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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Objective: Velocity Packet Tracker Visualisation\n",
+    "\n",
+    "## Google Summer of Code 2024\n",
+    "\n",
+    "Author: Sarthak Srivastava\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/archil/Documents/tardis/tardis/__init__.py:20: UserWarning: Astropy is already imported externally. Astropy should be imported after TARDIS.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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+       "model_id": "8de6446381ab43658dd4638a3568cf10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Iterations:          0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69b2154c53c44eca905dcf6de4fdfa31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Packets:             0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "from astropy import units as u\n",
+    "\n",
+    "from tardis import run_tardis\n",
+    "from tardis.io.atom_data.util import download_atom_data\n",
+    "from tardis.util.base import int_to_roman\n",
+    "from tardis.visualization import SDECPlotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Run simulation and generate SDEC Plot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Atomic Data kurucz_cd23_chianti_H_He already exists in /Users/archil/Downloads/tardis-data/kurucz_cd23_chianti_H_He.h5. Will not download - override with force_download=True.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download atom data\n",
+    "download_atom_data(\"kurucz_cd23_chianti_H_He\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  \n",
+      "\t/Users/archil/Documents/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n",
+      "  (g_lower * n_upper) / (g_upper * n_lower)\n",
+      " (\u001b[1mwarnings.py\u001b[0m:109)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
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+      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  \n",
+      "\t/Users/archil/Documents/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n",
+      "  (g_lower * n_upper) / (g_upper * n_lower)\n",
+      " (\u001b[1mwarnings.py\u001b[0m:109)\n"
+     ]
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+      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  \n",
+      "\t/Users/archil/Documents/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n",
+      "  (g_lower * n_upper) / (g_upper * n_lower)\n",
+      " (\u001b[1mwarnings.py\u001b[0m:109)\n"
+     ]
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+      "\n",
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+      "\t/Users/archil/Documents/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning:\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\t/Users/archil/Documents/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning:\n",
+      "\n",
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+       "                            \n",
+       "var gd = document.getElementById('acbd3159-8f04-4486-8e13-105befe8a307');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim = run_tardis(\n",
+    "    \"tardis_example.yml\",\n",
+    "    virtual_packet_logging=True,\n",
+    "    show_convergence_plots=True,\n",
+    "    export_convergence_plots=True,\n",
+    "    log_level=\"ERROR\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Wavelength $[\\\\mathrm{\\\\AA}]$', ylabel='$L_{\\\\lambda}$ [erg $\\\\mathrm{s^{-1}}$ $\\\\mathrm{\\\\AA^{-1}}$]'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "SDECPlotter.from_simulation(sim).generate_plot_mpl()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Abundance of Elements vs Shell Velocity\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[11000. 11450. 11900. 12350. 12800. 13250. 13700. 14150. 14600. 15050.\n",
+      " 15500. 15950. 16400. 16850. 17300. 17750. 18200. 18650. 19100. 19550.] km / s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get x-axis values: shell velocities\n",
+    "shell_velocities = sim.simulation_state.v_inner.si.to(u.km / u.s)\n",
+    "\n",
+    "print(shell_velocities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
+       "      <th>13</th>\n",
+       "      <th>14</th>\n",
+       "      <th>15</th>\n",
+       "      <th>16</th>\n",
+       "      <th>17</th>\n",
+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>atomic_number</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>8</th>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 0     1     2     3     4     5     6     7     8     9   \\\n",
+       "atomic_number                                                               \n",
+       "8              0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19   \n",
+       "12             0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03   \n",
+       "14             0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52   \n",
+       "16             0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19   \n",
+       "18             0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04   \n",
+       "20             0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03   \n",
+       "\n",
+       "                 10    11    12    13    14    15    16    17    18    19  \n",
+       "atomic_number                                                              \n",
+       "8              0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  \n",
+       "12             0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  \n",
+       "14             0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52  0.52  \n",
+       "16             0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  0.19  \n",
+       "18             0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  \n",
+       "20             0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.03  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get y-axis values: element abundances\n",
+    "abundances = sim.plasma.abundance\n",
+    "abundances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot data in Matplotlib\n",
+    "fig, axis = plt.subplots(figsize=(15, 10))\n",
+    "\n",
+    "ATOM_DATA = sim.plasma.atomic_data.atom_data\n",
+    "\n",
+    "for atomic_number in sim.plasma.selected_atoms:\n",
+    "    axis.step(\n",
+    "        shell_velocities,\n",
+    "        abundances.loc[atomic_number],\n",
+    "        marker=\".\",\n",
+    "        linestyle=\"--\",\n",
+    "        where=\"post\",\n",
+    "        label=ATOM_DATA.symbol.loc[atomic_number],\n",
+    "    )\n",
+    "\n",
+    "_ = axis.set(\n",
+    "    title=\"Elemental Abundance vs Shell Velocity\",\n",
+    "    xlabel=f\"Inner Velocity ({str(shell_velocities.unit)})\",\n",
+    "    ylabel=\"Abundance (Log)\",\n",
+    "    yscale=\"log\",\n",
+    ")\n",
+    "_ = axis.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+            "ticks": "",
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+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
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+            "linecolor": "white",
+            "ticks": ""
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+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
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+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
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+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Elemental Abundance vs Shell Velocity"
+        },
+        "xaxis": {
+         "title": {
+          "text": "Inner Velocity (km / s)"
+         }
+        },
+        "yaxis": {
+         "title": {
+          "text": "Abundance (Log)"
+         },
+         "type": "log"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot data in Plotly\n",
+    "\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "for atomic_number in sim.plasma.selected_atoms:\n",
+    "    fig.add_trace(\n",
+    "        go.Scatter(\n",
+    "            x=shell_velocities,\n",
+    "            y=abundances.loc[atomic_number],\n",
+    "            mode=\"lines+markers\",\n",
+    "            name=ATOM_DATA.symbol.loc[atomic_number],\n",
+    "            line=dict(dash=\"dot\"),\n",
+    "            line_shape=\"hv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Update layout for a common legend and titles\n",
+    "fig.update_layout(\n",
+    "    title=\"Elemental Abundance vs Shell Velocity\",\n",
+    "    xaxis_title=f\"Inner Velocity ({str(shell_velocities.unit)})\",\n",
+    "    yaxis_title=\"Abundance (Log)\",\n",
+    "    legend_title=\"Ion\",\n",
+    "    yaxis_type=\"log\",\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Number of interactions (escaped) for each element\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LINE_REFERENCE = sim.plasma.atomic_data.lines  # To look up line data by ID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def interaction_count_by_ion(line_ids):\n",
+    "    \"\"\"\n",
+    "    Looks up the line_ids in the line reference table and counts the interactions grouping by (atomic_number, ion_number)\n",
+    "    Returns a pd.Series with index containing ion name (Eg: \"Si II\") and corresponding interaction count.\n",
+    "    \"\"\"\n",
+    "    # Filter out non-escaped packets\n",
+    "    line_ids = [line_id for line_id in line_ids if line_id != -1]\n",
+    "\n",
+    "    # Group by ion\n",
+    "    num_lines_per_ion = (\n",
+    "        LINE_REFERENCE.iloc[line_ids]\n",
+    "        .groupby(level=[\"atomic_number\", \"ion_number\"])\n",
+    "        .size()\n",
+    "    )\n",
+    "\n",
+    "    # Create and return new Series\n",
+    "    combined_idx = num_lines_per_ion.index.map(\n",
+    "        lambda x: f\"{ATOM_DATA.symbol.loc[x[0]]} {int_to_roman(x[1]+1)}\"\n",
+    "    )\n",
+    "    combined_idx.name = \"Ion\"\n",
+    "    return pd.Series(\n",
+    "        num_lines_per_ion.values, index=combined_idx, name=\"interaction_count\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get line interaction IDs and compute counts by ion\n",
+    "out_lines = sim.transport.transport_state.last_line_interaction_out_id\n",
+    "virt_out_lines = (\n",
+    "    sim.transport.transport_state.virt_packet_last_line_interaction_out_id\n",
+    ")\n",
+    "\n",
+    "# Get count per ion\n",
+    "out_line_count_by_ion = interaction_count_by_ion(out_lines)\n",
+    "virt_out_line_count_by_ion = interaction_count_by_ion(virt_out_lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ion\n",
+       "O I          70\n",
+       "O II         92\n",
+       "O III       586\n",
+       "Mg II      2835\n",
+       "Si II      6055\n",
+       "Si III    10229\n",
+       "Si IV       137\n",
+       "S I           2\n",
+       "S II       4238\n",
+       "S III      1556\n",
+       "S IV         19\n",
+       "Ar I          6\n",
+       "Ar II      1468\n",
+       "Ar III       13\n",
+       "Ca II      1936\n",
+       "Name: interaction_count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Print the data before plotting\n",
+    "out_line_count_by_ion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ion\n",
+       "O I         9560\n",
+       "O II        2200\n",
+       "O III      27640\n",
+       "Mg II      75800\n",
+       "Si II     241460\n",
+       "Si III    407050\n",
+       "Si IV      17110\n",
+       "S I           80\n",
+       "S II      165550\n",
+       "S III      51000\n",
+       "S IV        2780\n",
+       "Ar I         480\n",
+       "Ar II      30180\n",
+       "Ar III      2880\n",
+       "Ar IV         10\n",
+       "Ca II      37650\n",
+       "Name: interaction_count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "virt_out_line_count_by_ion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 5))\n",
+    "\n",
+    "# Plotting for real packets\n",
+    "labels = out_line_count_by_ion.index\n",
+    "values = out_line_count_by_ion.values\n",
+    "\n",
+    "xticks = np.arange(len(labels))\n",
+    "bars = axes[0].bar(xticks, values, align=\"center\", alpha=0.7, color=\"b\")\n",
+    "\n",
+    "axes[0].set_xticks(xticks, labels, rotation=\"vertical\")\n",
+    "axes[0].set_xlabel(\"Ion\")\n",
+    "axes[0].set_ylabel(\"Counts (Log)\")\n",
+    "axes[0].set_yscale(\"log\")\n",
+    "axes[0].set_title(\"Last Out Line Interactions per Ion (Real Packets)\")\n",
+    "\n",
+    "# Plotting for virtual packets\n",
+    "labels = virt_out_line_count_by_ion.index\n",
+    "values = virt_out_line_count_by_ion.values\n",
+    "\n",
+    "xticks = np.arange(len(labels))\n",
+    "bars = axes[1].bar(xticks, values, align=\"center\", alpha=0.7, color=\"b\")\n",
+    "\n",
+    "axes[1].set_xticks(xticks, labels, rotation=\"vertical\")\n",
+    "axes[1].set_xlabel(\"Ion\")\n",
+    "axes[1].set_ylabel(\"Counts (Log)\")\n",
+    "axes[1].set_yscale(\"log\")\n",
+    "axes[1].set_title(\"Last Out Line Interactions per Ion (Virtual Packets)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+          "O II",
+          "O III",
+          "Mg II",
+          "Si II",
+          "Si III",
+          "Si IV",
+          "S I",
+          "S II",
+          "S III",
+          "S IV",
+          "Ar I",
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+          "O II",
+          "O III",
+          "Mg II",
+          "Si II",
+          "Si III",
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+          "S I",
+          "S II",
+          "S III",
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+          "xanchor": "center",
+          "xref": "paper",
+          "y": 1,
+          "yanchor": "bottom",
+          "yref": "paper"
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+          "showarrow": false,
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+          "xref": "paper",
+          "y": 1,
+          "yanchor": "bottom",
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+          "mesh3d": [
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+            "type": "mesh3d"
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+            "type": "parcoords"
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+            "type": "pie"
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+            "marker": {
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+            "type": "scatter3d"
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+          "surface": [
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+            "type": "surface"
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+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
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+             "line": {
+              "color": "white"
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+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
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+             "line": {
+              "color": "white"
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+            "type": "table"
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+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
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+          "autotypenumbers": "strict",
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+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
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+          "font": {
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+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
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+          "hoverlabel": {
+           "align": "left"
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+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
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+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          0.45
+         ],
+         "title": {
+          "text": "Ion"
+         }
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "domain": [
+          0.55,
+          1
+         ],
+         "title": {
+          "text": "Ion"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Counts (Log)"
+         },
+         "type": "log"
+        },
+        "yaxis2": {
+         "anchor": "x2",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Counts (Log)"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Creating plots in plotly\n",
+    "\n",
+    "fig = make_subplots(\n",
+    "    rows=1,\n",
+    "    cols=2,\n",
+    "    subplot_titles=(\n",
+    "        \"Last Out Line Interactions per Ion (Real Packets)\",\n",
+    "        \"Last Out Line Interactions per Ion (Virtual Packets)\",\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Trace for Real packets\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=out_line_count_by_ion.index,\n",
+    "        y=out_line_count_by_ion.values,\n",
+    "        name=\"Real Packets\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=1,\n",
+    ")\n",
+    "\n",
+    "# Trace for Virtual packets\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=virt_out_line_count_by_ion.index,\n",
+    "        y=virt_out_line_count_by_ion.values,\n",
+    "        name=\"Virtual Packet\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=2,\n",
+    ")\n",
+    "\n",
+    "fig.update_layout(yaxis_type=\"log\")\n",
+    "\n",
+    "fig.update_xaxes(title_text=\"Ion\", row=1, col=1)\n",
+    "fig.update_xaxes(title_text=\"Ion\", row=1, col=2)\n",
+    "fig.update_yaxes(title_text=\"Counts (Log)\", row=1, col=1)\n",
+    "fig.update_yaxes(title_text=\"Counts (Log)\", row=1, col=2)\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tardis",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}