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   "version": "2.7.6"
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  "colab": {
   "name": "Copy of tutorial03.ipynb",
   "provenance": [
    {
     "file_id": "1HqdyHBbruwoS6e5FUHcxEmFgUV7CJtyE",
     "timestamp": 1583293097635
    },
    {
     "file_id": "https://github.com/openforcefield/openff-evaluator/blob/master/docs/tutorials/tutorial03.ipynb",
     "timestamp": 1583293048478
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   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
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    "id": "YSV5DrHtlHp-",
    "colab_type": "text"
   },
   "source": [
    "# Tutorial 03 - Analysing Data Sets\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openforcefield/openff-evaluator/blob/master/docs/tutorials/tutorial03.ipynb)\n",
    "\n",
    "In this tutorial we will be analysing the results of the calculations which we performed in the [second tutorial](tutorial02.ipynb). \n",
    "The tutorial will cover:\n",
    "\n",
    "- comparing the estimated data set with the experimental data set.\n",
    "- plotting the two data sets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "pvunG4GblHqA",
    "colab_type": "text"
   },
   "source": [
    "*Note: If you are running this tutorial in google colab you will need to run a setup script instead of following the \n",
    "installation instructions:*"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "BRDQOezZlHqC",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# !wget https://raw.githubusercontent.com/openforcefield/openff-evaluator/master/docs/tutorials/colab_setup.ipynb\n",
    "# %run colab_setup.ipynb"
   ],
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "kETNDAhSlHqJ",
    "colab_type": "text"
   },
   "source": [
    "For the sake of clarity all warnings will be disabled in this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "DDquWWI4lHqL",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import logging\n",
    "logging.getLogger(\"openforcefield\").setLevel(logging.ERROR)"
   ],
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    },
    "id": "D5fOLXw1lHqS",
    "colab_type": "text"
   },
   "source": [
    "## Loading the Data Sets\n",
    "\n",
    "We will begin by loading both the experimental data set and the estimated data set:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "4aHO4zhClHqX",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from openff.evaluator.datasets import PhysicalPropertyDataSet\n",
    "\n",
    "experimental_data_set_path = \"filtered_data_set.json\"\n",
    "estimated_data_set_path = \"estimated_data_set.json\"\n",
    "\n",
    "# If you have not yet completed the previous tutorials or do not have the data set files \n",
    "# available, copies are provided by the framework:\n",
    "\n",
    "# from openff.evaluator.utils import get_data_filename\n",
    "# experimental_data_set_path = get_data_filename(\n",
    "#     \"tutorials/tutorial01/filtered_data_set.json\"\n",
    "# )\n",
    "# estimated_data_set_path = get_data_filename(\n",
    "#     \"tutorials/tutorial02/estimated_data_set.json\"\n",
    "# )\n",
    "\n",
    "experimental_data_set = PhysicalPropertyDataSet.from_json(experimental_data_set_path)\n",
    "estimated_data_set = PhysicalPropertyDataSet.from_json(estimated_data_set_path)"
   ],
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    },
    "id": "5vl3MxX5lHqh",
    "colab_type": "text"
   },
   "source": [
    "if everything went well from the previous tutorials, these data sets will contain the density and $H_{vap}$ of ethanol \n",
    "and isopropanol:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "4vI-dJ3VlHqj",
    "colab_type": "code",
    "outputId": "930607e6-2a25-4c00-9362-c6b4b4ba1613",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1583292817554,
     "user_tz": 420,
     "elapsed": 293013,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    }
   },
   "source": [
    "experimental_data_set.to_pandas().head()"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
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       "      <th>Temperature (K)</th>\n",
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       "      <th>N Components</th>\n",
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       "      <th>Density Uncertainty (g / ml)</th>\n",
       "      <th>EnthalpyOfVaporization Value (kJ / mol)</th>\n",
       "      <th>EnthalpyOfVaporization Uncertainty (kJ / mol)</th>\n",
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       "      <td>0.78507</td>\n",
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       "      <td>10.1021/je1013476</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>298.15</td>\n",
       "      <td>101.325</td>\n",
       "      <td>Liquid + Gas</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>42.26</td>\n",
       "      <td>0.02</td>\n",
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       "      <td>Liquid + Gas</td>\n",
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       "      <td>45.34</td>\n",
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       "   Temperature (K)  ...                         Source\n",
       "0           298.15  ...    10.1016/j.fluid.2013.10.034\n",
       "1           298.15  ...              10.1021/je1013476\n",
       "2           298.15  ...  10.1016/S0021-9614(71)80108-8\n",
       "3           298.15  ...  10.1016/S0021-9614(71)80108-8\n",
       "\n",
       "[4 rows x 13 columns]"
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     "metadata": {
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     "execution_count": 4
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   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
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    "colab_type": "code",
    "outputId": "17f510e9-2e7e-458d-cf8b-f70626df919f",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1583292817643,
     "user_tz": 420,
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   },
   "source": [
    "estimated_data_set.to_pandas().head()"
   ],
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (K)</th>\n",
       "      <th>Pressure (kPa)</th>\n",
       "      <th>Phase</th>\n",
       "      <th>N Components</th>\n",
       "      <th>Component 1</th>\n",
       "      <th>Role 1</th>\n",
       "      <th>Mole Fraction 1</th>\n",
       "      <th>Exact Amount 1</th>\n",
       "      <th>Density Value (g / ml)</th>\n",
       "      <th>Density Uncertainty (g / ml)</th>\n",
       "      <th>EnthalpyOfVaporization Value (kJ / mol)</th>\n",
       "      <th>EnthalpyOfVaporization Uncertainty (kJ / mol)</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>298.15</td>\n",
       "      <td>101.325</td>\n",
       "      <td>Liquid</td>\n",
       "      <td>1</td>\n",
       "      <td>CCO</td>\n",
       "      <td>Solvent</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.000705</td>\n",
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       "      <td>SimulationLayer</td>\n",
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       "      <th>1</th>\n",
       "      <td>298.15</td>\n",
       "      <td>101.325</td>\n",
       "      <td>Liquid + Gas</td>\n",
       "      <td>1</td>\n",
       "      <td>CCO</td>\n",
       "      <td>Solvent</td>\n",
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       "      <td>39.434820</td>\n",
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       "      <td>Solvent</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>298.15</td>\n",
       "      <td>101.325</td>\n",
       "      <td>Liquid + Gas</td>\n",
       "      <td>1</td>\n",
       "      <td>CC(C)O</td>\n",
       "      <td>Solvent</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>45.649979</td>\n",
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       "   Temperature (K)  ...           Source\n",
       "0           298.15  ...  SimulationLayer\n",
       "1           298.15  ...  SimulationLayer\n",
       "2           298.15  ...  SimulationLayer\n",
       "3           298.15  ...  SimulationLayer\n",
       "\n",
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     "execution_count": 5
    }
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  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "X9NjUh89lHqx",
    "colab_type": "text"
   },
   "source": [
    "## Extracting the Results\n",
    "\n",
    "We will now compare how the value of each property estimated by simulation deviates from the experimental measurement. \n",
    "\n",
    "To do this we will extract a list which contains pairs of experimental and evaluated properties. We can easily match \n",
    "properties based on the unique ids which were automatically assigned to them on their creation:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "kc65RHp_lHqz",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "properties_by_type = {\n",
    "    \"Density\": [],\n",
    "    \"EnthalpyOfVaporization\": []\n",
    "}\n",
    "\n",
    "for experimental_property in experimental_data_set:\n",
    "\n",
    "    # Find the estimated property which has the same id as the\n",
    "    # experimental property.\n",
    "    estimated_property = next(\n",
    "        x for x in estimated_data_set if x.id == experimental_property.id\n",
    "    )\n",
    "    \n",
    "    # Add this pair of properties to the list of pairs\n",
    "    property_type = experimental_property.__class__.__name__\n",
    "    properties_by_type[property_type].append((experimental_property, estimated_property))"
   ],
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    },
    "id": "I6lPQul9lHq5",
    "colab_type": "text"
   },
   "source": [
    "## Plotting the Results\n",
    "\n",
    "We will now compare the experimental results to the estimated ones by plotting them using `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    },
    "id": "m71PXnHPlHq6",
    "colab_type": "code",
    "outputId": "9d9ed590-6749-4bef-b4e1-43f014e1a764",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1583292818301,
     "user_tz": 420,
     "elapsed": 293750,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    }
   },
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "# Create the figure we will plot to.\n",
    "figure, axes = pyplot.subplots(nrows=1, ncols=2, figsize=(8.0, 4.0))\n",
    "\n",
    "# Set the axis titles\n",
    "axes[0].set_xlabel('OpenFF 1.0.0')\n",
    "axes[0].set_ylabel('Experimental')\n",
    "axes[0].set_title('Density $kg m^{-3}$')\n",
    "\n",
    "axes[1].set_xlabel('OpenFF 1.0.0')\n",
    "axes[1].set_ylabel('Experimental')\n",
    "axes[1].set_title('$H_{vap}$ $kJ mol^{-1}$')\n",
    "\n",
    "# Define the preferred units of the properties\n",
    "from openff.evaluator import unit\n",
    "\n",
    "preferred_units = {\n",
    "    \"Density\": unit.kilogram / unit.meter ** 3,\n",
    "    \"EnthalpyOfVaporization\": unit.kilojoule / unit.mole\n",
    "}\n",
    "\n",
    "for index, property_type in enumerate(properties_by_type):\n",
    "\n",
    "    experimental_values = []\n",
    "    estimated_values = []\n",
    "\n",
    "    preferred_unit = preferred_units[property_type]\n",
    "\n",
    "    # Convert the values of our properties to the preferred units.\n",
    "    for experimental_property, estimated_property in properties_by_type[property_type]:\n",
    "        \n",
    "        experimental_values.append(\n",
    "            experimental_property.value.to(preferred_unit).magnitude\n",
    "        )\n",
    "        estimated_values.append(\n",
    "            estimated_property.value.to(preferred_unit).magnitude\n",
    "        )\n",
    "\n",
    "    axes[index].plot(\n",
    "        estimated_values, experimental_values, marker='x', linestyle='None'\n",
    "    )\n"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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JC8m+JHwrIqIuUZuZmbWYup/xS+oHjAZuTUUnAqdHxMbA6cD1qfxS4OyI+HAZuzwsIrYD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\nd2YMcEuaPw24W9J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BTEuPvvftVF4+2txBFba9SNLLyxpBS9I5ykZRe1rS56qss6laeLQ1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\n38zMrED+P13KmSHw3vp2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    },
    "id": "tx8U-ExOlHrA",
    "colab_type": "text"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "And that concludes the third tutorial!\n",
    "\n",
    "If you have any questions and / or feedback, please open an issue on the\n",
    "[GitHub issue tracker](https://github.com/openforcefield/openff-evaluator/issues)."
   ]
  }
 ]
}