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    "# Advanced Physical Modelling\n",
    "\n",
    "In the previous tutorial, we simply modelled the chemistry of a static cloud for 1 Myr. This is unlikely to meet everybody's modelling needs and UCLCHEM is capable of modelling much more complex environments such as hot cores and shocks. In this tutorial, we model both a hot core and a shock to explore how these models work and to demonstrate the workflow that the UCLCHEM team normally follow."
   ]
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   "source": [
    "import uclchem\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b2153e",
   "metadata": {},
   "source": [
    "## The Hot Core\n",
    "\n",
    "### Initial Conditions (Stage 1)\n",
    "UCLCHEM typically starts with the gas in atomic/ionic form with no molecules. However, this clearly is not appropriate when modelling an object such as a hot core. In these objects, the gas is already evolved and there should be molecules in the gas phase as well as ice mantles on the dust. To allow for this, one must provide some initial abundances to the model. There are many ways to do this but we typically chose to run a preliminary model to produce our abundances. In many UCLCHEM papers, we refer to the preliminary model as *stage 1* and the science model as *stage 2*. Stage 1 simply models a collapsing cloud and stage 2 models the object in question.\n",
    "\n",
    "To do this, we will use `uclchem.model.cloud()` to run a model where a cloud of gas collapses from a density of $10^2 cm^{-3}$ to our hot core density of $10^6 cm^{-3}$, keeping all other parameters constant. During this collapse, chemistry will occur and we can assume the final abundances of this model will be reasonable starting abundances for the hot core."
   ]
  },
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   "id": "478960cc",
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    "execution": {
     "iopub.execute_input": "2026-01-23T14:08:07.112339Z",
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   "source": [
    "# set a parameter dictionary for cloud collapse model\n",
    "param_dict = {\n",
    "    \"endAtFinalDensity\": False,  # stop at finalTime\n",
    "    \"freefall\": True,  # increase density in freefall\n",
    "    \"initialDens\": 1e2,  # starting density\n",
    "    \"finalDens\": 1e6,  # final density\n",
    "    \"initialTemp\": 10.0,  # temperature of gas\n",
    "    \"finalTime\": 6.0e6,  # final time\n",
    "    \"rout\": 0.1,  # radius of cloud in pc\n",
    "    \"baseAv\": 1.0,  # visual extinction at cloud edge.\n",
    "}\n",
    "df_stage1_physics, df_stage1_chemistry, df_stage1_rates, final_abundances, result = (\n",
    "    uclchem.model.cloud(\n",
    "        param_dict=param_dict,\n",
    "        return_dataframe=True,\n",
    "    )\n",
    ")"
   ]
  },
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       "      <td>1.000000e-30</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.823239e-04</td>\n",
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       "      <td>5.000000e-01</td>\n",
       "      <td>9.674345e-17</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>9.479987e-17</td>\n",
       "      <td>7.800679e-23</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.054000e-17</td>\n",
       "      <td>1.950169e-24</td>\n",
       "      <td>1.000051e-10</td>\n",
       "      <td>1.770000e-04</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.823239e-04</td>\n",
       "      <td>5.479492e-22</td>\n",
       "      <td>7.386762e-14</td>\n",
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       "      <td>9.479870e-16</td>\n",
       "      <td>7.796568e-21</td>\n",
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       "      <td>1.000514e-10</td>\n",
       "      <td>1.770000e-04</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.823239e-04</td>\n",
       "      <td>5.461842e-20</td>\n",
       "      <td>7.376787e-13</td>\n",
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       "      <td>9.478701e-15</td>\n",
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       "      <td>2.054000e-15</td>\n",
       "      <td>1.948788e-20</td>\n",
       "      <td>1.005144e-10</td>\n",
       "      <td>1.770000e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.000001e-30</td>\n",
       "      <td>1.823239e-04</td>\n",
       "      <td>5.458412e-18</td>\n",
       "      <td>7.374509e-12</td>\n",
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       "      <td>2.771799e-08</td>\n",
       "      <td>1.276230e-08</td>\n",
       "      <td>0.040089</td>\n",
       "      <td>6.657004e-15</td>\n",
       "      <td>1.254442e-10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.005204e-10</td>\n",
       "      <td>1.496219e-15</td>\n",
       "      <td>8.792586e-14</td>\n",
       "      <td>2.268114e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>7.695199e-07</td>\n",
       "      <td>1.030074e-08</td>\n",
       "      <td>1.065493e-30</td>\n",
       "      <td>1.098211e-12</td>\n",
       "      <td>1.481852e-08</td>\n",
       "      <td>1.379987e-08</td>\n",
       "      <td>2.825312e-08</td>\n",
       "      <td>1.303557e-08</td>\n",
       "      <td>4.601581e-01</td>\n",
       "      <td>4.981617e-06</td>\n",
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       "      <td>0.040089</td>\n",
       "      <td>6.656996e-15</td>\n",
       "      <td>1.254478e-10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.005205e-10</td>\n",
       "      <td>1.496215e-15</td>\n",
       "      <td>8.866789e-14</td>\n",
       "      <td>2.214815e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>7.695199e-07</td>\n",
       "      <td>1.030074e-08</td>\n",
       "      <td>1.092545e-30</td>\n",
       "      <td>1.098211e-12</td>\n",
       "      <td>1.481852e-08</td>\n",
       "      <td>1.379987e-08</td>\n",
       "      <td>2.825312e-08</td>\n",
       "      <td>1.303519e-08</td>\n",
       "      <td>4.601628e-01</td>\n",
       "      <td>4.981617e-06</td>\n",
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       "    <tr>\n",
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       "      <td>1.276161e-08</td>\n",
       "      <td>0.040089</td>\n",
       "      <td>6.656988e-15</td>\n",
       "      <td>1.254510e-10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.005206e-10</td>\n",
       "      <td>1.496211e-15</td>\n",
       "      <td>8.929956e-14</td>\n",
       "      <td>2.168243e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>7.695199e-07</td>\n",
       "      <td>1.030074e-08</td>\n",
       "      <td>1.116798e-30</td>\n",
       "      <td>1.098211e-12</td>\n",
       "      <td>1.481852e-08</td>\n",
       "      <td>1.379987e-08</td>\n",
       "      <td>2.825312e-08</td>\n",
       "      <td>1.303486e-08</td>\n",
       "      <td>4.601672e-01</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2.771998e-08</td>\n",
       "      <td>1.276133e-08</td>\n",
       "      <td>0.040090</td>\n",
       "      <td>6.656981e-15</td>\n",
       "      <td>1.254537e-10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.005207e-10</td>\n",
       "      <td>1.496207e-15</td>\n",
       "      <td>8.983887e-14</td>\n",
       "      <td>2.127335e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>7.695199e-07</td>\n",
       "      <td>1.030074e-08</td>\n",
       "      <td>1.138790e-30</td>\n",
       "      <td>1.098211e-12</td>\n",
       "      <td>1.481852e-08</td>\n",
       "      <td>1.379987e-08</td>\n",
       "      <td>2.825313e-08</td>\n",
       "      <td>1.303456e-08</td>\n",
       "      <td>4.601713e-01</td>\n",
       "      <td>4.981617e-06</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
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       "      <td>0.040090</td>\n",
       "      <td>6.656975e-15</td>\n",
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       "      <td>0.1</td>\n",
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       "      <td>9.030016e-14</td>\n",
       "      <td>2.091235e-13</td>\n",
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       "      <td>7.695199e-07</td>\n",
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       "      <td>2.825313e-08</td>\n",
       "      <td>1.303430e-08</td>\n",
       "      <td>4.601752e-01</td>\n",
       "      <td>4.981617e-06</td>\n",
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       "<p>97 rows × 335 columns</p>\n",
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       "               H            H+        H2           H2+           H3+   HE  \\\n",
       "0   5.000000e-01  1.000000e-30  0.250000  1.000000e-30  1.000000e-30  0.1   \n",
       "1   5.000000e-01  9.674341e-18  0.250000  9.479999e-18  7.975011e-25  0.1   \n",
       "2   5.000000e-01  9.674345e-17  0.250000  9.479987e-17  7.800679e-23  0.1   \n",
       "3   5.000000e-01  9.674388e-16  0.250000  9.479870e-16  7.796568e-21  0.1   \n",
       "4   5.000000e-01  9.674822e-15  0.250000  9.478701e-15  7.795795e-19  0.1   \n",
       "..           ...           ...       ...           ...           ...  ...   \n",
       "92  2.771799e-08  1.276230e-08  0.040089  6.657004e-15  1.254442e-10  0.1   \n",
       "93  2.771874e-08  1.276193e-08  0.040089  6.656996e-15  1.254478e-10  0.1   \n",
       "94  2.771940e-08  1.276161e-08  0.040089  6.656988e-15  1.254510e-10  0.1   \n",
       "95  2.771998e-08  1.276133e-08  0.040090  6.656981e-15  1.254537e-10  0.1   \n",
       "96  2.772049e-08  1.276107e-08  0.040090  6.656975e-15  1.254562e-10  0.1   \n",
       "\n",
       "             HE+          HEH+             C            C+  ...          @OCS  \\\n",
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       "2   2.054000e-17  1.950169e-24  1.000051e-10  1.770000e-04  ...  1.000000e-30   \n",
       "3   2.054000e-16  1.949126e-22  1.000514e-10  1.770000e-04  ...  1.000000e-30   \n",
       "4   2.054000e-15  1.948788e-20  1.005144e-10  1.770000e-04  ...  1.000001e-30   \n",
       "..           ...           ...           ...           ...  ...           ...   \n",
       "92  4.005204e-10  1.496219e-15  8.792586e-14  2.268114e-13  ...  7.695199e-07   \n",
       "93  4.005205e-10  1.496215e-15  8.866789e-14  2.214815e-13  ...  7.695199e-07   \n",
       "94  4.005206e-10  1.496211e-15  8.929956e-14  2.168243e-13  ...  7.695199e-07   \n",
       "95  4.005207e-10  1.496207e-15  8.983887e-14  2.127335e-13  ...  7.695199e-07   \n",
       "96  4.005208e-10  1.496204e-15  9.030016e-14  2.091235e-13  ...  7.695199e-07   \n",
       "\n",
       "            @C4N         @SIC3          @SO2           @S2          @HS2  \\\n",
       "0   1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "1   1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "2   1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "3   1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "4   1.000001e-30  1.000001e-30  1.000001e-30  1.000001e-30  1.000001e-30   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "92  1.030074e-08  1.065493e-30  1.098211e-12  1.481852e-08  1.379987e-08   \n",
       "93  1.030074e-08  1.092545e-30  1.098211e-12  1.481852e-08  1.379987e-08   \n",
       "94  1.030074e-08  1.116798e-30  1.098211e-12  1.481852e-08  1.379987e-08   \n",
       "95  1.030074e-08  1.138790e-30  1.098211e-12  1.481852e-08  1.379987e-08   \n",
       "96  1.030074e-08  1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08   \n",
       "\n",
       "           @H2S2            E-          BULK       SURFACE  \n",
       "0   1.000000e-30  1.823239e-04  1.000000e-30  1.000000e-30  \n",
       "1   1.000000e-30  1.823239e-04  5.753474e-24  7.485971e-15  \n",
       "2   1.000000e-30  1.823239e-04  5.479492e-22  7.386762e-14  \n",
       "3   1.000000e-30  1.823239e-04  5.461842e-20  7.376787e-13  \n",
       "4   1.000001e-30  1.823239e-04  5.458412e-18  7.374509e-12  \n",
       "..           ...           ...           ...           ...  \n",
       "92  2.825312e-08  1.303557e-08  4.601581e-01  4.981617e-06  \n",
       "93  2.825312e-08  1.303519e-08  4.601628e-01  4.981617e-06  \n",
       "94  2.825312e-08  1.303486e-08  4.601672e-01  4.981617e-06  \n",
       "95  2.825313e-08  1.303456e-08  4.601713e-01  4.981617e-06  \n",
       "96  2.825313e-08  1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "\n",
       "[97 rows x 335 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stage1_chemistry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158202fd",
   "metadata": {},
   "source": [
    "With that done, we now have a file containing the final abundances of a cloud of gas after this collapse: `param_dict[\"abundSaveFile\"]` we can pass this to our hot core model to use those abundances as our initial abundances.\n",
    "\n",
    "### Running the Science Model (Stage 2)\n",
    "\n",
    "We need to change just a few things in `param_dict` to set up the hot core model. The key one is that UCLCHEM saves final abundances to `abundSaveFile` but loads them from `abundLoadFile` so we need to swap that key over to make the abundances we just produced our initial abundances.\n",
    "\n",
    "We also want to turn off freefall and change how long the model runs for.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "598dfa33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:08:15.860105Z",
     "iopub.status.busy": "2026-01-23T14:08:15.859930Z",
     "iopub.status.idle": "2026-01-23T14:09:12.233538Z",
     "shell.execute_reply": "2026-01-23T14:09:12.232696Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " At T(=R1) and step size H(=R2), the                                             \n",
      " corrector convergence failed repeatedly                                         \n",
      " or with ABS(H) = HMIN.                                                          \n",
      "In the above message, R1 =   0.7588386563546D+13   R2 =   0.5177419283488D+00\n",
      " ISTATE -5 - shortening step at time   240010.00000000000      years\n"
     ]
    }
   ],
   "source": [
    "# change other bits of input to set up stage 2\n",
    "param_dict[\"initialDens\"] = 1e6\n",
    "param_dict[\"finalTime\"] = 1e6\n",
    "param_dict[\"freefall\"] = False\n",
    "\n",
    "# freeze out is completely overwhelmed by thermal desorption\n",
    "# so turning it off has no effect on abundances but speeds up integrator.\n",
    "param_dict[\"freezeFactor\"] = 0.0\n",
    "\n",
    "# param_dict[\"abstol_factor\"]=1e-18\n",
    "# param_dict[\"reltol\"]=1e-12\n",
    "\n",
    "df_stage2_physics, df_stage2_chemistry, df_stage2_rates, final_abundances, result = (\n",
    "    uclchem.model.hot_core(\n",
    "        temp_indx=3,\n",
    "        max_temperature=300.0,\n",
    "        param_dict=param_dict,\n",
    "        return_dataframe=True,\n",
    "        starting_chemistry=final_abundances,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcfcc061",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:12.235382Z",
     "iopub.status.busy": "2026-01-23T14:09:12.235190Z",
     "iopub.status.idle": "2026-01-23T14:09:12.238452Z",
     "shell.execute_reply": "2026-01-23T14:09:12.237745Z"
    }
   },
   "outputs": [],
   "source": [
    "df_stage2 = pd.concat((df_stage2_physics, df_stage2_chemistry), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29a52de1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:12.240291Z",
     "iopub.status.busy": "2026-01-23T14:09:12.240116Z",
     "iopub.status.idle": "2026-01-23T14:09:12.264172Z",
     "shell.execute_reply": "2026-01-23T14:09:12.263313Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Density</th>\n",
       "      <th>gasTemp</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.276107e-08</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.098211e-12</td>\n",
       "      <td>1.481852e-08</td>\n",
       "      <td>1.379987e-08</td>\n",
       "      <td>2.825313e-08</td>\n",
       "      <td>1.303430e-08</td>\n",
       "      <td>4.601752e-01</td>\n",
       "      <td>4.981617e-06</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.603291e-06</td>\n",
       "      <td>1.992348e-13</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>3.855009e-08</td>\n",
       "      <td>8.935888e-29</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>3.839686e-08</td>\n",
       "      <td>8.944810e-29</td>\n",
       "      <td>1.324098e-22</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.610524e-06</td>\n",
       "      <td>1.985598e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>3.824463e-08</td>\n",
       "      <td>8.953731e-29</td>\n",
       "      <td>1.324098e-22</td>\n",
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       "    <tr>\n",
       "      <th>281</th>\n",
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       "      <td>300.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.614064e-06</td>\n",
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       "      <td>...</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>3.809338e-08</td>\n",
       "      <td>8.962654e-29</td>\n",
       "      <td>1.324098e-22</td>\n",
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       "    <tr>\n",
       "      <th>282</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>1.000000e-30</td>\n",
       "      <td>3.794308e-08</td>\n",
       "      <td>8.971577e-29</td>\n",
       "      <td>1.324098e-22</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>283 rows × 343 columns</p>\n",
       "</div>"
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      "text/plain": [
       "             Time    Density     gasTemp    dustTemp       Av  radfield  zeta  \\\n",
       "0    0.000000e+00  1000000.0   10.000000   10.000000  193.875       1.0   1.0   \n",
       "1    1.000000e-07  1000000.0   10.000000   10.000000  193.875       1.0   1.0   \n",
       "2    1.000000e-06  1000000.0   10.000000   10.000000  193.875       1.0   1.0   \n",
       "3    1.000000e-05  1000000.0   10.000000   10.000000  193.875       1.0   1.0   \n",
       "4    1.000000e-04  1000000.0   10.000003   10.000003  193.875       1.0   1.0   \n",
       "..            ...        ...         ...         ...      ...       ...   ...   \n",
       "278  9.600100e+05  1000000.0  300.000000  300.000000  193.875       1.0   1.0   \n",
       "279  9.700100e+05  1000000.0  300.000000  300.000000  193.875       1.0   1.0   \n",
       "280  9.800100e+05  1000000.0  300.000000  300.000000  193.875       1.0   1.0   \n",
       "281  9.900100e+05  1000000.0  300.000000  300.000000  193.875       1.0   1.0   \n",
       "282  1.000010e+06  1000000.0  300.000000  300.000000  193.875       1.0   1.0   \n",
       "\n",
       "     dstep             H            H+  ...          @OCS          @C4N  \\\n",
       "0      1.0  2.772049e-08  1.276107e-08  ...  7.695199e-07  1.030074e-08   \n",
       "1      1.0  2.772049e-08  1.276107e-08  ...  7.695199e-07  1.030074e-08   \n",
       "2      1.0  2.772049e-08  1.276107e-08  ...  7.695199e-07  1.030074e-08   \n",
       "3      1.0  2.772049e-08  1.276107e-08  ...  7.695199e-07  1.030074e-08   \n",
       "4      1.0  2.772050e-08  1.276107e-08  ...  7.695199e-07  1.030074e-08   \n",
       "..     ...           ...           ...  ...           ...           ...   \n",
       "278    1.0  2.603291e-06  1.992348e-13  ...  1.000000e-30  1.000000e-30   \n",
       "279    1.0  2.606933e-06  1.988969e-13  ...  1.000000e-30  1.000000e-30   \n",
       "280    1.0  2.610524e-06  1.985598e-13  ...  1.000000e-30  1.000000e-30   \n",
       "281    1.0  2.614064e-06  1.982234e-13  ...  1.000000e-30  1.000000e-30   \n",
       "282    1.0  2.617553e-06  1.978879e-13  ...  1.000000e-30  1.000000e-30   \n",
       "\n",
       "            @SIC3          @SO2           @S2          @HS2         @H2S2  \\\n",
       "0    1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08  2.825313e-08   \n",
       "1    1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08  2.825313e-08   \n",
       "2    1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08  2.825313e-08   \n",
       "3    1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08  2.825313e-08   \n",
       "4    1.158645e-30  1.098211e-12  1.481852e-08  1.379987e-08  2.825313e-08   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "278  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "279  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "280  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "281  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "282  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30  1.000000e-30   \n",
       "\n",
       "               E-          BULK       SURFACE  \n",
       "0    1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "1    1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "2    1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "3    1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "4    1.303430e-08  4.601752e-01  4.981617e-06  \n",
       "..            ...           ...           ...  \n",
       "278  3.855009e-08  8.935888e-29  1.324098e-22  \n",
       "279  3.839686e-08  8.944810e-29  1.324098e-22  \n",
       "280  3.824463e-08  8.953731e-29  1.324098e-22  \n",
       "281  3.809338e-08  8.962654e-29  1.324098e-22  \n",
       "282  3.794308e-08  8.971577e-29  1.324098e-22  \n",
       "\n",
       "[283 rows x 343 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stage2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d6a2e0",
   "metadata": {},
   "source": [
    "Note that we've changed made two changes to the parameters here which aren't strictly necessary but can be helpful in certain situations.\n",
    "\n",
    "Since the gas temperature increases throughout a hot core model, freeze out is much slower than thermal desorption for all but the first few time steps. Turning it off doesn't affect the abundances but will speed up the solution.\n",
    "\n",
    "We also change abstol and reltol here, largely to demonstrate their use. They control the integrator accuracy and whilst making them smaller does slow down successful runs, it can make runs complete that stall completely otherwise or give correct solutions where lower tolerances allow issues like element conservation failure to sneak in. If your code does not complete or element conservation fails, you can change them.\n",
    "\n",
    "### Checking the Result\n",
    "With a successful run, we can check the output. We first load the file and check the abundance conservation, then we can plot it up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68f48409",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:12.265882Z",
     "iopub.status.busy": "2026-01-23T14:09:12.265711Z",
     "iopub.status.idle": "2026-01-23T14:09:12.290284Z",
     "shell.execute_reply": "2026-01-23T14:09:12.289479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': '0.000%', 'N': '0.000%', 'C': '0.000%', 'O': '0.000%'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uclchem.analysis.check_element_conservation(df_stage2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e4be6f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:12.291890Z",
     "iopub.status.busy": "2026-01-23T14:09:12.291719Z",
     "iopub.status.idle": "2026-01-23T14:09:12.296862Z",
     "shell.execute_reply": "2026-01-23T14:09:12.296054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time        0.000000e+00\n",
       "Density     1.000000e+06\n",
       "gasTemp     1.000000e+01\n",
       "dustTemp    1.000000e+01\n",
       "Av          1.938750e+02\n",
       "                ...     \n",
       "@HS2        1.379987e-08\n",
       "@H2S2       2.825313e-08\n",
       "E-          1.303430e-08\n",
       "BULK        4.601752e-01\n",
       "SURFACE     4.981617e-06\n",
       "Name: 0, Length: 343, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stage2.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddd38b10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:12.298517Z",
     "iopub.status.busy": "2026-01-23T14:09:12.298316Z",
     "iopub.status.idle": "2026-01-23T14:09:13.118969Z",
     "shell.execute_reply": "2026-01-23T14:09:13.118153Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species = [\"CO\", \"H2O\", \"CH3OH\", \"#CO\", \"#H2O\", \"#CH3OH\", \"@H2O\", \"@CO\", \"@CH3OH\"]\n",
    "fig, [ax, ax2] = plt.subplots(1, 2, figsize=(16, 9))\n",
    "ax = uclchem.analysis.plot_species(ax, df_stage2, species)\n",
    "settings = ax.set(\n",
    "    yscale=\"log\",\n",
    "    xlim=(1e2, 1e6),\n",
    "    ylim=(1e-10, 1e-2),\n",
    "    xlabel=\"Time / years\",\n",
    "    ylabel=\"Fractional Abundance\",\n",
    "    xscale=\"log\",\n",
    ")\n",
    "\n",
    "ax2.plot(df_stage2[\"Time\"], df_stage2[\"Density\"], color=\"black\")\n",
    "ax2.set(xscale=\"log\")\n",
    "ax3 = ax2.twinx()\n",
    "ax3.plot(df_stage2[\"Time\"], df_stage2[\"gasTemp\"], color=\"red\")\n",
    "ax2.set(xlabel=\"Time / year\", ylabel=\"Density\")\n",
    "ax3.set(ylabel=\"Temperature\", facecolor=\"red\", xlim=(1e2, 1e6))\n",
    "ax3.tick_params(axis=\"y\", colors=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8f16ac",
   "metadata": {},
   "source": [
    "Here, we see the value of running a collapse stage before the science run. Having run a collapse, we start this model with well developed ices and having material in the surface and bulk allows us to properly model the effect of warm up in a hot core. For example, the @CO abundance is $\\sim10^{-4}$ and #CO is $\\sim10^{-6}$. As the gas warms to around 30K, the #CO abundance drops drastically as CO's binding energy is such that it is efficiently desorbed from the surface at this temperature. However, the rest of the CO is trapped in the bulk, surrounded by more strongly bound H2O molecules. Thus, the @CO abundance stays high until the gas reaches around 130K, when the H2O molecules are released along with the entire bulk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d930c7",
   "metadata": {},
   "source": [
    "## Shocks\n",
    "\n",
    "Essentially the same process should be followed for shocks. Let's run a C-type and J-type shock through a gas of density $10^4 cm^{-3}$. Again, we first run a simple cloud model to obtain some reasonable starting abundances, then we can run the shocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "503c4b17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:13.121029Z",
     "iopub.status.busy": "2026-01-23T14:09:13.120827Z",
     "iopub.status.idle": "2026-01-23T14:09:17.493208Z",
     "shell.execute_reply": "2026-01-23T14:09:17.492347Z"
    }
   },
   "outputs": [],
   "source": [
    "# set a parameter dictionary for stage 1 collapse model\n",
    "\n",
    "param_dict = {\n",
    "    \"endAtFinalDensity\": False,  # stop at finalTime\n",
    "    \"freefall\": True,  # increase density in freefall\n",
    "    \"initialDens\": 1e2,  # starting density\n",
    "    \"finalDens\": 1e4,  # final density\n",
    "    \"initialTemp\": 10.0,  # temperature of gas\n",
    "    \"finalTime\": 6.0e6,  # final time\n",
    "    \"rout\": 0.1,  # radius of cloud in pc\n",
    "    \"baseAv\": 1.0,  # visual extinction at cloud edge.\n",
    "    # \"abundSaveFile\": \"../examples/test-output/shockstart.dat\",\n",
    "}\n",
    "df_stage1_physics, df_stage1_chemistry, df_stage1_rates, final_abundances, result = (\n",
    "    uclchem.model.cloud(\n",
    "        param_dict=param_dict,\n",
    "        return_dataframe=True,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c98a5b",
   "metadata": {},
   "source": [
    "### C-shock\n",
    "\n",
    "We'll first run a c-shock. We'll run a 40 km s $^{-1}$ shock through a gas of density $10^4$ cm $^{-3}$, using the abundances we just produced. Note that c-shock is the only model which returns an additional output in its result list. Not only is the first element the success flag indicating whether UCLCHEM completed, the second element is the dissipation time of the shock. We'll use that time to make our plots look nicer, cutting to a reasonable time. You can also obtain it from `uclchem.utils.cshock_dissipation_time()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b7dfc46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:17.495182Z",
     "iopub.status.busy": "2026-01-23T14:09:17.494996Z",
     "iopub.status.idle": "2026-01-23T14:09:31.484481Z",
     "shell.execute_reply": "2026-01-23T14:09:31.483644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Cannot have freefall on during cshock\n",
      " setting freefall=0 and continuing\n"
     ]
    }
   ],
   "source": [
    "# change other bits of input to set up phase 2\n",
    "param_dict[\"initialDens\"] = 1e4\n",
    "param_dict[\"finalTime\"] = 1e6\n",
    "if \"abundSaveFile\" in param_dict:\n",
    "    param_dict.pop(\"abundSaveFile\")\n",
    "# param_dict[\"abundLoadFile\"]=\"../examples/test-output/shockstart.dat\"\n",
    "# param_dict[\"outputFile\"]=\"../examples/test-output/cshock.dat\"\n",
    "\n",
    "\n",
    "(\n",
    "    df_stage2_physics,\n",
    "    df_stage2_chemistry,\n",
    "    df_stage2_rates,\n",
    "    dissipation_time,\n",
    "    final_abundances,\n",
    "    result,\n",
    ") = uclchem.model.cshock(\n",
    "    shock_vel=40,\n",
    "    param_dict=param_dict,\n",
    "    return_dataframe=True,\n",
    "    starting_chemistry=final_abundances,\n",
    ")\n",
    "# result,dissipation_time=result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f79d865",
   "metadata": {},
   "source": [
    "The code completes fine. We do get a couple of warnings though. First, we're informed that `freefall` must be set to False for the C-shock model. Then we get a few integrator warnings. These are not important and can be ignored as long as the element conservation looks ok. However, it is an indication that the integrator did struggle with these ODEs under these conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d1217ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:31.486253Z",
     "iopub.status.busy": "2026-01-23T14:09:31.486067Z",
     "iopub.status.idle": "2026-01-23T14:09:31.508534Z",
     "shell.execute_reply": "2026-01-23T14:09:31.507799Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': '0.000%', 'N': '0.000%', 'C': '0.000%', 'O': '0.000%'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stage2 = pd.concat((df_stage2_physics, df_stage2_chemistry), axis=1)\n",
    "uclchem.analysis.check_element_conservation(df_stage2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "995f674b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:31.510272Z",
     "iopub.status.busy": "2026-01-23T14:09:31.510096Z",
     "iopub.status.idle": "2026-01-23T14:09:31.512595Z",
     "shell.execute_reply": "2026-01-23T14:09:31.511905Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_stage2.rename(columns={\"age\":\"Time\", \"density\":\"Density\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5dcb8066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:31.514295Z",
     "iopub.status.busy": "2026-01-23T14:09:31.514126Z",
     "iopub.status.idle": "2026-01-23T14:09:32.283142Z",
     "shell.execute_reply": "2026-01-23T14:09:32.282301Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species = [\"CO\", \"H2O\", \"CH3OH\", \"NH3\", \"$CO\", \"$H2O\", \"$CH3OH\", \"$NH3\"]\n",
    "\n",
    "fig, [ax, ax2] = plt.subplots(1, 2, figsize=(16, 9))\n",
    "ax = uclchem.analysis.plot_species(ax, df_stage2, species)\n",
    "settings = ax.set(\n",
    "    yscale=\"log\",\n",
    "    xlim=(1, 20 * dissipation_time),\n",
    "    ylim=(1e-10, 1e-2),\n",
    "    xlabel=\"Time / years\",\n",
    "    ylabel=\"Fractional Abundance\",\n",
    "    xscale=\"log\",\n",
    ")\n",
    "\n",
    "ax2.plot(df_stage2[\"Time\"], df_stage2[\"Density\"], color=\"black\")\n",
    "ax2.set(xscale=\"log\")\n",
    "ax3 = ax2.twinx()\n",
    "ax3.plot(df_stage2[\"Time\"], df_stage2[\"gasTemp\"], color=\"red\")\n",
    "ax2.set(xlabel=\"Time / year\", ylabel=\"Density\")\n",
    "ax3.set(ylabel=\"Temperature\", facecolor=\"red\", xlim=(1, 20 * dissipation_time))\n",
    "ax3.tick_params(axis=\"y\", colors=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7dc9c2",
   "metadata": {},
   "source": [
    "### J-shock\n",
    "Running a j-shock is a simple case of changing function. We'll run a 10 km s $^{-1}$ shock through a gas of density $10^3$ cm $^{-3}$ gas this time. Note that nothing stops us using the intial abundances we produced for the c-shock. UCLCHEM will not check that the initial density matches the density of the `abundLoadFile`. It may not always be a good idea to do this but we should remember the intial abundances really are just a rough approximation.\n",
    "\n",
    "By default UCLCHEM uses 500 timepoints for a model, but this turns out not be enough, which is why we increase the number of timepoints to 1500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1bca1451",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:09:32.285165Z",
     "iopub.status.busy": "2026-01-23T14:09:32.284980Z",
     "iopub.status.idle": "2026-01-23T14:10:05.710250Z",
     "shell.execute_reply": "2026-01-23T14:10:05.709357Z"
    }
   },
   "outputs": [],
   "source": [
    "# TODO: maybe add a function/method to adjust the number of timepoints in UCLCHEM WITHOUT restarting the kernel\n",
    "\n",
    "param_dict[\"initialDens\"] = 1e3\n",
    "param_dict[\"freefall\"] = False  # lets remember to turn it off this time\n",
    "param_dict[\"reltol\"] = 1e-12\n",
    "\n",
    "shock_vel = 10.0\n",
    "df_jshock_physics, df_jshock_chemistry, df_jshock_rates, final_abundances, result = (\n",
    "    uclchem.model.jshock(\n",
    "        shock_vel=shock_vel,\n",
    "        param_dict=param_dict,\n",
    "        return_dataframe=True,\n",
    "        starting_chemistry=final_abundances,\n",
    "        timepoints=1500,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3520be",
   "metadata": {},
   "source": [
    "This time, we've turned off the freefall option and made reltol a little more stringent. The j-shock ends up running a bit slower but we get no warnings on this run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "514d961e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:10:05.712115Z",
     "iopub.status.busy": "2026-01-23T14:10:05.711923Z",
     "iopub.status.idle": "2026-01-23T14:10:05.739545Z",
     "shell.execute_reply": "2026-01-23T14:10:05.738753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': '0.000%', 'N': '0.000%', 'C': '0.000%', 'O': '0.000%'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jshock = pd.concat((df_jshock_physics, df_jshock_chemistry), axis=1)\n",
    "# df_jshock.rename(columns={\"age\":\"Time\", \"density\":\"Density\"}, inplace=True)\n",
    "uclchem.analysis.check_element_conservation(df_jshock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "442ef730",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:10:05.741226Z",
     "iopub.status.busy": "2026-01-23T14:10:05.741050Z",
     "iopub.status.idle": "2026-01-23T14:10:05.745163Z",
     "shell.execute_reply": "2026-01-23T14:10:05.744187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1317, 343)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jshock.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "387aea69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-23T14:10:05.746739Z",
     "iopub.status.busy": "2026-01-23T14:10:05.746571Z",
     "iopub.status.idle": "2026-01-23T14:10:06.529688Z",
     "shell.execute_reply": "2026-01-23T14:10:06.528901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species = [\"CO\", \"H2O\", \"CH3OH\", \"NH3\", \"$CO\", \"$H2O\", \"$CH3OH\", \"$NH3\"]\n",
    "\n",
    "fig, [ax, ax2] = plt.subplots(1, 2, figsize=(16, 9))\n",
    "ax = uclchem.analysis.plot_species(ax, df_jshock, species)\n",
    "settings = ax.set(\n",
    "    yscale=\"log\",\n",
    "    xlim=(1e-7, 1e6),\n",
    "    ylim=(1e-10, 1e-2),\n",
    "    xlabel=\"Time / years\",\n",
    "    ylabel=\"Fractional Abundance\",\n",
    "    xscale=\"log\",\n",
    ")\n",
    "\n",
    "ax2.plot(df_jshock[\"Time\"], df_jshock[\"Density\"], color=\"black\")\n",
    "ax2.set(xscale=\"log\", yscale=\"log\")\n",
    "ax3 = ax2.twinx()\n",
    "ax3.plot(df_jshock[\"Time\"], df_jshock[\"gasTemp\"], color=\"red\")\n",
    "ax2.set(xlabel=\"Time / year\", ylabel=\"Density\")\n",
    "ax3.set(ylabel=\"Temperature\", facecolor=\"red\", xlim=(1e-7, 1e6))\n",
    "ax3.tick_params(axis=\"y\", colors=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5aa24c",
   "metadata": {},
   "source": [
    "That's everything! We've run various science models using reasonable starting abundances that we produced by running a simple UCLCHEM model beforehand. One benefit of this method is that the abundances are consistent with the network. If we start with arbitrary, perhaps observationally motivated, abundances, it would be possible to initiate the model in a state our network could never produce.\n",
    "\n",
    "However, one should be aware of the limitations of this method. A freefall collapse from low density to high is not really how a molecular cloud forms and so the abundances are only approximately similar to values they'd truly have in a real cloud. Testing whether your results are sensitive to things like the time you run the preliminary for or the exact density is a good way to make sure these approximations are not problematic.\n",
    "\n",
    "Bear in mind that you can use `abundSaveFile` and `abundLoadFile` in the same model run. This lets you chain model runs together. For example, you could run a c-shock from a cloud model as we did here and then a j-shock with the c-shock's abundances as the initial abundances."
   ]
  }
 ],
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