{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03058ac5",
   "metadata": {},
   "source": [
    "# Multifluid+Association\n",
    "\n",
    "Version 0.22 of teqp adds a new sort of hybrid model -- a combination of multifluid model plus an association model. Conceptually the pure fluid EOS consider the self-association implicitly, so you may want to consider the association model to only allow cross-assocation, which be enforced by supplying the \"self_assocation_mask\" logical mask in the \"options\" of the model. See below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae2f6b60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:24:20.442635Z",
     "iopub.status.busy": "2024-10-29T15:24:20.442138Z",
     "iopub.status.idle": "2024-10-29T15:24:21.272526Z",
     "shell.execute_reply": "2024-10-29T15:24:21.271853Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Don't understand \"kind\" of: multifluid-association",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 46\u001b[0m\n\u001b[1;32m     20\u001b[0m jassoc \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDufal\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     35\u001b[0m     }\n\u001b[1;32m     36\u001b[0m }\n\u001b[1;32m     38\u001b[0m j \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmultifluid-association\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m: {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     43\u001b[0m     }\n\u001b[1;32m     44\u001b[0m }\n\u001b[0;32m---> 46\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mteqp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/checkouts/readthedocs.org/user_builds/teqp/conda/stable/lib/python3.11/site-packages/teqp/__init__.py:47\u001b[0m, in \u001b[0;36mmake_model\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_model\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     43\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;124;03m    This function is in two parts; first the make_model function (renamed to _make_model in the Python interface)\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;124;03m    is used to make the model and then the model-specific methods are attached to the instance\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 47\u001b[0m     AS \u001b[38;5;241m=\u001b[39m \u001b[43m_make_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     48\u001b[0m     attach_model_specific_methods(AS)\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m AS\n",
      "\u001b[0;31mValueError\u001b[0m: Don't understand \"kind\" of: multifluid-association"
     ]
    }
   ],
   "source": [
    "import teqp, json, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas \n",
    "\n",
    "BIP = [{\n",
    "    \"Name1\": \"Water\",\n",
    "    \"Name2\": \"Ammonia\",\n",
    "    \"betaT\": 1.0,\n",
    "    \"gammaT\": 1.0,\n",
    "    \"betaV\": 1.0,\n",
    "    \"gammaV\": 1.0,\n",
    "    \"F\": 0.0\n",
    "}]\n",
    "jmf = {\n",
    "    \"components\": [\"Water\", \"Ammonia\"],\n",
    "    \"root\": teqp.get_datapath(),\n",
    "    \"BIP\": BIP,    \n",
    "}\n",
    "\n",
    "jassoc = {\n",
    "    \"kind\": \"Dufal\",\n",
    "    \"model\": {\n",
    "        \"sigma / m\": [3.0555e-10, 3.3309e-10],\n",
    "        \"epsilon / J/mol\": [3475.445374388054, 323.70*8.3124462618],\n",
    "        \"lambda_r\": [35.823, 36.832],\n",
    "        \n",
    "        # Note the scaling factors of 0.2 on the bonding energy to yield more reasonable behavior\n",
    "        \"epsilon_HB / J/mol\": [0.2*13303.140189045183, 0.2*1105.0*8.314462618], \n",
    "        \n",
    "        \"K_HB / m^3\": [496.66e-30, 560.73e-30],\n",
    "        \"kmat\": [[0.0,0.0],[0.0, 0.0]],\n",
    "        \"Delta_rule\": \"Dufal\",\n",
    "        \"molecule_sites\": [[\"e\",\"e\",\"H\",\"H\"],[\"e\",\"H\",\"H\",\"H\"]],\n",
    "        \"options\": {\"self_association_mask\": [False, False]}\n",
    "    }\n",
    "}\n",
    "    \n",
    "j = {\n",
    "    'kind': 'multifluid-association',\n",
    "    'model': {\n",
    "        'multifluid': jmf,\n",
    "        'association': jassoc\n",
    "    }\n",
    "}\n",
    "\n",
    "model = teqp.make_model(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "469bdad6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:24:21.274823Z",
     "iopub.status.busy": "2024-10-29T15:24:21.274571Z",
     "iopub.status.idle": "2024-10-29T15:24:21.277462Z",
     "shell.execute_reply": "2024-10-29T15:24:21.276904Z"
    }
   },
   "outputs": [],
   "source": [
    "# model.get_assoc_calcs(300, 300, np.array([0.5, 0.5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d9a26b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:24:21.279349Z",
     "iopub.status.busy": "2024-10-29T15:24:21.279125Z",
     "iopub.status.idle": "2024-10-29T15:24:21.336569Z",
     "shell.execute_reply": "2024-10-29T15:24:21.336020Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m pure \u001b[38;5;241m=\u001b[39m teqp\u001b[38;5;241m.\u001b[39mbuild_multifluid_model([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAmmonia\u001b[39m\u001b[38;5;124m\"\u001b[39m], teqp\u001b[38;5;241m.\u001b[39mget_datapath())\n\u001b[1;32m      3\u001b[0m anc \u001b[38;5;241m=\u001b[39m pure\u001b[38;5;241m.\u001b[39mbuild_ancillaries()\n\u001b[0;32m----> 4\u001b[0m j \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241m.\u001b[39mtrace_VLE_isotherm_binary(T, np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m, anc\u001b[38;5;241m.\u001b[39mrhoL(T)]), np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m, anc\u001b[38;5;241m.\u001b[39mrhoV(T)]))\n\u001b[1;32m      5\u001b[0m df \u001b[38;5;241m=\u001b[39m pandas\u001b[38;5;241m.\u001b[39mDataFrame(j)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "T = 293.15 # K\n",
    "pure = teqp.build_multifluid_model([\"Ammonia\"], teqp.get_datapath())\n",
    "anc = pure.build_ancillaries()\n",
    "j = model.trace_VLE_isotherm_binary(T, np.array([0, anc.rhoL(T)]), np.array([0, anc.rhoV(T)]))\n",
    "df = pandas.DataFrame(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9760392",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:24:21.338789Z",
     "iopub.status.busy": "2024-10-29T15:24:21.338285Z",
     "iopub.status.idle": "2024-10-29T15:24:21.353298Z",
     "shell.execute_reply": "2024-10-29T15:24:21.352744Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdf\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxL_0 / mole frac.\u001b[39m\u001b[38;5;124m'\u001b[39m], df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpL / Pa\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1e3\u001b[39m)\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxV_0 / mole frac.\u001b[39m\u001b[38;5;124m'\u001b[39m], df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpL / Pa\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1e3\u001b[39m)\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39myscale(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(df['xL_0 / mole frac.'], df['pL / Pa']/1e3)\n",
    "plt.plot(df['xV_0 / mole frac.'], df['pL / Pa']/1e3)\n",
    "plt.yscale('log')\n",
    "plt.gca().set(xlabel='$x_1,y_1$ / mole frac.', ylabel='p / kPa')\n",
    "plt.title(f'Water(1) + Ammonia(2) @ {T} K')\n",
    "plt.show()"
   ]
  }
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