{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "heading",
   "metadata": {},
   "source": [
    "## Molecule and Bond Counts from Reactive MD\n",
    "\n",
    "This example performs molecule and bond counting from a reactive MD trajectory. It first runs a short ReaxFF simulation, then tabulates and plots counts per frame from the generated `ams.rkf`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "note-license",
   "metadata": {},
   "source": [
    "The MD run requires a ReaxFF license. If you already have a reactive MD trajectory with molecule data, you can skip the MD cell and set `ams_rkf_path` to your own file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imports-md",
   "metadata": {},
   "source": [
    "### Initial Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "imports-code",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "from typing import Dict, List, Literal, Optional, Sequence, Set, Tuple\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "from scm.plams import AMSJob, Molecule, Settings, Trajectory, from_smiles, packmol, view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "run-md-section",
   "metadata": {},
   "source": [
    "### Run a short reactive MD simulation\n",
    "\n",
    "This produces an `ams.rkf` trajectory with molecule and bond information for analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "643c6826-6180-43ed-810c-425b05ebbfaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30.03|16:56:24] JOB reactive_md_toy STARTED\n",
      "[30.03|16:56:24] JOB reactive_md_toy RUNNING\n",
      "[30.03|16:56:26] JOB reactive_md_toy FINISHED\n",
      "[30.03|16:56:26] JOB reactive_md_toy SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "o2 = from_smiles(\"O=O\")\n",
    "h2 = from_smiles(\"[HH]\")\n",
    "mixture = packmol(molecules=[o2, h2], n_molecules=[4, 4], density=1.0)\n",
    "view(mixture, direction=\"tilt_z\", width=300, height=300)\n",
    "\n",
    "md_settings = Settings()\n",
    "md_settings.input.reaxff.forcefield = \"CHO.ff\"\n",
    "md_settings.input.ams.task = \"MolecularDynamics\"\n",
    "md_settings.input.ams.MolecularDynamics.NSteps = 3000\n",
    "md_settings.input.ams.MolecularDynamics.TimeStep = 0.5\n",
    "md_settings.input.ams.MolecularDynamics.InitialVelocities.Temperature = 3500\n",
    "md_settings.input.ams.MolecularDynamics.Trajectory.WriteMolecules = \"Yes\"\n",
    "md_settings.input.ams.MolecularDynamics.Trajectory.WriteBonds = \"Yes\"\n",
    "md_settings.runscript.nproc = 1\n",
    "md_settings.runscript.preamble_lines = [\"export OMP_NUM_THREADS=1\"]\n",
    "\n",
    "md_job = AMSJob(name=\"reactive_md_toy\", settings=md_settings, molecule=mixture)\n",
    "md_results = md_job.run();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "molecule-analysis-section",
   "metadata": {},
   "source": [
    "### Molecule counts per frame\n",
    "\n",
    "For each frame, count how many molecules of each detected species are present.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "molecule-analysis-code",
   "metadata": {
    "tags": []
   },
   "outputs": [
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      "text/plain": [
       "    Frame  O2  H2  H2O2  HO  HO2  H2O  H2O3  H  H3O  H4O2  O\n",
       "0       1   4   4     0   0    0    0     0  0    0     0  0\n",
       "1       2   4   4     0   0    0    0     0  0    0     0  0\n",
       "2       3   4   4     0   0    0    0     0  0    0     0  0\n",
       "3       4   4   4     0   0    0    0     0  0    0     0  0\n",
       "4       5   4   4     0   0    0    0     0  0    0     0  0\n",
       "5       6   4   4     0   0    0    0     0  0    0     0  0\n",
       "6       7   4   4     0   0    0    0     0  0    0     0  0\n",
       "7       8   4   4     0   0    0    0     0  0    0     0  0\n",
       "8       9   4   4     0   0    0    0     0  0    0     0  0\n",
       "9      10   4   4     0   0    0    0     0  0    0     0  0\n",
       "10     11   4   4     0   0    0    0     0  0    0     0  0\n",
       "11     12   4   4     0   0    0    0     0  0    0     0  0\n",
       "12     13   4   4     0   0    0    0     0  0    0     0  0\n",
       "13     14   4   4     0   0    0    0     0  0    0     0  0\n",
       "14     15   4   4     0   0    0    0     0  0    0     0  0\n",
       "15     16   4   4     0   0    0    0     0  0    0     0  0\n",
       "16     17   4   4     0   0    0    0     0  0    0     0  0\n",
       "17     18   4   4     0   0    0    0     0  0    0     0  0\n",
       "18     19   4   4     0   0    0    0     0  0    0     0  0\n",
       "19     20   4   4     0   0    0    0     0  0    0     0  0\n",
       "20     21   4   4     0   0    0    0     0  0    0     0  0\n",
       "21     22   3   3     1   0    0    0     0  0    0     0  0\n",
       "22     23   2   2     2   0    0    0     0  0    0     0  0\n",
       "23     24   2   2     1   2    0    0     0  0    0     0  0\n",
       "24     25   1   1     1   1    1    1     0  0    0     0  0\n",
       "25     26   0   0     1   0    1    1     1  1    0     0  0\n",
       "26     27   1   0     1   0    1    1     0  0    1     0  0\n",
       "27     28   0   0     0   2    2    2     0  0    0     0  0\n",
       "28     29   0   0     0   2    2    2     0  0    0     0  0\n",
       "29     30   1   0     0   2    0    1     0  0    0     1  1\n",
       "30     31   1   0     1   2    0    2     0  0    0     0  0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "n_molecules = int(md_job.results.readrkf(\"Molecules\", \"Num molecules\"))\n",
    "\n",
    "molecule_type_ids = list(range(1, n_molecules + 1))\n",
    "molecule_names = [\n",
    "    md_job.results.readrkf(\"Molecules\", f\"Molecule name {i}\") for i in molecule_type_ids\n",
    "]\n",
    "mols_type_history = md_job.results.get_history_property(\"Mols.Type\")\n",
    "\n",
    "molecule_counts = []\n",
    "frame = 1\n",
    "for mol_types in mols_type_history:\n",
    "    counter = Counter(mol_types)\n",
    "    counts = {\"Frame\": frame}\n",
    "    for i in molecule_type_ids:\n",
    "        counts[molecule_names[i - 1]] = counter[i]\n",
    "    molecule_counts.append(counts)\n",
    "    frame += 1\n",
    "\n",
    "molecule_counts_df = pd.DataFrame(molecule_counts)\n",
    "\n",
    "molecule_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc41e8a4-c611-4768-96eb-39035946503c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "x = molecule_counts_df[\"Frame\"]\n",
    "\n",
    "for i, name in enumerate(molecule_names):\n",
    "    y = molecule_counts_df[name]\n",
    "    ax.plot(x, y, label=name)\n",
    "\n",
    "ax.set_xlabel(\"Frame\")\n",
    "ax.set_ylabel(\"Molecule Count\")\n",
    "ax.legend(loc=\"best\", fontsize=8)\n",
    "fig.tight_layout()\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bond-analysis-section",
   "metadata": {},
   "source": [
    "## Bond counts per frame\n",
    "\n",
    "Count bond types per frame, using bond order rounded to either integer or half-integer values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bond-analysis-code",
   "metadata": {
    "tags": []
   },
   "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>Frame</th>\n",
       "      <th>H---O (BO 0.0)</th>\n",
       "      <th>H-H (BO 1.0)</th>\n",
       "      <th>H-O (BO 1.0)</th>\n",
       "      <th>O---O (BO 0.0)</th>\n",
       "      <th>O-O (BO 1.0)</th>\n",
       "      <th>O=O (BO 2.0)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frame  H---O (BO 0.0)  H-H (BO 1.0)  H-O (BO 1.0)  O---O (BO 0.0)  \\\n",
       "0       1               0             4             0               0   \n",
       "1       2               0             4             0               0   \n",
       "2       3               0             4             0               0   \n",
       "3       4               0             4             0               0   \n",
       "4       5               0             4             0               0   \n",
       "5       6               0             4             0               0   \n",
       "6       7               0             4             0               0   \n",
       "7       8               0             4             0               0   \n",
       "8       9               0             4             0               0   \n",
       "9      10               0             4             0               0   \n",
       "10     11               0             4             0               0   \n",
       "11     12               0             4             0               0   \n",
       "12     13               0             4             0               0   \n",
       "13     14               0             4             0               0   \n",
       "14     15               0             4             0               0   \n",
       "15     16               0             4             0               0   \n",
       "16     17               0             4             0               0   \n",
       "17     18               0             4             0               0   \n",
       "18     19               0             4             0               0   \n",
       "19     20               0             4             0               0   \n",
       "20     21               0             4             0               0   \n",
       "21     22               1             3             2               0   \n",
       "22     23               0             2             4               1   \n",
       "23     24               0             2             4               0   \n",
       "24     25               0             1             7               0   \n",
       "25     26               0             0             7               0   \n",
       "26     27               0             0             9               0   \n",
       "27     28               0             0             8               0   \n",
       "28     29               0             0             9               0   \n",
       "29     30               0             0             9               0   \n",
       "30     31               0             0             9               0   \n",
       "\n",
       "    O-O (BO 1.0)  O=O (BO 2.0)  \n",
       "0              0             4  \n",
       "1              1             3  \n",
       "2              4             0  \n",
       "3              0             4  \n",
       "4              0             4  \n",
       "5              3             1  \n",
       "6              3             1  \n",
       "7              0             4  \n",
       "8              2             2  \n",
       "9              3             1  \n",
       "10             2             2  \n",
       "11             0             4  \n",
       "12             2             2  \n",
       "13             3             1  \n",
       "14             1             3  \n",
       "15             1             3  \n",
       "16             3             1  \n",
       "17             2             2  \n",
       "18             0             4  \n",
       "19             3             1  \n",
       "20             2             2  \n",
       "21             3             1  \n",
       "22             3             1  \n",
       "23             2             1  \n",
       "24             3             0  \n",
       "25             4             0  \n",
       "26             3             0  \n",
       "27             2             0  \n",
       "28             1             0  \n",
       "29             0             1  \n",
       "30             0             1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trajectory = Trajectory(md_job.results.rkfpath())\n",
    "n_frames = len(trajectory)\n",
    "\n",
    "\n",
    "def bo_to_symbol(bo):\n",
    "    s = \"---\"\n",
    "    if bo == 0.5:\n",
    "        s = \"--\"\n",
    "    if bo == 1.0:\n",
    "        s = \"-\"\n",
    "    if bo == 1.5:\n",
    "        s = \"==\"\n",
    "    if bo == 2.0:\n",
    "        s = \"=\"\n",
    "    if bo == 2.5:\n",
    "        s = \"≡≡\"\n",
    "    if bo == 3.0:\n",
    "        s = \"≡\"\n",
    "    return s\n",
    "\n",
    "\n",
    "all_bonds = {}\n",
    "for frame_index, mol in enumerate(trajectory):\n",
    "    frame_counts: Counter = Counter()\n",
    "    seen_pairs: Set[Tuple[int, int]] = set()\n",
    "\n",
    "    for bond in mol.bonds:\n",
    "        pair = tuple(sorted((mol.index(bond.atom1), mol.index(bond.atom2))))\n",
    "        if pair in seen_pairs:\n",
    "            continue\n",
    "        seen_pairs.add(pair)\n",
    "\n",
    "        symbols = sorted((bond.atom1.symbol, bond.atom2.symbol))\n",
    "        bo = float(round(bond.order))\n",
    "        label = f\"{symbols[0]}{bo_to_symbol(bo)}{symbols[1]} (BO {bo:.1f})\"\n",
    "        frame_counts[label] += 1\n",
    "\n",
    "    for label, count in frame_counts.items():\n",
    "        if label not in all_bonds:\n",
    "            all_bonds[label] = [0] * n_frames\n",
    "        all_bonds[label][frame_index] = int(count)\n",
    "\n",
    "bond_names = sorted(all_bonds.keys())\n",
    "bond_counts = []\n",
    "for frame_index in range(n_frames):\n",
    "    counts = {\"Frame\": frame_index + 1}\n",
    "    for name in bond_names:\n",
    "        counts[name] = all_bonds[name][frame_index]\n",
    "    bond_counts.append(counts)\n",
    "\n",
    "bond_counts_df = pd.DataFrame(bond_counts)\n",
    "bond_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b24bcf6f-2fb5-41e0-9d42-88da44041c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "x = bond_counts_df[\"Frame\"]\n",
    "\n",
    "for i, name in enumerate(bond_names):\n",
    "    y = bond_counts_df[name]\n",
    "    ax.plot(x, y, label=name)\n",
    "\n",
    "ax.set_xlabel(\"Frame\")\n",
    "ax.set_ylabel(\"Bond Count\")\n",
    "ax.legend(loc=\"best\", fontsize=8)\n",
    "fig.tight_layout()\n",
    "ax;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43dd7dc6-a306-4087-aa3a-1550bb8f2107",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}