From 83ef13241c4a9481eefbf559d6a8ea83ca56f36f Mon Sep 17 00:00:00 2001
From: Adam Erispaha <aerispaha@gmail.com>
Date: Thu, 19 Dec 2024 16:10:36 -0500
Subject: [PATCH] Added example with pyswmm and swmmio, incluiding animation

---
 docs/conf.py                         |    8 +-
 docs/index.ipynb                     |    7 +-
 docs/usage/getting_started.ipynb     |    2 +
 docs/usage/working_with_pyswmm.ipynb | 8340 ++++++++++++++++++++++++++
 4 files changed, 8350 insertions(+), 7 deletions(-)
 create mode 100644 docs/usage/working_with_pyswmm.ipynb

diff --git a/docs/conf.py b/docs/conf.py
index e82afed..890cb46 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -212,4 +212,10 @@
 epub_exclude_files = ['search.html']
 
 
-numpydoc_class_members_toctree = False
\ No newline at end of file
+numpydoc_class_members_toctree = False
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3/", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable", None),
+    "pyswmm": ("https://pyswmm.github.io/pyswmm/", None),
+}
\ No newline at end of file
diff --git a/docs/index.ipynb b/docs/index.ipynb
index ede695b..d2d9d22 100644
--- a/docs/index.ipynb
+++ b/docs/index.ipynb
@@ -13,7 +13,7 @@
     "## Introduction\n",
     "`swmmio` is a Python tool for engineers and hydrologists who need to supercharge their ability to modify and analyze EPA SWMM models and results. Using a familiar Pandas interface, users can replace manual procesess that used to live in spreadsheets with scripts and automation.\n",
     "\n",
-    "The core {py:class}`~swmmio.core.Model` object provides accessors to related elements in the INP and RPT. For example, the {py:obj}`Model.subcatchments <swmmio.core.Model.subcatchments>` property provides a DataFrame (or GeoDataFrame) accessor joining data from the `[SUBCATCHMENTS]` and `[SUBAREAS]` tables in the model.inp file and, if available, the `Subcatchment Runoff Summary` from the model.rpt file. \n",
+    "The core {py:class}`~swmmio.core.Model` object provides accessors to related elements in the INP and RPT. For example, the {py:obj}`Model.subcatchments <swmmio.core.Model.subcatchments>` property provides a {py:obj}`~pandas.DataFrame` (or GeoDataFrame) accessor joining data from the `[SUBCATCHMENTS]` and `[SUBAREAS]` tables in the model.inp file and, if available, the `Subcatchment Runoff Summary` from the model.rpt file. \n",
     "\n",
     "Additionally, `swmmio` provides a lower-level {py:class}`~swmmio.core.inp` API for reading and writing (almost) all of the sections of the model.inp file which is useful for programmatically modifying EPA SWMM models.\n",
     "\n",
@@ -26,11 +26,6 @@
     "For more examples and tutorials, see the [User Guide](usage/index.md) section."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/usage/getting_started.ipynb b/docs/usage/getting_started.ipynb
index a564964..cc99623 100644
--- a/docs/usage/getting_started.ipynb
+++ b/docs/usage/getting_started.ipynb
@@ -119,6 +119,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Edit Model Parameters\n",
+    "\n",
     "Now let's use the lower-level {py:obj}`Model.inp <swmmio.core.inp>` API to access and modify the sections of the model. We'll change the outfall type to FIXED and set a stage elevation. \n",
     "\n",
     ":::{note}\n",
diff --git a/docs/usage/working_with_pyswmm.ipynb b/docs/usage/working_with_pyswmm.ipynb
new file mode 100644
index 0000000..7cd17ae
--- /dev/null
+++ b/docs/usage/working_with_pyswmm.ipynb
@@ -0,0 +1,8340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with PySWMM\n",
+    "\n",
+    "If we _really_ want to supercharge our modeling workflow, we can tap into the excellent functionality provided by [pyswmm](https://www.pyswmm.org/). Here we'll walk through a simple example that runs a model with pyswmm and post-processes the results with `swmmio`.\n",
+    "\n",
+    "We'll start by opening a {py:obj}`pyswmm.Simulation` context to run a model. Then we'll visualize the results using the {py:func}`swmmio.create_map` function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'num_subcatchments': 8,\n",
+       " 'num_conduits': 13,\n",
+       " 'num_junctions': 13,\n",
+       " 'num_outfalls': 1,\n",
+       " 'num_raingages': 1,\n",
+       " 'catchment_area': np.int64(71),\n",
+       " 'mean_subcatchment_slope': np.float64(0.010000000000000002),\n",
+       " 'total_conduit_length': np.int64(4300),\n",
+       " 'invert_range': np.int64(35)}"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import swmmio\n",
+    "import pyswmm \n",
+    "\n",
+    "# path to a SWMM model from swmm-nrtestsuite\n",
+    "model_path = 'https://raw.githubusercontent.com/USEPA/swmm-nrtestsuite/refs/heads/dev/public/examples/Example1.inp'\n",
+    "model = swmmio.Model(model_path)\n",
+    "model.summary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>InletNode</th>\n",
+       "      <th>OutletNode</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MaxDPerc</th>\n",
+       "      <th>coords</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>[(4042.11, 9600.0), (4105.26, 6947.37)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19</td>\n",
+       "      <td>20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>[(7768.42, 6736.84), (5957.89, 6589.47)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>[(5957.89, 6589.47), (4926.32, 6105.26)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10</td>\n",
+       "      <td>21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>[(4105.26, 6947.37), (4926.32, 6105.26)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>[(4926.32, 6105.26), (4421.05, 4715.79)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>[(4421.05, 4715.79), (4821.05, 3326.32)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>[(6252.63, 2147.37), (6673.68, 1368.42), (6631.58, 505.26)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>[(2336.84, 4357.89), (3157.89, 4294.74)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>[(3157.89, 4294.74), (3221.05, 3242.11)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>[(3221.05, 3242.11), (4821.05, 3326.32)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>23</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>[(6484.21, 3978.95), (5389.47, 3031.58)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>[(4821.05, 3326.32), (5389.47, 3031.58)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>24</td>\n",
+       "      <td>17</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>[(5389.47, 3031.58), (6252.63, 2147.37)]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     InletNode OutletNode  ...  MaxDPerc  \\\n",
+       "Name                       ...             \n",
+       "1            9         10  ...      0.38   \n",
+       "4           19         20  ...      0.22   \n",
+       "5           20         21  ...      0.17   \n",
+       "6           10         21  ...      1.00   \n",
+       "7           21         22  ...      0.29   \n",
+       "8           22         16  ...      0.39   \n",
+       "10          17         18  ...      0.53   \n",
+       "11          13         14  ...      0.27   \n",
+       "12          14         15  ...      0.31   \n",
+       "13          15         16  ...      0.76   \n",
+       "14          23         24  ...      0.35   \n",
+       "15          16         24  ...      0.54   \n",
+       "16          24         17  ...      0.57   \n",
+       "\n",
+       "                                                           coords  \n",
+       "Name                                                               \n",
+       "1                         [(4042.11, 9600.0), (4105.26, 6947.37)]  \n",
+       "4                        [(7768.42, 6736.84), (5957.89, 6589.47)]  \n",
+       "5                        [(5957.89, 6589.47), (4926.32, 6105.26)]  \n",
+       "6                        [(4105.26, 6947.37), (4926.32, 6105.26)]  \n",
+       "7                        [(4926.32, 6105.26), (4421.05, 4715.79)]  \n",
+       "8                        [(4421.05, 4715.79), (4821.05, 3326.32)]  \n",
+       "10    [(6252.63, 2147.37), (6673.68, 1368.42), (6631.58, 505.26)]  \n",
+       "11                       [(2336.84, 4357.89), (3157.89, 4294.74)]  \n",
+       "12                       [(3157.89, 4294.74), (3221.05, 3242.11)]  \n",
+       "13                       [(3221.05, 3242.11), (4821.05, 3326.32)]  \n",
+       "14                       [(6484.21, 3978.95), (5389.47, 3031.58)]  \n",
+       "15                       [(4821.05, 3326.32), (5389.47, 3031.58)]  \n",
+       "16                       [(5389.47, 3031.58), (6252.63, 2147.37)]  \n",
+       "\n",
+       "[13 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.links.dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "link_flows = dict()\n",
+    "# Run Simulation PySWMM\n",
+    "with pyswmm.Simulation(model.inp.path) as sim:\n",
+    "    \n",
+    "    for i, step in enumerate(sim):\n",
+    "        \n",
+    "        # store each link's flow in a dictionary \n",
+    "        link_flows[sim.current_time] = {\n",
+    "            link_id: pyswmm.Links(sim)[link_id].flow \n",
+    "            for link_id in model.inp.conduits.index\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "pd.DataFrame(link_flows).T.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img1624ab0047f046feab057333bb190412\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider1624ab0047f046feab057333bb190412\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim1624ab0047f046feab057333bb190412.set_frame(parseInt(this.value));\">\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim1624ab0047f046feab057333bb190412.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim1624ab0047f046feab057333bb190412.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim1624ab0047f046feab057333bb190412.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim1624ab0047f046feab057333bb190412.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim1624ab0047f046feab057333bb190412.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim1624ab0047f046feab057333bb190412.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim1624ab0047f046feab057333bb190412.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim1624ab0047f046feab057333bb190412.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim1624ab0047f046feab057333bb190412.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select1624ab0047f046feab057333bb190412\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_1624ab0047f046feab057333bb190412\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_1624ab0047f046feab057333bb190412\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_1624ab0047f046feab057333bb190412\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_1624ab0047f046feab057333bb190412\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_1624ab0047f046feab057333bb190412\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_1624ab0047f046feab057333bb190412\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img1624ab0047f046feab057333bb190412\";\n",
+       "    var slider_id = \"_anim_slider1624ab0047f046feab057333bb190412\";\n",
+       "    var loop_select_id = \"_anim_loop_select1624ab0047f046feab057333bb190412\";\n",
+       "    var frames = new Array(22);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+       "bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9h\\\n",
+       "AAAPYQGoP6dpAABLUklEQVR4nO3deXwU9f3H8feeOcnBkYRwGQUCCHILQeQoFLC03lYUFRWlKqiI\\\n",
+       "VeGnUtt6Ym3rUcGrQuuJbfEWTTlVYoAAch8KQjgCKCQLAXLt9/dHzMoW0KDJzu7O6/l47INk5rsz\\\n",
+       "n5m62Xe/M/P9OowxRgAAALANp9UFAAAAILQIgAAAADZDAAQAALAZAiAAAIDNEAABAABshgAIAABg\\\n",
+       "MwRAAAAAmyEAAgAA2AwBEAAAwGYIgAAAADZDAAQAALAZAiAAAIDNEAABAABshgAIAABgMwRAAAAA\\\n",
+       "myEAAgAA2AwBEAAAwGYIgAAAADZDAAQAALAZAiAAAIDNEAABAABshgAIAABgMwRAAAAAmyEAAgAA\\\n",
+       "2AwBEAAAwGYIgAAAADZDAAQAALAZAiAAAIDNEAABAABshgAIAABgMwRAAAAAmyEAAgAA2AwBEAAA\\\n",
+       "wGYIgAAAADZDAAQAALAZAiAQ4Q4ePKjf/e53GjZsmBo2bCiHw6Hp06efsP1TTz2l9u3bKyYmRs2a\\\n",
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+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim1624ab0047f046feab057333bb190412 = new Animation(frames, img_id, slider_id, 200.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "links_gdf = model.links.geodataframe\n",
+    "links_gdf = links_gdf.join(pd.DataFrame(link_flows))\n",
+    "\n",
+    "# Example data: list of Axes objects\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.set_axis_off()\n",
+    "ax_list = [ax]\n",
+    "\n",
+    "# Example function to update each Axes object\n",
+    "def update_axes(ax, frame):\n",
+    "    ax.clear()\n",
+    "    links_gdf.plot(linewidth=links_gdf[frame]+1, ax=ax, figsize=(10,10))\n",
+    "    ax.set_title(f'{frame}')\n",
+    "\n",
+    "# Function to update the plot for each frame\n",
+    "def update(frame):\n",
+    "    for ax in ax_list:\n",
+    "        update_axes(ax, frame)\n",
+    "\n",
+    "# Create the animation\n",
+    "ani = animation.FuncAnimation(fig, update, frames=list(link_flows)[::100 + 1], repeat=True)\n",
+    "\n",
+    "HTML(ani.to_jshtml())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}