CODE_1_load_ML_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86c710dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "### run this tab twice to remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import peslearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a40cead",
   "metadata": {},
   "source": [
    "### Load ML model from file and print ML energies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a3bb64",
   "metadata": {},
   "source": [
    "#### Use following if you encounter a error regarding precision:\n",
    "##### ERROR: \"RuntimeError: expected scalar type Double but found Float\"\n",
    "    This bug is only encountered in NN model (in my Mac/Linux Machines) \n",
    "    However to solve it: \n",
    "    (a) open compute_energy.py (file) --> goto pes (function) --> \n",
    "    (b) replace \"E = model(x)\" with \"E = model(x.double())\"\n",
    "    or add line x = x.double() after     x = torch.Tensor(data=newX)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73f5c8c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select appropriate path and model name (path uses UNIX separator)\n",
    "input_dir = os.getcwd()+'/'\n",
    "out_peslearn = input_dir + 'peslearn_out/'\n",
    "test_path2 = out_peslearn + 'model1_data/'\n",
    "os.chdir(test_path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f63d82b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from compute_energy import pes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a252b8e",
   "metadata": {},
   "source": [
    "### plot trial curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ec77e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-76.97605685, -76.97661403, -76.97701655, -76.97731353,\n",
       "       -76.97752333, -76.97766474, -76.97775514, -76.97780853,\n",
       "       -76.97783568, -76.97784481, -76.97784207, -76.97783203,\n",
       "       -76.97781792, -76.97780196, -76.97778559, -76.97776971,\n",
       "       -76.97775485, -76.97774125, -76.97772901, -76.97771813,\n",
       "       -76.97770855, -76.97770016, -76.97769287, -76.97768655,\n",
       "       -76.97768111, -76.97767642, -76.9776724 , -76.97766895,\n",
       "       -76.977666  , -76.97766348, -76.97766133, -76.97765948,\n",
       "       -76.97765791, -76.97765657, -76.97765541, -76.97765443,\n",
       "       -76.97765358, -76.97765285, -76.97765222, -76.97765167,\n",
       "       -76.97765119, -76.97765077, -76.9776504 , -76.97765007,\n",
       "       -76.97764977, -76.9776495 , -76.97764926, -76.97764903,\n",
       "       -76.97764882, -76.97764862, -76.97764843])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating r_n points at 5 degree\n",
    "r_n = 51 # number of points with 0.1 Ang. interval\n",
    "R_ini = 3.0 # initial R value (adjust for plot)\n",
    "E_inf = -76.97764842\n",
    "b =np.zeros(4) # array with dimentions of input coordinates\n",
    "e1 = np.zeros(r_n)\n",
    "r =  np.zeros(r_n)\n",
    "\n",
    "# angular coordinates at which curve needs to be tested\n",
    "phi_pes = 0.0\n",
    "theta2_pes = 0.0\n",
    "theta1_pes = 40.0\n",
    "\n",
    "for i in range (r_n):\n",
    "    r_pes = ((i/10.0)+R_ini)   # input R 3. to 8.\n",
    "    b =np.array([r_pes,phi_pes,theta2_pes,theta1_pes])\n",
    "    E = pes(b, cartesian=False)  # cartition is false for r/theta r\n",
    "    e1[i] = E[0,0]\n",
    "    r[i] = r_pes\n",
    "e1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06a79a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa3a3bafef0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the bayesian/nn learned curve for testing\n",
    "e2 = (e1-E_inf)*219474.63   # converting to cm-1\n",
    "x = np.arange(len(e2))/10.0+R_ini\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(x,e2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eed13e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy minimum at tested curve\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-43.1016326396712"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Energy minimum at tested curve\")\n",
    "min(e2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77636651",
   "metadata": {},
   "source": [
    "### Use below section once to create array file (containing coordinates) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c76fa315",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 43.98it/s]\n"
     ]
    }
   ],
   "source": [
    "# template for creating 4D collision coordinates (step size halved)\n",
    "\n",
    "A = np.ndarray(shape=(1,4)) # junk array initialization\n",
    "r1 = np.arange(3.1,8.1,0.1) \n",
    "r_n = len(r1) # saving number of R data points\n",
    "r4 = np.atleast_2d(r1).T   # creating r column vector\n",
    "for i_phi in tqdm(range (0,91,15)):\n",
    "    for j_theta2 in range (0,91,15):\n",
    "        for k_theta1 in range (0,181,15):\n",
    "            b = np.array([i_phi,j_theta2,k_theta1])\n",
    "            c = np.tile(b,(r_n,1))  # creating angles as columns\n",
    "            d = np.c_[ r4, c ]        # joining r and columns\n",
    "            A = np.vstack([A, d]) # repeating for different geoms and joining\n",
    "A = np.delete(A, 0, 0) # deleting first row (empty)\n",
    "\n",
    "# save coordinates\n",
    "np.savetxt(\"geom_dense.csv\", A, delimiter=\",\",fmt='%.4f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de13500a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31850"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load coordinates directly from previously saved files\n",
    "B_var2 = np.loadtxt(\"geom_dense.csv\", delimiter=\",\")\n",
    "len(B_var2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "35b57c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = B_var2\n",
    "predicted_energies = pes(result, cartesian=False)\n",
    "final_data = np.c_[ result, predicted_energies ]\n",
    "np.savetxt(\"E_PESLearn.dat\", final_data, delimiter=\",\",fmt='%.2f,%d,%d,%d,%.16f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faff418a",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}