diff --git a/Readme.ipynb b/Readme.ipynb
index 4da047f..14cd2a1 100644
--- a/Readme.ipynb
+++ b/Readme.ipynb
@@ -118,7 +118,10 @@
     "* [**Group Metrics**](tutorials/Stackdriver Monitoring/Group metrics.ipynb) - How to list Stackdriver groups, and query time series data for a given group.\n",
     "\n",
     "\n",
-    "* [**Time-shifted Data**](tutorials/Stackdriver Monitoring/Time-shifted data.ipynb) - How to compare today's metric data against the past week by time-shifting the time series data."
+    "* [**Time-shifted Data**](tutorials/Stackdriver Monitoring/Time-shifted data.ipynb) - How to compare today's metric data against the past week by time-shifting the time series data.",
+    "\n",
+    "\n",
+    "* [**Distribution Metrics**](tutorials/Stackdriver Monitoring/Distribution Metrics.ipynb) - How to visualize the timeseries for Distribution metrics."
    ]
   },
   {
diff --git a/tutorials/Stackdriver Monitoring/Distribution Metrics.ipynb b/tutorials/Stackdriver Monitoring/Distribution Metrics.ipynb
new file mode 100644
index 0000000..f315ad1
--- /dev/null
+++ b/tutorials/Stackdriver Monitoring/Distribution Metrics.ipynb	
@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribution metrics\n",
+    "\n",
+    "In this tutorial, we show how to extract the various components of a distribution metric and visualize them. \n",
+    "\n",
+    "**Note:** If your project has data in the last 1 hour for the pubsub metric for message sizes, then that will be used. Otherwise sample data will be loaded from a Google Cloud Storage bucket."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the monitoring module and set the default project\n",
+    "\n",
+    "If there is no default project set already, you must do so using 'set_datalab_project_id'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from datalab.stackdriver import monitoring as gcm\n",
+    "\n",
+    "# set_datalab_project_id('my-project-id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "METRIC_TYPE = 'pubsub.googleapis.com/topic/message_sizes'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table><tr><th>Metric type</th><th>Display name</th><th>Kind</th><th>Value</th><th>Unit</th><th>Labels</th></tr><tr><td>pubsub.googleapis.com/subscription/backlog_bytes</td><td>Backlog size</td><td>GAUGE</td><td>INT64</td><td>By</td><td></td></tr><tr><td>pubsub.googleapis.com/subscription/byte_cost</td><td>Subscription cost</td><td>DELTA</td><td>INT64</td><td>By</td><td>operation_type, response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/config_updates_count</td><td>Subscription updates</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code, operation_type</td></tr><tr><td>pubsub.googleapis.com/subscription/mod_ack_deadline_message_operation_count</td><td>ModifyAckDeadline operations</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/mod_ack_deadline_request_count</td><td>ModifyAckDeadline requests</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_class, response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/num_outstanding_messages</td><td>Outstanding messages</td><td>GAUGE</td><td>INT64</td><td>1</td><td></td></tr><tr><td>pubsub.googleapis.com/subscription/num_undelivered_messages</td><td>Undelivered messages</td><td>GAUGE</td><td>INT64</td><td>1</td><td></td></tr><tr><td>pubsub.googleapis.com/subscription/oldest_unacked_message_age</td><td>Oldest message age</td><td>GAUGE</td><td>INT64</td><td>s</td><td></td></tr><tr><td>pubsub.googleapis.com/subscription/pull_ack_message_operation_count</td><td>Acknowledge operations</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/pull_ack_request_count</td><td>Acknowledge requests</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_class, response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/pull_message_operation_count</td><td>Pull operations</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/pull_request_count</td><td>Pull requests</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_class, response_code</td></tr><tr><td>pubsub.googleapis.com/subscription/push_request_count</td><td>Push requests</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_class, response_code, delivery_type</td></tr><tr><td>pubsub.googleapis.com/subscription/push_request_latencies</td><td>Push latency</td><td>DELTA</td><td>DISTRIBUTION</td><td>us</td><td>response_code, delivery_type</td></tr><tr><td>pubsub.googleapis.com/topic/byte_cost</td><td>Topic cost</td><td>DELTA</td><td>INT64</td><td>By</td><td>operation_type, response_code</td></tr><tr><td>pubsub.googleapis.com/topic/config_updates_count</td><td>Topic updates</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code, operation_type</td></tr><tr><td>pubsub.googleapis.com/topic/message_sizes</td><td>Publish message size</td><td>DELTA</td><td>DISTRIBUTION</td><td>By</td><td></td></tr><tr><td>pubsub.googleapis.com/topic/send_message_operation_count</td><td>Send operations</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_code</td></tr><tr><td>pubsub.googleapis.com/topic/send_request_count</td><td>Send requests</td><td>DELTA</td><td>INT64</td><td>1</td><td>response_class, response_code</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%monitoring list metrics -t pubsub*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load timeseries data for the pubsub topic message size\n",
+    "\n",
+    "In case the default project has no data, then sample data is load from a Google Cloud Storage bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>conductor_fast</th>\n",
+       "      <th>conductor_fast_aws</th>\n",
+       "      <th>conductor_initial</th>\n",
+       "      <th>conductor_initial_aws</th>\n",
+       "      <th>conductor_kicker</th>\n",
+       "      <th>conductor_realtime</th>\n",
+       "      <th>conductor_slow</th>\n",
+       "      <th>conductor_slow_aws</th>\n",
+       "      <th>conductor_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-12-08 18:02:52.409</th>\n",
+       "      <td>{u'count': u'50850', u'bucketCounts': [u'0', u...</td>\n",
+       "      <td>{u'count': u'4509', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{u'count': u'57', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'472', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'501', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'77', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'95', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'56534', u'bucketCounts': [u'0', u...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-08 18:03:52.409</th>\n",
+       "      <td>{u'count': u'51121', u'bucketCounts': [u'0', u...</td>\n",
+       "      <td>{u'count': u'4549', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{u'bucketOptions': {u'exponentialBuckets': {u'...</td>\n",
+       "      <td>{u'count': u'465', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'423', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'73', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'87', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'56265', u'bucketCounts': [u'0', u...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-08 18:04:52.409</th>\n",
+       "      <td>{u'count': u'50852', u'bucketCounts': [u'0', u...</td>\n",
+       "      <td>{u'count': u'4546', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{u'bucketOptions': {u'exponentialBuckets': {u'...</td>\n",
+       "      <td>{u'count': u'584', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'540', u'bucketCounts': [u'0', u'0...</td>\n",
+       "      <td>{u'count': u'73', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'84', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'56064', u'bucketCounts': [u'0', u...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-08 18:05:52.409</th>\n",
+       "      <td>{u'count': u'51105', u'bucketCounts': [u'0', u...</td>\n",
+       "      <td>{u'count': u'4421', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{u'bucketOptions': {u'exponentialBuckets': {u'...</td>\n",
+       "      <td>{u'count': u'1191', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>{u'count': u'1188', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>{u'count': u'80', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'81', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'56705', u'bucketCounts': [u'0', u...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-08 18:06:52.409</th>\n",
+       "      <td>{u'count': u'51318', u'bucketCounts': [u'0', u...</td>\n",
+       "      <td>{u'count': u'4488', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{u'bucketOptions': {u'exponentialBuckets': {u'...</td>\n",
+       "      <td>{u'count': u'1307', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>{u'count': u'1205', u'bucketCounts': [u'0', u'...</td>\n",
+       "      <td>{u'count': u'63', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'66', u'bucketCounts': [u'0', u'0'...</td>\n",
+       "      <td>{u'count': u'56686', u'bucketCounts': [u'0', u...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                            conductor_fast  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'50850', u'bucketCounts': [u'0', u...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'51121', u'bucketCounts': [u'0', u...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'50852', u'bucketCounts': [u'0', u...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'51105', u'bucketCounts': [u'0', u...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'51318', u'bucketCounts': [u'0', u...   \n",
+       "\n",
+       "                                                        conductor_fast_aws  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'4509', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'4549', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'4546', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'4421', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'4488', u'bucketCounts': [u'0', u'...   \n",
+       "\n",
+       "                        conductor_initial  \\\n",
+       "2016-12-08 18:02:52.409               NaN   \n",
+       "2016-12-08 18:03:52.409               NaN   \n",
+       "2016-12-08 18:04:52.409               NaN   \n",
+       "2016-12-08 18:05:52.409               NaN   \n",
+       "2016-12-08 18:06:52.409               NaN   \n",
+       "\n",
+       "                                                     conductor_initial_aws  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'57', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:03:52.409  {u'bucketOptions': {u'exponentialBuckets': {u'...   \n",
+       "2016-12-08 18:04:52.409  {u'bucketOptions': {u'exponentialBuckets': {u'...   \n",
+       "2016-12-08 18:05:52.409  {u'bucketOptions': {u'exponentialBuckets': {u'...   \n",
+       "2016-12-08 18:06:52.409  {u'bucketOptions': {u'exponentialBuckets': {u'...   \n",
+       "\n",
+       "                                                          conductor_kicker  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'472', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'465', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'584', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'1191', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'1307', u'bucketCounts': [u'0', u'...   \n",
+       "\n",
+       "                                                        conductor_realtime  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'501', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'423', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'540', u'bucketCounts': [u'0', u'0...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'1188', u'bucketCounts': [u'0', u'...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'1205', u'bucketCounts': [u'0', u'...   \n",
+       "\n",
+       "                                                            conductor_slow  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'77', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'73', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'73', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'80', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'63', u'bucketCounts': [u'0', u'0'...   \n",
+       "\n",
+       "                                                        conductor_slow_aws  \\\n",
+       "2016-12-08 18:02:52.409  {u'count': u'95', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:03:52.409  {u'count': u'87', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:04:52.409  {u'count': u'84', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:05:52.409  {u'count': u'81', u'bucketCounts': [u'0', u'0'...   \n",
+       "2016-12-08 18:06:52.409  {u'count': u'66', u'bucketCounts': [u'0', u'0'...   \n",
+       "\n",
+       "                                                          conductor_status  \n",
+       "2016-12-08 18:02:52.409  {u'count': u'56534', u'bucketCounts': [u'0', u...  \n",
+       "2016-12-08 18:03:52.409  {u'count': u'56265', u'bucketCounts': [u'0', u...  \n",
+       "2016-12-08 18:04:52.409  {u'count': u'56064', u'bucketCounts': [u'0', u...  \n",
+       "2016-12-08 18:05:52.409  {u'count': u'56705', u'bucketCounts': [u'0', u...  \n",
+       "2016-12-08 18:06:52.409  {u'count': u'56686', u'bucketCounts': [u'0', u...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import StringIO\n",
+    "import json\n",
+    "import pandas \n",
+    "import datalab.storage as storage\n",
+    "\n",
+    "query = gcm.Query(METRIC_TYPE, hours=1)\n",
+    "df = query.as_dataframe(label='topic_id')\n",
+    "\n",
+    "# If there is data, load some sample data from a GCS bucket\n",
+    "if df.empty:\n",
+    "  bucket_name = 'cloud-datalab-samples'\n",
+    "  topic_messages_data = 'stackdriver-monitoring/timeseries/topic-message-sizes-20161208.csv'\n",
+    "  \n",
+    "  # Load the CSV from the bucket, and intialize the dataframe using it.\n",
+    "  bucket_item = storage.Bucket(bucket_name).item(topic_messages_data)\n",
+    "  data_string = StringIO.StringIO(bucket_item.read_from())\n",
+    "  df = pandas.DataFrame.from_csv(data_string)\n",
+    "  df = df.applymap(lambda x: json.loads(x.replace('\\'', '\"'))\n",
+    "                   if isinstance(x, basestring) else x)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the distribution fields, and display them\n",
+    "\n",
+    "We can use the `applymap` method to extract the distribution fields. The following fields are numeric, and can be displayed as standard linecharts / heatmaps:\n",
+    "\n",
+    "* mean\n",
+    "* count\n",
+    "* sumOfSquaredDeviation\n",
+    "\n",
+    "Lets see a single distribution below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{u'bucketCounts': [u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'0',\n",
+       "  u'47304',\n",
+       "  u'3546'],\n",
+       " u'bucketOptions': {u'exponentialBuckets': {u'growthFactor': 2,\n",
+       "   u'numFiniteBuckets': 32,\n",
+       "   u'scale': 1}},\n",
+       " u'count': u'50850',\n",
+       " u'mean': 933.3828435757302,\n",
+       " u'sumOfSquaredDeviation': 99326089.09674126}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "single_distribution = df.iloc[0, 0]\n",
+    "single_distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MzziLSMzgQF0LvmAMpybjcqip58DlUHA6lG7PhxmM6NQ1h6lvjuB1awwtSOt0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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a04d8e2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_mean = df.applymap(lambda x: x.get('mean', 0) if isinstance(x, dict) else x)\n",
+    "\n",
+    "# Display the data as a linechart\n",
+    "df_mean.plot().legend(loc=\"upper left\", bbox_to_anchor=(1,1));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the count\n",
+    "\n",
+    "The count field is a string, so we need to cast it to an integer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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I1y+GW1E4cqqOgydqMfpoCQs0EBaoJzRAj0Z97g1cBUEQBEG4MD/7YNFqd1JW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\n/wE9gJtSWk1WmgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a052e4890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_count = df.applymap(lambda x: int(x.get('count', 0)) if isinstance(x, dict) else x)\n",
+    "\n",
+    "# Display the data as a linechart\n",
+    "df_count.plot().legend(loc=\"upper left\", bbox_to_anchor=(1,1));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the sum of square deviation\n",
+    "\n",
+    "Extracting maximum value is similar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fz69//WvUajXbt28nPT2dpKQkFi9e7Eymr5ec4CKEEEKIThESEkJmZiYA+/fv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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a053f2e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_ssd = df.applymap(lambda x: x.get('sumOfSquaredDeviation', 0) if isinstance(x, dict) else x)\n",
+    "\n",
+    "# Display the data as a linechart\n",
+    "df_ssd.plot().legend(loc=\"upper left\", bbox_to_anchor=(1,1));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display values in a specific bucket\n",
+    "\n",
+    "Here, we pick the values for a specific bucket from all distribution metric values. This shows you how the messages in a given size bucket changed over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KJbZO9qjVzfOupeZMqVQ0eruVpPxO2hfLyfr+Owp+OUzJ6VOUXf4DbVYmUktL\nLDp1QpebS8nvSdj290N63ztOm5/7tZtRryf/px9IWxONJlWFpYcnbWb9DZvH+2HTtx/lqSrUyUmo\nf0/G2rd3xXxy/+PKrl8jfc0q5FZWtHl7NvJarAgiNTOrmFLn9ElKzpym+L+JSBQKFG6ta0zsNLfS\nufnJErQZGbQY+Byuk1675927EokEqULxwIYFSM3NserWncKEoxSfOoG1T896XQ6+33NOX1xM6ufL\nKL1wHisvH1qHv/VAbg6SSKVYdu1KwX9+RZ18Fpu+/RrlcnTB4UOUnDmNw5BhWHnef+k1mZUS235P\nYt27D4ayMkovnKf45AkKjx4BiRRF6zZI5PIH8h7XGJTKR+99T2g+/pKXoY+MrOHSjEyG1Nwco1Zr\nuoOvOhK5nNajRmDxbMBf4kO3MTX2JZrCY0e5tfFrJBIJFp06o2jdGvPWbVC4tcG8tZvpQyX3wP8j\n+/t/ovTt9dBXhmgM92q3smvXyNi8kfIb15EqlRXrCPs9dcc5GvV6MrfGUPDLYcycnGj91jsVvV4P\nmKGsFIn5g0uMaqLNzUX18WK0Odl0/8d76NrVbVoZbV4eOTtjKTyeAHo9cnt77AJepMXTz9zxZaPs\n6hVSv1iOvrgIh1EvYT90eLN5bhWfOkFa9ErkDg60nzu/zsvn3es5p83NJfXzT9GkpWHzxJO4Tgp9\n4L1rhUePcGvDV1h06kybd/7esJuQDAauvTcHXUE+HT9ZXq/eUG1ONnkHD1Dw688YNRqk1tbYPfcC\nHiHjyckrrXfdHhRxGVpoiL9ksvj7yrUYNdqKnkWtFqNGg+HPn0atBoncDKmVFTIrK6RWyj9/VyJV\nWoFOR+7+fehyc5G1bInT2HHY9Hvyvh8QmowMChP+Q+mFCxUbpH/2ZEqlf/ZqSkAqxdzFFYdRox+Z\nSXbrqjGTxcoEUGppiduMWffsHTAaDKg++4TS8+dwCZlMi2cGNEodHpbq2s1QXk72rh3k/3gQjEZs\nn/TDcVwgchvbasswGo3k7N5F7p5/I7OxofWsCCw6dHgg9TXqdGRs2khhwhGQSpHZ2iK3bYG8RQtk\nLVr++bMFUoUCQ3k5xrIyDOXlGMrLMJSVYyyv+FvRth32Q4cjNa/966H0j0ukrV6JvrAQ++Ej6RYW\nUu/nnDYnh7yD+00JgczahpbPv0DLgc9Rdu0qaatXYtRocA6eWOupVR6myvF+ll270eatiDoldDW9\nVjXpaahS8tU0AAAgAElEQVSWf4ouN5eWzwfgNG78Q/kyYDQaubX+S4qOH6v40jd1er0T1KKTJ0hf\nvZIWzzyLS8ikBtVLX1RE3qEfyT/0I4aSElr4eOP42pRGvxxfX0aDAfW5FDo8+2RTV0V4hP0lk8WG\nJiuG8nLKf/kR1Y5dGLVaLDp3wfmVICzad7hjP71aTdFviRQePULZH5cqNlYmlfdodqtu3XGbMeuR\nvFx6P42RLBoNBrK+/yf5PxxAbmdH61lv3zEVSk20uTlcnzcXo15P+3kLMHd58CtDNJaq7aY+f46M\nTRvQZmVh5uyCS/BErLp1r1VZ+YcPkbk1Bom5ArdpM1D28AIqxnBpMzLQZN6q+HnrFrqCfGyf7E8L\nv6drXVeDRkP62mhKzp7BzNUVmdIafWEBuoICjPWYcNa8lRuuoW/UKrEt+PVnMrZsBqMRp3Gv0PK5\n53F2tm3wc05fVETeTz9UJARqNRKFBUadFolEgusbb2LTu0+Dyn9QjAYDaWtWUXLqJC39n8f51Qm1\nPra616r64gXSVq/EUFyM40tjsXtx6EPtSTVotaSt+Bz1ud8rejRfe71eierNjxdTevEC7RcsQuHW\nunHqVlZK+tdfUXLqJGYuLrSe+beHsvpMTfTqEgqPHiE//hDajFv4/Tu2yeryMKjVaoYNG8ahQ4fq\ndNzBgwcJCAiod9zs7GxWrlzJhx9+WO3jv/76K6mpqYwfP54DBw4waNAgzp8/z48//siMGTOqPWbV\nqlXY2dkRFBRU73o1NpEs1pOTkw1p566Q9f0/KT7xG0gktHj6GRxGjqb85g0Kjx6h+NRJjFotSCRY\nde2ObX8/rHv3QapQVMzkbjBgNBj+76dOx61NGyg5fQrLLh64hf/todyMYNTrKT51kuJTJ5BaKTFz\ndMTMwQEzB0fkjo7IrG0a7QOhocmiQaslY+N6ihKPY97KjdZvRWDmUPu7nAsTj3Fr3Vos3DvSds57\njXLpzKjTYTQY6tT7VVeV7aYvLSX7X9sp+Dm+YoqUgME4jBxd59hFJ37j1ldrMRqNWHbshCYzA/09\n1iS19Xsa51cn3PcLjF6trrgj+OIFrHp44TZtpukYo9GIoawMfUEBusIC9AUFGMrLkVookCoskCgU\nSC0skP75UyKVkbN3N/k//QAyGQ7DRlRMAVTNlDZGnY6s7dvIP/QTUqUSt6nTTclzY/ZmG8pKyf/5\nMHkHD2DUanCbHl6r8W5NyVBWyo2oj9CkpeIy6TVaPPVMrY6r2m6mRBxwCZ5Y63Iam6GsDNVnn1B2\n5TItBj6H86sT6vT+VHb9GjcWzseqhxdt/vZOo9bNaDCgPrCH1NidSK2scHtzRq2/xDWW8ps3yY//\nicJjRzFqNEjkcqwf74tPZMRDrcfDVlJSwogRI/jpp59qfYxKpeLjjz9mxYoVD7Bm/2fMmDHExt4/\naRfJYjPR2FPnqM+lkLltK5q01Dv2MXNxpYXfU9g88WStp20x6nSkr19H8W+JKDq40+atiAd2OUOv\nLqHg11/I/+lHdLk5Ne4nMTfHzMERczc3HIaPrFUvXk0a8sGtLy0lLXoFpefPYdG5C61nzKpX26Sv\n/5KiYwmNMsddye/JpH+1FkNxMVIrJfKWLZC3sEPesiWyFi2Qt7RDbmeHsodXg8a3OjnZcC3+CBmb\nv0GXm4u5W2tcJ4di4V7/yZfVF86TtmYVhpISzBwcMXP5cwJpF1fM//xnNBpJ/3I15devYd6mLW5v\nTq+xR1ZXVEjq8mWU37iO9WOP4xr6RqNM1VOS8jsZG79Gl5eLhXtHXEPfuKPHRl9URNqXqyk9fw5z\nt9a4zZyFuZOz6fEHMZWJUafDqNM9MmOWNZmZ3PjoQ4yactrMjrxr8unqVLbb7T35UqWyIgHq2u0h\n1Lpm+pISbn68GE2qCvthw3EcVfspgm59/RWFCUdo/dbbKL0af3YEJycbLv97PxmbN2I0GHAOCqbl\ngOrX/K5k1OkovfwHSCRYuHes8+tGX1xMye/JFBw+ROmliwDI7R1o+exAbJ9+BrmNbbMds6jT6Zgz\nZw5paWlYWFiwaNEiVq5ciUqlQqvVEh4eTv/+/QkICCAwMJD4+Hi0Wi0bN27EYDAQHh6ORqOhd+/e\nxMXF8dNPP+Hv709cXByWlpYsXboUT09Phg0bdkecJUuW8MEHH5CUlMSECRMICQkhMjKSwsJC9Ho9\nc+fOpVu3bgQEBODl5YWfnx9jxtz9PEtNTSU8PJzY2Nhq63jgwAEuXryIo6Mjy5cvx9/fnwkTJrBl\nyxZWrFhh2sdoNPLMM88wffr0+yaL58+fZ8GCBZiZmSGVSvn8889ZuHAhISEh+Pj4EBoaylNPPcXk\nyZP58ssvcXFxISsrix9++AGpVIq/vz9vvPFGnf6fxP3+jcCqW3faz1tA/s/xFP7nVyzcO2Lb3w+L\njp3q3CMnkctp9cZUMszNKDx6BNWypbR+e3aN49DqQ3MrnbyffqDw6BGM5eVIzM1pMdCfls88i9Fo\nRJeTgzYnG212Ntqc7Iq/s7PRpKdRfPoU9i8OrRhH9hDn7NPl55P6xTLKb95E2as3rV6fWu+ePOdX\ngym9eJHcvbtRennX6oOzKqPRSP4PB8j6/p9IZDIsu3ZDX1iILj8fTVraXfubOTnTasqbWHRwr3Ms\nfXExl779hsxDhyumwxk+Evshwxrc/laeXen06ecYjYZ7jpFtG/keWd9to+DneG589CEuk0LvmmpE\nm5uD6rNP0N66VTFHYcikRhvHpuzeg/bzF5L57RaKjidwfcEHOI0dR4uBz6FJVZG66gt02dlY9+qD\na2gYUosH3xsvkcsfqelSzJ2daTV1GqnLPyVt9UrazZ2PmZ3dfY/Tq9Wkr1uDOjkJ81ZuuM18C3Nn\n5/se96DJlEravP0ON5cuJnfvHqSWVtgPevG+x+kK8ilMPIa5ayusuns9sPrZ9vfDzMmJtOiVZMZs\nQpOWVjG287Zecb1ajTo5ieIzpyhJOotBXbEMrUQux6JTZyw9PLHy7IpFx053vdfp8vMovXgR9aUL\nlF68iCZVZXrMqnsPWg58DmVP3zq9Bn/Yk0LKmbvfuxqie083Xhh+757VnTt34uzszLJly9i3bx87\nduzAwsKCmJgYMjMzCQ4O5sCBA+h0Ojp37kxoaCgREREkJCSQkZGBh4cHkZGR7Nu3j7i4uFrHiY+P\nJywsjC1btjBt2jSio6Px9fUlLCyM5ORkoqKiiImJQaVSsWbNGjp16lRj2ZWf89XVsfLx0NBQ1q9f\nz4oVK0hMTDQdI5FI2LZtGxKJhOeee45Jkybdt11zc3P54IMP6Nq1KytWrGDPnj3069ePM2fO4O3t\njVwuJykpCYBTp04xf/58Ro8ezZEjR5BKpXz33Xf3jVHVo/Nu18xJZDLs/J+v1XJQ9y1LKsVlUigS\ncwUFhw+h+ngJbSL+jrxly3qXqS8tpfTSRQrif6Ik6SwAcnt7Wg4fSYunByBT3jYVRbv21ZZRfPY0\nmVs2k7t3N8W//ReXia9h2aVud5nWltFgQJtxi9Irlym7coXiM6fQ5+fTYsBAnIOCG5SIyKyscA17\nA9UnS7i1/kvaz1tQpwTDoNWQuXkThQlHkLVogdu0mXcknAaNpuJSa34+uoKKN/X8Qz9yY/FHOI15\nmZbPB9Sq/kaDgaJjCWTFbkdfUICiXXtcJ4eiaNuuXuddHYlczv2+zkjNzHEJnohl585kxGwifc0q\nygIGV8xBKJdXTFj82SfocnOwGzQYx7GBjT6OTaZU0ur1KVj36kVGzKaKxPG/iZTduI6xvBz74SNx\nGD7yL7tKTW0ou/fA6eXxZG3fhurTpbR46mmUPr6Yu7lV+/9Vmn6Lm4sXoUlPw8rLh1ZvTH1o62bX\nhrxFyz8TxijTjW73u8ko/3A86PW0fP6FB/5cseziQbv3PyB15efk//QDmoxbOL08ntIL5yg+fQr1\nhfOg11eci719xY2SMimlF85TevECpRfOk7vn3xXJo3tHLLt4oCssoPTiRbSZGaY4EnNzLLt2w8rD\nE5u+/TB3bfVAz6uxpaSk0L9/xdyZQ4YM4aOPPqJv34q5QZ2dnVEoFBT8OUSmT58+pu1FRUVcvnzZ\ntG+/fv3qFAcgMTHR9HhycjJvvvkmAF5eXty4cQMAS0vLeyaKVVWt4+2qu5CrUCgICgpCJpORn59v\nOtd7cXBw4NNPP6WsrIzMzEyGDx/OsGHDWLlyJY8//jjdunXj4sWKHuacnBxcXV0ZPHgwEydONO1b\nVyJZbKYkUmlFUmRmRv4PB7j58WLaRPy9VuPzDBoN5TdvUHb1KmXXr1J+9SqajFumm2osOnXG7oUA\nrHv1qdOSZtY+vlgt8CR7x7/Ijz/EzY+jaPGsP44vjW3w2Ep9URFl169SevkyZVcuU3b1iumbNlS8\nITqMHlMxZq0REhErD0/sBg8h7//Fkfndt7hOCq3Vcdq8PNKiV1B+7SoW7h1pNW3mXT00UnNzpE5O\nmDk5AWDzWF+UPX259fU6srZ/R0lKCq6vhSG3rfmu5eJTJ8nZFYsmLQ2JXE774CDMn/Kv1xJ0jcX2\nST8U7dqTtmYVeQf3U3rlMg5Dh3Nrw3r0RYUP5YYHm8f6YtnZg1vfbECdfBaJQkGrN2fcd1JloULL\nFwLQZmeSH3+I7NjvyY79HrmDA0ofX6x9emLZtStSM3PU589x5ctodEXFtHxhEE4vBzbLRNzM0YnW\nf5uN6uPFZMZsQmZhiU3f/0sadAUFlKtuUn7zBuWqm5ScPoXUSontk34Pp35OTrR9dy631q2hJOks\n15OTTI8p2rXH2rcXSt9eKNq2u+N1oy8upvSPS6gvnK9IHv+4ZLq8LLW0ROnTE8sunlh6eGDRvkOj\n9HK/MLz7fXsBHwSZTIbBYDD9LZFI7kiqtFot0j+fe7Jq3v8qH6taRiXdn9PgVY1TVdX3Lf2fibxZ\nHa/gVFfHmqSlpfHNN9/w73//GwsLC4YPH16r4xYtWsSUKVPw8/Njw4YNqNVqOnToQFpaGidPnqR3\n794UFxfz888/4+lZsab6vHnzuHr1Kvv27SM4OJjY2FhT29WGSBabMYlEgtO48UgV5uTu3cPNpVG4\nhr4OgEFdgl6txlCiRl+qxlBSgr6kBE1aKuVpqaZvrABSCwssPbti0cEdmz6PNWicm9TCEudXg7Hp\n+wQZmzZW9FSePoVzcAjWPr73PNao06FWqShOuYzmVjqaW7cqfmbcwlBcfMe+Zi4uFW+IHTth0bEz\nijZtGv2yn+PI0ah/T6bwP7+i9O5534Sj9PIfpK1ehb4gH5sn++MSMqnWUxwpe3jRft5Cbm34CnXy\nWa5/+A9ahU25Y/C70WhEfS6F7B3/ovzaVZBIsH3qGRyGj8Sta4dmsYSYonUb2s+dx61vNlL8WyKp\nX3wGEgnOQSG0HOj/UOogb9mS1rP+RsmZ05i7tmrSO04fNRKJBOdXg7EfPhJ1chIlZ89QkpxEQfxP\nFMT/VNFL1bkL6gvnkUgkuEycTIunm/c0Uwo3N1r/LQLVp0tJ/3od6osX0GZmUK66ib6w8I59JQoF\nzi+Pe6gzTcgsLXGb+Ra5cXsou34NZQ8vlD197zmOXWZtjbVvL6x9ewEV48vLrlxBZmuLok3bZpm4\n15eXlxfHjh1j0KBBHD58GDs7O44fP86QIUNIT09HKpViU8Mcoe7u7iQlJfHCCy9w7Ngx03YbGxuy\nsrJo3bo1Z86coUePHnh7e5OQkMCgQYOIj4/n0qVL9O7d25RMent7c+zYMXx8fDh9+jQeHh6Nfq5V\nexbz8vJwcHDAwsKC33//nbS0NDS1mDEiPz+ftm3botFo+Pnnn/H1rfjsdXNz46effmL58uXk5+ez\nefNmRo0aRXFxMZs2bWL69OlMnz6dEydOUFxcjG0NHRbVEcliMyeRSHAcNQaJmTk5O2NRfbLk3vub\nmWHRvgMWHdyxcHfHooM7Zi6ujf7mYtm5C+0++JDcfXvJ3beXtBWfY9nFA4mZWcXgf622Yu5KndY0\nybm+qAiqfrOTSjFzcsayU2cUbdti0bEzlu4d6zyBcH1I5HJcw6ZwY+E80tdGk+3igqJ1G8zdWpt+\nmru4IJHJKDjyK5kxmzDq9RVTsrwQUOceNHmLFrSe9TZ5B/eTvTMW1WefYP/iUBxGjKLs+jWyd8ZS\nev4cANaPPY7jqJea5SUlqYUlraa8SX6XLuT/cBCH0WPuua7ugyCRSEwfpELdyW1ssX3SD9sn/Uw3\nV5ScPU3JmTOoU35Ham1N9/f+jsa58YY8PEgW7TvgNvMtUpd/SsHhiqlT5A4OFb12bdqiaNsWRZu2\nmDk5N0miJZFKcRg+st7Hy6yUKL28G7FGzcfQoUNJSEggODgYuVxOVFQU0dHRhISEoNPpWLBgAXBn\nz1/l7yNHjmT69OlMnjyZPn36mLYHBQUxZcoUOnbsSJc/h0oNHTqUo0ePmuIsXboUuVzOuXPnWLJk\nCeHh4URGRjJx4kSMRiPz5s27K+79VFfH23Xr1o1x48Yxe/ZsALp3746lpSWvvvoqffr0ITAwkAUL\nFtC7d+97xpkwYQLTpk2jXbt2BAcH89FHHzFkyBAef/xxYmJisLW1xdfXl8jISKKiorC2tiYvL4+X\nX34ZpVJJr1696pQogrgbut6aYrH4ov8mUnrpAlIrZZUJw62QKSt+l7do+dAH3penqsjY/A1ll/8w\nbZOYmVWMhzMz+/N3M2TW1th2aIehpSPmrq6Yu7pWvHk38Y0CxadPkbt/H5q01DsufUNFQil3dER7\n6xZSKytaTZlmmpewIUqvXOHWV2vQZmUht7OvWL8asPLywXH0S3fN2dkUz7f/FaLt6kabk4NMqcSl\nrdMj127a3Iqb8RRt2jTKkoD10Vyfb831bmjh0SCSxXpqrm8ITcVoNGIsLwNZxV2iNX0ba87tZjQa\nK+5mTlVRnqpCk5b2589UzF1b0WrKtEZdIk+vVpO5ZRNFicex6NwFx5fGYuXhWe2+zbndmjvRdvUj\n2q1+mmu7iWSxYbZv386ePXtMn21GoxGJREJERAQ9e/Zs9HharZbXXnvtrs9Sd3f3GicAf5BEslhP\nzfUNobl7FNut8k3hQZWty89H3rLlPWM8iu3WXIi2qx/RbvXTXNtNJItCQ4gxi4JwHw/y7l6JRFKr\n+e4EQRAEoan879xSJQiCIAiCIDQ6kSwKgiAIgiAINRLJoiAIgiAIglAjkSwKgiAIgvBIU6vV+PvX\nfWGAgwcPNihuamoqY8aMuWNbdna2aZ7G6vj7+1NaWtqguA+bSBYFQRAEQXik1WfWCpVKxd69exsc\nu2pcR0fHe05v8yBvmnxQRLIoCIIgCEKT0Ol0RERE8MorrzB58mTS0tJ49913CQ4OZvz48Rw9ehSA\ngIAAvv76ayZMmEBgYCBqtZri4mJee+01JkyYwJdffmkq8/aeu6VLl7Jr16674mRkZLBw4UJ+++03\nVq9eTXFxMTNmzCAkJISgoCDOnTtnivv2228TGxt733P5+eefmTJlCjdv3jT1Nh45coSxY8cyfvx4\nNm/eDPzfsn/p6em89NJLZGdn89tvvxEUFMSkSZN499130el0JCYmMnXqVEJCQkhJSWm8Rq8HMXWO\nIAiCIPzFqS7sJS/jbKOWaefiQxvPYffcZ+fOnTg7O7Ns2TL27dvHjh07sLCwICYmhszMTIKDgzlw\n4AA6nY7OnTsTGhpKREQECQkJZGRk4OHhQWRkJPv27SMuLq7WceLj4wkLC2PLli1MmzaN6OhofH19\nCQsLIzk5maioKGJiYlCpVKxZs4ZOnTrd8zxu3LjB2rVrWb9+Pfn5+abewwULFvDPf/4TW1tbpk2b\nRmBgIBKJhLKyMv7+978TFRWFo6Mjr7/+Ops2bcLW1pZPPvmE/fv34+zszMWLFzl48CDyJl7pTPQs\nCoIgCILQJFJSUkxrIQ8ZMoT8/Hz69u0LgLOzMwqFgoKCAgD69Olj2l5UVMTly5fp1atijfh+/frV\nKc748eO5fU2S5ORkU1wvLy9u3LgBgKWl5X0TRbVazfTp0/nggw9QKv9vmcnc3FwUCgUtW7ZEKpWy\ndu1aFAqFae3p5557jq5du5KTk8O1a9eYMWMGwcHBJCYmkpGRAUDXrl2bPFEE0bMoCIIgCH95bTyH\n3bcX8EGQyWQYDAbT3xKJ5I4kTqvVIpVKTftWVflY1TIq6XS6auNUVXUcoV6vB8DMzOy+53Dr1i1G\njhzJ1q1b+eijj+6oW00xW7Vqxe7duwkKCsLMzAxXV1fTZepKiYmJtYr/MIieRUEQBEEQmoSXlxfH\njh0D4PDhw9jZ2XH8+HGgYkyfVCrFxqb6pQrd3d1JSkoCMJUBYGNjQ1ZWFnq9njNnzgDg7e1NQkIC\nAPHx8axbtw6pVGpKJr29vU1lnD59Gg8Pj1qfQ8eOHZk3bx43b97kyJEjpu0tW7bEYDCQmZmJ0Whk\n6tSpFBVVLAX51ltv4e/vz6pVq7C1tQXg8uXLAGzZsoWLFy/WOv7DIJJFQRAEQRCaxNChQ1Gr1QQH\nB7Np0yZGjx6NXq8nJCSEiIgIFixYANzZ81f5+8iRIzl9+jSTJ0/m+vXrpu1BQUFMmTKF8PBwunTp\nYopTWlpKcHAwmzdvZtSoUXTs2JFz586xZMkSJk6cSHJyMhMnTuSzzz7j/fffvyvu/Xz00UcsXrwY\ntVpt2vbBBx8QHh7OK6+8Qv/+/bGxsTGVOXXqVH799VdSUlJYtGgR7777LhMmTODkyZO4u7s3oFUb\nn8R4e3/vX0RjLPLeXBeLb+5Eu9WPaLf6E21XP6Ld6qe5tpuTU/W9c4JQG2LMoiAIgiAIwj1s376d\nPXv2mHoFK+d1jIiIoGfPnk1cuwdPJIuCIAiCIAj3MG7cOMaNG9fU1WgyYsyiIAiCIAiCUCORLAqC\nIAiCIAg1EsmiIAiCIAiCUCORLAqCIAiCIAg1EsmiIAiCIAiPNLVajb+/f52PO3jw4AOoTe0EBwfz\nxx9/UFxcbJrMe926daaJxJsTkSwKgiAIgvBIq5zKpi5UKhV79+59QDW6v8r6pqSk8J///AeAN954\no1lOxSOmzhEEQRAEoUnodDrmzJlDWloaFhYWLFq0iJUrV6JSqdBqtYSHh9O/f38CAgIIDAwkPj4e\nrVbLxo0bMRgMhIeHo9Fo6N27t6lMf39/4uLisLS0ZOnSpXh6ejJs2LA74ixZsoSFCxeSlJTE6tWr\nCQkJITIyksLCQvR6PXPnzqVbt24EBATg5eWFn58fY8aMuav+qampvPPOO1hbWzNhwgSUSiXLly/H\nzMyMVq1asXDhQiQSCXPmzCEjI4PS0lJmzpzJgAEDTGtgL1iwgJKSEjp27MjJkycZPHgwubm5JCYm\nkpeXx+XLl3nrrbfYu3cvV65c4ZNPPsHHx4dvv/2WPXv2IJPJeP7555k0adID+39qNslieXk5Q4cO\nZcaMGTzxxBPMnj0bo9GIk5MTH3/8MWZmZuzevZvNmzcjk8kYN24cY8aMQafTERkZSVpaGjKZjMWL\nF9OmTZumPh1BEARBeGR8f07FiVv5jVpmH9eWvNzt3p/HO3fuxNnZmWXLlrFv3z527NiBhYUFMTEx\nZGZmEhwczIEDB9DpdHTu3JnQ0FAiIiJISEggIyMDDw8PIiMj2bdvH3FxcbWOEx8fT1hYGFu2bGHa\ntGlER0fj6+tLWFgYycnJREVFERMTg0qlYs2aNXTq1KnGss+fP8/PP/+Mra0to0ePZtOmTdja2vLJ\nJ5+wf/9++vfvz1NPPcWoUaO4efMms2bNYsCAAabjQ0NDuXTpEi+//DInT540bb9x4wZbt27l+++/\nZ926dezatYvY2Fji4uKwt7dn//79bNu2DYDx48czePBgXF1da/vfUyfN5jL06tWrsbOzA+CLL74g\nODiYLVu20K5dO2JjYyktLWX16tVs2rSJzZs3880331BYWMjevXtp0aIF3377LVOnTmXZsmVNfCaC\nIAiCINRGSkqKqVdwyJAh5Ofn07dvXwCcnZ1RKBQUFBQA0KdPH9P2oqIiLl++TK9evQDo169fneKM\nHz+e21c7Tk5ONsX18vLixo0bAFhaWt4zUQRo164dtra25OTkcO3aNWbMmEFwcDCJiYlkZGRga2tL\nUlISr7zyCpGRkabzuR8vLy8AnJyc8PT0RCKR4OjoSFFREWfPnuX69euEhIQQHByMWq1GpVLVqtz6\naBY9i1euXOHKlSumbtn//ve/psXDBw4cyIYNG+jQoQM+Pj4olUoAevfuzYkTJ0hISGDUqFEA9O/f\nn/fee6/JzkMQBEEQHkUvd2tz317AB0Emk2EwGEx/SySSO5I4rVaLVCo17VtV5WNVy6ik0+mqjVNV\n1fGOer0eADMzs/ueQ+U+ZmZmuLq6snnz5jse37VrFwUFBWzbto28vDzGjh173zIr61zd70ajEXNz\nc5599lk+/PDDWpXVUM2iZ3Hp0qVERkaa/i4tLTU1voODA5mZmeTk5GBvb2/ax97enqysLLKzs03b\nJRIJUqnU9OQQBEEQBKH58vLy4tixYwAcPnwYOzs7jh8/DkB6ejpSqRQbG5tqj3V3dycpKQnAVAaA\njY0NWVlZ6PV6053F3t7eJCQkABAfH8+6devuyBe8vb1NZZw+fRoPD49an0NlcmtrawvA5cuXAdiy\nZQsXLlwgLy/PNDzu4MGDaLXaO46XSCSm5LS2evTowfHjxykrK8NoNLJo0SI0Gk2dyqiLJu9Z3LVr\nF7169aJ169ambbdn+JV3ON3+TeP27VUZDIY63xElCIIgCMLDN3ToUBISEggODkYulxMVFUV0dDQh\nISHodDrTVcbbP9crfx85ciTTp09n8uTJ9OnTx7Q9KCiIKVOm0LFjR7p06WKKc/ToUVOcpUuXIpfL\nOXfuHEuWLCE8PJzIyEgmTpyI0Whk3rx5d8Wtye37LFq0iHfffRdzc3OcnZ0JDAzE2tqaN998kzNn\nzrcAhp4AACAASURBVDBmzBhcXV1ZvXq16bgePXqwbNmyOo03bNWqFSEhIQQF/X/27jw+qvre//hr\ntkz2lSSEfUcQEELFhWorIlbUUosFi0SvwqOtWrjXcilYvdWfK7ZVbhf01trrgnqRirQIlFgFtAIi\nNUBZBDQIISRk3yaTzHp+fwSmAhlITpYJ8H4+HjxIZs6c8z3vzPKZ7znn+70du93OtddeS1RUVIsf\n31oW49QqrJPdf//9FBYWYrVaKSkpCfUorlmzhqioKLZt28Zrr73GzJkzWbZsWeicxAceeIBvfetb\nrFu3jptuuonx48fj9/u59tpr+eCDD864Tb8/gN1+ene2iIiIiJws4j2LixcvDv38u9/9jl69epGX\nl8e6dev49re/TW5uLldddRWjRo3ioYcewuVyYbFY2L59Ow8++CB1dXWsW7eO8ePHs379+rOe5ApQ\nVeVuc7vT0xMoK6tr83ouNMrNHOVmnrIzR7mZ01VzS09v/lCutMzy5ct55513Qr2BJ45uzps3r0uO\ni9jeIl4sNmfu3Ln89Kc/Zfny5fTo0YNbbrkFm83GvHnzuPvuu7FarcyZM4f4+HgmT57Mpk2bmDFj\nBk6nk0WLFkW6+SIiInIemTZtGtOmTYt0MyIm4oehI6E9vvV11W+PXZ1yM0e5mafszFFu5nTV3NSz\nKG3RJa6GFhEREZGuScWiiIiIiISlYlFEREREwlKxKCIiIuc0t9vNhAkTWv24d999t93bcvToUaZO\nndru640kFYsiIiJyTgs3UceZFBYWsnr16g5pz/k2OYiKRREREYkIv9/PvHnz+P73v89dd91FUVER\nDzzwADk5Odx2221s3rwZgEmTJvHHP/6RmTNnMn36dNxuNy6Xi7vvvpuZM2fy+9//PrTOCRMm0NDQ\nADRNJ/znP//5tO2UlJTw2GOP8Y9//IPnnnsOl8vFj3/849CsKJ999llouz/5yU9YsWJF2Pbff//9\n5OTkMH36dD766KOT7t+6dSu33XYbOTk5zJ8/H6/Xyw033IBhGAQCAbKzs9mzZw8As2bNori4uN0z\nbg9dcpxFERER6Tz/+84eNu082q7rHH9JT+6++eIzLrNy5UoyMjJ45plnWLt2LW+//TbR0dEsXbqU\n0tJScnJyyM3Nxe/3M2jQIGbNmsW8efPYsmULJSUlDBkyhIULF7J27VrWrFnT4u1s2LCB2bNn89pr\nr3HvvfeyZMkSRo8ezezZs9m9ezdPPvkkS5cupbCwkOeff56BAwc2u94DBw5QXV3N0qVLcblcp80g\n98gjj/Dyyy+TmZnJ448/zurVqxkxYgQHDhzA6/UycuRIduzYwfDhw6msrCQrK6v1QXcC9SyKiIhI\nROzdu5fs7GwAJk+eTHV1NePGjQMgIyMDp9NJTU0NAGPHjg3dXldXR35+PmPGjAE46+xtp27ntttu\n46vDTO/evTu03REjRlBQUABATExM2EIRYMCAAdTX17NgwQK2bNnCjTfeGLqvpqYGq9VKZmYmAOPG\njWPv3r2MGzeOHTt2kJeXR05ODjt37mT//v1cfPGZC+tIUs+iiIjIBe7umy8+ay9gR7DZbASDwdDv\nFovlpCLO5/NhtVpDy57qxH2nruMEv9/f7HZOdeo5hoFAAACHw3HG9kdHR7N8+XLy8vJYuXIlGzZs\n4L777gut86vb9Pl82Gw2Lr30Un7/+9/j9Xr53ve+x1tvvUVeXl6oWO2K1LMoIiIiETFixAg+/vhj\nADZu3EhKSgpbt24FoLi4GKvVSkJC87PP9O/fn127dgGE1gGQkJBAWVkZgUCAnTt3AjBy5Ei2bNkC\nwIYNG3jhhRewWq2hYnLkyJGhdezYsYMhQ4a0qP179+5l1apVZGdn8/DDD3Pw4MHQfYmJiVitVo4d\nOwbAJ598wogRI+jXrx/Hjh2jrq6O2NhY0tPTef/998/aOxpJ6lkUERGRiLjxxhvZsmULOTk52O12\nnnzySZYsWcIdd9yB3+/n0UcfBU7u+Tvx85QpU7jvvvu46667GDt2bOj222+/nR/+8IcMGDCAwYMH\nh7azefPm0Haefvpp7HY7n332GYsWLWLu3LksXLiQO++8E8MwePjhh0/bbnN69erFs88+y5tvvonN\nZmP27Nkn3f/oo4/yk5/8BLvdTu/evUOHqdPS0kJF8KhRo9i2bVvocHVXpLmhTeqq8392dcrNHOVm\nnrIzR7mZ01Vz09zQ0hbqWRQRERE5g+XLl/POO++EehpPjOs4b948Lrnkkgi3ruOpWBQRERE5g2nT\npjFt2rRINyNidIGLiIiIiISlYlFEREREwlKxKCIiIiJhqVgUERERkbBULIqIiMg5ze12M2HChFY/\n7t133233thw9epSpU6e2+3ojScWiiIiInNNODGXTGoWFhaxevbpD2tPatnR1GjpHREREIsLv97Ng\nwQKKioqIjo7miSee4Le//S2FhYX4fD7mzp3LlVdeyaRJk5g+fTobNmzA5/Px0ksvEQwGmTt3Ll6v\nl+zs7NA6J0yYwJo1a4iJieHpp59m6NCh3HTTTSdtZ9GiRTz22GPs2rWL5557jjvuuIOFCxdSW1tL\nIBDgoYceYtiwYUyaNIkRI0Ywfvz4ZnsL/X4/8+fPp7y8HK/Xy5w5c+jfv3/o/q1bt7J48WIcDgfd\nu3fniSeeYMqUKaxdu5ZgMMill17K0qVLufjii5k1axaPP/44WVlZp22npKSE+fPnY7FY8Pv9LFq0\niLfeeouLLrqIG264gZ///OdERUXx0EMPsXr1ag4dOkSvXr14/fXXiYqK4qKLLuK//uu/TP+dVCyK\niIhc4JbuWMHHR/LadZ2X984mZ/SZD8euXLmSjIwMnnnmGdauXcvbb79NdHQ0S5cupbS0lJycHHJz\nc/H7/QwaNIhZs2Yxb948tmzZQklJCUOGDGHhwoWsXbuWNWvWtHg7GzZsYPbs2bz22mvce++9LFmy\nhNGjRzN79mx2797Nk08+ydKlSyksLOT5559n4MCBza73wIEDVFdXs3TpUlwuFx988MFJ9z/yyCO8\n/PLLZGZm8vjjj7N69WpGjBjBgQMH8Hq9jBw5kh07djB8+HAqKyubLRQBSktL+fGPf8y4ceNYsWIF\nb7zxBldffTUffPABN9xwAxUVFaFlt2/fzg033MCjjz7KH/7wBzIzM1m5ciVer5eoqKgz/j3C0WFo\nERERiYi9e/eGegUnT55MdXU148aNAyAjIwOn00lNTQ0AY8eODd1eV1dHfn4+Y8aMAeCyyy5r1XZu\nu+02vjrb8e7du0PbHTFiBAUFBQDExMSELRQBBgwYQH19PQsWLGDLli2huZ8BampqsFqtoTmfx40b\nx969exk3bhw7duwgLy+PnJwcdu7cyf79+7n44ovDbic9PZ1XX32VmTNn8sorr1BdXc3o0aPZs2cP\ndXV1xMfHEx0dTWNjI3v27OGSSy7hpptu4t577+WVV17h6quvNl0ognoWRURELng5o6eetRewI9hs\nNoLBYOh3i8VyUhHn8/mwWq2hZU914r5T13GC3+9vdjunOvUcw0AgAIDD4Thj+6Ojo1m+fDl5eXms\nXLmSDRs2cN9994XW+dVt+nw+bDYbl156Kb///e/xer1873vf46233iIvLy9UrDbn17/+NVdddRXT\np08nNzeXjRs3EhMTg8Vi4eOPP2b06NE0Njby8ccfEx8fj8Ph4Ac/+AHf/va3WbduHXfeeSevv/46\nSUlJZ9yfcNSzKCIiIhExYsQIPv74YwA2btxISkoKW7duBaC4uBir1UpCQkKzj+3fvz+7du0CCK0D\nICEhgbKyMgKBADt37gRg5MiRbNmyBYANGzbwwgsvYLVaQ8XkyJEjQ+vYsWMHQ4YMaVH79+7dy6pV\nq8jOzubhhx/m4MGDofsSExOxWq0cO3YMgE8++YQRI0bQr18/jh07Rl1dHbGxsaSnp/P++++fsXe0\nurqaPn36APD+++/j8/kAGD16NG+88QZjxoxh1KhRLF26lK997WsYhsHixYvp1q0b//Zv/8bo0aMp\nKipq0T41Rz2LIiIiEhE33ngjW7ZsIScnB7vdzpNPPsmSJUu444478Pv9PProo8DJPX8nfp4yZQr3\n3Xcfd911F2PHjg3dfvvtt/PDH/6QAQMGMHjw4NB2Nm/eHNrO008/jd1u57PPPmPRokXMnTuXhQsX\ncuedd2IYBg8//PBp221Or169ePbZZ3nzzTex2WzMnj37pPsfffRRfvKTn2C32+ndu3foMHVaWlqo\nCB41ahTbtm0LHa5uzvTp03nsscfo2bMnM2fO5Oc//zmbN2/m0ksv5fXXX2fo0KF4vV62bdvGj3/8\nYywWC3FxcUyfPp3ExER69+7NsGHDWvx3OZXF+Gp/7wWirKyuzetIT09ol/VcaJSbOcrNPGVnjnIz\np6vmlp7efO+cSEuoZ1FERETkDJYvX84777wT6mk8Ma7jvHnzuOSSS9ptO3PmzAld0HNiO4mJiSxZ\nsqTdtmGGehZN6qrfHrs65WaOcjNP2Zmj3MzpqrmpZ1HaQhe4iIiIiEhYKhZFREREJCwViyIiIiIS\nlopFEREREQlLxaKIiIic09xuNxMmTGj14959990OaA3k5uae8f79+/dz+PDhDtl2R1CxKCIiIue0\nE0PZtEZhYSGrV6/ukPa88MILZ7z/b3/7G19++WWHbLsjaJxFERERiQi/38+CBQsoKioiOjqaJ554\ngt/+9rcUFhbi8/mYO3cuV155JZMmTWL69Ols2LABn8/HSy+9RDAYZO7cuXi9XrKzs0PrnDBhAmvW\nrCEmJoann36aoUOHctNNN520nUWLFvHYY4+xa9cunnvuOe644w4WLlxIbW0tgUCAhx56iGHDhjFp\n0iRGjBjB+PHjmTr19Lmz/X4/8+fPp7y8HK/Xy5w5c9i/fz/79+9n7ty5LF68mAULFlBSUkJDQwNz\n5swhKyuLZcuWkZqaSmpqKv/xH/9xWnsvu+wy5s+fj81mIxAI8Mtf/pKsrKzO/NOcRMWiiIjIBe7L\nl16hYvOWdl1n2pVX0P+uO8+4zMqVK8nIyOCZZ55h7dq1vP3220RHR7N06VJKS0vJyckhNzcXv9/P\noEGDmDVrFvPmzWPLli2UlJQwZMgQFi5cyNq1a1mzZk2Lt7NhwwZmz57Na6+9xr333suSJUsYPXo0\ns2fPZvfu3Tz55JMsXbqUwsJCnn/+eQYOHNjseg8cOEB1dTVLly7F5XLxwQcfMGvWLF588UV+85vf\nUFlZyde//nW+853vcOTIEf793/+dt99+m6uuuopvfetbjBo1qtke0dzcXMaPH88999zDZ599RllZ\n2YVdLDY2NrJw4UIqKirwer3cc889XHTRRcyfPx/DMEhPT+cXv/gFDoeDVatW8eqrr2Kz2Zg2bRpT\np07F7/ezcOFCioqKsNlsPPXUU/Tq1SvSuyUiIiJnsXfvXq688koAJk+ezOOPP864ceMAyMjIwOl0\nhmY0GTt2bOj2uro68vPzQ8tedtllrdoOwCeffBK6f/fu3dxzzz0AjBgxgoKCAgBiYmLCFooAAwYM\noL6+ngULFjBx4sTQ3M8nJCYmsmvXLt58802sVutJs7Oc0NzcKF//+te57777qK2t5frrr2f06NFn\n3L+OFvFicf369YwcOZJZs2ZRVFTEXXfdRXZ2NjNnzuT6669n8eLFrFixgilTpvDcc8+xYsUK7HY7\nt956K9dddx3r168nKSmJX/3qV2zatIlnnnmGxYsXR3q3REREzhn977rzrL2AHcFmsxEMBkO/WyyW\nk4onn8+H1WoNLXuqE/eduo4T/H5/s9s51am9e4FAAACHw3HG9kdHR7N8+XLy8vJYuXIlGzdu5Ikn\nngjtw+rVq6mpqeH//u//qKqq4tZbbz3jtk+0d9CgQaxatYqPPvqIZ599lqlTpzJlypQztqUjRfwC\nl8mTJzNr1iwAioqKyMrKYtu2baGrmq655ho2b97Mzp07GTVqFHFxcTidTrKzs/n000/ZsmULEydO\nBODKK68kLy8vYvsiIiIiLTdixAg+/vhjADZu3EhKSgpbt24FoLi4GKvVSkJC81MV9u/fn127dgGE\n1gGQkJBAWVkZgUCAnTt3AjBy5Ei2bGk6zL5hwwZeeOEFrFZrqDgbOXJkaB07duxgyJAhLWr/3r17\nWbVqFdnZ2Tz88MPk5+cD/+otrKqqCh3tfPfdd/H5fEBTgXhi2821d+3atezfv59rr72Wf//3f2f3\n7t0tak9HiXjP4gm33XYbpaWlPP/889x9992haj4tLY3S0lIqKipITU0NLZ+amkpZWRnl5eWh2y0W\nS+iPb7d3mV0TERGRZtx4441s2bKFnJwc7HY7Tz75JEuWLOGOO+7A7/fz6KOPAif3vp34ecqUKdx3\n333cddddjB07NnT77bffzg9/+EMGDBjA4MGDQ9vZvHlzaDtPP/00drudzz77jEWLFjF37lwWLlzI\nnXfeiWEYPPzww6dttzm9evXi2Wef5c0338RmszF79mwAhg0bxrRp0/jv//5vfvSjH7Fz506mTp1K\n9+7def755/na177GE088QVxcXLPt7devHw8//DBxcXHYbDYefPDBdky99SxGcwfLI2Tfvn3Mnz+f\nqqoqPvroIwAKCgpYsGABt99+O7t372bhwoUA/Pd//zc9e/Zk3bp1/PSnP2Xo0KEAfOMb32D9+vXN\ndlef0B6TvHfVyeK7OuVmjnIzT9mZo9zM6aq5pac33zsn0hIR737bs2cPaWlpdO/enYsuuohgMEhM\nTAxer5eoqChKSkrIyMggMzOTDRs2hB5XUlLCmDFjyMjIoLy8nKFDh550bsKZpKTEYrefeZmW0IvP\nHOVmjnIzT9mZo9zMUW7nn+XLl/POO++EehpPjOs4b948Lrnkkgi3ruNFvFjctm0bRUVF/OxnP6O8\nvBy3281VV13FunXr+Pa3v01ubi5XXXUVo0aN4qGHHsLlcmGxWNi+fTsPPvggdXV1rFu3jvHjx7N+\n/fqzXhEFUFXlbnO7u+q3x65OuZmj3MxTduYoN3O6am4qYNtm2rRpTJs2LdLNiJiIF4vf//73+dnP\nfsbtt9+Ox+PhkUce4eKLL+anP/0py5cvp0ePHtxyyy3YbDbmzZvH3XffjdVqZc6cOcTHxzN58mQ2\nbdrEjBkzcDqdLFq0KNK7JCIiInLe6FLnLHYWnbMYOcrNHOVmnrIzR7mZ01VzU8+itEXEh84RERER\nka5LxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiIiEhYKhZFREREJCwViyIiIiIS\nlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiIiEhYKhZFREREJCwV\niyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiIiEhYKhZF\nREREJCwViyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiI\niEhYKhZFREREJCwViyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGR\nsOyRbgDAL37xC/Ly8ggEAvzgBz9g5MiRzJ8/H8MwSE9P5xe/+AUOh4NVq1bx6quvYrPZmDZtGlOn\nTsXv97Nw4UKKioqw2Ww89dRT9OrVK9K7JCIiInJeiHixuHXrVvLz81m2bBnV1dXccsstXH755cyc\nOZPrr7+exYsXs2LFCqZMmcJzzz3HihUrsNvt3HrrrVx33XWsX7+epKQkfvWrX7Fp0yaeeeYZFi9e\nHOndEhERETkvRPww9Lhx4/j1r38NQFJSEm63m23btjFhwgQArrnmGjZv3szOnTsZNWoUcXFxOJ1O\nsrOz+fTTT9myZQsTJ04E4MorryQvLy9i+yIiIiJyvol4sWixWIiOjgbgT3/6E9/85jdpaGjA4XAA\nkJaWRmlpKRUVFaSmpoYel5qaSllZGeXl5aHbLRYLVqsVv9/f+TsiIiIich6KeLF4wnvvvceKFSv4\nr//6r5NuNwwDi8WCYRjN3n6qYDDY7O0iIiIi0noRP2cR4O9//zsvvPACf/zjH4mPjyc2Nhav10tU\nVBQlJSVkZGSQmZnJhg0bQo8pKSlhzJgxZGRkUF5eztChQ0M9ijab7YzbS0mJxW4/8zItkZ6e0OZ1\nXIiUmznKzTxlZ45yM0e5yfkm4sWiy+Xil7/8JS+//DIJCU0vsCuuuILc3FxuvvlmcnNzueqqqxg1\nahQPPfQQLpcLi8XC9u3befDBB6mrq2PdunWMHz+e9evXc9lll511m1VV7ja3Oz09gbKyujav50Kj\n3MxRbuYpO3OUmzldNTcVsNIWES8W165dS3V1Nf/xH/8ROrT89NNP8+CDD/Lmm2/So0cPbrnlFmw2\nG/PmzePuu+/GarUyZ84c4uPjmTx5Mps2bWLGjBk4nU4WLVoU6V0SEREROW9YjFNPBrwAtMe3vq76\n7bGrU27mKDfzlJ05ys2crpqbehalLbrMBS4iIiIi0vWoWBQRERGRsFQsioiIiEhYKhZFREREJCwV\niyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiIiEhYKhZF\nREREJCwViyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGRsFQsioiI\niEhYKhZFREREJCwViyIiIiISlopFEREREQlLxaKIiIiIhKViUURERETCUrEoIiIiImGpWBQRERGR\nsFQsioiIiEhYKhY7iRH0U130PsX7XsDvqYp0c0RERERaRMViJ/C6j3Fs/x+pLdmEr+EY1cUbI90k\nERERkRaxR7oB5zPDCFJbsomaYx+AESQ+bSwe91HcVbvwZl5JVExmpJsoIiIickbqWewgvsYKSg68\nRE3xBmz2ONIHziC1z40kZ10DQI16F0VEROQcoJ7FdmYYBq7ybVQffQ/D8BObMoKUXjdgs8cAEJ04\nCGdcbxpq9uOpL8QZ1yvCLRYREREJTz2L7SgY8FCW/xpVheuwWB1063cr3fp9N1QoAlgsFpJ6TACg\numhDpJoqIiIi0iLqWWxH9RU7aKz7kuiEgaT1nYLNEd/sctHxfYlOGEhjXT6NdQeJThjQyS0VERER\naRn1LLajhrp8AFL73By2UDwhuUfTuYvVRRswDKPD2yYiIiJihorFdmIE/XjqDuGIzsAelXjW5aNi\nexCTPAyv+ygNtQc6oYUiIiIiradisZ00ug5jGH6iE1t+SDm5+zcBCzXqXRQREZEuqksUiwcOHOC6\n667j9ddfB+DYsWPk5OQwc+ZM7r//fnw+HwCrVq3i1ltvZfr06axYsQIAv9/Pf/7nfzJjxgxycnIo\nLCyMyD401jYdgo5OGNjixzhi0olLHYWvsRR31e6OapqIiIiIaREvFhsaGnj88ce54oorQrf9+te/\nJicnh9dee40+ffqwYsUKGhoaeO6553jllVd49dVXefnll6mtrWX16tUkJSXxxhtv8KMf/Yhnnnkm\nIvvRWJePxWInOr5vqx6X1P1qsFipKd6IYQRMb98wDBpqDuBrLDe9DhEREZFTRbxYdDqdvPjii2Rk\nZIRu++STT7jmmqYLQK655ho2b97Mzp07GTVqFHFxcTidTrKzs/n000/ZsmULEydOBODKK68kLy+v\n0/fB763B11iGM6EfFmvrLjC3O1OITxuL31tFfcUOU9sPBrxUHHqbsoPLKDu4XIe0RUREpN1EvFi0\nWq1ERUWddFtDQwMOhwOAtLQ0SktLqaioIDU1NbRMamoqZWVllJeXh263WCxYrVb8fn/n7QDQWHcQ\ngJhWHIL+qqTuX8disVNz7EOCQV+rHutrLOPYgRdxV+8Biw2/pxyP67CpdoiIiIicKuLFYnMsFkvo\nZ8MwsFgsp/WWnbj9VMFgsNnbO1JD7RcARCeaKxZtjgQS0scR8NXhKv9Hix9XX7WHY/tfxN9YTkL6\nZWQM/D4ArorO710VERGR81OXHJQ7NjYWr9dLVFQUJSUlZGRkkJmZyYYN/5rxpKSkhDFjxpCRkUF5\neTlDhw4N9SjabLYzrj8lJRa7/czLtER6egJGMMDRXYeIik4hq1c/04VqSvL17KrMo/bYhzgsLpIz\nR5KQMgCL9fR2BoN+jh5YQ0XBR1htTvqPmklK90swDIPa4gwaqj8jOcmCI+rMYz1GSnp6QqSbcE5S\nbuYpO3OUmznKTc43XbJYvOKKK8jNzeXmm28mNzeXq666ilGjRvHQQw/hcrmwWCxs376dBx98kLq6\nOtatW8f48eNZv349l1122VnXX1XlbnMb09MTKCurw+M6QsDfQEzScMrLXW1aZ0rPG6gqXEdZ4RbK\nCrdgtcUQkzSU2ORhRCf0x2K14/fWUn7oLbz1hTii0+nW/3v4bd0oK6sDIDp5DI31uRQc2ERi5pVt\n3s/2diI3aR3lZp6yM0e5mdNVc1MBK20R8WJxz549LFq0iKKiIux2O7m5ufzqV79i4cKFvPnmm/To\n0YNbbrkFm83GvHnzuPvuu7FarcyZM4f4+HgmT57Mpk2bmDFjBk6nk0WLFnVq+0/M2mL2EPRXxaWO\nJDblYjyuw7irP6Oheh/1lTuor9yBxeokJmkwjXUHCfrdxKaMILX3TVhtJ5/vGZ86ipqi93FV5JGQ\ncUWnH5JvjhH0E/C7sX5ljmwRERE5N1iMC/DS2fb41nfi2+Ox/S/idR+j16j5WG3OdmjdvxiGgbf+\nCO7qz3BX7yPgqwGLlZSe1xPf7WthC8GKw3+hvnInGQNntmqQ8LbwuIuor/wnQb+boN9NwN/Q9HPA\njXH8oh27M5UR4+dRWeXplDadT7pqb8W5QNmZo9zM6aq5qWdR2iLiPYvnsoDfjdddhDO+T7sXJZ97\nKgAAIABJREFUitB0oY8zvg/O+D4k95yEr6EEqy0auzP5jI+L75ZNfeVO6io+7ZRi0VN/lNIvXg0V\nhQAWqwOrPRa7sxs2ewzBoBdvfSHFB9/FmfKNDm+TiIiItA8Vi23QWPcl0LpZW8yyWCxExXZv0bJR\nsb1wRGfQUL2fgM+FzdFxF7r4GsspO/h/GEE/qX2mEJ3QH5s99rTxJoNBH8WfPU/J4b/T3TmEqNis\nDmuTiIiItJ8uOXTOuaLx+JA5Me1wvmJ7slgsxHcbCwRxVWzvsO34fXWU5r9O0O8mtfeNxKddgj0q\nsdmBya1WB6m9bwQjSGXBagwj2GHtEgn4XJQdfJOKw6soLdiMp/4oRrBzx18VETlfqGfRJMMwaKzN\nx2qPxRHT9XrJ4lJHUl30Hq6K7SRmfr3dL3QJ+hso++J1At4akrKuIb5b9lkfE5M4kNSsbCqL86gr\n+4TEjMvbtU0i0PTarDj8FxqPX3xWX3l8ZiSLjaiYTKJiexAV2wNnXC8c0d0i2FIRkXODikWTGlzF\nBPwuYlNGdokrjk9ltUUTmzKC+ortNNblE5M4qN3WHQz6KDu4DF9jKfHp40jM/HqLH9tr6M1Ul35G\nTfEGYpMvwh515vMvRVqrrvRjGuvyiU4cRHKPiThtlVSUHMTrLsLbcAyvuyi0bGLm10nKuqZLvoZF\nRLoKFYsm1ZYfALreIeiviu82lvqK7bjKP223YtEwgpR/+Rae+iPEpowgpef1rfqgdUTFk9xzEpUF\nf6HyyF9JH3CbPqgvUO7qfXgbjpHU/WoslvY5I8bjLqK6+H2s9njS+kzB5oijW/pAjKiLgKZhnLwN\nJXjdxdSVfUxtyUcEg95WP49FRC4kOmfRpNqK/UDnXNxiljO2B46YLBpqDuD31rZ5fYZhUFmwmsba\nz4lOGEBanymmPmDjUkfhjO9HY+3nuKv3trldcu5pqDlA+Zd/ovbYh1QWrDptOk8zggEPFYfeBiNI\nt77fweaIO20Zi9WOM64nCelfI3PwnTiiM3CVfUJlwTs6j1ZEJAwViyYEA15cVV/iiMlq9gOpK0no\nlg0Y1LfDhS41Re9TX7mDqNgedOs/rdmpCFvCYrGQ2ucmLBY7VYXrCPob2ty2C3C40HOWp/4o5YdW\nYLHYcERnUl/5T6qO5rb5b1h55K/4PZUkZlzZoiGjbI4EMgbfQVRsD+ord1Bx6G2MYKBNbRAROR+p\nWDTB4zqEYQSI6aQBr9siNmUEFmsUrortpntODCNA1dG/UVu6GbszjfQB3z9t5pjWcjhTSex+FUF/\nPdVF77f68cGAh4aaA1QV5lK87/cU7nyKyiN/JRjwtqldZhhGUMVqC/k8laGhltL6TyVjcA6O6HRc\nZZ9Qe+xD0+utr/wn7qp/EhXbg6Qe17T4cTZ7LBmDcnDG9cFdvZeyL5frqmkRkVPonEUTGmpPTPHX\nfheNdBSrzUlc6khc5Z/SUPs5sUlDW/V4v7eG8kMr8NYXYnemkjHo9nbrTU3MuBJ31R5cFXnEpo4k\nOr5v2GWNoB9P/REa676k0XUIb/1R4HiBZrFhs8XgKt9GQ+0BUnvf2K4X9JyJ111Maf4b2KOSSO19\nU4vHwrwQBfxuyvLfIOh3k9J7cui5mD5oJiUHXqLm2AdYbdEkZJx9fvev8nkqqTyyFos1im79pmKx\ntK7H22pzkj7odsoPvklj7eeU5r9B+oDb2vyFSETkfKFi0YTGunysNifOuF6RbkqLxKeNxVX+Ka7y\nvFYVi+7q/VQW/IVgoPH4XNQ3tutMNRarjdQ+N1Jy4CUqC9aQddEPMIwgfk85vsav/POU4/dUQqhn\n1EJUbA+iE/oTndCfqLheWLBQc+xDaks2UZb/BnGpl5DccxK2DpyP2uMqoDT//zCCHrz+eo7tf5HE\nzPEkdb+q2bEmL2TBoI+y/P9rOkycOZ6Ebl8L3Wd3JJAxaCYlB16m6mguVnsMcamjWrReIxg4fvjY\nS1rf72J3pphqn9XqIH3AbZQfepuGmn2UfrGUjIEzNJ+5iAgqFlvN56nE76kkOePiVvdgREpUbHei\nYnvQWPs5fm/1WYerMYIBqoveo65sKxaLndTeNxGXNqZDrhZ1xvUmvtuluMq3cXT3YoKB089ftFid\nRMVk4YzrRXRCf5zxfZstWpN7TCA2eTgVBe9QX7mThtovSO11A7Epw9u93Y21Byn78k2MYIC0flOx\n2pxUFqyhtuTvuKs/I63PzTjje7f7djubYRht/rsbRpCKQ2/jdR8lNmUkSVkTTlvGcbzXuvTzV6g4\n/BcsNmeLvthUF6/H6y4iLvUS4lJHtKmdFqudbv1vpeLwX3BX7aLki6WkD/w+dofm1L0QNNTmU1e2\nFbszldiki3DG92m3q/RFznUW4wI82aotk7zXlW2jqvCv9Bn2XYhu24dTZ3JV7KCyYBVWexzR8f1w\nJvQjOr4vdmfaScWAz1N5/IO9CLuzG936TyUqJrPd2pGennBa/sGAh5IDLxEMNGB3dsMRffI/qz2+\nVQWLYQSpK91CdfFGMALEJF1Eau8bsLXTh767Zj/lX74FQLf+t4aKmmDAQ3XxBlxlnwAQnz6O5KwJ\n7XI4s7ncOpJhBKkr+4SaYx9itdixR6fjiEnHEZ1+/O+Sjs0e24L1GFQVrsNVvg1nfH8yBs4444VR\nnvpCSr9YimEEyRg4g+iE/mGXbaj9grL8N7A7U+k+9Adhc25tdk1tXour/FNsjgTSB9x2QU5P2dnP\nOWg6rcNds5+E9HEten61h4CvjqrCd3FX7znpdqs9jtikocQkDyM6oV+LOwcikVtLpKfrS4+Yp2Kx\ntY89uIyGmgOM+PoD1NY72rFVHcsI+qk6mou7eh9Bf33o9qbisS/O+L5YrHaqCt/FCHqIS72ElF43\ntPt5W535RuprrKCy4B089QVYbdGkD5zR5lMH6it3U3F4JRarnfQB04lOOP0iJ4+rgIqCd/B7KrAd\nP5exreNxdmZu3oZSKgtW4XUXYbVFY7E5CXhrTlvOao/DEd0Ne1Qytqgk7FFJ2B1JoZ8tVju1JZup\nLnoPR3QGmUP+Dast+qzbb6jNp+zgMiwWGym9rscI+gn4XAT8LgI+F0F/fdPvvjqwWOg+ZNYZizkz\n2RmG0fSFo+g9LBY7af1uITZ5WKvWca7r7KLH21BKyecvYwQasdrjSO1zU6vPsW4NwwjiKv+U6qL1\nGEEPUbE9Sel9A0F/Aw3Vn+Gu2R96r7TaoolJGkps8nCiEwed8curikU5H6lYbIVgwMvR3c9icyRw\nyTcWdsk3hLMxDAO/p4JG12E8dYfwuA4T8LtC91usDlJ6TSY+7ZIO2X7n95AZuMr/QVXhOiw2J5mD\nckz3ErnK86g8shqLzUnGgBlnPMxsBP2hcyjBICq2BzFJQ4hJHIwjpnurD+12Rm5GMEBtyUfUlPwd\njGBo0HWbI45gwIuvsez4eaRloX8Bb3XY9VntcQT99dgciWQOuRt7VGKL2+Ku/ux47+3pb08Wix2r\nIx6bI56E9MuIS7n4jOtqS3bu6v1UHH4bI+gjKeuaDpk6s6vqzNeq31tDyYH/JeCrIy51FPVVe8AI\nEJc6ipSe17f7uaNedzGVR9bgdRdhsTlJ7nEt8WljT/rbGkYQj6sAd80+Gqo/a/pyAsSmjCKtz81h\ne8hVLMr5SMViC3ncRVQcWonfU0FCxpUMGX1Ll3xDaK2m4rESj+swPk8l8WmX4IhO77DtReqNtL5y\nFxWHV2K1xZAx+E6iYjJa9fja0o+pPvouVnssGQNvb3HB6XUfo7roPRrrDgFNF+jYHAlEJw4iJnEI\n0Qn9m+29NYwgwUAjwUAjFouVrJ69OzQ3T/1RKgvewddYis2R0HRFedKQsz7OCPrxe2sI+Grwe2sJ\neGvwH/8X8NWEeuXMnMrQ6CrA6y7C5kjAZo/DdrxAtFidrSrY2vqc87qPUXZwGQFfLbEpI48XCl33\ndG8j6Mfvq8XhTG3TejrrtRrwN1Dy+Uv4G8tJ7jGRxMwrT+rdbno+3kRM0uA2bysY8FBTvJG6sk8A\n4/gXoknYHPFnfJxhGHjdR6kqzMXrPoozvj/pA77XbE+5mdz8niqqi97H6ognpeekVp8raQT91Fft\nIiomK+yIDCoWpS1ULJ6FYQSpLdlETfEHQJCE9MtI7nEtGZkp50Wx2Nki+a3bVbGdyoJ3sNrjyBz8\nbzii0876GCMYoObYRmpLNjUN4jxwJo6Y1hfTQX8jDXX5NNR8TmPdFwT97qY7LLbQofFgoJGgv5Fg\noAEjePJ4kcmZo4hJu7rNBcBp7Qr6qCneQF3pVsAgvttYkntc26LDxeeK9njOBXwuyg6+idd9lKi4\nXqT3n3bWAqOzGYaBu2oX1UXrCfhq2zzvdWe8VoNBH6VfLMVbX9j03tpzUqi9offeYx+AESQudTQp\nvSaZem4Ggz7qy/OoLd1MwFeH3ZlKaq/JLRq8/dT1VBx6m4aa/TiiM0gfOOO0HvPW5NZ0fvXH1BRv\nxDCaxveMSR5Gt77fbfGkB8GAh7KDy/C4DgPgiOlOfNoYYlNGnDQahIpFaQsVi2fg91RRcfjPeOqP\nYHMkkNZnSujNpaseaujqIp1bXdknVBWuazo0OvjOMw610lj35fFZQcqxRSWTOSjH9NAsX2UYQbzu\nozTUfE5D7ef4GkoAsNicWG3RWG0xWG3O4/9H42ssw+s+ChYrCd0uJbH7VW0++T8YaMRVsZ260k8I\n+GqaPjx730R0Qr82719X017POSPop6JgFe6q3dgcSaQPvK1dL/5qi8a6L6k6+jd8Dceaxh21xxLw\n1RGTOIS0freYGvKqo1+rhhGk/OByGmoPEJsygrS+tzRb2HobSqg4/Bd8DcewORJJ6XU9MYmDW9S7\nGwx4cZV/Sm3pZoL+eixWBwkZV5CU+XXTvcOGEaSqMBdX+bamC6AGzjjpedDS3Dz1R6k8sgZfwzGs\n9liSe0ykvnInHtdhohMH0a3/97Baz3xefMDnojT/DXwNx4hOHIzFYqWh5gBggMVGbNJFxKWNJjqh\nPxkZSab2VwRULDbLMIymKcgK/4oR9BKbPJyU3jee9i1NxWLrdYXcTlx0YYtKJnPwv53WMxDwuag6\n+jfcVbsAiO92KclZ12C1d0xvWzDgxWK1hz30ZBgGjuCXFOxbg99bhcUWTVLmVSSkX9rqDzxfYwV1\n5duor9iBEfRisdiJTx9HUtY3zvrBdK5qz+ecYRjUlvydmuKNWCx2knpcS0L6uIidx+hrLKPq6Hs0\n1n4OQGzKSJJ7XIPF6qTi0Fs01n2JPbob6f2nt6gn/as68rVqGAaVR1ZTX7Gd6IT+pA8481XyhhGg\n9thH1Bz7OxDEYnXgjO9HTOJAohMHndbjHgx4qCvbRl3ZxwT9bizWKBLSx5GQcXm7XGV90gVQVifp\nA74XutjtbLkFA96m3vzjh8LjUkeT3HMiNnsswaCP8oPLaazLxxnf74yDw/s8lZR98Tp+bxXxadmk\n9J6MxWIl4HNRX/lPXBU78HvKAbA5khh9zUNt3m+5cF2QxeKhAx9jtTqwhP5FhX43jCBVR3NpqP4M\nizWK1N6TiU0ZedqHQVcoes5FXSW3muIPqDn2AXZnGpmD78TmiD9+dWQe1cXvYwQ8RMVkkdLnRpyx\nPSLdXNLTEygtqaKu/B/UHvuQYKARW1Ty8bElLz5jsWIYBh7Xl9SWbg0VFTZHIgnplxKXlt2hA5d3\nBR3xnHNX76Oy4B2CgQaccX1I7fvtdj9F4EwCPhc1xz7AVZ4HGDjj+5Lc87qTnquGEaT66N+axku1\nRdOt39RWXZXfka/V6qIN1Jb8HUdMFpmD72hxz6e3oYT6yp001ubjaywL3W53pjadB5wwAK+7mLqy\nrU3n+9qim4rE9Ms65HleX7WHisN/BsMgtc/NxKddcsbc3DX7qTryVwK+WuzONFJ733hab74R9IcG\nh4+K60XGgBmnfVE9MXNU0F9PYverSer+jdPeA06cZ+mq2IG7ajfZE59o132XC8sFWSx++u78sy7j\njOtDWt/vYHc2P4B1Vyl6zjVdJTfDMKgpXk9tySYc0Rmk9JwUGuDZYnWS3GMC8d3GdplBeb+aW9Df\nQM2xv1NX/gkYQWxRSdjscU29k9YoLFbHv74MWew01n2Jr7EUgKjYniRkXE5s8kXnzKDybdVRz7mA\nz0XlkbU01OzDYrE3XVHbAb2MhmEQ8NXidRfjdRfhdRfjqS/ACPqwO9NI7jmRmMQhYbfrqthB5ZE1\nYARJ7nkdCemXtaiNzeVmGAbe+iNYHfGmi+MTY9Xao1LIHHKX6XM//d5qGmvzaaj9gsa6L086z9dq\niyEh43IS0i/t8PNvG12HKTv4JkagsamXPjmNupqa0AVqoX/+BvzeKrBYm2Z6ygw/05NhBEODwzti\nupMxaGaoR7Sx7sum7QW9pPS6gYT0S8/axmDAS2b31vUsi3zVBVksHty7HiPowwj6CAa9GEE/RtB7\n/Hcf0Qn9jx9aCl8odJWi51zTlXIzDIOqo7mhQbSBFl8d2dmay83vqaK6eAONdQdDz+fmWYlNGU5C\n+rhzZorK9tTRh1Pd1XupOrL2rL2Mfm8NjXWH8LgO4XEVYGBgs8VgtcditTf9H/rd5sTXWN5UIDYU\n/+uCqOPsUSkkZFxOfLfsFhX9nvpCyg8uJ+B3EZc6itTeN531FIaTv6A04qrcjqvsH00FD+CM70tc\n6mhiU4af9RSGYNBHY+0XuKv24q7e03SR2ZC72q031ggGjs8dfxCrPZb4tDHtOjXp2fgayijNf4OA\n7/TxSOH4cE+2aBwxmaT0nNSii+QMI0jlkbXUV+ThiE4nY9BMPK4jlB9eCUC3vre0anYqXeAibXFB\nFovt8cHRlYqec0lXy80wDKqP/g1PfQHJPSY0O8h2V9CS3AzDCBWNX/0yZI9K6nLFb2fqjOdcc72M\nMcnD8LgO0+g6hKfuUKjIgqZBni3WqONXvocr8pvYopKJis06PixK0z8z5935fXWUH3zz+OxMqcQk\nDsYZ1xtnfJ9mnx/p6QkcLcjHVbaN+qp/YgR9WCx2YlOG4/fW4nEdAsBijSIuZQRxaaOJiu0Z6rUM\nBrxNBWL1XhpqPw/tp92ZSrd+U8+7WXECfjeeukMkpyRSV2+ELlBr+lubvZjGoProu9SVbcVmjyfg\nd2GxRh2fECD87EbNUbEobaFi0aSuVvScK5SbOcrNvM7K7tRexq+y2JxEx/U9Ps1mPxwxmf8qqoI+\ngn43QX8DQb+bQMBNMNCIPSrFdGEYto1BP5WF66iv3AlGIHS73ZkaKhydcb3xNZbRWJ2HqyofAFtU\nEgndvkZc2phQe/yeKlyVO6iv2EnAVwuAIzqd2OTheBtLaaz5PDQcjN2ZSmzyMGKTh5salP5c0t7P\nt6ZTZjZQW/IRVnscGQNnmCq0VSxKW6hYNEkf3uYoN3OUm3mdnV3A56K6eAMBn6tpKs2EfkTFdO8y\n579CU9HocRfhcRXgqS/AU38EI+A5bTlnfH8S0i8lJmnIGa7WD9JY9yX1Fdtx1+wPFaF2ZxqxycOJ\nTR52UnF8vuuo51tj3UHszm6tmgnpq1QsSlt03WkIRETOQTZHPGl9bo50M87IYrUTHd+H6Pg+QFPB\n52ssO148HsFqj6XP4KtxNZy9V9NisRKTOJCYxIEE/G4a6w7iiM7AEZ1+wRSInaGrniIjFwYViyIi\nFziLxUpUTCZRMZmhq2tj4hNwNbSuh8xmjyUuZURHNFFEIqjrHBcRERERkS5HxaKIiIiIhKViUURE\nRETCUrEoIiIiImGpWBQRERGRsFQsioiIiEhYKhZFREREJCwViyIiIiISlopFEREREQlLxaKIiIiI\nhHXeTPf31FNPsXPnTiwWCz/72c8YOXJkpJskIiIics47L4rFbdu2cfjwYZYtW0Z+fj4PPvggy5Yt\ni3SzRERERM5558Vh6C1btjBx4kQABg4cSG1tLfX19RFulYiIiMi577woFsvLy0lNTQ39npKSQnl5\neQRbJCIiInJ+OC8OQxuGcdrvFosl7PL3rnu4o5skIiLSZfwp59lIN0HOYedFsZiZmXlST2JpaSnd\nunULu7xeNCIiIiItc14chh4/fjy5ubkA7N27l8zMTGJjYyPcKhEREZFz33nRszhmzBguvvhibrvt\nNmw2Gz//+c8j3SQRERGR84LFOPWEPxERERGR486Lw9AiIiIi0jFULIqIiIhIWCoWRURERCQsFYsi\nIiIiEpaKxTBKS0vx+/2RbsY5R7mZp+zMUW7mKDfzlJ1caGyPPPLII5FuRFfi8Xh45pln+MMf/sDe\nvXsJBAIMGDDgrLPCXOiUm3nKzhzlZo5yM0/ZyYVKPYunePfddykrK+Ott95iwoQJ/Pa3v6WiokJv\nBGeh3MxTduYoN3OUm3nKTi5UKhaBffv2sW/fPgDi4uKIiYkB4Jvf/CbZ2dm88sorOuTQDOVmnrIz\nR7mZo9zMU3YiF/hhaJfLxbx58/jwww/ZuHEjCQkJ1NXVYbfbSUxMJC0tjWHDhvHiiy+SnZ1NWlqa\nDjeg3NpC2Zmj3MxRbuYpO5F/uaCLxb1793L06FGeeeYZMjIy2LNnDxUVFZSVlREbG0tWVhapqakc\nOnSIjRs3cv311+uNAOXWFsrOHOVmjnIzT9mJ/MsFdxh6zZo1bNq0iUAggMfjYceOHQCMHz+ewYMH\n43A4MAyDL774InTf1KlTSUlJuaAPNSg385SdOcrNHOVmnrITad4F0bNoGAZVVVXMnz+f/Px8qqur\nWbVqFddddx2HDh3C6/UyePBgYmNjKSwsJCEhgaSkJNauXUtlZSX/+7//S3Z2NmPGjIn0rnQq5Wae\nsjNHuZmj3MxTdiJnd0H0LFosFjweD/Hx8fz2t79lwYIF9OjRgyeeeIIbb7yR9957D6/XS1ZWFk6n\nk5qaGmbMmMGsWbOoqKjgjjvuICcnJ9K70emUm3nKzhzlZo5yM0/ZiZydPdIN6EjPPfccaWlpZGdn\nY7FYQm8KTqeT+++/n+uvv57i4mL69u3LkiVLuP/++7n00kvZvHkzPp+P7OxssrOzI70bnU65mafs\nzFFu5ig385SdSMudlz2L1dXVPPDAA5SUlBAMBlmwYAE2m438/Hzy8vJCy91zzz2sW7eO73//++Tm\n5vK73/2OBQsWcPnll2Oz2TAMI4J70fmUm3nKzhzlZo5yM0/ZibTeedWz6Ha7iY2NxePx8OWXX7Js\n2TIAjh49yqZNmxg/fjzPP/88l19+OYZhMH78eP7+978THx/PH/7wB7744gvGjx9/wZ17otzMU3bm\nKDdzlJt5yk7EvPPiApfKykp+85vfsGbNGhITE0lPT6eoqIjGxkYGDBjAoEGDWLVqFRMmTOCf//wn\nFRUVDB06lIqKCj799FMmT55MUlIS/fv3JysrK9K702mUm3nKzhzlZo5yM0/ZibSdxTjH+9INw2Dh\nwoX07t2bwYMH849//IO4uDiSk5MxDIMpU6aQmprK66+/TmFhITNmzGDZsmUcOXKE8vJyJk6cyN13\n333BDaaq3MxTduYoN3OUm3nKTqR9nLPFYnl5Od26daO6upoHH3yQJUuWAJCXl8fatWsJBAJkZWXR\np08fvvWtbwEwceJEXnnlFXr27MnOnTvp3r07mZmZkdyNTqfczFN25ig3c5SbecpOpH2dc4ehCwoK\n+PnPf87f/vY3Dh48yBVXXMHatWupra1l5MiRxMfH4/V6+fzzz0Mv+hPzeebn5zNhwgRiYmLo3r07\n8fHxkd6dTqPczFN25ig3c5SbecpOpGOcc1dD/8///A+jR4/mN7/5DbW1tTz66KPMmzePlStX4vV6\nSUhIYMCAAXTv3p3hw4czfvx4XnnlFe69917Gjh1LSkpKpHchIpSbecrOHOVmjnIzT9mJdBDjHBEI\nBAyfz2f86le/MjZu3Bi6fdKkScaHH35oLFq0yHjqqacMwzAMv99v3HPPPcbnn39uGIZhlJSUGB6P\nJyLtjjTlZp6yM0e5maPczFN2Ih2rSw+d09DQQExMDABWqzU0aGptbW1o8NQf/ehH/O53v+OPf/wj\n3/nOd7jiiiuIi4vD5/OF5urMyMiI5G50OuVmnrIzR7mZo9zMU3YinadLnrNYUFDA4sWL2bBhAxkZ\nGSQlJWG327FYLHi9XlavXs2ll15KQkICw4YNY/ny5QwbNoxrrrmGHTt2sGLFCmbNmnXBja6v3MxT\nduYoN3OUm3nKTqTzdbli0ePx8LOf/Yzs7Gx69+7Npk2bKC4uZtSoUQAMGDCADz74gJqaGrKysoiP\nj6e6uprExETGjx/PuHHj+O53v0ufPn0ivCedS7mZp+zMUW7mKDfzlJ1IZHSZYrGqqoqYmBiKi4vZ\nunUrCxYsYPjw4TidTt5//30yMjLo3r070PSGsHnzZj799FNKS0v585//zM0330x6enqE96LzKTfz\nlJ05ys0c5WaeshOJrIgXi7t37+aBBx7gk08+IT8/n4kTJ/KHP/yBHj160Ldv39A3w23btnH11VcD\nkJqayogRI6itrWXfvn3MmTOH4cOHR3I3Op1yM0/ZmaPczFFu5ik7kS4iklfXeL1e49577zXeeecd\no76+3pgzZ46xZMkSY9OmTcadd94ZWm779u3GY489Zhw8eNDweDzGiy++GLlGdwHKzTxlZ45yM0e5\nmafsRLqOiIyzGAwGMQwDj8dDVlYWffv2JTY2lv/3//4ff/rTn0hKSiItLY1XX30VgD59+lBYWEhS\nUhJRUVGkpaWF1nMhUW7mKTtzlJs5ys08ZSfS9XRqsZifn4/H4wkNcxAVFUVjYyNVVVX4fD5SUlL4\n7ne/y//8z//wn//5n7z88svs3LmTHTt2AOB2uwH4zne+09R46zk3prgpys08ZWeOcjNHuZmn7ES6\nrk55Ne3bt4+5c+fy0ksvhca2evrppykoKGD06NH89a9/pa6uDoA5c+bw+eefEwwGeeAgH21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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a04d22c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bucket_no = 9\n",
+    "df_single_bucket = df.applymap(lambda x: x.get('bucketCounts', []) if isinstance(x, dict) else [])\n",
+    "df_single_bucket = df_single_bucket.applymap(lambda x: float(x[bucket_no]) if len(x) > bucket_no else 0) \n",
+    "\n",
+    "# Display the data as a linechart\n",
+    "df_single_bucket.plot().legend(loc=\"upper left\", bbox_to_anchor=(1,1));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display all bucket values for a specific timeseries\n",
+    "\n",
+    "In order to display the size of all the buckets in a given timeseries, we create a new dataframe, where each column corresponds to a specific bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OQCDQa92qqiomTZrEnj17WLRoEQaDgRUrVoTC2NVAJsMLIYQQYsgcOnQIpRRN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\nXNbgNq1BVAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a0424f8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas \n",
+    "\n",
+    "df_single_column = pandas.DataFrame(index=df.index)\n",
+    "column_name = df.columns[0]\n",
+    "series_col = df[column_name]\n",
+    "\n",
+    "max_buckets = max(series_col.apply(lambda x: len(x.get('bucketCounts', [])) if isinstance(x, dict) else 0))\n",
+    "\n",
+    "for i in xrange(max_buckets):\n",
+    "  series = series_col.apply(lambda x: x.get('bucketCounts', []) if isinstance(x, dict) else [])\n",
+    "  series = series.apply(lambda x: float(x[i]) if len(x) > i else 0) \n",
+    "  df_single_column['bucket_%s' % i] = series\n",
+    "\n",
+    "if not df_single_column.empty:\n",
+    "  df_single_column.plot(title=column_name).legend(loc=\"upper left\", bbox_to_anchor=(1,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display a specific value as a histogram\n",
+    "\n",
+    "Here we display the disbribution initialized earlier - `single_distribution`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0ffp0VVVVqbu7W6WlpSovL1dFRYXKysoUDAa1c+dORUdHq7KyUitWrND69eslSYWFhVq9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\nDtzthyEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a0424f810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load the bucket values, and map to integers\n",
+    "bucket_values = map(int, single_distribution['bucketCounts'])\n",
+    "\n",
+    "# Load the information about the bucket bins\n",
+    "bucket_options = single_distribution['bucketOptions']['exponentialBuckets']\n",
+    "scale = bucket_options['scale']\n",
+    "growth_factor = bucket_options['growthFactor']\n",
+    "\n",
+    "# Set up the bucket bins and their widths.\n",
+    "num_values = len(bucket_values)\n",
+    "bucket_bins = [0] + [scale * (growth_factor ** i) for i in xrange(num_values)]\n",
+    "bucket_widths = [bucket_bins[i+1] - bucket_bins[i] for i in xrange(len(bucket_bins) - 1)]\n",
+    "bucket_bins = bucket_bins[:-1]\n",
+    "\n",
+    "# Display values as a histogram\n",
+    "plt.bar(left=bucket_bins, height=bucket_values, width=bucket_widths)\n",
+    "plt.gca().set_xscale(\"log\", basex=growth_factor)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}