free rides upto 15 dollars each only on weekends), when the program was expanded to cover all days (10 rides of up to 15 dollars each) and when the program was reduced from to 7 rides up to 10 dollars in Summer 2023.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "414550d7-9d43-4f5c-8f75-4756974014af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ('spark.executorEnv.OPENBLAS_NUM_THREADS', '1'),\n",
       " ('spark.repl.local.jars',\n",
       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
       " ('spark.sql.cbo.enabled', 'true'),\n",
       " ('spark.executorEnv.PYTHONPATH',\n",
       "  '/usr/lib/spark/python/lib/py4j-0.10.9-src.zip:/usr/lib/spark/python/:<CPS>{{PWD}}/pyspark.zip<CPS>{{PWD}}/py4j-0.10.9-src.zip<CPS>{{PWD}}/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar<CPS>{{PWD}}/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar<CPS>{{PWD}}/com.typesafe_config-1.4.2.jar<CPS>{{PWD}}/org.rocksdb_rocksdbjni-6.29.5.jar<CPS>{{PWD}}/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar<CPS>{{PWD}}/com.github.universal-automata_liblevenshtein-3.0.0.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-storage-2.16.0.jar<CPS>{{PWD}}/com.navigamez_greex-1.0.jar<CPS>{{PWD}}/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar<CPS>{{PWD}}/it.unimi.dsi_fastutil-7.0.12.jar<CPS>{{PWD}}/org.projectlombok_lombok-1.16.8.jar<CPS>{{PWD}}/com.google.guava_guava-31.1-jre.jar<CPS>{{PWD}}/com.google.guava_failureaccess-1.0.1.jar<CPS>{{PWD}}/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar<CPS>{{PWD}}/com.google.errorprone_error_prone_annotations-2.16.jar<CPS>{{PWD}}/com.google.j2objc_j2objc-annotations-1.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-1.42.3.jar<CPS>{{PWD}}/io.opencensus_opencensus-contrib-http-util-0.31.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-jackson2-1.42.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-gson-1.42.3.jar<CPS>{{PWD}}/com.google.api-client_google-api-client-2.1.1.jar<CPS>{{PWD}}/commons-codec_commons-codec-1.15.jar<CPS>{{PWD}}/com.google.oauth-client_google-oauth-client-1.34.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-apache-v2-1.42.3.jar<CPS>{{PWD}}/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar<CPS>{{PWD}}/com.google.code.gson_gson-2.10.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-2.9.0.jar<CPS>{{PWD}}/com.google.auto.value_auto-value-annotations-1.10.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-http-2.9.0.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-appengine-1.42.3.jar<CPS>{{PWD}}/com.google.api_gax-httpjson-0.105.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-grpc-2.9.0.jar<CPS>{{PWD}}/io.grpc_grpc-core-1.51.0.jar<CPS>{{PWD}}/com.google.api_gax-2.20.1.jar<CPS>{{PWD}}/com.google.api_gax-grpc-2.20.1.jar<CPS>{{PWD}}/io.grpc_grpc-alts-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-grpclb-1.51.0.jar<CPS>{{PWD}}/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-1.51.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-credentials-1.13.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar<CPS>{{PWD}}/com.google.api_api-common-2.2.2.jar<CPS>{{PWD}}/javax.annotation_javax.annotation-api-1.3.2.jar<CPS>{{PWD}}/io.opencensus_opencensus-api-0.31.1.jar<CPS>{{PWD}}/io.grpc_grpc-context-1.51.0.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-3.21.10.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-util-3.21.10.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-common-protos-2.11.0.jar<CPS>{{PWD}}/org.threeten_threetenbp-1.6.4.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.fasterxml.jackson.core_jackson-core-2.14.1.jar<CPS>{{PWD}}/com.google.code.findbugs_jsr305-3.0.2.jar<CPS>{{PWD}}/io.grpc_grpc-api-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-auth-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-stub-1.51.0.jar<CPS>{{PWD}}/org.checkerframework_checker-qual-3.28.0.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-lite-1.51.0.jar<CPS>{{PWD}}/com.google.android_annotations-4.1.1.4.jar<CPS>{{PWD}}/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar<CPS>{{PWD}}/io.grpc_grpc-netty-shaded-1.51.0.jar<CPS>{{PWD}}/io.perfmark_perfmark-api-0.26.0.jar<CPS>{{PWD}}/io.grpc_grpc-googleapis-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-xds-1.51.0.jar<CPS>{{PWD}}/io.opencensus_opencensus-proto-0.2.0.jar<CPS>{{PWD}}/io.grpc_grpc-services-1.51.0.jar<CPS>{{PWD}}/com.google.re2j_re2j-1.6.jar<CPS>{{PWD}}/dk.brics.automaton_automaton-1.11-8.jar<CPS>{{PWD}}/org.slf4j_slf4j-api-1.7.16.jar'),\n",
       " ('spark.yarn.dist.jars',\n",
       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
       " ('spark.dataproc.sql.parquet.enableFooterCache', 'true'),\n",
       " ('spark.driver.memory', '4g'),\n",
       " ('spark.sql.warehouse.dir', 'file:/spark-warehouse'),\n",
       " ('spark.yarn.executor.failuresValidityInterval', '1h'),\n",
       " ('spark.app.startTime', '1701129773992'),\n",
       " ('spark.yarn.am.memory', '640m'),\n",
       " ('spark.driver.port', '40637'),\n",
       " ('spark.cores.max', '4'),\n",
       " ('spark.executor.cores', '4'),\n",
       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
       "  'hub-msca-bdp-dphub-students-test-harshpachisia-m'),\n",
       " ('spark.jars.packages',\n",
       "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
       " ('spark.executor.instances', '2'),\n",
       " ('spark.dataproc.listeners',\n",
       "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
       " ('spark.eventLog.dir',\n",
       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/9596200d-6e6e-4d74-a57e-00bf53fa6d0e/spark-job-history'),\n",
       " ('spark.serializer.objectStreamReset', '100'),\n",
       " ('spark.submit.deployMode', 'client'),\n",
       " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
       " ('spark.shuffle.service.enabled', 'true'),\n",
       " ('spark.scheduler.mode', 'FAIR'),\n",
       " ('spark.sql.adaptive.enabled', 'true'),\n",
       " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
       " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
       " ('spark.master', 'yarn'),\n",
       " ('spark.ui.port', '0'),\n",
       " ('spark.rpc.message.maxSize', '512'),\n",
       " ('spark.rdd.compress', 'True'),\n",
       " ('spark.task.maxFailures', '10'),\n",
       " ('spark.yarn.isPython', 'true'),\n",
       " ('spark.dynamicAllocation.enabled', 'true'),\n",
       " ('spark.ui.showConsoleProgress', 'true')]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in packages create spark environment\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import functions as F\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "spark = SparkSession.builder.appName('supervised').getOrCreate()\n",
    "\n",
    "#change configuration settings on Spark \n",
    "conf = spark.sparkContext._conf.setAll([('spark.executor.memory', '4g'), ('spark.app.name', 'Spark Updated Conf'), ('spark.executor.cores', '4'), ('spark.cores.max', '4'), ('spark.driver.memory','4g')])\n",
    "\n",
    "#print spark configuration settings\n",
    "spark.sparkContext.getConf().getAll()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6932b302-6ff0-49ba-a436-8a48d93f0f92",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ac5c49-7dbc-4aac-a702-1a5ac3ee0097",
   "metadata": {},
   "source": [
    "### Reading in cleaned data, partitioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bfe115e-abb4-4a36-8508-1bd17ce2c55c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
      "|                  ID|    start_timestamp|      end_timestamp|seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|  dropoff_lat|   dropoff_lon|month|day_of_month|hour|day|\n",
      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
      "|625e77ae6e0ff7191...|2018-11-06 19:00:00|2018-11-06 19:15:00|   1142|  5.8| 17031063400|  17031010400|          6|           1|12.5|  0| 15.0|41.9346591566|-87.6467297286| 42.004764559| -87.659122427|   11|           6|  19|  3|\n",
      "|62945fdb2e70957f0...|2018-11-06 19:00:00|2018-11-06 19:00:00|    341|  1.2| 17031081800|  17031833000|          8|          28| 5.0|  0|  7.5|41.8932163595|-87.6378442095|41.8852813201|-87.6572331997|   11|           6|  19|  3|\n",
      "|6dc03f91e4480d237...|2018-11-06 19:00:00|2018-11-06 19:00:00|    558|  1.2| 17031070400|  17031061500|          7|           6| 7.5|  0| 10.3|41.9289672664|-87.6561568309|41.9452823311|-87.6615450961|   11|           6|  19|  3|\n",
      "|773894079a526afa1...|2018-11-06 19:00:00|2018-11-06 19:30:00|   1047|  2.8| 17031832200|  17031062100|         22|           6|10.0|  2| 14.5|41.9204515116|-87.6799547678|41.9426918444|-87.6517705068|   11|           6|  19|  3|\n",
      "|7acf0a7f2edfbe546...|2018-11-06 19:00:00|2018-11-06 19:00:00|    502|  1.3| 17031839100|  17031081700|         32|           8| 2.5|  0|  5.0|41.8809944707|-87.6327464887|41.8920421365|-87.6318639497|   11|           6|  19|  3|\n",
      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read in rideshare data for all years, concatenate, create appropriate partitioning\n",
    "# we are dropping 2020 because covid will affect the performance of our model\n",
    "\n",
    "df_2018 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2018.csv\", inferSchema=True, header=True)\n",
    "df_2019 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2019.csv\", inferSchema=True, header=True)\n",
    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2021.csv\", inferSchema=True, header=True)\n",
    "df_2022 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2022.csv\", inferSchema=True, header=True)\n",
    "df_2023 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2023.csv\", inferSchema=True, header=True)\n",
    "\n",
    "# dropping new columns that are only in 2023\n",
    "df_2023 = df_2023.drop('Shared Trip Match','Percent Time Chicago','Percent Distance Chicago')\n",
    "\n",
    "df_all = df_2018.union(df_2019).union(df_2021).union(df_2022).union(df_2023)\n",
    "df_all.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18e30586-4bdd-4217-b55d-e41522df062b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partitions: 544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 16:=====================================================>(541 + 3) / 544]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+------+\n",
      "|partitionId| count|\n",
      "+-----------+------+\n",
      "|         42|305254|\n",
      "|         41|305316|\n",
      "|         40|305420|\n",
      "|         38|305471|\n",
      "|         39|305480|\n",
      "|         37|305618|\n",
      "|         36|305676|\n",
      "|         35|305871|\n",
      "|         34|305890|\n",
      "|         33|305962|\n",
      "|         32|305971|\n",
      "|         31|306010|\n",
      "|         29|306031|\n",
      "|         30|306038|\n",
      "|         28|306086|\n",
      "|         27|306127|\n",
      "|         26|306402|\n",
      "|         25|306467|\n",
      "|         24|306633|\n",
      "|         23|306731|\n",
      "|         22|307226|\n",
      "|        543|364094|\n",
      "|        542|364374|\n",
      "|        541|364493|\n",
      "|        537|364581|\n",
      "|        538|364599|\n",
      "|        539|364616|\n",
      "|        540|364617|\n",
      "|        536|364654|\n",
      "|        534|364709|\n",
      "|        535|364756|\n",
      "|        532|364784|\n",
      "|        533|364810|\n",
      "|        529|364899|\n",
      "|        530|364903|\n",
      "|        531|364944|\n",
      "|        528|364957|\n",
      "|        527|364961|\n",
      "|        524|364971|\n",
      "|        525|364988|\n",
      "|        526|365006|\n",
      "|        522|365011|\n",
      "|        523|365051|\n",
      "|        521|365057|\n",
      "|        520|365079|\n",
      "|        518|365083|\n",
      "|        517|365090|\n",
      "|        519|365097|\n",
      "|        516|365122|\n",
      "|        514|365165|\n",
      "|        515|365179|\n",
      "|        513|365224|\n",
      "|        509|365252|\n",
      "|        506|365253|\n",
      "|        511|365255|\n",
      "|        508|365272|\n",
      "|        510|365277|\n",
      "|        512|365278|\n",
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      "|        499|365454|\n",
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      "|        496|365528|\n",
      "|        495|365536|\n",
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      "|        489|365547|\n",
      "|        488|365552|\n",
      "|        487|365554|\n",
      "|        490|365569|\n",
      "|        493|365574|\n",
      "|        484|365576|\n",
      "|        494|365595|\n",
      "|        485|365602|\n",
      "|        486|365622|\n",
      "|        491|365622|\n",
      "|        483|365650|\n",
      "|        482|365684|\n",
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      "|        479|365750|\n",
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      "|        462|366150|\n",
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      "|        458|366193|\n",
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      "|         21|380513|\n",
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      "|          6|381763|\n",
      "|          5|381783|\n",
      "|          4|381827|\n",
      "|          3|381971|\n",
      "|          1|382022|\n",
      "|          2|382029|\n",
      "|          0|382095|\n",
      "|        342|420259|\n",
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      "|        244|423762|\n",
      "|         43|457702|\n",
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      "|        346|574185|\n",
      "|        345|574318|\n",
      "|        344|574727|\n",
      "+-----------+------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#display number of records by partition\n",
    "def displaypartitions(df):\n",
    "    #number of records by partition\n",
    "    num = df.rdd.getNumPartitions()\n",
    "    print(\"Partitions:\", num)\n",
    "    df.withColumn(\"partitionId\", F.spark_partition_id())\\\n",
    "        .groupBy(\"partitionId\")\\\n",
    "        .count()\\\n",
    "        .orderBy(F.asc(\"count\"))\\\n",
    "        .show(num)\n",
    "\n",
    "df_all.rdd.getNumPartitions()\n",
    "displaypartitions(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abf8091a-9662-4378-8fe5-b2ece46a6a14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 19:=====================================================>(542 + 2) / 544]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partitions: 600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 22:===================================================> (586 + 14) / 600]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+------+\n",
      "|partitionId| count|\n",
      "+-----------+------+\n",
      "|         24|413573|\n",
      "|         12|413574|\n",
      "|         25|413574|\n",
      "|        532|413574|\n",
      "|          9|413574|\n",
      "|         13|413574|\n",
      "|         15|413574|\n",
      "|         10|413575|\n",
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      "|        184|413589|\n",
      "|        263|413589|\n",
      "|        394|413589|\n",
      "|        487|413589|\n",
      "|        274|413589|\n",
      "|         98|413589|\n",
      "|        386|413589|\n",
      "|        483|413589|\n",
      "|        407|413589|\n",
      "|         89|413589|\n",
      "|        405|413589|\n",
      "|        484|413589|\n",
      "|        403|413589|\n",
      "|         97|413589|\n",
      "|        202|413589|\n",
      "|        488|413589|\n",
      "|        485|413589|\n",
      "|        384|413590|\n",
      "|        388|413590|\n",
      "|        346|413590|\n",
      "|        390|413590|\n",
      "|        402|413590|\n",
      "|         90|413590|\n",
      "|        201|413590|\n",
      "|        389|413590|\n",
      "|        200|413590|\n",
      "|        396|413590|\n",
      "|        348|413590|\n",
      "|        349|413590|\n",
      "|        401|413590|\n",
      "|        400|413590|\n",
      "|        395|413590|\n",
      "|        344|413590|\n",
      "|        199|413590|\n",
      "|        191|413591|\n",
      "|        268|413591|\n",
      "|        398|413591|\n",
      "|        273|413591|\n",
      "|         91|413591|\n",
      "|         92|413591|\n",
      "|        345|413591|\n",
      "|        198|413591|\n",
      "|        399|413591|\n",
      "|        270|413591|\n",
      "|        397|413591|\n",
      "|        269|413591|\n",
      "|        271|413591|\n",
      "|        197|413592|\n",
      "|        391|413592|\n",
      "|        347|413592|\n",
      "|        392|413592|\n",
      "|        192|413592|\n",
      "|        272|413592|\n",
      "|        193|413593|\n",
      "|        194|413593|\n",
      "|        195|413593|\n",
      "|        196|413594|\n",
      "+-----------+------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# repartitioning to 600 partitions, seems to be balanced now with each partition under 2GB as well. \n",
    "df_all = df_all.repartition(600)\n",
    "displaypartitions(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c7c7fa9-7a39-46eb-93fd-c7006d01c03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will need a year column in this model\n",
    "df_all = df_all.withColumn('year', F.year(df_all.start_timestamp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fae5317c-df84-47ae-a003-440c01c25d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ID: string (nullable = true)\n",
      " |-- start_timestamp: timestamp (nullable = true)\n",
      " |-- end_timestamp: timestamp (nullable = true)\n",
      " |-- seconds: integer (nullable = true)\n",
      " |-- miles: double (nullable = true)\n",
      " |-- pickup_tract: long (nullable = true)\n",
      " |-- dropoff_tract: long (nullable = true)\n",
      " |-- pickup_area: integer (nullable = true)\n",
      " |-- dropoff_area: integer (nullable = true)\n",
      " |-- Fare: double (nullable = true)\n",
      " |-- Tip: integer (nullable = true)\n",
      " |-- total: double (nullable = true)\n",
      " |-- pickup_lat: double (nullable = true)\n",
      " |-- pickup_lon: double (nullable = true)\n",
      " |-- dropoff_lat: double (nullable = true)\n",
      " |-- dropoff_lon: string (nullable = true)\n",
      " |-- month: integer (nullable = true)\n",
      " |-- day_of_month: integer (nullable = true)\n",
      " |-- hour: integer (nullable = true)\n",
      " |-- day: integer (nullable = true)\n",
      " |-- year: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_all.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "540c7bff-6eac-40c2-a9fe-9b6843f7d546",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we initially had taken a sample to test these operations out on first\n",
    "#sample_df = df_all.sample(fraction=1/1000000)\n",
    "\n",
    "# get only the columns needed for the model\n",
    "selected_columns = [\"pickup_area\",\"dropoff_area\",\"day\",\"month\",\"year\",\"ID\"]\n",
    "selected_df = df_all.select(selected_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdccfb9c-6f47-4d6a-a42b-ad7a1f707f4a",
   "metadata": {},
   "source": [
    "### Daily counts for each community area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23b5405-6491-43ed-90fb-cf7aa2056553",
   "metadata": {},
   "source": [
    "We group by pickup area and dropoff area seperately then sum to create daily counts of number of trips to that particular community area when it was either a pickup or dropoff area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14b0b19a-36ad-4e17-bf30-1e9fcdaea452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate daily counts for pickup areas\n",
    "pickup_counts = selected_df.groupby('day', 'month', 'year', 'pickup_area').count().withColumnRenamed('count', 'pickup_count')\n",
    "pickup_counts = pickup_counts.withColumnRenamed('pickup_area', 'area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "388446e2-2cd4-4bb8-bfa0-f204b1359427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate daily counts for dropoff areas\n",
    "dropoff_counts = selected_df.groupby('day', 'month', 'year', 'dropoff_area').count().withColumnRenamed('count', 'dropoff_count')\n",
    "dropoff_counts = dropoff_counts.withColumnRenamed('dropoff_area', 'area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e028095-2ff1-4e5f-aa03-7dafe417168e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 26:==================================================>    (95 + 9) / 104]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----+----+----+-----------------+------------------+------------+\n",
      "|day|month|year|area|sum(pickup_count)|sum(dropoff_count)|total_counts|\n",
      "+---+-----+----+----+-----------------+------------------+------------+\n",
      "|  6|    7|2022|   8|           167867|            172693|      340560|\n",
      "|  4|    8|2022|   6|            40523|             39734|       80257|\n",
      "|  7|   11|2022|  42|             7351|              7392|       14743|\n",
      "|  7|   12|2022|  76|            18491|             29572|       48063|\n",
      "|  6|    1|2022|  76|            13224|             18168|       31392|\n",
      "|  7|   11|2022|  73|             2196|              2170|        4366|\n",
      "|  2|    1|2022|   1|             6929|              7019|       13948|\n",
      "|  4|    8|2022|  56|             9706|             11844|       21550|\n",
      "|  1|   12|2022|   5|             7162|              7406|       14568|\n",
      "|  5|    4|2022|   1|             7085|              6183|       13268|\n",
      "+---+-----+----+----+-----------------+------------------+------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "pickup_counts = pickup_counts.withColumn('dropoff_count', F.lit(0))\n",
    "dropoff_counts = dropoff_counts.withColumn('pickup_count', F.lit(0))\n",
    "\n",
    "# ensuring same column order\n",
    "pickup_counts = pickup_counts.select('day', 'month', 'year', 'area', 'pickup_count', 'dropoff_count')\n",
    "dropoff_counts = dropoff_counts.select('day', 'month', 'year', 'area', 'pickup_count', 'dropoff_count')\n",
    "\n",
    "# Union the pickup and dropoff dataframes\n",
    "combined_df = pickup_counts.union(dropoff_counts)\n",
    "\n",
    "# Group by day, month, year, and area, summing up the counts\n",
    "daily_counts_by_area = combined_df.groupby('day', 'month', 'year', 'area').sum('pickup_count', 'dropoff_count')\n",
    "\n",
    "daily_counts_by_area = daily_counts_by_area.withColumn('total_counts', F.col('sum(pickup_count)') + F.col('sum(dropoff_count)'))\n",
    "daily_counts_by_area.drop('sum(pickup_count)','sum(dropoff_count)')\n",
    "daily_counts_by_area.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b68b7b0-9db1-47c2-a896-f1875023ed66",
   "metadata": {},
   "source": [
    "Pivoting the dataset for community areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d57f2088-9ca6-4cc8-a8fe-0cca835fecf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23/11/25 23:32:49 WARN org.apache.spark.sql.catalyst.util.package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n",
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+-----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+-----+----+-----+-----+-----+-----+----+----+----+----+-----+----+----+----+----+----+---+-----+----+----+----+-----+----+----+----+----+----+-----+----+-----+-----+----+-----+----+----+----+----+-----+-----+\n",
      "|day|month|year|    1|    2|    3|    4|    5|     6|     7|     8|   9|  10|  11|  12|  13|   14|   15|   16|  17|  18|   19|  20|   21|   22|   23|    24|   25|  26|   27|    28|   29|   30|   31|    32|   33|   34|   35|  36|  37|   38|   39|  40|   41|   42|   43|   44|  45|  46|  47|  48|   49|  50|  51|  52|  53|  54| 55|   56|  57|  58|  59|   60|  61|  62|  63|  64|  65|   66|  67|   68|   69|  70|   71|  72|  73|  74|  75|   76|   77|\n",
      "+---+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+-----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+-----+----+-----+-----+-----+-----+----+----+----+----+-----+----+----+----+----+----+---+-----+----+----+----+-----+----+----+----+----+----+-----+----+-----+-----+----+-----+----+----+----+----+-----+-----+\n",
      "|  5|   10|2021|12199| 9107|24884|10934|12710| 62904| 55515|180042| 674|3314|3259|1323|3077| 7226| 7209|10819|2898|1116| 8828|3030|11178|34577|10307| 59723|14084|3590| 5678| 99556| 8672| 7147|12736|104048|24877| 3684| 7049|1553| 824| 6866| 5464|3602|21490| 8095|10986| 8677|1848|3884| 560|2047| 6449|2113|2097| 622|2949| 898|326|15899|2444|4881|2795| 5363|6014|1560|3048|1163|2742| 5662|4618| 5772| 7720|3439| 7108|1518|3534| 550|2453|47198|16199|\n",
      "|  2|    7|2021| 9005| 7260|17397| 7642| 7999| 47541| 33715|117551| 521|2660|2410|1168|1936| 5671| 5780| 8063|2010| 913| 6787|2356| 7659|21888| 7997| 36890|12275|3120| 4611| 53566| 7252| 5243| 8074| 64260|16000| 3827| 5687|1408| 671| 5674| 3668|2866| 9282| 5301| 9215| 6354|1275|3264| 461|1337| 4831|1325|1588| 472|2478| 835|255|11814|1417|3425|1885| 4057|4469|1062|2325| 917|2063| 4523|3978| 4877| 6248|2526| 5897|1059|2890| 387|1875|36306|12384|\n",
      "|  2|    7|2023|  452|  347|  900|  431|  381|  2452|  1759|  6299|  27| 131| 135|  59| 113|  331|  309|  460| 114|  44|  345| 130|  398| 1214|  403|  1923|  633| 156|  200|  3404|  379|  264|  452|  4042| 1131|  196|  349|  62|  47|  280|  231| 167|  632|  336|  452|  372|  84| 165|  37|  90|  317|  86| 114|  23| 157|  38| 17|  755|  83| 200| 121|  206| 244|  55| 149|  54|  99|  233| 220|  282|  355| 136|  342|  74| 202|  24| 119| 1999|  624|\n",
      "|  4|   12|2022|13534|10789|26607|13908|14898| 73450| 56285|192604|1031|4105|3962|1865|3414| 8913| 9367|13438|3610|1451|10446|3656|13370|41366|12193| 65188|17799|4458| 7188|124088|11197| 8620|15505|123219|29952| 5589| 8510|2051|1159| 9143| 6917|4444|25007|10275|14128|11708|2262|5106| 862|2495| 8716|2723|2908| 753|4145|1309|439|17321|2865|5458|3348| 5973|7057|1971|3514|1572|3215| 7133|5790| 7826| 9908|4047| 9907|2026|4732| 695|3250|49998|18831|\n",
      "|  6|    1|2022|13941|10289|29467|13586|15451| 94575| 80801|210493|1000|3892|3486|1586|2974| 8731| 9212|13110|3319|1318|10251|3698|13416|46027|12440| 79758|17915|4286| 6360|118913|10588| 8039|15913| 93354|22282| 4688| 8157|1862|1026| 8014| 6155|4489|25584| 9771|13227|10423|2183|4636| 739|2299| 7620|2338|2513| 752|3682|1190|487|10984|2607|5532|3128| 6166|6861|1899|3610|1470|3237| 7135|5899| 7034| 9444|3664| 9049|1921|4355| 746|2923|31392|19582|\n",
      "|  7|    1|2022|18806|12136|42743|19325|22814|172792|143120|352533|1718|5127|4171|2165|3057|11874|10871|16888|3747|1437|11811|4244|18677|72617|14263|127816|19298|4748| 6853|141436|10020| 8756|20224|120688|34626| 7479| 9024|2030|1129| 8667| 6848|4660|28835|11337|14475|10306|2194|4961| 667|2345| 7874|2120|2467| 710|3958|1205|346| 9587|2493|5522|3424| 7502|6996|1920|3879|1636|3363| 7526|6009| 6965|10058|3664| 9436|2334|4479|1115|3095|27717|26732|\n",
      "|  4|    7|2023|  463|  365|  965|  463|  444|  2559|  1962|  7393|  34| 127| 131|  59| 112|  341|  302|  429|  97|  56|  403| 132|  443| 1310|  433|  2120|  759| 192|  267|  4459|  391|  336|  526|  4889| 1197|  239|  321|  56|  46|  344|  243| 175|  792|  343|  541|  363|  83| 191|  31|  84|  375| 132|  92|  28| 155|  41| 13|  700|  90| 197| 120|  223| 256|  69| 149|  41| 109|  247| 257|  267|  370| 152|  375|  74| 194|  25| 124| 1821|  609|\n",
      "|  2|   12|2018|19802|15539|43393|21356|23301|117250| 90763|301912|1100|4603|4795|2425|4764|12802|11522|19919|4383|1636|14022|5417|21532|68183|16957|110900|18190|4607| 8161|171992|12190|11649|23754|198173|29469| 8323|13998|2705|1938|12021|10056|5742|27483|10491|15387|11743|2257|5055| 782|2719| 8513|2142|2348| 713|3407| 864|430|21408|3815|8522|4970|11152|9706|2631|5835|2237|4789| 8749|6969| 8467|11051|4597| 9713|2362|4425| 929|3084|51232|28031|\n",
      "|  1|    8|2022|13296| 9439|32329|13087|15270|105985| 73132|228688|1446|4394|3636|1833|2587| 9013| 8194|13321|3025|1106| 9205|3234|14227|46734|10686| 76607|14257|3440| 5457|101705| 7272| 7232|16326|101827|32238| 8668| 8570|1524| 928| 6713| 5401|3969|17821| 7527|11246| 7882|1540|3706| 520|1768| 6252|1636|1870| 700|3050| 903|280|22217|1966|4391|2438| 6302|5531|1702|3129|1605|2887| 5693|4800| 5282| 7928|2816| 7186|1695|3311| 955|2488|55147|20351|\n",
      "|  3|    5|2023|16807|13341|33578|15704|15697| 86879| 63584|227565| 929|4833|4777|2111|4279|10515|10783|15338|4314|2029|13129|4497|14692|42292|15713| 69735|23553|6249| 9546|138643|14677|11315|17235|155372|44067| 9692|12508|2625|1687|11341| 9104|6239|43562|16333|18605|14355|2980|6865|1210|3395|11945|3708|3893|1018|5759|1775|633|24394|3771|7014|4114| 7801|9179|2355|5080|2115|4261| 9532|8674|10757|14098|5573|14056|2570|6896| 842|4693|71329|22182|\n",
      "|  3|    4|2021|10752| 7700|17863| 8097| 8166| 44654| 31723| 88534| 462|2339|2907| 982|2451| 6301| 6522| 8472|2233|1073| 8403|2923| 8328|22146|10603| 36694|18019|4651| 6295| 52198|10596| 6663| 9319| 47537|11252| 3260| 7161|1718| 942| 7237| 4423|3662|11469| 7158|12603| 8795|1757|4851| 675|2046| 7168|1849|2450| 576|3634|1059|260| 7632|1939|4179|2405| 4556|6223|1398|2987|1097|2590| 6248|5597| 7130| 8685|3223| 8347|1322|3891| 432|2563|15590|12931|\n",
      "|  5|    6|2023|18451|14352|36525|18935|20186|117204| 90040|294778|1256|5320|5346|2493|4144|11275|11802|17490|4709|1878|13671|5098|17893|55472|17084| 92824|26242|6684|10091|173452|15695|11370|20425|191290|51359| 7999|12958|2871|1842|12451| 9709|6780|35207|14715|20536|15518|3404|7328|1271|3774|12771|4065|4041|1064|6282|1765|679|30124|3856|6929|4332| 8925|9870|2420|4868|2239|4358|10023|8958|11184|15503|5661|14911|2880|7158|1015|4907|82087|25320|\n",
      "|  5|    5|2021|11218| 8218|19190| 9168|10185| 52738| 43469|121878| 623|2684|2927|1140|2395| 6527| 7022| 9465|2317|1118| 8419|2878| 9256|27314|10519| 47596|16945|4436| 6204| 65966|10314| 6442|10467| 61026|14390| 3246| 6851|1672| 952| 7261| 4677|3774|12909| 7149|12641| 8868|1756|4445| 633|2014| 6898|1690|2256| 556|3487|1025|298|12786|1859|4265|2512| 4701|6010|1440|2950|1087|2530| 6310|5523| 6606| 8666|3082| 8409|1446|3907| 453|2599|30137|14018|\n",
      "|  7|   12|2022|23637|14853|58130|25327|30532|208802|152553|414877|2300|6833|5894|2958|4240|14899|14723|22845|5023|1855|15093|5562|25574|90194|18059|148096|25837|6463| 9180|176444|13566|10724|27875|164483|41645| 9796|11170|2773|1570|11916| 9239|6316|36135|15092|19341|15278|2961|6456| 812|3017|10799|3019|3245|1041|5472|1697|510|19533|3201|7159|4301| 9625|8666|2587|4921|2288|4379| 9689|8330| 9378|13109|4768|13450|3104|6268|1498|4847|48063|35303|\n",
      "|  6|    6|2021|12371| 8913|24889|11603|13836| 86502| 71364|199110| 988|3458|3230|1648|2548| 7349| 7707|11729|2636|1114| 8371|3000|11113|39300|10637| 72459|16010|4238| 5986| 91405| 9266| 6328|12768| 85373|22234| 5523| 7431|1743| 965| 7506| 5138|3736|15434| 7379|11968| 8401|1773|4176| 562|1845| 6354|1716|2192| 532|3307| 970|295|11939|2007|4147|2444| 5603|5676|1520|2841|1217|2649| 6014|5326| 6131| 8192|3181| 8166|1683|3668| 530|2565|32995|17873|\n",
      "|  5|    7|2019|19329|14198|47419|22225|26378|133951|124257|379055|1178|5080|4944|2693|4217|11916|11382|20303|4299|1760|13068|5321|23236|77784|16224|134828|18868|4342| 8478|207091|12300|11208|24766|241322|48453|11700|16106|2933|1651|12022| 9580|5574|27064|10211|15156|11706|2465|5030| 743|2539| 8769|2205|2576| 883|3502| 863|433|24153|3681|7892|4599|11856|9344|2679|5295|2469|4613| 8268|6481| 8123|10662|4611| 9820|2479|4919| 921|3009|62043|30475|\n",
      "|  3|   11|2022|15880|12131|31078|14865|15863| 73919| 60160|214459| 959|4542|4438|1943|4045| 9870|10023|14586|4147|1719|11962|4314|14547|42118|14140| 67054|20024|4836| 7883|133125|12585|10273|16786|148457|35501| 5557|10284|2489|1280|10137| 8355|5071|39935|14530|16406|13048|2599|5804| 962|2849| 9968|3281|3300| 941|4640|1404|545|22147|3389|6356|3749| 6760|8016|2197|4209|1791|3813| 8234|6939| 9057|11424|4844|11041|2208|5570| 876|3765|63512|20986|\n",
      "|  2|    2|2021| 9183| 7546|14935| 7747| 7354| 33977| 24718| 67195| 409|2337|2404| 833|2225| 5894| 5945| 7652|2068|1018| 7260|2612| 7590|19048| 8670| 30216|13278|3523| 5011| 40704| 8110| 5834| 8311| 35505| 8889| 1823| 5562|1330| 715| 5581| 3495|3075| 9154| 5433|10297| 7210|1345|3967| 598|1805| 5907|1611|2049| 456|3202| 851|291| 6280|1670|4015|2282| 3874|5551|1243|2706| 980|2302| 5554|4482| 5622| 6722|2786| 6740|1064|3181| 389|2023|14378|10746|\n",
      "|  4|    2|2023|15458|11260|28204|14529|14833| 74643| 58242|189020| 864|4181|4240|1726|4031| 9422| 9329|13571|3771|1497|11336|4042|14024|40407|13643| 66096|20899|5449| 8260|129142|13489| 9685|14687|133399|24428| 4547| 9962|2307|1329| 9933| 8143|5676|41225|14456|16586|13385|2594|6035|1137|2847|10257|3385|3169| 804|4796|1549|533|15089|3033|5810|3539| 6367|7949|1994|4156|1693|3454| 8092|7348| 9805|12322|4483|11787|2050|5439| 787|3873|46148|19838|\n",
      "|  6|    2|2021|13911| 9972|22922|11569|11977| 68007| 55550|135886| 760|3083|3241|1270|2707| 7962| 8118|11141|2829|1274| 9956|3524|11010|32483|12558| 59532|20673|5550| 7129| 73369|12222| 7617|12828| 57966|14662| 2978| 8001|1928|1169| 8170| 5241|4483|13561| 8155|14385|10377|2016|5580| 822|2450| 8428|2214|2879| 650|4344|1145|363| 8688|2171|5180|2843| 5245|7489|1767|3403|1369|3128| 7447|6572| 7860|10370|3904| 9873|1713|4660| 554|2927|19113|16875|\n",
      "+---+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+-----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+-----+----+-----+-----+-----+-----+----+----+----+----+-----+----+----+----+----+----+---+-----+----+----+----+-----+----+----+----+----+----+-----+----+-----+-----+----+-----+----+----+----+----+-----+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# pivot so that each community area is a column\n",
    "# one row for each day, each column represents a community area (with its entry being daily count of rides for that area).\n",
    "\n",
    "# Pivot the DataFrame\n",
    "pivoted_df = daily_counts_by_area.groupBy(\"day\", \"month\", \"year\").pivot(\"area\").sum(\"total_counts\")\n",
    "\n",
    "# Show the results\n",
    "pivoted_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0df4c64-d1e4-48d1-a5fe-ec6bf18f65fe",
   "metadata": {},
   "source": [
    "### Adding in weather dataset\n",
    "\n",
    "Read in weather data, merge with rideshare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b501c963-15c8-4341-b9c6-7d2f07cc5015",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df_weather_1 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2018-01-01 to 2020-01-01.csv\", inferSchema=True, header=True)\n",
    "df_weather_2 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2020-01-01 to 2022-08-31.csv\", inferSchema=True, header=True)\n",
    "df_weather_3 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2022-09-01 to 2022-12-31.csv\", inferSchema=True, header=True)\n",
    "df_weather_4 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2023-01-01 to 2023-11-22.csv\", inferSchema=True, header=True)\n",
    "df_weather = df_weather_1.union(df_weather_2).union(df_weather_3).union(df_weather_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2858a6da-ef1b-4561-b8e2-f93073b8e803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- datetime: string (nullable = true)\n",
      " |-- temp: double (nullable = true)\n",
      " |-- precip: double (nullable = true)\n",
      " |-- snow: double (nullable = true)\n",
      " |-- snowdepth: double (nullable = true)\n",
      " |-- sunset: string (nullable = true)\n",
      "\n",
      "root\n",
      " |-- day: integer (nullable = true)\n",
      " |-- month: integer (nullable = true)\n",
      " |-- year: integer (nullable = true)\n",
      " |-- 1: long (nullable = true)\n",
      " |-- 2: long (nullable = true)\n",
      " |-- 3: long (nullable = true)\n",
      " |-- 4: long (nullable = true)\n",
      " |-- 5: long (nullable = true)\n",
      " |-- 6: long (nullable = true)\n",
      " |-- 7: long (nullable = true)\n",
      " |-- 8: long (nullable = true)\n",
      " |-- 9: long (nullable = true)\n",
      " |-- 10: long (nullable = true)\n",
      " |-- 11: long (nullable = true)\n",
      " |-- 12: long (nullable = true)\n",
      " |-- 13: long (nullable = true)\n",
      " |-- 14: long (nullable = true)\n",
      " |-- 15: long (nullable = true)\n",
      " |-- 16: long (nullable = true)\n",
      " |-- 17: long (nullable = true)\n",
      " |-- 18: long (nullable = true)\n",
      " |-- 19: long (nullable = true)\n",
      " |-- 20: long (nullable = true)\n",
      " |-- 21: long (nullable = true)\n",
      " |-- 22: long (nullable = true)\n",
      " |-- 23: long (nullable = true)\n",
      " |-- 24: long (nullable = true)\n",
      " |-- 25: long (nullable = true)\n",
      " |-- 26: long (nullable = true)\n",
      " |-- 27: long (nullable = true)\n",
      " |-- 28: long (nullable = true)\n",
      " |-- 29: long (nullable = true)\n",
      " |-- 30: long (nullable = true)\n",
      " |-- 31: long (nullable = true)\n",
      " |-- 32: long (nullable = true)\n",
      " |-- 33: long (nullable = true)\n",
      " |-- 34: long (nullable = true)\n",
      " |-- 35: long (nullable = true)\n",
      " |-- 36: long (nullable = true)\n",
      " |-- 37: long (nullable = true)\n",
      " |-- 38: long (nullable = true)\n",
      " |-- 39: long (nullable = true)\n",
      " |-- 40: long (nullable = true)\n",
      " |-- 41: long (nullable = true)\n",
      " |-- 42: long (nullable = true)\n",
      " |-- 43: long (nullable = true)\n",
      " |-- 44: long (nullable = true)\n",
      " |-- 45: long (nullable = true)\n",
      " |-- 46: long (nullable = true)\n",
      " |-- 47: long (nullable = true)\n",
      " |-- 48: long (nullable = true)\n",
      " |-- 49: long (nullable = true)\n",
      " |-- 50: long (nullable = true)\n",
      " |-- 51: long (nullable = true)\n",
      " |-- 52: long (nullable = true)\n",
      " |-- 53: long (nullable = true)\n",
      " |-- 54: long (nullable = true)\n",
      " |-- 55: long (nullable = true)\n",
      " |-- 56: long (nullable = true)\n",
      " |-- 57: long (nullable = true)\n",
      " |-- 58: long (nullable = true)\n",
      " |-- 59: long (nullable = true)\n",
      " |-- 60: long (nullable = true)\n",
      " |-- 61: long (nullable = true)\n",
      " |-- 62: long (nullable = true)\n",
      " |-- 63: long (nullable = true)\n",
      " |-- 64: long (nullable = true)\n",
      " |-- 65: long (nullable = true)\n",
      " |-- 66: long (nullable = true)\n",
      " |-- 67: long (nullable = true)\n",
      " |-- 68: long (nullable = true)\n",
      " |-- 69: long (nullable = true)\n",
      " |-- 70: long (nullable = true)\n",
      " |-- 71: long (nullable = true)\n",
      " |-- 72: long (nullable = true)\n",
      " |-- 73: long (nullable = true)\n",
      " |-- 74: long (nullable = true)\n",
      " |-- 75: long (nullable = true)\n",
      " |-- 76: long (nullable = true)\n",
      " |-- 77: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# keeping only relevant weather variables\n",
    "df_weather = df_weather.select('name', 'datetime', 'temp','precip','snow','snowdepth','sunset')\n",
    "df_weather = df_weather.withColumn('sunset', F.concat(F.hour(df_weather.sunset),F.minute(df_weather.sunset)))\n",
    "df_weather.printSchema()\n",
    "pivoted_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7aba8bfb-6be3-4af5-86ac-39666270bfb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+----+------+----+---------+------+\n",
      "|   name|  datetime|temp|precip|snow|snowdepth|sunset|\n",
      "+-------+----------+----+------+----+---------+------+\n",
      "|chicago|2018-01-01|-2.7|   0.0| 0.0|      0.6|  1630|\n",
      "|chicago|2018-01-02|-0.7|   0.0| 0.0|      0.3|  1631|\n",
      "+-------+----------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_weather.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb54fa3a-399b-4b9d-b67d-6726a362cf9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 116:==================================================> (578 + 22) / 600]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+\n",
      "|day|month|year|    1|   2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|  19|  20|   21|   22|   23|   24|   25|  26|  27|   28|  29|  30|   31|    32|   33|  34|  35|  36| 37|  38|  39|  40|   41|  42|   43|  44|  45|  46| 47|  48|  49|  50|  51| 52|  53| 54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|  datetime|\n",
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+\n",
      "|  5|   10|2021|12199|9107|24884|10934|12710|62904|55515|180042|674|3314|3259|1323|3077|7226|7209|10819|2898|1116|8828|3030|11178|34577|10307|59723|14084|3590|5678|99556|8672|7147|12736|104048|24877|3684|7049|1553|824|6866|5464|3602|21490|8095|10986|8677|1848|3884|560|2047|6449|2113|2097|622|2949|898|326|15899|2444|4881|2795|5363|6014|1560|3048|1163|2742|5662|4618|5772|7720|3439|7108|1518|3534|550|2453|47198|16199|2021-10-05|\n",
      "|  2|    7|2021| 9005|7260|17397| 7642| 7999|47541|33715|117551|521|2660|2410|1168|1936|5671|5780| 8063|2010| 913|6787|2356| 7659|21888| 7997|36890|12275|3120|4611|53566|7252|5243| 8074| 64260|16000|3827|5687|1408|671|5674|3668|2866| 9282|5301| 9215|6354|1275|3264|461|1337|4831|1325|1588|472|2478|835|255|11814|1417|3425|1885|4057|4469|1062|2325| 917|2063|4523|3978|4877|6248|2526|5897|1059|2890|387|1875|36306|12384|2021-07-02|\n",
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import concat_ws, to_date, lpad\n",
    "\n",
    "# pad the days and months with zeros for converting to datetime\n",
    "padded_month = lpad(pivoted_df[\"month\"], 2, \"0\")\n",
    "padded_day = lpad(pivoted_df[\"day\"], 2, \"0\")\n",
    "\n",
    "# concatenate columns and create datetime column for merging\n",
    "date_string_column = concat_ws(\"-\", pivoted_df[\"year\"], padded_month, padded_day)\n",
    "pivoted_df = pivoted_df.withColumn('datetime',F.to_date(date_string_column,'yyy-MM-dd'))\n",
    "pivoted_df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d80ab44-61f4-4ef0-9209-7c05710e9023",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+---------+\n",
      "|day|month|year|    1|   2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|  19|  20|   21|   22|   23|   24|   25|  26|  27|   28|  29|  30|   31|    32|   33|  34|  35|  36| 37|  38|  39|  40|   41|  42|   43|  44|  45|  46| 47|  48|  49|  50|  51| 52|  53| 54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|  datetime|area_sums|\n",
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+---------+\n",
      "|  5|   10|2021|12199|9107|24884|10934|12710|62904|55515|180042|674|3314|3259|1323|3077|7226|7209|10819|2898|1116|8828|3030|11178|34577|10307|59723|14084|3590|5678|99556|8672|7147|12736|104048|24877|3684|7049|1553|824|6866|5464|3602|21490|8095|10986|8677|1848|3884|560|2047|6449|2113|2097|622|2949|898|326|15899|2444|4881|2795|5363|6014|1560|3048|1163|2742|5662|4618|5772|7720|3439|7108|1518|3534|550|2453|47198|16199|2021-10-05|    35049|\n",
      "|  2|    7|2021| 9005|7260|17397| 7642| 7999|47541|33715|117551|521|2660|2410|1168|1936|5671|5780| 8063|2010| 913|6787|2356| 7659|21888| 7997|36890|12275|3120|4611|53566|7252|5243| 8074| 64260|16000|3827|5687|1408|671|5674|3668|2866| 9282|5301| 9215|6354|1275|3264|461|1337|4831|1325|1588|472|2478|835|255|11814|1417|3425|1885|4057|4469|1062|2325| 917|2063|4523|3978|4877|6248|2526|5897|1059|2890|387|1875|36306|12384|2021-07-02|    18251|\n",
      "+---+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+----+-----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+----------+---------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create the program area sum column\n",
    "pivoted_df = pivoted_df.withColumn('area_sums', pivoted_df['39'] + pivoted_df['41'] + pivoted_df['42'])\n",
    "pivoted_df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "408996cf-b9ff-4f5c-b6c8-8a5211eb3a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+-------+----+------+----+---------+------+\n",
      "|  datetime|month|year|    1|   2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|  19|  20|   21|   22|   23|   24|   25|  26|  27|   28|  29|  30|   31|    32|   33|  34|  35|  36| 37|  38|  40|   43|  44|  45|  46| 47|  48|  49|  50|  51| 52|  53| 54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|area_sums|   name|temp|precip|snow|snowdepth|sunset|\n",
      "+----------+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+-------+----+------+----+---------+------+\n",
      "|2021-10-05|   10|2021|12199|9107|24884|10934|12710|62904|55515|180042|674|3314|3259|1323|3077|7226|7209|10819|2898|1116|8828|3030|11178|34577|10307|59723|14084|3590|5678|99556|8672|7147|12736|104048|24877|3684|7049|1553|824|6866|3602|10986|8677|1848|3884|560|2047|6449|2113|2097|622|2949|898|326|15899|2444|4881|2795|5363|6014|1560|3048|1163|2742|5662|4618|5772|7720|3439|7108|1518|3534|550|2453|47198|16199|    35049|chicago|66.6|   0.0| 0.0|      0.0|  1824|\n",
      "|2021-07-02|    7|2021| 9005|7260|17397| 7642| 7999|47541|33715|117551|521|2660|2410|1168|1936|5671|5780| 8063|2010| 913|6787|2356| 7659|21888| 7997|36890|12275|3120|4611|53566|7252|5243| 8074| 64260|16000|3827|5687|1408|671|5674|2866| 9215|6354|1275|3264|461|1337|4831|1325|1588|472|2478|835|255|11814|1417|3425|1885|4057|4469|1062|2325| 917|2063|4523|3978|4877|6248|2526|5897|1059|2890|387|1875|36306|12384|    18251|chicago|66.6|   0.0| 0.0|      0.0|  2029|\n",
      "+----------+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+-------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Drop day and the community areas that were summed earlier, will use year and month in regression\n",
    "pivoted_df = pivoted_df.drop('day','39','41','42')\n",
    "\n",
    "df_weather = df_weather.withColumn('datetime', F.to_date('datetime', 'yyyy-MM-dd'))\n",
    "\n",
    "# Perform the left join with weather data\n",
    "merged_df = pivoted_df.join(df_weather, on='datetime', how='left')\n",
    "merged_df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e325a6ba-0463-43db-8275-5708fb3817bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|month|year|    1|   2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|  19|  20|   21|   22|   23|   24|   25|  26|  27|   28|  29|  30|   31|    32|   33|  34|  35|  36| 37|  38|  40|   43|  44|  45|  46| 47|  48|  49|  50|  51| 52|  53| 54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|\n",
      "+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|    7|2021| 9005|7260|17397| 7642| 7999|47541|33715|117551|521|2660|2410|1168|1936|5671|5780| 8063|2010| 913|6787|2356| 7659|21888| 7997|36890|12275|3120|4611|53566|7252|5243| 8074| 64260|16000|3827|5687|1408|671|5674|2866| 9215|6354|1275|3264|461|1337|4831|1325|1588|472|2478|835|255|11814|1417|3425|1885|4057|4469|1062|2325| 917|2063|4523|3978|4877|6248|2526|5897|1059|2890|387|1875|36306|12384|    18251|66.6|   0.0| 0.0|      0.0|  2029|\n",
      "|   10|2021|12199|9107|24884|10934|12710|62904|55515|180042|674|3314|3259|1323|3077|7226|7209|10819|2898|1116|8828|3030|11178|34577|10307|59723|14084|3590|5678|99556|8672|7147|12736|104048|24877|3684|7049|1553|824|6866|3602|10986|8677|1848|3884|560|2047|6449|2113|2097|622|2949|898|326|15899|2444|4881|2795|5363|6014|1560|3048|1163|2742|5662|4618|5772|7720|3439|7108|1518|3534|550|2453|47198|16199|    35049|66.6|   0.0| 0.0|      0.0|  1824|\n",
      "+-----+----+-----+----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+----+----+-----+-----+-----+-----+-----+----+----+-----+----+----+-----+------+-----+----+----+----+---+----+----+-----+----+----+----+---+----+----+----+----+---+----+---+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "merged_df = merged_df.drop('datetime','name') #since no longer needed. \n",
    "merged_df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a2e0d72f-0152-4cf8-83b4-777997cbac56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# write completed data to a GCS bucket so we don't have to rerun things\n",
    "merged_df.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/supervised_dataset_final.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6dadc9-db41-4492-adaa-be633c0c4afa",
   "metadata": {},
   "source": [
    "## ML Model\n",
    "\n",
    "1. Create Datasets that are for data pre-program (Oct 2021) and for data between Oct 2021 up to not including july 2023.\n",
    "2. Train model on first dataset. predict for october, november, december 2021\n",
    "3. train new model on second dataset, make more future predictions\n",
    "4. Plot graphs of all predictions versus actual ride counts over the years \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52bce528-7f87-4a2d-9e10-c764fefc2e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# skip all the above and just run this line to get the final dataset loaded in to use with the ML model\n",
    "merged_df = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/supervised_dataset_final.csv\", inferSchema=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe839d92-0f0e-4b33-8a0a-a3e48ff8b0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the data for predicting the first policy change\n",
    "df_1 = merged_df.filter((merged_df.year < 2021) | ((merged_df.year == 2021) & (merged_df.month < 10))) \n",
    "# this is the data for predicting the second policy change\n",
    "df_2 = merged_df.filter(((merged_df.year == 2021) & (merged_df.month >= 10)) | (merged_df.year == 2022) | ((merged_df.year == 2023) & (merged_df.month < 7)))\n",
    "# this is the data after the second policy change\n",
    "df_3 = merged_df.filter(((merged_df.year == 2023) & (merged_df.month >= 7)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "975dadc7-cb0c-4e75-89fb-b73b8e7ba743",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import LinearRegression\n",
    "from pyspark.ml.feature import StringIndexer,OneHotEncoder, IndexToString, VectorAssembler\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.sql.types import FloatType\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder, CrossValidatorModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba889428-ef35-43d5-91d3-e49f1ef7bcad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure sunset is an integer so it works in the model\n",
    "df_1 = df_1.withColumn(\"sunset\", F.col(\"sunset\").cast(\"int\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8682ec74-9d84-454e-980c-074da8e08284",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_2.withColumn(\"sunset\", F.col(\"sunset\").cast(\"int\"))\n",
    "df_3 = df_3.withColumn(\"sunset\", F.col(\"sunset\").cast(\"int\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ce90d7-7024-4830-a4f8-6f1efa911d10",
   "metadata": {},
   "source": [
    "## Impact of Program on rides in Hyde Park"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1884f85e-70c8-4d83-8d5c-f1a90e7b7cee",
   "metadata": {},
   "source": [
    "### Building model to predict rides in program area based on pre-program data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370b8f4e-f6bc-4245-889d-e1803afed90c",
   "metadata": {},
   "source": [
    "This model will be for predicting the first policy change using df_1. Using df1 (all pre-program data) to predict what would happen to count of rides if the program had not happened at all. Do this by creating the model using only pre-program data. Predict outcomes for future dates and compare to the actual ride counts on those dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e0b37ea7-3f52-4698-8d63-114d69164046",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input features are everythin but the area_sums column which is what we are trying to predict\n",
    "input_features = ['month','year','1','2','3','4','5','6','7','8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20','21','22','23','24','25',\n",
    " '26','27','28','29','30','31','32','33','34','35','36','37','38','40','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66',\n",
    " '67','68','69','70','71','72','73','74','75','76','77','temp','precip','snow','snowdepth','sunset']\n",
    "\n",
    "vectorAssembler = VectorAssembler(inputCols=input_features,\n",
    "                                  outputCol=\"features\", handleInvalid='skip')\n",
    "\n",
    "# splitting first and then doing vector assembly to avoid errors\n",
    "train_df, test_df = df_1.randomSplit([.7,.3],seed=1234)\n",
    "\n",
    "train_df = vectorAssembler.transform(train_df)\n",
    "test_df = vectorAssembler.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd8be7aa-1cc7-4753-bf08-6142797364c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23/11/26 00:20:31 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n",
      "23/11/26 00:20:31 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n",
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-141.17821491897232,-745.3452416033055,0.04069589047839324,0.10523630495219816,0.002416215491090195,0.025009680242684473,0.015991560747858,-0.000809268481126041,-0.0003083326046310907,0.001719524462734702,-0.34154509015643614,-0.08238655427681342,-0.0,-0.0,0.779895783342685,0.05086694893622908,0.025653865341783114,0.024961895150708617,0.19582968856485264,0.2782530128506548,0.05452784089546298,0.17385879886338018,0.0187564547338862,0.003440291549253111,0.050322500767793425,0.0028592082630942497,-0.025445117595504444,0.0,0.14513263303240942,0.010301942556421948,0.02382458946533733,0.13529300450184567,0.0461338629846489,0.009441369903795505,0.01915890689447327,0.030501308888032312,0.16639747544742067,0.372961105529036,1.0630578240307724,0.15210946145832405,0.3574990068792217,-0.009678684217525414,0.05007541974459699,0.3291731701458939,-0.13681731725138238,1.8145586373952591,0.17587067975956647,0.10694274072585269,1.1307091745949909,-0.32397755445680976,0.7275761869829841,0.0,-4.3914935300680344,2.803775923420458,0.06630059546553475,0.6118236814711749,0.2231250018854385,0.3552694333131243,0.11002835175648276,0.19352160818980538,0.5766994392678659,0.34223581743721654,0.22754882093817158,0.3015178177562475,0.06567033036361472,0.05159515210734938,0.14042357457635468,0.05271453557563555,0.36247842456150564,0.0,-0.5013060546057359,0.10421665258028001,-0.4851376212929765,-0.03691767085960801,0.029653477467609077,0.014410898627229617,-18.758338427871184,534.5990989483481,1413.626713599575,-197.24250326523736,1.094024899581077]\n",
      "Intercept: 1502177.818453082\n",
      "RMSE: 3547.182496\n",
      "r2: 0.965341\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 448:====================================================>  (19 + 1) / 20]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+------------------+\n",
      "|features                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |area_sums|prediction        |\n",
      "+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+------------------+\n",
      "|[7.0,2021.0,9005.0,7260.0,17397.0,7642.0,7999.0,47541.0,33715.0,117551.0,521.0,2660.0,2410.0,1168.0,1936.0,5671.0,5780.0,8063.0,2010.0,913.0,6787.0,2356.0,7659.0,21888.0,7997.0,36890.0,12275.0,3120.0,4611.0,53566.0,7252.0,5243.0,8074.0,64260.0,16000.0,3827.0,5687.0,1408.0,671.0,5674.0,2866.0,9215.0,6354.0,1275.0,3264.0,461.0,1337.0,4831.0,1325.0,1588.0,472.0,2478.0,835.0,255.0,11814.0,1417.0,3425.0,1885.0,4057.0,4469.0,1062.0,2325.0,917.0,2063.0,4523.0,3978.0,4877.0,6248.0,2526.0,5897.0,1059.0,2890.0,387.0,1875.0,36306.0,12384.0,66.6,0.0,0.0,0.0,2029.0]                                             |18251    |18633.118334107567|\n",
      "|[11.0,2019.0,31301.0,20571.0,72163.0,34279.0,40909.0,214270.0,184080.0,553240.0,2052.0,7234.0,7293.0,3814.0,6445.0,18618.0,17940.0,30061.0,6311.0,2393.0,20390.0,8016.0,35837.0,124274.0,24830.0,210548.0,28231.0,7284.0,12059.0,313372.0,18903.0,17359.0,40780.0,317356.0,55213.0,15558.0,19109.0,3502.0,2606.0,16664.0,8029.0,23451.0,19175.0,3615.0,8133.0,1312.0,3933.0,13494.0,3497.0,3799.0,1276.0,5579.0,1630.0,890.0,32696.0,5663.0,12046.0,7249.0,17380.0,14692.0,4072.0,8155.0,3444.0,6762.0,13198.0,10621.0,12938.0,17027.0,7086.0,15616.0,3550.0,7423.0,1570.0,4810.0,77674.0,44762.0,40.4,0.003,0.0,0.0,1638.0]|82642    |77658.68311885605 |\n",
      "|[11.0,2018.0,24441.0,15075.0,52214.0,23778.0,26500.0,177030.0,132956.0,359952.0,1817.0,5422.0,4540.0,2236.0,3735.0,14001.0,12197.0,21433.0,4340.0,1529.0,13116.0,5356.0,25214.0,92699.0,15159.0,143693.0,16025.0,3772.0,6588.0,157780.0,8952.0,10114.0,25784.0,161527.0,60265.0,12294.0,12096.0,2217.0,1700.0,10215.0,4856.0,14280.0,9544.0,1864.0,4154.0,488.0,2166.0,6643.0,1346.0,1924.0,676.0,2827.0,796.0,276.0,26145.0,2801.0,7425.0,4322.0,11516.0,8106.0,2490.0,4856.0,2313.0,4084.0,7322.0,5504.0,6204.0,8881.0,3517.0,8240.0,2502.0,3849.0,1464.0,2712.0,56067.0,33937.0,49.6,0.0,0.0,0.0,1744.0]                 |48053    |45113.59325499227 |\n",
      "|[4.0,2021.0,13500.0,8516.0,26863.0,12093.0,13898.0,110488.0,87020.0,200307.0,1233.0,3432.0,3362.0,1559.0,2317.0,7982.0,8325.0,12108.0,2869.0,1202.0,9252.0,3202.0,11952.0,44393.0,11736.0,87212.0,18243.0,4644.0,6120.0,86325.0,9252.0,6761.0,13300.0,68326.0,20362.0,4862.0,6875.0,1657.0,924.0,7051.0,3768.0,12655.0,9209.0,1773.0,4661.0,605.0,1917.0,6639.0,1833.0,2033.0,553.0,3401.0,1073.0,257.0,7300.0,1808.0,4263.0,2628.0,5369.0,6158.0,1565.0,3205.0,1223.0,2857.0,6542.0,5606.0,6420.0,8774.0,2882.0,8396.0,1621.0,3721.0,696.0,2779.0,17087.0,18032.0,70.4,0.0,0.0,0.0,1923.0]                                 |25550    |24605.566335394047|\n",
      "|[9.0,2019.0,19539.0,13004.0,39713.0,20213.0,25689.0,127372.0,112930.0,355301.0,1189.0,4628.0,4699.0,2400.0,5147.0,11212.0,10800.0,19062.0,4815.0,1505.0,12736.0,4831.0,21310.0,68785.0,15359.0,123804.0,17553.0,4304.0,8337.0,211111.0,12235.0,11235.0,23736.0,244834.0,52115.0,9774.0,14359.0,2475.0,1710.0,11178.0,5128.0,14788.0,11767.0,2449.0,4980.0,875.0,2536.0,8806.0,2437.0,2584.0,913.0,3575.0,829.0,568.0,28341.0,4058.0,7966.0,4721.0,11188.0,9751.0,2651.0,4954.0,2468.0,4630.0,8627.0,6540.0,8567.0,10951.0,4686.0,9570.0,2237.0,4867.0,870.0,2990.0,72793.0,26796.0,64.9,0.0,0.0,0.0,1917.0]                 |46530    |53888.15131104388 |\n",
      "+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "train_df = train_df.na.drop()  # Remove rows with null values\n",
    "\n",
    "# Train Model\n",
    "from pyspark.ml.regression import LinearRegression\n",
    "\n",
    "#Elastic Net\n",
    "lr = LinearRegression(featuresCol = 'features', labelCol='area_sums', regParam=0.3, elasticNetParam=0.8, maxIter=10)\n",
    "lrm = lr.fit(train_df)\n",
    "\n",
    "#coefficients\n",
    "print(\"Coefficients: \" + str(lrm.coefficients))\n",
    "print(\"Intercept: \" + str(lrm.intercept))\n",
    "\n",
    "#model summary\n",
    "print(\"RMSE: %f\" % lrm.summary.rootMeanSquaredError)\n",
    "print(\"r2: %f\" % lrm.summary.r2)\n",
    "\n",
    "# Run the classifier on the test set\n",
    "predictions = lrm.transform(test_df)\n",
    "predictions.select('features','area_sums','prediction').show(5,truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e904f1bc-2143-439c-a6fd-e30ef8cfac28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 4090.238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 16730047.318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 3102.571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 538:===================================================> (392 + 8) / 400]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.950\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#print evaluation metrics\n",
    "e = RegressionEvaluator(labelCol='area_sums', predictionCol= 'prediction', metricName= 'rmse')\n",
    "\n",
    "# Root Mean Square Error\n",
    "rmse = e.evaluate(predictions)\n",
    "print(\"RMSE: %.3f\" % rmse)\n",
    "\n",
    "# Mean Square Error\n",
    "mse = e.evaluate(predictions, {e.metricName: \"mse\"})\n",
    "print(\"MSE: %.3f\" % mse)\n",
    "\n",
    "# Mean Absolute Error\n",
    "mae = e.evaluate(predictions, {e.metricName: \"mae\"})\n",
    "print(\"MAE: %.3f\" % mae)\n",
    "\n",
    "# r2 - coefficient of determination\n",
    "r2 = e.evaluate(predictions, {e.metricName: \"r2\"})\n",
    "print(\"r2: %.3f\" %r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e42c45-e8c6-4d6d-b4fd-6c30b38a026c",
   "metadata": {},
   "source": [
    "The R-squared value is high, indicating that the model explains most of the variability in the daily rides data. These statistics suggest that the linear regression model performs quite well in terms of fitting the data and predicting daily ride counts for the program area, although the RMSE and MAE indicate that there are still significant errors in the predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "296059d9-78af-4ac7-b53a-eee5fad98434",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# save model\n",
    "model_path = \"gs://msca-bdp-student-gcs/bdp-rideshare-project/models/pre_program_model\"\n",
    "lrm.save(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da4fa2f-e810-441a-883d-0346543f021b",
   "metadata": {},
   "source": [
    "### Pre-program trends and predictions\n",
    "\n",
    "Pre-program ride trends- what would the ridership be in the program areas for 2018 - Sept 2021 based on the model- predict for area_sums against actual area_sums before the program started (pre-trends)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "092e3097-0029-4d0c-b30e-c1bfc497beee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|month|year|    1|    2|    3|    4|    5|     6|     7|     8|   9|  10|  11|  12|  13|   14|   15|   16|  17|  18|   19|  20|   21|    22|   23|    24|   25|  26|  27|    28|   29|   30|   31|    32|   33|   34|   35|  36|  37|   38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51|  52|  53| 54| 55|   56|  57|  58|  59|   60|   61|  62|  63|  64|  65|  66|  67|  68|   69|  70|   71|  72|  73|  74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|            features|        prediction|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|   10|2019|29231|17263|71395|36099|42629|257725|214809|553952|2207|7122|6108|3430|4891|17926|16443|28228|5642|2027|17239|6727|35113|133338|19446|223133|21584|5012|9019|243039|12333|13862|36085|235168|80315|16449|15174|2820|1914|13071|6112|17193|12961|2539|5394|727|2773|8829|2193|2763|1052|3938|952|703|18892|4074|9492|5964|14861|10619|3148|6504|2837|5680|9947|7136|8439|11685|4809|10787|2999|5129|1682|3586|47665|44315|    66329|59.6|   0.0| 0.0|      0.0|  1822|[10.0,2019.0,2923...| 60641.37608649512|\n",
      "|    1|2019|19333|15209|37813|20437|22238|111367| 89150|272925| 851|4290|4442|2078|4735|12263|10975|18766|4329|1600|13523|5190|20481| 64069|15803|104733|17215|4207|7807|171554|11963|11761|22381|182757|28563| 7562|13755|2527|1725|10899|5501|14607|10856|2135|4816|877|2490|8106|2139|2291| 724|3246|839|448|19506|3788|8377|4689|11395| 9566|2721|5781|2271|4428|8209|6626|7957|10502|4158| 9128|1770|4354| 688|2932|51541|26065|    52615|29.3| 0.003| 0.0|      0.0|  1630|[1.0,2019.0,19333...|51590.197710192995|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "only showing top 2 rows\n",
      "\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|month|year|    1|    2|    3|    4|    5|     6|     7|     8|   9|  10|  11|  12|  13|   14|   15|   16|  17|  18|   19|  20|   21|    22|   23|    24|   25|  26|  27|    28|   29|   30|   31|    32|   33|   34|   35|  36|  37|   38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51|  52|  53| 54| 55|   56|  57|  58|  59|   60|   61|  62|  63|  64|  65|  66|  67|  68|   69|  70|   71|  72|  73|  74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|   10|2019|29231|17263|71395|36099|42629|257725|214809|553952|2207|7122|6108|3430|4891|17926|16443|28228|5642|2027|17239|6727|35113|133338|19446|223133|21584|5012|9019|243039|12333|13862|36085|235168|80315|16449|15174|2820|1914|13071|6112|17193|12961|2539|5394|727|2773|8829|2193|2763|1052|3938|952|703|18892|4074|9492|5964|14861|10619|3148|6504|2837|5680|9947|7136|8439|11685|4809|10787|2999|5129|1682|3586|47665|44315|    66329|59.6|   0.0| 0.0|      0.0|  1822|\n",
      "|    1|2019|19333|15209|37813|20437|22238|111367| 89150|272925| 851|4290|4442|2078|4735|12263|10975|18766|4329|1600|13523|5190|20481| 64069|15803|104733|17215|4207|7807|171554|11963|11761|22381|182757|28563| 7562|13755|2527|1725|10899|5501|14607|10856|2135|4816|877|2490|8106|2139|2291| 724|3246|839|448|19506|3788|8377|4689|11395| 9566|2721|5781|2271|4428|8209|6626|7957|10502|4158| 9128|1770|4354| 688|2932|51541|26065|    52615|29.3| 0.003| 0.0|      0.0|  1630|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+------+-----+------+-----+----+----+------+-----+-----+-----+------+-----+-----+-----+----+----+-----+----+-----+-----+----+----+---+----+----+----+----+----+----+---+---+-----+----+----+----+-----+-----+----+----+----+----+----+----+----+-----+----+-----+----+----+----+----+-----+-----+---------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load pre-program model\n",
    "from pyspark.ml.regression import LinearRegressionModel\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# Path to saved model on GCS\n",
    "model_path = \"gs://msca-bdp-student-gcs/bdp-rideshare-project/models/pre_program_model\"\n",
    "\n",
    "# Load the Linear Regression Model\n",
    "lrm = LinearRegressionModel.load(model_path)\n",
    "\n",
    "# dataframe that is the true counts\n",
    "df_real = df_1\n",
    "\n",
    "# input features are everything but the area_sums column which is what we are trying to predict\n",
    "input_features = ['month','year','1','2','3','4','5','6','7','8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20','21','22','23','24','25',\n",
    " '26','27','28','29','30','31','32','33','34','35','36','37','38','40','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66',\n",
    " '67','68','69','70','71','72','73','74','75','76','77','temp','precip','snow','snowdepth','sunset']\n",
    "\n",
    "vectorAssembler = VectorAssembler(inputCols=input_features, outputCol=\"features\", handleInvalid='skip')\n",
    "\n",
    "# take the real data and create predictions to compare\n",
    "df_real_vector = vectorAssembler.transform(df_real)\n",
    "df_pre_program_predictions = lrm.transform(df_real_vector)\n",
    "\n",
    "df_pre_program_predictions.show(2)\n",
    "df_real.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c88dfe23-98e7-4373-8339-4fc1693416db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now group by month and sum counts and plot\n",
    "\n",
    "#df_real.withColumn(\"month\", F.format_string(\"%02d\", df_real.month.cast(\"int\")))\n",
    "#df_real.select('month').show(5)\n",
    "monthly_real = df_real.withColumn(\"year_month\", F.concat_ws(\"-\", df_real.year, df_real.month))\n",
    "df_pre_program_predictions = df_pre_program_predictions.withColumn(\"year_month\", F.concat_ws(\"-\", df_pre_program_predictions.year, df_pre_program_predictions.month))\n",
    "\n",
    "#monthly_real.select('year_month').distinct().show(30)\n",
    "monthly_real = monthly_real.groupBy('year_month').sum('area_sums')\n",
    "monthly_pre_program_preds = df_pre_program_predictions.groupby('year_month').sum('prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "b52a1292-af1b-4146-b8d0-01843287ee44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10</td>\n",
       "      <td>435438</td>\n",
       "      <td>418766.823378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-4</td>\n",
       "      <td>458738</td>\n",
       "      <td>437424.914776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11</td>\n",
       "      <td>428752</td>\n",
       "      <td>411241.410744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-3</td>\n",
       "      <td>178045</td>\n",
       "      <td>193006.695556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-4</td>\n",
       "      <td>176680</td>\n",
       "      <td>178816.816038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-9</td>\n",
       "      <td>351479</td>\n",
       "      <td>395490.044955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-2</td>\n",
       "      <td>452100</td>\n",
       "      <td>419238.609782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-5</td>\n",
       "      <td>456471</td>\n",
       "      <td>446927.511582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-6</td>\n",
       "      <td>422260</td>\n",
       "      <td>417559.979058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-5</td>\n",
       "      <td>183403</td>\n",
       "      <td>177115.769091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-9</td>\n",
       "      <td>193462</td>\n",
       "      <td>182082.974058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-8</td>\n",
       "      <td>163498</td>\n",
       "      <td>158309.023479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-3</td>\n",
       "      <td>474323</td>\n",
       "      <td>486828.015158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-6</td>\n",
       "      <td>181285</td>\n",
       "      <td>165413.667248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-2</td>\n",
       "      <td>156257</td>\n",
       "      <td>158592.509085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-11</td>\n",
       "      <td>424978</td>\n",
       "      <td>393264.032867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-8</td>\n",
       "      <td>346070</td>\n",
       "      <td>391839.729404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-12</td>\n",
       "      <td>360239</td>\n",
       "      <td>387170.099655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>365057</td>\n",
       "      <td>403329.566396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-7</td>\n",
       "      <td>172026</td>\n",
       "      <td>172070.713297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-7</td>\n",
       "      <td>372019</td>\n",
       "      <td>400838.564600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-1</td>\n",
       "      <td>434428</td>\n",
       "      <td>418610.505910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-1</td>\n",
       "      <td>177280</td>\n",
       "      <td>179217.039411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_month  sum(area_sums)  sum(prediction)\n",
       "0     2019-10          435438    418766.823378\n",
       "1      2019-4          458738    437424.914776\n",
       "2     2019-11          428752    411241.410744\n",
       "3      2021-3          178045    193006.695556\n",
       "4      2021-4          176680    178816.816038\n",
       "5      2019-9          351479    395490.044955\n",
       "6      2019-2          452100    419238.609782\n",
       "7      2019-5          456471    446927.511582\n",
       "8      2019-6          422260    417559.979058\n",
       "9      2021-5          183403    177115.769091\n",
       "10     2021-9          193462    182082.974058\n",
       "11     2021-8          163498    158309.023479\n",
       "12     2019-3          474323    486828.015158\n",
       "13     2021-6          181285    165413.667248\n",
       "14     2021-2          156257    158592.509085\n",
       "15    2018-11          424978    393264.032867\n",
       "16     2019-8          346070    391839.729404\n",
       "17    2018-12          360239    387170.099655\n",
       "18    2019-12          365057    403329.566396\n",
       "19     2021-7          172026    172070.713297\n",
       "20     2019-7          372019    400838.564600\n",
       "21     2019-1          434428    418610.505910\n",
       "22     2021-1          177280    179217.039411"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_real_pd = monthly_real.toPandas()\n",
    "monthly_pre_program_preds_pd = monthly_pre_program_preds.toPandas()\n",
    "combined_df = monthly_real_pd.merge(monthly_pre_program_preds_pd, left_on='year_month', right_on='year_month', how='inner')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7b2e2d29-471e-4fe6-a4cf-52866411961b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RUVH4+/tXiBHKKirKY3R3d7e+yC9X3d0Vn3nmGdq3b09ERAT/+Mc/KC0tZeHChTYvwgFuu+02m89//vlnVCoVd911l02cfn5+tGnTxhpnecLw8n9/16tXL9q3b0/37t15/vnnadeuHcuWLcPZ2ZmdO3fi5ubG7bffbnOf8oRldV6UGwwG9uzZwx133FGhwutyUVFRtGnThs8++8x6rLwh+ujRo6t8vSupbInr559/zvDhwwkPD6ddu3a0b9+e33///bp2JCyv+Lk84fPZZ5/RunVrunfvXqVz3HvvvbRv354uXbrw6KOPotfrWbx48TWTb1X9vqzO8zE8PJzt27czd+5cdu3aRVFRUZW+hvLd564211BW3TVhwgQCAwOZNWtWhduvpxF+cXExTz31FMnJySxYsAB3d/dqn+NyDRo0IDMz84rNyoUQQtw4qYASQgjhEM6ePcuePXu47bbbsFgs1i3mb7/9dtauXcuaNWt47rnnAMjKyropu48dPnyYRx55hMjISF5//XUCAwPRarVs3ryZJUuWVPlF3uVGjhzJhx9+yLfffsuYMWP45ZdfSE9Pt6lu6t69O++++y6ffPIJ06ZNw2g00qpVKyZMmFCl5EnXrl158cUXUalUuLq60qRJk0qbi/99yUx5D5o5c+YwZ86cSs+dlZUFlFU9VLZ8rCpLyi5evIi/v3+1erxcLjMzk59//vmKVRs1EePlpk6dSo8ePdBoNPj4+NCoUaNKx/n7+1eI02Kx2FS7Xa5JkyZAWUXM3/t7nThxwubzjz/+GA8PD7RaLQEBATbLKcu/zr8nARo0aGCzfK0qcnNzKS0trdLza+zYscyYMYP4+HiaNGnCqlWrGDRoUIXvq+uRnJyMTqezVtZ89NFHvPnmm4wZM4YpU6bg4+ODWq1mwYIFxMfHV/v8fn5+DB48mC+//JLHH3+cU6dOsXfvXl577bUqn2POnDm0aNGCgoICvvvuO7788kueffZZli1bdtX7VfX7sjrPxxkzZhAYGMh3333HBx98gLOzM7179+aFF1646pLA4uJioKwa8kqSkpIYN24cGo2GFStWWBuKl/P29ub48eMV7ldYWIjJZKowHsp6wD3xxBPs27eP999/v0Y2mnB2dsZisVBcXHzDFXhCCCEqJz9dhRBCOIQ1a9ZgsVj48ccf+fHHHyvcvm7dOp5++mlrEuB6mgSX0+l05OfnVzhe/mKu3LfffouTkxPvv/++zQu0zZs3X/e1W7ZsSceOHVm7di1jxoxhzZo1+Pv707t3b5txAwYMYMCAARiNRg4ePMj777/Pc889R3BwsE3fmsp4enpaq6iu5u8Ji/Kkxj//+c8r9vBp3rw5UPais7KmyVVp8O3r68u+ffswm83XlYTy8fEhLCysQkVXufJEkLe3N4cPH76uGC/XpEmTKj2elcWpUqn47LPPKk0Alh/r37+/tfn+lYSFhV2xSsXb25tDhw5hsVhs5rS8GuTvvb+uxsvLC41GU6Xn19ChQ5k7dy7/93//R0REBOnp6VfcZbE6UlNTOXr0KN27d7cmEr755hsiIyOZOXOmzdiCgoLrvs64ceP4+uuv2bJlCzt27ECv11urjaqiRYsW1u+LHj16YDabWbVqFT/88EOFarTLVfX7sjrPRzc3NyZPnmzdiGH79u28/fbbTJgwgR9++OGKsZRfIycnp0ICFcqST+UN1FeuXFlpH73WrVvz7bffkp6ebpN8PHnyJPDXcuhyRqORSZMmsWvXLhYvXkzPnj2vGF91ZGdno9PpbriSSgghxJXJEjwhhBB2r7S0lHXr1hESEsLKlSsr/Bs/fjzp6els374dgL59+7Jr166rVj6Uv7ivrEopODiYM2fO2OwQlpWVVWFXLJVKhUajsUmSFBUV8c0339zQ1ztixAgOHTrE3r17+fnnnxk+fLhNI+e/fx2RkZHWXkHHjh27oWtfTWhoKM2aNePPP/8kPDy80n/ljcCjoqL4/fffbV40l5aWVrpb3t/16dOH4uLiCjuv/Z1Op6t0/vr168fJkycJCQmpNMby6p2oqCgKCgrYsmWLzf2r0ii9JvTr1w+LxUJqamqlcZY3afbx8alwW3X07NmTwsLCConR9evXW2+vKhcXF7p3784PP/zAxYsXrzrW2dmZ0aNHs27dOj766CPatm1L165dqxX73xUVFfHSSy9ZG9WXU6lUFZJ4f/75JwcPHrzua3Xo0IHOnTvzwQcfsGHDBoYPH17pzm9V9fzzz+Pl5cXChQsr3emyXFW/L6vzfLycn58fI0aM4M477yQhIaHSXUQvvwb81dT+csnJyYwdOxaz2cyKFSsIDg6u9By33norKpWKdevW2Rxfu3YtLi4u9OnTx3qsvPJp586dLFq0yOa2G3X+/PkbamQuhBDi2qQCSgghhN3bvn07aWlpTJ06tdJmwq1ateLTTz9l9erV9O/fnylTprB9+3YeeOAB/vnPf9K6dWvy8vLYsWMHDz30EC1atCAkJAQXFxc2bNhAixYtcHNzw9/fn4CAAIYNG8aXX37J1KlTuffee8nOzmbZsmUVXsxFR0fz0Ucf8dxzzzF69Giys7NZvnx5pdUs1TFkyBDefPNNnnvuOYxGo02DdShrsn7hwgV69uxJYGAgubm5rFy5Eq1WS2Rk5A1d+1pmzpzJY489xiOPPMLw4cMJCAggJyeHuLg4jh49ysKFCwGYOHEiW7du5cEHH+SJJ57AxcWFzz777KovdssNGTKEtWvX8uqrr5KQkEBUVBQWi4VDhw7RokULaw+w1q1bs3v3brZu3UrDhg1xd3cnNDSUyZMn89tvvzFmzBjGjh1L8+bNMRqNnD9/nu3btzNz5kwCAwO5++67+fjjj5k2bRrPPPMMTZs2JTY2ll9++aVWH8NyXbt2ZfTo0fzrX//iyJEjdO/eHVdXV9LT09m3bx+tW7eukYqhu+++m88++4xp06aRlJRE69atrUuboqOjr7gE8EpefPFF7rvvPu69914ef/xxQkJCyMzMZOvWrcycOdPmeXL//fezbNkyjh49WmlvoKtJSUnh4MGDmM1m8vLyOH78OGvWrCE5OZnp06fbVAX269ePxYsXs3DhQrp3705CQgKLFy+mcePGlJaWVuu6lxs3bhzPPPMMKpXqhufCy8uLxx9/nP/+979s2LCBYcOGVTquOt+XVX0+jho1in79+hEWFoaXlxdxcXF8/fXXdO7cGVdX1yvG3LFjR1xcXDh06JBNT6rMzEzGjRtHeno6//nPf8jMzLTpeRcYGGithmrVqhX33HMPixYtQqPREB4ezq+//spXX33F008/bbMEb/LkyWzfvp0JEybg7e1tk0D08PCwSSCdPn2a06dPA2U7XxoMBms1V8uWLW3Gms1mDh8+LDvgCSFELZMElBBCCLu3evVqtFrtFZtS+/r6MnDgQH788UcyMjIICAhg9erVLFy4kA8++IDs7Gx8fHzo2rWr9cWOq6srs2fP5p133uGRRx7BZDLx5JNP8tRTT9G1a1fmzJnD0qVLmTRpEk2aNOGJJ55g+/bt1sbQUFY5Mnv2bD744AMmTJhAQEAA9957L76+vrz00kvX/fV6enoyYMAANm7cSJcuXazLaMpFRERw5MgR5s6dy8WLF9Hr9XTo0IGPP/64wnKWmtajRw9WrVrFkiVLmD17Nrm5uXh7e9OiRQubXd9at27NRx99xJw5c5g2bRpeXl7cddddDBo0iJdffvmq13BycuKDDz7g/fff59tvv2XFihW4u7vTpk0bm4qIl156iZkzZ/Lss89iMBiIjIzkk08+wd/fn9WrV7N48WKWL19Oamoq7u7uBAcH06dPH/R6PVD2PbBy5Ur+85//MHfuXFQqFb1792bevHmMGTOmdh7Av3nttdeIiIjgyy+/5PPPP8dsNuPv70+XLl3o2LFjjVzD2dmZlStXMn/+fJYtW2btkTZ+/HiefPLJap+vTZs21ufX22+/TUFBAQ0bNqRHjx4Vkq8BAQF06dKFkydPVmv5GsAnn3zCJ598gkajwcPDg8aNG9O/f3/uvffeCpUsEyZMwGAwsHr1apYtW0bLli159dVX2bx5s81ztroGDBiATqcjKirqqr2Sqmrs2LF89tlnLF68mCFDhlRa2Vid78uqPh979OjB1q1bWbFiBQaDgYCAAO6++24mTJhw1Xh1Oh2DBg1iy5YtPPvss9bjp0+fJjExEaDSnRrLf5aW+/e//01AQACffvop6enpBAcH89JLL1mX75X7+eefAViyZAlLliyxua38+V3u+++/55133rEZM2XKlEqvv2vXLvLy8qr9PSiEEKJ6VJbKtgkRQgghhBCilmVmZtK/f38eeOABXnjhBaXDqbatW7cyceJEli5dSnR0tNLhKOKPP/7gnnvu4auvvqqRZuBKeP7550lMTOSLL75QOhQhhHBokoASQgghhBA31YULF0hMTGT58uXs3LmTH3/88absTFlTTp8+TVJSErNnz8bV1ZV169ZVaMpfnzz99NMYDAbef/99pUOptnPnzjF48GA+/vhjunXrpnQ4Qgjh0KQJuRBCCCGEuKlWrVrF2LFjOXXqFHPnzrWr5BOU9VaaNGkSer2et99+u14nnwCmT59OeHh4pbuD1nXJycm8/PLLknwSQoibQCqghBBCCCGEEEIIIUStkgooIYQQQgghhBBCCFGrJAElhBBCCCGEEEIIIWqVk9IBnD17luXLl3Po0CFOnTpFaGgoGzdutBljsVhYtmwZn3/+OWlpaTRr1oxJkyYxePBgm3ExMTEkJSVVuMbhw4dxdna2fp6fn89bb73Fjz/+iNFoJCoqipdffpng4GCb+yUkJDBr1iz27duHq6srd955J1OnTsXFxcVmXGxsLPPnzycuLo7AwEAeeugh/vGPf1z3Y3LgwAEsFgtarfa6zyGEEEIIIYQQQghRm0wmEyqVis6dO19zrOIJqFOnThEbG0tERARms5nKWlItW7aM//3vf0ycOJHOnTuzZcsWnn32WVxcXIiJibEZO2jQIMaPH29zTKfT2Xz+3HPPcfToUV5++WU8PDxYuHAhDz/8MN988401uZSbm8uDDz5IUFAQCxcu5OLFi7zxxhtkZ2czd+5c67kOHDjApEmTGDZsGNOnT2f//v3MmjULnU7HqFGjrusxsVgslT4O9sRisWAymdBqtfW+MWd9IPNt/2QO6xeZb8cm81u/yHw7Ppnj+kXmu35xhPmuTu5C8QRUTEwMAwYMAMp20Dhy5IjN7Uajkffee4+xY8fy5JNPAtCrVy+SkpL43//+VyEB5efnR6dOna54vUOHDrFt2zaWLl1KdHQ0AK1bt2bgwIGsW7eO++67D4AvvviC3Nxc1q9fj6+vLwAajYapU6cyceJEWrRoAcC7775Lu3btmD17NgA9evQgJSWFBQsWMHLkSNTq6q9yLK98Cg8Pr/Z964rCwkKOHz9Oy5YtcXNzUzocUctkvu2fzGH9IvPt2GR+6xeZb8cnc1y/yHzXL44w33/88UeVxyreA+paCZrExEQKCgro3bu3zfE+ffpw4sQJkpOTq3W92NhY9Ho9ffv2tR4LCgqiS5cuxMbGWo9t376dnj17WpNPUFZdpdPprOOMRiM7d+7kzjvvtLnG0KFDSU9P59ixY9WKTQghhBBCCCGEEMIRKV4BdS3FxcUAFfohlS+ri4uLIygoyHp8w4YNfPXVV2i1Wrp168bUqVMJCwuz3h4XF0fz5s0rlLe1bNmSX375xWbcyJEjK1wzJCSEuLg4AM6dO4fJZCI0NLTCucrP0aFDh+v6ui0WC4WFhdd137rAYDDYfBSOTebb/skc1i8y345N5rd+kfl2fDLH9YvMd/3iCPNtsViqvHywziegQkJCUKvVHD58mKioKOvxgwcPApCTk2M9FhMTQ8eOHQkKCiIxMZElS5Zw//33s379epo0aQKU9Xby9PSscB29Xm9zrtzcXPR6/VXHlX/8+7jyzy8/X3WZTCaOHz9+3fevK86cOaN0COImkvm2fzKH9YvMt2OT+a1fZL4dn8xx/SLzXb/Y+3z/ve/2ldT5BJSHhwfDhg1j2bJltG7dmk6dOvHzzz/z7bffArZL+GbMmGH9f7du3ejVqxd33HEHy5cv59VXX7XedqXsXFWydpVl927kfFei1WqtlVT2yGAwcObMGZo1a4arq6vS4YhaJvNt/2QO6xeZb8cm81u/yHw7Ppnj+kXmu35xhPk+ffp0lcfW+QQUlDUnT09P5/HHHwfAx8eHKVOmMGfOHPz8/K54P39/f7p27crRo0etx/R6PSkpKRXG/r3iSa/Xk5ubW2FcXl6etQG5l5cXULHSqfx+lVVQVZVKpbLbJmSXc3V1dYivQ1SNzLf9kzmsX2S+HZvMb/0i8+34ZI7rF5nv+sWe57s6hTeKNyGvCm9vb5YvX8727dvZsGED27dvp1GjRmi1Wtq1a3fV+/59S8AWLVqQkJBQ4fjp06etiaXyceW9nsoZjUbOnTtnHRcSEoJWqyU+Pr7CucrPIYQQQgghhBBCCFHf2UUCqlxAQACtW7dGo9Hw+eefM3jwYDw8PK44PjU1lf379xMeHm49Fh0dTW5uLjt27LAeS0lJYf/+/URHR1uP9e3bl507d5KVlWU9tmnTJoxGo3WcTqejR48efP/99zbX3bhxIw0bNrxmckwIIYQQQgghhBCiPlB8CZ7BYCA2NhaApKQk8vPz+eGHHwCIjIzE19eXb775huLiYkJCQkhLS+PLL7/k/PnzzJ0713qejRs3sm3bNvr27Yu/vz+JiYksXboUjUbDww8/bB0XERFBv379eOmll5g+fToeHh4sWLCA4OBghg8fbh03ZswYPv30UyZNmsSkSZPIzMzkzTffZOjQoTaVTU888QQPPPAAM2bMYOjQoezfv59Vq1bx2muv2fSnEkIIIYQQQgghhKivFE9AZWZmMmXKFJtj5Z+vXLmSqKgoLBYLH374IefPn8fNzY3o6Gjmzp2Lv7+/9T6NGzcmNTWV2bNnk5eXh6enJz169GDy5MnWHfDKvf3228yZM4eZM2diMpmIiopi0aJFuLi4WMfo9XpWrFjBrFmzeOqpp3BxcWHIkCFMnTrV5lydO3dm8eLFzJs3j/Xr1xMYGMiMGTMYNWpUTT9UQgghhBBCCCGEEHZJ8QRU48aNOXHixFXHDBs2jGHDhl11TKdOnfjkk0+qdE0PDw9ef/11Xn/99auOa968OcuXL7/m+aKjo22W7wkhhBBCCCGEEEKIv8gaMSGEEEIIIYQQQghRqyQBJYQQQgghhBBCCCFqlSSghBBCCCGEEEIIIUStkgSUEEIIIYQQQgghhKhVkoASQgghhBBCCCGEUICLi4vSIdw0koASQgghhBBCCCGEuImKjCVodS40ahyKVudCkbFE6ZBqnZPSAQghhBBCCCGEEELUF0ZTKWt+Ps2GHfEUGEy4u2q5q08o98S0QqfVKB1erZEElBBCCCGEEEIIIcRNUGQsYc3Pp/nipxPWYwUGE59f+nxE/5a46BwzVSNL8IQQQgghhBBCCCFuAo1azYYd8ZXe9s2OeDRqx03TOO5XJoQQQgghhBBCCFGHFBSZKDCYKr/NYKKwqPLbHIEkoIQQQgghhBBCCCFuAncXLe6u2spvc9Xi5lL5bY5AElBCCCGEEEIIIYQQN0Gp2cxdfUIrve2uPqGUms03OaKbxzE7WwkhhKgys9FA4R/bUJn1SocihBBCCCGEQ3PROTG8X0vMZgsbf02QXfCEEELUH9m/riH3t3V4eAVhad8BcFM6JCGEEEIIIRzWxxuO0rmNPyv+PYhCgxEPN2dKzWaHTj6BLMETQoh6r+DEbgCccpLJ3fIxFotF4YiEEEIIIYRwTFm5Rfyw6yz/+Wg3SReySE6Mx2QswkXn+PVBkoASQoh6zHQxBVNmEqjUWFBhOLaD3L3fKx2WEEIIIYQQDunnfYmYzRbaNPUhsIEbRUVFSod000gCSggh6rGCU3sB0DVuiyGsPwCZmz/GcPaokmEJIYQQQgjhcCwWC5t2nwNgQGRThaO5+SQBJYQQ9Vjh6X0AOId2orhZFC5hPcFcSurauZTkpCscnRBCCCGEEI7jxLkszqflo9Nq6NMpSOlwbjpJQAkhRD1lLiqg6NwxAJxDO4NKhdfAR9AFNMdcmMuF1W9hNhUrHKUQQgghhBCOYfOl6qfeEUG4uWgVjubmkwSUEELUU4XxB8FcitavMU7eAQCotM4EjHoBtZse44V4Mr5bIk3JhRBCCCGEuEFFxhK2H0gCYED3EIWjUYYkoIQQop4qvNT/ya1lV5vjWi9/AoY/Cyo1+Ue2k7N7oxLhCSGEEEII4TB+O5yCobiEwAZutA9toHQ4ipAElBBC1EMWcymFcfsBcGvVrcLtrs3CaTDgQQAublmJIeHwTY1PCCGEEEIIR7JlT9nyu1u7h6BWqxSORhmSgBJCiHqo6PwJzIZ81K4euDQOq3SMvvudeIT3A4uZ1HXzMGWn3twghRBCCCGEcAAXMgs4fDoDlQpiujVROhzFSAJKCCHqofLd79xadEGl1lQ6RqVS4XfH4zg3aoHZkEfqqrcwG4tuZphCCCGEEELYvS17EgGIaNUQfx83haNRjiSghBCiHrL2f6pk+d3l1FpnAu55AY27F8a0M6R/u1iakgshhBBCCFFFZrOFLXvLlt8NjKyfzcfLSQJKCCHqGVPWBUwZ50GtwTW00zXHO+n98B8xFdQaCo79Ss7Or2s/SCGEEEIIIRzA4dPppGcZcHfV0qNDI6XDUZQkoIQQop4pr35yadIWjYt7le7jGtKOBgPHA3Dx588ojDtQa/EJIYQQQgjhKDbvLlt+17dzMDpt5a0v6gtJQAkhRD1T1eV3f6fvOgjPiFvBYiZt/XxMF1NqIzwhhBBCCCEcQr7BxO9/JAOy/A4kASWEEPWKuagAw7ljALhXMwGlUqnwu/0xnINbYy4q4MLqOZiNhtoIUwghhBBCCLu348B5jCVmmgZ60rKxt9LhKE4SUEIIUY8UJhwCcynaBkFofau/Bl3lpCVg5PNo3L0xpSeS9s0iaUouhBBCCCFEJTbtLms+PiCyKSqVSuFolCcJKCGEqEeud/nd5Zw8fQm45wVQO1F4YhfZv66pqfCEEEIIIYRwCGdTcjmVmI1GraJ/18ZKh1MnSAJKCCHqCYu5lMLT+wFwa3n9CSgAl8Zh+N3+GABZsV9QeGrfDccnhBBCCCGEo9i8p6z6KbJ9IF4ezgpHUzdIAkoIIeqJ4qRTmA15qF08cGnS5obPp+88AM8utwEWUr/+H8bMpBsPUgghhBBCCDtXUmrm531lu98N6C7Nx8tJAkoIIeqJglN7AHBr0RmVuma2gPW7bTzOjdtgKS4kddUczMWFNXJeIYQQQggh7NWeY6nk5Bvx8XSmaxt/pcOpMyQBJYQQ9UTh6bJlcm6tutbYOVUaLQEjp6Lx9MWUmUTa1wuxWMw1dn4hhBBCCCHszZZLy+/6d22CRiNpl3LySAghRD1gyk7FlJ4IKjWuoZ1r9NxOHj4E3DMNlUZL4ak9ZO1YVaPnF0IIIYQQwl5k5Rax53gqAAMiZfnd5SQBJYQQ9UD57ncuIW3RuHrU+Pldglrid8fjAGTv+IqCE7tr/BpCCCGEEELUdT/vO4/ZbCGsqQ9NAjyVDqdOkQSUEELUA+W71N3o7ndX4xkRg77bYADSvlmAMT2x1q4lhBBCCCFEXWOxWKy73w2U6qcKJAElhBAOzlxciOHsUQDcWtVeAgqgwYAHcQlpj8VYROrqOZQWFdTq9YQQQgghhKgrTp7LIjE1D51WQ59OwUqHU+dIAkoIIRxcYfwhMJeg9Q1C1yCoVq+l0jgRMOI5nPR+mC6mkLZ+PhZzaa1eUwghhBBCiLpg856yFQC9OjbCzUWrcDR1jySghBDCwRWeLuv/VJO7312Nxt2rrCm5kw5D3AGyYr+4KdcVQgghhBBCKUXGErYfOA9I8/ErUTwBdfbsWV555RWGDRtGu3btGDJkSIUxFouFDz74gJiYGDp06MCQIUP47rvvKj3f8uXLiYmJITw8nJEjR7Jr164KY/Lz83nllVeIioqic+fOTJgwgaSkpArjEhISeOSRR+jUqRM9e/Zk1qxZFBUVVRgXGxvL3XffTXh4OAMHDuSzzz67jkdCCCFqnsVcSuHp/UDtL7+7nHOjUPzunAhA9m9ryT/++027thBCCCGEEDfbzj9SKCwqwd/XjQ6hfkqHUycpnoA6deoUsbGxNG3alBYtWlQ6ZtmyZfzvf/9jxIgRvP/++0RGRvLss8+ydetWm3HLly9n/vz5/OMf/2Dp0qU0bdqUxx57jBMnTtiMe+6559i6dSsvv/wy8+fPJy0tjYcfftgmuZSbm8uDDz5IQUEBCxcuZNq0aWzYsIEZM2bYnOvAgQNMmjSJdu3a8cEHHzB8+HBmzZrFqlWyDbkQQnnFyacwF+aidnHHpXGbm3ptzw598YoaCkD6hncwpp29qdcXQgghhBDiZtm0u6z5+IDuIajVKoWjqZuclA4gJiaGAQMGADB9+nSOHDlic7vRaOS9995j7NixPPnkkwD06tWLpKQk/ve//xETE2Mzbty4cTzyyCMAREZGMnToUJYsWcL8+fMBOHToENu2bWPp0qVER0cD0Lp1awYOHMi6deu47777APjiiy/Izc1l/fr1+Pr6AqDRaJg6dSoTJ060Jsveffdd2rVrx+zZswHo0aMHKSkpLFiwgJEjR6JWK57jE0LUY+W737mGdkKlufk/8n1jxmJMPYPhzB9cWDWH4PFz0LjKdrRCCCGEEMJxpF4s5PDpDFQquLVbE6XDqbMUz45cK0GTmJhIQUEBvXv3tjnep08fTpw4QXJyMgD79+8nLy/PZgmfRqNh8ODBxMbGYrFYgLLlcnq9nr59+1rHBQUF0aVLF2JjY63Htm/fTs+ePa3JJ4BBgwah0+ms44xGIzt37uTOO++0iW3o0KGkp6dz7Nix6jwUQghR4wpOlfV/cm/VXZHrq9Qa/Ic/h5OXPyXZqaStk6bkQgghhBDCsWzZU1b9FNGyIf6+bgpHU3cpXgF1LcXFxQBotbYd5HU6HQBxcXEEBQURFxcHQGhoqM24Fi1aUFBQQGpqKoGBgcTFxdG8eXNUKtuSuJYtW/LLL79YP4+Li2PkyJEVrhkSEmK91rlz5zCZTBWu2bJlS+s5OnTocF1ft8ViobCw8LruWxcYDAabj8KxyXzXTSU56ZjSz4FKDUFhV/2ZUrtzqMFr6GQufvEahoRDpP30MZ5976uF64iqkuesY5P5rV9kvh2fzHH9IvNtf8xmC5t3l7Wa6BMRUK3X8Y4w3xaLpUJ+5UrqfAIqJCQEtVrN4cOHiYqKsh4/ePAgADk5OUBZzyadToeLi4vN/b28vADIzs4mMDCQ3NxcPD0rLv/Q6/XWc5WfT6/XX3Vc+ce/jyv//PLzVZfJZOL48ePXff+64syZM0qHIG4ime+6xfnsXtwAk3cwJxISq3Sf2pxDbfvBeBxaT8G+70g1aTEFta+1a4mqkeesY5P5rV9kvh2fzHH9IvNtP+IvFJGeXYSzVoVencXx49nVPoe9z3d5gdC11PkElIeHB8OGDWPZsmW0bt2aTp068fPPP/Ptt98Ctkv4Ksu6lS+9u/y2K2XnqpK1qyy7dyPnuxKtVmutpLJHBoOBM2fO0KxZM1xdXZUOR9Qyme+66eLxDRgB3w69aNK27VXH3pQ5bNuWPF0JBXs24nHsexqEd0fr37R2riWuSp6zjk3mt36R+XZ8Msf1i8y3/dl85A8A+nYKpmP41f/m/jtHmO/Tp09XeWydT0BBWXPy9PR0Hn/8cQB8fHyYMmUKc+bMwc+vbHtDvV5PcXExxcXFODs7W++bm5sL/FUJpdfrSUlJqXCNv1c86fV6630vl5eXZ21AXn7Ov1c6ld+vsgqqqlKpVLi52f/aUVdXV4f4OkTVyHzXHeZiA8bzZVWUXu16oqvivNT2HLoOGMeFzCQM8QfI2biA4PFvoXG7/p+V4sbIc9axyfzWLzLfjk/muH6R+bYP+QYTu4+lAXD7LaHXPWf2PN/VKbxRvAl5VXh7e7N8+XK2b9/Ohg0b2L59O40aNUKr1dKuXTsAa1KovD9Tubi4ONzd3QkICLCOS0hIsFZGlTt9+rT1HOXj/n4uo9HIuXPnrONCQkLQarXEx8dXONflMQkhxM1mSDgEpSU4+QSibRCsdDhWKrUG/7ufxsknkJKcdFLXvi1NyYUQQgghhF3acTAJY4mZkEBPWjXxVjqcOs8uElDlAgICaN26NRqNhs8//5zBgwfj4eEBQJcuXfD09OS7776zji8tLeX7778nOjrampWLjo4mNzeXHTt2WMelpKSwf/9+oqOjrcf69u3Lzp07ycrKsh7btGkTRqPROk6n09GjRw++//57mzg3btxIw4YNrckxIYS42f7a/a7bDS0Hrg0aVw8CR01DpXWh6OwRMresVDokIYQQQgghqm3L7rLd7wZGhtS5v7nrIsWX4BkMBmJjYwFISkoiPz+fH374AYDIyEh8fX355ptvKC4uJiQkhLS0NL788kvOnz/P3LlzrefR6XRMnDiR+fPn4+vrS7t27Vi1ahWJiYnMmzfPOi4iIoJ+/frx0ksvMX36dDw8PFiwYAHBwcEMHz7cOm7MmDF8+umnTJo0iUmTJpGZmcmbb77J0KFDbSqbnnjiCR544AFmzJjB0KFD2b9/P6tWreK1116z6U8lhBA3i8VipvD0PgDcWnVTOJrK6RqG4H/XU6Su+S+5uzfiHNgcz/B+SoclhBBCCCFElZy7kMuJc1lo1Cr6dWmidDh2QfEEVGZmJlOmTLE5Vv75ypUriYqKwmKx8OGHH3L+/Hnc3NyIjo5m7ty5+Pv729xv/PjxWCwWPvnkEzIyMmjdujVLly4lLCzMZtzbb7/NnDlzmDlzJiaTiaioKBYtWmSzg55er2fFihXMmjWLp556ChcXF4YMGcLUqVNtztW5c2cWL17MvHnzWL9+PYGBgcyYMYNRo0bV5MMkhBBVVpx8GnNhLmpnN1yaVK8R4s3k3qYH3r3vIfuX1WR8uwRdg8Y4B9nv5gtCCCGEEKL+2LynbJfp7u0C8PZ0vsZoAXUgAdW4cWNOnDhx1THDhg1j2LBh1zyXSqXi0Ucf5dFHH73qOA8PD15//XVef/31q45r3rw5y5cvv+Z1o6OjbZbvCSGEkgpP7gHAtUVnVBrFf8xflU/f0RgvJFB4eh8XVr9F8Pi3cPLwVjosIYQQQgghrqik1MzPe8sSUAO6hygcjf2QNWJCCOFgrMvvWnZVOJJrU6nU+A+bgrZBEKV5maStnYultETpsIQQQgghhLiifcdTyc4vxtvDma5tA5QOx25IAkoIIRyIKScNY9pZUKlxa9FF6XCqRO3iTsA901DpXClKPE7mpo+UDkkIIYQQQogr2nSp+Xj/bk1w0khaparkkRJCCAdSeKqs+smlcRgaN0+Fo6k6nV9j/IeV9f/L3fcDuQe3KByREEIIIYQQFWXlFbH3eCoAA7pL8/HqkASUEEI4kPIEVF3d/e5q3Ft3x6fvGAAyflhKUdJJhSMSQgghhBDC1rZ95yk1WwgL8SEkUK90OHZFElBCCOEgzEYDhrN/APaZgALw7j0St9aRUFpC6ur/UpKXpXRIQgghhBBCAGCxWKzL726NlObj1SUJKCGEcBCG+MNQWoKTTyDaBsFKh3NdVCo1/ndNRuvXmNL8i6Su+S+WEpPSYQkhhBBCCMGpxGwSU/PQOanp28k+/95WkiSghBDCQRSe3guU7X6nUqkUjub6qZ1dCRw1DbWzG8VJJ8j4abnSIQkhhBBCCMHmS9VPt3QMwt1Vq3A09kcSUEII4QAsFjOFp/cD4G6ny+8up/UNwv/uZwAVeQc2kbv/J6VDEkIIIYQQ9VixqZTtB84DMECW310XSUAJIYQDKE4+TWlBNipnN1xC2iodTo1wa9kF3/73A5Dx43KKEv9UOCIhhBBCCFFf/f5HCgVFJfj7uhHewk/pcOySJKCEEMIBWHe/C41ApXGccmCvnsNxb9sTzCWkrvkvJbmZSockhBBCCCHqoS2Xlt8N6NYEtdp+210oSRJQQgjhAApPXer/5ADL7y6nUqloOOQJdP4hlBZkk7rmv5hLjEqHJYQQQggh6pG0i4UcOp0OQEx3WX53vSQBJYQQdq4kJx1j2hlQqXFr0UXpcGqcWudKwD3TULt4UJx8iozvl2KxWJQOSwghhBBC1BNb9iZisUBEKz8CfN2UDsduSQJKCCHsXOHpsuV3zsGt0bjpFY6mdmh9AvEf/iyo1OQf/pncvd8rHZIQQgghhKgHzGYLm/dcWn4n1U83RBJQQghh5wouLb9zhN3vrsYtNALfmLEAZG7+GMPZowpHJIQQQgghHN2R+AzSLhbi5uJEj/BGSodj1yQBJYQQdsxsLKLozBEA3Fp1VTia2ucVNRSP9n3AXErq2rmU5KQrHZIQQgghhHBgmy41H+/buTEuOieFo7FvkoASQgg7Zkg4jKXUhJO3P1q/JkqHU+tUKhV+d05EF9Acc2EuF1a/hdlUrHRYQgghhBDCARUYTPx2OAWAAd0d/2/t2iYJKCGEsGOX736nUtWP7WDVWmcCRr2A2k2P8UI8Gd8tkabkQgghhBCixu04mITRVEqTAE9ah/goHY7dkwSUEELYKYvFbG1A7tbSsfs//Z3Wy5+A8qbkR7aTs3uj0iEJIYQQQggHc3nz8fryZm9tkgSUEELYqeKUeEoLslHpXHFt2k7pcG4612bhNBj4EAAXt6zEkHBY2YCEEEIIIYTDSEzN48TZLNRqFf27NVY6HIcgCSghhLBThaf2AOAW2gmVRqtwNMrQdxuMR8d+YDGTum4epuxUpUMSQgghhBAOYPOl5uPd2wbg4+micDSOQRJQQghhpwpPXVp+Vw92v7sSlUqF3x3/xLlRC8yGPFJXvYXZWKR0WEIIIYQQwo6VlJrZui8RgAGRIQpH4zgkASWEEHaoJDcTY2oCoMKtRRelw1GU2klHwD0voHH3wph2hvRvF0tTciGEEEIIcd32/5lGdl4x3h7OdGsboHQ4DkMSUEIIYYfKd79zbtwajbuXwtEoz0nvh/+IqaDWUHDsV3J2fq10SEIIIYQQwk6VNx/v17UxThpJm9QUeSSFEMIO1dfd767GNaQdfreNB+Diz59RGHdA4YiEEEIIIYS9yc4rZvfRC4Asv6tpkoASQgg7YzYVYzjzBwDurSQBdTnPLoPw7DQALGbS1s/HdDFF6ZCEEEIIIYQd2bb/PKVmC61DvGkaqFc6HIciCSghhLAzhoTDWEqMOHn5o23YROlw6hSVSoXfoEdxDm6NuaiAC6vnYDYalA5LCCGEEELYAYvFwubdZwEY0F2qn2qaJKCEEMLOlPd/cmvVFZVKpXA0dY/KSUvAyOfRuHtjSk8k7ZtF0pRcCCGEEEJc0+nz2Zy9kIfOSU2fzo2VDsfhSAJKCCHsiMVi/qv/kyy/uyInT18C7nkB1E4UnthF9q9rlA5JCCGEEELUcZt2lzUf7xkehIerVuFoHI8koIQQwo4YU+Ipzc9CpXPBNaS90uHUaS6Nw/C7/TEAsmK/oPDUPoUjEkIIIYQQdVWxqZTtB5IAGBApbS5qgySghBDCjhRcqn5ybR6ByknelbkWfecB6LsMAiykfv0/jJlJSockhBBCCCHqoJ1/pFBgMNHQx5WOLRsqHY5DkgSUEELYkfL+T7L7XdU1uO1hXJq0xVJcSOqqOZiLC5UOSQghhBBC1DGb95Qtv7u1WwhqtfRZrQ2SgBJCCDtRkpuJ8UI8oMKtZVelw7EbKo0W/xFT0Xj6YspMIu3rhVgsZqXDEkIIIYQQdURaViGHTqUDcGt3WX5XWyQBJYQQdqK8+bhzcCs07l4KR2NfnDy8CbhnGiqNlsJTe8jasUrpkIQQQgghRB2xdW8iFgt0bOlHYAN3pcNxWJKAEkIIO1G+/E52v7s+LkEt8bvjcQCyd3xFwYldCkckhBBCCCGUZjZb2Hxp97sBkSEKR+PYJAElhBB2wGwqxnDmDwBZfncDPCNi0HcfDEDaNwsxpicqHJEQQgghhFDS0fhMUi8W4ubiRM/wRkqH49AkASWEEHbAcOYPLCVGnPR+6PybKh2OXWtw64O4NG2PxVhE6uo5lBYVKB2SEEIIIYRQyKbdZwHo0ykYF52TwtE4NklACSGEHbh8+Z1KJbty3AiVxomA4c/hpPfDdDGFtPXzsZhLlQ5LCCGEEELcZIVFJn49nALI8rubQRJQQghRx1ksFgpPlTUgl/5PNUPj7lXWlNxJhyHuAFmxXygdkhBCCCGEuMl2HEzGaCqlSYAHYSE+Sofj8CQBJYQQdZzxQgKl+RdRaV1wadpe6XAchnOjUPzunAhA9m9ryT/+u8IRCSGEEEKIm2nzpeV3A7qHyCqDm0ASUEIIUceVL79zDY1A7aRTOBrH4tmhL15RdwGQvuEdjGlnFY5ICCGEEELcDImpefx5Ngu1WkX/rk2UDqdekASUEELUcYWnL/V/kt3vaoVvzAO4Nu+IxVTEhVVzKDXkKR2SEEIIIYSoZVv2nAOgW5sAfPQuCkdTP0gCSggh6rCSvIsUp8QBKklA1RKVWoP/3c/i5O1PSXYqaeukKbkQQgghhCMrLTWzdW8iAAMipfrpZlE8AXX27FleeeUVhg0bRrt27RgyZEiFMSUlJSxdupTbb7+diIgIYmJimDVrFrm5uTbjYmJiCAsLq/CvuLjYZlx+fj6vvPIKUVFRdO7cmQkTJpCUlFThugkJCTzyyCN06tSJnj17MmvWLIqKiiqMi42N5e677yY8PJyBAwfy2Wef3eCjIoQQZQpPlzUfdw5qiZOHt7LBODCNm2dZU3KtM4aEQ1z8WX6OCyGEEEI4qn0n0sjKK8bLQ0e3toFKh1NvOCkdwKlTp4iNjSUiIgKz2YzFYqkw5t1332Xp0qU89dRTdOrUibi4OObPn8/58+dZsmSJzdhBgwYxfvx4m2M6nW3PlOeee46jR4/y8ssv4+HhwcKFC3n44Yf55ptvcHEpK73Lzc3lwQcfJCgoiIULF3Lx4kXeeOMNsrOzmTt3rvVcBw4cYNKkSQwbNozp06ezf/9+Zs2ahU6nY9SoUTX1MAkh6inZ/e7mcQ5oRsMhT5C2bh45O7/GObA5Hu37KB2WEEIIIYSoYZt3ly2/69elCVonxety6g3FE1AxMTEMGDAAgOnTp3PkyJEKYzZu3MiQIUOYMGECAD169KCwsJB58+ZRWFiIm5ubdayfnx+dOnW64vUOHTrEtm3bWLp0KdHR0QC0bt2agQMHsm7dOu677z4AvvjiC3Jzc1m/fj2+vr4AaDQapk6dysSJE2nRogVQlhxr164ds2fPtsaWkpLCggULGDlyJGq1fDMLIa6P2VSMIeEQIAmom8WjXS+MqQlk/7aO9I2L0TZojHNgc6XDEkIIIYQQNSQnv5jdRy8AMDAyROFo6hfFsyNVSdCUlJTg6elpc0yv12OxWCqtmLqa2NhY9Ho9ffv2tR4LCgqiS5cuxMbGWo9t376dnj17WpNPUFZdpdPprOOMRiM7d+7kzjvvtLnG0KFDSU9P59ixY9WKTQghLld05giWEiMavR86/6ZKh1Nv+ETfh2toZywlRlJXz6G0MPfadxJCCCGEEHZh2/7zlJottGziTdNGeqXDqVeuqwLqzJkzfPnll8TFxVXoiaRSqVixYkWNBFdu9OjRLF++nJiYGCIiIoiPj+fDDz9k+PDhuLu724zdsGEDX331FVqtlm7dujF16lTCwsKst8fFxdG8eXNUKpXN/Vq2bMkvv/xiM27kyJE2Y3Q6HSEhIcTFxQFw7tw5TCYToaGhFc5Vfo4OHTpc19dssVgoLCy8rvvWBQaDweajcGwy37Uj58+dAOiaRdT6YytzaMtz0D8xfv5vSrJTSVn9Fj4jpqFSa5QOq8bIfDs2md/6Rebb8ckc1y8y37XLYrHw084zAER3ClT8NbcjzLfFYqmQX7mSaiegTp48yejRo/H39+fcuXOEhYWRlZVFamoqjRo1okmTmu8gP2HCBEpKShg/fry14um2227jtddesxkXExNDx44dCQoKIjExkSVLlnD//fezfv16a1y5ubkVqqmgrKIqJyfH+nlubi56fcVs6OXjyj/+fVz555efr7pMJhPHjx+/7vvXFWfOnFE6BHETyXzXIIsFr5N7UANpWj+Sb9LPA5nDv6jb34V+5wqMicc5u/5dDG0HKh1SjZP5dmwyv/WLzLfjkzmuX2S+a0fyRSPnUvPRqMHPObfOvOa29/n+e9/tK6l2AmrevHn07t2b+fPn06FDB/7zn//Qvn17tm3bxr/+9S+efvrp6p7ymj799FM+/vhjpk+fTvv27UlISGDBggXMmDGDOXPmWMfNmDHD+v9u3brRq1cv7rjjDpYvX86rr75qve1K2bmqZO0qy+7dyPmuRKvVWiup7JHBYODMmTM0a9YMV1dXpcMRtUzmu+aZ0s6SWZSHyklHy1tuQ+VUtR/q10vmsDJtKfJxJXvjQlzO7iGgTRdc2/VWOqgaIfPt2GR+6xeZb8cnc1y/yHzXrt82lCWcerQPpEtEe4WjcYz5Pn36dJXHVjsBdezYMf79739bezeZzWYA+vXrx/jx45k3bx6ffvppdU97RVlZWcyZM4fnn3+ecePGAdC9e3d8fX154oknGDduHO3bV/6N4+/vT9euXTl69Kj1mF6vJyUlpcLYv1c86fV6cnMr9v3Iy8uzNiD38vICKlY6ld+vsgqqqlKpVDbN1e2Vq6urQ3wdompkvmtOVmLZhgyuoRG4671v2nVlDm25RURDdjLZv6wmd/OHeASF4hxkv28O/J3Mt2OT+a1fZL4dn8xx/SLzXfOMplJ+/SMVgEE9m9epx9ee57s6hTfVbkKem5uLl5cXarUaJycnmyRNhw4dbJI9NSExMRGj0Ujbtm1tjpd/fu7cuave/+9Nylu0aEFCQkKF46dPn7YmlsrHlfd6Kmc0Gjl37px1XEhICFqtlvj4+ArnKj+HEEJcj8JTewHZ/a4u8Ok7GreWXbGUmriw+i1K8rOVDkkIIYQQQlTTriMXKDCYaOjjSsdWDZUOp16qdgIqICCA7OxsAJo2bcqePXust504caJCU/AbFRQUBFAhsXXkSFl1QHBw8BXvm5qayv79+wkPD7cei46OJjc3lx07dliPpaSksH//fqKjo63H+vbty86dO8nKyrIe27RpE0aj0TpOp9PRo0cPvv/+e5vrbty4kYYNG9KuXbvqfrlCCEFJfhbFKWWJbLcWXRWORqhUavyHTUHbIIjSvEzS1s7FUlqidFhCCCGEEKIaNu0+C0BMtyZo1NffLkdcv2ovwevSpQv79+9nwIABDB06lEWLFpGeno5Wq2XdunXcdddd1TqfwWAgNjYWgKSkJPLz8/nhhx8AiIyMxM/Pj0GDBrFgwQJKSkro0KED8fHxLFq0iM6dO1t3mdu4cSPbtm2jb9+++Pv7k5iYyNKlS9FoNDz88MPW60VERNCvXz9eeuklpk+fjoeHBwsWLCA4OJjhw4dbx40ZM4ZPP/2USZMmMWnSJDIzM3nzzTcZOnSoTWXTE088wQMPPMCMGTMYOnQo+/fvZ9WqVbz22mvWZYpCCFEdhaf3AeDcqCVOnj4KRyMA1C7uBNwzjaSPplOUeJzMTR/hd/tjSoclhBBCCCGqID3LwMFT6QAM6B6icDT1V7UTUBMnTiQtLQ2Axx57jIyMDDZs2ADAHXfcwbRp06p1vszMTKZMmWJzrPzzlStXEhUVxezZs3nvvff46quvWLhwIX5+ftx2221MmTLFmuRp3LgxqampzJ49m7y8PDw9PenRoweTJ0+usDPf22+/zZw5c5g5cyYmk4moqCgWLVqEi4uLdYxer2fFihXMmjWLp556ChcXF4YMGcLUqVNtztW5c2cWL17MvHnzWL9+PYGBgcyYMYNRo0ZV63EQQohysvyubtL5NcZ/2BRSV71J7r4f0AWGou90q9JhCSGEEEKIa9i69xwWC4S38COwQc2u2hJVV+0EVEhICCEhZRlDjUbDjBkzbHafq67GjRtz4sSJq47x8PDg+eef5/nnn7/imE6dOvHJJ59U6ZoeHh68/vrrvP7661cd17x5c5YvX37N80VHR9ss3xNCiOtlLjFiSDgMgFsrWX5X17i37o5P3zFkbf+CjB+WomvYBJfg1kqHJYQQQgghrsBstrB5T1nv6AGRTa4xWtSmG1ojVlRURGpqKiUl0gtDCCFqQtGZI1hMxWg8G6ALaK50OKIS3r1H4hYWBaUlpK7+LyV5Wde+kxBCCCGEUMTRhEwuZBbi6uzELeFBSodTr11XAmrnzp2MHj2aLl260L9/f2sF08yZM/npp59qNEAhhKhP/lp+17VaW5qKm0elUuM/9Cm0fo0pzb9I6pr/YikxKR2WEEIIIYSoxObdZdVPfToF4+Jc7UVgogZVOwH1+++/88gjj1BcXMz48eMxm83W23x8fFi7dm2NBiiEEPWFxWKh4FIDcveW0v+pLlM7uxI4ahpqZzeKk06Q8dO1l2sLIYQQQoibq7DIxK+HkwEYGCnNx5VW7QTUwoUL6du3L+vXr+fpp5+2ua1Nmzb8+eefNRWbEELUK8a0s5TmZqBy0uHSrIPS4Yhr0PoG4X/3M4CKvAObyN0vFcBCCCGEEHXJL4eSKTaWEtzQg7Cmsru00qqdgDp+/DhjxowBqLA8xNfXl8zMzJqJTAgh6pny5XeuzSNQa50VjkZUhVvLLvj2vx+AjB+XU5Qob8KIuuHynX2FEEKI+qp8+d3AyBBpb1EHVDsBpdFoMJkq73WRmZmJu7tsaSiEENfj8v5Pwn549RyOe9ueYC4hdc1/KcmVN2KEcoqMJWh1LjRqHIpW50KRUTaKEUIIUT+dT8vj+JmLqNUq+neT3e/qgmp34AoPD+ebb75hwIABFW778ccf6dSpU03EJYQQ9UpJfjbFyacBcJP+T3ZFpVLRcMiTmDKTMKadI3XNf2k09jXUTjqlQxP1jNFUypqfT7NhRzwFBhPurlru6hPKPTGt0Gk1SocnhBBC3FTl1U9d2/jjq5fK4Lqg2hVQjz/+OJs2beKJJ55g69atqFQqDh06xGuvvcaPP/7Io48+WhtxCiGEQys8vQ+w4NyoBU6esj7d3qh1LgTcMw21iwfFyafI+H4pFotF6bBEPVJkLGHV1lN88dMJCgxlleoFBhOf/3SC1VtPSSWUEEKIeqW01MzP+xIBGNBdmo/XFdVOQN1yyy28+eab7N27l6eeegqLxcJrr73Gxo0beeONN+jWTd65F0KI6iq8tPudVD/ZL61PIP7DnwWVmvzDP5O793ulQxL1iEatZsOO+Epv+2ZHPBp1tf/kE0IIIezWgZPpXMwtRu+uo3u7QKXDEZdUawleaWkp586do3///gwaNIgDBw6QkZGBj48PXbp0wc3NrbbiFEIIh2UuMWKIPwSAWytJQNkzt9AIfGPGcnHLCjI3f4zOvymuTdsrHZaoBwoMJmvlU2W3XcwtYsmaQ+h0GkKDvGh+6Z+ft4s0ZRVCCOFwNu0+C0C/ro3ROsmbMHVFtRJQFouFO++8k/fee4/o6Gh69uxZW3EJIUS9UXT2KBZTERoPX3SBzZUOR9wgr6ihGC/Ek390B6lr59J4/Fs4eTVUOizhwE4lZhES4Im7q7bSJJS7qxYvdx0nE7PJLTDy2+EU620erlpCg71oFqS3JqaaBHjKH+tCCCHsVk5+MbuPXgBgYGRThaMRl6tWAsrJyQk/Pz/payGEEDXo8t3vpBLB/qlUKvzunIgx4zzG1AQurH6LoHGzUGudlQ5NOJjcAiMrvj3GT7vO8tLDkQzp1ZwvN5+sMO6uPqGUlJp54YFuxCfnkJCcQ0JyLompeeQbTBw+ncHh0xnW8U4aFY39PQkN9qJ5kN5aLaV3l8b6Qggh6r7Y/ecpKbXQsrEXzRrplQ5HXKbau+DdeeedrF+/nn79+tVCOEIIUb9YLJbLElCy/M5RqLXOBIx6gaQPp2G8EE/Gd0toeNdkSTCKGmE2W9i85xwfbzxGXqERgBPnshgzMAy1WsU3V9gFL6J1QyJa/1WNZyop5dyFPGtCKv7SxwKDiTMpuZxJybW5rp+XC82CvAgN9rpULaUnsIE7arV8XwshhKgbLBYLmy7tfifNx+ueaieg2rRpw3fffce4ceO47bbbaNiwYYU/qG+77bYaC1AIIRyZKf0cJbkZqJx0uDYLVzocUYO0Xv4EjHiOlM9mkn9kO7rAULyjhiodlrBzCck5LF59iD/PZgHQNNCTiSMjaB/aAIAR/Vsy6tbW5BcW4+HmTKnZjE6rqfRcWicNLRp706Kxt/WYxWIhPctAQnIO8cm5l5JTOVzILCQjp4iMnCL2Hk+1jnfRaWjWSE/z4LIqqdAgPU0D9bg4V/tPTCGEEOKGxSXlcCYlF62Tmr5dGisdjvibav91MG3aNABSU1PZvXt3hdtVKhXHjx+/8ciEEKIeKLhU/eTaLFyWaDkg16YdaDDwITJ/+pCLW1bi7N8U1+YdlQ5L2KHCIhOf/fgnG39JwGy24Oqs4f5BbRjSOxQnzV/9mlx0ThQWFpKcmEDz5s2rvUGMSqXC39cNf183ojo0srl+QnIuZy5LTJ1NyaXIWMqfZ7OsCbGyc0CQnwfNg/SXlvGVVUv56qXhuRBCiNq15VL1U48OjfB0k6XjdU21E1ArV66sjTiEEKJekuV3jk/fbTDFF+LJP7yN1HXzCB4/B613gNJhCTthsVj45WAyy775g4u5xQD0igji0bs64OftesX7FRUV1Wgcbi5a2oc2sFZaAZSWmklKzyfBWilVtowvO6+YpPR8ktLz+eVQsnW8l4eO5o28LlVLlTU9D/b3sEmgCSGEENfLaCpl2/7zAAyIlOV3dVG1E1CRkZG1EYcQQtQ7pQU5FCedAsCtZVeFoxG1RaVS4XfHPzGln6c45TSpq94i6MH/oNa5KB2aqOOS0vNZsuYwB0+lA9DIz50JwzvSpY2/wpGV0WjUhATqCQnUE33ZMoes3CJrUqq8r1RSWh45+UYOnkq3fj0ATho1TRt5XkpM/dXw3MNVq8SXJIQQwo7tOnqBfIMJPy8XIlrJDsR1kSzQF0IIhRSe3gdY0AWG4qRvcM3xwn6pnXQE3PMCSR8+jzHtDOnfLsb/7mdkOZKoVJGxhNVbTrHm59OUlJrROqkZdWtrRvZvecV+TnWJj94FH72LTaKs2FTKuQu5xCeVL+MrS0wZikuIO59D3Pkc2PPXOfx9XK3JqNBLiakAXzd5zgghhLiizZeW393aPQSNbJBRJ11XAurMmTN8+eWXxMXFVSjxVqlUrFixokaCE0IIR1Ygy+/qFSd9A/xHTCXls1cpOPYrOYGhePe8W+mwRB2z+9gF3l/3B2kXCwHo2saffw7vSCM/d4UjuzHOWg2tmvjQqomP9ZjZbCEtq7CsUirpr4bnaVkG679dRy9Yx7u5OJUlpaxNz8uqr5ztICknhBCidqVnGThwMg0oS0CJuqnaCaiTJ08yevRo/P39OXfuHGFhYWRlZZGamkqjRo1o0qRJbcQphBAOxVJiwpBwCAB3WX5Xb7iGtMPvtvFk/PABF7d+is6/KW4tOisdlqgD0i4WsnT9H9aEi5+XC48PD6dHh0YOW/WjVqsIbOBOYAN3eoYHWY/nFxpJSMklIemvvlLnLuRRWFTC0fhMjsZn2pwjuKEHoZcanZcnpnw8ZYmrEELUJ1v3ncNigQ4tGtj9mzaOrNoJqHnz5tG7d2/mz59Phw4d+M9//kP79u3Ztm0b//rXv3j66adrIUwhhHAshnNHsRiL0Hj4oGsUqnQ44iby7DKI4gsJ5B3cTNr6+QQ/PAetb6Nr31E4JFOJmfWxp/li00mMplI0ahV3R7dg9MAwXJ3rZ6cEDzcd4S38CG/hZz1WUmrmfFr+pWqpHM5cSkzlFhhJTM0jMTWP2AN/ncPH09m6+17ZMj4vghp6yJIMIYRwQBaLhS17EgEYINVPdVq1/7I5duwY//73v1Gry3YsMZvNAPTr14/x48czb948Pv3005qNUgghHIx197uWXVGpZAeo+kSlUuE36FGM6ecoTjrJhdVzCH7oDdS6K+9oJhzT4dPpvLfmMOfT8gFoH9qAiSM70jRQr3BkdY+TRk2zRnqaNdLTv2tZtb3FYuHi5Q3PL1VMJWfkk5VXTNaJNPafSLOeQ6fV0DTQk9Dgv5bxNWukx81FGp4LIYQ9O5ZwkZSMAlydNfTqGHTtOwjFVDsBlZubi5eXF2q1GicnJ3Jzc623dejQgXfffbdGAxRCCEdjsVgoPLUPkP5P9ZXKSUvAyOdJ+vAFTOmJpH2ziICRzzvsUithKyu3iA83HLVuFe3t4czDQ9vTv2tj+R6oBpVKRQMvVxp4udKtbYD1eFFxCWcv5BKfXL6ML4czKbkUGUs5lZjNqcRsm/M0auBOsyC9TWKqoberzIUQQtiJTbvPAtA7IhgXO6seLs3NQHvhOJY2bZQO5aao9uwEBASQnZ0NQNOmTdmzZw+9evUC4MSJE7i7y3pLIYS4GlN6IiU5aaicdLg276h0OEIhTp6+BIx8nuRPXqHwxC6yf12DT+97lA5L1KJSs4Xvf0vgk++PU1hUgkoFd/Rsxtg72uLhplM6PIfh4uxEWFNfwpr6Wo+ZzRYuZBZYd98rW8aXQ0ZOESmZBaRkFvD7HynW8R6u2gpL+JoEeKB1kobnQghRlxQWmfj1UDIAAyObKhxN9VgsZrLWv41H5nlMYR2gZSelQ6p11U5AdenShf379zNgwACGDh3KokWLSE9PR6vVsm7dOu66667aiFMIIRxG+e53rs3CUWudFY5GKMmlcRh+tz9GxnfvkRX7Bc4BzXFrJU3pHdGJsxdZvOYw8Uk5ALRs4s2kkR1tdoUTtUetVhHU0IOghh70jgi2Hs8tMFp33ytPTCWm5pFvMPFHXAZ/xGVYx2rUKpoEeNLcWi3lRbMgPV4e8nNcCCGU8uuhZIqMpQQ39KBNM/v6nVp4cg8lmeexODnj5Bt87Ts4gGonoCZOnEhaWtl6+scee4yMjAw2bNgAwB133MG0adNqNkIhhHAwhaf/6v8khL7zAIwX4snd/yOpX/+P4IffRNegfvwRUh/kFRpZ8e0xftp1FosF3F21PDi4Lbf1aCYNsesAvbuOiFYNiWjV0HrMVFJKYmp+WU+plBwSksp6TOUbTJxJyeVMSi4/7ztvHd/Ay8VaLRUa7EXzIC8aNXBHLfMrhBC1bvOecwDc2r2JXS2dtlgsZP+6BoCikK6oXerHSrJqJ6BCQkIICSnrLK/RaJgxYwYzZsyo8cCEEMIRlRbkUHz+JCD9n8RfGtz2MMb0cxQlHid11RyCH34TtbOb0mGJG2A2W9i69xwfbTxGboERgJhuTXh4SHu8PaVipi7TOmkIDS5bdlfOYrGQnm2w7r6XkFyWmErJLCAzp4jMnCL2Hk+1jnfRaWjaSE9o+TK+YC+aBertrjeJEELUZUnp+RxLuIhaVfY71p4YEg5RnBKHyklHcbPuSodz08hvQSGEuIkK4/YDFnQBzXHSN1A6HFFHqDRa/EdMJenD5zFlJpH29UICRr0gOyTaqYTkHN5bc5jjZy4CEBLoycQRHenQwk/hyMT1UqlU+Pu44e/jRmT7QOvxwqKyqqjynfgSknM4k1zW8PzE2SxOnM267BwQ5Od+qVrK61K1lB5fvcsNv2vv4uJyQ/cXQgh7tOVS9VOXNgE08LKv3YTLq59cw/tzUVc/qp9AElBCCHFT/bX7nSy/E7acPLwJuGcaKStnUHhqD1k7VuHbd7TSYYlqKCwy8flPJ/hmRzxmswUXnYb7bmvDXX1DcdJIMtERubloade8Ae2a//WGQqnZQnJ6/l99pZLLGp5fzC0mKb2ApPQCfrnUMBfKlgGWNzsvT0w19veo0vdMkbEErc6FRo1D0eqcKTKW4KKTP++FEI6v1Gxhy55EAAZEhigcTfUUJR6n6NwxUDvh3m0wJKZe+04OQn5DCSHETWIpNVEYfxAAt1b1p9RWVJ1LUEv8Bv+T9A3vkL3jK5wDmuEeFqV0WOIaLBYLvx5O5oP1R7iYWwTALR0b8ehd4TT0sa93ZMWNK29W3iTAk76d/zqelVdEQnIuZ5JziE/KJSElh/Np+eQWGDl0KoNDp/5qeO6kURMSeKnheZAXzS/1lvJw1VrHGE2lrPn5NBt2xFNgMOHuquWuPqHcE9MKnVZ26xNCOLYDJ9K4mFuEp5uOyHaB175DHZL1S1n1k2fHfmg8fAFJQAkhhKhhhnPHsBgNaNy9cW4UqnQ4oo7y7Nif4gvx5O75jrRvFhL80JvoGtpXX4P6JCk9nyVrD3PwZDoAjRq4888R4XRtE6BwZKKu8fF0wSfMhS5h/tZjRlMp5y7k/dVX6tJSvsKiEuKTcohPymELidbx/j6uNA/y4oE72vDroWS+2HTSeluBoawCD2BE/5ZSCSWEcGibd5ctv+vftTFaJ/upMi5OiccQfwBUarxvGY5J6YBuMvnNJIQQN0nhqb92v5PePuJqGtz6IMa0sxSdPUrq6jkEPTwHTT3ZHcVeFJtKWbXlJGu2nqak1IzWSc09Ma2k+kRUi06roWUTb1o28bYes1gspF4stOkrFZ+cS9rFQtKyDBQZSwn0dWfDLwmVnvObHfGMurX1TfoKhBDi5svJL2bX0RTA/pbfZf9WVv3k0b43Wp9ATIWFCkd0c0kCSgghbgKLxfJXAkp2vxPXoNI4ETD8OZI+fAHTxRTS1s8n8N4XUaklsVEX7D2eypK1h0m9WPZHY5cwf/45IpwgPw+FIxOOQKVSEdjAncAG7vQMb2Q9nm8wcSY5h/RsA3kGEwWGyt83LzCYKCwy4eUhuy0KIRxT7IHzlJRaaNG4bHmyvTBmnKfgz10AeN8yXOFolHHdCahTp06RnJxMcXFxhdtuu+22GwpKCCEcjSnjPCXZaag0Wlybd1Q6HGEHNO5eBIyaRvKKlzDEHSAr9gt8+/9D6bDqtbSsQpZ9fYTf/yh717WBlwuP3R3OLeGNbngXMyGuxcNVa91J0VRixt1VW2kSyt1Vi5uLtsJxIYRwFFt2X2o+3t3eqp/WAhbcwqLQNbSv2GtKtRNQ586dY/LkyZw4UbbG3GKx2NyuUqk4fvx4zUQnhBAOorz6yaVZB9Q62S5bVI1zYCgN75xE2tf/I/u3tegCQ/Fo21PpsOodU4mZb7bH8fmmExQbS1GrVQzr24IxA1vLC32hiFKzmbv6hFp7Pl1uSK/mxCdl0zzIS5aDCiEcTtz5bOKTc3DSqInu0ljpcKrMlHWB/CM7APC5ZYTC0Sin2gmol19+mYyMDF588UVatGiBVit/eAkhxLUUXEpAucvyO1FNHh36UHwhnpxd35C+4R10DYLQ+TdVOqx644+4DN5bc5jE1DwA2jX3ZdLICJo20iscmajPXHRO3BPTCijr+VS+C97Q3s0Z0juU6e/+grurlpcejsTHU970EEI4js17ypqP9+gQiKebTuFoqi7796/BYsY1NALnoJZKh6OYaiegDh8+zKxZs7jzzjtrIx4hhHA4pYW5FCeV7VTk1rKrwtEIe+Qb8wDGtDMYEg5zYdUcgsfPQePqqXRYDi0rr4gPNxxl277zAHh56Hh4SHtiujWR5XaiTtBpNYzo35JRt7Ymv7AYDzdnSs1mziTnkp1XzPm0fJ5bsJ2Xx0fZVY8UIYS4ElNJKbH7y34vD4y0nzfjSvIuknd4KwDevUYqHI2yqr0Nk6+vLx4e0mRTCCGqqjBuP1jM6Pyb4eTVUOlwhB1SqTX43/0sTt7+lGSnkrZuPhZzqdJhOaRSs4Vvf4ln4ptb2LbvPCoV3HFLM5ZMu5Vbu4dI8knUKS46J0zGIpIT4zEZi3DROdGmmS9zp/QluKE76VkGpr2zg91HLygdqhBC3LBdRy+QV2jCz8uFiNb28zd1zq5voLQElyZtcQ1pr3Q4iqp2Auq+++5j1apVtRGLEEI4JNn9TtQEjZsnAfdMQ6V1xpBwiIs/f6Z0SA7n5Lkspi6IZcm6PygoKqFlYy/mTu7LpJEReNhRmb+of4qKimw+D27owdzJfenY0g9DcSmzPtrFum2nK/RuFUIIe7J5d9nyu/7dmqBR28cbQqWFueTu/wmQ6ie4jiV4jz76KG+++SYjRoygT58+eHt729yuUql46KGHaig8IYSwb5ZSE4XxhwBwayXL78SNcQ5oRsMhT5C2bh45O7/GObA5Hu37KB2W3csvNLLyu+P8sPMMFgu4uzgxdnA7bu/ZzG7+wBXi7zzcdMx8vCfvr/uDH34/w4cbjpKYmsfEkRFonar9HrQQQigqM8fAgRNpAAyItJ8d5HJ2f4vFVIwusAWuoZ2UDkdx1U5AHTp0iHXr1pGTk8OxY8cq3C4JKCGE+EvRueNYigvRuHvX64aDouZ4tOuFMTWB7N/Wkb5xMdoGjXEObK50WHbJYrGwdW8iH208Sk6+EYD+XRvz8ND20rhZOAQnjZpJIzvSJMCD5V8fYdPuc6RkFvDig5Ho3aWqTwhhP7buTcRsgfahDQjys4+WQOaiAnL3fgeAT68Rsoyf60hAvfbaa/j4+DB79uwa2QXv7NmzLF++nEOHDnHq1ClCQ0PZuHGjzZiSkhI+/PBD1q5dS0pKCg0aNCAmJobJkyej19vuQrN8+XI+++wz0tPTad26NS+88AJRUVE2Y/Lz83nrrbf48ccfMRqNREVF8fLLLxMcHGwzLiEhgVmzZrFv3z5cXV258847mTp1Ki4utn+UxsbGMn/+fOLi4ggMDOShhx7iH//4xw09LkIIx1C++51byy6oVPKOs6gZPtH3UXzhDIb4A6SunkPw+LfQuMmubNVxNiWX99Ye5mh8JgBNAjyZOLIj4S38FI5MiJqlUqm4q08Lgvw8eOuTvRyJy2Tqgu28/EgUTQJkMwMhRN1nsVjYdGn53YDudlT9tO9HzMWFaP0a4xYWqXQ4dUK1Xw2dPn2a559/nltvvZVmzZoRHBxc4V91nDp1itjYWJo2bUqLFi0qHfPuu++yYMEC7r77bt5//30eeeQR1q9fzwsvvGAzbvny5cyfP59//OMfLF26lKZNm/LYY49x4sQJm3HPPfccW7du5eWXX2b+/PmkpaXx8MMP26yfz83N5cEHH6SgoICFCxcybdo0NmzYwIwZM2zOdeDAASZNmkS7du344IMPGD58OLNmzZI+WUIILBbLX/2fWkr/J1FzypqSP42TTyAlOemkrn1bmpJXkaG4hA83HGXyvG0cjc/EWafhoTvbseDZfpJ8Eg6tW9sA/ju5DwG+bqRkFvD8wu3sv7ScRQgh6rJjCRdJySjARaehV0SQ0uFUidlUTM7uDQB43zJc3oi+pNoVUI0aNarRBoYxMTEMGDAAgOnTp3PkyJEKYzZu3MiQIUOYMGECAD169KCwsJB58+ZRWFiIm5sbRqOR9957j3HjxvHII48AEBkZydChQ1myZAnz588HypYQbtu2jaVLlxIdHQ1A69atGThwIOvWreO+++4D4IsvviA3N5f169fj6+sLgEajYerUqUycONGaLHv33Xdp164ds2fPtsaWkpLCggULGDlyJGq1fKMJUV+ZMpMoyU4FjROuoR2VDkc4GI2rB4GjppH08YsUnT1C5paV+A18WOmw6iyLxcJvf6Twwfo/yMwpe8OpZ3gjHh3WAX8fN4WjE+LmaBqo5+0pfZn98W6OJVxk5rKdPDasA0N6hyodmhBCXFF58/E+nYJxda52CkMReQc3Yy7MxcnbX/p1Xqba2ZHHH3+cDz/8kOLi4poJoAoJmpKSEjw9bUuE9Xo9FovFmgzbv38/eXl5DBkyxDpGo9EwePBgYmNjreNiY2PR6/X07dvXOi4oKIguXboQGxtrPbZ9+3Z69uxpTT4BDBo0CJ1OZx1nNBrZuXMnd955p01sQ4cOJT09vdIeWUKI+qO8+sm1aThqnavC0QhHpGsYgv/QyQDk7t5I3h/bFI2nrkrOyOfVD3by5oo9ZOYUEdjAjX8/2oN/PRQpySdR73h5ODNrwi3EdGuC2Wzh/XV/8N6aQ5SWmpUOTQghKjAUl/DLoSQAbrWT5XeWUhPZv38NgHfP4ajUGoUjqjuqnT48duwYqampDBgwgKioqAq74AEVlqndqNGjR7N8+XJiYmKIiIggPj6eDz/8kOHDh+Pu7g5AXFwcAKGhtu/gtGjRgoKCAlJTUwkMDCQuLo7mzZtXaADWsmVLfvnlF+vncXFxjBxpu02iTqcjJCTEeq1z585hMpkqXLNly5bWc3To0OG6vmaLxUJhYeF13bcuMBgMNh+FY5P5rlzeid0AODUNr/PPZ5lD+6UKCcc96m4Kdq0n/dslmN390AZevZqhvsy30VTK1zvO8PWOM5hKzDhpVAzr05y7+zZDp9XU+efl9aov8yvKXO98P35XGIG+Lny+6RTf/XaG86m5PD26I+6uN9bfVdQ8eU7XLzLftn7en0SRsZRGDdxoFuBiF7+7C//YRmleJmp3HzQto64asyPMt8ViqXKD9WonoD799FPr///eLBzKGh3WdAJqwoQJlJSUMH78eGsl02233cZrr71mHZObm4tOp6vQINzLywuA7OxsAgMDyc3NrVBNBWUVVTk5OTbn+3uD87+PK//493Hln19+vuoymUwcP378uu9fV5w5c0bpEMRNJPP9F5WxEK+kk6iA82ZPzHbyfJY5tFPebXFveARd+mnS180lt+fDWJyvvUOMI8/3qWQD3+3NJiu/rDdWi0BnBnfzpoHeSNzpkwpHd3M48vyKiq5nvlv7wb29G7D2t4scjrvIC+/8wn3RfjTwtI8lLvWNPKfrF5nvMt/9Utarrl1jJ/7880+Fo6kCsxn9b2vRAPlNupJ56nSV7mbv863TVW1n1Wr/dlFi0j/99FM+/vhjpk+fTvv27UlISGDBggXMmDGDOXPmWMdVlnUrT1hdftuVsnNVydpVlt27kfNdiVartVZS2SODwcCZM2do1qwZrq6y9MjRyXxXZDj+KzlYcPJrQliXHkqHc00yh/bP3OJ5Mj9/FbJSaHjyR3xHTkelqfzXvCPPd0ZOESu/O8GuY2W72/nqnRl3Rxg92vvXm+2PHXl+RUU3Ot9t20KX8Dze+vQAGbnFfLQ5k2fv60j75r7XvrO4KeQ5Xb/IfP8lJbOAc+nnUangntsi8NW7XPtOCjP8+Rs5hVmoXDxoNmA0au3VY3aE+T59umpJNriOBNTNlpWVxZw5c3j++ecZN24cAN27d8fX15cnnniCcePG0b59e/R6PcXFxRQXF+Ps7Gy9f25uLvBXJZReryclJaXCdf5e8aTX6633vVxeXp61AXn5Of9e6VR+v8oqqKpKpVLh5mb/fSlcXV0d4usQVSPz/Ze8c38A4NG6u109JjKHdszNDd3oF0n6aDqmpBMYfv0Sv9sfu+pdHGm+S0rNfLM9ns9/+pMiYylqtYq7+oRy321huLnUzyVFjjS/4tpuZL7btXBj/jP9mPXRLk6ey+Y/H+9n0j0R3BbVtGaDFDdEntP1i8w3/LrtDABdwvxpHFj3k+IWi5nMvd8C4B01FA+vqsdsz/NdnTf46vwWbYmJiRiNRtq2bWtzvPzzc+fKOuKXJ4XK+zOVi4uLw93dnYCAAOu4hISECjv5nT592nqO8nF/P5fRaOTcuXPWcSEhIWi1WuLj4yuc6/KYhBD1i6W0BEPcAQDcWndXOBpRn+gaBOM/bAqgInffD+Qe3KJ0SDfFkbgMpszbxkcbj1JkLKVtM18WPNuPR+7qUG+TT0JUl4/ehdmTetO3UzClZguLvjrI8m+OUGquud2vhRCiqkrNFrbuTQRgYKR9JMMLT+7FlH4OlbMb+m53KB1OnXRdCaivv/6aESNG0KlTJ9q2bVvhX00KCgoC4OjRozbHjxw5AkBwcDAAXbp0wdPTk++++846prS0lO+//57o6GhrVi46Oprc3Fx27NhhHZeSksL+/fuJjo62Huvbty87d+4kKyvLemzTpk0YjUbrOJ1OR48ePfj+++9tYtu4cSMNGzakXbt2N/z1CyHsT1HicczFhajd9Dg3kkS0uLncW3XDp+9oADJ+WEpRkuP2O8rOK2b+5/t5cfGvnLuQh95dx5TRnXnzid40a3T9VchC1FfOWg1TH+jK/beFAbA+No7/fLSLwiKTwpEJIeqbgyfTyMwpwtNNS2T7AKXDuSaLxUL2r2sA8Op6OxoXd4UjqpuqvQRvy5Yt/Otf/2L48OEcO3aMkSNHUlxczNatW/H392fIkCHVOp/BYCA2NhaApKQk8vPz+eGHHwCIjIzEz8+PQYMGsWDBAkpKSujQoQPx8fEsWrSIzp07W3eZ0+l0TJw4kfnz5+Pr60u7du1YtWoViYmJzJs3z3q9iIgI+vXrx0svvcT06dPx8PBgwYIFBAcHM3z4cOu4MWPG8OmnnzJp0iQmTZpEZmYmb775JkOHDrWpbHriiSd44IEHmDFjBkOHDmX//v2sWrWK1157DbW6zheYCSFqQeGpvQC4tewq264KRXj3HklxagKFJ3aRuvq/BI9/CydPH6XDqjGlZgs/7jzDyu+OU2AwoVLBoB7NGDe4LZ5uVWuCKYSonEql4r5BbWjs78n/vtjPnmOpTHvnF14eH4W/r30uDxFC2J/Nu8tWOkV3aYzWqe7/PW1IOExxymlUTjq8IquXE6lPqp2A+uCDD3jooYd49tlnWb16Nffffz/t27cnPT2df/zjHwQGBlbrfJmZmUyZMsXmWPnnK1euJCoqitmzZ/Pee+/x1VdfsXDhQvz8/LjtttuYMmWKTZKnfJe8Tz75hIyMDFq3bs3SpUsJCwuzOf/bb7/NnDlzmDlzJiaTiaioKBYtWmSzg55er2fFihXMmjWLp556ChcXF4YMGcLUqVNtztW5c2cWL17MvHnzWL9+PYGBgcyYMYNRo0ZV63EQQjgGi8VCwaUElHurbgpHI+orlUqN/9CnSMpMwpRxntQ1/yXogZmonOx/OdqpxCwWrznM6cRsAEKDvZg0siNhTet+bwgh7EmfzsEENHBj1oe7OJOSy3MLtvOvhyJpK83JhRC1LK/QyM4jFwD7WX5XXv3k2XkgGncvhaOpu6qdgEpISOCpp56yLmkrLS3b3rhhw4ZMnDiR5cuXc88991T5fI0bN+bEiRNXHePh4cHzzz/P888/f9VxKpWKRx99lEcfffSa53v99dd5/fXXrzquefPmLF++/KpjoGxZ3+XL9wSYDXmoTAalwxDipjNdTKYk6wJonHBtHqF0OKIeUzu7EjhqOkkfTaM46QQZPy2n4eAJSod13fILjXzy/XG+//0MFgu4uTgx7o623H5LczTq+rG7nRA3W+sQH+Y9Hc3ry3cRn5zDv977lSmjO9GvaxOlQxNCOLDY/ecpKTUTGuRFaHDdT+YUJf5J0bmjoHbCu8cwpcOp06q9Rqy0tBStVotarcbV1ZX09HTrbY0aNSIxMbFGAxT2x2Ixk7FyOvpflmEuKlA6HCFuqvLld65N26N2ts+tVIXj0Po2wv/uZwAVeQc2kbv/J6VDqjaLxcLWveeYOGcr3/1Wlnzq17UxS6bdyp29QyX5JEQt8/N2Zc6TvenRIZCSUjNv/99+Pvn+OGZpTi6EqCWbLi2/GxAZonAkVZNVXv3UsR9O+gYKR1O3VTsB1bhxY9LS0gBo06YN3377rfW2H3/8kYYNG9ZcdMIuqVRq1K561MV5FOzZoHQ4QtxUf/V/kuV3om5wa9EZ3/73A5Dx43KKEv9UOKKqO3shlxcX/8r8zw+QnV9MkwAP/jPxFp67vys+epdrn0AIUSNcnJ148cFIRt3aCoCvNp9kzid7KCouUTgyIYSjiU/KIT4pByeNmugujZUO55qKL8RjiNsPKjXePe9WOpw6r9oJqJ49e/Lbb78BMG7cOL777jsGDhzI4MGD+eKLLxgzZkyNBynsj0fvsh2YCg78hCk7TeFohLg5Sg151hf3btL/SdQhXj2H4962J5hLSF3zX0rzLyod0lUZikv4aMNRpry9jaPxmTjrNDx4ZzsWPNufji3ljS4hlKBWqxg3uB3P3NcZJ42a3w6nMH3xL2TmSMsFIUTN2bynrPopqkMgeve6v7FI9q9rAfBo1wutbyOFo6n7qt0D6plnnsFoNAJwxx13oNFo2LBhg7X/0ogRI2o8SGF/nJtHYPJtivbiWbK2/R/+dz+tdEhC1DpD3EGwmNE2DEHr7a90OEJYqVQqGg55ElNmEsa0c2RvWADhVe/XeLNYLBZ+/yOFD9b/QUZOEQA9OgTy2LBw2X1LiDoiplsIAb7uzP54N3Hnc3j2f9t5eXwULZt4Kx2aEMLOmUpK2bbvPAADutf95XfGjPMU/LkTAO9bJA9SFdVKQBmNRnbv3k1oaCgeHh4A3Hbbbdx22221EpywXyqVCkPYrWh//5D8ozvwihyCc1BLpcMSolYVnNoDyO53om5S61wIuGcaSR9Nw3QhHn3+h2QntcEY0BStbzBav2C0PgGoNMrslJeSUcD76w6z78+yqll/Xzf+OTycyHbV211XCFH72oc24O0pfXn9w12cu5DHtHd/4dn7utArIkjp0IQQdmz30VTyCo008HKhc1jdfzM3+7d1gAW31pHo/Ot+wqwuqFYCysnJiQkTJvDBBx8QFCS/YMTVlXoF4tK2F0XHfyVzy0oaPTDTunuiEI7GUlqCIf4gAG6tuiobjBBXoPUJxH/4s1z4cjaa/AyKjv1C0bFf/hqgUqP1CUDrG1SWkPINRtsgCF2DYNRu+lr5GW40lbLm59Os2nISU4kZJ42akf1bcs+trXDRVbtQWwhxkwQ2cOe/T/Xhv5/uY+/xVN5cuYcHbm/DvQNay997QojrUr78LqZbkzq/yYgpO5X8I9sB8O41UuFo7Ee1/rJTq9UEBASQn59fW/EIB+N5yz0Un9xN0bmjFJ7ai3vr7kqHJEStKDr/J+aiAtRuepyDWikdjhBX5NY8goYPzyVhbyyBbmrITS9bmpeZhMVYhOliCqaLKXB6n8391C4eaBv8lZDSNggq+/wGqqb2/5nGknWHScko2zG1U6uGTBjZkeCGHjf8dQohap+bi5YZ46P4cMMRvtkez6c//Eliaj6TR3dCp9UoHZ4Qwo5k5hjY/2cqYB/L73J+/xosZlybR+AiK32qrNpvLd5zzz189tlnxMTEoNHILxZxdRq9H15RQ8j+bR0Xt67ErUVnVBp5R1s4nr92v+uCSi0/G0XdpvFsgCmwLR5t2+LmVtZbyWKxUJqfhSkz6VJCKvnS/5MpyUnHXJRPcdIJipNO2J6svGrqsqRUWYIqGI2bvtLrZ2QbWPb1EX49nAyAr96ZR4eF0zsiSConhLAzGrWKx4aF08TfkyVrDxN74DwXLhbw0sOR+HjKbpVCiKrZujcRswXaNfclqI6/EVWSl0Xeoa2AVD9VV7UzAVqtloSEBAYPHkxMTAwNGza0+WNRpVLx0EMP1WSMws559xxO7sEtmDKTyTu4GX3X25UOSYgaV3iqrFrEraX0fxL2SaVS4eTpi5OnL67Nwm1uM5uKyyqjLiWkTBeTMWYkYbr4t6qpU7bnVLteqpryDUbXIAi1TxC/JJSyYkcGhUYLarWKob1DuX9QGG4uyvSeEkLUjNt7NqORnztvrtjDibNZPLegrDl58yAvpUMTQtRxFouFLZeW3w2MtIPqp13fYCk14dy4DS4h7ZQOx65UOwE1d+5c6/8/+uijCrdLAkr8ndrFHZ/eo8j8aTlZO77Co0Nf1M6ym5FwHMZLL8hRO+EWGqF0ONfNxUXeqRaVU2udcQ5ohnNAM5vjFouF0ryLNgkpm6opQz7F509QfP6vqqk2wCx3FXl6b7yDm+LlkU3Jn6kUXaqgulLVlBCi7oto1ZC5U/ry+vKdJKUXMO2dHUz9Rzci28tmAkKIKzt+5iJJ6QW46DT0ighWOpyrKi3MI3f/TwD49BopldvVVO0E1JYtW2ojDuHg9F1uI3fvd5guppD9+3p8+92vdEhC1Jjy5XeuTdvZZXK1yFiCVudCo8ahaHXOFBlLpPmzqBKVSoWTvgFO+gZXrJrKSTrL3p0Hyb+QSIAmB39NLs6qErzNWZCYRU7iQZv7qV09L+szFfzX0j7vAFnCLYQdCG7owdzJfXlz5R4Oncpg1ke7eOjO9gzv10JeqAkhKrV5d1n1U6+IIFyd6/bv+pw9G7GYitAFhuLaorPS4didas9ucHDdzkiKukmlccK3/1hS17xFzq4N6LsMwknfQOmwhKgRhacv9X9qZX/L78p3INuwI54Cgwl3Vy139QnlnphW0kBW3BCLRsfWeFj5bQH5hmZAMwb1aMrYO9ribs637TN1MQlTRhIluRmYDXkVqqYAUGsu7dAXfGmHviB0l3bq07h5KvElCiGuwMNNx6uP9WTpuj/4/vczfLTxKOfT8pg4MgKtk1rp8IQQdUhRcQm/HEoCYGBkU4WjuTpzcSG5e78HwLvXCEmqX4e6nV4UDsUtLBKXJm0pSjzOxdjP8R/6pNIhCXHDSg35FJ07DoBby64KR1M9RcYS1vx8mi9++uuFfoHBxOeXPh/Rv6VUQonrcjoxm/fWHuLkuWwAQoO8mHhPR9o09b00wrmsaqp5R5v7ma39pJIxZSRhvHip51RmMhZTkfX/nNpjcz+1m74sIXX57nwNgtF6+0vVlBAKcdKomTiyI40DPFj+9RE27T5HSmYBLz4Yid5dp3R4Qog64tfDyRiKS2nk50675r7XvoOCcvf9gLmoAG2DYNzDopQOxy5V+6+ymJiYK2b61Go1np6ehIeHM27cOFq0aHHDAQrHoVKp8L11HMkfv0j+4W14RQ6p0E9ECHtjiD8IFjNav8Zofeyrx4VGrWbDjvhKb/tmRzwj+rfk9eU7cXPV4u/jRkNv17KPPq409HGV5JSoIN9g4rPvj/PdbwmYLeDm4sQDt7dl8C3N0GiuXfWg1rngHNgc58DmNsfLe00ZM89fSkKVJaaMmUmU5mZgLsyluDCX4vN//u2EGrQ+gRV259M2CELjKlVTQtQ2lUrFXX1aEOTnwVuf7OVIXCZTF2zn5UeiaBIgz0EhBGy6tPxuQPeQOl1RZDYVk71rAwDet4xApZJqzutR7VcPkZGR7N69m7S0NLp06YKfnx/p6ekcOHAAf39/GjVqxKZNm/j666/55JNPCA8Pv/ZJRb3hEtwa93a9KDj2Kxe3rCDwvlfq9A8aIa6lvP+TPS6/KygyUWAwVX6bwUROvpHUi4WcvZBX6Ri9u46GPpclpbzd8L/sc727Tp7f9YTFYmHb/vN8uOEo2XnFAER3bsz4u9rjq7/x5vaX95qiuW2jf2vVlDUpdd66U5/FVHzpeBJQsWpK16BsKd/lS/qcvANQqWX5qRA1qVvbAP47uQ+vL99FSmYBzy/czgvjutMlzF/p0IQQCkrOyOdofCZqFcR0a6J0OFeVd3AL5sJcnLz88WjfW+lw7Fa1E1C9e/fm4MGDbNq0iUaNGlmPJycnM378eAYMGMCbb77J2LFjWbRoEUuXLq3RgIX98+3/DwpO7MKQcBhD/EHcpHmbsFMWcymFcQcAcG/VXeFoqs/dRYu7q7bSJJS7qxZfvTP3D2pDckYBaVmFpGcZSM8qJC3LgKG4hNwCI7kFRuLO51R6fmedhobermWVU762SaqGPm408HLBqQpVMaJuO3chl/fWHuZIXCZQ1oB44siORLRqeFOuf+WqKXNZ1VTGpZ35LiZb+06VV00VFeZSlHj8byd0QusbWJaY+lszdI2rx035moRwRE0D9bw9pS+zP97NsYSLzFy2k8eGdWBI71ClQxNCKGTLnkQAOoX54+ftqnA0V2YpNZH9+3oAvHveLcv7b0C1H7klS5bw1FNP2SSfAIKCgnjiiSdYvHgxw4cP56GHHmL27Nk1FqhwHFrvALy63UHOrg1kblmJa/OO8m6zsEtF5//EXJSP2tUT5+BWSodTbUZTKUN6NefLzScr3HZXn1BKzRZu6RhU4TaLxUKBwUR6toG0i4VlH7MMpGUVknHpY1ZeMcXGUs6n5XM+Lb/S66tV4Ovlal3a5+9b9v+GPn8lqer6Tij1WVFxCV9sOsH62DhKzRZ0Wg1jBrbm7uiWdaLJsEqlxknvh5PeD0Irq5pKtm2EfnnVVMZ5TBnnK5zTWjV12VI+XYMgqZoSooq8PJyZNeEW3ll1iK17E3l/3R8kpubx+N3hVVqmK4RwHKVmC1v3/LX8ri7L+yOW0rxMNB4+eET0Vzocu1btv+zPnj2Lh0fl7wDq9XqSkso62AcHB2MwGG4sOmHXXFyuvOzCu9dI8g79jCn9HHmHf0bfacBNjEyImmFdfteyi12++Iw9cJ6hfcreed74a0KVd8FTqVR4uOnwcNPRPMir0jFGUykZOQbSLxpIzy60JqjKqqgMpGcbKCk1k5FtICPbwPEzFys9j6eblobeZdVT/r4V+1B5ezjLMr+bzGKxsPPIBZau/4OM7LLf81HtA3ns7nACfN0Ujq5qyqqmQnEOtK28sFjMlOZmXpaUSvqraiov89pVUw2C0V3eBN03SKqmhPgbrZOGp8d0JiTAkxXfHeO7386QnFHAtHHd8XDVKh2eEOImOXQynYycIjzdtPToUHf7qFrMpWT/tg4Arx53oXaSTRRuRLUTUEFBQaxbt47o6OgKt61Zs8ZaGZWdnY2XV+UvTIRjKzKWoNW50KhxKFqdM0XGkgrNijWunnj3HsnFzSvIiv0Cj3a9UetuvE+IEDeTPfd/KjKW8NkPf7JhRzyvPBLF6IFh5BcW4+HmTKnZfMXkU1XptBqC/DwI8qv8xbfZbCE7v7jC0r50a6KqkIKiEvIKTeQV5hCfXPkyP62T+m9JKds+VA28XOtENY6juJBZwPvr/mDv8VQA/H3d+Ofd4US2r7t/OFaHSqXGyashTl4NK6maMmDKTMF0MalsWd+lnfpMF5OxlBitVVOFfzunxt3r0nK+4MsaoQfh5O1vl4lrIWqCSqViZEwrghp6MO//9nHwZDpTF2znlUejrvh7QwjhWDZfqn6K7twYrVPd/X1YcPw3SrIuoHb1QN95oNLh2L1qJ6AeeeQRXnnlFcaMGcPtt9+On58fGRkZ/PDDDxw6dIjXXnsNgF27dtGhQ4caD1jUbUZTKWt+Ps2GHfHXrKbw6noHuXu/pyQ7jZxd3+DT516Fohai+sqW7ySDWoPb35oi24NNu86RW2DE1dkJfx83iouLSE5MoHnz5ri51X4Vi1qtwlfvgq/ehTZNKx9Tvszvr+RUoTVBlZZlICuvCFOJmeSMApIzCio9h0oFvnqXSpNU5R/dXOQd92sxlZT9bF+1+STGEjNOGhUj+rdi1K2t6s1uiGqdK86NQnFuVLFqqiQ349LufMmXVU0lUZp3kdKCHEoLcipWTWmcLu3QZ7s7n7ZBMBoX95v4lQmhnJ7hjZjzZB9e/3AXSen5TF2wnRcfjCS8pZ/SoQkhalFeoZHf/0gBYEBk3V1+Z7GYyfp1LQBe3Yeg1tXdPlX2otp/Nd57771YLBYWLVrEm2++aT3u5+fHzJkzGTVqFAATJkxAp5PytPqkyFjCmp9P88VPJ6zHCgwmPr/0+Yj+LW1eqKictPj2f4C0dfPI/v1rPDsPxMnD56bHLcT1KDi1DwDXkHao7ezFYkmpmXWxp4Gy52V5342ioiIlw6rA3bWsSXqzRvpKbzeVmMnM+Wtp39+TVOnZhktjisjMKeLPs1lXvI61D9WlpX0NLyWr/H3c8PZwRq2uv8v8DpxIY8naw9YkX0QrPyaM6Ehjf9lCHcqqprRe/mi9/CG0k81t5mKDNVlt3Z2vvNfUVaumvK3JqMsboTt5NZSqKeFwQoO9mDelL7M+2sXJc9m8/P5vTLongtuirvDuhBDC7m3ff56SUjPNg/S0aOytdDhXVHhqH6b0c6h0rui73aF0OA7hut62HD16NPfeey/x8fFkZ2fj7e1NaGioTR8OPz9556K+0ajVbNgRX+lt3+yIZ9StrSscd297C867NlCcfIqs7V/ScPCE2g5TiBphz8vvYvefJz3LgLenc51v+ng1Wic1gQ3cCWxQeQLQYilb5pd++dK+8sbpWWW9qfIKTf/P3n1HR1WtfRz/nmmZmfTeILTQe++hi9KrigX7ey1XbFzFa0PFrqhYr16uCnZAEBCk9w6h94QA6T2Z9Gnn/WMgEilJIDNnkuzPWizIZM+cXziZZGaf/TybohLHn7Oppis+jkatutAc/a9VVCGXTFIF+xnceun49crOL+G/vx9h68EUAPy9PXhwbDv6d4oUfbeqSOVhwCO8GR7hzSrcXr5q6pJSPvOFv22FOdiK8rAV5VF6/ljFB1Rr0AaEow2ouDufLjCi1k2EC8Kl/H30vPloP+b8vJ/NB5L55NcDJKYXcO+otqjr8QUAQairLpbfuffqJ5m8bYsA8O12s+jpWEOue928JEk0a9as8oFCvVFUarnidu7gWAlVXGrB18ujwu2SJBE49B5S5r1IwYF1+HYfgS7YfX8QCQKArbSovJymtk1A2e0yC9efBmBsTLMb7vXkziRJwt9bj7+3nhZRV15dWVx6sczvwuqpvBIycv6arMrJdzRLT80uIjX7ymV+AAE+HuXN0v/ehyrY31irGuvabHaWbU3gx1XHKSmzoZJgVL+m3DG8FZ616OtwZxVWTTXrXOFzF1dNmS9pgu5YNZXqWDWVmYglM/Eqq6YcK6bwCUZt1gOtXfY1CcKN8tCqmX5XVxqEevPjqhMs2RTvKMu7s6solRaEOiQhJZ+4pHw0aokBnRsoHeeqSs4eoizlNJJGh2+P0UrHqTOuawLq/PnzfPLJJ+zYsYO8vDz8/f3p06cPjz32GFFRYvKgvvLUO8plrjQJ5WnQXvXFg75ha4wte1J8chc5678n7LZ/OzuqINyQkjMHwG5DG9QArX/tar6880gqSRmFeOo1jOjTWOk4ijPqtTQK09Io7Mplflabo4SvvA9V3oUSv5wLk1W5JZgtNnJMZeSYyjh5/splfka95sIqqor9p4L9jIQEGPD31ru8zO9KO5UeS8jmi0WHyleDtWrkzyMTO9I0Umwq4irXXDWVn1VhUsp84e+Kq6aOAuANlHhrMHYfrsBXIQjXR5IkptzUkgYhXnz0Uyx7jqXz3Kdbeen+noTUkl02BUG4tourn3q0DbtscYI7ubj6ybvzUNSe4nVQTan2BFR8fDy33347ZWVl9OrVi5CQEDIyMli5ciUbN27kxx9/FCuj6imb3c6Y/k3Lez5dakz/ptjsdrRceTeqgEF3UXx6L8Vx+yhJOIShSQdnxxWE61Zby+9kWWbBhdVPI/s1FVeUq0CjVhEaYCT0Km98ZFnGVGSuWOL3t539TEVmikutnEsr4FxawVWOIxHoe+mqqQv/9jMQEuD4u6ZWq11pp1KLxc43y4+yZrfjRaG3Ucu9o9oytHtUve5/5U4kSYXWLwSt35VWTRVXmJAqTjyO+fxR8td8jYfRiFfb/gqlFoTr079TJKEBRmb9bxdnU0088/Fm/n1vD1o3CVA6miAIN8BitbNxXxIAw3q4b5+30qQTlJ47CioNfr3GKh2nTqn2BNSHH36In58f8+fPJyzsryv/aWlp3HPPPXz00Ud88sknNRpSqB30Og2TBjcHHD2fLu6CN6pvEyYOao6H7upvnnSBEfh0GY5p7wqy180j8oF3kSSxdbrgfmS7jeL4WACM0V0VTlM9B09nEpeYh06rZkz/ppXfQaiUJEn4enng6+VBdEO/K44pLbP+VeaX55iUunSSKiu/FKtNJj2nmPScvxdW/cXPy6NCH6q/T1J5GbSV9ma60k6lo/s1YXS/phw/mwPATT0bMXVEa7e+KilUpPIw4hERjUdENAAeRUWcX/QhHon7yfh9Dqg0eLXurXBKQaieFlH+zH5yAK/P3cWZlHz+/cU2pt3WiUFdGyodTRCE67TnWBqmIjMBPh50bhGsdJyryruw8513+wFofERv65pU7QmoPXv28MILL1SYfAIICwvj0Ucf5Y033qixcELto9OqmTAomslDWlBYXIZRryX2ZCY/rj7BfaPaXvO+/v0nU3B4I+b0BAqPbMa7/UCXZBaE6ihNOom9pBCVwQt9g5ZKx6mWBescq59u6hklJhdcSO+hoWGoNw1Dr7xrnM1mJ8dUVr6Cqrzc75K/S8028grLyCss43Ri3hUfx+ChJsjvbyV+Fyeo/I14GbX8tvHynUp/XnMKWYZ/jO+AXqemVWOxwqC2kySJ4jY34+fjTcnRzWQs+RBJpcazZQ+lowlCtQT5GXjnn/2Y/VMsOw6nMvvHWBLTC7jr5tZidaYg1EIXV1oP7hZVvguzuylLS6A4bh9IKvz6jFc6Tp1T7QmokpIS/Pz8rvg5f39/t9vGW3A9vU5DcXExKYkJqAxBvPntbiQJBnRucM0+ImqjD/59JpCz4XtyNv6EZ6veqLTiTbLgXsrL75p1qVXboZ86n8uhuCzUKonxA6OVjiNcQq1Wla9ouhJZlikssVzSd6q44s5+uSXkFZZRUmYjMb2AxPTLy/x8PHXMfWHYVXcqXb4tgduGtUSrcc8Xg8J1kCR8hj6AWoLCI5tJ/+0DwiY/W+tWbgqC3kPDjKnd+f7P4yxYd5oF606TnFnIU7d3Qe9x3fspCYLgYtn5JcSeSAfce/e7vO2O3k+ebfqgDQhXOE3dU+2f2k2aNGHZsmXExMRc9rk//viDpk1FWYfgUFpaSusm3sR0imTzgWS+XX6U1/7R55r38ek+gvx9f2IzZWHa8wd+fSa4KK0gVE1x3D6g9vV/WrDuFAADujQgxF80cq1NJEnC26jD26ijWQO/K44ps9jIumT11N8nqTz1GvILy6q9U6lQu0kqFcGj/4lss1J0fDvpC98j9NbnMTbtqHQ0QagWlUpi6og2NAjx4pNfD7L9UCrpOY7m5IG+V568FwTBvWzYl4RdhtaNA4gM9lI6zhWZs5IoOr4TAP8+ExVOUzdVewLq7rvv5sUXX6SgoIDx48cTHBxMZmYmS5cuZf369cyaNcsZOYVa7O4Rrdl+OIX9pzI5cCqDTi1CrjpWpfUgYOAdZC6dQ+623/DuOETsOiC4DUtuGpasJFCpMTTtpHScKjufZmLnkTQkifI+bULd4qFVExnsddUXdDa7jN0uX9dOpULtJqnUhIx9gnS7jeKTu0hf8DZht7+AoVE7paMJQrUN7hZFaIAnb367m/ikfJ7+aBMv3t+T5g39lY4mCMI1yLLM2gvld269+mnHYkDG2KI7uhD3zVmbVXut/aRJk3jqqafYuXMnTz75JHfeeSdPPPEE27Zt46mnnmLiRDFTKFQUFujJLX2aAPDN8mPY7fI1x3u1648urCmyuYTcLb+6IqIgVMnF8jt9w9ao9Z4Kp6m6RRviAOjVLvyqfYiEuk2tksp3Kr2SizuVCnWTpNYQOv4pjNFdka1m0n55i9LE40rHEoTr0rZpIB88EUNUmDc5pjJmfLaNbQdTlI4lCMI1nDibS3JmIR46Nf06Rigd54oseRkUHt4MgJ9Y/eQ01ZqAstlsJCQkMGXKFLZs2cJ//vMf3nnnHb766iu2bNnC//3f/zkrp1DL3Ta0BQYPDWeS89l8IPmaYyVJReCQqQCY9q/BnH3t8YLgKuX9n2pR+V1GTjGbYh3b3YrVT/XbxZ1Kp9zUEk+DY7WTp0HLlJtaMmlwc/Q60UulLpPUWkImTsfQtCOypZTUn9+gNPmU0rEE4bqEBXry3uP96dY6FLPFxtvz9vDLmpPI8rUvcgqCoIy1exyrn/p2iHDbFdf5O38H2Y6hSQf0keI1s7NUawJKlmVGjhzJ/v378fb2JiYmhjFjxhATE4O3t7iqLlydr5cHEwc7Gh/PX3kci9V2zfGGxu0djVLtNnLWf++KiIJwTfbSIkrOHwPAsxZNQC3eGIfNLtOxeRAtokSJQn13cafS+TNvZt4rNzF/5s1MGBSNTlt7GuoL10+l0RE66Tn0jdsjm0tI++l1ylLjlY4lCNfFqNfy4v09GRPjWNn5/Z8n+OCHWMyWa7/GFATBtUrLrGw54LgYOsxNy++sBbkUHFgHgF9fsfrJmao1AaXRaAgKChJXF4TrMrZ/MwJ8PMjIKWbF9rOVjg8YfDdIKopP7S5/4y8ISilOOAh2G9rAiFqzI0ZeQRmrd50DYPLgFgqnEdyFXqfBYi4lJfEMFnOpWPlUz6i0HoRNnoG+YWvsZcWk/vgaZelnlY4lCNdFrZJ4aGx7HpvUEbVKYtP+JP79xTZyC8Su3ILgLrYfTqGkzEZ4oCdtmwYqHeeK8ncvRbZZ8GjQEn1UW6Xj1GnV7gE1cuRIlixZ4oQoQl2n99Bwx/BWAPyy5tRVd2O6SBfcEO9OQwHIWfsdsiz6kwjKqY3ld0u3xGO22mne0I8OzYOUjiO4mdJS8QatvlLp9ITd9gIekS2xlxaS+uOrmDPPKx1LEK7bzb0b8+r/9cbLoOXkuVye/mgzCSn5SscSBAFYuzsRgCE9GiJJksJpLmcrLsC0bzUA/n0numXGuqTaE1CtWrVi//79TJ06le+//55Vq1axevXqCn8E4WqGdo+iQYgXBcVmFm04Xel4/5hbkXR6ylLjKDq23QUJBeFyst1GcVwsUHsmoIpLLazYlgDA5CHNxS9TQRAqUHkYCL/9BTzCm2EvNpH6w6uYs5KUjiUI161j82A+eCKGyGBPsvJKePaTLew+mqZ0LEGo19Kyizgcn4UkweCu7ll+l7/nD2RLKbrQJhiadVE6Tp1X7XX3zz33HADp6ens3r37ss9LksTx42JnFeHK1GoV94xswxvf7Ob3zWcY2bcJgb6Gq47XePnj12scuZt/JmfDD3i27Imkcc/GdULdVZZ8GntJASq9F/oGrZSOUyUrtp+lqNRKw1AveratHSWDgiC4lkrvSdiUl0n9YSbm9ARSf5hJxN2v15oyY0H4u4hgL96fFsPb8/Zw8HQWs77Zxb0j2zJ+YDNxIUYQFHCx+XjnFiEE+1/9PZ9S7GXFmPauABy9n8TPCeer9gTUvHnznJFDqEd6tg2jdeMAjp/N4cdVJ3n81k7XHO/bczSm2NVY8zPI37sSv15jXBNUEC4oOr0HAGOzzkgq92/WXGax8ftmR2PhiYOao1KJX6aCIFyZ2uBF+B0vk/L9K1gyz5Pyw0wi7n4NrV+o0tEE4bp4GXXMfKg3Xy0+zModZ/lm+VGSMgp4ZGJHtJpqF38IgnCdbHaZdXsc5XdDu7vn6idT7GrspUVoAyPwbNlD6Tj1QrV/Cvfo0aPSP4JwLZIkcd8oR3O3tbvPcT7NdM3xKp0e/wG3A5C3bSG2kgKnZxSES9W2/k/r9pwnr6CMYH8DA7o0UDqOIAhuTm30IeLOmWgDI7GZskj9fibW/EylYwnCddOoVTwysQP/N649KgnW7D7PS//ZTn5hmdLRBKHeOHQ6k6y8EjwNWnq2C1M6zmXsljLydy0DwK/PhFpxkbkuuO7LAGVlZcTGxrJ+/XpiY2MpKxM/0IWqa90kgN7tw7HLMG9F5SWb3h0GoguJwl5aRN7WhS5IKAgOltw0LFlJIKkwNO2kdJxK2Wx2Fm2IA2DCwGg0anG1VxCEyqk9fQm/81W0AeFY8zNI+WEmVlO20rEE4bpJksTo/k15+cFeGPUajp7JZvqczSSmiwuZguAKa3c7yu8GdmmATut+kzsFB9ZhK8pD4xuCV9v+SsepN67rnck333xDv379uPPOO3n00Ue588476du3L//73/9qOp9Qh919S2tUKoldR9M4eubaL3IllZqAIfcAkL/3Tyy5oqmk4BrFcfsA0Ee1Rm3wUjhN5TYfSCYjpxhfLx1De7jncmdBENyTxtuf8DtfReMXgjU3jdQfZ2ItzFU6liDckK6tQnnv8f6EBhhJyy7mX3M2E3syQ+lYglCnFRab2XEkFXDP8jvZZiFv5+8A+PUei6Sudmci4TpVewJq/vz5vPPOO3To0IE333yTr7/+mjfffJMOHTrw3nvviR5RQpU1DPVm2IU3yN8sP4osy9ccb2zaCUPTjmC3krPhB1dEFIRaVX5nt8ssXO/YXXJM/2bodeKXqSAI1aPxCXRMQvkEYclOIfXHV7EVie3shdotKsyHD56IoU2TAIpKrbz6350s33pG6ViCUGdt2p+MxWqncbgPzRr4Kh3nMgWHN2MzZaH28ser42Cl49Qr1Z6A+u677xgzZgxz585l/Pjx9O/fn/Hjx/O///2PkSNHVnsC6ty5c7z88suMHTuWNm3aMGrUqMvGtGzZ8qp/MjL+uoIxePDgK475e3lgYWEhL7/8Mj179qRz5848/PDDJCcnX3bchIQEHnjgATp16kTv3r2ZNWsWpaWll43btGkT48aNo3379gwbNowffhCTI1V1x/BWeOjUnDyXy47DqZWODxg8FZAoOr6d0uRTzg8o1Gv2smJKzh0DwBjt/hNQe46lcT6tAIOHhhF9mygdRxCEWkrrF0L4Xa+i9g7AkplI6o+vif6LQq3n6+XBrIf7MLhbQ+x2mf8sPswXiw5itdmVjiYIdc7F3e+G9ohyu53lZLuN/B2LAfDtOQaVRqdwovql2pfHMzIyGD169BU/N3bsWFavXl2txzt9+jSbNm2iY8eO2O32K66C+eWXXy677bnnnsNgMBASElLh9uHDh3P//fdXuE2nq/hN9cwzz3D06FFeeuklvLy8mDNnDvfddx9Lly5Fr9cDYDKZuOeee4iIiGDOnDnk5OTw1ltvkZeXx/vvv1/+WPv37+fRRx9l7NixzJgxg9jYWGbNmoVOp2Py5MnV+r+ojwJ89IyLacYva08xb8UxerQNu2bPGo/Qxnh1GEThofVkr/2OiKmz3O6HmlB3FJ85CHYr2oAIdIERSse5JlmWWXBh9dOIPo3xMmgVTiQIQm2m9Q8j/M6ZpM5/GXPGWVJ/fJ3wO19BrfdUOpogXDetRs2Tt3cmKtSb71YcY8X2s6RkFvHcPd3F701BqCFnU03EJeahUUsMdMPNcIpO7MSSk4rK4IVPl2FKx6l3qj0B1bhxY7Kzr9yvJzMzk0aNGlXr8QYPHszQoUMBmDFjBkeOHLlsTKdOnSp8nJSUxNmzZ/nXv/512digoKDLxl/q4MGDbNy4ka+++ooBAwYA0KJFC4YNG8bixYuZMmUKAD///DMmk4klS5YQEBAAgFqtZvr06TzyyCM0a9YMgM8++4w2bdrw5ptvAtCrVy9SU1P5+OOPmThxIiqVaABcmQmDolm54yzJmUWs2XWOW/pce+VGwIDbKTq2lbKkExSf3IVnq14uSirUN3+V33VVOEnljsRnc/JcLlqNirExzZSOIwhCHaALjCT8zpmkfP8y5rR40n56nfA7XkblYVQ6miBcN0mSmDi4OZEhXnzwwz4OnM5k+sebefnBnkQEuX+vR0Fwdxebj3dvE4avl4fCaSqSZZm8bYsA8O0+EpXOoHCi+qfasyPTpk1jzpw5nDpVsfzpxIkTfPrpp0ybNq16Aa5jgmb58uVIknTFcr3KbNq0CR8fH2JiYspvi4iIoEuXLmzatKn8ts2bN9O7d+/yySdwrK7S6XTl48xmMzt37mTkyJEVjjF69GgyMzM5duxYtfPVR0a9ltuHtQTgx9UnKSmzXnO8xicQ315jAMhePx/ZZnF6RqH+ke02iuNjgdrR/2nBOsfP5KE9ovD30SucRhCEukIX3JDwO15BZfCiLOU0qT+/gd1conQsQbhhvdqF884/+xPkZyA5s5DpH2/mcFyW0rEEoVazWO1s2JcIUN7r150Un96LOeMcks6AT7cRSsepl6q9AmrhwoXYbDbGjRtHdHQ0wcHBZGZmEhcXR0hICIsWLWLRIsesoiRJfPHFFzUe+o8//qB79+6EhYVd9rlly5bx66+/otVq6datG9OnT6dly5bln4+Pj6dJkyaXlW1FR0ezdevWCuMmTpxYYYxOpyMqKor4+HgAzp8/j8VioWnTppc91sXHaNeu3XV9jbIsU1xcfF33dQclJSUV/q7MgI4h/L7ZQHpOCQvWHmfSoGuv4NB1vAlV7GqsuWlk7fwDz8433XBm4fpV93zXBuaUU9iLTUgeRuwBUW79fDyTbGL/qUxUKokRvRpcV9a6eA6FqxPnu26r8fPrHYL/+OfIWfQWZUknSPnpDfzHPYOkda8r2/WVeD5fvzB/LW/8X3fe+/EAcUkmXvrPdh4Y3Yoh3dyrbEic4/qlNp/v3ccyMBWZ8ffW0aqhl1u9fpZlmZwtCwAwdhhCmawCN8hXm8/3RbIsV7ktTrUnoE6dOoVarSYsLIzCwkIKCwsByieDLl0Z5YzePCdOnODUqVO89tprl31u8ODBdOjQgYiICBITE/nyyy+54447WLJkCQ0bNgQcvZ28vb0vu6+Pjw/5+X/t8mIymfDx8bnmuIt//33cxY8vfbzqslgsHD9+/Lrv7y7Onj1b5bH9WxtYuK2E3zcn0MivBC+9+prjdY374HnsT/K3LSRRHYysFas+lFad8+3u9Cc3YADK/Btz4tRppeNc069bHGXRbaMMZKedJTvt+h+rLp1DoXLifNdtNX1+1Z1vxXvPj5iTjpP00xsUdpkMYutqtyGez9fvtj7e/L7LwpFzJXz1+3EOn0xkWCdfVCr36jMqznH9UhvP97LNjlWEbRp6cOrUSYXTVKTJTsA7LR5ZpSHZuymym73Xro3n+1J/77t9NdV+1bB+/fpqh6lJy5YtQ6vVMnz48Ms+9+KLL5b/u1u3bvTt25dbbrmFuXPnMnPmzPLPXW1irCoTZlea3buRx7sarVZbvpKqNiopKeHs2bM0btwYg6FqtbUtW8rEnt3NmWQTh5M13D+q1TXHyy1bkJV2CHJSiMw/hXf/22oiunAdrud8u7usPfOwAsGdBxDVqrXSca4qJbOI40lJAEwd1ZGo0OvrX1EXz6FwdeJ8123OO7+tMTdqSO5v76LNTiAsbhV+o55A0ojmzUoSz+ea0b6dzKKNZ1iw/gw7ThRSJuuZNrk9Bg/lJ1nFOa5fauv5zisoIy7VsbP8pKHtiQh2r00rchYuxgx4dhhEeKfuSscpV1vP96Xi4uKqPFb5n6jVIMsyK1asoH///vj5+VU6PiQkhK5du3L06NHy23x8fEhNTb1s7N9XPPn4+GAymS4bV1BQUN6A3NfXF7h8pdPF+11pBVVVSZKE0Vj7m3waDIZqfR0PjGnHC19sZ+2eJCYMblF5M8ih95D+61sU719FQK+RaH1Drj1ecKrqnm93ZcnLwJqdBJIKv9Y9URvc92tasfMksgw92oTRqsmNf//XlXMoVI0433WbM86vMboTHre/QNpPsyhLOEjBqi8InTAdSayEUpx4Pt+4qSPb0yQygI9+iiX2ZBav/HcvLz3Qi9AA9/h/Fee4fqlt5/vPXcnY7TKtGwcQ3ShY6TgVlCadxJx4HFRqgvpNROOG/6+17XxfqjoLb2rVFm379u0jJSWF0aNHV/k+sixX+LhZs2YkJCRcdntcXFz5xNLFcRd7PV1kNps5f/58+bioqCi0Wi1nzpy57LEuPoZQPR2ig+naKgSbXWb+isqXRRqju6Jv1A7ZZiF3408uSCjUBxd3v9M3bIXacHnJrrvIyispb/Q4eUhzhdMIglBfGKLaEnbr80gaHcWn9pCx5CNku03pWIJQI/p3iuStx/rh7+3BubQCnvl4E8cTcpSOJQhuTZZl1lzY/W5Id/drPn5x5zvv9gPQ+LrX5Fh9U6smoJYtW4bRaGTQoEFVGp+enk5sbCzt27cvv23AgAGYTCa2bNlSfltqaiqxsbEMGDCg/LaYmBh27txJbm5u+W1r1qzBbDaXj9PpdPTq1YuVK1dWOO7y5csJDg6mTZs21/V11nf3jGyDJMHWgymcOp97zbGSJBE4ZCoAhUc2U5Yaf83xglAVxXGOCSh33/1u8aY4rDaZds0CadU4oPI7CIIg1BBDkw6ETnoW1BqKTuwgY+kcMQkl1BktovyZ/eQAmkb6kl9o5t9fbCu/4CMIwuVOns8lKaMQnVZN/04RSsepoCz9LMVx+xyVDX3GKx2n3lN8AqqkpIQ///yTP//8k+TkZAoLC8s/zsn562qD1Wpl1apVDB069Iq1kcuXL2f69OksXbqUnTt3smDBAu666y7UajX33Xdf+biOHTsycOBAXnjhBf744w82bdrEY489RmRkJOPH//UNefvtt+Pt7c2jjz7Kli1bWLJkCa+//jqjR4+usLLpscce48iRI7z44ovs2rWLL774ggULFvDEE0+gUin+31srNYnwZVBXR9P4b5cfu2y12t95hDfDq10MANnrvqt0vCBci72shJJzjrJdd56Ayi8sY9XOcwBMHtxC4TSCINRHxmadCZ0wHVRqio5uJfOPz5Flu9KxBKFGBPkZeOexfvRuH47VZmf2j7HMW3EMu128zhSEv1t7YfVTv44RGPXu1Rfw4uonz9a90Qa41+RYfaR4wX52djZPPPFEhdsufjxv3jx69uwJwNatW8nNzWXUqFFXfJwGDRqQnp7Om2++SUFBAd7e3vTq1Ytp06aV74B30QcffMA777zDq6++isVioWfPnnzyySfo9X/toubj48N3333HrFmzePzxx9Hr9YwaNYrp06dXeKzOnTvz+eefM3v2bJYsWUJYWBgvvvgikydPvuH/m/rszptbseVAMofjs9h3IoNurUOvOd5/4BSKju+g9NxRiuP24enGEweCeytJOAg2Kxr/MLf+JbV8awJlZhtNI33p3FIsJRYEQRmeLboTOv5p0n/7gMJDG5FUGoJG/ANJEhfhhNpP76FhxtTufP/ncRasO82CdadJyijk6Sld0LtBc3JBcAelZiub9zuajw91s/I7c3YyRcd3AODfd6LCaQRwgwmoBg0acPJk5Vs0Dhw48JrjOnXqxPz586t0TC8vL15//XVef/31a45r0qQJc+fOrfTxBgwYUKF8T7hxIf5GRvVryuKNcXy7/CidW4agvsZWuFrfEHx6jCR/xxJy1s3D2KwzkkrtwsRCXVF0of+TZ/NuN7STpTMVl1pYvtXRe+7WIS3cNqcgCPWDZ6tehIx7kowlH1FwYC2SWkPg8AfFzyahTlCpJKaOaEODEC8++fUgOw6nMiN3Ky/d35NA39q5Y5Ug1KTth1IpKbMSFmikbdNApeNUkLd9MSBjbN4dXUgjpeMIuEEJniBczeQhzfE0aDmXVsCGvZXX3fv3mYDK4I0lO5mCA+tckFCoa2S7zVEjjnuX363aeY7CEguRwZ70ah+udBxBEAS82vQlePRjgIRp359kr/1WlMQLdcrgblG88UgffL10xCfl8/RHmzideO1epYJQH6zb81fzcdU1Fgy4miU/g8IjmwHw6ztB4TTCRdWegLJYLHz++eeMGDGCTp060bp16wp/RONtoaZ4G3XcemFnrx/+PE6Z5drNTVV6T/z73wpA7uafsZeVOD2jULeUpcZjLzah8jCib9ha6ThXZLHaWLLJsdPmxEHNr7kyUBAEwZW82w8kaOQjAJh2Lydnw/diEkqoU9o0CeSDJwYQFeZNjqmMGZ9tY9vBFKVjCYJi0rKLOBSXhSTB4G4NK7+DC+Xv+B3sNgyN26OPFP1S3UW1S/Bmz57Nt99+S0xMDEOHDkWn0zkjlyAAMKpfU5ZtTSArr4TlW84wcfC1t5r36TIM094VWHJSyduxhICBU1yUVKgLik/tAcDQrDOSWvEK5StatyeRHFMZQb56BnZ1r1/0giAIPp2GgM1K1p9fkb9jCZJaS8CA25WOJQg1JjTAyHuP9+e97/ex93g6b8/bw103t+LWoaIkXqh/1u1xVKl0bB5MiL9R4TR/sRbmllfE+IneT26l2u+wVq5cyWOPPcY///lPZ+QRhAp0WjV33dyKj37ez4L1p7mpVyO8jVef9JTUWgIG3UX6ovfI37UUny43ofFxr1pkwX0Vxzn6P7lr+Z3NZue3DY7VT+MGRqPViCpqQRDcj0/X4ch2K9mr/0fe1gVIag3+/SYpHUsQaoxRr+XF+3vyv2VHWLr5DN//eYLE9EKm3dYJnVb0IBXqB7tdZt1eR/ndsB7u1Xw8f9cyZJsFj8iW6Bu1UzqOcIlqv3vJz8+nWzf3fHMm1E0DuzakcbgPRSUWFqw7Xel4Y8ueeDRohWw1k7PpZxckFOoCS34G5ozzIKkwNu2sdJwr2nYohdTsIryNOob3FI0UBUFwX77dRxIwZCoAuZt+Im/HEmUDCUINU6skHhrbnscmdUStkti0P4l/f76NXFOp0tEEwSUOxWWSmVuCp0FLr3bu05PUVlKAKXYV4Nj5TqxMdC/VnoDq3r07J06ccEYWQbgitUrinpGO3mLLt54hI7f4muMlSSJw6D0AFB7aQFn6WWdHFOqA4tOO5uP6Bi1RG70VTnM5WZZZuN4xATu6f1Ox/bMgCG7Pr9dY/Ac4SuFz1s8nf/dyhRMJQs27uXdjXvtHb7wMWk6ez+XpjzeTkJKvdCxBcLq1ux3ldwM6R7rVyr/8PSuQzaXoQhpjiO6idBzhb6o9AfXiiy+ycOFCVq9ejdlsdkYmQbhM11YhdIgOwmK188OflU+A6iNb4Nm6DyCTs36e8wMKtV7xafcuv9t3IoOEFBMGDzWj+jVROo4gCEKV+PebhF+/yQBkr/kG074/FU4kCDWvQ3QwHzwRQ2SwJ1l5JTz7yRZ2H01TOpYgOE1hiYUdhx0N+Ie6UfmdvawE054VgGPnO7H6yf1UewJq7NixnDt3jieeeIJOnTrRpUuXCn+6du3qjJxCPSdJf62C2rAvsUpXlgIG3QkqDSVnDlIcv9/ZEYVazG4uoeTcEcB9J6AWrDsFwPBeja/ZB00QBMHd+Mfchl+f8QBk/fk1pgNrFU4kCDUvItiL96fF0LF5EKVmG7O+2cVvG+LETpBCnbRlfxJmq53G4T5EN/BTOk45U+wq7KWFaAMj8GzVS+k4whVUu4Zj+PDhYiZRUESLKH/6dYxg68EUvv3jGK8+1Pua47X+Yfh2u5n83cvJWT8PQ5MOSCr3WR4quI+SM4fAZkXjH4Y2MFLpOJc5eiabYwk5aNQqxg1opnQcQRCEapEkCf+BdyJbLeTvXk7WH18iqTR4dxiodDRBqFFeRh0zH+rNV4sPs3LHWb5ZfpTE9AIendRRbBwi1Clrdjuajw/pHuU2cwN2Sxn5u5YB4Nd7vHjf56aqPQH19ttvOyOHIFTJ3SNas+NwKrEnMjh4OpOOzYOvOd6v3yQKDm3AnHGegkMbHdtDC8LfFF0sv4vu6ja/RC91sffTkO4NCfQ1KJxGEASh+iRJImDovcg2K6Z9f5K5/DMktQavtv2UjiYINUqjVvHIxA40DPXmv78fZu2e86RmF/H8Pd3x9fJQOp4g3LBzqSZOJ+ahVkkM6tpA6TjlCg6ux1aUh8Y3GK92MUrHEa5CTMULtUpEkBe39G4MwLfLj2K3X3tZs9rgjV9fx9bPuZt+xm4WO5MIFcmynZJ4RwNyTzcsv0tIyWfv8XRUEkwYFK10HEEQhOsmSRKBwx/Au9NQkO1k/P4xhSd2KB1LEGqcJEmM7t+Ulx/shVGv4eiZbKbP2UxieoHS0QThhq3d41j91KNtmNtMqso2K/kXdlv17TUOSS0263FXVZqA2rNnD0VFReX/ruyPIDjTbcNaYvBQE5eUz9aDyZWO9+12CxrfEGyFOeXLMgXhorKUOGxF+UgeRvRRrZWOc5mF6xyrn/p2jCQiyEvhNIIgCDdGklQEjfgHXh0GOSahFn9I0Snx2lGom7q2CuW9x/sTGmAkLbuY6XM2E3siQ+lYgnDdrDY7G/Y5dr8b2t19mo8XHtmM1ZSF2tMP706DlY4jXEOVpgbvvvtufv31Vzp06MDdd9991RIVWZaRJInjx4/XaEhBuJSftwcTBjXnhz9PMH/lcXq3j7hmXb2k0RIw+C4yFs8mb8cSvDsPRePl78LEgjsrPu1Y/WRs2hFJrVU4TUUpWYXlk6yTBjdXOI0gCELNkCQVwSMfAZuVwqNbSP/tfcImz8DYrLPS0QShxkWF+fDBEzG89d0ejp7J5tX/7uChce0Z1a+p0tEEodr2HEsnv9CMv7cHXVuFKB0HANluI2/7bwD49hqDSiM263FnVZqAmjdvHs2aNSv/tyAobVxMM1ZsSyAtu5iVOxIY0//ajZk9W/fBY9cyylJOk7v5V4JH/MNFSQV3V3yx/5Mblt/9tiEOuwxdW4XQNNJX6TiCIAg1RlKpCR7zOLLNStGJHaQveIfQ257H2KSj0tEEocb5ennw+j9689nCg6zbk8h/Fh8mMb2Ah8a1R6MWHVGE2mPdhfK7QV0bonaT792iEzux5KSi0nvh0/kmpeMIlajSBFSPHj2u+G9BUIreQ8OU4a34fOFBfllziqHdozDqr756RZIkAoZMJXX+SxQcWItv9xHoghu6MLHgjqz5mZgzzoKkwtisi9JxKsjOL2HdHscS58lDWiicRhAEoeZJKjUh454k/Tcrxaf2kP7r24Td/iKGRm2VjiYINU6rUfPEbZ2JCvXm2z+OsWL7WVIyi3junu54GdxrBbYgXEmuqZQ9x9MBGNrDPcrvZFkmb9uF1U/dR6LyEJv1uDv3mLYUhOtwU48oIoO9MBWZWbQhrtLxhqg2GFv0ANlOzvr5LkgouLviOEf5nUdkC9RGH4XTVPT75jNYbXbaNAmgbdNApeMIgiA4haTWEDr+GQzNuiBbzaT98ialiSeUjiUITiFJEhMGNeff9/ZAr1Nz4HQm0z/eTEpWodLRBKFSG/YlYbfLtGzkT8NQb6XjAI7X8uaMs0g6PT7db1E6jlAFYgJKqLXUahX3jHQ0jV6yKZ4cU+U73AUMvhtUaorj9lFy9rCzIwpuruhC+Z277X5XUGzmzx0JgFj9JAhC3SdptIRO+heGJh2RLaWk/jyL0uTTSscSBKfp1S6cd/7ZnyA/A8mZhTzz0WYOx2UpHUsQrkqWZdbuOQfAMLda/bQIAJ+uN6M2uMekmHBtYgJKqNV6tQundeMAzBYbP66q/IqpLjACny6O2uDsdfOQZbuzIwpuym4upfTsEcD9+j8t35pASZmNJhE+btPgURAEwZlUGh2hk59D36gdsrmEtJ9eoyz1jNKxBMFpmkb6MvuJGFpG+VNYYuGl/2xn1c5zSscShCs6dT6XxPRCdFo1/TtFKh0HgNJzRyhLPoWk0eHbY7TScYQqEhNQQq0mSRL3jmoDwJrd50lML6j0Pv79JiN5GDGnnaHwyBZnRxTcVEnCIWSbBY1fCNqgBkrHKVdaZmXZFsebrkmDm19111FBEIS6RqX1IOzWGegbtsZeVkzqT69Sln5W6ViC4DT+PnreeLQvMZ0jsdllPl1wgLlLj2Czy0pHE4QK1ux2NB/v2yH8mn13XSn3wuon705D0Hj5KRtGqDIxASXUem2aBNKzbRh2u8y8FccqHa/29MW/z3gAcjb+iN1S5uyIghu6dPc7d5rkWbXrHAXFZsIDPenbIULpOIIgCC6l0hkIu+3feEQ0x15SSOqPr2LOTFQ6liA4jYdWzfQ7u3LH8FaAo63ErP/torjUUmGcXq9XIp4gUGq2suVAMuA+zcdLk09RevYwqNT49RqrdByhGmpsAqqsTLyJF5QzdURrVBLsPJLGsYTsSsf7dB+J2icImykL054VLkgouBNZtpc3IHen8juL1c6SjY6G+hMGRbvN9raCIAiupPIwEjblJXRhzbAXm0j9YSbm7GSlYwmC00iSxJSbWvLs3d3QaVTsPZ7Os59sISuvhFKzFa1OT3iDpmh1ekrNVqXjCvXMzsOpFJdaCQ0w0q5pkNJxAMp7P3m1G4DGN1jhNEJ1VPvdzYoVK/jhhx/KPz537hwjRoygU6dO3HHHHeTn59doQEGoiqgwH4b2aATAt8uPIcvXXrqs0noQMHAKALnbf8NWJL5v65Oy1DPYivKQdAYMUW2UjlNu475EsvJLCfDxYEj3hkrHEQRBUIxa70n4HS+hC2mMrSiP1O9nYslNUzqWIDhV/06RvPVYPwJ8PLDZZXRaFYvWn+bumX9yz2uruXvmn/y2IQ6zxaZ0VKEeuVh+N6R7FCqV8lUDZelnHZUMkgq/C1UtQu1R7QmouXPnUlJSUv7xu+++i8lkYurUqZw5c4Yvv/yyRgMKQlXdMbwlOq2a42dz2Hmk8hepXu1i0IU2QS4rJnfrQhckFNxF8ek9ABibdkJSu0cdu80us2iDY9ensTHRaDVqhRMJgiAoS23wJvyOl9EGN8RWmEPq969gyctQOpYgOFWLKH8+eGIAD0/owNLNZ/h1zQkaWM5hkMooKrHw0+qTLFx/WqyEElwiPaeYQ3FZSBIM6eYeF0fztv8GgGfr3ugCRbuK2qbaE1BJSUk0b94ccJTdbd26lenTp/P888/z5JNPsm7duhoPKQhVEehrYGxMUwDmrTiGzXbtHe4kSUXg0HsAMMWuwpyd4vSMgnsoPu1+5Xc7D6eSnFmEl0HLzb0bKR1HEATBLag9fQm/YybawAispixSf3gFq0lsVy/UbUF+Bto0CeTQjp084/MHj/qs5SmflRgkMwBLt5xBrRJl+oLzrdvjWP3UMTqYkACjwmnAnJ1C0bHtAPj1maBwGuF6VPsnV0lJCUaj45vv4MGDmM1mYmJiAIiOjiY9Pb1mEwpCNUwc1Bxvo46kjMLy5aLXYmjcHmN0V7DbyNnwvQsSCkqzmrIxpycAEsZmnZWOA4AsyyxYfwqAkf2auM3uIoIgCO5A4+VH+J2vovEPw5qXQeoPM7EW5CgdSxCcxlaUT/qyT3nYYzkNNLkAhKpN3OO5GRV2ikoslzUpF4SaZrfL5RNQQ9yk+Xje9sWAjLF5NzxCGysdR7gO1Z6ACg4O5vjx4wBs2bKFJk2aEBAQAEB+fr7YoUFQlKdBy+3DWgDw46oTlJZVvjw5YPDdIKkoPrmL0sTjzo4oKOzi7nceDVqg9vRVOI3D/lOZxCfl46FTM7pfU6XjCIIguB2NdwARd72Kxi8ES06qYxKqME/pWIJQo2S7DdO+P0n88nHMxzYBsKM0ms9NQzHLalrrUhhr3IenQSsuVglOdzgui4zcEjz1Gnq3D1c6Dtb8TAqPOJ4Xfn0nKpxGuF7VnoC66aab+PDDD3n88ceZN28eI0aMKP/cyZMniYpyj9lRof66pU9jQgOM5BaU8fvm+ErH64Ib4t1pCADZa7+rtIG5ULsVXZiAMka7T/ndwnWO3k/DezbC18tD4TSCIAjuSeMTRPidr6L2CcKSnUzqj69iKzYpHUsQakRp8mmSv3merD+/xl5ahDakMUda/YOfi/tw0hrBD4V9ARioP879LbNJTC/AWkm7CUG4EWsvrH6K6dwAD63yvUnzdv4Odhv6xu3RR7ZQOo5wnao9AfXEE08wevRozp49y6hRo3jwwQfLP7dx40b69OlTowEFobq0GjV339IagEUb4sgvLKv0Pv4xtyFp9ZSlnKbo+HZnRxQUYjeXUnr2MACebtL/6cS5HA7HZ6FRS4wbEK10HEEQBLem9Qsh4s6ZqL0CsGSeJ/XH17CVFCgdSxCum624gMw/viDl2+cxp8Wj8jASOPxBGjzwLjePGcKUm1riadBywNKYtRZH64DopGX8NH8pMz7bSkZOscJfgVAXFZZY2H7I0R93qBuU31kL8yg44Og17S9WP9VqmureQa/X89prr13xc7/++usNBxKEmtC/UyRLNsURl5TPL2tP8X/j2l9zvMbLH7/eY8nd/As5G77Hs0UPJI1Y2lzXlJw9jGyzoPENQRvsHjt5XFz9NLBLQ4L9DQqnEQRBcH/agHDC75pJ6vyXMacnkPbT64Tf8QoqvafS0QShymTZTsGBdeRs+B57SSEAXh0GEjh4anmLAJ0KJgyKZvKQFhQWl+FpGEnW7x9SenInY+Q/eT/xFp6YXciTt3emZzvlS6SEumPLgWTMVjtRYd40b+indBzydy9DtprxiGyBvlE7peMIN+CGtk84c+YM+/bto7hYzLwL7kWlkrh3ZFsAVm5PIC27qNL7+PYcg9rLH2teBvn7Vjo7oqCAi/2fjM27IkmSwmngXKqJXUfTkCSYOFisfhIEQagqXWAk4Xe+gsroQ1lqPKk/z8JeVqJ0LEGokrLUeFK+/TdZK77EXlKILqQREVNnETL68cv6U+p1GizmUlISz2C1mAkbOw1dWFM8pVL+GbAZS0kxs77ZzX9/P4LFKkryhJqxdvc5AIb1iFL8NbOtpADTvj8BR+8npfMIN+a6JqCWLFlCTEwMI0eO5K677iIhIQFwlOeJVVCCu+jYIpguLUOw2mTmr6i8ubhKp8d/wO0A5G1dJJb01zGybKc4bh8ARjcpv1u4wbH6qXf7cBqEeCucRhAEoXbRBUddWPnkRVnyKdJ+eQO7uVTpWIJwVbaSArJWfkXy/56jLOU0ks5A4LD7iHzgPfQNW1/zvqWlju9tldaDsMkzUHv6EWjP5l+NYpGw8/vmeGZ8toV0UZIn3KBzaSZOnc9DrZIY2EX5igHTnpXI5lJ0IY0cu5cLtVq1J6BWrlzJjBkzaNOmDS+99FKFhs1t27Zl5UqxckRwH/eMbIMkweYDyZxOzK10vHeHQWiDo7CXFpK3bZELEgquYk49g60wF0mnxxDVVuk4pGUXsXl/MgCTB4tGioIgCNfDI7Qx4Xe8jMrDSGnicdJ+fQu7pfLej4LgSrJsx3RgHYlfTsMUuwqQ8WoXQ8OHP8G3xygkVfUaPGt8Agmd/BySWktwwUne7JGOl0HLqfN5PDF7IzsOpzrnCxHqhbW7Hc3Hu7cJxc9b2c1x7GUl5O/5AxCrn+qKak9AffXVV0yYMIEvv/yS2267rcLnmjZtSlxcXI2FE4Qb1TTSlwFdGgDw7fJjle5wJ6nUBA6ZCkD+3pVYctOcnlFwjaILq58MTTq6RX+vxRvjsNtlOrcIJtoNausFQRBqK4/wZoRNeQlJZ6D03BHSF76D3WpWOpYgAFCWlkDKdy+S9cfn2ItNaIMbEn7Xa4SMfQKNt/91P64+sgXBox4DwBi3hvdHamjZyJ+iEgtvfrubr5ccFiV5QrVZbXY27ksCYGh35ZuPm/avxl5aiDYgAs9WvZSOI9SAak9AxcfHM3LkyCt+zs/Pj7y8vBvNJAg16q6bW6NRqzgUl0XsyYxKxxubdcbQpCPYrORs/NEFCQVXuNj/yR12v8s1lbLmwtWlyUPE6idBEIQbpY9sQfjtLyJp9ZScOUjGoveRbRalYwn1mK20iKxVc0n+37OUJZ9E0ukJGHIPDR54H0OjmlmJ7dWuP359JgBQtmkur44LYfxAR0/JpVvO8OynW6rUB1UQLtp3PJ28wjL8vD3o2jpU0Sx2q5n8nUsB8OszvtorBQX3VO0JKIPBQEHBlXvjpKen4+vre8XPCYJSQgOMjOrXBHCsgrLZr70KCiBgyFRAoujYNkqTTzk5oeBsVlM25rQzgOQWteO/b47HYrXTspE/7ZoFKh1HEAShTtA3bEXYbc8jaXQUx+0j/bfZyDar0rGEekaWZQoObSTpy8cx7V0Bsh3PNn1p+I85+PUag6Su9ibk1+Q/cArGFt3BZiXrt/eYOiCUlx7oibdRS1xiHk/O3sj2Qyk1ekyh7rp4gXRQ14Zo1De0X9kNKziwHltRHhqfILzaxSiaRag51f6u6ty5Mz/88MMVS5l+++03evToUSPBBKEmTR7SAk+9hrOpJjbFJlY63iO0MV4dBgKQs25epaV7gnu72HzcI7L5ZbvLuFphiYUV288CMHlwc1HLLgiCUIMMjdoReusMJLWW4lO7yfj9I2S7TelYQj1hzjhH6vyXyFz2CbaifLSBkYTf8Qqh459G4+OcC06SpCJk7BPoQhphK8oj7de36Rbtx8dPD6J14wCKSq289d0e/vPbISxW8VwQri63oJS9x9MBGNpd2ebjss1K/s4lAPj2HlfjE7eCcqo9AfXYY49x4MABJk2axPz585EkidWrV/Pwww+zd+9eHn74YWfkFIQb4uOpY9KFUqfv/zyB2VL5L+CAAVOQNDpKE49TfHK3syMKTnSx/M4ddr9bsS2BkjIrUWHedG8TpnQcQRCEOsfYpCOhk54FtYai4zvIXPapmIQSnMpeVkzWmm9I+u90ShOPI2k9CBh0Fw0e+gBDkw5OP75KZyD01hmojD6Y0xPIXPYJQX4evPloXyYOcpTkLd+WwLOfbCE1S5TkCVe2cV8SNrtMyyh/osJ8FM1SeGQz1vxM1J5+eHccrGgWoWZVewKqffv2fP311xQXF/P2228jyzL/+c9/SEhI4KuvvqJFC9HPRHBPo/s3JchXT2ZuCcu3JlQ6XuMTiG/PMQDkbJgvlvHXUnZLGSVnDwPK938qNVtZuiUegEmDm6NSidVPgiAIzmCM7kLohOmgUlN4ZDOZf3yBLIuGzELNkmWZwiNbSPzicUy7lzvK7Vr1puHDcxw9a9Su2/RE6xtC2KRnQaWh6MROcjf/ikat4t5RbXnlwV54G3XEJeXz5Icb2Xow2WW5hNpBluXy8rshPZRtPi7bbeTtWAyAb8/RqLTK7sQn1KzrKuzs1asXK1euZPXq1fz444+sXLmSVatW0bNnz5rOJwg1xkOr5s6bWwGwYN0pCosr3yHHr/c41J6+WHJSMcWudnZEwQlKzh5GtprR+AShDVb2F+ra3efJLzQTEmAkplOkolkEQRDqOs8W3QkZ/xRIKgoPbSBr5VeipF6oMebMRFJ/eIWM3z/CVpSHNiCcsNtfJHTidDQ+QYpk0jdsTfCIfwCQt3UBhce2AdCtdShznhlI68YBFJdaeWfeXr5YdLBKFQFC/XA6MY/E9AJ0GpXir1GLTu7Ckp2CSu+FT5fhimYRat4NdRaLioqiS5cuNGnSpKbyCIJTDeoWRaMwbwpLLCxcf7rS8SoPA/79bwMgd+sC7KVi2XJtc2n5nZL9lqw2O79tjANgwsBo1Ao3dhQEQagPvFr1JmTsNJBUFOxfQ/bquWISSrgh9rISstd9R9J/n6H03FEkjQ7/gXfQ4KEPMTbrrHQ8vDsOLl/Bn7nsU8pSHK89gvwMvPVoXyYPaQ7Aiu1n+decLaRkFiqWVXAfay+sfurTIQJPg+tW7v2dLMvkbV0EgE/3Eag8DIplEZyjSt28lixZUq0HHTdu3HVEEQTnU6sk7hnZhtfm7mLpljOM7NuUYP9r/2Dz7jyU/D1/YMlOJm/HYgIG3eWitMKNkmWZ4tOOBuRK93/avD+JzNwS/Lw8GKrw0mZBEIT6xKttf2Sblcxln2HauxJJrSFgyD1iEwihWmRZpuj4drLXfoutIAcAY4seBA67D61fiMLpKgoYfBfmrERK4veTtuAdIu9/B413AGq1iqkj2tC2aSCzf4zlTEo+T364iX9O7khM5wZKxxYUUmaxsXl/EoDir1FL4mIxZ5xF0unx7T5C0SyCc1RpAmrGjBkVPr74C/vSK0iX/hKvzgTUuXPnmDt3LgcPHuT06dM0bdqU5cuXVxjTsmXLq95/y5YthIT89UN/7ty5/PDDD2RmZtKiRQueffbZy0oDCwsLeffdd1m1ahVms5mePXvy0ksvERlZcblhQkICs2bNYt++fRgMBkaOHMn06dPR6/UVxm3atIkPP/yQ+Ph4wsLCuPfee7nzzjur/H8guFa31qG0axbIkfhsflh1nCdv73LN8ZJKTcDgu0lf8Db5u5bj02U4Gt9gF6UVboQ5LQFbYQ6SVo++UVvFctjtcvmKuzExTfHQqhXLIgiCUB95dxiEbLORteIL8nctQ1Jr8R94h5iEEqrEnJVE9uq5lCQcAkDjF0rQTQ9gbN5V4WRXJqnUhI57iuTv/o0lK4n0Be8Qfvdr5b10urZylOS99/0+jp7J5r3v93E4PpsHx7YTr1HqoR2HUykqtRISYKR9M2XKR8Ext5C77cLqpy7DURu8FcsiOE+VJqDWrVtX/u+srCyeeuop+vXrx6hRowgKCiIrK4tly5axbds2Pvzww2oFOH36NJs2baJjx47Y7fYrLov+5ZdfLrvtueeew2AwXDb59OGHH/LUU0/Rpk0bFixYwEMPPcSCBQsqTGI988wzHD16lJdeegkvLy/mzJnDfffdx9KlS8snl0wmE/fccw8RERHMmTOHnJwc3nrrLfLy8nj//ffLH2v//v08+uijjB07lhkzZhAbG8usWbPQ6XRMnjy5Wv8XgmtIksR9o9ryzMebWb83kXEDomkcfu2dHozNu6Fv1JbSc0fJ2fQTIWOmuSitcCMult8ZmnZEpdEplmPX0TQS0wsx6jWM6CNKlgVBEJTg03koss1K9qqvydv+m2MSKuZWpWMJbsxuLiVv20Lydi4DuxVJrcWvzwR8+4xT9HVFVaj0noTd+jzJ3zxHWWocmX98TsjYJ8snXQN9DbzxcB9+Wn2SX9ed4s8dZzl5LofnpnYnMthL4fSCK63dfQ6Aod0aKrpBTum5I5Qln0RSa/HtOVqxHIJzVakJSWRkZPmf7777jqFDh/Laa6/Ro0cPmjZtSo8ePXj99dcZOnQo33zzTbUCDB48mE2bNjFnzhzatr3yCoVOnTpV+BMUFMTZs2cZNWpU+Riz2cwXX3zB1KlTeeCBB+jduzfvvfceDRo04Msvvywfd/DgQTZu3Mgbb7zBqFGjGDhwIJ9++inJycksXry4fNzPP/+MyWTi888/JyYmhnHjxvHiiy+ybNky4uPjy8d99tlntGnThjfffJNevXrx6KOPMmnSJD7++GPsdrHbirtqEeVP3w4RyDJ898exSsdLkkTgkHsAKDy8ibLUM86OKNSAoov9n6KVu0IpyzIL158CYGTfJorW1QuCINR3vt1uJnDYfQDkbvml/Gq7IFxKlmWKTuwk8T9PkLd9MditGKO70uAfH+Efc6vbTz5dpPUPK98NsujoVvK2/1bh82q1irtuac3Mh3rj66UjIcXEUx9uZGNskkKJBVdLzynmUFwWAIO7K1t+d/H707vTEDRe/opmEZyn2l1wN2/ezMCBA6/4uQEDBrB169bqBVBVvxHv8uXLkSSpwgRUbGwsBQUFFW5Tq9WMGDGCTZs2la+s2rRpEz4+PsTExJSPi4iIoEuXLmzatKn8ts2bN9O7d28CAgLKbxs+fDg6na58nNlsZufOnYwcObJCvtGjR5OZmcmxY5VPbAjKmTqiNWqVxN7j6Ry+8IP3WjzCm+HVzvF9k73uO9HE1M1ZC3Iwp8UDkqITUIfisjh1Pg+dRsWY/s0UyyEIgiA4+PYYRcDguwHI3fgjeTuXKpxIcCeWnBTSfp5F+qL3sJmy0PgGEzp5BmG3/Rutf5jS8arN0Lg9QcMfBBzf70Undl02pkvLED5+eiDtmwVRUmbjgx/28emCA5SJXfLqvPV7ziPL0LF5EKEBRsVylCafdpS4qtT49h6rWA7B+apUgncpu93O2bNn6dOnz2WfO3v2rEvelP/xxx90796dsLC/fglcXJXUtGnTCmObNWtGUVER6enphIWFER8fT5MmTS6r+Y+Ojq4weRYfH8/EiRMrjNHpdERFRZUf6/z581gslsuOGR0dXf4Y7dq1u66vUZZliouLr+u+7qCkpKTC3+7Iz1PFkG6RrN6dxNylh3njHz0q7QVh6DmewuM7KD13hNyjO9A37eSasG7OHc938bEdAGjDmmJW6TAr9Hz6Zc0JAAZ1jUSntrnt89odz6HgPOJ8123i/FZO1/EmvEqLKdy+iJx132Gx2fHsfJPSsa6LON81Q7aUUbhnGUV7/wCbFdQaPLuNwqv7KCSth6K/v2/0HGta9cOYeobiA2vI+P0jAvQvow1pVGGMQQv/ntqJhRvO8NumM6zaeY7jCdk8eVsHIoM9b/hrEKrOVc9pu11mzYXyu/4dwxT9Hs/d/CsAhlZ9sGi9sLjp62VnqAs/w2VZrnJPxWpPQPXv35+PPvqIiIiICiuhNmzYwMcff0y/fv2q+5DVcuLECU6dOsVrr71W4XaTyYROp7usQbivry8AeXl5hIWFYTKZ8Pa+vKGZj48P+fn5FR7Px+fyvkCXjrv499/HXfz40serLovFwvHjx6/7/u7i7NmzSke4pg6RNjZqJOKTTSxcFUu7RpXP/BuiuqJP2En22m8x9X0IrmMVX13lTufb8+AWdIDJO5IMhZ5LydlmDsfnIEnQKrR2PKfd6RwKzifOd90mzm8lfFqib9YXQ/w2CjbOJy0jE3PUtTcmcWfifF8nWUabeRrD8TWoSxyv3S1BTSlufRO5ngEQ5z5tF27oHId0xSvwNNrss2QuehdT73uRPS7v9dQ+AoyDgvhtew7n0wt57vMdjOruR8cmYhLK1Zz9nE5ILyUzrxQPrYSvKpfjx/OceryrURVk4HsmFhlIC2hDSi14vewMtf1nuE5XtdLkak9AvfDCC9x777088sgjeHp6EhgYSHZ2NkVFRTRq1IgXXnih2mGrY9myZWi1WoYPH37Z564063ZxRdaln7va7FxVZu2uNLt3I493NVqttnwlVW1UUlLC2bNnady4MQaDQek41zQmz8DCDWfYcryE8UM7o9Fce0LJ3iSKzG+OoC7KppEtHWPbwS5K6r7c7XzLVjPpax1XdBr0vAltcKNK7uEcK346CED/juH06X59qyFdxd3OoeBc4nzXbeL8Vp3cqhWFW/0o2vsHnsf+JDyyAcZ2A5SOVS3ifF8/a14GBRvnU5ZwAACVdyA+A+7EI7qbW+2QWFPn2N5sBtk/zYS8NIJPrCRg0vNImst7U7ZuDf26lfHJwiMcOZPD4h255JUZuG9kKzx0Ypc8Z3PVc3rd0SMA9O8UQYf2bZx2nMrkrVhPKWBo0ZPw7s5dzOKO6sLP8Li4uCqPrfYEVEhICIsXL+a3335j9+7d5OXl0aZNG3r27Mm4ceMuW4FUk2RZZsWKFfTv3x8/P78Kn/Px8aGsrIyysjI8PDzKbzeZTMBfK6F8fHxITU297LH/vuLJx8en/L6XKigooFmzZhUe8+8rnS7e70orqKpKkiSMRuXqcGuKwWBw+6/j1mGtWbs3mfScEjYfymBUv6bXvoPRiC3mVrJX/4+inYsJ6DwElUft/GFR09zlfBefPg5WM2qfIHyiWinyIjIxvYA9xzMAuG1YK7f4f6kKdzmHgmuI8123ifNbNcab7iNbAtOePzCtmYuHwYh3+9o1CQXifFeH3VJG/o7fydv+G7LNAioNfr3G4Nd3Iiqd897L3KgbPsdGI7rb/03KNzOwpJ6maNN8gkc9dsXXSUajkVmP9OPXNSf5ac1JNsSmEJ9SwHN3dyMq7Prf4whV58zndFGJhV3HHK9Tb+nTTLGfHZacFEpP7QYgMOZWPOrxz7Da/DO8Ou+1rqt2yMPDgylTpvDhhx/yzTff8OGHH3L77bc7dfIJYN++faSkpDB69OXbMl6cFLp0h7qLH3t6ehIaGlo+LiEh4bJeVXFxceWPcXHc3x/LbDZz/vz58nFRUVFotVrOnDlz2WNdmklwbwYPDVNuagnAz2tOUlxqqfQ+Pl1uQuMfhq0oj7ydvzs7olBNRXGO3e88o7sqdgVz0YbTyDL0ahcmXqgJgiC4MUmSCBx2Hz5dhgMymcs+pfDYNqVjCU5SHLePpK+eInfLL8g2C4YmHWjw0AcEDLrTrSefaoouMJKQCc+ApKLw0Abyd129Cb9aJTFleCtmPdwHf28PzqcV8PTHm1m357wLEwvOsOVAMmaLjYah3jRv6KdYjrztS0C2Y4zuikdoY8VyCK5Tq5rXLFu2DKPRyKBBgy77XJcuXfD29mbFihXlt9lsNlauXMmAAQPK34QOGDAAk8nEli1byselpqYSGxvLgAF/Xe2KiYlh586d5Obmlt+2Zs0azGZz+TidTkevXr1YuXJlhSzLly8nODiYNm2UW8ooVM9NPRsRGexJfqGZxRvjKx0vqbUEDL4LgPydv2M1ZTs7olBFsixTfHofAMbm3RTJkJFbzMZ9ji2MJw9poUgGQRAEoeokSSLw5gfx7jQUZDsZSz664m5hQu1lycsgbcHbpP3yJta8dNReAYSMf5qwKS+jC2qgdDyXMjbtROCwewHIWTe//HXT1XSIDubjZwbSqXkwZWYbH/28n49+jqW0zOqCtIIzrL0wiTi0e5RiF2utpiwKDjt2l/frO7GS0UJdUaUSvKlTp/LKK6/QrFkzpk6des2xkiTx3XffVTlASUkJmzY5vvGSk5MpLCzkzz//BKBHjx4EBAQAYLVaWbVqFUOHDr1ibaROp+ORRx7hww8/JCAggDZt2rBgwQISExOZPXt2+biOHTsycOBAXnjhBWbMmIGXlxcff/wxkZGRjB8/vnzc7bffzvfff8+jjz7Ko48+SnZ2Nm+//TajR4+usLLpscce46677uLFF19k9OjRxMbGsmDBAl577TVUojl1raFRq7h7RBve/m4PSzbFMaJPY/x9rn0VzLNlLzwatKQs6SS5m38meNRjLkorXIs5PQFbQTaS1gN9Y2X6Li3eGIfNLtMhOogWUf6KZBAEQRCqR5JUBI34B7LNSuHhjaQvnk3opH/hqdDFDKFmyFYLeTt/J2/bImSr2bHNe49R+PebXK9bKPh0G4E54zwFB9aSvuRDIu99E11w1FXH+3vrmfl/vVmw7hQ/rTrBuj2JnDqfx3NTu9FIrPSuVRLTCzh5LheVSmJQN+UmX/N2/A52K/pG7dA3aKlYDsG1qjQBdWm52t9L1641tiqys7N54oknKtx28eN58+bRs2dPALZu3Upubi6jRo266mPdf//9yLLM/PnzycrKokWLFnz11Ve0bFnxG/qDDz7gnXfe4dVXX8VisdCzZ08++eSTCiWEPj4+fPfdd8yaNYvHH38cvV7PqFGjmD59eoXH6ty5M59//jmzZ89myZIlhIWF8eKLLzJ58uRq/T8IyuvTPpyWjfw5eS6Xn1af5NFJHa85XpIkAofeS8q3z1NwcAM+3UeKpaNu4OJVPEOTDqg0VduNoSblF5axepfjqtLkIc1dfnxBEATh+kmSiuBRjyLbrRQd3Ur6ovcImzwDY7POSkcTrkNx/H6yV8/FkuPo/6pv1Jag4Q9ec6KlvpAkiaCbH8SSk0rp+aOk/fo2kfe9jdp49ckktUri9mEtads0kPe/30tiegFPf7SZRya0Z4iCK2mE6lm72/E6tXvrUPy9lSk7tRbmUXBgLQD+YvVTvVKlCaj58+df8d81oUGDBpw8ebLScQMHDqx0nCRJPPjggzz44IPXHOfl5cXrr7/O66+/fs1xTZo0Ye7cuZVmGzBgQIXyPaF2kiSJ+0a1ZcZnW1m16xxjYprSIMT7mvfRR7bAs3Vvio7vIGf9fMKnvOSitMLVFJ929H9Sqvxu6ZYzmC02ohv60bF5sCIZBEEQhOsnqdSEjJlGhs1K0YmdpC98l7Bbn8fQpIPS0YQqspqyyF7zDUUndgKg9vIncOg9eLbpJyZJLiGptYROnE7yN89hzUsnfdH7hN/xEpL68p3xLtW+WRAfPz2I2T/uY/+pTD7+5QCH4rJ4ZGJHDB7V3uNKcCGrzc76fYkADO2h3ERs/u5lyFYzHhHN0Tdur1gOwfWqVSNWWlrKM888w969e52VRxAU1bZpID3ahGG3y8xbcbxK9wkYdBeoNJScOUDxmQPODShck7Ugl7JUxyYAxuiuLj9+camFP7Y6NiWYPLi5eJErCIJQS0kqNSHjnsTYvDuy1UzagrcpOX9U6VhCJWSbhbzti0n8cppj8klS4dtjFA0fnoNX2/7i9/IVqI0+hN36PJJOT+n5o2StmlulihY/bw9mPtSbu29pjUqCDfuSePqjTZxNvXwXccF9xJ7IIK+gDD8vD7q1DlUkg62kENO+VYCj95N4XtYv1ZqA0uv1rFu3rtpldoJQm0wd6fhFuuNwKifO5lQ6Xusfhk+3mwHIWTcP2W5zdkThKorjHeV3HuHRaLxc33tp5fazFJVaaRDiRa924S4/viAIglBzJLWW0AnPYGjWGdlSRtovb1KaVPmqfUEZJQmHSPr6aXI2fI9sKUPfsDWRD7xH4LD7UHnUzq3NXUUXHEXIuKcAiYL9azDtXVnpfQBUKolbh7bgjUf6EuCjJymjkGc+2sTqXefE+0U3tWb3OQAGdm2ARq1Mv2LT3pXI5hJ0IY0Uq1gQlFPt77pWrVpx6tQpZ2QRBLfQKMyHId0dS1K/WX60Sr9A/ftOQqX3xJxxjsILuzkIrqdk+Z3ZYuP3zY4dFCcOao5KJa7mCIIg1HaSRkvoxH9haNIB2VxK6s+zKE2JUzqWcAmrKZv0xbNJ/fFVLNkpqD19CR7zOOF3vy56c1aDZ/Nu5Ts8Z6/5plqr+ts1C2LOMwPp0ioEs9XOJ78eYPaPsRSXWpyUVrgeeQVl7DmWDihXfmc3l5C/ZzkAfn0miNVP9VC1J6CmT5/O3Llz2b17tzPyCIJbuGN4K3QaFccScth9NK3S8WqjN359JwGQs/En7JYyZ0cU/sZuNVOScAhQZgJq3Z7z5BaUEeRnYECX+rWdsyAIQl2m0noQOnkG+qi2yGXFpP30GmVpZ5SOVe/JNit5O5eS+J9pFB3bBpIKn24jaPDwJ3i3Hyje2F4H315j8eowEGQ7Gb99gDk7uer39fLglQd6cc/INqhUEhtjHSV5CSn5zgssVMvG2ERsdpkWUX6K7Vxoil2DvaQQbUA4nq17K5JBUFa1J6BeffVVioqKuOeee+jZsyejR4+u8GfMmDHOyCkILhXkZ2BMTDMAvltxDJvNXul9fLvdgsY3BFthDvm7ljk7ovA3pWePIFvKUHsHonPxFU+bzc6iDY4r4uMHNkOrUWZJsyAIguAcKq0HYbc9j0eDVthLi0j98TXMGeeUjlVvlZw7QtJ/nyFn3XfI5lI8IlsSef+7BA1/ALXeU+l4tZYkSQTf8jAekS2xlxWT/uvb2EoKq3x/lUpi0uDmvPVoX4J89SRnFvHMx5v5c8dZUZKnMFmWy3e/G9pdodVPVjP5O38HwLf3eCSVWpEcgrKq/S7Jz8+PFi1a0K1bN1q0aIGfn1+FP76+vs7IKQguN3Fwc7yNWhLTC1m7J7HS8ZJGS8CgOwHI27EYa2GekxMKl/qr/K6ry696bjmYQnpOMT6eOm7q2cilxxYEQRBcQ6UzEH77C3hENMdeUkDKDzMxZyUpHatesRbkkvH7x6R+/wqWrCRURh+CRz1GxD2z8AhronS8OkHSaAmd9CxqnyAsOSlkLP6g2v1N2zQJ5KOnB9KtdSgWq53PFh7k/e/3iZI8BcUl5XEurQCdRkX/zsqs1C88uB5bUR5qnyC828cokkFQXrX3yZw/f74zcgiC2/EyaLl1aEvmLj3Cj6tOMKBLJHrdtZ8ynm364rFrGWWpceRu+YXgW/7horT1myzLFMU5GpB7Rru2/E6WZRatPw3AmP5NK/0eEQRBEGovlYeRsCkvkfrDTMxpZ0j9/hXC734dXWCE0tHqNNluw7R3JTmbfkY2lwASPl1uwn/gFNQGb6Xj1TkaLz/CJs8gZd4LlCQcInvNtwQNf6Baj+Hr5cFL9/dkyaY4vltxnM0HkjmdlMdzd3ejWQM/5wQXrmrNhdVPvdtH4GXQuvz4ss1K3o4lAPj1Goukdn0GwT2IOhFBuIaRfRsTEmAkx1TK0s2V93uQJImAoVMBKNi/VlwZdRFzxjlspiwkjQ5943YuPfae4+mcTTVh8FAzsq+4+ioIglDXqfWehE95GV1II2xFeaT+8AqW3Mr7RQrXpzTxOMlzp5O95htkcwkeEc2JvO9tgm75PzH55EQeYU0IGTMNANPeFZhiV1f7MVQqiQmDmvP2o/0I8jOQmlXEvz7ZwortCaIkz4XKLDY273f08xqmUPPxwqNbseZnovb0xbvTEEUyCO5BTEAJwjVoNWruvrkVAIs2nCa/sPLm4oaothhb9ADZTs56sWLQFS6W3xmadESl9XDZcWVZZuE6x+qnEX2a4GXUuezYgiAIgnLURm/C73gFbVADbAU5jpKw/AylY9Up1sI8MpZ+Qsq8FzFnnEdl8CJoxMNE3PsmHhHRSserFzxb9cJ/wBQAslb9l5JzR67rcVo3CeDjpwfSo00YFqudLxYd4t35eykqESV5rrDzcCpFJRZC/A20jw5y+fFl2U7e9t8A8O0x2qWv1QX3IyagBKESMZ0b0DTSl+JSK7+uO1Wl+wQMvgskFcWn9173L2uh6i7t/+RKR89kc/xsDlqNirEXmtYLgiAI9YPa05fwO2eiDYjAasoi9fuZWE3ZSseq9WS7jfw9K0j68nEKD28EJLw7DaXhw5/g03kYkiTevriSX9+JeLbpC3Yb6Yveu+7Vfj6eOl68vwcPjGmLWiWx9WAKT324ibikvJoNLFxm7R5H+d3gblGoVK7fHbLoxC4s2cmo9J74dB3u8uML7kX8BBeESqhUEveObAPAim0JpGUXVXofXWAkPl1uAiB77TxkufJd9ITrYy3MoyzFsQrJ6OL+Twsu9H4a2j0Kfx+9S48tCIIgKE/j5U/4nTPR+IdhzUsn9YdXsBbkKh2r1ipNOkny/54je/Vc7GXF6MKaEXHvmwSPfAS1UZlt4+s7SZIIHvUYHuHNsJcUkvbrW9jLiq/7scYNiObtf/YjxN9AanYR/5qzhT+2nhEleU6SkVvMwdOZAAzp3tDlx5dlmbxtiwDw6TYClYfR5RkE9yImoAShCjq3DKFTi2CsNpnvV56o0n38+9+KpDNgToun8OhWJyesv4ovNB/3CG+GxtvfZceNT8oj9kQGKgkmDBKlAIIgCPWVxieQiDtnovENwZKTSuqPM7EV5Ssdq1axFeWTufwzUr77N+b0BFR6T4JufojI+95CH9lC6Xj1nkrrQejkGai9ArBkJZG++MNq74x3qVaNHCV5PduGYbXZ+XLxYd6ZJ0rynGH93kRkGTpEBxEW6Ony45fEx2JOT0DS6vHtPtLlxxfcj5iAEoQquufCKqhN+5OqtFxY7emLX58JAORu+AG71ezMePXWxQkopVY/9e/UQJFf6IIgCIL70PgGE37XTNTegViykhyTUMUFSsdye7LdhmnfKhK/nEbBwfUAeHcc7Ci363ozkkqtcELhIo13AKGTn0PS6CiJjyVnw/c39HheRh0v3NeDB8e2Q6OW2HYohSc/3MjpRLGCsKbY7TJrL+x+N1SB5uOyLJN7cfVT15tQG8WmAcJ1TECNHj2an3/+mZKSEmfkEQS3Fd3AjwGdGwDw3fJjVbqPb4+RqL0DsZqyMO1Z4cx49ZLdaqbkzEEAjM1dNwGVnFnI9kMpAEwa0txlxxUEQRDcl9YvlIi7ZqL28seccZ7UH1/FVlKodCy3VZoSR8q3z5P151fYSwvRhTQm4p43CB71GGpPX6XjCVegj4gmePQ/AcjfubR80vB6SZLE2JhmvPPP/oQEGEnLLubZT7awdEu8KMmrAUfPZJOeU4xRr6F3+3CXH7/0/FHKkk4iqbX49hjj8uML7qnaE1D+/v7MnDmTmJgY3nzzTc6ePeuEWILgnu66pRUatYoDpzOJPVn5bjcqrQcBA+8AIHfbImzFJmdHrFdKzx1FtpSi9gpAF9bEZcddtP40sgzd24TSOFz0pBAEQRActAERhN85E7WnL+b0BNJ+eh17aeW9I+sTW3EBmSu+JOWbGZSlxiN5GAm86QEiH3gXfYNWSscTKuHVpi9+/SYBkLnyP5QmVq01xbW0iPLn46cH0rt9OFabzNdLjvDWd3soFCV5N2TN7nMA9O8UiV6ncfnx87Y5dr7z7jjYpW0yBPdW7QmoefPmsXz5ckaOHMnChQsZMWIEDzzwABs2bHBGPkFwK2GBnozo2xhwrIKy2yu/OuPVPgZdaBPksmJyty5wcsL65dLd7yTJNbt6ZOWVsGFfIgCTB4u+FIIgCEJFuqAGhN8xE5XBm7LUOFJ/eQN7magckGU7pv1rSfzycQr2rwFkvNoPoOHDn+DbfYQot6tF/GNuw9iyJ9ispC18B0t+5RdlK+Nl0PL8Pd15aJyjJG/H4VSemL2RU+dFSd71KC61sO1QKqBM+V1pShwlCQdBUuHbe5zLjy+4r+vqARUdHc3MmTPZvHkzM2bMICUlhUcffZQhQ4Ywd+5c8vNF40Wh7rp1SAuMeg1nUvLZtD+p0vGSpCJwyFQATPtWYclJcXbEekGW5UsmoFxXfrdkUzxWm0zbpoG0bhLgsuMKgiAItYcuJIrwO15BpfeiLOkkab++id1cqnQsxZSlniHl23+TteIL7CUFaIOjCL/7NULGTEPj5ad0PKGaJElFyJhp6EKbYC82kf7r29jNNz7JKkkSY/o3493H+xMaYCQjp5jnPt3C75tFSV51bTmQjNlio2GoFy2jXL/6KG/bQsBxIV7rF+Ly4wvu64aakHt5eTF16lTmzZtHjx49SE5O5r333mPgwIG8/fbbok+UUCf5enkwabCj78/3K49jtlS+C4ihSQcMzbqA3UbOhh+cHbFeMGecw2rKQtLoMDRu75JjmorMrNp5FoDJoveTIAiCcA0eYU0In/ISkoeR0vPHSFvwNnZLmdKxXMpWUkjWn1+T/L9nKUs5jaQzEDD0Xho88B6GqLZKxxNugEqnJ2zyc6g9/TBnnCPj9znIsr1GHrt5Q38+enogfTo4SvL++/sR3vhmNwXFYkOfqipvPt49ymVVAheZM85RfGoPIJVvyCQIF93QBNT+/fuZPn06gwcP5tChQ0yZMoX58+dz22238dNPP/Hvf/+7pnIKglsZ3b8pgb56MnJLWLE9oUr3CRxyN0gqik7srJF6+fru4u53hsbtUWk9XHLM5VvPUGq20TTSly4txdUcQRAE4do8IqIdk1A6PaVnD5O+8N16sSuuLNspOLiexC8fx7TvT0DGq21/Gj48B7+eo5HUru9HI9Q8jW8woZOeBbWG4lO7yd30c409tpdBy4yp3Xl4fHs0ahW7jqbxxOyNnDiXU2PHqKsS0ws4cS4XlUpiUNeGLj9+7nZH7yfP1r3QBUa6/PiCe6v2BFRZWRkLFy5kwoQJ3HHHHezfv5+nnnqKzZs388orr9C9e3dmzJjBq6++ysaNG50QWRCUp9dpuGO4o1Hmr2tPValJoi44Cu+OgwHIXvedWEp8g1xdfldSZmXZljMATBrc3OVXkwRBEITaSR/ZgvDbX0TSelBy5gAZi95HttXd5spl6WdJmfcSmcs/w15sQhvUgPA7ZxIy7kk03qJ0va7RN2hJ8IhHAMjbtojCI1tq7LElSWJkv6a8N60/4YGeZOaWMOPTrSzZFCdeR1/Duj2O1U/dWoXi76N36bEtOakUHdsOgF+fiS49tlA7VHsCKiYmhpdeeglPT08++eQT1q5dy/3334+3t3eFcU2aNBEleEKdNqRbQxqGelNQbGHR+tNVuo9/zO1IWj1lyacoOrHDyQnrLltRPmXJjv9zY3RXlxxz1c6zFJZYiAjypE+HCJccUxAEQagb9A1bE3br80gaHcVx+0hf/CGyzap0rBplLy0ia/Vckuf+i7KkE0haPQFDptLgwfddViovKMO7w8DyRtOZyz+jNLlqr4urKrqBHx8+NYB+HSOw2WXmLj3KrP+JkrwrsdnsrN/r2CxHiebjeTuWgGzHGN0VDxfuUC3UHtWegBo6dChLlixh/vz5DB069KqrADp27MiJE6LMSKi71GoV945sA8DSzfFk5VU+4arx9sev11gActZ/j2ytu1dAnclRfiejC2uKxifQ6cezWG0s3hgPwIRBzVGrxOonQRAEoXoMjdsTOvk5R7nSyV1kLJ2DbK+8j6S7k2WZgsMbSfxyGqY9K0C249m6j6PcrtdYJLVW6YiCCwQMvANjdFdkm4X0BW9jNWXX6ON7GrQ8e3c3Hp3YAa1Gxe5jaUz7YCMnzoqSvEvtO5lBbkEZvl46urcJdemxraYsCg5tBMCvr+j9JFxZtSeg3njjDVq2bOmMLIJQ63RvE0rbpoGYrXZ+XFW1CVffXmNQe/ljzUvHFLvKyQnrpiIXl9+t35tEjqmUAB89g7s1cMkxBUEQhLrH2LQTYROfBZWGomPbyFz+Wa2ehDJnnCN1/ktkLv0EW1Ee2oAIwqa8TOiEZ1xygUhwH5JKTci4p9AGR2EryiNtwTs13nRfkiRu6dOE96fFEBHkSVZeCTM+28pvG05jt4uSPPir+fjALg3RqG+o3XO15e38HexW9I3aom/QyqXHFmoP135XCkIdI0kS945yrIJat+c859JMld5HpdPjH3M7ALlbFmArKXRqxrpGtlooSTgIgKcLyu9sdpnfNjiWko8f2AytRu30YwqCIAh1l7F5V0InPAMqNYWHN5G14ssa2z3MVexlxWSv/Zak/06nNPE4kkaH/8A7afDQbIxNOyodT1CIysNA2K0zUBm8MafFk7nsU6f0amoa6cuHTw0gplMkNrvMN8uP8fr/dpFfWL92mfy7/MIydh9NA2CYi8vvbEX5FOxfC4BfX9H7Sbi6Km1B0apVqyo33JUkiWPHjt1QKEGoTVo1CqB3+3B2HE7luz+O8fIDvSq9j3fHQeTv+QNL5nnyti0icOg9LkhaN5ScP4psLkXt5Y8uvKnTj7f9UAopWUV4G7UM79XY6ccTBEEQ6j7Plj0IGfckGYs/pODgelBrCLr5/9x+gwtZlik6tpXstd9hK8wFwNiyJ0HD7kPjG6xwOsEdaP1CCZ30L1J/eJWi49vJC26If/9ba/w4Rr2W6Xd1pX10EF8tOcze4+k8OXsj0+/qRtum9XP13cbYJGx2meiGfjQK93HpsfN3L0O2mvGIaI6hcQeXHluoXao0AfXYY4+5/S9EQVDS1BGt2XU0jT3H0jkSn0W7ZkHXHC+p1AQOmUraz7PI37sCn243o/VzbZ12bVW++110VyTJuYs4ZVlm4TrH6qfR/Zpi8BDbRguCIAg1w6t1H7DZyPj9YwpiVyOpNQQOu99tX3ObMxPJWvVfSs8dAUDjH0bQ8AcxNuuscDLB3Rii2hJ08/+RteILcjf/gjaoIV6te9f4cSRJ4ubejWnZyJ935u0hObOIf3+xjbtubsXEQc1R1aOenbIsl5ffuXz1U0kh+Xv/BByrn9z1Z5jgHqr0burxxx93dg5BqNUahHgzvGcjVu44y7fLj/HetP6V/vA1NO2EoUkHShIOkbPhB0LHP+2itLWXLMt/TUC5oP9T7MkMzqTko9epGdXf+autBEEQhPrFq11/ZJuFzOWfYdqzAkmtIWDwVLd6A2c3l5C7dSH5u5aB3Yak0eHXdyK+vcag0uiUjie4KZ/OQzFnnse05w8yl85B6x+KR5hzXks1ifBl9pMD+GLRITbGJjFvxXGOnMnm6Sld8PXycMox3U18Uj5nU01oNSpiOkW69NimfX8im0vQhURhbO6a3amF2kv0gBKEGjLlppbodWpOns9l+6HUSsdLkkTA4KmARNGxbTW+ZW1dZMlMxJqfiaTRYWji/OW9Cy6sfrq5d2O8jeJFtiAIglDzvDsOJuiWfwCQv3MpuRt/dErfnOqSZZnC49tJ/HIa+TuWgN2GsXl3GvzjI/z7TRKTT0KlAofeg6FpJ2SrmbRf38Z6oWzTGYx6LU/f0YXHb+2ETqMi9kQG0z7YyNEzNbsbn7tas/scAL3bh+PlwtesdnMJ+buXA+DXZ4LTqxOE2q9KK6D27NlDmzZt8PT0ZM+ePZWO7969+w0HE4Taxt9Hz7gB0fy85iTzVhyjZ7uwSnef8AhrgleHARQe2kjOuu8Iv/t1t7rq6W4u7n5naNwelda5V7SOJWRz9Ew2GrXEuAHNnHosQRAEoX7z6XITst1G9qr/krf9NySN1il9c6rKnJ1M9qq55Zt+aPxCCLzpATxdtPusUDdIKjUh458m5dsZWLJTSF/4LuF3veq0yUtJkripZyNaRDlK8pIyCvn351u54+ZWTB7cos6W5JktNjbtTwZgaHfXlt+Z9q/BXlKAxj8Mz9Z9XHpsoXaq0gTU3Xffza+//kqHDh24++67r/oGWZZlJEni+PHjNRpSEGqL8QOb8eeOs6RkFbFq5zlG9m1S6X0CBtxB0bHtlCYep/jUbjxb9nRB0trp0v5PznZx9dPgblEE+hqcfjxBEAShfvPtdguyzUrO2m/J3fwLklqDX58JLs1gN5eSt20ReTuXgt2KpNbi22c8fr3HOf3Cj1A3qfWehN36PMnfPE9Z8imy/viC4DHTnHrBtXG4z4WSvINs2JfE9ytPcDQ+m6fv6Iqfd937Pt55JJWiEgvB/gY6NHfdZgB2q5n8nUsB8OszHkkldooWKlelCah58+bRrFmz8n8LgnBlRr2W24e14MvFh/l59UkGdW2AUa+95n00PoH49hxN3rZF5Kz/3tFcWy2aXf+drSifsuRTgPP7PyWk5LP3eDoqCSYOinbqsQRBEAThIr+eo8FmIWfDD+Rs+AHUWsdtTibLMsUnd5O95n9YTVkAGJp1JuimB9AGhDv9+ELdpg2IIGTC06T9NIvCI5vRBUfh12e8U49p8NDw1JQudIgO4ovfDrP/VCZPzN7A9Lu60b6SzYJqm4vNxwd3a4jahau8Cg9uwFaYi9onCO/2A1x2XKF2q9K73B49elzx34IgXG5478b8vuUMqVlF/L4pninDW1V6H7/e4zDtX4MlJwXT/jX4drvFBUlrl+L4WEBGF9oEjY9zt9dduN6x+qlPhwgigr2ceixBEARBuJRfnwnINiu5m38hZ+23SGqNU18XWHJSyVo9l5L4/QBofIMJHHY/xhbdRVsAocYYm3Qk8Kb7yV71X3I2/IA2qAGeLZzbtkWSJIb2aETzCyV5iemFvPjFNqYMb8XkIS1cOlnjLBm5xRw4nQm4tvxOtlnJ27EEAL9eY5HU177gLggXiS5hglDDNGoVU0e0BuC3jXHkFpRWeh+Vh5GAmNsAyN3yK/bSIqdmrI2KT+8DcPruGqlZRWw94KijnzS4uVOPJQiCIAhX4tdvMn59JwKQveq/mGJX1/gx7JYycjb9ROJXTzomn9Qa/PpOpME/PsazZQ8x+STUON9ut+DTZTggk/H7R5gzzrnkuI3CfJj9xACGdG+IXYYf/jzBzK92VOk1urvbsDcRWYb2zYIIC/R02XELj23Fmp+B2tMX705DXHZcofa7rjqfvLw8li9fTnx8PKWlFZ+4kiTx5ptv1kg4Qait+naIoEWUH6fO5/Hz6pM8MrFjpffx7jSU/D1/YMlOIW/HEgIG3emCpLWDbLNQfOYAAMbmzr1a9tvGOOwydGkVQrMGfk49liAIgiBciSRJ+A+YgmyzkL9zKVkrv0JSa/DuOLhGHr/o1B6yV/8Pa34GAIYmHQkc/iC6wIgaeXxBuJrAm+7HnJ1M6bkjpP36NpH3vY3a09fpx9V7aHjy9i60bxbEF78d4sDpTJ74YCPP3NmVji7sm1ST7HaZtXsc5XdDe7hw9ZNsJ2/7YgB8e4wS/eGEaqn2BFRKSgqTJk2ipKSE0tJS/P39yc/Px2az4evri5eXKFcRBEmSuHdUW/79+TZW7TzH2JhmlZZySWoNAYOnkr7gbfJ3L8en63A0PnWrRv16lZw7hmwuQe3ph0d4U6cdJ8dUWl5HP1msfhIEQRAUJEkSAYOnItusmPasIHP550hqLV7t+l/3Y1ry0sleNZfiOMeqYrV3IIE33Ydny15ixZPgEpJaQ+iE6SR/OwNrbhrpi94j/M5XXFbCNaR7FM0b+vHO/L2cTyvgpf9sZ8qwltw6rGWtK8k7mpBNWnYxBg8NfTq4rldb0cldWLKSUOk98el6s8uOK9QN1S7B++CDD4iOjmb79u3IsszXX3/N/v37eemll9DpdHz11VfOyCkItU77ZkF0ax2KzS4zb0XVdoY0Nu+GPqotstVMzsafnJyw9iiO+2v3O0lyXuXw75visdrstG4cQNumzu0zJQiCIAiVkSSJwGH3493lJkAmY+kcCo9vr/bj2K1mcrf8StJ/nnRMPqnU+PYeR8OHP8arVW8x+SS4lNroTditzyN5GClNPE7Wyq+QZdllx48K8+GDJ2IY1iMKWYYfV5/k5f9sJ9dUu0ryLl407d8pEr3ONRsYybJM3tZFAPh0uwWVh9ElxxXqjmq/k9u/fz9TpkzBw8Ox1E6WZXQ6HXfeeSeTJk3i3XffrfGQglBb3TOyDZIE2w6lcPJcTqXjJUkicMhUAAoPb6Is7YyzI7o9WZYpPn1hAsqJu98VFptZuSMBgElDmosX44IgCIJbkCSJoJsfcpTfyXYylnxE0cldVb5/cVwsSV89Re7mX5CtZvSN29PgodkEDr4blc7gxOSCcHW6oAaEjnsKJBUFB9dj2vOHS4+v12mYdltnnprSBb1OzaG4LKZ9sJEDpzJcmuN6FZda2HYoBYBhLiy/K4nfjzk9AUmrx7f7KJcdV6g7qj0BlZ2dTXBwMCqVCrVaTWFhYfnnevTowb59+2o0oCDUZo3DfRjcrSEA3yw/VqWrOx4R0Xi17Q/IZK+b59IrQu7IkpWINS8DSa3F0KSD047zx7YESspsNA73oXvrUKcdRxAEQRCqS5JUBI14GK/2A8BuI/232eWbc1yNJT+DtAXvkPbLG1hz01B7BRAy/mnC73gFXVADFyUXhKszRnch4MKF1+y131F8YSdGVxrcrSGznxxAozBv8grLePmrHXz/53Fsdvd+/b31YAplZhuRwV60bOTvkmPKskzutgurn7rchNro7ZLjCnVLtSegAgMDyc/PByAyMpIjR46Ufy4pKQm1Wl1z6QShDrhzeGt0GhVHz2Sz53h6le7jP/AOUGsoPXuYkvhYJyd0bxdfYOsbt0Ol0zvlGKVmK0u3OFabTRwsVj8JgiAI7kdSqQke9RiebfqC3Ur6ovfKN+i4lGy1kLttEUlfPkHxqd0gqfDtOYaGD8/Bq01f8TtOcCu+PUaVr+5LXzwbc1aSyzM0DPXmgycHMLxXI2QZfllzihe/3EZ2fonLs1TVxfK7YT2iXPacLk08RlnSCVBr8O05xiXHFOqeak9AderUiePHHf1shg0bxmeffcbnn3/O119/zQcffECvXr1qPKQg1GbB/gZG93c0zv7uj2NVuqKi9QvBt/tIAMcqKLvNqRndWdGF8jtPJ5bfrd51DlORmbBAI/07ih2ABEEQBPckqdSEjJmGsWVPZJuF9AXvUJZ4rPzzxWcOkPT10+Ru/NFRbhfVhgYPfkDg0HtQeYhyO8H9OEpM/w99w9bIZcWk/foWtpICl+fw0Kr55+ROPHNnVwweao7EZ/PE7I3EnnS/krykjAKOn81BpZIYdKHSwhXyLqx+8u44GI23a1ZdCXVPtSeg7r//fpo3d+wO9dhjj9GzZ08++eQTPvjgA5o3b84LL7xQ4yEFobabNLg5XgYt59MKWH9hu9TK+PWdiMrghSUriYKD652c0D3Zik2UJZ8CHA3IncFitbN4YzwAEwY1R612XpNzQRAEQbhRklpD6PinMEZ3RbaayVvyAdq04+Qun0PaT69jyUlB7elH8NgnCL/rNXQhrusPIwjXQ9JoCZ34LzS+wY6d8X77ANlmVSTLwC4N+PCpgTSJ8CG/0MzMr3cwf+VxbDa7Inmu5OLqp66tQgjwcU51wN+VpsRRcuYgSCr8eo9zyTGFuqna77TatWvH8OHDATAajXz55Zfs2bOHvXv3Mn/+fEJCQmo8pCDUdl5GHbcObQHAD6tOUGqu/JeqWu+Jf7/JAORu+hm72X2XATtLcXwsyHZ0IY3R+AY75RibYpPIyivB39uDIS68iiQIgiAI10tSO96wG5p2Rraa8TqwmLLTe0BS4dN9JA0fnoN3uxhRbifUGmpPX8fOeDo9pWcPk73mG8WyRAZ78d60GG7u3RhZhl/XnuKFL7e7RUmezWZnw75EAIZ2d93k8sXVT17tYtD6iV6pwvWrkUv9Xl5eeHl5Xdd9z507x8svv8zYsWNp06YNo0ZduZt+aWkps2fPZtCgQbRr147Bgwfz6aefVhgzePBgWrZsedmfsrKyCuMKCwt5+eWX6dmzJ507d+bhhx8mOTn5smMmJCTwwAMP0KlTJ3r37s2sWbMoLb18e85NmzYxbtw42rdvz7Bhw/jhhx+u6/9CqNtG9m1CsL+B7PxSlm9NqNJ9fLoOR+Mfhq0oj7ydS52c0P04e/c7u11m0YbTAIwb0AydVvSwEwRBEGoHSaMldNK/0DVsC4A2ogWRD7xH0E33o9J7KpxOEKpPF9KIkLFPAhKmfX+Sv/dPxbJ4aNU8Nqkj/7rLUZJ39Ew20z7YyL4TVevn6iyxJzPIMZXh46mje5swlxzTnHHe0U8OCb8+411yTKHu0lRncE5ODj///DN79+4lI8NRDxsSEkLPnj259dZb8fevfi3o6dOn2bRpEx07dsRut19xxy+bzcY//vEP0tLSmDZtGpGRkaSkpJCamnrZ2OHDh3P//fdXuE2n01X4+JlnnuHo0aO89NJLeHl5MWfOHO677z6WLl2KXu9YxmgymbjnnnuIiIhgzpw55OTk8NZbb5GXl8f7779f/lj79+/n0UcfZezYscyYMYPY2FhmzZqFTqdj8uTJ1f7/EOounVbNXTe35sOfYlm47hQ39WyEj6fumveR1FoCBt1Fxm/vk7/zd3w6D0PjHeCixMqSbRaK4w8AYGzunPK7nUdSScooxNOg5ebejZ1yDEEQBEFwFpXWA/8J/+LU3q206N4fD8/ruyAsCO7Cs0V3AgbdQc6GH8hePRddUCSGxu0VyxPTuQHRDfx4Z95ezqTkM/PrnUwa3Jy7bm6lSNuGtRdaeQzs2gCtxjXHz9v+GwCerXqJHTSFG1blCagdO3bw+OOPU1hYiFqtxt/fH1mWSUhIYPv27fzvf//j008/pXv37tUKMHjwYIYOHQrAjBkzKuyqd9HChQs5duwYK1euJCgo6JqPFxQURKdOna76+YMHD7Jx40a++uorBgwYAECLFi0YNmwYixcvZsqUKQD8/PPPmEwmlixZQkCA4w2/Wq1m+vTpPPLIIzRr1gyAzz77jDZt2vDmm28C0KtXL1JTU/n444+ZOHEiKpXoJyP8ZWCXBizZFEdCiokF607xwJh2ld7Hs1UvPCJbUpZ8ktzNvxA88hEXJFVe6fnjyOYS1J5+eERE1/jjy7LMgvWO1U+j+jbBqNfW+DEEQRAEwdkklRq7dwiSJF5zCnWDb+/xmDMTKTyymfRF7xN531toA5TbJCYi2Iv3pvVn7tIjrNh+loXrT3MsIZt/3dWNID/XNffPLyxj99E0AIb1aOSSY1py0yg8tg0Av74TXHJMoW6r0m+qnJwcnnzySby9vfnoo4/Yu3cvW7duZdu2bezdu5fZs2djMBiYNm0aubm51QtQhQmahQsXcsstt1Q6+VQVmzZtwsfHh5iYmPLbIiIi6NKlC5s2bSq/bfPmzfTu3bt88gkcq6t0Ol35OLPZzM6dOxk5cmSFY4wePZrMzEyOHTuGIFxKpZK4d6RjqfzyrQmk5xRXeh9Jkggceg8ABQfXY84459SM7uLi7nfG6C5OeVF94FQmcYl56LTq8l0KBUEQBEEQBGVJkkTQyEfwiGiOvbTQsTNeaZGimXRaNY9M7Mizd3fD4KHhWEIO0z7YyN7jrivJ2xSbhNUmE93Al8bhPi45Zt72xSDbMTTrjEeYeL0s3LgqrYBauHAhdrudn376ibCwirWmBoOBESNG0KlTJ8aOHcvChQt56KGHaiyg2Wzm2LFjDBo0iH/961+sXr0atVrNoEGDePHFFy8r+1u2bBm//vorWq2Wbt26MX36dFq2bFn++fj4eJo0aXJZU8bo6Gi2bt1aYdzEiRMrjNHpdERFRREf79gx6/z581gsFpo2bXrZY118jHbtKl/hciWyLFNcXPnkhLsqKSmp8Lfwl5YNPWnXNIAjZ3L4bvkR/jmpCt8jAQ3xaN6dstN7yFjzLQHj/+X8oNVQ0+dblmWKTu0BQB3V3inPhV/WnABgSNcItCpbrX6+1QTxnK1fxPmu28T5rV/E+a776us59hk1jewfX8GSnULqwvfwH/cMkkrZfp1dW/jz9iM9+ejXQySkFPDqf3cyul8jbh8ajaaGSvKudL5lWWbVzrMAxHQKd8nrVltBDgWHNgBg6Dqq3r9Wdpa68PyWZbnKm15UaQJq69atTJw48bLJp0tFREQwYcIEtmzZUqMTUHl5eVitVr7++mt69uzJZ599RmZmJu+++y5PP/0033zz1w4JgwcPpkOHDkRERJCYmMiXX37JHXfcwZIlS2jY0LG7lclkwtvb+7Lj+Pj4kJ+fX/6xyWTCx+fymeVLx138++/jLn586eNVl8Vi4fjx49d9f3dx9uxZpSO4pT4tNBw5A1sOptImwka4/7V7QQGowrriE7cP89lDnN6yAmtQExckNYv4qQAAeBxJREFUrZ6aOt+qwix88zOQJTUJJVqo4edCUlYZRxNyUUnQMrRuPNdqinjO1i/ifNdt4vzWL+J813318Ryr24/De9c8zOcOc27J55S0Hqp0JADu7O/D6v12dp8qYtnWc+w/kcqkvgH4eVarxfI1XXq+U3LMnE8vRK2CYL3JJa9dDcfXoLfbsPhHEW+ygUm8Xnam2v78/nvf7aup0jPkzJkz3H333ZWO69atG3/88UeVDlxVF5uS+/j4MGfOnPIvzNPTk8cff5xDhw7RoUMHAF588cUKWfr27cstt9zC3LlzmTlzZvnnrjY7V5VZuyvN7t3I412NVqstX0lVG5WUlHD27FkaN26MweC62ujaojVwJPkQ2w+ns+O0jRfuaV2l+5kKEyjevwr/c9sI7Hszkpv0GKvp8124ZzmFgEejtrRu3/HGA/7N8h8OANC/UwS9u7Wt8cevjcRztn4R57tuE+e3fhHnu+6r3+e4NaUBRvL++BT9ud2EtGiPsd1ApUMB0L4d7DqazheLj5GUZea/q7N5dEJburYKvqHHvdL53rHcsXK/R5tQunR0/mtXW3E+mWsPAhAy6HYaNqraexWh+urC8zsuLq7KY6s0AWUymSr0QrqagIAATCZTlQ9eFRdXE3Xp0qXCrFqvXr0Axy56Fyeg/i4kJISuXbty9OjRCo93pd3z/r7iycfH54pfS0FBQXkDcl9fX+DylU4X73elFVRVJUkSRqPxuu/vLgwGQ534OpzhvtHt2X0sg0Nx2ZxKKqRTi5BK7+MxcAqJx7ZgzTyP/cwevDsMckHSqqup85137hAA3i171Pj3z7k0E3tPZCJJcNuwVuL782/Ec7Z+Eee7bhPnt34R57vuq6/n2NhpEJIpk9wtv2Ba9x2eYY0wRLnHBcRB3ZvQumkI78zfS1xiHu/+cIBxA5pxz8g2N1ySd/F8my02th1yNB+/uU9Tl3wP5OxaDFYzHuHR+LXqcUMLK4Sqqc3P7+p8f1TpWWE2m9FqK98hSqPRYLFYqnzwqjAYDERGRl7185U1Mb+4guqiZs2akZCQcNntcXFx5RNLF8dd7PV0kdls5vz58+XjoqKi0Gq1nDlz5rLHuvgYgnA1YYGe3NLHUUb3zfJj2O1yJfcAtdEbv36TAMjZ+CN2S5lTMyrBVlxAadJJAIzNu9b44y+6sPNdr3bhNAy9vBxXEARBEARBcC9+/Sfh2bo32K2kL3ofS57rmn9XJizQk3f/2Y8xFza1WbIpnhmfbiWjCpsNVcWuo2kUllgI8tXTsfmNra6qCltpEfn7/gTAr+9EMfkk1KgqT8ueOXOGo0ePXvPP3ydiasrAgQPZt28fZrO5/Lbt27cD0KpVq6veLz09ndjYWNq3b19+24ABAzCZTGzZsqX8ttTUVGJjYxkwYED5bTExMezcubPCrn5r1qzBbDaXj9PpdPTq1YuVK1dWOO7y5csJDg6mTZs21/kVC/XFbUNbYPDQcCY5n80Hkqt0H59ut6DxDcZWkEP+rmVOTuh6xfGxINvRhTRC61v5qrDqSM8pZtN+x//z5CHNa/SxBUEQBEEQBOeQJBXBox9HF9YUe7GJtF/fxl7mPk2btRo1D41rz7/v7Y6nXsPJ87lMm72RnUcur7yprrW7zwMwpHsUapXzJ4NMe1cilxWjDY7C2KKb048n1C9V7pL2/PPPVzqmOt3PLyopKWHTpk0AJCcnU1hYyJ9/OmZce/ToQUBAAA888ABLly7lscce46677iIjI4MPPviAoUOH0rq1ox51+fLlbNy4kZiYGEJCQkhMTOSrr75CrVZz3333lR+vY8eODBw4kBdeeIEZM2bg5eXFxx9/TGRkJOPHjy8fd/vtt/P999/z6KOP8uijj5Kdnc3bb7/N6NGjK6xsupjpxRdfZPTo0cTGxrJgwQJee+21SldnCYKvlwcTB0fz/coTzF95nL4dwtFqrr27h0qjI2DQnWQs+Yi8HYvx7jQUjZefawK7QHHcPgCM0TW/+mnxxjjsdplOzYNp3tC/8jsIgiAIgiAIbkGl9SBs8gyS//cslszzZPz+EaGTnlV8Z7xL9W4fQZMIX96dv5fTiXm88c1uxsY4SvK0muq/N8zMLWH/qQzAMQHlbHZzKfm7lwPg32cCkiTezwo1q0oTUG+99ZbTAmRnZ/PEE09UuO3ix/PmzaNnz55ERkby7bff8vbbb/P4449jMBgYPnw4zz77bPl9GjRoQHp6Om+++SYFBQV4e3vTq1cvpk2bVr4D3kUffPAB77zzDq+++ioWi4WePXvyySefoNfry8f4+Pjw3XffMWvWLB5//HH0ej2jRo1i+vTpFR6rc+fOfP7558yePZslS5YQFhbGiy++yOTJk2v6v0qoo8b2b8aKbQlk5BSzYvtZxsZUXrrp2aYvHruWUZYaT96WXwm65f9ckNT5ZJuVkvj9ABhbdK/Rx84tKGXNrnMATBKrnwRBEARBEGodjU8goZOfI3X+yxSf3kvOxh8JHFz5ZlmuFBboyTv/7M93fxzj983x/L45nuNns/nXXd0IC/Ss1mOt33ceWYZ2zQIJD6refa+Haf8a7CUFaPzD8GzTx+nHE+qfKk1AXboyqKY1aNCAkydPVjquXbt2fP/991f9fKdOnZg/f36Vjunl5cXrr7/O66+/fs1x/9/efYc3WbZ9HP8m3aWLssqGFiiUDQXKhgKyFRAQRIaiCKgoij7o614MRZShiPIo4kYEBQFBGSoyVPZQoKwyCmV0rzTJ+0dtHkoLLbQlNPl9joNDc+e67vvMfXblzDVq1qzJggUL8j1fx44dc0zfE7kenh6u3N29LnMW7+KrtQfp2qIapbyuveaawWAksMtIznz6PAk71uLXohfuZavcpIiLT1r0ASzpKRi9/fCoVLS7QC7/9QgZmRbqVAugUa2yRXpuEREREbk5PCvXoVyfhzj33dvEb16Ge7mq+DbsZO+wcnBzNXL/HQ1oEFKGt7/cwcETcTz21gYeHdKU1g0rFegcVquVn7dFA9D1Jox+smaaiN/yPQABrfvfUiPLxHFoTJ3ILaBri2pUKe9DYkoGS9YfKlAfr+r1s0YJWS1cXHf14mxJknLoTyBr+l1RDvlNTjXxw6ajAAyMrKPFFEVERERKMJ8G7QloMwCA2B/eI+3UQTtHlLeIBhWZ9XgnQquVJjktk9c//oP5y/ZgyjTn2/fA8TjOXEjGy8OFto0KVrQqjMTd6zEnXcTFtwy+jTS4QoqHClAitwAXFyMje2ctWv/dL0e4EF+wRRUDI4eDwUjKoT9IPb6vOEMsdlarleR/C1Clahftgocrfz9KSlomVSv40qp+UJGeW0RERERuvtKdhmZ9GGvO5OziaWQmnLd3SHkqH+jNlIfa0a9j1jIby389wlOzfyXmQvI1+23YfhqAdo0r4+lR4KWbb4jVYiZu81IAAlrfgcHl2rMxRG6UClAit4hW9YOoVyOQDJOZz3/Mf1oqgHuZyvg1uw2Aiz8vxGq1FGeIxcp04RSZl2LAxRWvmo2L7LzpJjPf/5K1Q+fAyNoYb8LuISIiIiJSvAwGI+XveBT38tUxJ8dl7YyXkWbvsPLk5mpk9O0NeG50K3y93Th8Mp5H39rApt2n82yfbrKwZW8MAN1aVi/2+JL2/UZm3DmM3n74Nula7NcT56UClMgtwmAwcG+f+gD8tO04J2ISCtSvdPvBGNy9SD8TRfK+TcUZYrHK3v3Oq3p9jB5eRXben7adIC4pnfKlvejQtHKRnVdERERE7Mvo7kWFwZMxevuRcfYosctn39IfyLYMC+LtxztRt3ppUtIymbrwD97/dneuKXmn4w14uLtSuZwPdWsU787NVquFuN+/BcC/ZV+Mbh7Fej1xbipAidxC6tUMJKJBEBYrfLLyQIH6uJTyJ6BN1kYBFzd8hiUzozhDLDb/W/+p6Kbfmc0Wvt1wGIABnWrh6qIfeSIiIiKOxM2/PEEDnwKjK8l/b+HSL1/bO6RrKl86a0renZ2zNtxZsekoT87+lXOXkknLyMTN3ZMOLcJY8H/deHpUi2JfuzTln22Yzp/E6OGNf/PuxXotEb0bE7nFjOgVhtFoYOu+GPYduVCgPv4t++DiW4bM+FgS/lhZzBEWPXNqImnRfwPgXYTrP/2y8xTnLqYQ4ONB11bFP3xZRERERG4+z6r1KNfrQQDifltM0v5be1aAq4uRUX3q88L9Efh6u5OeYcbDzZUl6w4x/MXVjH5tLaNeWcOmXafJMOW/YPmNslqtXNq0BAC/8F4YPUsV27VEQAUokVtO1Qq+dGuZtdXqRyv2YbVa8+1jdPMgsNNQAOI2LcGcklisMRa11KidYLXgVq4abgHli+ScFouVb9Zl7Sh4e4dgPNy0layIiIiIo/JtHIl/q9sBiF0+h/TTh+0cUf7C61Vg1hOdGH9nY5b/eoQv1x4kOdUEZO3i/MWaf/hm3SHSMjKL5fqpR3aSEXMEg5sH/i17F8s1RC6nApTILeju7nXxcHfhn+OX2LznTIH6+DTogHuFmljSU7j02+JijrBoJR/6Ayja3e+27Y/hREwi3p6u9GpTs8jOKyIiIiK3psDIe/AKaYo1M4OYxdPITLxo75DyVTbAi7o1Almx6Wiez3//6xFcjMXztj0ue/RTs9tw8fYrlmuIXE4FKJFbUKCfJ/06ZG3V+snK/WSa819M0WB0IbDLcAAS/lqN6WLBClf2ZjVnkhq1AwDv2s2L5pxWK9/8nDX6qVebmpTy0layIiIiIo7OYHShQr+JuJWtgjnpImcXT8NiSrd3WPlKTjPZRj7lei7VREpa3s8VRuqJ/aRFHwAXV9vIMZHipgKUyC1qQOda+JVy51RsMmu3Hi9QH++ajfEKaQoWMxfXf1bMERaNtJN/Y0lPwejth0el2kVyzj1R5/nnxCXcXY3c3iG4SM4pIiIiIrc+o2cpggY/jdHLh/Qzh4n94d0CLWlhT6U83a76gWkpLze8PYv+w9Ts0U++jSJx9Q0s8vOL5EUFKJFblLenG0O6hQLw+Zp/SE0v2NzvMpEjwGAk+e/NpJ38uzhDLBL/2/2uGQZj0azTtPjf0U9dW1ajtK9nkZxTREREREoGt9JBVBgwCYwuJO/7jbjfv7V3SNdktli4vX3eH5re3j4YsyX/2RDXI/30YVKP7ASDkYA2/Yr03CLXogKUyC2sR+saVCxTirjEdL77JapAfdzLV8O3cSQAF3765Jb/xOd/BaiiWf/pcHQcOw/GYjQaGNC5aEZUiYiIiEjJ4lWjIWW73w/ApQ2fk/z3VjtHdHWe7q4MjKzN0NtCbSOhSnm5MfS2UAZG1sbT3bVIr3fp34KcT4P2uAVUKNJzi1yLClAitzA3VyPDe9YD4Nv1h4hLLNgc9tIdhmBw8yD91D8k/72lOEMslIwLp7PWqjK64h3cuEjOuXjdQQA6NK1MhUDvIjmniIiIiJQ8fs1uwy+8JwDnvp9F+tlj9g3oGtzdXBjQuRaLXuzBJy/cxqIXezCgcy3ci3gn54zYE6T8sxUwENBmQJGeWyQ/KkCJ3OLaNq5EraoBpKab+WrtPwXq4+pbGv+IOwC4uP5TrOaiX7iwKGSPfvKqXh+jR+GLRdFnE227Bg6M1OgnEREREWdXptu9eNVshNWUxtmvp5CZFGfvkK7K090VU0Yap6OPYMpIK/KRTwBxvy8FoFTdVriXrVLk5xe5FhWgRG5xRqOBe/uEAbBq8zFOn08qUL+AiNtxKRVA5qUYEv76sThDvGEph/+dfldEu999u/4wViu0qh9E9SBtJSsiIiLi7AxGF8r3fwK3wIpkJpzn7JLpWDNvzQ9ns6WlpRXLeU2XYkja9xsAAW3uLJZriFyLClAiJUCjWuVoVrc8ZouVRSsPFKiP0d2L0h2HAnDpt8WY05KLM8TrZk5NIu1E1mvxrl349Z9iL6Wy/q9oAAZ20egnEREREcni4uVDhcFPY/TwJv3kP8Suev+WXye1OMRtXgZWC17BTfGoqJ2i5eZTAUqkhBjVOwyDAX7bdZqDJy4VqI9v4864lauKJTXJttXqrSL1yA6wWnArV7VIFj9ctvEwZouVRrXKUre6tpIVERERkf9xL1OZ8gOeAIORpN3rid+63N4h3VSZCRdI3L0egNLtNPpJ7EMFKJESomYlfzo3rwrAxyv2F+hTG4PRhTKRIwCI/+MHTHHnijXG65Fy6C8AvGsVfvpdfFI6P249DmjtJxERERHJm3dwE8p0GwXAxZ8/IeXwX/YN6CaK2/o9mDPxrBaGZ9V69g5HnJQKUCIlyLAedXFzNbIn6jx//V2wYpJXSFO8ajQEcyYXN3xWzBEWjNViJiVqBwClarco9PmW/3aE9Awztar406ROuUKfT0REREQck194L3ybdAWsnF06k4zYE/YOqdiZk+NJ3L4GgIC2Gv0k9qMClEgJUr60N33aZc3X/njFPsyWAoyCMhgI7DISMJC87zfSTh8u5ijzlxb9N5a0JIxevnhULtyIpZQ0Eyt+OwrAwC51MBgMRRGiiIiIiDggg8FA2R7341mtPtaMVGK+noo5JcHeYRWr+G0rsGZm4FExBK+aje0djjgxFaBESphBXWpTysuN4zGJrP8zukB9PIJq4tOoIwAXf15o90UXbbvf1WqGwehSqHOt3nyc5FQTlcv50LpBxaIIT0REREQcmMHFjQp3TsI1oDyZcWc5u+RNrOZbe2e8G2VJSyb+r9VA1ugnfVgr9qQClEgJ4+vtzuB/d3n7bPUB0k3mAvUL7Hg3Bld30k7sJ+XQn8UZYr6yr1/Y3e9MmWa++yVrRNfAyFoYjfqFKiIiIiL5c/H2I2jw0xjcPUk7sY/zPy6w+4e0xSH+r9VY01NwK1cV7zqFX/pCpDBUgBIpgfq0C6ZsgBfn49P44bcjBerj6lcG/5Z9ALi47hOs5sziDPGqTBdPY7pwGowueBdyCPDPf0RzMSGdsv6edGxWtYgiFBERERFn4F6uGuX7TQQMJO5YS8Kfq+wdUpGyZKQRv20FAAFtBmAw6O2/2Je+AkVKIHc3F+7pUReAr38+RGJKRoH6BbTpj9HbD9OF0yTs+Kk4Q7yq5H93v/OqFobRs9QNn8dstrBk/SEA+neqhZurfpyJiIiIyPUpVTucwMh7ALiw9iNSjuyyc0RFJ3HnT1hSEnANqIBPWFt7hyOiApRISdWpeVVqVPQjOdXE4p8PFaiP0cOb0u3vAuDSr19hSU8pzhDzVFTT737bdZqYCyn4ertzW6vqRRGaiIiIiDgh/4g78GnUCawWzn37JhkXTtk7pEKzZpqI2/wdkPUhdGHXXRUpCipAiZRQLkYDI3uHAbDityOcu1SwYpJf0664BVbCkpJA3O9LizPEXMxpyaRFHwAKV4CyWq18sy6r6HZ7h2A8PVyLJD4RERERcT4Gg4FyPcfiUTkUS3oKZ7+eijk1yd5hFUri7vWYky7i4huIb8NO9g5HBFABSqREa163PI1qlcWUaeGz1X8XqI/BxZXAyOFA1pasmQkXijPEHFKP7ASLGbeyVXArHXTD5/nzwFmOnUnAy8OFPm1rFl2AIiIiIuKUDK5uVBj4FC5+ZTFdPM25pW9htRRss59bjdViJm7zMgACIu7A4Opm34BE/qUClEgJZjD8bxTU+r+iOXo6vkD9vOu0wLNaGNbMDC5u/Lw4Q8yhqKbfZU857NG6Jj7e7oWOS0RERETE1SeAoEGTMbh5kHp0Fxd++tjeId2QpP2byIw7i9HbD9+m3ewdjoiNClAiJVydaqVp17gSVit8/MP+AvUxGAwEdhkJQNLujaTHHC3OEIGsT2JSDm8HwLtW8xs+z74jFzhw7CKuLkbu6BBcVOGJiIiIiOARVJPyt08AIOGPlSRsX2PniK6P1WohbtMSAPxb9sHo5mHniET+RwUoEQcwvFc9XIwGtv99jl2HYgvUx7NSLUrVbwdYufjzQqxWa7HGmHbyHyxpSRi9fPCsEnrD51n880EAurSoShl/r6IKT0REREQEgFJ1IyjdcSgA53/8kNTje+0cUcGl/PMHpvMnMXp449+8h73DEclBBSgRB1CprA89W9cA4OMV+7BYClZMCuw0DFxcST22h9SoHcUY4WXT70Ka3fAuHEdOxfPX3+cwGuDOzrWLMjwREREREZuAtndSKqwtWMycXfIGpksx9g4pX1arlUv/jn7yC++J0bOUnSMSyUkFKBEHcVe3ULw8XDh8Mp7fdhVs61i3gPL4t+gFwIV1nxTrQosph/8CCrf+U/bOd+0aV6ZiWf1CFREREZHiYTAYKNfnITwqhmBJTSLm6ylY0gu267S9pB7ZSUZMFAY3D/xb9rF3OCK5qAAl4iACfD0Y8O+ooEWrDmDKtBSsX5s7MXr5YIqNJnHX+mKJzXQpBtP5k2B0wTu4yQ2d4/T5JDb9W1gb2EWjn0RERESkeBndPKgwaDIuPoGYzp/k3LK3b+md8eJ+/xYAv6bdcPH2s3M0IrmpACXiQPp1CKG0rwcxF1JYvflYgfq4ePlQut0gAC798iWWjNQijyt7+p1ntbAbHgr87frDWKwQXq8CNSv5F2V4IiIiIiJ5cvUNpMKg/2BwdSfl8F9cXP+pvUPKU1r0AdJO7AcXV/xb3W7vcETypAKUiAPx9HBlaPe6AHy59h9S0kwF6ufXvDuupYMwJ10ifsvyIo/Ltv7TDe5+dyE+lZ//iAZgkEY/iYiIiMhN5FmpFuX6PgxA/JbvSdy1zs4R5Xbpt6y1n3wbdcbVr4ydoxHJmwpQIg7mtpbVqFzOh4TkDL5df7hAfQwubgR2HgZA3JZlZCZeKrJ4LGnJpJ7YD0CpG1z/adnGKDLNFuoHlyGspn6hioiIiMjN5RPWloB2AwGIXfU+adF/2zmi/0k/E0XqkR1gMBLQup+9wxG5KhWgRByMi4uRkb3rAbDslyguJqQVqF+puq3xqFwHqymdS798WWTxpBzZCRYzbmUq4xZY8br7J6Zk2KYTDozU6CcRERERsY/SHe7CO7QVmDM5u2Q6pvhz9g4JwLbznU/9driVDrJzNCJXpwKUiAOKaFCRejUCSc8w8/mPBft0xmAwUKbrSAASd60jI/ZEkcTyv93vbmz63YrfjpKWYSa4kj/N65YvkphERERERK6XwWCk/O0TcC9fA3NyPGe/nlos66dej4zYaFL+2QpAQJsBdo1FJD8qQIk4IIPBwMjeYQCs3XaC6LOJBernWaUupepGgNXChZ8XFToOq8VMyuHtAHjfwPS7tPRMlv96BMga/WQwGAodk4iIiIjIjTK6exI0eDIupQLIOHecc9/Nwmot2O7TxSFu81IAvENb4V6uqt3iECkIFaBEHFT94DK0qh+ExWLlk5X7C9wvsPM9YHQhNWo7KUd3FSqG9FMHsaQmYvT0wbNK3evu/+PW4ySmZFCxbCnaNK5UqFhERERERIqCq385Kgx8ClxcSTm4jUsbi275iuthuhRD0t5fASjd9k67xCByPVSAEnFgI3rVw2iALXtj2H/0QoH6uAVWxK95dwAu/ryoUJ/oJGfvfhfSFIPR5br6mjLNLN2QtYj6nZ1r4WLU6CcRERERuTV4VgmlXK9xAMRtWmIrBN1McZu/A6sFr+AmeFQMuenXF7ledi9AHT9+nOeff5477riDsLAw+vTpk2e7tLQ03nrrLTp37kyDBg2IjIxkzpw5udotWLCAyMhIGjZsyJ133snWrVtztUlKSuL555+nVatWNG3alLFjx3Lq1Klc7Y4ePcro0aNp0qQJrVu35tVXXyUtLfeCzhs3bqRfv340bNiQbt268dlnn93AnRApetWC/OjasjoAH6/Yj9VqLVC/0u0GYfTwJuPsUZL2/HLD10/JLkDdwPS79X+d5EJ8GoF+nkSGazixiIiIiNxafBt1wv/fXediV8wl7dShm3btzIQLJO5eB0CARj9JCWH3AtShQ4fYuHEj1atXJyQk76qt2WzmwQcf5Mcff2TChAn897//ZcKECbi45BxRsWDBAmbOnMmwYcOYP38+1atX54EHHuCff/7J0e6JJ55g3bp1PPfcc8ycOZNz585x77335iguJSQkMHLkSJKTk5k1axb/+c9/WL58Oc8++2yOc+3YsYPx48cTFhbGBx98QP/+/Xn11VdZvHhxEd0hkcK5u3so7m4uHDh2kS17YwrUx8Xbz/aL7OKGz7GY0q/7uqZLMZjOnwSDEa/gJtfV12yxsmRd1i/wfh1DcHO9vtFTIiIiIiI3Q2Cnu/Gu1Ryr2cTZb6aRmVCwWQeFFb/1ezBn4lm1Hl7Vwm7KNUUKy9XeAURGRtK1a1cAJk+ezN69e3O1+eabb9i/fz+rVq2ibNmyeZ4nIyOD9957jxEjRjB69GgAWrZsSd++fZk3bx4zZ84EYNeuXWzYsIH58+fTsWNHAOrUqUO3bt1YunQpQ4cOBeDLL78kISGBZcuWERgYCICLiwuTJk1i3LhxtmLZ3LlzCQsL4/XXXwcgIiKCM2fO8M4773DnnXdiNNq9xidOroy/F3d0CGbxz4f4ZOV+WoZVwMUl/69Lvxa9SPhrNZnxscRv+4HSba9vV43s3e88q9XDxcvnuvpu3nOa0+eT8fFyo3tE9evqKyIiIiJysxiMLpTvN5FTC5/BFHuCmMXTqDTiFYxuHsV2TXNKAgk71gIa/SQli92rIwUp0HzzzTf07NnzqsUngO3bt5OYmJhjCp+Liwu9evVi48aNtqlHGzduxM/Pjw4dOtjaVapUiWbNmrFx40bbsV9++YXWrVvbik8A3bt3x93d3dYuIyODLVu20Lt37xyx9O3bl9jYWPbvL/jCzyLF6c7OtfH1dufkuSR++uNEgfoYXd0J7DQMgLjfv8WcHH9d17zR6XdWq5XFP2eNfurTLhhvT7fr6i8iIiIicjMZPbwIGjwZo5cvGTFRxC6fU+ClL25E/LYfsJrScQ8Kue6ZBiL2ZPcRUPnJyMhg//79dO7cmSeffJI1a9bg4uJC586defbZZyldujQAUVFRAAQHB+foHxISQnJyMmfPniUoKIioqChq1qyZazv3WrVq8dtvv9keR0VFceedOavJ7u7uVKtWzXatEydOYDKZcl2zVq1atnM0aNDghl631WolJSXlhvreClJTU3P8V+zLAAzoVJOFK//hs9UHaFG3DJ7u+U9rM9Rsimv5mmSeO0rs+s/wixyVZ7sr821JTyX1+D4AjFXqX9fX8s5D5zlyKh4PNyNdw4NK9PdBSaLvWeeifDs25de5KN+OTzkuIdx9CegzgYtLppJ84HdiAyriE9Hvuk+TX74t6SnE/7kSAO8WffR1UcI5wve31WrNVV+5mlu+ABUXF0dmZiYffPABrVq1Yu7cucTGxjJ9+nQef/xxPvroIyBrzSZ3d3c8PT1z9Pf397edJygoiISEBHx9fXNdx8/Pj/j4/43wSEhIwM/P75rtsv97Zbvsx5ef73qZTCYOHDhww/1vFceOHbN3CPKvqr5WAkq5cCkxg4Xf/UmHBrm/vvPiWr0NvueOkrx7HWd8a2HxKXPVttn5dos5gI/FjNk7kEMxcRATV+A4P//pHABNg705eTyqwP2kaOh71rko345N+XUuyrfjU45LBvd63Sm1byVJm5cQk2rFFFT3hs5ztXx7Rv2OV3oKZp+yHM3wBgd4zygl//vb3d29QO1u+QJU9tBFPz8/Zs2aZXthpUqV4pFHHmH37t00atQIIM+qW3b/y5+7WnWuIFW7vKp7hTnf1bi5udlGUpVEqampHDt2jBo1auDl5WXvcORfIyylmbV4L5v/SWZor6b4lSrID4p6XLpwgPQj2yl/5g9K3z4xV4sr8x13/BfSAN96rahcr16B4zt4Io7j507i4mJgRN+mlPH3zL+TFAl9zzoX5duxKb/ORfl2fMpxCVOvHgnuZlJ2/Ijv3hUE1m+KW/kaBe5+rXxbTenEbpyNBQhsN5DK9bT4eEnnCN/fhw8fLnDbW74AlT2aqFmzZjmqahEREUDWLnqNGjXCz8+P9PR00tPT8fD434JvCQkJwP9GQvn5+XHmzJlc17lyxJOfn5+t7+USExNtC5Bnn/PKkU7Z/fIaQVVQBoMBb2/vG+5/q/Dy8nKI1+EourQMZuXmaA6fjOf7TdGM6dewQP1cu43k5PydpEdtx3D+KF7V6ufZzsvLCy9PD84d3w2Af70IvK4j/8s37QEgsnlVqlYMzKe1FAd9zzoX5duxKb/ORfl2fMpxyeHVYzQx8WdJPbKTuO/fpvJ903D1KX1958gj3/Hb1mFJTcQ1oAKBTSMxGLVTtKMoyd/f1zPw5pYvQHl5eVG5cuWrPp+9iHl2USgqKoqwsP9VgqOioihVqhQVKlSwtfv9999zjWQ6fPiw7RzZ7bLXesqWkZHBiRMnbGtDVatWDTc3N44cOZJjUfPsCuDl5xO5FRiNBkb1rs+z7//Oqt+Pcnv7YILKlMq3n3vZKvg17UbC9h+5+NMnVLp3CgZD3hsIpJ8+hCUlAaNnKTyrFHzI8fEzCWzbH4PBAHdG1i5wPxFHZDabMZlMxXb+9PR023+1W6vjUX6dy62Sbzc3N1xc9GZYBP7dGa//45z+eDKmC6c5+810Kt7zEkbXgk1Tyos100Tclu8ACGjdT8UnKZFu+QIUQKdOnfjpp5/IyMiwjYL6/fffAahbN+sNbrNmzfD19WXlypW2ApTZbGbVqlV07NjRVmzq2LEjc+fO5ddff7UVjc6cOcP27dt59tlnbdfs0KED7733HpcuXbItdL527VoyMjLo2LEjkDXPMSIiglWrVjFq1Chb3xUrVlCuXLkchTCRW0XjOuVoWqccOw7GsmjlAZ4cXrBd6gLaDyZx70bSzxwmef8mfOq3z7Nd9u53XiFNMbgU/EfMN+uydr5r06gSlcv5FLifiCOxWq3ExMQQFxdXrNexWCy4urpy+vRpFSgckPLrXG6lfAcEBBAUFFSoZShEHIWLZymCBj/NqY+eJv3UQc6vnEe5vo/c8PdH4p6NmBMv4uIbiG+jzkUcrcjNYfcCVGpqKhs3bgTg1KlTJCUlsXr1agBatmxJYGAgo0eP5vvvv+ehhx7innvu4dy5c8yYMYOuXbtS79/1Zdzd3Rk3bhwzZ84kMDCQsLAwFi9eTHR0NG+99Zbteo0bN6ZTp0783//9H5MnT8bHx4d33nmHypUr079/f1u7IUOG8OmnnzJ+/HjGjx/PhQsXmDp1Kn379s0xsik7pmeffZa+ffuyfft2Fi9ezMsvv2z3PwJErmZUn/rsnLmBX3aeol+nEGpXzX9IsKtPAAGt+3Np4xdcXP8Z3qGt8vwUJ/nQXwCUqlWwwhZAzIVkftl5CoBBGv0kTiy7+FS+fHm8vb2L7U2c2Wy2TVnXiAXHo/w6l1sh39m7N587l7WRSMWKFe0Sh8itxi2wEuUHPE7MF6+StGcj7uWqEdC633Wfx2oxE7d5KQABEXdgcHUr4khFbg67F6AuXLjAo48+muNY9uNPPvmEVq1aUblyZT7++GOmTp3KI488gpeXF927d+epp57K0e++++7DarWyaNEizp8/T506dZg/fz6hoaE52s2YMYNp06bx0ksvYTKZaNWqFbNnz86xg56fnx8LFy7k1Vdf5ZFHHsHT05M+ffowadKkHOdq2rQp7777Lm+99RbLli0jKCiIZ599lkGDBhXlbRIpUsGV/enYrAob/jrJxyv28+rYNgV6o+vfqi8J238kMz6WhD9XERBxR47nM+NjMcWeAIMRr5CmBY7n2w2HsVisNAstT0iVgOt9OSIOwWw224pPZcpcfbfJoroWgKenpwoUDkj5dS63Sr6zF889d+4c5cuX19eeyL+8azamzG33ceHHD7m47lPcylSmVJ0W13WO5P2/k3kpBqO3H75NuhZTpCLFz+4FqCpVqvDPP//k265BgwZ8+umn12xjMBi4//77uf/++6/ZzsfHh1deeYVXXnnlmu1q1qzJggUL8o2tY8eOtml5IiXFPT3q8dvO0+w+fJ7t/5yjed0K+fYxunkQ2HEosSvmErdpCb6NInHx9rU9n35kBwCeVevh4lWwaXSXEtL4adsJAAZ20egncV7Zaz6V1AUoRUSyf36ZTCYVoEQu4x/eE1NsNAnbf+Tcd29TeeTruJevXqC+VquFS78vyTpPi94Y3bVLtJRcmiMm4qQqBHrTp11NAD5esR+zxVqgfj4NO+JevgaWtGQubfomx3PpR3cC4F27eYHj+O6XKEyZFupWL02D4OId9SFSEmjtFBEpqfTzS+Tqytx2H57VG2DNSCPm66mYk+Pz7wSkHPwDU2w0Bg9v/MJ7FnOUIsVLBSgRJzaoSx1Kebpy7EwCG7dHF6iPwehCYJcRACT8uRrTpZisJzLTyTh5AADv2gVb/ykp1cTK34/ZYtEfriIiIiLiiAwurlQYMAnX0kFkxp/j7JI3sJqvveOt1WolbtO/o5+a98DFM//dq0VuZSpAiTgxv1LuDOxSB4BPV/9NhslcoH7ewY3xCm4Klkwurs+aGut2/iiYM3EtHYRbYKUCneeHTUdITc+kepAv4fXynwIoIiIiIlJSuXj7EjT4aQwe3qRFH+D8qg+wWq8+CyH16G7Sz0RhcPPAv2WfmxipSPFQAUrEyfVtH0xZf09iL6Xyw6ajBe5XpssIMBhJPrCZjNOHcIs9DECp2uEFGsmUlpHJ978cAWBgZG2MRo1+EnFE/fv3JzQ0lK1bt95Q/48//ti2W25xGT58OA8++OA128yePZvQ0FDbv1atWjF06NA8Y4uMjOTll1++5vkuXrxIaGgo3377baFiL4jZs2fTtGnBN4bIz759+xg8eDCNGzcmNDSUhISEIju3iIijcy9bhQr9JoLBSOKun0n444erts0e/eTbtBsupfxvVogixUYFKBEn5+HmwrAedQH4+qeDJKVkFKife/lq+DbqDEDiL5/bClAFnX63dusJEpIzqBDoTfsmlW8gchG51UVFRbF//34Ali9ffkPn+OSTT4q9AFVQnp6efPXVV3z11Ve88sormEwmxo4dy/bt23O0mzNnDvfdd5+doix+L7/8Mmazmffff5+vvvqKUqU0JURE5Hp412pmW9Liwk8LSYnakatNxqmDpJ3YB0ZXAlrdfrNDFCkWKkCJCJ3Dq1E9yJekVBPfrDtU4H6lOw7B4OaB6cxhjBkpGDy88axaL99+mWYLSzdmFawGdK6Fi4t+FIkUtbSMTEyZFuKS0jFlWkjLyLzpMSxfvhwXFxdat27Njz/+SEZGwQrctyqj0UiTJk1o0qQJt912G++++y5Wq5Vly5blaBcWFkaVKlXsE+RNEBUVRYcOHYiIiKBJkyaF2u0sPT29CCMruLS0NIe4hoiUXP4t++DbOBKsFs4ufYuM8ydzPJ+07TsAfBt1wtVPG/WIY9C7PhHBxWhgZO8wAL7/9Qixl1IL1M/VNxD/yz6R8ajeEIOLa779Nm4/SeylVAJ8PejaotqNBS0iV5VhMrNk/WGGv7ia4S+sZviLq/l2/eECr/NWVFasWEFERAT33nsvCQkJ/PLLL7nanD17lqeeeoo2bdrQqFEjevTowcKFC4GsqWynTp3is88+s019y56yFhoayoIFC3Kca8GCBYSGhtoep6Sk8PLLL9O9e3caN25MZGQkzz//PImJiUXy+sqXL09gYCCnT5/OcTyvKXhff/01kZGRNG7cmJEjR3LixIk8z/ntt9/St29fGjZsSPv27Zk5cyaZmf8rHiYkJPDss8/Svn17GjZsSMeOHZk4cWKB4t29ezcDBw6kYcOG9OzZk/Xr1+dqs2HDBgYNGkSjRo2IiIjghRdeICUlBYCtW7cSGhpKYmIi7777LqGhoQwfPhwAi8XCvHnziIyMpEGDBtx22218/PHHOc6dPRVw9+7d3HXXXTRs2JBFixYBWUWtcePG0bx5c5o0acKYMWOueo+ynTx5ktDQUJYuXcozzzxD8+bNadmyJVOmTMlxz7799ltCQ0PZsWMH9957L02aNGHatGkAHDx4kNGjR9O0aVOaNWvGuHHjOH78eI7rJCYmMmnSJJo2bUpERATTp09n/vz5Ob7Wsu/Nhg0bmDBhAs2aNePRRx8FYNmyZQwdOpSWLVvSokULhg8fzu7du/O8N3v37rXd/379+rF3717S09N54YUXaNmyJR06dMh1X0WkZDIYDJTtMQbPqvWwpqcQ8/UUzKlZv59c4mPIOLYbDEYC2vS3c6QiRSf/d4oi4hTC61WgQUgZ9kZd4LMfD/DYkGYF6hfQ+g4Stq/BkhKPR0j+fSwWK0vWZ42yuqNDCO5uN/7JuYgzsFqtpGcUvHBksVpZujGKL9f8YzuWnGrii38f9+sYgtVqIS3DDMZMXIxXX/wUwMPd5YZ2qNy5cyfR0dGMGzeOtm3bUrp0ab7//nu6du1qa3Pp0iXuuusuACZOnEiVKlU4fvy4rfAwZ84cxowZQ7NmzWxT2qpVK3jROi0tDbPZzMSJEwkMDOTMmTPMmzePhx56iE8++eS6X9OVkpOTiY+Pzzem9evX89xzzzFgwAB69erF3r17efzxx3O1++ijj3jjjTcYOXIkkydPJioqipkzZ2I2m5k0aRIAU6ZM4ddff+WJJ56gcuXKxMbG5lnYu5LJZGLixIncd999VKlShS+++IKHH36YpUuXUqdO1mYUq1evZuLEiQwYMIBHHnmE2NhYZsyYQUJCAjNnzqR+/fp89dVXjBw5kj59+jBo0CB8fHwAmD59OgsXLuTBBx8kPDycTZs2MWXKFJKTk3nooYdyxDFp0iRGjhzJ448/jr+/P9HR0QwZMoTatWszdepUDAYD8+bNY9SoUaxevRp3d/drvra33nqLdu3a8fbbb7N//35mzZqFm5ub7Z5lmzRpEnfddRcPPvggnp6enDlzhmHDhlG5cmWmTp2K2Wxm9uzZDBs2jO+//57AwEAAnn76abZs2cKTTz5J5cqV+fLLL21TS6/0/PPPc/vttzN37lzb983Jkyfp168f1apVIyMjgxUrVtiuUbNmzRz35plnnmHUqFGUKVOGN998k0ceeYRmzZpRtmxZZs6cyc8//8yUKVNo1KgRzZoV7Pe0iNy6DK5uVLjzSU599B8yL8Vw7tsZ+N3+OJ5HNgHgU78dbqWD7BylSNFRAUpEgKxPYUb1DmPSrF9Z92c0/TrWokZFv3z7Gd29KN1vEtF/baBCnYh822/dd4bos0mU8nSlV5saRRC5iOOyWq38Z85vHDh2sUDt/Uq5s+D/urH81yN5Pv/9r0cY0KkWo19fR0JywabD1asRyLSH2113EWr58uW4u7tz22234erqSs+ePVmyZAlJSUm2osXHH3/MhQsXWLVqlW3KWuvWrW3nCAsLw93dnbJly9KkSZPruj5AYGAgL730ku1xZmYmVapU4e677+bo0aM53vwXVPbImtjYWN588018fHwYMWLENfu89957hIeHM2XKFADat29Pamoq77//vq1NUlISs2bN4v7777cVp9q2bYuLiwvTp09n9OjRlC5dmj179tCnTx/69//fJ+K9e/fON26TycS4ceMYOHAgAO3ataNbt268//77zJgxA6vVyvTp0+nVqxevvfaarV/ZsmV58MEHGT9+PLVr16ZJkyYYjUaCgoJsObl48SKffvop9957L4899pjt/MnJyXz44YeMGjXKtk5UdiGsZ8+etmv85z//wc/Pj48++ggPDw8AmjVrRpcuXVi8eDHDhg275murVq1arnv78ccf88ADD+Dv/79Fe4cOHcr9999vezxlyhRMJhP//e9/bcWmxo0b0717dz777DMeeeQRDh8+zNq1a5k2bRr9+vWzvbbu3bvnGUuXLl1yFb4efvhh2/9bLBbatm3Lnj17WLp0aY5CZHZxrkOHDra2Y8eOpUmTJjz99NMAREREsHr1alavXq0ClIiDcCnlT9Dgpzm18BlSj+3B8sMc3M5mfWik0U/iaDQFT0RsQqsH0rZRJaxWWPhD3p/u5sWtQg3Sa7bCYLz2jxSr1crin7NGP/VqWxNvT7dCxSsiOZX29SA+KZ3kVFOezyenmohPzqC0r0exxmE2m1m1ahWdOnXC19cXgL59+5Kens6aNWts7TZv3kxERESxrpe0bNky+vXrR9OmTalfvz533303AMeOHbvuc6WkpFC/fn3q169Pp06dWLVqFdOnT6dGjRpX7WM2m9m3bx/dunXLcfzKAsaOHTtISUmhR48eZGZm2v5FRESQlpbGoUNZPzvDwsJYunQpCxYs4ODBg9cV/+UxuLi4EBkZyc6dOwE4evQop06domfPnjmu36JFCwwGA3v37r3qeXfv3o3JZKJXr145jvfu3ZuUlBQOHDiQ43jHjh1zPN60aRNdunTBxcXFdl0/Pz9CQ0Oved28XhfAbbfdRmpqaq77c+V1//zzTyIiImzFJ4DKlSvTtGlT/vzzTwD27NkDZBWWsrm4uNCpU6c8Y7nyGpA1vfChhx6iTZs21KtXj/r163P06NFcX4NGo5GIiP99kJP9ddWmTZsc165WrRoxMTF5Xl9ESib38tUpf8djgIH0qL8wAB61wnEvp6UqxLFoBJSI5DCiVz227D3DnwfOsufweRrWKltk59596DyHouNwd3Ph9vYhRXZeEUdlMBiY9nC765qC5+JipJSXW55FqFJebgT6eTLt4bakpaXj6emBi/Ha02BvZArepk2buHDhAp07dyYhIQGAWrVqERQUxPLlyxkwYAAAcXFx1K5d+7rOfT3Wrl3Lf/7zH+666y4mTpxIQEAAsbGxPPTQQze0+LWnpyeffvopVquVY8eOMWPGDJ566imWL19O+fLl8+xz8eJFMjMzcxQ5IGtk0eUuXboEkGNk0+XOnDkDwHPPPYe/vz8fffQR06dPp2LFiowZM8ZWWLsaNze3HKOBAMqUKUNsbGyO618+XS6v6+clPj4egHLlyuU4nv0a4+LibMe8vLzw9vbO0e7SpUssXLjQtvbX5Tw9Pa963WxX3tsyZbIW681+bVcez5aQkEC9erk3zihbtixHjx61ncPNzc1WSL3aua4WS1JSEvfddx+BgYFMnjyZSpUq4eHhwbPPPpvra9DT0zPHdEM3t6wPaa68tpubm90WbxeR4lOqTgsCO9/NxfWfAeDToq+dIxIpeipAiUgOlcr50D2iOit/P8ZHK/Yx49EON7T+S14Wr8v6NPq2VtUIKOYRGCKOwmAw4OlR8F/XaRmZ3N4+2Lbm0+Vubx+M2WLB090VLJl4ursWagezq1m+fDmQtXZO9tShbOfOnSM2NpZy5coREBDAuXPnbuga7u7umEw5i2zZhZBsq1evpl69ejkWBN+2bdsNXQ+yRqg0bNgQgEaNGhEcHMygQYOYO3dujql+lwsMDMTV1ZWLF3NOozx//nyOx9nFoTlz5hAUlHu9j+xRYr6+vvzf//0f//d//8c///zDJ598wksvvUTt2rVp0aLFVWM3mUzEx8fnKEJduHDBVjQKCAgAstYwatSoUa7+VyuwXd73/PnzVKhQIddrzH4eyPP3ib+/Px07dsyziJY9de9arry3Fy5cAHIXxPK67pV5yI47+z6VK1cOk8lEYmJijkJQ9jWudOXr27lzJzExMbz//vvUrVvXdjwxMTHPPIuIc/Nv3R+T2cLZ8xcJCgq2dzgiRU5T8EQklyG3heLl4cKh6Dh+23U6/w4FcPDEJXYdOo+L0UD/TrWK5JwikpunuysDI2sz9LZQSnlljaAo5eXG0NtCGRhZO6v4VIxSU1P56aef6Nq1K5988kmOf2+//TYWi4UffvgByFrvacuWLbl2kbvc1UZ7BAUFERUVlePY77//nuNxWlqabRRJtuziWFFo0KABvXv35ttvv8012iabi4sLYWFhrF27NsfxH3/8McfjZs2a4eXlRUxMDA0bNsz1r3Tp0rnOHRoaaivwHTmS97pfl7s8BrPZzLp162jcuDEAwcHBBAUFER0dnef1Ly8sXalhw4a4ubmxatWqHMdXrlyJt7c3YWFh14yrdevWHDp0iLCwsFzXDQ7O/w3Ylfd2zZo1eHl52RZXv5rmzZuzZcsW2+gvyBrptWPHDsLDw22vDeDnn3+2tTGbzXnuIJiXtLQ0gBxfh9u3b+fUqVMF6i8izsVgMFCqeS/SqzW3dygixUIjoEQkl9K+nvTvWIvP1/zDopUHaN2wIq4uhatXf7Mua/2Sjs2qUL60dz6tRaQw3N1cGNC5FoO61CElzYS3pxtmi+Wm7Dq5bt06UlJSGD58OK1atcr1/IIFC1i+fDmjRo1i1KhRfPfdd9xzzz2MGzeOqlWrEh0dzbFjx3jyySeBrMLIli1b2LRpE35+flSpUoXSpUvTvXt3Fi5cSKNGjahRowbLli3LNZqlTZs2vPzyy8yZM4dmzZrxyy+/sHnz5iJ9vePHj+eHH35g4cKFuRafzjZ27FjGjx/P008/bdsFb8WKFTna+Pr6MmHCBN544w1iYmJo1aoVRqOR6Ohofv75Z2bPno2XlxdDhgyhW7du1K5dGxcXF5YtW4abm5utYHI1bm5uvPfee6Snp9t2wTt79ixjxowBst70TJ48mUmTJpGSkkKnTp3w8vLi9OnTbNy4kYkTJ1510fbAwECGDx/Of//7X9zd3WnWrBmbN2/mq6++4pFHHsk15e5KEyZMYODAgYwePZrBgwdTtmxZzp8/z7Zt2wgPD6dPnz7X7H/ixAnbvd2/fz8ffvghI0aMyDXl8EqjRo3i22+/ZfTo0YwdO9a2C56/v79t4fNatWrRrVs3Xn31VVJTU6lUqRJffvklmZmZBRod3KRJE7y9vXnppZcYM2YMZ8+eZc6cOdcs6ImIiDgqFaBEJE/9OtVi5eZjnLmQzI+bj9G73Y0PA44+m8jmPWcwGGBgZPGt9yIi/5M90snfJ2u6q9tNGvS8fPlyKlWqlGfxCbLWOHr55Zdtu9B98cUXzJgxgzfffJPU1FQqV66cYyrW448/zosvvsgjjzxCcnIyU6ZMYcCAAYwfP54LFy4wZ84cjEYjgwcPpm7durz55pu2vkOGDOHkyZN89tln/Pe//6Vdu3bMmDGDwYMHF9nrDQ4Opnfv3nzxxRc8+OCDudbrgawFrF966SXmzZvHDz/8QOPGjZkxYwZDhgzJ0e6+++6jQoUKfPTRR3z66ae4urpSrVo1OnXqZBtB06xZM5YtW8bJkycxGo3UqVOHefPmERJy7XX13NzceOutt3jppZc4ePAgVapUYdasWTmmhfXs2RM/Pz/mzZtnGylWuXJl2rdvn2vNqis9+eST+Pn5sXjxYubPn0/FihWZPHkyo0aNyvceVq9encWLF/P222/z0ksvkZKSQrly5WjRogWhoaH59p84cSLbtm3j0UcfxcXFhaFDhzJx4sR8+1WsWJFPP/2U6dOn89RTT2EwGGjVqhWTJ0/OsZbT66+/zssvv8z06dNxd3enf//+1KpViy+//DLfa5QtW5Z33nmH6dOnM378eGrUqMGLL77Ihx9+mG9fERERR2OwWq1WewchOWXvuJI97Lskyt71pl69evl+8im3rpW/H+W9Jbvx93Fn/tNdr7prXX75nvnFdtb9GU3rhhV5ZlTL4g5bboC+Z+0vLS3NVpQpyMLLhWE2m0lLS8PT07NY1oAS+3KW/J48eZIuXbrwzjvv0KNHj5t67aFDh+Lq6sqiRYtu6nXzcivl+2b+HHMm+h3tXJRv5+II+b6e+oVGQInIVd3Wqjrf/xLFqdhklm6IYliPuvl3usK5Syls3H4S0OgnEREpeX788UfOnDlDaGgoKSkprFixgu3btzN37lx7hyYiIlKiqAAlIlfl6mJkeK8wpi78g2UbD9OrTQ1K+13fJ5pLNxzGbLHSuHZZ6lTLvYiuiIjIrczb25vvvvuOY8eOYTKZCA4O5o033qBr1672Dk1ERKREUQFKRK6pTcOKhFYvzT/HL/HFmn8YP7BxgfvGJaazZusJAAZFXns3IhERketRpUoV/vnnn2K/Tvv27Wnfvn2xX0dERMTR3ZwVSUWkxDIYDIzqnbWF9o9bj3PyXGKB+37/axQZJjO1qwbQqPa1F7AVERERERERx6UClIjkq0FIWVqEVcBisfLJygMF6pOSZmLlpqMADOpSu0DbVYuIiIiIiIhjUgFKRApkZO8wjAbYvOcMfx+7mG/7lb8fIzktk6oVfGhVv+JNiFBERERERERuVSpAiUiBVA/yo0uLagB8tGIfVqv1qm3TTWa++yUKgDs718Zo1OgnERERERERZ6YClIgU2N3d6+LuamT/0Yts2xdz1XY//3GCuMR0ypX2omOzKjcxQhEREREREbkVqQAlIgVWNsCL2zuEALBw5X7MZkuuNmazhW/XHwagf8dauLrox4yIiIiIiIiz0ztDEbkud0bWxtfbjeizSfz8Z3Su53/deYqzF1Pw93GnW6tqdohQROxt9uzZhIaG2v5FREQwcuRI/vzzz2K97ssvv0xkZKTt8datWwkNDWXPnj0FPsfWrVuZN29ekca1YMECQkND873u5fesadOm9O3bl4ULF2I2m3O0nTx5Mn369Mn3urfffjuTJ08uVOzF5crXcODAAWbPnk1qamqOdt9++y2hoaFcvJj/2oMiIiJya3O1dwAiUrL4eLkxuGsoC77fy2er/6ZD08q25ywWK9+sOwTA7e1D8HTXjxgRZ+Xp6cnChQsBiImJ4b333mPUqFEsWbIk32JMUalfvz5fffUVISEhBe6zbds2/vvf/zJ27NhijOzqpkyZQnBwMImJiXz//fe8/vrrpKenM2bMGFub8ePHk5KSYpf4isqVr+HAgQPMmTOHYcOG4eXlZcfIREREpLhoBJSIXLfebWtQPtCbiwlpLP/1iO34joPnOR6TiJeHK73a1rRjhCJib0ajkSZNmtCkSRN69OjBe++9R2ZmJl999VWe7a1WKxkZGUUag4+PD02aNMHb27tIz1ucateuTZMmTWjfvj3Tp08nJCSEJUuW5GhTrVo16tata6cIi4YjvIYblZaW5hDXEBERuV4qQInIdXNzdWF4j6w3Dt+sO0RiiglPT0/W/nESgF5tauDj5WbPEEXkFlOpUiVKly7NyZNZPyeyp2Bt3LiR22+/nYYNG/Lzzz8DsGPHDkaMGEGTJk1o3rw5TzzxBBcuXMhxvrNnzzJ27FgaN25M+/bt+fDDD3NdM68peBaLhY8++oiePXvSoEED2rZty4QJE0hMTGT27NnMmTOHlJQU21S44cOH2/pGRUUxbtw4mjdvTpMmTRgzZgwnTpzIcc2kpCSeeuopmjZtSkREBNOnT881ha6gDAYDderU4cyZMzmO5zUFb/v27QwYMICGDRty++23s2nTpjzPWZB7O3/+fLp160bDhg1p3bo1o0aNIjo695TrbF27duWdd96xPf7pp58IDQ1lypQptmO///47oaGhxMTE5HoN3377LU8//TQArVu3JjQ0NMdUSoAzZ85w//3306RJE2677TaWLVt21XiyRUZG8vLLL/Ppp5/SuXNnmjdvzvjx43NN5zt9+jQTJkwgPDycxo0bM2LEiAJN2wwNDWX+/PlMnz6diIgImjZtyuTJk0lKSrK1yf4a3LBhAxMmTKBZs2Y8+uijBb5uRkYGr776Ki1btqR58+Y888wzLF26lNDQUNv30qlTp2jWrBlLly7l2WefpVWrVgwcOBCADRs2cO+999K6dWuaNWvGoEGD+OWXX3JcI3ua4+7duxk5ciSNGzeme/fu/Prrr1gsFt5++23atm1L69atmTFjBhZL7vUfRURECkLzY0TkhnRoWoXf95whMrwqAf4+uLrW5JlR7uw6FEvdGoH2Dk/EYVitVqym9CI9p8VsxmpKx2IEg4vLNdsa3DwwGAyFvmZSUhLx8fGUL1/eduzcuXO89tprjBs3jqCgICpWrMiOHTsYPnw4HTt2ZObMmaSmpvL2228zbtw4vv76a1vf8ePHc/bsWV588UV8fX2ZP38+MTExuLpe+0+bV155ha+++oqRI0fStm1bkpOT2bBhAykpKQwaNIiYmBhWrFhhmz7o4+MDQHR0NEOGDKF27dpMnToVg8HAvHnzGDVqFKtXr8bd3R2AZ555hl9//ZVJkyZRpUoVPvvsM/7+++8bvm9nzpyhWrVrr6cXGxvL6NGjCQ0N5e233yYuLo7XX3891zS9gtzbZcuW8c477zBhwgSaNGlCYmIif/31F8nJyVe9fosWLfjjjz9sj//44w88PDxyHatSpQpBQUG5+nfq1Ilx48bx3nvv8eGHH+Lr62u7n9mefPJJBg8ezL333stXX33F5MmTadCgAbVq1brmvVm3bh3Hjx/n+eef59KlS7z++uu88sorzJw5E8j6urznnnsAeP755/H29ubDDz9kxIgRfPPNN/lO31y0aBH169dn2rRpnDx5kjfffJP09HTb+bM9//zz3H777cydOxeDwVDg686YMYMvv/ySCRMmUK9ePVatWsXbb7+dZywzZ84kMjKSGTNm2IqeJ0+epHPnztx3330YjUZ++eUXxowZw8KFC2nVqlWO/pMnT2bo0KHcf//9zJ8/nwkTJjBgwACSkpKYOnUqu3btYvbs2dSpU4e+ffte876IiIjkRQUoEbkhRqOBiUObsWTdId7+cgfJqSZKebnRt11NmoaWz/8EIpIvq9XK6U/+j/ST/9gtBo8qdak04tUbKkJlZmYCWWtATZs2DbPZTPfu3W3Px8fH8+GHH9KoUSPbsWeffZYGDRowZ84c2zVr165N37592bhxIx07duSXX35h7969fPzxx7Ru3RrIKoJ06tSJ0qVLXzWeo0eP8sUXXzBx4kQefPBB2/HLYwoKCrJNH7zcnDlz8PPz46OPPsLDwwOAZs2a0aVLFxYvXsywYcOIiopizZo1vPrqq7YRKG3btqVbt24FvmcWi4XMzEySkpJYtmwZu3btumrBIdvChQsxGAzMnz8fPz8/zGYz/v7+PPTQQznazZgxI997u3v3bkJDQ3Pcn65du17z+uHh4axYsYL09HRb4WnQoEF8/vnnJCYm4uvry59//kmLFi3y7B8YGGgrstWvX5/AwNwfYgwbNoxhw4YB0LhxYzZs2MCaNWvyLUBZrVbee+89W0Hr+PHjLFiwAIvFgtFo5Ntvv+X06dMsX76c2rVrA1mjsCIjI/nggw+YOnXqNc/v7u7O3Llzcfm3kOvu7s5zzz3Hww8/nKN41aVLFyZNmmR7/Mknn+R73bi4OL744gvGjRtnWwOsffv2DB8+3DaS7HJhYWG88sorOY5lF7kg62urVatWHD58mK+//jpXAWr48OEMHToUgAoVKtC3b1/27NljK062b9+edevWsXr1ahWgRETkhmgKnojckLSMTL7dcJivfjpIcqoJgORUE1+uPcg36w6RlpFp5whFHEXhRx/ZQ0pKCvXr16d+/fp06dKFrVu38vzzz9O+fXtbm9KlS+coPqWmprJ9+3Z69OiB2WwmMzOTzMxMatasSbly5WzTk3bv3o2vr6+t+ATg7++f6w31lbZs2YLVarUVh67Hpk2b6NKlCy4uLra4/Pz8CA0NZe/evba4rFZrjoKTq6srXbp0KfB1Bg8eTP369WnVqhVTpkzhgQceoEePHtfss2vXLlq1aoWfn5/tWOvWrW2jt6Dg9zYsLIz9+/czZcoU/vzzT0wmU74xt2jRgoyMDHbt2kVSUhJ///03Q4YMISAggL/++sv2XHh4eIHvw5XatWtn+38fHx8qVqyYZxEmr9guH01Vq1YtTCaTbdrhn3/+Se3atW1FIIBSpUrRuXPnAu3a2LlzZ1vxCeC2227DarXmmkrXsWPHHI8Lct2DBw+Snp6e6+vnal9PHTp0yHUsJiaG//znP7Rv356wsDDq16/Pb7/9xtGjR3O1bdOmje3/a9SoAZDjewygZs2auaaEioiIFJRGQInIDXExGnMsQH657389wqAudW5yRCKOx2AwUGnEq0U+Bc9sNttGq7gU0xQ8T09PPv30UwwGA6VLl6ZixYoYjTk/9ypTpkyOxwkJCZjNZqZMmZJj/aBs2W98z507l+combJly3Lw4MGrxhQXF4erq2uu6xbEpUuXWLhwoW1q3uU8PT2BrKlwbm5u+Pv753j+eq43bdo0QkJCuHjxIvPnz+eDDz6gRYsWeRYXssXGxlK9evVcxy+/RwW9twMGDCA5OZmvv/6ajz/+GF9fX/r168ekSZNsr/NK1apVIygoiD/++IO0tDT8/PyoVasW4eHh/PHHH/j4+JCenk7Lli0LfB+u5Ovrm+Oxm5tbgRatv7wol90PID0963sqISGBsmXL5upXtmxZ4uPj8z3/lbn19/fHzc2Nc+fO5Th+5ddrQa4bGxsLkGtUX15f+3kdt1gsjBs3jsTERCZMmED16tXx8vJi1qxZeRaRLr/H2UW7vO5fUW8WICIizkMFKBG5IclpJtvIp1zPpZpISTPh7+Nxk6MScTwGgwGDe95v/G+U1WzGYAGjuyfGfApQN8poNNKwYcNrtrmysOXr64vBYODBBx/Mc9pX9hvx8uXL51pIGuD8+fPXvF5AQACZmZlcuHDhuotQ/v7+dOzYkbvvvjvXc6VKlQKgXLlymEwm4uPjcxShrlzk+1pCQkJs9y08PJyePXsybdo02rdvf9VCYLly5fK8xuX3qKD31mg0MnLkSEaOHMnZs2f54YcfmDFjBqVLl841pe9y2cWm9PR0wsPDMRgMhIeH88MPP+Dr60v58uXzXcvKHvz9/TlyJPeHKefPn89VSMzLlfc9Pj4ek8mUY60zyP21XpDrlitXDsgqflaoUMHWJq+v/byucfz4cfbv38/cuXNz5Fw75ImIiL1oCp6I3JBSnm6UuspOd6W83PD21C54InJ9vL29adKkCUeOHKFhw4a5/lWpUgWAhg0bkpiYyObNm2194+Pj2bp16zXPHxERgcFgYMmSJVdtc7URHq1bt+bQoUOEhYXliis4ONgWl8FgYO3atbZ+mZmZtt39rlepUqV45JFHOHz4MD/99NNV2zVq1IitW7eSmJhoO7Z58+Ycu7EV9N5erkKFCtx3332EhobmWSy5XHh4ODt37uT333+3rfXUsmVL9u3bx8aNG6+6/lO27JFJN3t0TfPmzTl06BCHDx+2HUtJSWH9+vUFmjK4fv36HLscrlmzBoPBkG/xtSDXrVOnDh4eHrlyf62vhctlj/LKvreQtWPejh07CtRfRESkqGkElIjcELPFwu3tg/liTe7FkW9vH4zZYsFNNW4RuU5PPfUUI0eO5LHHHqN37974+fkRExPD77//zoABA2jVqhUdOnSgfv36PPnkk0yaNAlfX1/ef//9XNO0rlSzZk2GDBnCO++8Q3x8PK1btyYtLY0NGzbwyCOPUKFCBUJCQsjMzGThwoU0bdoUHx8fgoODmTBhAgMHDmT06NEMHjyYsmXLcv78ebZt20Z4eDh9+vShVq1adO3alddff5309HTbLniXFyiuV79+/Xjvvff44IMPrrqY+ciRI/n888954IEHeOCBB4iLi2P27Nm5RvAU5N4+//zz+Pn50aRJE/z8/Ni+fTt///23bXHqq2nRogWpqans2bOHl19+GYDQ0FC8vb3Zvn07L7zwwjX7Zy/Y/dlnn9G1a1c8PT0JDQ0t6G26YQMGDODjjz/mwQcf5LHHHrPtRpeens4DDzyQb/+MjAweeughhg4datsFr3v37vnunleQ6wYEBDB06FDmzZuHh4cH9erVY+XKlURHRwPkmtJ6peDgYIKCgpgxYwYWi4XU1FRmzZqVa3SWiIjIzaIClIjcEE93VwZGZi2e+v2vR2y74N3ePpiBkbVxdyueaT0i4tiaNWvG559/zuzZs3n66acxmUwEBQURERFhW+fIYDDw7rvv8sILL9gKJiNGjCAmJoYNGzZc8/zPP/88VapUYfHixSxcuJCAgABatGhhm0bXuXNn7r77bubPn8+FCxdo0aIFixYtonr16ixevJi3336bl156iZSUFMqVK0eLFi1yFEpef/11Xn75Zd58803c3d3p378/4eHhzJgx44buh5ubG+PGjePZZ59l69ateS60Xr58eT744ANeffVVHn30UapWrcrkyZOZPXv2dd/bpk2b8vXXX7N48WJSU1OpWrUqTz/9NIMGDbpmnLVq1SIwMBCTyUTdunWBrAJJ8+bNWb9+fb4joMLCwnjkkUdYvHgxH374IRUrVmTdunXXc6tuiI+PD59++ilTp07lxRdfJDMzk0aNGvHJJ5/kW0SCrJ3jLl68yFNPPUVGRgbdunXj+eefL7LrPvHEE2RmZjJ//nwsFgvdunVj9OjRvPbaa/kWXN3d3Zk9ezYvv/wyjz76KBUrVmTcuHFs2bLFtnC+iIjIzWSwWq1WewchOWXvnJLf8O1bWUpKCgcOHKBevXp4e3vbOxwpRmkZmbgYjSSlpOPj7YHZYsHTXbXtkkbfs/aXlpbG0aNHqVmz5lUXey4qZrOZtLQ0PD09812EXEoe5ffmCA0N5amnnmL06NE39bqTJk1i+/bttgLdrZTvm/lzzJnod7RzUb6diyPk+3rqF3qXKCKF4unuSkpKCqejs/7gLKk/OEVERG4127ZtY/v27dSvXx+LxcKGDRtYsWIFkydPtndoIiIi100FKBEpEtpVR0REpGh5e3uzYcMGPvzwQ9LS0qhcuTKTJ09m1KhR9g5NRETkuqkAJSIiIiJynf75J/cmHEWtQYMGfPnll8V+HRERkZtBW1SJiIiIiIiIiEixsvsIqOPHj7NgwQJ27drFoUOHCA4OZsWKFTnaTJ48maVLl+bq+8EHH9ChQwfb48jISE6dOpWr3e7du/Hw8LA9TkpKYvr06fz4449kZGTQqlUrnnvuOSpXrpyj39GjR3n11Vf566+/8PLyonfv3kyaNCnXooobN25k5syZREVFERQUxKhRoxg2bNgN3Q8REREREREREUdj9wLUoUOH2LhxI40bN8ZisXC1TfmqVq3Km2++meNYXtvjdu/enfvuuy/HMXd39xyPn3jiCfbt28dzzz2Hj48Ps2bN4t577+X777+3FZcSEhIYOXIklSpVYtasWVy8eJEpU6YQFxeXI44dO3Ywfvx47rjjDiZPnsz27dt59dVXcXd3z3fLYhERkStpc1oRKan080tERK7F7gWoyMhIunbtCmSNdNq7d2+e7Tw9PWnSpEm+5ytbtuw12+3atYsNGzYwf/58OnbsCECdOnXo1q0bS5cuZejQoQB8+eWXJCQksGzZMgIDAwFwcXFh0qRJjBs3zlb8mjt3LmFhYbz++usAREREcObMGd555x3uvPNOjEbNchQRkfy5ubkBWdvxenl52TkaEZHrl5KSAvzv55mIiMjl7F6AutkFmo0bN+Ln55dj6l6lSpVo1qwZGzdutBWgfvnlF1q3bm0rPkHW6KpnnnmGjRs3EhISQkZGBlu2bGHSpEk5rtG3b1++/vpr9u/fT4MGDW7OCxMRkRLNxcWFgIAAzp07B2TtfmUwGIrlWmazmfT0dNt1xbEov87lVsi31WolJSWFc+fOERAQoK87ERHJk90LUAV14sQJwsPDSUtLo06dOowfP942cupyy5cv5+uvv8bNzY3w8HAmTZpEaGio7fmoqChq1qyZ64/6WrVq8dtvv+Vod+edd+Zo4+7uTrVq1YiKirLFZDKZCA4OznWu7HPcaAEq+xd5SZWamprjv+LYlO+STzm8Nfj5+WEymYiJiSnW61itVsxmMy4uLsVW5BL7UX6dy62Ub19fX/z8/Er037C3Iv2Odi7Kt3NxhHxbrdYC//4pEQWoevXq0bBhQ2rVqkViYiJffPEFDz30EO+88w49evSwtYuMjKRRo0ZUqlSJ6Oho5s2bx913382yZcuoWrUqkLW2k6+vb65r+Pn5ER8fb3uckJCAn5/fNdtl//fKdtmPLz/f9TKZTBw4cOCG+98qjh07Zu8Q5CZSvks+5dC5ZGZm2jsEKUbKr3O5FfKdlpZGbGysvcNwWPod7VyUb+dS0vN95brbV1MiClAjR47M8TgyMpIhQ4Ywa9asHAWoZ5991vb/4eHhtG3blp49e7JgwQJefPFF23NXq84VpGqXV3WvMOe7Gjc3N9tIqpIoNTWVY8eOUaNGDa1l4gSU75JPOXQuyrdjU36di/Lt+JRj56J8OxdHyPfhw4cL3LZEFKCuZDQaue2223jjjTdIS0uz7Vx3pfLly9O8eXP27dtnO+bn58eZM2dytb1yxJOfnx8JCQm52iUmJtoWIPf39wdyj3TK7pfXCKqCMhgMeHt733D/W4WXl5dDvA4pGOW75FMOnYvy7diUX+eifDs+5di5KN/OpSTn+3oG3pTYLdoKus3rle1CQkI4evRoruOHDx+2FZay22Wv9ZQtIyODEydO2NpVq1YNNzc3jhw5kutc2ecQEREREREREXF2JbIAZbFY+PHHH6ldu/ZVRz8BnD17lu3bt9OwYUPbsY4dO5KQkMCvv/5qO3bmzBm2b99Ox44dbcc6dOjAli1buHTpku3Y2rVrycjIsLVzd3cnIiKCVatW5bjuihUrKFeuHGFhYYV+rSIiIiIiIiIiJZ3BWtChRMUkNTWVjRs3AvDZZ58RHR3N5MmTAWjZsiWpqalMnjyZPn36UK1aNeLj4/niiy/YunUrs2fPplu3bkBW0WfDhg106NCB8uXLEx0dzfz584mPj2fJkiW2RcgBHnzwQfbv38/kyZPx8fHhnXfeISkpie+//95W0EpISKBPnz5UrlyZ8ePHc+HCBaZOnUq7du148803befasWMH99xzD/3796dv375s376dWbNm8fLLLzNo0KAbuifbt2/HarUWeCGvW5HVasVkMuHm5mb3HVmk+CnfJZ9y6FyUb8em/DoX5dvxKcfORfl2Lo6Q74yMDAwGA82aNcu3rd0LUCdPnqRLly55PvfJJ58QGhrK008/zb59+7h48SJubm40aNCAMWPG0L59e1vbnTt3MmPGDA4dOkRiYiK+vr5EREQwYcIEgoODc5w3KSmJadOm8eOPP2IymWjVqhXPPfcclStXztHu6NGjvPrqq/z11194enrSp08fJk2alGvU1caNG3nrrbeIiooiKCiIe++9l2HDht3wPdmxYwdWqxU3N7cbPoeIiIiIiIiISHEymUwYDAaaNm2ab1u7F6BERERERERERMSxlcg1oEREREREREREpORQAUpERERERERERIqVClAiIiIiIiIiIlKsVIASEREREREREZFipQKUiIiIiIiIiIgUKxWgRERERERERESkWKkAJSIiIiIiIiIixUoFKBERERERERERKVYqQImIiIiIiIiISLFSAUpERERERERERIqVClAiIiIiIiIiIlKsVIASEREREREREZFipQKUiIiIiIiIiIgUKxWgRERERERERESkWKkAJSIiIiIiIiIixUoFKBERESeUkZFh7xBERERExIm42jsAcQ5RUVGsWrWKhx9+2N6hSBH4888/OXfuHMHBwdStWzfX82fPnmXx4sXK9y0oLi6OTZs2YTKZ6Nq1Kz4+PsTExPDhhx9y/PhxqlWrxogRI6hevbq9Q5VilJiYSMuWLVm0aBHh4eH2DkeKkNlsZu3atezbtw+ARo0a0aVLF4xGfeZY0h0/fpy0tDRCQ0Ntx7Zv387777/PoUOHMBqN1KtXj4ceeijP381ya3vxxRfp2LEj7du3x9VVb9GcxZ49e1izZg1Wq5X+/fsTEhLC33//zaxZs4iOjqZy5coMHz6ctm3b2jtUKSI//fQT69evJyoqivj4eIxGI2XLlqVJkyb079+fGjVq2DvEYmWwWq1Wewchju/HH3/kscce48CBA/YORQohKSmJ+++/n127dmG1WjEYDLRp04bXXnuNoKAgW7tdu3YxZMgQ5fsWc+zYMUaOHMnZs2cBqFSpEh999BGjRo3CZDJRs2ZNDh06hNlsZunSpVSpUsXOEUthfPTRR1d9Lj09nbfffpuhQ4dSrVo1DAYDo0aNunnBSZEYMmQIr732GiEhIQDEx8dz7733sn//fry9vbFaraSmptKoUSM++ugjSpUqZeeIpTCGDRtG69atbR/urFy5kscff5zQ0FBatWqF1Wpl69atREVF8f7779OuXTs7RyzXo27duhgMBvz8/OjRowd9+/bVBwQO7tdff2XcuHF4e3vj4eFBSkoK7733Hg899BBVq1YlNDSU/fv3c/DgQd599106d+5s75ClEC5dusTYsWPZtWsXAQEBuLu7Exsbi4uLC+3btyc6Oppjx44xYcIExowZY+9wi43K61IocXFxBWqXnJxcvIHITTF37lyio6OZO3cuYWFh/PHHH7z99tvceeedzJ8/n/r169s7RLmGmTNn4u/vzyeffIK/vz+vvPIKY8aMoVKlSnz44Yd4eXmRkJDAyJEjee+993jttdfsHbIUwrRp0zAYDFztcyaDwcAXX3xh+38VoEqenTt35vj9+uabb3LixAnef/99OnbsCMC6det48sknmTNnDv/5z3/sFaoUgX/++YexY8faHs+ZM4c77riDadOm5Wj3+OOP89Zbb6kAVQI98cQTHD58mB9++IGvv/6aChUq0Lt3b/r27atRbQ7o3XffpUOHDrzzzju4ubkxe/ZsHn74YTp16sQbb7wBgNVq5dFHH+X9999XAaqEmzJlCrGxsXzzzTc0aNAAgNOnT/P000/j5ubGihUr2Lx5M+PGjaN8+fL069fPvgEXE43HlkKJiIigdevW+f575pln7B2qFIGff/6Zxx57jMjISIKCgujbty/Lli2jfv36DB8+nF9//dXeIco1bN++nbFjx1K9enUCAgKYOHEix48fZ9SoUXh5eQHg5+fHiBEj+OOPP+wcrRRW586dKVOmDFOmTOHvv//O8W/btm1YrVYWLVrE33//rdGKDuLnn39m3LhxtuITQGRkJGPGjGHNmjV2jEyKgtlszjE16/jx4/Tv3z9XuwEDBnD48OGbGZoUkZYtWzJ16lR+//13Zs6cSYMGDVi0aBH9+/end+/ezJs3j+joaHuHKUXk0KFDDBs2DDc3NwCGDx9OQkICAwYMsLUxGAwMHDiQI0eO2CtMKSIbNmxg0qRJtuITZM1GeOWVV1i7di2xsbG0bt2aBx54gIULF9ox0uKlEVBSKF5eXoSHh9OrV69rttuzZ4/tk3Ypuc6dO5drXrKvry/z5s3j2WefZdy4cbz22msOP3e5pIqPj6d8+fK2x9nTJitWrJijXZUqVWzT9KTkeu+999i4cSOvv/46n3/+Oc899xyNGjUCsv6gFccTFxdH48aNcx1v1KgRs2fPtkNEUpQaNWrEzz//TOvWrYGsn9XHjx8nIiIiR7tjx45RunRpe4QoRcTd3Z0ePXrQo0cPkpKSWLVqFT/88AOzZs3inXfeoXHjxnz55Zf2DlMKyd3dPceGINn/n5mZmaNdZmYmLi4uNzU2KXomk8n2ge/lPDw8sFqtxMfHU65cOZo2bcr8+fPtEOHNoQKUFEr9+vWxWCx5fgJ3OW9vbxWgHECFChU4evQoLVq0yHHcaDTy+uuvExAQwNNPP03Pnj3tFKFcS+nSpXMUllxcXOjbt2+uNyoXL17E29v7ZocnxaBjx460bt2aBQsWMHLkSHr06METTzyBh4eHvUOTIrJ161ZiYmIACAgIICEhIVebxMTEPP/olZLlkUce4d5778XHx4dhw4bx5JNP8uyzz2K1WmnVqhWAbeTMyJEj7RytFBUfHx8GDRrEoEGDiI2N5YcffmDFihX2DkuKQMOGDZk3bx6hoaH4+Pgwc+ZMatSowcKFC4mIiMDNzY2MjAwWLVpEnTp17B2uFFJ4eDhz586lSZMmtr+9MzIyeOutt/D397d9gJ+enu7Qf4erACWF0qhRI5YsWZJvOy8vr1yjLKTkCQ8P57vvvmPw4MF5Pv/UU09RunRpZsyYoREWt6B69eqxdetWevfuDWSNgsleY+By27dvp3bt2jc7PCkm7u7ujBs3jjvuuIMpU6bQvXt3hg8fru9RBzFjxowcj3/77TciIyNzHNu1axfVqlW7mWFJMQgPD2fevHk899xzvP/++wQEBJCamspLL71ka+Pi4sLdd9+tXWgdVLly5Rg1apTW7HMQjz/+OKNGjaJLly4AlClThs8++4zx48fTpUsXgoODiYqK4tKlS9fcWERKhsmTJzNy5EgiIyOpW7cubm5uHDx4kKSkJKZOnWqbYv3HH3849Lq62gVPCsVkMpGWloavr6+9Q5GbYM+ePaxcuZIxY8Zcc3j/8uXL+f3335kyZcpNjE7yc/r0aVJSUqhVq9Y1282ZM4ewsLBcb2LFMfz222+89tprHD16lEWLFuUa0Sglx6lTp3Idc3d3p1y5cjmOTZs2jZCQEAYOHHizQpNiZDab+e2339i9ezexsbFYrVb8/f0JCQmhXbt2ufIvJcOcOXMYNGgQFSpUsHcochNduHCBTZs2kZmZSZcuXfD39+fixYt88MEHREVFUalSJe666y7q1atn71ClCCQlJfH5559z4MAB0tPTqVGjBnfddRfVq1e3tUlNTcVoNDrsaHUVoEREREREREREpFhpCp4UqbNnzxIfH4/BYMDPz0+f4jg45btkU/6ci/Lt+JRj56J8Ozbl1/ko587FWfOtApQU2qFDh5g3bx4bN24kOTk5x3OlSpWiY8eOjBs3Lt9pP1IyFCTfY8eO1RpCtyjlz7no57Pj0/e0c9H3tGNTfp2PfoY7F32PawqeFNK2bdt44IEHqFKlCj179qRWrVr4+/tjtVpJSEjg8OHDrF69mpMnT/LBBx9orZESTvku2ZQ/56J8Oz7l2Lko345N+XU+yrlzUb6zqAAlhTJw4ECqV6/OG2+8gdFozLONxWLhySef5MSJEyxevPgmRyhFSfku2ZQ/56J8Oz7l2Lko345N+XU+yrlzUb6z5P3KRQro4MGDDB48+KrfRABGo5HBgwdz8ODBmxiZFAflu2RT/pyL8u34lGPnonw7NuXX+SjnzkX5zqIClBRK2bJlOXDgQL7t9u/fT5kyZW5CRFKclO+STflzLsq341OOnYvy7diUX+ejnDsX5TuLFiGXQhk2bBhvvvkmFy5coHfv3gQHB+Pu7g5ARkYGR44cYeXKlXz00UdMnDjRztFKYSnfJZvy51yUb8enHDsX5duxKb/ORzl3Lsp3Fq0BJYW2YMEC5s2bR1JSEgDu7u4YDAbS09MB8PHxYdy4cdx33332DFOKiPJdsil/zkX5dnzKsXNRvh2b8ut8lHPnonyrACVFJCMjgx07dhAVFUVCQgIAfn5+hISE0LRpU1t1VxyD8l2yKX/ORfl2fMqxc1G+HZvy63yUc+fi7PlWAUpERERERERERIqVFiGXYmGxWBgxYgTHjh2zdyhyEyjfJZvy51yUb8enHDsX5duxKb/ORzl3Ls6WbxWgpFhYrVa2bdtGcnKyvUORm0D5LtmUP+eifDs+5di5KN+OTfl1Psq5c3G2fKsAJSIiIiIiIiIixUoFKBERERERERERKVZahFyKzbZt22jQoAHe3t72DkVuAuW7ZFP+nIvy7fiUY+eifDs25df5KOfOxZnyrQKUiIiIiIiIiIgUK03Bk5siKiqKOXPm2DsMuUmU75JN+XMuyrfjU46di/Lt2JRf56OcOxdHz7cKUHJTHD58mLlz59o7DLlJlO+STflzLsq341OOnYvy7diUX+ejnDsXR8+3q70DkJItLi6uQO2cZVtJR6d8l2zKn3NRvh2fcuxclG/Hpvw6H+XcuSjfWVSAkkKJiIjAYDDk285qtRaondzalO+STflzLsq341OOnYvy7diUX+ejnDsX5TuLClBSKF5eXoSHh9OrV69rttuzZw9ffPHFTYpKiovyXbIpf85F+XZ8yrFzUb4dm/LrfJRz56J8Z1EBSgqlfv36WCwW+vfvf8123t7eDv2N5CyU75JN+XMuyrfjU46di/Lt2JRf56OcOxflO4sWIZdCadSoEXv37s23nZeXFxUrVrwJEUlxUr5LNuXPuSjfjk85di7Kt2NTfp2Pcu5clO8sBqvVarV3EFJymUwm0tLS8PX1tXcochMo3yWb8udclG/Hpxw7F+XbsSm/zkc5dy7KdxYVoEREREREREREpFhpCp6IiIiIiIiIiBQrFaCk0M6ePcvcuXN54YUXWLRoEYmJibnaREVFMWLECDtEJ0VN+S7ZlD/nonw7PuXYuSjfjk35dT7KuXNRvjUFTwrpxIkTDBo0iJSUFIKCgjh9+jT+/v5MmTKFjh072trt2rWLIUOGcODAATtGK4WlfJdsyp9zUb4dn3LsXJRvx6b8Oh/l3Lko31k0AkoKZcaMGVSuXJmNGzeydu1afvrpJ5o2bcr48eP58ssv7R2eFDHlu2RT/pyL8u34lGPnonw7NuXX+SjnzkX5/pdVpBDatWtnXbNmTa7j8+bNs9atW9f69ttvW61Wq3Xnzp3WunXr3uzwpIgp3yWb8udclG/Hpxw7F+XbsSm/zkc5dy7KdxZXexfApGRLTk7Gz88v1/EHH3yQ8uXL89xzz3H+/Hn69+9vh+ikqCnfJZvy51yUb8enHDsX5duxKb/ORzl3Lsp3FhWgpFCqVavGrl27aNWqVa7n+vfvj5+fH48//jg7duywQ3RS1JTvkk35cy7Kt+NTjp2L8u3YlF/no5w7F+U7i9aAkkJp06YN33zzDRaLJc/nu3TpwgcffEBMTMxNjkyKg/Jdsil/zkX5dnzKsXNRvh2b8ut8lHPnonxn0S54UiixsbHs27eP8PBwfHx8rtruyJEj7Nq1y+GHFDo65btkU/6ci/Lt+JRj56J8Ozbl1/ko585F+c6iApSIiIiIiIiIiBQrrQElRWb//v1ERUURHx+PwWDAz8+PkJAQwsLC7B2aFAPlu2RT/pyL8u34lGPnonw7NuXX+SjnzsWZ860ClBTaN998w6xZs4iNjeXKAXUGg4Fy5crx6KOPcuedd9opQilKynfJpvw5F+Xb8SnHzkX5dmzKr/NRzp2L8q0ClBTS559/zquvvsrAgQPp27cvISEh+Pv7AxAfH09UVBTLly/n+eefJyMjg6FDh9o5YikM5btkU/6ci/Lt+JRj56J8Ozbl1/ko585F+c6iNaCkUG677TYGDBjA2LFjr9nuvffeY+nSpaxZs+YmRSbFQfku2ZQ/56J8Oz7l2Lko345N+XU+yrlzUb6zGO0dgJRsMTExNGvWLN92zZs3d/gtJZ2B8l2yKX/ORfl2fMqxc1G+HZvy63yUc+eifGdRAUoKJSQkhOXLl+fbbvny5YSEhNyEiKQ4Kd8lm/LnXJRvx6ccOxfl27Epv85HOXcuyncWrQElhfLYY4/x0EMPcfDgQXr37k1wcDB+fn4AJCQkEBUVxapVq9i7dy/vvvuunaOVwlK+Szblz7ko345POXYuyrdjU36dj3LuXJTvLFoDSgptx44dzJ07l61bt2IymTAYDABYrVbc3NyIiIjgoYceokmTJvYNVIqE8l2yKX/ORfl2fMqxc1G+HZvy63yUc+eifKsAJUUoIyOD6Oho4uPjAfD396dq1aq4u7vbOTIpDsp3yab8ORfl2/Epx85F+XZsyq/zUc6dizPnWwUoKRImk4n4+HjKlCljq+ReLikpiQMHDtCiRQs7RCdFTfku2ZQ/56J8Oz7l2Lko345N+XU+yrlzcfZ8axFyKRSr1cobb7xBixYtaN++Pa1bt+b999/HbDbnaBcVFcWIESPsFKUUFeW7ZFP+nIvy7fiUY+eifDs25df5KOfORfnOokXIpVC+/PJLFi5cyD333EO9evX4888/mT17Nr/88gvvvvsu/v7+9g5RipDyXbIpf85F+XZ8yrFzUb4dm/LrfJRz56J8/8sqUgh9+/a1zpo1K8ex3bt3Wzt16mTt3bu39cyZM1ar1WrduXOntW7duvYIUYqQ8l2yKX/ORfl2fMqxc1G+HZvy63yUc+eifGfRFDwplOjoaFq1apXjWMOGDfn6669xdXVl8ODBHDp0yE7RSVFTvks25c+5KN+OTzl2Lsq3Y1N+nY9y7lyU7ywqQEmh+Pv7c/78+VzHy5Urx6effkq1atUYNmwYf/31lx2ik6KmfJdsyp9zUb4dn3LsXJRvx6b8Oh/l3Lko31lUgJJCqV+/Pj/99FOez/n4+PDf//6XZs2aMX369JscmRQH5btkU/6ci/Lt+JRj56J8Ozbl1/ko585F+c6iApQUSp8+fTh16hSXLl3K83l3d3fmzp3L4MGDqVix4k2OToqa8l2yKX/ORfl2fMqxc1G+HZvy63yUc+eifGcxWK1Wq72DEBERERERERERx6URUCIiIiIiIiIiUqxUgBIRERERERERkWKlApSIiIiIiIiIiBQrFaBERERERERERKRYqQAlIiIicgMefPBBwsPDOXPmTK7n4uLiaNeuHUOGDMFisdghOjh58iShoaGEhoYye/bsPNs8/fTTtjbF6ezZs8yePZsDBw7kem7y5Mk0bdq0WK8vIiIi9qcClIiIiMgNePXVV3FxceHZZ5/N9dwrr7xCcnIy06ZNw2i0759bpUqVYunSpbkKYcnJyaxevRofH59ij+HcuXPMmTMnzwKUiIiIOAcVoERERERuQLly5XjhhRf47bff+PLLL23H165dy4oVK3jyySepXr16scZgNpvJyMi4ZptevXpx6tQpNm/enOP4ypUrsVgsREZGFmeIIiIiIoAKUCIiIiI3rFevXvTu3Ztp06Zx8uRJLl26xAsvvEDbtm25++672bNnD2PHjqVly5Y0bNiQfv36sXLlyhznuHjxIi+++CK9evWiadOmtG7dmhEjRvDnn3/maJc9pe6DDz7g3XffJTIykoYNG7Jly5ZrxlizZk2aNm3KkiVLchxfsmQJ3bp1w9fXN1cfi8XCBx98QI8ePWjQoAGtW7fmqaeeIiYmJke74cOH06dPH3bv3s3dd99N48aN6dKlC/Pnz7eNuNq6dSsDBw4Eck75u3Ja4PHjx3nggQdo2rQpHTt2ZOrUqfkW10RERKTkcLV3ACIiIiIl2fPPP8+2bdt45plnCAwMxGQy8frrr7Nlyxbuv/9+GjduzIsvvoivry8rV65k4sSJpKWlMWDAACBrvSiAhx9+mLJly5KSksLatWsZPnw4H3/8Ma1atcpxvUWLFlGjRg3+85//4OPjU6BRVgMHDuTll18mPj4ef39/jhw5wo4dO3jsscdYs2ZNrvYvvvgiX331Fffccw+dOnXi1KlTvPPOO2zbto1vv/2WwMBAW9vY2FiefPJJ7r33Xh5++GHWrl3LjBkzKF++PP369aN+/fpMmTKFp59+mnHjxtGpUycAgoKCbOcwmUyMGzeOgQMHct999/HHH3/w7rvv4uPjw8MPP3y9KREREZFbkApQIiIiIoUQEBDAa6+9xpgxYwCYPn06QUFB3HvvvdSuXZuFCxfi6pr1J1f79u25dOkSb731Fv369cNoNBIcHMyLL75oO5/ZbKZdu3acOnWKRYsW5SpAeXh4sGDBAtzc3AocY8+ePXnttddYsWIFw4YN45tvvqFKlSq0atUqVwEqKiqKr776irvvvpvnnnvOdjwsLIxBgwaxcOFCJk6caDseFxfHBx98QKNGjQBo06YN27ZtY/ny5fTr1w8fHx9q164NQLVq1WjSpEmu+EwmE4888gg9e/YEoHXr1uzdu5cVK1aoACUiIuIgNAVPREREpJA6duxIkyZNqFGjBnfccQfHjx/nyJEj9O3bF4DMzEzbvw4dOhAbG8vRo0dt/b/44gv69+9Pw4YNCQsLo379+mzevJmoqKhc14qMjMxRfLr83JmZmVit1lx9SpUqRY8ePViyZAmZmZl89913DBgwAIPBkKvt1q1bAejfv3+O440aNSIkJCTXWlLlypWzFZ+yhYaGcvr06fxum43BYMi1FtX1nkNERERubRoBJSIiIlIE3N3dbYWh8+fPAzBt2jSmTZuWZ/tLly4B8NFHHzF16lSGDBnCo48+SunSpTEajbzzzjscOXIkV79y5crleFy/fv0cj6dMmWKb3ne5gQMHcvfddzNv3jwuXryYZxv435TA8uXL53qufPnyuYpCAQEBudq5u7uTnp6e5/nz4uXlhYeHR6HOISIiIrc2FaBEREREiljp0qUBePDBB+nWrVuebWrWrAnA999/T8uWLXnppZdyPJ+cnJxnvytHLX3zzTc5HlepUiXPfs2bN6dmzZrMnTuXNm3aULFixTzbZReUzp07l2Odpuxj2a9NRERE5HqoACUiIiJSxIKDg6lRowZ///03jz/++DXbGgwG3N3dcxz7+++/2blz51WLRJdr2LBhgeMaN24cP/74I8OGDbtqm4iICCCrMHb51Lrdu3cTFRXF2LFjC3y9bNmvLy0t7br7ioiIiGNQAUpERESkGLz00ks88MADjB49mv79+1OhQgXi4+OJiopi3759zJo1C4BOnTrx7rvvMmvWLFq0aMHRo0d59913qVKlCmazuUhjuuOOO7jjjjuu2SY4OJi77rqLTz/9FKPRSIcOHWy74FWsWJFRo0Zd93WrVauGp6cny5cvJyQkBG9vb8qXL0+FChVu8JWIiIhISaMClIiIiEgxiIiIYPHixcybN4/XX3+dhIQEAgICCAkJse32BjB27FhSU1P55ptv+PDDD6lVqxYvvvgiP/30E9u2bbNL7C+++CJVq1blm2++4fPPP8fHx4f27dvzxBNP3NAUPC8vL15//XXmzJnD6NGjMZlMPPzwwzzyyCPFEL2IiIjcigzWvLZKERERERERERERKSJGewcgIiIiIiIiIiKOTQUoEREREREREREpVipAiYiIiIiIiIhIsVIBSkREREREREREipUKUCIiIiIiIiIiUqxUgBIRERERERERkWKlApSIiIiIiIiIiBQrFaBERERERERERKRYqQAlIiIiIiIiIiLFSgUoEREREREREREpVipAiYiIiIiIiIhIsfp/HcOPoXCmdmMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df['year_month'] = pd.to_datetime(combined_df['year_month'], format='%Y-%m')\n",
    "\n",
    "# Filter the DataFrame to exclude the year 2020 since we don't keep it in our dataframe due to COVID\n",
    "df_2018_2019 = combined_df[(combined_df['year_month'].dt.year >= 2018) & (combined_df['year_month'].dt.year <= 2019)]\n",
    "df_2021 = combined_df[combined_df['year_month'].dt.year == 2021]\n",
    "\n",
    "# Plot for 2018-2019\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=df_2018_2019, marker='o', label='Actual Rides before program')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=df_2018_2019, dashes=True, label='Predicted Rides with no program')\n",
    "plt.title('Actual vs Predicted Pre-Policy Daily Rides (2018-2019)')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot for 2021\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=df_2021, marker='o', label='Actual Rides before program')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=df_2021, dashes=True, label='Predicted Rides with no program')\n",
    "plt.title('Actual vs Predicted Pre-Policy Daily Rides (2021)')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0cbf3260-bb29-406d-80da-894a3aa0ca55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# joining different datasets together\n",
    "final_dataset = df_2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685b3a94-eede-4b4f-94fc-a1b762aed181",
   "metadata": {},
   "source": [
    "### Program starts- assessing initial Impact\n",
    "\n",
    "Using the trained model for predicting count of rides in program area if program had not started for the period of Oct 2021- Dec 2021 when in program rides were only on weekends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8fecbaee-05af-4446-8809-89207ec651c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|month|year|    1|    2|    3|    4|    5|     6|     7|     8|   9|  10|  11|  12|  13|   14|   15|   16|  17|  18|   19|  20|   21|   22|   23|    24|   25|  26|  27|    28|  29|  30|   31|    32|   33|   34|  35|  36|  37|  38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51| 52|  53|  54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|            features|        prediction|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|   10|2021|21509|12594|48695|21907|26742|181400|148061|415628|1743|5526|5094|2380|3538|12512|11743|19057|4262|1510|12471|4463|21272|81001|14405|152551|18170|4720|6931|172852|9447|9429|26324|160188|50419|11257|9288|2039|1109|8899|4652|13903|10586|1950|4766|671|2280|7651|2291|2283|737|3648|1028|423|13507|2671|6013|3917|9364|7225|2137|4146|1739|4101|7443|5723|6493|9414|3769|8805|2216|4261|958|3199|38242|30834|    54264|68.9| 0.483| 0.0|      0.0|  1821|[10.0,2021.0,2150...| 38974.35118074482|\n",
      "|   11|2021|13303| 8395|28113|11644|13194| 92033| 66378|199483|1149|3613|3246|1488|2366| 8071| 7243|11571|2521|1004| 8151|2891|12715|41766| 9589| 68719|12257|3070|4350| 87701|6175|5995|14830| 88191|27226| 5414|5787|1357| 707|5792|3054| 9727| 6829|1222|3108|397|1429|4835|1534|1474|469|2458| 746|285|16331|1673|3780|2345|5188|4594|1321|2824|1178|2406|4714|4063|4586|6621|2254|5829|1365|2692|778|2055|48696|18708|    34078|41.5|   0.0| 0.0|      0.0|  1744|[11.0,2021.0,1330...|23039.636769989273|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "only showing top 2 rows\n",
      "\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|month|year|    1|    2|    3|    4|    5|     6|     7|     8|   9|  10|  11|  12|  13|   14|   15|   16|  17|  18|   19|  20|   21|   22|   23|    24|   25|  26|  27|    28|  29|  30|   31|    32|   33|   34|  35|  36|  37|  38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51| 52|  53|  54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|  69|  70|  71|  72|  73| 74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|   10|2021|21509|12594|48695|21907|26742|181400|148061|415628|1743|5526|5094|2380|3538|12512|11743|19057|4262|1510|12471|4463|21272|81001|14405|152551|18170|4720|6931|172852|9447|9429|26324|160188|50419|11257|9288|2039|1109|8899|4652|13903|10586|1950|4766|671|2280|7651|2291|2283|737|3648|1028|423|13507|2671|6013|3917|9364|7225|2137|4146|1739|4101|7443|5723|6493|9414|3769|8805|2216|4261|958|3199|38242|30834|    54264|68.9| 0.483| 0.0|      0.0|  1821|\n",
      "|   11|2021|13303| 8395|28113|11644|13194| 92033| 66378|199483|1149|3613|3246|1488|2366| 8071| 7243|11571|2521|1004| 8151|2891|12715|41766| 9589| 68719|12257|3070|4350| 87701|6175|5995|14830| 88191|27226| 5414|5787|1357| 707|5792|3054| 9727| 6829|1222|3108|397|1429|4835|1534|1474|469|2458| 746|285|16331|1673|3780|2345|5188|4594|1321|2824|1178|2406|4714|4063|4586|6621|2254|5829|1365|2692|778|2055|48696|18708|    34078|41.5|   0.0| 0.0|      0.0|  1744|\n",
      "+-----+----+-----+-----+-----+-----+-----+------+------+------+----+----+----+----+----+-----+-----+-----+----+----+-----+----+-----+-----+-----+------+-----+----+----+------+----+----+-----+------+-----+-----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# dataframe that is the true counts\n",
    "# only Oct, Nov, Dec of 2021. \n",
    "df_real = df_2.filter(df_2.year == 2021)\n",
    "\n",
    "# take the real data and create predictions to compare\n",
    "df_real_vector = vectorAssembler.transform(df_real)\n",
    "df_first_predictions = lrm.transform(df_real_vector)\n",
    "\n",
    "df_first_predictions.show(2)\n",
    "df_real.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "0c752d74-61ab-4c16-a1f6-62a5eebd3963",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_real = df_real.withColumn(\"year_month\", F.concat_ws(\"-\", df_real.year, df_real.month))\n",
    "df_first_predictions = df_first_predictions.withColumn(\"year_month\", F.concat_ws(\"-\", df_first_predictions.year, df_first_predictions.month))\n",
    "\n",
    "#monthly_real.select('year_month').distinct().show(30)\n",
    "monthly_real = monthly_real.groupBy('year_month').sum('area_sums')\n",
    "monthly_first_preds = df_first_predictions.groupby('year_month').sum('prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8dacd81e-05ea-4243-8370-b6b6b56fd75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month  sum(area_sums)  sum(prediction)\n",
       "0    2021-11          275975    201316.311757\n",
       "1    2021-10          265236    210299.350589\n",
       "2    2021-12          258103    207578.423603"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_real_pd = monthly_real.toPandas()\n",
    "monthly_first_preds_pd = monthly_first_preds.toPandas()\n",
    "combined_df = monthly_real_pd.merge(monthly_first_preds_pd, left_on='year_month', right_on='year_month', how='inner')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "cd3da028-b75d-4145-ac69-3caa69704db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df['year_month'] = pd.to_datetime(combined_df['year_month'], format='%Y-%m')\n",
    "\n",
    "# Plot for 2021\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=combined_df, marker='o', label='Actual Rides post-program start')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=combined_df, dashes=True, label='Predicted Rides with no program')\n",
    "plt.title('Actual vs Predicted Post-Program Daily Rides (2021)')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7ad53625-0ac8-4b5f-9fd5-515e7c5d688e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>178045</td>\n",
       "      <td>193006.695556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>176680</td>\n",
       "      <td>178816.816038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>183403</td>\n",
       "      <td>177115.769091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>193462</td>\n",
       "      <td>182082.974058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>163498</td>\n",
       "      <td>158309.023479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>181285</td>\n",
       "      <td>165413.667248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>156257</td>\n",
       "      <td>158592.509085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>172026</td>\n",
       "      <td>172070.713297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>177280</td>\n",
       "      <td>179217.039411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_month  sum(area_sums)  sum(prediction)\n",
       "0  2021-03-01          178045    193006.695556\n",
       "1  2021-04-01          176680    178816.816038\n",
       "2  2021-05-01          183403    177115.769091\n",
       "3  2021-09-01          193462    182082.974058\n",
       "4  2021-08-01          163498    158309.023479\n",
       "5  2021-06-01          181285    165413.667248\n",
       "6  2021-02-01          156257    158592.509085\n",
       "7  2021-07-01          172026    172070.713297\n",
       "8  2021-01-01          177280    179217.039411\n",
       "9  2021-11-01          275975    201316.311757\n",
       "10 2021-10-01          265236    210299.350589\n",
       "11 2021-12-01          258103    207578.423603\n",
       "12 2021-11-01          275975    201316.311757\n",
       "13 2021-10-01          265236    210299.350589\n",
       "14 2021-12-01          258103    207578.423603"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = pd.concat([final_dataset, combined_df], ignore_index=True)\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb93c332-190e-42fb-bc05-cffb78f0a455",
   "metadata": {},
   "source": [
    "### Impact of policy expansion:to all days (5pm-4am), 10 rides a month\n",
    "\n",
    "Use the same model (of pre-program rides) to predict for the entirety of df2 (between Oct 2021 - June 2023) when the policy was expanded. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "df0ac0c2-03c0-4296-b913-6781fdba946a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|month|year|    1|    2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|   19|  20|   21|   22|   23|   24|   25|  26|  27|    28|   29|  30|   31|    32|   33|  34|  35|  36|  37|  38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51| 52|  53|  54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|   69|  70|   71|  72|  73| 74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|            features|        prediction|\n",
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "|    6|2023|13269|10762|27012|13889|13854|78625|59169|218863|903|3944|3861|1902|3214|8622|8552|12716|3441|1474|10175|3706|12489|38430|12245|63626|18512|4684|7302|128708|11464|8604|13839|146555|42171|6911|9581|2128|1259|9161|5271|14640|11170|2313|5170|971|2714|9084|3022|2995|800|4383|1326|536|21202|2955|5248|3254|6489|7274|1793|3738|1704|3260|7394|6669|8175|10885|4274|10874|2110|5162|719|3616|59391|18202|    41533|70.9|   0.0| 0.0|      0.0|  2021|[6.0,2023.0,13269...| 35531.93121489859|\n",
      "|    1|2022|11893| 9661|22646|11116|11805|63492|52275|146428|678|3225|2966|1201|3003|7803|7971|11069|3062|1176| 9408|3341|11137|33134|11780|55447|16303|3974|5958| 89606|10078|7868|12262| 81189|16779|3382|7738|1640|1010|7685|4228|12634| 9867|2100|4501|684|2124|7370|2379|2413|635|3497|1123|429| 9887|2544|5007|2860|5234|6441|1594|3343|1349|2951|6427|5262|7174| 8987|3411| 8288|1625|4106|622|2666|30481|16017|    36209|19.7|   0.0| 0.0|      1.1|  1633|[1.0,2022.0,11893...|28938.437764938688|\n",
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+--------------------+------------------+\n",
      "only showing top 2 rows\n",
      "\n",
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|month|year|    1|    2|    3|    4|    5|    6|    7|     8|  9|  10|  11|  12|  13|  14|  15|   16|  17|  18|   19|  20|   21|   22|   23|   24|   25|  26|  27|    28|   29|  30|   31|    32|   33|  34|  35|  36|  37|  38|  40|   43|   44|  45|  46| 47|  48|  49|  50|  51| 52|  53|  54| 55|   56|  57|  58|  59|  60|  61|  62|  63|  64|  65|  66|  67|  68|   69|  70|   71|  72|  73| 74|  75|   76|   77|area_sums|temp|precip|snow|snowdepth|sunset|\n",
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "|    6|2023|13269|10762|27012|13889|13854|78625|59169|218863|903|3944|3861|1902|3214|8622|8552|12716|3441|1474|10175|3706|12489|38430|12245|63626|18512|4684|7302|128708|11464|8604|13839|146555|42171|6911|9581|2128|1259|9161|5271|14640|11170|2313|5170|971|2714|9084|3022|2995|800|4383|1326|536|21202|2955|5248|3254|6489|7274|1793|3738|1704|3260|7394|6669|8175|10885|4274|10874|2110|5162|719|3616|59391|18202|    41533|70.9|   0.0| 0.0|      0.0|  2021|\n",
      "|    1|2022|11893| 9661|22646|11116|11805|63492|52275|146428|678|3225|2966|1201|3003|7803|7971|11069|3062|1176| 9408|3341|11137|33134|11780|55447|16303|3974|5958| 89606|10078|7868|12262| 81189|16779|3382|7738|1640|1010|7685|4228|12634| 9867|2100|4501|684|2124|7370|2379|2413|635|3497|1123|429| 9887|2544|5007|2860|5234|6441|1594|3343|1349|2951|6427|5262|7174| 8987|3411| 8288|1625|4106|622|2666|30481|16017|    36209|19.7|   0.0| 0.0|      1.1|  1633|\n",
      "+-----+----+-----+-----+-----+-----+-----+-----+-----+------+---+----+----+----+----+----+----+-----+----+----+-----+----+-----+-----+-----+-----+-----+----+----+------+-----+----+-----+------+-----+----+----+----+----+----+----+-----+-----+----+----+---+----+----+----+----+---+----+----+---+-----+----+----+----+----+----+----+----+----+----+----+----+----+-----+----+-----+----+----+---+----+-----+-----+---------+----+------+----+---------+------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load pre-program model\n",
    "from pyspark.ml.regression import LinearRegressionModel\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# Path to saved model on GCS\n",
    "model_path = \"gs://msca-bdp-student-gcs/bdp-rideshare-project/models/pre_program_model\"\n",
    "\n",
    "# Load the Linear Regression Model\n",
    "lrm = LinearRegressionModel.load(model_path)\n",
    "\n",
    "# filter to get entirety of time when program was expanded\n",
    "# dataframe that is the true counts\n",
    "# time period after 2021\n",
    "df_real = df_2.filter(df_2.year != 2021)\n",
    "\n",
    "# input features are everything but the area_sums column which is what we are trying to predict\n",
    "input_features = ['month','year','1','2','3','4','5','6','7','8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20','21','22','23','24','25',\n",
    " '26','27','28','29','30','31','32','33','34','35','36','37','38','40','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66',\n",
    " '67','68','69','70','71','72','73','74','75','76','77','temp','precip','snow','snowdepth','sunset']\n",
    "\n",
    "vectorAssembler = VectorAssembler(inputCols=input_features, outputCol=\"features\", handleInvalid='skip')\n",
    "\n",
    "# take the real data and create predictions to compare\n",
    "df_real_vector = vectorAssembler.transform(df_real)\n",
    "df_second_predictions = lrm.transform(df_real_vector)\n",
    "\n",
    "df_second_predictions.show(2)\n",
    "df_real.show(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "af5b8f79-3c4f-48fc-8cae-2e63aca5cefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now group by month and sum counts and plot\n",
    "monthly_real = df_real.withColumn(\"year_month\", F.concat_ws(\"-\", df_real.year, df_real.month))\n",
    "monthly_second_preds = df_second_predictions.withColumn(\"year_month\", F.concat_ws(\"-\", df_second_predictions.year, df_second_predictions.month))\n",
    "monthly_real = monthly_real.groupBy('year_month').sum('area_sums')\n",
    "monthly_second_preds = monthly_second_preds.groupby('year_month').sum('prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "8a419823-8529-4d2e-816c-03ed49fa7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-1</td>\n",
       "      <td>461079</td>\n",
       "      <td>263854.436567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-4</td>\n",
       "      <td>365044</td>\n",
       "      <td>229855.316322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-4</td>\n",
       "      <td>463392</td>\n",
       "      <td>280383.624732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-5</td>\n",
       "      <td>469985</td>\n",
       "      <td>291879.252644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-7</td>\n",
       "      <td>273441</td>\n",
       "      <td>238003.298985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-11</td>\n",
       "      <td>403456</td>\n",
       "      <td>243251.328962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-5</td>\n",
       "      <td>365878</td>\n",
       "      <td>237826.757514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-10</td>\n",
       "      <td>439440</td>\n",
       "      <td>262934.652192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-9</td>\n",
       "      <td>310420</td>\n",
       "      <td>231850.364094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-1</td>\n",
       "      <td>274172</td>\n",
       "      <td>210230.981548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-6</td>\n",
       "      <td>339336</td>\n",
       "      <td>273999.704574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-3</td>\n",
       "      <td>356928</td>\n",
       "      <td>243639.120057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-12</td>\n",
       "      <td>340977</td>\n",
       "      <td>256011.843615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-6</td>\n",
       "      <td>294611</td>\n",
       "      <td>226426.620388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023-2</td>\n",
       "      <td>453236</td>\n",
       "      <td>256310.650196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-2</td>\n",
       "      <td>328935</td>\n",
       "      <td>197677.798013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-3</td>\n",
       "      <td>463721</td>\n",
       "      <td>302917.852905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-8</td>\n",
       "      <td>263644</td>\n",
       "      <td>221321.815883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_month  sum(area_sums)  sum(prediction)\n",
       "0      2023-1          461079    263854.436567\n",
       "1      2022-4          365044    229855.316322\n",
       "2      2023-4          463392    280383.624732\n",
       "3      2023-5          469985    291879.252644\n",
       "4      2022-7          273441    238003.298985\n",
       "5     2022-11          403456    243251.328962\n",
       "6      2022-5          365878    237826.757514\n",
       "7     2022-10          439440    262934.652192\n",
       "8      2022-9          310420    231850.364094\n",
       "9      2022-1          274172    210230.981548\n",
       "10     2023-6          339336    273999.704574\n",
       "11     2022-3          356928    243639.120057\n",
       "12    2022-12          340977    256011.843615\n",
       "13     2022-6          294611    226426.620388\n",
       "14     2023-2          453236    256310.650196\n",
       "15     2022-2          328935    197677.798013\n",
       "16     2023-3          463721    302917.852905\n",
       "17     2022-8          263644    221321.815883"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_real_pd = monthly_real.toPandas()\n",
    "monthly_second_preds_pd = monthly_second_preds.toPandas()\n",
    "combined_df = monthly_real_pd.merge(monthly_second_preds_pd, left_on='year_month', right_on='year_month', how='inner')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "85ec2c3b-443d-44c7-9eb2-a6d68884eb38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df['year_month'] = pd.to_datetime(combined_df['year_month'], format='%Y-%m')\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=combined_df, marker='o', label='Actual Rides with expansion')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=combined_df, dashes=True, label='Predicted Rides with no expansion')\n",
    "plt.title('Actual vs Predicted Program Expansion Daily Rides (2022-23)')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3ee929ce-fba3-41c0-9e6d-92409b729fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>178045</td>\n",
       "      <td>193006.695556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>176680</td>\n",
       "      <td>178816.816038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>183403</td>\n",
       "      <td>177115.769091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>193462</td>\n",
       "      <td>182082.974058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>163498</td>\n",
       "      <td>158309.023479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>181285</td>\n",
       "      <td>165413.667248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>156257</td>\n",
       "      <td>158592.509085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>172026</td>\n",
       "      <td>172070.713297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>177280</td>\n",
       "      <td>179217.039411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>461079</td>\n",
       "      <td>263854.436567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>365044</td>\n",
       "      <td>229855.316322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>463392</td>\n",
       "      <td>280383.624732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>469985</td>\n",
       "      <td>291879.252644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>273441</td>\n",
       "      <td>238003.298985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>403456</td>\n",
       "      <td>243251.328962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>365878</td>\n",
       "      <td>237826.757514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>439440</td>\n",
       "      <td>262934.652192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>310420</td>\n",
       "      <td>231850.364094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>274172</td>\n",
       "      <td>210230.981548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>339336</td>\n",
       "      <td>273999.704574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>356928</td>\n",
       "      <td>243639.120057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>340977</td>\n",
       "      <td>256011.843615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>294611</td>\n",
       "      <td>226426.620388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>453236</td>\n",
       "      <td>256310.650196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>328935</td>\n",
       "      <td>197677.798013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>463721</td>\n",
       "      <td>302917.852905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>263644</td>\n",
       "      <td>221321.815883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_month  sum(area_sums)  sum(prediction)\n",
       "0  2021-03-01          178045    193006.695556\n",
       "1  2021-04-01          176680    178816.816038\n",
       "2  2021-05-01          183403    177115.769091\n",
       "3  2021-09-01          193462    182082.974058\n",
       "4  2021-08-01          163498    158309.023479\n",
       "5  2021-06-01          181285    165413.667248\n",
       "6  2021-02-01          156257    158592.509085\n",
       "7  2021-07-01          172026    172070.713297\n",
       "8  2021-01-01          177280    179217.039411\n",
       "9  2021-11-01          275975    201316.311757\n",
       "10 2021-10-01          265236    210299.350589\n",
       "11 2021-12-01          258103    207578.423603\n",
       "12 2021-11-01          275975    201316.311757\n",
       "13 2021-10-01          265236    210299.350589\n",
       "14 2021-12-01          258103    207578.423603\n",
       "15 2023-01-01          461079    263854.436567\n",
       "16 2022-04-01          365044    229855.316322\n",
       "17 2023-04-01          463392    280383.624732\n",
       "18 2023-05-01          469985    291879.252644\n",
       "19 2022-07-01          273441    238003.298985\n",
       "20 2022-11-01          403456    243251.328962\n",
       "21 2022-05-01          365878    237826.757514\n",
       "22 2022-10-01          439440    262934.652192\n",
       "23 2022-09-01          310420    231850.364094\n",
       "24 2022-01-01          274172    210230.981548\n",
       "25 2023-06-01          339336    273999.704574\n",
       "26 2022-03-01          356928    243639.120057\n",
       "27 2022-12-01          340977    256011.843615\n",
       "28 2022-06-01          294611    226426.620388\n",
       "29 2023-02-01          453236    256310.650196\n",
       "30 2022-02-01          328935    197677.798013\n",
       "31 2023-03-01          463721    302917.852905\n",
       "32 2022-08-01          263644    221321.815883"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = pd.concat([final_dataset, combined_df], ignore_index=True)\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a84bc4-d9c8-4dec-a569-55799550c451",
   "metadata": {},
   "source": [
    "### Program reduction - assessing impact\n",
    "Predicting when policy changed to 7 rides days, 10 dollars a month. \n",
    "\n",
    "Creating another model based on in-program real data from df_2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c1994d2c-ca99-4b65-91c6-57fbeee7f2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorAssembler = VectorAssembler(inputCols=input_features,\n",
    "                                  outputCol=\"features\", handleInvalid='skip')\n",
    "\n",
    "# splitting first and then doing vector assembly to avoid errors\n",
    "train_df, test_df = df_2.randomSplit([.7,.3],seed=1234)\n",
    "\n",
    "train_df = vectorAssembler.transform(train_df)\n",
    "test_df = vectorAssembler.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "303cf0e0-f2af-4a84-bdf4-98ec84046c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                ]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-217.34093676028456,1578.480812555171,0.5537942505135528,0.062080882287470274,0.03118791057209453,0.0746681159031738,0.036376208836090634,-0.0013722080654734968,0.0031201423180575566,-0.0014115479528669977,-0.49732250406216444,-0.13616525364015059,0.410592256809017,-0.9702291764676397,1.6554338324869962,0.13647510986857794,0.14239425694967234,0.07276673041681377,0.07688233314150066,0.5481713121277239,0.07727026195361791,0.2571312316898789,0.07029743643850779,0.014518759690937135,0.07243090130519246,0.0026475035032199976,0.07445054051041215,0.33409007019994763,0.21356579199400424,0.010553094784998544,-0.016908040264507998,0.13391110120468358,0.07456021110309917,-0.0035608334233764786,-0.006186040779805305,-0.03033246856426237,-0.5088825212553649,-1.8384196499638998,1.3896041569375672,-0.0,0.31416388620658353,0.13970873112685042,0.16015505985305448,-0.11223977048607371,0.3920010021748861,1.2457638390022423,-0.08761068454762237,0.19243817040700043,0.3077046392950376,0.5080466928030754,-2.239813504731119,0.4562449568458568,2.6740159264962187,2.906894795490214,0.033187424583189684,0.1370322863937649,0.11931712236367185,0.2349254828878703,0.01025824491620768,-0.03861836327340833,-0.7297678258284158,-0.08977684132400188,-0.45683460927084374,-0.23191212747963358,0.056968009822920405,0.2599042680686446,0.2713364919932726,0.16094930470813487,0.25920263791400067,0.14672061954464297,-1.4421085407069856,0.02171346269475358,0.18846555266464746,0.3915217762275989,0.027417483220527512,0.05709267632223363,-41.2056332525515,5571.518111892086,18.525326365068025,1357.3950332795182,1.9281593434528261]\n",
      "Intercept: -3192679.1584404353\n",
      "RMSE: 5061.244492\n",
      "r2: 0.853017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----------------+\n",
      "|features                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |area_sums|prediction       |\n",
      "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----------------+\n",
      "|[10.0,2021.0,12199.0,9107.0,24884.0,10934.0,12710.0,62904.0,55515.0,180042.0,674.0,3314.0,3259.0,1323.0,3077.0,7226.0,7209.0,10819.0,2898.0,1116.0,8828.0,3030.0,11178.0,34577.0,10307.0,59723.0,14084.0,3590.0,5678.0,99556.0,8672.0,7147.0,12736.0,104048.0,24877.0,3684.0,7049.0,1553.0,824.0,6866.0,3602.0,10986.0,8677.0,1848.0,3884.0,560.0,2047.0,6449.0,2113.0,2097.0,622.0,2949.0,898.0,326.0,15899.0,2444.0,4881.0,2795.0,5363.0,6014.0,1560.0,3048.0,1163.0,2742.0,5662.0,4618.0,5772.0,7720.0,3439.0,7108.0,1518.0,3534.0,550.0,2453.0,47198.0,16199.0,66.6,0.0,0.0,0.0,1824.0]            |35049    |36155.24312346475|\n",
      "|[5.0,2022.0,17302.0,10504.0,41467.0,17711.0,21337.0,149196.0,115617.0,325139.0,1709.0,4991.0,4437.0,2222.0,3102.0,10479.0,10187.0,17046.0,3773.0,1500.0,11274.0,4011.0,18070.0,66516.0,12716.0,112921.0,16796.0,4269.0,6324.0,135076.0,8657.0,8241.0,24360.0,141404.0,49033.0,12462.0,9371.0,1822.0,1172.0,8271.0,5041.0,12605.0,9392.0,1964.0,4288.0,572.0,2034.0,6706.0,1676.0,2233.0,677.0,3726.0,979.0,344.0,13677.0,2385.0,5182.0,3188.0,8379.0,6498.0,1899.0,3368.0,1688.0,3483.0,6736.0,5235.0,6157.0,8949.0,3413.0,8445.0,2252.0,3985.0,932.0,2861.0,38306.0,24753.0,55.9,0.0,0.0,0.0,1955.0]  |54875    |47005.3645400377 |\n",
      "|[12.0,2021.0,15334.0,11839.0,30581.0,14637.0,15938.0,84279.0,72666.0,232096.0,1016.0,4185.0,4262.0,1698.0,3693.0,9655.0,9731.0,14002.0,3570.0,1432.0,11428.0,4051.0,14444.0,44767.0,13778.0,75868.0,19517.0,4816.0,7073.0,118158.0,11234.0,9203.0,17681.0,126362.0,27419.0,5278.0,9174.0,2017.0,1188.0,9343.0,4944.0,14861.0,11699.0,2349.0,5020.0,848.0,2660.0,8718.0,2824.0,2832.0,644.0,4265.0,1237.0,468.0,17543.0,3150.0,6327.0,3647.0,6606.0,7771.0,2008.0,4083.0,1536.0,3817.0,7780.0,6562.0,8032.0,10688.0,4158.0,10085.0,1988.0,4732.0,744.0,3144.0,54636.0,21202.0,38.5,0.193,0.0,0.0,1619.0]|44651    |50539.29755615583|\n",
      "|[12.0,2021.0,11030.0,8798.0,22502.0,9729.0,10909.0,54911.0,44628.0,150773.0,636.0,2999.0,3138.0,1292.0,2647.0,7134.0,7151.0,10014.0,2514.0,999.0,8570.0,2931.0,10060.0,28673.0,9890.0,47390.0,13852.0,3306.0,4965.0,79970.0,8147.0,7049.0,11133.0,90970.0,19143.0,3575.0,6627.0,1462.0,813.0,6489.0,3470.0,10615.0,8189.0,1696.0,3625.0,563.0,1850.0,6121.0,1968.0,1994.0,542.0,2914.0,839.0,348.0,11601.0,2223.0,4611.0,2586.0,4799.0,5586.0,1512.0,3023.0,1181.0,2954.0,5735.0,4590.0,5690.0,7401.0,2985.0,7117.0,1370.0,3270.0,473.0,2189.0,35940.0,14810.0,45.1,0.0,0.0,0.0,1619.0]                |30397    |32994.72140055988|\n",
      "|[11.0,2022.0,12706.0,9878.0,22455.0,11161.0,11416.0,56014.0,43704.0,152851.0,725.0,3513.0,3279.0,1489.0,3409.0,8088.0,7912.0,11317.0,3309.0,1401.0,9693.0,3514.0,11212.0,31451.0,11453.0,48328.0,15547.0,3836.0,6123.0,95624.0,9961.0,8231.0,13713.0,103379.0,29815.0,4813.0,8222.0,1874.0,981.0,7902.0,4159.0,13198.0,10010.0,1921.0,4570.0,853.0,2244.0,7891.0,2716.0,2833.0,686.0,3649.0,1160.0,419.0,21663.0,2670.0,5213.0,2846.0,5413.0,6384.0,1742.0,3472.0,1429.0,3113.0,6546.0,5476.0,7342.0,9143.0,3745.0,8607.0,1675.0,4305.0,666.0,3018.0,60583.0,16149.0,59.9,0.0,0.0,0.0,1743.0]          |48964    |43063.5390418279 |\n",
      "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_df = train_df.na.drop()  # Remove rows with null values\n",
    "\n",
    "# Train Model\n",
    "from pyspark.ml.regression import LinearRegression\n",
    "\n",
    "#Elastic Net\n",
    "lr = LinearRegression(featuresCol = 'features', labelCol='area_sums', regParam=0.3, elasticNetParam=0.8, maxIter=10)\n",
    "lrm = lr.fit(train_df)\n",
    "\n",
    "#coefficients\n",
    "print(\"Coefficients: \" + str(lrm.coefficients))\n",
    "print(\"Intercept: \" + str(lrm.intercept))\n",
    "\n",
    "#model summary\n",
    "print(\"RMSE: %f\" % lrm.summary.rootMeanSquaredError)\n",
    "print(\"r2: %f\" % lrm.summary.r2)\n",
    "\n",
    "# Run the classifier on the test set\n",
    "predictions = lrm.transform(test_df)\n",
    "predictions.select('features','area_sums','prediction').show(5,truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4a85f573-488a-40f1-b502-642a52298c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 5874.389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 34508445.347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4927.196\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 1225:==================================================> (195 + 5) / 200]]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#print evaluation metrics\n",
    "e = RegressionEvaluator(labelCol='area_sums', predictionCol= 'prediction', metricName= 'rmse')\n",
    "\n",
    "# Root Mean Square Error\n",
    "rmse = e.evaluate(predictions)\n",
    "print(\"RMSE: %.3f\" % rmse)\n",
    "\n",
    "# Mean Square Error\n",
    "mse = e.evaluate(predictions, {e.metricName: \"mse\"})\n",
    "print(\"MSE: %.3f\" % mse)\n",
    "\n",
    "# Mean Absolute Error\n",
    "mae = e.evaluate(predictions, {e.metricName: \"mae\"})\n",
    "print(\"MAE: %.3f\" % mae)\n",
    "\n",
    "# r2 - coefficient of determination\n",
    "r2 = e.evaluate(predictions, {e.metricName: \"r2\"})\n",
    "print(\"r2: %.3f\" %r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db61ad06-9ff0-4a14-a43a-07376b3b3c15",
   "metadata": {},
   "source": [
    "The R-squared value is decent, indicating that the model explains a lot of the variability in the daily rides data. These statistics suggest that the linear regression model performs well in terms of fitting the data and predicting daily ride counts for the program area, although the RMSE and MAE indicate that there are still significant errors in the predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d909cf9-c75b-4156-9cbc-393fb6ec519c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "model_path = \"gs://msca-bdp-student-gcs/bdp-rideshare-project/models/program_reduction_model\"\n",
    "lrm.save(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "0b2dad59-d006-4aaf-ad0a-0d8be9843801",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-9</td>\n",
       "      <td>228913</td>\n",
       "      <td>262678.673214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-8</td>\n",
       "      <td>6681</td>\n",
       "      <td>6186.443012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-7</td>\n",
       "      <td>10666</td>\n",
       "      <td>36928.352373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month  sum(area_sums)  sum(prediction)\n",
       "0     2023-9          228913    262678.673214\n",
       "1     2023-8            6681      6186.443012\n",
       "2     2023-7           10666     36928.352373"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load pre-program model\n",
    "from pyspark.ml.regression import LinearRegressionModel\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# Path to saved model on GCS\n",
    "model_path = \"gs://msca-bdp-student-gcs/bdp-rideshare-project/models/program_reduction_model\"\n",
    "\n",
    "# Load the Linear Regression Model\n",
    "lrm = LinearRegressionModel.load(model_path)\n",
    "\n",
    "# dataframe that is the true counts\n",
    "df_real = df_3\n",
    "\n",
    "# take the real data and create predictions to compare\n",
    "df_real_vector = vectorAssembler.transform(df_real)\n",
    "df_second_predictions = lrm.transform(df_real_vector)\n",
    "\n",
    "monthly_real = df_real.withColumn(\"year_month\", F.concat_ws(\"-\", df_real.year, df_real.month))\n",
    "df_second_predictions = df_second_predictions.withColumn(\"year_month\", F.concat_ws(\"-\", df_second_predictions.year, df_second_predictions.month))\n",
    "\n",
    "#monthly_real.select('year_month').distinct().show(30)\n",
    "monthly_real = monthly_real.groupBy('year_month').sum('area_sums')\n",
    "monthly_second_preds = df_second_predictions.groupby('year_month').sum('prediction')\n",
    "\n",
    "\n",
    "monthly_real_pd = monthly_real.toPandas()\n",
    "monthly_second_preds_pd = monthly_second_preds.toPandas()\n",
    "combined_df = monthly_real_pd.merge(monthly_second_preds_pd, left_on='year_month', right_on='year_month', how='inner')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "82ce6fa9-a173-492a-91de-24f7e93d7d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df['year_month'] = pd.to_datetime(combined_df['year_month'], format='%Y-%m')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=combined_df, marker='o', label='Actual Rides post program contraction')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=combined_df, dashes=True, label='Predicted Rides with no contraction')\n",
    "plt.title('Actual vs Predicted Post-Program Contraction Daily Rides (2023)')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b9c9cce4-a73a-4a1d-8e5e-a02818b398ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>sum(area_sums)</th>\n",
       "      <th>sum(prediction)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>178045</td>\n",
       "      <td>193006.695556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>176680</td>\n",
       "      <td>178816.816038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>183403</td>\n",
       "      <td>177115.769091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>193462</td>\n",
       "      <td>182082.974058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>163498</td>\n",
       "      <td>158309.023479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>181285</td>\n",
       "      <td>165413.667248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>156257</td>\n",
       "      <td>158592.509085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>172026</td>\n",
       "      <td>172070.713297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>177280</td>\n",
       "      <td>179217.039411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>275975</td>\n",
       "      <td>201316.311757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>265236</td>\n",
       "      <td>210299.350589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>258103</td>\n",
       "      <td>207578.423603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>461079</td>\n",
       "      <td>263854.436567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>365044</td>\n",
       "      <td>229855.316322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>463392</td>\n",
       "      <td>280383.624732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>469985</td>\n",
       "      <td>291879.252644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>273441</td>\n",
       "      <td>238003.298985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>403456</td>\n",
       "      <td>243251.328962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>365878</td>\n",
       "      <td>237826.757514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>439440</td>\n",
       "      <td>262934.652192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>310420</td>\n",
       "      <td>231850.364094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>274172</td>\n",
       "      <td>210230.981548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>339336</td>\n",
       "      <td>273999.704574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>356928</td>\n",
       "      <td>243639.120057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>340977</td>\n",
       "      <td>256011.843615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>294611</td>\n",
       "      <td>226426.620388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>453236</td>\n",
       "      <td>256310.650196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>328935</td>\n",
       "      <td>197677.798013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>463721</td>\n",
       "      <td>302917.852905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>263644</td>\n",
       "      <td>221321.815883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>228913</td>\n",
       "      <td>262678.673214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>6681</td>\n",
       "      <td>6186.443012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>10666</td>\n",
       "      <td>36928.352373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_month  sum(area_sums)  sum(prediction)\n",
       "0  2021-03-01          178045    193006.695556\n",
       "1  2021-04-01          176680    178816.816038\n",
       "2  2021-05-01          183403    177115.769091\n",
       "3  2021-09-01          193462    182082.974058\n",
       "4  2021-08-01          163498    158309.023479\n",
       "5  2021-06-01          181285    165413.667248\n",
       "6  2021-02-01          156257    158592.509085\n",
       "7  2021-07-01          172026    172070.713297\n",
       "8  2021-01-01          177280    179217.039411\n",
       "9  2021-11-01          275975    201316.311757\n",
       "10 2021-10-01          265236    210299.350589\n",
       "11 2021-12-01          258103    207578.423603\n",
       "12 2021-11-01          275975    201316.311757\n",
       "13 2021-10-01          265236    210299.350589\n",
       "14 2021-12-01          258103    207578.423603\n",
       "15 2023-01-01          461079    263854.436567\n",
       "16 2022-04-01          365044    229855.316322\n",
       "17 2023-04-01          463392    280383.624732\n",
       "18 2023-05-01          469985    291879.252644\n",
       "19 2022-07-01          273441    238003.298985\n",
       "20 2022-11-01          403456    243251.328962\n",
       "21 2022-05-01          365878    237826.757514\n",
       "22 2022-10-01          439440    262934.652192\n",
       "23 2022-09-01          310420    231850.364094\n",
       "24 2022-01-01          274172    210230.981548\n",
       "25 2023-06-01          339336    273999.704574\n",
       "26 2022-03-01          356928    243639.120057\n",
       "27 2022-12-01          340977    256011.843615\n",
       "28 2022-06-01          294611    226426.620388\n",
       "29 2023-02-01          453236    256310.650196\n",
       "30 2022-02-01          328935    197677.798013\n",
       "31 2023-03-01          463721    302917.852905\n",
       "32 2022-08-01          263644    221321.815883\n",
       "33 2023-09-01          228913    262678.673214\n",
       "34 2023-08-01            6681      6186.443012\n",
       "35 2023-07-01           10666     36928.352373"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = pd.concat([final_dataset, combined_df], ignore_index=True)\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce92eb1b-0567-4a2f-8374-3ce0533e1e25",
   "metadata": {},
   "source": [
    "### Overall Impact of Program from 2021-2023 across program changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "08b87349-9cb2-4ed6-b5bf-96593ad39835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='year_month', y='sum(area_sums)', data=final_dataset, marker='o', label='Actual Daily Rides (with program)')\n",
    "sns.lineplot(x='year_month', y='sum(prediction)', data=final_dataset, dashes=True, label='Predicted Rides (with no program)')\n",
    "\n",
    "plt.axvline(pd.to_datetime('2021-10-01'), color='purple', linestyle='--', linewidth=2, label='Program Starts (Oct 2021)')\n",
    "\n",
    "plt.axvline(pd.to_datetime('2022-01-01'), color='green', linestyle='--', linewidth=2, label='Program Expands (Jan 2022)')\n",
    "\n",
    "\n",
    "plt.axvline(pd.to_datetime('2023-07-01'), color='red', linestyle='--', linewidth=2, label='Program Contracts (July 2023)')\n",
    "#plt.text(pd.to_datetime('2023-07-01'), final_dataset['sum(area_sums)'].max(), 'Program Contracts',\n",
    "   #      rotation=0, verticalalignment='bottom', horizontalalignment='right', color='red')\n",
    "\n",
    "plt.title('Actual vs Predicted Daily Rides in Program Area')\n",
    "plt.xlabel('Year-Month')\n",
    "plt.ylabel('Daily rides in program area')\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pyspark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}