{
 "cells": [
  {
   "attachments": {
    "b51ca3f4-e8bd-4b0a-a599-5f6724ad8fe5.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "305d1216-9791-43dd-8d7f-ec90aa72a307",
   "metadata": {
    "tags": []
   },
   "source": [
    "# `pyEQL` Tutorial: Calculating Osmotic Pressure\n",
    "\n",
    "![pyeql-logo.png](attachment:b51ca3f4-e8bd-4b0a-a599-5f6724ad8fe5.png)\n",
    "\n",
    "`pyEQL` is an open-source `python` library for solution chemistry calculations and ion properties developed by the [Kingsbury Lab](https://www.kingsburylab.org/) at Princeton University.\n",
    "\n",
    "[Documentation](https://pyeql.readthedocs.io/en/latest/) | [How to Install](https://pyeql.readthedocs.io/en/latest/installation.html) | [GitHub](https://github.com/rkingsbury/pyEQL) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c14b65-36f0-4bb2-a3bf-f8e725cc897e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Installation\n",
    "\n",
    "Uncomment and run the code cell below, if you do not already have `pyEQL`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abb1f51c-38fc-4543-8b59-057b8c591ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pyEQL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8efc81-17bb-46dc-8a22-63e71ae458b7",
   "metadata": {},
   "source": [
    "## First, create a `Solution`\n",
    "\n",
    "`pyEQL`'s built-in property database contains Pitzer model parameters for many simple (binary) electrolytes. If such parameters are available, `pyEQL` will use them by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55f1aea5-a5b8-49b8-a8e8-20856c4be6e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyEQL import Solution\n",
    "\n",
    "# 2 mol/L NaCl\n",
    "s1 = Solution({\"Na+\": \"2 mol/L\", \"Cl-\": \"2 mol/L\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b21981-7568-4e29-a93c-4017a32629b4",
   "metadata": {},
   "source": [
    "## Get the osmotic pressure\n",
    "\n",
    "Note that the osmotic pressure (and most `Solution` properties) are returned as `pint` `Quantity` objects (see [Converting Units](https://pyeql.readthedocs.io/en/latest/units.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c037d7c-0a16-4d0a-b2e7-ad2d43a4d3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10224795.903885065 Pa"
      ],
      "text/latex": [
       "$10224795.903885065\\ \\mathrm{Pa}$"
      ],
      "text/plain": [
       "10224795.903885065 <Unit('pascal')>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.osmotic_pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7800c8-e23a-4d6b-9d36-77ac51226fb4",
   "metadata": {},
   "source": [
    "If you want the osmotic pressure in different units, or you only want the magnitude, use `to()` and `magnitude`, respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55f0b945-fe6f-464e-a855-30ec0e5292c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "102.24795903885065 bar"
      ],
      "text/latex": [
       "$102.24795903885065\\ \\mathrm{bar}$"
      ],
      "text/plain": [
       "102.24795903885065 <Unit('bar')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.osmotic_pressure.to(\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51adb7d2-40f0-4072-adce-9ca2d980e5ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102.24795903885065"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.osmotic_pressure.to(\"bar\").magnitude"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b754001-1ee7-4348-882a-6e6f6e046b93",
   "metadata": {},
   "source": [
    "## Use a `for` loop for multiple calculations\n",
    "\n",
    "You can rapidly get estimates for multiple concentrations (or temperatures, or solutes) by using a `for` loop. Notice how in the example below, we use [f-strings](https://realpython.com/python-f-strings/) to insert the desired concentration (from the `for` loop) into the argument passed to `Solution` and to print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53ab967d-1cac-4e3a-9a4d-11f9b28f5bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At C=0.1 M, the osmotic pressure is 4.63 bar.\n",
      "At C=0.5 M, the osmotic pressure is 23.07 bar.\n",
      "At C=1.0 M, the osmotic pressure is 47.40 bar.\n",
      "At C=2.0 M, the osmotic pressure is 102.25 bar.\n",
      "At C=4.0 M, the osmotic pressure is 246.95 bar.\n"
     ]
    }
   ],
   "source": [
    "for conc in [0.1, 0.5, 1, 2, 4]:\n",
    "    s1 = Solution({\"Na+\": f\"{conc} mol/L\", \"Cl-\": f\"{conc} mol/L\"})\n",
    "    print(f\"At C={conc:.1f} M, the osmotic pressure is {s1.osmotic_pressure.to('bar'):.2f}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4ff669-b6c0-4592-b9be-1b088b548cfc",
   "metadata": {},
   "source": [
    "## Compare different modeling engines\n",
    "\n",
    "`pyEQL` contains several different [modeling engines](https://pyeql.readthedocs.io/en/latest/engines.html) that can calculate activity coefficients or osmotic pressures. At present, there are three options:\n",
    "\n",
    "1. The `native` or built-in engine, which includes an implementation of the Piter model (Default).\n",
    "2. the `phreeqc` engine, which utilizes the USGS PHREEQC model with the `phreeqc.dat` database.\n",
    "3. An ideal solution model (`ideal`) which does not account for solution non-ideality.\n",
    "\n",
    "You select a modeling engine using the `engine` keyword argument when you create a `Solution`. Let's compare the preditions from the three models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6962da9e-f510-483f-8ab6-df29a054441a",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_ideal = Solution({\"Na+\": \"2 mol/L\", \"Cl-\": \"2 mol/L\"}, engine=\"ideal\")\n",
    "s_phreeqc = Solution({\"Na+\": \"2 mol/L\", \"Cl-\": \"2 mol/L\"}, engine=\"phreeqc\")\n",
    "s_native = Solution({\"Na+\": \"2 mol/L\", \"Cl-\": \"2 mol/L\"}, engine=\"native\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a627a29-e7b4-458e-86bb-e23b71ae8f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "95.73878553477434 bar"
      ],
      "text/latex": [
       "$95.73878553477434\\ \\mathrm{bar}$"
      ],
      "text/plain": [
       "95.73878553477434 <Unit('bar')>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_ideal.osmotic_pressure.to(\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29ef5458-fc42-44de-a3b5-05e80987f9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "95.73878553477434 bar"
      ],
      "text/latex": [
       "$95.73878553477434\\ \\mathrm{bar}$"
      ],
      "text/plain": [
       "95.73878553477434 <Unit('bar')>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_phreeqc.osmotic_pressure.to(\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5807fbde-dddd-4883-8bf0-1014ce08833b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "102.24795903885065 bar"
      ],
      "text/latex": [
       "$102.24795903885065\\ \\mathrm{bar}$"
      ],
      "text/plain": [
       "102.24795903885065 <Unit('bar')>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_native.osmotic_pressure.to(\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49fb2e3-71a3-49ff-9f17-35e64f6f6d54",
   "metadata": {},
   "source": [
    "## Plot the comparison vs. experiment\n",
    "\n",
    "We can make a plot showing how the 3 models compare by combining the two previous steps (using a `for` loop plus changing the `engine` keyword argument. Note that this example makes use of `matplotlib` for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d38dd675-0767-4eea-ab11-d85c4613e59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create empty lists to hold the results\n",
    "pi_ideal = []\n",
    "pi_phreeqc = []\n",
    "pi_native = []\n",
    "\n",
    "concentrations = [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.4, 2, 2.5, 3, 3.5, 4]\n",
    "\n",
    "for conc in concentrations:\n",
    "    s_ideal = Solution({\"Na+\": f\"{conc} mol/kg\", \"Cl-\": f\"{conc} mol/kg\"}, engine=\"ideal\")\n",
    "    s_phreeqc = Solution({\"Na+\": f\"{conc} mol/kg\", \"Cl-\": f\"{conc} mol/kg\"}, engine=\"phreeqc\")\n",
    "    s_native = Solution({\"Na+\": f\"{conc} mol/kg\", \"Cl-\": f\"{conc} mol/kg\"}, engine=\"native\")\n",
    "\n",
    "    # store the osmotic pressures in the respective lists\n",
    "    # note that we have to just store the .magnitude because matplotlib can't plot Quantity\n",
    "    pi_ideal.append(s_ideal.osmotic_pressure.to(\"bar\").magnitude)\n",
    "    pi_phreeqc.append(s_phreeqc.osmotic_pressure.to(\"bar\").magnitude)\n",
    "    pi_native.append(s_native.osmotic_pressure.to(\"bar\").magnitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a651b33f-5572-40a2-8587-bd7af7a71170",
   "metadata": {},
   "source": [
    "We will include experimental data from the [IDST](https://idst.inl.gov/) as a benchmark. The IDST gives us water activity, which we convert into osmotic pressure according to\n",
    "\n",
    "$$\n",
    "\\Pi = -\\frac{RT}{V_{w}} \\ln a_{w}\n",
    "$$\n",
    "\n",
    "Where $\\Pi$ is the osmotic pressure, $V_{w}$ is the partial molar volume of water (18.2 cm**3/mol), and $a_{w}$ is the water\n",
    "            activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "008d1e80-536b-4346-9527-462deacd6b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# water activity at [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4] mol/kg\n",
    "water_activity_idst = [\n",
    "    0.99664,\n",
    "    0.993353,\n",
    "    0.99008,\n",
    "    0.986804,\n",
    "    0.98352,\n",
    "    0.966828,\n",
    "    0.953166,\n",
    "    0.93191,\n",
    "    0.913072,\n",
    "    0.89347,\n",
    "    0.872859,\n",
    "    0.85133,\n",
    "]\n",
    "\n",
    "# calculate osmotic pressure as -RT/Vw ln(a_w). Factor 10 converts to bar.\n",
    "pi_idst = [-8.314 * 298.15 / 18.2 * math.log(a) * 10 for a in water_activity_idst]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1082bad0-c9e1-4e16-941d-fd908ea402fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'pyEQL prediction of NaCl osmotic pressure')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results!\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(concentrations, pi_ideal, label=\"engine='ideal'\", ls=\"--\", color=\"gray\")\n",
    "ax.plot(concentrations, pi_phreeqc, label=\"engine='phreeqc'\", ls=\":\", color=\"green\")\n",
    "ax.plot(concentrations, pi_native, label=\"engine='native'\", ls=\"-\", color=\"navy\")\n",
    "ax.plot(concentrations, pi_idst, label=\"experiment\", ls=\"\", color=\"red\", marker=\"x\")\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Solute Concentration (M)\")\n",
    "ax.set_ylabel(\"Osmotic Pressure (bar)\")\n",
    "fig.suptitle(\"pyEQL prediction of NaCl osmotic pressure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd14cb5f-f9a4-4006-ae26-2ef827d2412d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (conda: skagit2)",
   "language": "python",
   "name": "skagit2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}