test-rescure-jason.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rescure import initial_random_matrix\n",
    "from rescure import get_rates_simplified\n",
    "from rescure import get_rates_phenol\n",
    "from rescure import choose_event\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 0. 1. 1. 0. 2. 0. 0. 1. 1. 2. 2. 0. 0.]\n",
      " [0. 2. 1. 0. 1. 2. 0. 1. 0. 1. 0. 1. 2. 1. 1.]\n",
      " [1. 2. 0. 1. 2. 1. 1. 0. 2. 1. 1. 0. 1. 1. 2.]\n",
      " [2. 0. 2. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 2. 1.]\n",
      " [1. 2. 2. 1. 1. 2. 1. 1. 1. 1. 2. 1. 0. 2. 0.]\n",
      " [1. 2. 1. 2. 1. 1. 2. 1. 2. 0. 0. 0. 1. 1. 1.]\n",
      " [1. 0. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 1. 1. 1.]\n",
      " [1. 1. 2. 1. 1. 0. 0. 2. 1. 1. 1. 1. 1. 2. 1.]\n",
      " [2. 2. 2. 2. 1. 0. 1. 2. 0. 2. 1. 1. 1. 1. 1.]\n",
      " [0. 2. 2. 0. 1. 1. 2. 0. 1. 1. 2. 1. 1. 1. 0.]\n",
      " [1. 0. 1. 1. 1. 1. 1. 1. 1. 2. 1. 1. 1. 0. 2.]\n",
      " [2. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 2. 0. 1. 0.]\n",
      " [2. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 2. 2. 1.]\n",
      " [0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
      " [1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 2. 1.]]\n"
     ]
    }
   ],
   "source": [
    "ratio = (1, 2, 1)\n",
    "\n",
    "inital_state = initial_random_matrix(15, ratio)\n",
    "\n",
    "print(inital_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n",
      "123\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "# Test to see if the distrubution is the correct ratio:\n",
    "\n",
    "n = len(inital_state)\n",
    "\n",
    "voids = []\n",
    "phenols = []\n",
    "coals = []\n",
    "\n",
    "for i in range(n):\n",
    "    for j in range(n):\n",
    "        if inital_state[i][j] == 0:\n",
    "            voids.append((i, j))\n",
    "        elif inital_state[i][j] == 1:\n",
    "            phenols.append((i, j))\n",
    "        elif inital_state[i][j] == 2:\n",
    "            coals.append((i, j))\n",
    "\n",
    "print(len(voids))\n",
    "print(len(phenols))\n",
    "print(len(coals))\n",
    "\n",
    "# Looks like it's working!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the kernel / filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.0, 2.0, 2.0, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "start_mol = (5, 4)\n",
    "i, j = start_mol\n",
    "\n",
    "periphery = []\n",
    "\n",
    "for k in range(i-1, j+2):\n",
    "    for l in range(j-1, j+2):\n",
    "        periphery.append(initial_state[k, l]) #NOT WORKING\n",
    "\n",
    "# Reshape the periphery array to be a 3x3 array:\n",
    "\n",
    "# periphery = np.array(periphery).reshape(3, 3)\n",
    "print(periphery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different method of setting up the kernel / mask:\n",
    "\n",
    "start_mol = (5, 4)\n",
    "i, j = start_mol\n",
    "\n",
    "kernel = np.zeros((3, 3))\n",
    "\n",
    "kernel[0, 0] = 1 # Still need to figure out how to do this automatically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing a simple rate function calculator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n"
     ]
    }
   ],
   "source": [
    "# Let's say that the position of the molecule\n",
    "# we are looking at is (5, 4) in the initial state\n",
    "# matrix:\n",
    "\n",
    "pos = (5, 4)\n",
    "\n",
    "rate_matrix = get_rates_simplified(initial_state, pos)\n",
    "print(rate_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3.04, 0.03999999999999998, 0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.0, 0.0, 0.0, [(2, 5), (2, 6), (2, 7), (3, 5), (3, 7), (4, 5), (4, 6), (4, 7)], [1.0, 1.0, 0.0, 0.0, 0.0, 2.0, 1.0, 1.0])\n",
      "\n",
      "\n",
      "(5.32, 0.03999999999999998, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, [(7, 11), (7, 12), (7, 13), (8, 11), (8, 13), (9, 11), (9, 12), (9, 13)], [1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n",
      "\n",
      "\n",
      "(4.5600000000000005, 0.0, 0, 0.0, 0.0, 0.16666666666666666, 0.0, 0.0, 0.0, 0.16666666666666666, 0.0, [(12, 1), (12, 2), (12, 3), (13, 1), (13, 3), (14, 1), (14, 2), (14, 3)], [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0])\n"
     ]
    }
   ],
   "source": [
    "state = initial_state\n",
    "\n",
    "pos_1 = (3, 6)\n",
    "pos_2 = (8, 12)\n",
    "pos_3 = (13, 2)\n",
    "\n",
    "T = 1\n",
    "\n",
    "rates_1 = get_rates_phenol(state, pos_1, T)\n",
    "rates_2 = get_rates_phenol(state, pos_2, T)\n",
    "rates_3 = get_rates_phenol(state, pos_3, T)\n",
    "\n",
    "print(rates_1)\n",
    "print(\"\\n\")\n",
    "print(rates_2)\n",
    "print(\"\\n\")\n",
    "print(rates_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "move_ur\n",
      "pp_rxn\n",
      "pp_rxn\n"
     ]
    }
   ],
   "source": [
    "event1 = choose_event(rates_1[0:11])\n",
    "event2 = choose_event(rates_2[0:11])\n",
    "event3 = choose_event(rates_3[0:11])\n",
    "\n",
    "print(event1)\n",
    "print(event2)\n",
    "print(event3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's test the simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rescure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[300, 300.00804, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.0089, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905, 300.00905]\n",
      "[18, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0.008039999999999999, 0.00086, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00015, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "n = 20\n",
    "ratio = (30, 2, 1) # (voids, phenols, coals)\n",
    "T = 300\n",
    "n_iter = 30\n",
    "\n",
    "state_list, temps, crosslinks, coal_rxn, heat_rxn, = rescure.resin_cure_simulation(n, ratio, T, n_iter)\n",
    "\n",
    "print(temps)\n",
    "print(crosslinks)\n",
    "print(coal_rxn)\n",
    "print(heat_rxn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success! For now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 0. 0. 0. 1. 0. 0. 0. 0. 2.]\n",
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      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
      "\n",
      "\n",
      "[[0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0.]\n",
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      " [0. 0. 0. 0. 3. 0. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 3. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 3. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 3. 0. 0. 0. 0. 0. 0. 0.]\n",
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      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 3. 3. 2. 0. 0. 0. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 3. 0. 0. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 4. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 4.]\n",
      " [0. 0. 0. 0. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 3. 0. 0. 3. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 2. 0. 0. 1. 0. 0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "state_1 = state_list[0]\n",
    "state_2 = state_list[-1]\n",
    "\n",
    "print(state_1)\n",
    "print(\"\\n\")\n",
    "print(state_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we turn this into a heatmap?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1cf71aeabd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the states and turn them into heat maps so\n",
    "# we can visualize them better:\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(state_1, cmap='hot', interpolation='nearest')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1cf78337510>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(state_2, cmap='hot', interpolation='nearest')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}