{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Dimension tables: efficiently adding details of processes and flows\n",
    "\n",
    "In the [Quickstart tutorial](./Quickstart.ipynb) we saw how to draw some simple Sankey diagrams and partition them in different ways, such as this:\n",
    "\n",
    "![](./quickstart_example1.png)\n",
    "\n",
    "But to do the grouping on the right-hand side we had to explicitly list which people were \"Men\" and which were \"Women\", using a partition like this:\n",
    "\n",
    "```python\n",
    "customers_by_gender = Partition.Simple('process', [\n",
    "    ('Men', ['Fred', 'James']),\n",
    "    ('Women', ['Susan', 'Mary']),\n",
    "])\n",
    "```\n",
    "\n",
    "We can show this type of information more efficiently -- and with less code -- by using *dimension tables*.\n",
    "\n",
    "## Dimension tables\n",
    "\n",
    "The table we've seen before is a **flow fact table** -- it lists basic information about each flow:\n",
    "\n",
    "- *source*: where the flow comes from\n",
    "- *target*: where the flow goes to\n",
    "- *type* or *material*: what is flowing\n",
    "- *value*: the size (in tonnes, GJ, £ etc) of the flow\n",
    "\n",
    "An example of this type of table is shown at the top right of this diagram:\n",
    "\n",
    "![](./dimension_tables.png)\n",
    "\n",
    "The **dimension tables** add extra information about the source/target and type of the flows (the diagram above also shows extra information about the time period the flow relates to, but we're not worrying about time in this tutorial). For example, \"farm2\" has a *location* attribute set to \"Cambridge\".\n",
    "\n",
    "This tutorial will show how to use dimension tables in floweaver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>type</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>farm1</td>\n",
       "      <td>Mary</td>\n",
       "      <td>apples</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>farm1</td>\n",
       "      <td>James</td>\n",
       "      <td>apples</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>farm2</td>\n",
       "      <td>Fred</td>\n",
       "      <td>apples</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>farm2</td>\n",
       "      <td>Fred</td>\n",
       "      <td>bananas</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>farm2</td>\n",
       "      <td>Susan</td>\n",
       "      <td>bananas</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>farm3</td>\n",
       "      <td>Susan</td>\n",
       "      <td>apples</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>farm4</td>\n",
       "      <td>Susan</td>\n",
       "      <td>bananas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>farm5</td>\n",
       "      <td>Susan</td>\n",
       "      <td>bananas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>farm6</td>\n",
       "      <td>Susan</td>\n",
       "      <td>bananas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  source target     type  value\n",
       "0  farm1   Mary   apples      5\n",
       "1  farm1  James   apples      3\n",
       "2  farm2   Fred   apples     10\n",
       "3  farm2   Fred  bananas     10\n",
       "4  farm2  Susan  bananas      5\n",
       "5  farm3  Susan   apples     10\n",
       "6  farm4  Susan  bananas      1\n",
       "7  farm5  Susan  bananas      1\n",
       "8  farm6  Susan  bananas      1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the same data used in the quickstart tutorial\n",
    "import pandas as pd\n",
    "flows = pd.read_csv('simple_fruit_sales.csv')\n",
    "flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>location</th>\n",
       "      <th>organic</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>farm1</th>\n",
       "      <td>farm</td>\n",
       "      <td>Barton</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>farm2</th>\n",
       "      <td>farm</td>\n",
       "      <td>Barton</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>farm3</th>\n",
       "      <td>farm</td>\n",
       "      <td>Ely</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>farm4</th>\n",
       "      <td>farm</td>\n",
       "      <td>Ely</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>farm5</th>\n",
       "      <td>farm</td>\n",
       "      <td>Duxford</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>farm6</th>\n",
       "      <td>farm</td>\n",
       "      <td>Milton</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>customer</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>customer</td>\n",
       "      <td>Milton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Men</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fred</th>\n",
       "      <td>customer</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan</th>\n",
       "      <td>customer</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Men</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           type   location organic    sex\n",
       "id                                       \n",
       "farm1      farm     Barton     yes    NaN\n",
       "farm2      farm     Barton     yes    NaN\n",
       "farm3      farm        Ely      no    NaN\n",
       "farm4      farm        Ely     yes    NaN\n",
       "farm5      farm    Duxford      no    NaN\n",
       "farm6      farm     Milton     yes    NaN\n",
       "Mary   customer  Cambridge     NaN  Women\n",
       "James  customer     Milton     NaN    Men\n",
       "Fred   customer  Cambridge     NaN  Women\n",
       "Susan  customer  Cambridge     NaN    Men"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load another table giving extra information about the \n",
    "# farms and customers. `index_col` says the first column\n",
    "# can be used to lookup rows.\n",
    "processes = pd.read_csv('simple_fruit_sales_processes.csv', \n",
    "                        index_col=0)\n",
    "processes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `id` in this table matches a `source` or `target` in the flows table above. We can use this extra information to build the Sankey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setup\n",
    "from floweaver import *\n",
    "\n",
    "# Set the default size to fit the documentation better.\n",
    "size = dict(width=570, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we now have two tables (before we only had one so didn't have to worry) we must put them together into a Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = Dataset(flows, dim_process=processes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `type` column in the process table to more easily pick out the relevant processes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nodes = {\n",
    "    'farms': ProcessGroup('type == \"farm\"'),\n",
    "    'customers': ProcessGroup('type == \"customer\"'),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to how the same thing was written in the Quickstart:\n",
    "```python\n",
    "nodes = {\n",
    "    'farms': ProcessGroup(['farm1', 'farm2', 'farm3', \n",
    "                           'farm4', 'farm5', 'farm6']),\n",
    "    'customers': ProcessGroup(['James', 'Mary', 'Fred', 'Susan']),\n",
    "}\n",
    "```\n",
    "\n",
    "Because we already know from the process dimension table that James, Mary, Fred and Susan are \"customers\", we don't have to list them all by name in the ProcessGroup definition -- we can write the *query* `type == \"customer\"` instead."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. note::\n",
    "\n",
    "    See the API Documentation for :class:`floweaver.ProcessGroup` for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the Sankey diagram definition is the same as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "<p>Failed to display Jupyter Widget of type <code>SankeyWidget</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
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       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "</p>\n"
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      "text/plain": [
       "SankeyWidget(layout=Layout(height='300', width='570'), links=[{'type': '*', 'time': '*', 'target': 'customers^*', 'title': '*', 'source': 'farms^*', 'value': 46.0, 'opacity': 1.0, 'color': '#FBB4AE'}], margins={'left': 130, 'bottom': 10, 'top': 25, 'right': 130}, nodes=[{'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'farms^*', 'title': 'farms'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'customers^*', 'title': 'customers'}], order=[(('farms^*',),), (('customers^*',),)])"
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   "source": [
    "ordering = [\n",
    "    ['farms'],       # put \"farms\" on the left...\n",
    "    ['customers'],   # ... and \"customers\" on the right.\n",
    "]\n",
    "bundles = [\n",
    "    Bundle('farms', 'customers'),\n",
    "]\n",
    "sdd = SankeyDefinition(nodes, bundles, ordering)\n",
    "weave(sdd, dataset).to_widget(**size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we need to set the partition on the ProcessGroups to see something interesting. Here again, we can use the process dimension table to make this easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<p>Failed to display Jupyter Widget of type <code>SankeyWidget</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
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       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
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       "SankeyWidget(groups=[{'type': 'process', 'id': 'customers', 'title': '', 'nodes': ['customers^Men', 'customers^Women']}], layout=Layout(height='300', width='570'), links=[{'type': '*', 'time': '*', 'target': 'customers^Men', 'title': '*', 'source': 'farms^*', 'value': 21.0, 'opacity': 1.0, 'color': '#FBB4AE'}, {'type': '*', 'time': '*', 'target': 'customers^Women', 'title': '*', 'source': 'farms^*', 'value': 25.0, 'opacity': 1.0, 'color': '#FBB4AE'}], margins={'left': 130, 'bottom': 10, 'top': 25, 'right': 130}, nodes=[{'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'customers^Women', 'title': 'Women'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'farms^*', 'title': 'farms'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'customers^Men', 'title': 'Men'}], order=[(('farms^*',),), (('customers^Men', 'customers^Women'),)])"
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   "source": [
    "# Create a Partition which splits based on the `sex` column\n",
    "# of the dimension table\n",
    "customers_by_gender = Partition.Simple('process.sex', \n",
    "                                       ['Men', 'Women'])\n",
    "\n",
    "nodes['customers'].partition = customers_by_gender\n",
    "weave(sdd, dataset).to_widget(**size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, this is what we wrote before in the Quickstart:\n",
    "```python\n",
    "customers_by_gender = Partition.Simple('process', [\n",
    "    ('Men', ['Fred', 'James']),\n",
    "    ('Women', ['Susan', 'Mary']),\n",
    "])\n",
    "```\n",
    "\n",
    "And we can use other columns of the dimension table to set other partitions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<p>Failed to display Jupyter Widget of type <code>SankeyWidget</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
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       "SankeyWidget(groups=[{'type': 'process', 'id': 'farms', 'title': '', 'nodes': ['farms^yes', 'farms^no']}, {'type': 'process', 'id': 'customers', 'title': '', 'nodes': ['customers^Men', 'customers^Women']}], layout=Layout(height='300', width='570'), links=[{'type': '*', 'time': '*', 'target': 'customers^Men', 'title': '*', 'source': 'farms^yes', 'value': 10.0, 'opacity': 1.0, 'color': '#FBB4AE'}, {'type': '*', 'time': '*', 'target': 'customers^Women', 'title': '*', 'source': 'farms^yes', 'value': 25.0, 'opacity': 1.0, 'color': '#FBB4AE'}, {'type': '*', 'time': '*', 'target': 'customers^Men', 'title': '*', 'source': 'farms^no', 'value': 11.0, 'opacity': 1.0, 'color': '#FBB4AE'}], margins={'left': 130, 'bottom': 10, 'top': 25, 'right': 130}, nodes=[{'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'customers^Women', 'title': 'Women'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'farms^yes', 'title': 'yes'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'farms^no', 'title': 'no'}, {'style': {'hidden': False, 'type': 'process', 'direction': 'r'}, 'id': 'customers^Men', 'title': 'Men'}], order=[(('farms^yes', 'farms^no'),), (('customers^Men', 'customers^Women'),)])"
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   ],
   "source": [
    "farms_by_organic = Partition.Simple('process.organic', ['yes', 'no'])\n",
    "\n",
    "nodes['farms'].partition = farms_by_organic\n",
    "weave(sdd, dataset).to_widget(**size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a tip for doing quick exploration of the data with partitions: you can automatically get a Partition which includes all the values that actually occur in your dataset using the `dataset.partition` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  Widgets Documentation</a> for setup instructions.\n",
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   "source": [
    "# This is the logical thing to write but\n",
    "# it doesn't actually work at the moment :(\n",
    "# nodes['farms'].partition = dataset.partition('process.organic')\n",
    "\n",
    "# It works with 'source.organic'... we can explain later\n",
    "nodes['farms'].partition = dataset.partition('source.organic')\n",
    "\n",
    "# This should be the same as before\n",
    "weave(sdd, dataset).to_widget(**size)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "The process dimension table adds extra information about each process. You can use this extra information to:\n",
    "\n",
    "1. Pick out the processes you want to include in a ProcessGroup (selection); and\n",
    "2. Split apart groups of processes based on different attributes (partitions).\n",
    "\n",
    "Things to try:\n",
    "\n",
    "- Make a diagram showing the locations of farms on the left and the locations of customers on the right"
   ]
  },
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1rwGgX1B0SE8TvNYKnx3Wyyunx0plh1DTTwevq1K//57MY9MzuPAmALRQdEiShJq7JPd6/GxfOT1USvsF736U+v32ZJ56rYWKvnQ25ycADBqKDkma4HVls1o+LVZKu3VzxY6l0rzcyuekfn89mYemV4eKnsilGwDg0Sg6pI+i527uGYk4NrZl7stHBK+XpX8/s5+m6dJo5Wd2aX8AAEOHokP6LsG7UlFbdlBsNOZszMoczfZsmi5N/fp7kqpeVvjssDg2tuUs7RsAYChQdEjfJpibyL17z4aunB7NtgumHwumV6d+zT2Jlc+JJS7ACQAzQdEh/Z+qXhZMPzbdldOD11eGqi5P/hp7kGBuIlj22gT7CQAYWBQdMjAJplcHc2c2vXtWrNefnJs7K/Vr6sn79rqqWXWfiqXS9ql3GAAwaCg6ZKASvFtdeHdbMP1j8O5XqV/PrL9fU9f07rmpdxQAMKgoOmRgEry7MXi9N/jsobXJTe9rer0t9+6a1K+vy+/1iqKmR27sgGwAwCNRdEjfJ3h3TWHud8HrQ1NLziNi7oHc9M7c6/WpX+9mx/S8WK/PT71zAIBhQNEh/R3Tm4PXNessOB3JvT7UNHd38O6G5K99IxNMLy68Hph6pwAAw4SiQ/oywbvrgtd7ZlpwHh19KJj7U+7dL1O/l5mkWS2fFrNsp9Q7BAAYNhQd0ncpvLs1ePfAppeczrM87t7g3a9Tv6/pEqxsea28d+odAQAMK4oO6ZsE724ovPtLtwrONGd51uSW3Z7X9Nr071VXTnr9cKwv2ib1TgAAhhlFh6SPuWtyr3eud7BxF5ObPpib+13wLsnA5cL0/Jhlz0i98QPAKKDokNT5ZTB3Xy8KznTjeJpe/5B7d2Mv3mvw7tKm6dtSb/QAMEooOiRJgnfXFd7dnabgTBPTP+fmbpm192zurOi+/6TUGzwAjBqKDul5CtPfBO/uT15upvu3lnd/y727NfdZV95rMFcPWbZv6g0dAEbVHiKyZ26lF0fL9i3q2eub3r2pae6duemRk14/HEw/1jQ9pen19GDurLyq3wnmGqkPlmTwEmru58Fmc7BxN+Puz2vut5s6cDmYXj1Zcx+NZtul3sgBAJsg2pK5uZVe3KyUD2uajhWm3099ICX9meDd6tzrHcG7B9MXmI08w2Plh4LXu0LN/Xym77dpujSa7Zl6GwUAzIJY06dHy/bNzR2dm34leK2lPtCSdAne3VR497fUhaVLZ3nuyb27ad3vVS/LffmIODa2ZertEADQQ9FsbvC6YLLmTsi9Lg6mV6Y+AJNZLjim1xbe3dWrj4z3NOb+kj/qyul6biyV5qXe1gAAfSAuXrxttGzf4PVzjPcZxuiv+3WwcTeTm94XTH8ea+6A1NsUAKCP5VXdp2n66eD1J+kP0mRTE7y7Ppj7U+oC0sMzO3cHy67Nzf04N3d0bHCtKgDAesRSaavgx18evJ4evLsi9YGbzLjgrC58dnswHbjBxpuS1pgj94tHzwe9PHg9Odbr81NvSwCAPhdLpe0LKx9cePe91Adysp6Y3hS8uzd1+ehRwXmw8O6OMKP54s7K6+V/Sr0dAQAGQKy53YPXz7Q+1dIHB3eyOm8NNv79UA42nr7k/GVjPmK+NoXp+aHiXsMnsQAAGxRLpe2blfJhTe/Gkx/oRzihlt0SvFuTunz0JOYeyL27dbPnmWm1qJUPjVeUHpt6OwIADIBQda/Iq/qd1Af9UUrw7vrCuz8mLx+9O4vzh+DddV2dh+Yak5Ydw3WvAAAz0vTuWcHr6cH0qtRFYFjTGmzsbiu8eyB1+ehRwbkvN715Vuer6cpg2VicqO6aehsCAAyAWCvNm6y5jwbTn6UuBkMVczcGryMx2Dh4fSg397vg3TW9ncflc/Kq7pN6GwIADIBYqeyQe/eeYG4ieUkY4ATvrmkd9EdjsHHwem9u7sak8930wqKib4iNxpzU2xEAoM/FRmNOUVt2UDBdlro0DFxMby68uy99+Zj9FN49UHh324w+Mt6jhKouz335iFgqbZ96OwIADIBgpZcVNXdu6gNYvyd4d13wek/q8tHDkvPH4N31qef7OpeHuZ+Gip4YJ9zOqbchAMAAiMtLewTvvsDA5Wlza7DRGGwcvFuTP+qinP2bYHpVMP1iXF7aI/U2BAAYAHHC7Rys/JFg7qepD2KpE7y7ofDuL+nLRy+iDwXTu3LTa1PP901NYeVvBz/+8tTbEABgAMRSafvcl49ojYlIfxDracxdk5veOTqDjd3fgnc3JZ/vXUowLRc2fkhcvXrr1NsRAKDPxUZjTlF1BzRNl6Y+gPUovww2GoONg7kHC+9+20+DjbtaeLxeVNTd++JFpcen3o4AAAMg1nWfwut/pD6Azc5B0V1XeHd38vLRs+ifg+lGX59qEBO8XjZp7hPRSk9LvQ0BAAZArJR2C14/l5uuTH0Q60pMfxO8uz99+ehF3P256W+Sz/MUhcf06uCzr+RZaa/U2xAAYABEs7m5ZceFmrsk9UFsEw98Pw82KoONs4eCd3cHywZ2sHFXY+XvBa8LYoxbpN6OAAB9Ll5RemyzUj4seOeTH8BmUnC8W517vSN492D68tGDmLsv9+6Xqed7PyaYVpqmb4uLF2+bejsCAPS5WCptFWtuv9z0h6kPYOspOTe1PmXUBwVk1qMP5aZ35tbj61MNYILXn+RW/hBXTgcAzEhupRfnXs9OfQD73wOZ6bXBu7tG5SPjre//cTeknu+DlmB6ZdP0FK6cDgCYkThR3bVpOhZMr0x2ADP99QgNNn6g8O7W1IVhGFL4bFFupRen3oYAAAMguu8/adKyY4K5Rq8OVMG764O5P6UvH72K3hO8uy51QRi6VN2Sop69PpZKW6XejgAAfS4uXrxtUSsfGqxss1hwVhc+uz2YjsZgY69rgnc3Jy8EQ57gtVZUs3+PExOPS70dAQAGQPTjC3LvLujqAcn0puDdvenLR08KzkNFzf0ueAYb97TwmP5ssuY+Gmuleam3IQDAAMh99qLc3FnB66rNKDjXBu9+PyqDjYPXe4N3N6Y+6I9ygunVoea+yjgeAMCMxJo+PdT008G7KzbugJPdErxbk7589CCmD+amtw/r9akGNcG03Kxmb41m26XejgAAfS42sp0mffaBYHrxeg8uretT/TF5+ehZyXF/Ct5dn/qgTtZbeH4WvDs1r+q/8K3LAID1ivVF2zSr2VuDd+OdB5TCu9uCdw8kLx89SFFza4J3v0p9ECcbWXq81nIrHxPr9fmptyUAQB+LMW4RbNmrcyt/Lzd3YzD319TlozfRh4LXu4Ip16ca8ATT8qR3JwU//vLYaHC5CQDAI8X6om1yy44L3t0QvLsnfQmZ7bi/Be9uSn2AJrMU0/Pyqr43Trjnpt62AACJ5bXy3o/63p2auyF4vSu3YfuUlXuwfcHR9Adj0pMEc42mL3+5abqQf3MBwAiJ3j8hmH5xAweK63LTO4MNwXgdc38JNffz1Adekjjmfpx7PTdU9MSituygZrX8nFhftE3q7REA0EWFjR+ysZeLaHq9LTe9L3lh2fizOPcXpr9JfoAlfZ2m6Yq8qt8J3p2aez2qqLoD8hX6wphduEusX7Bj6m0WADADMbtwl9zr4s07KOivgnd/SV9gZhL9A9enIt1KqOry3PSHublvBl/+WvDuC6Gmnw5Vd9Lp7/73r4nIq1Jv4wAwkuLq1VtPmjs6N13ZxR3/Ta0LXfbhOB5z9+Xe/TL1gZGMTo47+MC7RWQs9bYOACMn99mLpvuunG4l1PTnwfpl4LI+VHi9MzeuT0V6G4oOAPRYrFR2aJqO9XBnf11uekcwd3+iszh/zY3rU5E0oegAQA8VFX1D69MlCXb6rbMpt/Zu4LJ7IPd6W+oDHRntUHQAoAdipfLU3Ou5qXf6/xtztwSbvYHLhXf35KZcn4okD0UHAGZRbDTm5FV9b/B6eeod/vTRm1qfgOrWOB5dE7y7Of37IqQVig4AzJKmd88NpstS7+hnkuDd9cG73wfTBzd1sHHw7vfBM9iY9FcoOgDQZbFU2j54PTl4XZV6J7/Rqem1udffBr8xA5fdvbkp16cifRmKDgB0Uai41wSvK1Lv3Dc/2ercu1tzc39b96ep9MHCZ7dzfSrSz6HoAEAXxFJpXm7lc1Lv1GclNXdL3jlw2dyfgncMNiZ9H4oOAGyGODa2ZeGzw/KqXpZ6h96D3Jib3p6bu6UPXgshMwpFBwA2UTTbs2m6NPWOvBcJXleFmn46lkrbR7O5k16PD95dmvp1EbKhUHQAYCPFRmPbUHUnBdOrU+/Ee5Fg6vKstNej5sPExOOKavbvwWst9WskZF2h6ADARggVfemoHNiD6ZW516NiozFnffMklkpbFVbeP6+6JalfMyGdoegAwAxEWzK36ctfTr3T7llMz4v1+vyNnU95rbz30A7KJgMZig4AbEDT9G2jMh4leP1JUVt20ObOszhR3TVYNpabrkz9nshoh6IDAOsQs+wZhen5qXfUvUrw7rTo/RO6Og/d95806fXDueklqd8fGc1QdACgQ6wv2iY3d2zwo3E2IljZ8lp571mdp1eUHtus6Tuapj71+yWjFYoOAEyR18p7Bytb6p1zLxK8rszNHRvri7bp1fyNY2NbBl9+XdPcBanfPxmNUHQAQESi908I3n0h9U65VylMz49Z9oyU8zyvlP85mDtrIK8JRgYmFB0AIy9adnAw10i9Q+5FgneXNr17e+p5PlWs6dOD188E765IPX/I8IWiA2BkxezCXXKvi1PviHsWc2fFev3Jqef7usRGtlPu9YPB60+SzysyNKHoABg5sdGYk5s7uvWFeOl3xLOdUNXlwesrU8/3mYqLF2/bNH1bMK2knndk8EPRATBS8qy0VzB1qXe+vUgwvTqYfiyabZd6vm+KGOMWweuCwrvvpZ6XZHBD0QEwEqItmRu8fi71TrdnqWVLm949N/V875ZYy/YKNfdVBi6TjQ1FB8BQi4sXb5t7PSqv6mWpd7i9SPB6WV7Td8WxsS1Tz/vZEOv1+cHrycHr5annNRmMUHQADK3CyvsHc/XUO9reRc+NlcpTU8/3XogXlR6fV/X9wdyP08930s+h6AAYOs3a+PNG6dINubkfx5rbL/V8TyHWF23TNF0YTLPky4H0ZSg6AIZGXF7ao/DZotQ71l6maXpKrFR2SD3v+0Hw+srC3P9NvUxIf4WiA2DgxUppt+DLX0u9Q+1lgqnLs9Jeqed9P2pWKs9v+vKXg+nVqZcTSZ91FJ0bRKQpIpMicpeIHDjlvueISBSRwzt+51ci8ncR+bOIHDYb6y4APEKcqO4aTM8YpU/iBNMrc3NHx0ZjTur53++ilZ4WrPyJ4EdjIDqZPus5o/MtESlE5Akd00+QVtH54TS/c7mI7NzN9RQAHiGuXr11rLn9cnPfHKWC04oujtmFu6ReBoMmVio7FDU9Mnj3o/TLkPQ66yk6b5ZWoVnYMX25iPxaWmdutpgyfScRWTULqygAiDSr5edMmn4yN70k9Y6z1wnmGoWNH5J6GQy6uHr11oWNHxJMy6mXKeld1lN0dpLWGZ1vT5m2jYjcJiIfklYJeuGU+xaKyOlTbr9ORFaKyK0icqeI/EBE/mHK/ce0nyOKyPtE5EwRub/92HeKyJYi8lURWSMid8ij/1UmInKAiKwWkd+0H/MtEXn8NM9/VPv510jrX3GfnOa5APSbOOF2zs0dHkyXpd5ZpkrT6+nR+85T69hMoab/lpuel3r5ktnPBgYjXy6tArHWq0XkfBF5hrQKxCem3PdtEXlV++cDpDW+59D27W1EpCqtQrL2wwE7iMiz289ztYh8QESeJ61CVIjIIhH5cHva+SKSi8jTpvy9g0Tkf+ThArSjtIrVJdI60zT1+VeJyLHt5zqjPe0l63jPAFKKZnOblfJho/61/8FrLa+V9069PIZdXF7ao+n19GB6VeplTplF82MAAAlJSURBVGYnGyg6p0irFOzRvn2GPDzQ+DYRaUx57C9E5DHtn28RkSs7nmv39nN9fMq07dvTlkyZtmt7mpsy7entaW+dMu1XInJNx9/Yr/24BR3Pv3TKY+aIyH+LyMkCoD9EK/1ju9x8N/VOsS9i2XGx0dg29XIZJbFWmjfp3UnB9GfJlz/pajZQdPaRVlH4cPv2NSLylPbP35TWJ7MeJ60iVG9Pn9/+nUXTPN8aEbloyu21ReTEKdO2aU/71JRpW3W8jrV/4+yO539Ke/qX1vP8IiK/FZFvTPP6APRCLJXmxZo7IFg2FrzWUu8I+yamP4xWfmbq5TPKYqm0fWHu3cHriuTrA+lKNlB0thKRv4mIiciTReTaKfcdIq0S8QZpFZDj2tP3ak8/dZrnu11Erptye20ROXLKtDntacd0rn4icnzH37hPWv9am5o10hqrs67nF2mNGzpnmtcHoNtifdE2ccI9t7DxQ5pV96lgWkm94+u7VPWywmd8L0cfiY3GnMLrgcG7UvL1g2xWZvCFgctE5EEROUJa/7paa0cRCdI6c2Mi8qz29HWdbRFZ9xmdjS06a//GGbJ+FB2gV2Ij2ynW3O6h6l6RV/X9TV/+ctO78dQ7uX5PMHdWnHB8L0cfCxV9ae713NTrCtm0zKDovE9aZeHX0hqMPNVl0ioNv+yYfou0BgZP9UxZ9xidjS06IiI3i8iKaV7v5+XhQdEUHWATPEVE5uVWenGo6b+FintNrLkDmqYLm+bemXv3ntzcscH0i3lVv8MZmk1L8Loi+vEFG1wa6Butb+B2pwWvK1OvP2TmmUHR+UdplYW/S2v8zFSfbd/33Y7paz919Y727ceIyLi0PnW145THbU7R2V9aZ5TePGXaW0Tk9yLyxPU8vwhFB1ivU0RkLPXOaVgTvK6aNPeJWCptn3pBY9PECbfzZM2dELy7NPX6RDacGV7r6mZpfVFgp72lVSTeNs19+4nIVdIqFb8TkQvkkd+j80ZpfWdOFJG/isjXReSl0hpnE0XkXhH5T2kNiF477W/t51nrtdL6dNft0ho/lMnDnxCb7vnntZ8riMgD0vooOoAOFJ1ZSjBd1vTuuakXMLojTkw8rvU9T66eet0i6w4X9QTQiaLT5QSvl+fevYfrUw2n2GjMKSr6htz0wtTrGnl0KDoAOlF0uho9N1YqT029UNEbeVX3Kcydk369I2tD0QHQiaLThQRzPy6q7oDUCxNpxInqrsG7U3Nj4HLqUHQAdKLobGaCZWMxy3ZKvSCRXjSbm5s7dhQvRtsvoegA6ETR2cQE78Zzn70o9QJE/4lXlB7bNPfO4J1PvZ6OWig6ADpRdDY2pitzrx+Mq1dvnXrhob/FUmmrUCntl3t3QfL1dkRC0QHQiaKzUdHFMbtwl9QLDYMn1ir/HMx9I/06PNyh6ADoRNGZSUwvaZouTL2wMPhiTZ8equ6zwfTK5Ov1EIaiA6ATRWcDCaZnRFsyN/WCwnCJ7vtPyq18TPD6k9Tr+DCFogOgE0VnHQmmlVDRl6ZeQBhucfHibQvv3h5Mq6nX+WEIRQcYTMdI6/omUUSOEpEzRWSNiNwlIp+c5vGvk9aVdm+V1rVRfiCPvCbLVBSdjgTTnxU1fRffbIxeimNjW4aKe01hen7qbWCQQ9EBBtMOIvJsaRWdVSJyrIg8T0TOaE97yZTHrr3K7qHt29uISFVaV9ndYZrnpuhMSfDuNP5NhdTyFfrCvKpfD15Xpd4mBi0UHWBwbS+tUrN0yrQ5IvLfInLylGm3SOvqt1Pt3v7dj0/zvBSdVv6rWS0/p3uLC9h8sV6f3zT9dPDuij7YRgYiFB1gcK0tOid2TP+tiHyj/fP89mMWTfP7a0Tkommmj3TRCaYXF3VdGGPcokvLCei62Mh2yqv6/uD1otTbTL+HogMMrrVF58iO6beKyDntn/dqP+bUaX7/dhG5bprpo1l0zP049+49sVTavkvLB5h1sdGY0/oCQl2cfBvq01B0gME1k6Kz9ozO2dP8Pmd0vFsdzNWLWvnQWF+0TZeWC5BErLndm6anBK+Xp96u+ikUHWBwzaToiLTG6KzseMwzZcTH6DS9G4+WHcwnqTBsYv2CHfOavqtpynW1KDrAQJtp0Vn7qat3tG8/RkTGpfWpqx2ned7hLjq1bGmolPZjDA5GQfDjLw/enZZ8u6PoANhIb5TW9+FEEfmriHxdROaJyB0iEkTkARG5ZMrj9xORq6RVgn4nIhfIKH2PjunK4PX0vKr/MgvLAuh70Wy7wuuBuddzR+0j6hQdAJ2GpugEK1vu3Xtilu2UeqYC/SKazc3NHZ7XsqWpt1GKDoAUBrroBNOr86p+PS53L+PfU8D6xZrbfdKyY5rVcjn1tkvRAdArA1l0gpUtN3d0rNefnHoGAoMo1krzCu/eXpj+Z266MvU2TdEBMFsGpugE08qkZcfE5aU9Us80YJjEiYnHRT++IHj9XGh9x1Ty7Z2iA6Bb+rroBO/8pNfjuTwD0Du5uRfkVX1vYe6cYPqz1PsBig6AzdGPReeC3OvxeVbaK/XMAUZdHBvbsunds5rm3hl89pVgenEf7CMoOgBmLGnRCaZXN81dMOn1+ODHX84lGYD+Fyul3Qrv3tI0HcurbknqckPRAbA+PS06wdxPc6+LJ2vuBIoNMBxiqbRVXFHarbDy/pNej8/NfTPVWB+KDoBOs1J0gtefFF6/06y6TxW18qF5VfeJE27n1G8WQO/ESuUpwdxLCu/eklt2XNOXv5xX3ZJQc5dQdAD0yiYVnWBuIvfuB8FnXwl+2cfzmr6rqOgb8hX6wmhL5qZ+UwD6W7yo9PjmeOnZ0Y8vyH35iFDRE5umn8+9np1794NgZduUgdAUHQCdXiEi++amR+bmDi+8e3uzWn5z4fXAWHP7BVv26uDHX55XdZ9mbfx5sVaal/oFAxgtccLtHGtu99xnLwrmXhKq7hXRjy8o6tnri9qyg5qmC4ta+dDcl4/40rsOP1NE9k39mgEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAwHD5/0EG2f8op6JKAAAAAElFTkSuQmCC",
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     "cb26b203b8c046b59cb30ae668b70015": {
      "model_module": "jupyter-sankey-widget",
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      "model_name": "SankeyModel",
      "state": {
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         "rect": {
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       "layout": "IPY_MODEL_1cbf16c85a674e368b9fd4149e22def2",
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1rwGgX1B0SE8TvNYKnx3Wyyunx0plh1DTTwevq1K//57MY9MzuPAmALRQdEiShJq7JPd6/GxfOT1USvsF736U+v32ZJ56rYWKvnQ25ycADBqKDkma4HVls1o+LVZKu3VzxY6l0rzcyuekfn89mYemV4eKnsilGwDg0Sg6pI+i527uGYk4NrZl7stHBK+XpX8/s5+m6dJo5Wd2aX8AAEOHokP6LsG7UlFbdlBsNOZszMoczfZsmi5N/fp7kqpeVvjssDg2tuUs7RsAYChQdEjfJpibyL17z4aunB7NtgumHwumV6d+zT2Jlc+JJS7ACQAzQdEh/Z+qXhZMPzbdldOD11eGqi5P/hp7kGBuIlj22gT7CQAYWBQdMjAJplcHc2c2vXtWrNefnJs7K/Vr6sn79rqqWXWfiqXS9ql3GAAwaCg6ZKASvFtdeHdbMP1j8O5XqV/PrL9fU9f07rmpdxQAMKgoOmRgEry7MXi9N/jsobXJTe9rer0t9+6a1K+vy+/1iqKmR27sgGwAwCNRdEjfJ3h3TWHud8HrQ1NLziNi7oHc9M7c6/WpX+9mx/S8WK/PT71zAIBhQNEh/R3Tm4PXNessOB3JvT7UNHd38O6G5K99IxNMLy68Hph6pwAAw4SiQ/oywbvrgtd7ZlpwHh19KJj7U+7dL1O/l5mkWS2fFrNsp9Q7BAAYNhQd0ncpvLs1ePfAppeczrM87t7g3a9Tv6/pEqxsea28d+odAQAMK4oO6ZsE724ovPtLtwrONGd51uSW3Z7X9Nr071VXTnr9cKwv2ib1TgAAhhlFh6SPuWtyr3eud7BxF5ObPpib+13wLsnA5cL0/Jhlz0i98QPAKKDokNT5ZTB3Xy8KznTjeJpe/5B7d2Mv3mvw7tKm6dtSb/QAMEooOiRJgnfXFd7dnabgTBPTP+fmbpm192zurOi+/6TUGzwAjBqKDul5CtPfBO/uT15upvu3lnd/y727NfdZV95rMFcPWbZv6g0dAEbVHiKyZ26lF0fL9i3q2eub3r2pae6duemRk14/HEw/1jQ9pen19GDurLyq3wnmGqkPlmTwEmru58Fmc7BxN+Puz2vut5s6cDmYXj1Zcx+NZtul3sgBAJsg2pK5uZVe3KyUD2uajhWm3099ICX9meDd6tzrHcG7B9MXmI08w2Plh4LXu0LN/Xym77dpujSa7Zl6GwUAzIJY06dHy/bNzR2dm34leK2lPtCSdAne3VR497fUhaVLZ3nuyb27ad3vVS/LffmIODa2ZertEADQQ9FsbvC6YLLmTsi9Lg6mV6Y+AJNZLjim1xbe3dWrj4z3NOb+kj/qyul6biyV5qXe1gAAfSAuXrxttGzf4PVzjPcZxuiv+3WwcTeTm94XTH8ea+6A1NsUAKCP5VXdp2n66eD1J+kP0mRTE7y7Ppj7U+oC0sMzO3cHy67Nzf04N3d0bHCtKgDAesRSaavgx18evJ4evLsi9YGbzLjgrC58dnswHbjBxpuS1pgj94tHzwe9PHg9Odbr81NvSwCAPhdLpe0LKx9cePe91Adysp6Y3hS8uzd1+ehRwXmw8O6OMKP54s7K6+V/Sr0dAQAGQKy53YPXz7Q+1dIHB3eyOm8NNv79UA42nr7k/GVjPmK+NoXp+aHiXsMnsQAAGxRLpe2blfJhTe/Gkx/oRzihlt0SvFuTunz0JOYeyL27dbPnmWm1qJUPjVeUHpt6OwIADIBQda/Iq/qd1Af9UUrw7vrCuz8mLx+9O4vzh+DddV2dh+Yak5Ydw3WvAAAz0vTuWcHr6cH0qtRFYFjTGmzsbiu8eyB1+ehRwbkvN715Vuer6cpg2VicqO6aehsCAAyAWCvNm6y5jwbTn6UuBkMVczcGryMx2Dh4fSg397vg3TW9ncflc/Kq7pN6GwIADIBYqeyQe/eeYG4ieUkY4ATvrmkd9EdjsHHwem9u7sak8930wqKib4iNxpzU2xEAoM/FRmNOUVt2UDBdlro0DFxMby68uy99+Zj9FN49UHh324w+Mt6jhKouz335iFgqbZ96OwIADIBgpZcVNXdu6gNYvyd4d13wek/q8tHDkvPH4N31qef7OpeHuZ+Gip4YJ9zOqbchAMAAiMtLewTvvsDA5Wlza7DRGGwcvFuTP+qinP2bYHpVMP1iXF7aI/U2BAAYAHHC7Rys/JFg7qepD2KpE7y7ofDuL+nLRy+iDwXTu3LTa1PP901NYeVvBz/+8tTbEABgAMRSafvcl49ojYlIfxDracxdk5veOTqDjd3fgnc3JZ/vXUowLRc2fkhcvXrr1NsRAKDPxUZjTlF1BzRNl6Y+gPUovww2GoONg7kHC+9+20+DjbtaeLxeVNTd++JFpcen3o4AAAMg1nWfwut/pD6Azc5B0V1XeHd38vLRs+ifg+lGX59qEBO8XjZp7hPRSk9LvQ0BAAZArJR2C14/l5uuTH0Q60pMfxO8uz99+ehF3P256W+Sz/MUhcf06uCzr+RZaa/U2xAAYABEs7m5ZceFmrsk9UFsEw98Pw82KoONs4eCd3cHywZ2sHFXY+XvBa8LYoxbpN6OAAB9Ll5RemyzUj4seOeTH8BmUnC8W517vSN492D68tGDmLsv9+6Xqed7PyaYVpqmb4uLF2+bejsCAPS5WCptFWtuv9z0h6kPYOspOTe1PmXUBwVk1qMP5aZ35tbj61MNYILXn+RW/hBXTgcAzEhupRfnXs9OfQD73wOZ6bXBu7tG5SPjre//cTeknu+DlmB6ZdP0FK6cDgCYkThR3bVpOhZMr0x2ADP99QgNNn6g8O7W1IVhGFL4bFFupRen3oYAAAMguu8/adKyY4K5Rq8OVMG764O5P6UvH72K3hO8uy51QRi6VN2Sop69PpZKW6XejgAAfS4uXrxtUSsfGqxss1hwVhc+uz2YjsZgY69rgnc3Jy8EQ57gtVZUs3+PExOPS70dAQAGQPTjC3LvLujqAcn0puDdvenLR08KzkNFzf0ueAYb97TwmP5ssuY+Gmuleam3IQDAAMh99qLc3FnB66rNKDjXBu9+PyqDjYPXe4N3N6Y+6I9ygunVoea+yjgeAMCMxJo+PdT008G7KzbugJPdErxbk7589CCmD+amtw/r9akGNcG03Kxmb41m26XejgAAfS42sp0mffaBYHrxeg8uretT/TF5+ehZyXF/Ct5dn/qgTtZbeH4WvDs1r+q/8K3LAID1ivVF2zSr2VuDd+OdB5TCu9uCdw8kLx89SFFza4J3v0p9ECcbWXq81nIrHxPr9fmptyUAQB+LMW4RbNmrcyt/Lzd3YzD319TlozfRh4LXu4Ip16ca8ATT8qR3JwU//vLYaHC5CQDAI8X6om1yy44L3t0QvLsnfQmZ7bi/Be9uSn2AJrMU0/Pyqr43Trjnpt62AACJ5bXy3o/63p2auyF4vSu3YfuUlXuwfcHR9Adj0pMEc42mL3+5abqQf3MBwAiJ3j8hmH5xAweK63LTO4MNwXgdc38JNffz1Adekjjmfpx7PTdU9MSituygZrX8nFhftE3q7REA0EWFjR+ysZeLaHq9LTe9L3lh2fizOPcXpr9JfoAlfZ2m6Yq8qt8J3p2aez2qqLoD8hX6wphduEusX7Bj6m0WADADMbtwl9zr4s07KOivgnd/SV9gZhL9A9enIt1KqOry3PSHublvBl/+WvDuC6Gmnw5Vd9Lp7/73r4nIq1Jv4wAwkuLq1VtPmjs6N13ZxR3/Ta0LXfbhOB5z9+Xe/TL1gZGMTo47+MC7RWQs9bYOACMn99mLpvuunG4l1PTnwfpl4LI+VHi9MzeuT0V6G4oOAPRYrFR2aJqO9XBnf11uekcwd3+iszh/zY3rU5E0oegAQA8VFX1D69MlCXb6rbMpt/Zu4LJ7IPd6W+oDHRntUHQAoAdipfLU3Ou5qXf6/xtztwSbvYHLhXf35KZcn4okD0UHAGZRbDTm5FV9b/B6eeod/vTRm1qfgOrWOB5dE7y7Of37IqQVig4AzJKmd88NpstS7+hnkuDd9cG73wfTBzd1sHHw7vfBM9iY9FcoOgDQZbFU2j54PTl4XZV6J7/Rqem1udffBr8xA5fdvbkp16cifRmKDgB0Uai41wSvK1Lv3Dc/2ercu1tzc39b96ep9MHCZ7dzfSrSz6HoAEAXxFJpXm7lc1Lv1GclNXdL3jlw2dyfgncMNiZ9H4oOAGyGODa2ZeGzw/KqXpZ6h96D3Jib3p6bu6UPXgshMwpFBwA2UTTbs2m6NPWOvBcJXleFmn46lkrbR7O5k16PD95dmvp1EbKhUHQAYCPFRmPbUHUnBdOrU+/Ee5Fg6vKstNej5sPExOOKavbvwWst9WskZF2h6ADARggVfemoHNiD6ZW516NiozFnffMklkpbFVbeP6+6JalfMyGdoegAwAxEWzK36ctfTr3T7llMz4v1+vyNnU95rbz30A7KJgMZig4AbEDT9G2jMh4leP1JUVt20ObOszhR3TVYNpabrkz9nshoh6IDAOsQs+wZhen5qXfUvUrw7rTo/RO6Og/d95806fXDueklqd8fGc1QdACgQ6wv2iY3d2zwo3E2IljZ8lp571mdp1eUHtus6Tuapj71+yWjFYoOAEyR18p7Bytb6p1zLxK8rszNHRvri7bp1fyNY2NbBl9+XdPcBanfPxmNUHQAQESi908I3n0h9U65VylMz49Z9oyU8zyvlP85mDtrIK8JRgYmFB0AIy9adnAw10i9Q+5FgneXNr17e+p5PlWs6dOD188E765IPX/I8IWiA2BkxezCXXKvi1PviHsWc2fFev3Jqef7usRGtlPu9YPB60+SzysyNKHoABg5sdGYk5s7uvWFeOl3xLOdUNXlwesrU8/3mYqLF2/bNH1bMK2knndk8EPRATBS8qy0VzB1qXe+vUgwvTqYfiyabZd6vm+KGOMWweuCwrvvpZ6XZHBD0QEwEqItmRu8fi71TrdnqWVLm949N/V875ZYy/YKNfdVBi6TjQ1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       "layout": "IPY_MODEL_3725dc91edae429499da3bab554c1081",
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