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    "# Chemical Analysis\n",
    "Chemical networks are complex systems where the interplay between many elements often means that small changes in one aspect of the network can greatly effect the outcome in unexpected ways. Nevertheless, there are cases where a simple chemical explanation can be found for some observed behaviour in the model outputs. This tutorial demonstrates how to use some of the functionality of the UCLCHEM library to analyse model outputs and discover these explanations.\n",
    "\n",
    "We do recommend caution when following the approach laid out in this tutorial. There are many pitfalls which we will try to point out as we go. Ultimately, a comprehensive view of how important a reaction is to the outcome of your model requires detailed statistical calculations such as the use of [SHAP values](https://github.com/slundberg/shap) to assign meaningful scores to how much various reactions in a network contribute to a species' abundance. Therefore, care must be taken by the user to ensure that the conclusions they draw from a simpler approach are sound. Examples of papers doing this for UCLCHEM include:\n",
    "- [Understanding molecular abundances in star-forming regions using interpretable machine learning Open Access](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526..404H/abstract) Heyl, J., Butterworth, J., & Viti, S. 2023, MNRAS, 526, 404\n",
    "- [A statistical and machine learning approach to the study of astrochemistry](https://ui.adsabs.harvard.edu/abs/2023FaDi..245..569H/abstract) Heyl, J., Viti, S., & Vermariën, G. 2023, Faraday Discussions, 245,\n",
    "569\n",
    "- [Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning](https://ui.adsabs.harvard.edu/abs/2025arXiv250508410V/abstract) Vermariën, G., Viti, S., Heyl, J., Fontani, F., 2025, A&A, 699, A18  \n",
    "\n",
    "We'll use an example from work that was published in 2022 [Energizing Star Formation: The Cosmic-Ray Ionization Rate in NGC 253 Derived from ALCHEMI Measurements of H3O+ and SO](https://ui.adsabs.harvard.edu/abs/2022ApJ...931...89H/abstract) to demonstrate the use of the rates coming out of UCLCHEM and how it can be used to draw conclusions about the most important reactions in a network for a given species/behaviour."
   ]
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   "source": [
    "import uclchem\n",
    "from glob import glob\n",
    "from joblib import Parallel, delayed\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5bc54f",
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   "source": [
    "## H3O+ and SO\n",
    "\n",
    "In a piece of inference work in which we measured the cosmic ray ionization rate (CRIR) in NGC 253 [(Holdship et al. 2022)](https://ui.adsabs.harvard.edu/abs/2022arXiv220403668H/abstract). We found that both H3O+ and SO were sensitive to the ionization rate. Furthermore, since H3O+ was increased in abundance by increasing CRIR and SO was destroyed, their ratio was extremely sensitive to the rate. \n",
    "\n",
    "In the work, we present the plot below which shows how the equilibrium abundance of each species changes with the CRIR as well as the ratio. We plot this for a range of temperatures to show that this behaviour is not particularly sensitive to the gas temperature.\n",
    "\n",
    "\n",
    "![crir_h3o_so_example](./assets/holdship_ngc253.png)\n",
    "\n",
    "In a sense, this is all the information we need. A complex array of reactions all compete and contribute to produce the outcome of the model. Whatever they are, we see the abundance of these species are very sensitive to the CR over a wide range of temperature and density. However, we can only trust this conclusion as far as we trust the entire chemical network since we don't know what exactly causes this behaviour.\n",
    "\n",
    "If we use the analysis function, we may find all of this is driven by a small handful of reactions. The benefit would then be that our trust in our conclusions only depends on how much we trust the rates of those specific reactions. If it is very important, we can evaluate those reactions and readers of our work can make the same decisions as information from experiment changes.\n",
    "\n",
    "### 1. Generate Test Cases\n",
    "Ideally, we should run a huge grid of models and find some rigorous way to evaluate how important each reaction is to the outcome of the model. However, if we run a small grid representative of different conditions then any simple chemical explanation will be evident if it exists. If it no simple explanation is appropriate, then the analysis module is not appropriate for our task and we could consider more complex approaches.\n",
    "\n",
    "Let's run a simple grid with all possible combinations of the following:\n",
    "- A low CRIR (zeta=1) and high CRIR (zeta=1e4)\n",
    "- A typical cloud density (n=1e4) and high density (n=1e6)\n",
    "- The lower temperature bound of NGC 253 CMZ (75 K)* and a high temperature (250 K) \n",
    "* The lower boundary is a bit lower, but the computational time of 50K models is a lot longer than 75K so we stick with a bit higher values for speed\n",
    "\n",
    "and that will give us enough to work with for our analysis.\n",
    "\n",
    "When we run the model and want to interact with the rates directly after running, UCLCHEM must be told to return it to the user. This \n",
    "can be done using both `return_dataframe=True` and `return_rates=True`. The model will then return the\n",
    "physics (temperature, density etc), abundances and rates as a function of time."
   ]
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    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 models to run\n"
     ]
    },
    {
     "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>temperature</th>\n",
       "      <th>density</th>\n",
       "      <th>zeta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_2</th>\n",
       "      <td>250.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_3</th>\n",
       "      <td>250.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_6</th>\n",
       "      <td>250.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_7</th>\n",
       "      <td>250.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature    density     zeta\n",
       "model_0         50.0    10000.0     10.0\n",
       "model_1         50.0    10000.0  10000.0\n",
       "model_2        250.0    10000.0     10.0\n",
       "model_3        250.0    10000.0  10000.0\n",
       "model_4         50.0  1000000.0     10.0\n",
       "model_5         50.0  1000000.0  10000.0\n",
       "model_6        250.0  1000000.0     10.0\n",
       "model_7        250.0  1000000.0  10000.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperatures = [50, 250]\n",
    "densities = [1e4, 1e6]\n",
    "zetas = [1e1, 1e4]\n",
    "\n",
    "parameterSpace = np.asarray(np.meshgrid(temperatures, densities, zetas)).reshape(3, -1)\n",
    "model_table = pd.DataFrame(parameterSpace.T, columns=[\"temperature\", \"density\", \"zeta\"])\n",
    "model_names = [f\"model_{i}\" for i in range(len(model_table))]\n",
    "model_table.index = model_names\n",
    "print(f\"{model_table.shape[0]} models to run\")\n",
    "\n",
    "\n",
    "def run_model(row):\n",
    "    # basic set of parameters we'll use for this grid.\n",
    "    ParameterDictionary = {\n",
    "        \"baseAv\": 10,  # UV shielded gas in our model\n",
    "        \"freefall\": False,\n",
    "        \"finalTime\": 1e6,\n",
    "        \"initialtemp\": float(row[\"temperature\"]),\n",
    "        \"initialdens\": float(row[\"density\"]),\n",
    "        \"zeta\": float(row[\"zeta\"]),\n",
    "        # Specify some custom tolerance since Silicon chemistry at 50K is tough to solve\n",
    "        \"abstol_factor\": 1e-7,\n",
    "        \"reltol\": 1e-6,\n",
    "        \"abstol_min\": 5e-20,\n",
    "    }\n",
    "    result = uclchem.model.cloud(\n",
    "        param_dict=ParameterDictionary, return_dataframe=True, return_rates=True\n",
    "    )\n",
    "    return result\n",
    "\n",
    "\n",
    "model_table\n"
   ]
  },
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   "id": "2217adeb",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    8.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   2 out of   8 | elapsed:    9.2s remaining:   27.5s\n",
      "[Parallel(n_jobs=10)]: Done   3 out of   8 | elapsed:    9.3s remaining:   15.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   4 out of   8 | elapsed:    9.7s remaining:    9.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   5 out of   8 | elapsed:   10.8s remaining:    6.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   6 out of   8 | elapsed:   11.6s remaining:    3.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done   8 out of   8 | elapsed:   16.0s finished\n"
     ]
    }
   ],
   "source": [
    "# Each result contains: physics, abundances, rates, final_abundances and succesflag\n",
    "results = Parallel(n_jobs=10, verbose=100)(\n",
    "    delayed(run_model)(row) for idx, row in model_table.iterrows()\n",
    ")\n",
    "results = {k: v for k, v in zip(model_names, results)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b3a3e67",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-01-23T13:20:17.360801Z"
    }
   },
   "outputs": [],
   "source": [
    "phys, abun, rates, _, _ = results[\"model_5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c86eacff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:17.363558Z",
     "iopub.status.busy": "2026-01-23T13:20:17.363311Z",
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     "shell.execute_reply": "2026-01-23T13:20:17.398186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': '0.000%',\n",
       " 'N': '0.000%',\n",
       " 'C': '0.000%',\n",
       " 'O': '0.000%',\n",
       " 'S': '0.000%',\n",
       " 'SI': '0.000%'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from uclchem.analysis import check_element_conservation, analyze_element_per_phase\n",
    "\n",
    "# We check that everything is conserved:\n",
    "check_element_conservation(abun, [\"H\", \"N\", \"C\", \"O\", \"S\", \"SI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d5755cb",
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    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we can see why the solve was slow, all of the silicon seems to get stuck on the grains.\n",
    "analyze_element_per_phase(\"SI\", abun).plot(logy=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992337fd",
   "metadata": {},
   "source": [
    "### 2. Analyze the rates\n",
    "UCLCHEM will compute and save the rates of each of your reactions during the simulation. It is then returned to the user to be inspected. \n",
    "\n",
    "\n",
    "For example, if we care about H3O+ and find that it is created by $H_2O$ + $H^+$, then UCLCHEM is called to get $k$, the rate of that reaction and then analysis calculates\n",
    "$$\n",
    "\\dot{Y(H_3O^+)} = k Y(H_2O) Y(H^+) n_H\n",
    "$$\n",
    "\n",
    "where $\\dot{Y(H_3O^+)}$ is the rate of change of the $H_3O+$ abundance due to the reaction. The $\\dot{Y}$ s of each reaction involving the $H_3O^+$ are then compared to see which reactions contribute the most to the destruction and formation of $H_3O+$.\n",
    "\n",
    "Our plan then is to do this for the low zeta case and the high zeta case. Hopefully, we'll see the same reactions are most important to the abundance of $H_3O^+$ and SO at all densities and tempertures. If we don't find that that's the case, then the chemistry is these species is fairly complex and perhaps trying to present a simple cause for the CRIR dependence is not a good idea.\n",
    "\n",
    "So, let's analyse the outputs!\n",
    "\n",
    "#### 2.1 $H_3O^+$\n",
    "\n",
    "Let's inspect the reaction file of H3O+ for a high temperature, low density and low radiation field model (model 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1040c51c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:17.675573Z",
     "iopub.status.busy": "2026-01-23T13:20:17.675362Z",
     "iopub.status.idle": "2026-01-23T13:20:17.933666Z",
     "shell.execute_reply": "2026-01-23T13:20:17.932687Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve the outputs from the grid:\n",
    "physics, abundances, rates, final_abundances, successflag = results[\"model_2\"]\n",
    "\n",
    "# Add everything together into one large dataframe\n",
    "super_df = pd.concat((physics, abundances, rates), axis=1)\n",
    "\n",
    "# Plot the evolution of H3O+:\n",
    "super_df.plot(\"Time\", \"H3O+\", logx=True, logy=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c772f96c",
   "metadata": {},
   "source": [
    "Above, we can see that the H3O+ is being formed effectively. If we then want to better understand which reactions are responsible for this formation process, we can easily obtain the production and struction routes using:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d0a2092",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:17.935512Z",
     "iopub.status.busy": "2026-01-23T13:20:17.935293Z",
     "iopub.status.idle": "2026-01-23T13:20:17.961416Z",
     "shell.execute_reply": "2026-01-23T13:20:17.960632Z"
    }
   },
   "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>CH+ + H2O -&gt; H3O+ + C</th>\n",
       "      <th>CH4 + H2O+ -&gt; H3O+ + CH3</th>\n",
       "      <th>CH4 + OH+ -&gt; H3O+ + CH2</th>\n",
       "      <th>CH4+ + H2O -&gt; H3O+ + CH3</th>\n",
       "      <th>CH5+ + H2O -&gt; H3O+ + CH4</th>\n",
       "      <th>H2 + H2O+ -&gt; H3O+ + H</th>\n",
       "      <th>H2+ + H2O -&gt; H3O+ + H</th>\n",
       "      <th>H2O + C2H2+ -&gt; C2H + H3O+</th>\n",
       "      <th>H2O + H2CO+ -&gt; HCO + H3O+</th>\n",
       "      <th>H2O + H2CL+ -&gt; HCL + H3O+</th>\n",
       "      <th>...</th>\n",
       "      <th>H2O+ + HCO -&gt; CO + H3O+</th>\n",
       "      <th>H3+ + CH3CHO -&gt; H3O+ + C2H4</th>\n",
       "      <th>H3+ + H2O -&gt; H3O+ + H2</th>\n",
       "      <th>H3+ + HCOOH -&gt; H3O+ + CO + H2</th>\n",
       "      <th>NH + H2O+ -&gt; H3O+ + N</th>\n",
       "      <th>NH+ + H2O -&gt; H3O+ + N</th>\n",
       "      <th>NH2+ + H2O -&gt; H3O+ + NH</th>\n",
       "      <th>O + CH5+ -&gt; H3O+ + CH2</th>\n",
       "      <th>OH + H2O+ -&gt; H3O+ + O</th>\n",
       "      <th>OH+ + H2O -&gt; H3O+ + O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CH+ + H2O -> H3O+ + C  CH4 + H2O+ -> H3O+ + CH3  CH4 + OH+ -> H3O+ + CH2  \\\n",
       "42           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "43           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "44           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "45           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "46           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "\n",
       "    CH4+ + H2O -> H3O+ + CH3  CH5+ + H2O -> H3O+ + CH4  H2 + H2O+ -> H3O+ + H  \\\n",
       "42              2.848157e-09              4.053147e-09                    0.0   \n",
       "43              2.848157e-09              4.053147e-09                    0.0   \n",
       "44              2.848157e-09              4.053147e-09                    0.0   \n",
       "45              2.848157e-09              4.053147e-09                    0.0   \n",
       "46              2.848157e-09              4.053147e-09                    0.0   \n",
       "\n",
       "    H2+ + H2O -> H3O+ + H  H2O + C2H2+ -> C2H + H3O+  \\\n",
       "42           3.724513e-09               2.409979e-10   \n",
       "43           3.724513e-09               2.409979e-10   \n",
       "44           3.724513e-09               2.409979e-10   \n",
       "45           3.724513e-09               2.409979e-10   \n",
       "46           3.724513e-09               2.409979e-10   \n",
       "\n",
       "    H2O + H2CO+ -> HCO + H3O+  H2O + H2CL+ -> HCL + H3O+  ...  \\\n",
       "42               2.848157e-09               2.190890e-09  ...   \n",
       "43               2.848157e-09               2.190890e-09  ...   \n",
       "44               2.848157e-09               2.190890e-09  ...   \n",
       "45               2.848157e-09               2.190890e-09  ...   \n",
       "46               2.848157e-09               2.190890e-09  ...   \n",
       "\n",
       "    H2O+ + HCO -> CO + H3O+  H3+ + CH3CHO -> H3O+ + C2H4  \\\n",
       "42             3.067246e-10                 1.139263e-09   \n",
       "43             3.067246e-10                 1.139263e-09   \n",
       "44             3.067246e-10                 1.139263e-09   \n",
       "45             3.067246e-10                 1.139263e-09   \n",
       "46             3.067246e-10                 1.139263e-09   \n",
       "\n",
       "    H3+ + H2O -> H3O+ + H2  H3+ + HCOOH -> H3O+ + CO + H2  \\\n",
       "42            6.463126e-09                   1.971801e-09   \n",
       "43            6.463126e-09                   1.971801e-09   \n",
       "44            6.463126e-09                   1.971801e-09   \n",
       "45            6.463126e-09                   1.971801e-09   \n",
       "46            6.463126e-09                   1.971801e-09   \n",
       "\n",
       "    NH + H2O+ -> H3O+ + N  NH+ + H2O -> H3O+ + N  NH2+ + H2O -> H3O+ + NH  \\\n",
       "42           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "43           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "44           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "45           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "46           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "\n",
       "    O + CH5+ -> H3O+ + CH2  OH + H2O+ -> H3O+ + O  OH+ + H2O -> H3O+ + O  \n",
       "42            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "43            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "44            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "45            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "46            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from uclchem.analysis import get_production_and_destruction\n",
    "\n",
    "production, destruction = get_production_and_destruction(\"H3O+\", rates)\n",
    "\n",
    "production.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "685850dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:17.963177Z",
     "iopub.status.busy": "2026-01-23T13:20:17.962994Z",
     "iopub.status.idle": "2026-01-23T13:20:18.303725Z",
     "shell.execute_reply": "2026-01-23T13:20:18.302793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from uclchem.plot import plot_rate_summary\n",
    "\n",
    "plot_rate_summary(production, destruction, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf9bc8f",
   "metadata": {},
   "source": [
    "We can then also effectively convert from rates constants k, to the actual RHS\n",
    "elements that contribute to each reaction; We can then get fluxes rather than rates:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13b6dd93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:18.305763Z",
     "iopub.status.busy": "2026-01-23T13:20:18.305565Z",
     "iopub.status.idle": "2026-01-23T13:20:22.764630Z",
     "shell.execute_reply": "2026-01-23T13:20:22.763739Z"
    }
   },
   "outputs": [],
   "source": [
    "from uclchem.analysis import rates_to_dy_and_flux\n",
    "from uclchem.utils import get_reaction_network\n",
    "\n",
    "network = get_reaction_network()\n",
    "dy, flux = rates_to_dy_and_flux(physics, abundances, rates, network=network)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e418b82",
   "metadata": {},
   "source": [
    "We can then inspect the RHS of the differential equation per reaction. This informs us that the only relevant term is actually the destruction of the molecule via its reaction with HCS and H2S. Explaining the small decrease at 1 million years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3de1ba50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T13:20:22.766527Z",
     "iopub.status.busy": "2026-01-23T13:20:22.766320Z",
     "iopub.status.idle": "2026-01-23T13:20:23.102570Z",
     "shell.execute_reply": "2026-01-23T13:20:23.101508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rate_summary(production, destruction, -10, \"flux\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79414b9",
   "metadata": {},
   "source": [
    "# UPDATE to UMIST22\n",
    "As of 11-10-2024, we use UMIST22 for the notebook, so we need to repeat this analysis. Some dominant formation and destruction pathways are no longer present for in UMIST22. The rest of the notebook is thus no longer up to-date!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bdea2d",
   "metadata": {},
   "source": [
    "\n",
    " \n",
    "We can then delve into specifically the low temperature, low density, low zeta case:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 7.90e+05 years\n",
    "Formation = 1.92e-18 from:\n",
    "H2 + H2O+ -> H3O+ + H : 78.49%\n",
    "H2O + HCO+ -> CO + H3O+ : 17.68%\n",
    "H3+ + H2O -> H3O+ + H2 : 3.40%\n",
    "\n",
    "Destruction = -1.92e-18 from:\n",
    "H3O+ + E- -> OH + H + H : 65.43%\n",
    "H3O+ + E- -> H2O + H : 15.21%\n",
    "H3O+ + E- -> OH + H2 : 11.52%\n",
    "H3O+ + H2S -> H3S+ + H2O : 5.17%\n",
    "H3O+ + E- -> O + H2 + H : 1.20%\n",
    "H3O+ + HCN -> HCNH+ + H2O : 0.33%\n",
    "H3O+ + SIO -> SIOH+ + H2O : 0.33%\n",
    "```\n",
    "\n",
    "What this shows is the reactions that cause 99.9% of the formation and 99.9% of the destruction of $H_3O^+$ at a time step. The total rate of formation and destruction in units of $s^{-1}$ is  given as well the percentage of the total that each reaction contributes. \n",
    "\n",
    "What we need to find is a pattern in these reactions which holds across time and across different densities and temperatures. It actually turns out that the reactions printed above are the dominate formation and destruction routes of $H_3O^+$ for all parameters for all times. For example, at high temperature, high density and high zeta, we get:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.00e+04 years\n",
    "Formation = 2.03e-14 from:\n",
    "H2 + H2O+ -> H3O+ + H : 95.47%\n",
    "H3+ + H2O -> H3O+ + H2 : 3.14%\n",
    "H2O + HCO+ -> CO + H3O+ : 1.05%\n",
    "\n",
    "Destruction = -2.03e-14 from:\n",
    "H3O+ + E- -> OH + H + H : 69.82%\n",
    "H3O+ + E- -> H2O + H : 16.23%\n",
    "H3O+ + E- -> OH + H2 : 12.29%\n",
    "H3O+ + E- -> O + H2 + H : 1.28%\n",
    "```\n",
    "The sole difference being that the formation rate is much higher. Since equilibrium is reached, the destruction rate is also higher and so we need to use the fact we know the $H_3O^+$ abundance increases with CRIR to infer that the destruction rate is only faster because there is more $H_3O^+$ to destroy.\n",
    "\n",
    "However, there are some problems here! Why does $H_2$ + $H_2O^+$ become so efficient at high CRIR? We know the rate of a two body reaction does not depend on CRIR ([see the chemistry docs](/docs/gas)) so it must be that $H_2O^+$ is increasing in abundance. We can run analysis on $H_2O^+$ to see what is driving that. In fact, as we report in the paper, following this thread we find that a chain of hydrogenations starting from $OH^+$ is the overall route of $H_3O^+$ formation and that this chain starts with small ions that are primarly formed through cosmic ray reactions. *It will very often be the case that analysing one species requires you to run analysis on another.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6254d85c",
   "metadata": {},
   "source": [
    "#### 2.2 SO\n",
    "\n",
    "With a strong explanation for $H_3O^+$, we can now look at SO. We start by running analysis again and then by looking at the reactions as before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f0249d",
   "metadata": {},
   "source": [
    "Again, let's start by looking at the low temperature, high density, low zeta case:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.31e+04 years\n",
    "Formation = 3.67e-22 from:\n",
    "#SO + THERM -> SO : 68.11%\n",
    "HSO+ + E- -> SO + H : 21.04%\n",
    "#SO + DEUVCR -> SO : 9.87%\n",
    "\n",
    "Destruction = -3.78e-22 from:\n",
    "SO + FREEZE -> #SO : 78.04%\n",
    "HCO+ + SO -> HSO+ + CO : 20.97%\n",
    "```\n",
    "\n",
    "An interesting point here is that equilbrium is reached at around $1.31 \\times 10^4$ yr in this model. Since `analysis()` only prints a time step when the most important reactions are different to the last one, this time step is the last output from the analysis. We can see that we broadly reach an equilibrium between thermal desorption and freeze out of SO, with some formation and destruction via ions. \n",
    "\n",
    "This doesn't hold up at lower densities or higher temperatures, looking at the high temperature, low density, low zeta case, we see a second pattern of reactions:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 9.50e+05 years\n",
    "Formation = 1.05e-20 from:\n",
    "O2 + S -> SO + O : 98.10%\n",
    "O + NS -> SO + N : 1.48%\n",
    "\n",
    "Destruction = -1.02e-20 from:\n",
    "C + SO -> CS + O : 39.10%\n",
    "C + SO -> S + CO : 39.10%\n",
    "C+ + SO -> SO+ + C : 4.33%\n",
    "C+ + SO -> CS+ + O : 4.33%\n",
    "C+ + SO -> S+ + CO : 4.33%\n",
    "C+ + SO -> S + CO+ : 4.33%\n",
    "N + SO -> S + NO : 3.46%\n",
    "H+ + SO -> SO+ + H : 0.28%\n",
    "```\n",
    "However, it's not so complex. All the low zeta outputs show one of these two sets of reactions. Essentially, higher temperatures are opening up gas phase reaction routes which form and destroy SO. These new routes are not particularly quick with a total rate of change of $10^{-20} s^{-1}$. Note that quite a lot of these are due to reactions with ions.\n",
    "\n",
    "Finally, we look at the high zeta cases and find that no matter the temperature or density, we see the same reactions:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.81e+04 years\n",
    "Formation = 5.64e-17 from:\n",
    "O2 + S -> SO + O : 85.30%\n",
    "HSO+ + E- -> SO + H : 13.19%\n",
    "O + NS -> SO + N : 1.19%\n",
    "\n",
    "Destruction = -5.64e-17 from:\n",
    "H+ + SO -> SO+ + H : 13.16%\n",
    "C+ + SO -> SO+ + C : 10.52%\n",
    "C+ + SO -> CS+ + O : 10.52%\n",
    "C+ + SO -> S+ + CO : 10.52%\n",
    "C+ + SO -> S + CO+ : 10.52%\n",
    "H3+ + SO -> HSO+ + H2 : 9.86%\n",
    "C + SO -> CS + O : 8.90%\n",
    "C + SO -> S + CO : 8.90%\n",
    "SO + CRPHOT -> S + O : 4.11%\n",
    "SO + CRPHOT -> SO+ + E- : 4.11%\n",
    "HCO+ + SO -> HSO+ + CO : 3.20%\n",
    "HE+ + SO -> S+ + O + HE : 1.93%\n",
    "HE+ + SO -> S + O+ + HE : 1.93%\n",
    "N + SO -> S + NO : 0.87%\n",
    "```\n",
    "\n",
    "Ions take over the destruction of SO when the CRIR is high and these reactions are proceeding about 1000 times faster than the low zeta rates. This makes sense as increasing the CRIR will greatly increase the abundances of these simple ionic species.\n",
    "\n",
    "All in all, we can conclude that a range of gas phase reactions control the abundance of SO at low zeta but once the CRIR is high, destruction via ions dominates the SO chemistry leading to the decreasing abundance that we see."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19cbebcd",
   "metadata": {},
   "source": [
    "## Summary Notes\n",
    "\n",
    "In this notebook, we've run some representative models to look at why the SO and $H_3O^+$ abundances seem to so heavily depend on the CRIR. We find that a chain of very efficient reactions starting with simple ions comprise the primary formation route of $H_3O^+$ so that the total production of this species depends entirely on the abundance of those ions. With high CRIR, those ions become very abundant and we get a lot of $H_3O^+$ formation.\n",
    "\n",
    "SO is complex at first glance since the formation routes vary by parameter. However, looking at the destruction routes we can see that it's simply that SO can be easily destroyed by simple ions and, again, these are very abundant in gas exposed to a high CRIR.\n",
    "\n",
    "An interesting note about SO is that we could potentially say that destruction via C$^+$ is the dominant destruction route. However, we would caution users to be careful with that kind of statement. Reactions with H$^+$, HE$^+$ and other also destroy SO. It could be that if we turned off the C$^+$ reactions, the SO chemistry would be unchanged as other ions take up the slack. Therefore, unless a single reaction is clearly dominant (as in $H_3O^+$ formation), it is best to test the importance of a reaction by removing it before concluding it is the single most important reaction.\n",
    "\n",
    "## Considerations\n",
    "\n",
    "`uclchem.analysis.analysis()` looks at a snapshot of the gas and calculates the instantaneous rate of change of important reactions. However, over the course of a time step, abundances change and reactions rise and fall in importance. More importantly, complex interplay between reactions can contribute to an outcome. This is ultimately a simple, first order look at what is happening in the network but in many cases, a deeper view will be required.\n",
    "\n",
    "If you struggle to find an explanation that fits all time steps in your outputs and is true across a range of parameters, then it is best not to report simple conclusions about the chemistry and to look for other ways to understand the network."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac57d51d",
   "metadata": {},
   "source": []
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