epoxy_fit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tm_develop import TrajectoryMatching\n",
    "from misc import plot_1component\n",
    "import time\n",
    "\n",
    "def basis_function(x, p):\n",
    "    return np.sign(x) * np.abs(x) ** p\n",
    "\n",
    "T = 350\n",
    "outfile = f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy_compass/epoxy_npt_{T}.out\"\n",
    "PATH = \"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/\"\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  00:08:52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 1000/1000 [02:25<00:00,  6.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fitting: 100%|██████████| 998/998 [3:44:25<00:00, 13.49s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonded weight =  -6.310332398906823\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = 1\n",
    "steps_between_points = 1\n",
    "configurations = 1000\n",
    "params = range(-1, -21, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.1, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  15:36:36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 10/10 [00:01<00:00,  7.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    }
   ],
   "source": [
    "run = 1\n",
    "steps_between_points = 1\n",
    "configurations = 10\n",
    "params = range(-1, -21, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data(only_r=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% JUST TESTING\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  04:14:06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 1000/1000 [02:21<00:00,  7.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fitting: 100%|██████████| 998/998 [3:36:48<00:00, 13.03s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonded weight =  -6.310363062932083\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = 2\n",
    "steps_between_points = 1\n",
    "configurations = 1000\n",
    "params = range(-1, -19, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.25, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  08:10:10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 1000/1000 [02:23<00:00,  6.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fitting: 100%|██████████| 998/998 [3:39:34<00:00, 13.20s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonded weight =  -6.310338034942909\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = 3\n",
    "steps_between_points = 1\n",
    "configurations = 1000\n",
    "params = range(-1, -17, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.25, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  16:31:54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 3000/3000 [07:26<00:00,  6.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fitting: 100%|██████████| 2998/2998 [11:34:17<00:00, 13.90s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonded weight =  -6.702985621845983\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = 4\n",
    "steps_between_points = 1\n",
    "configurations = 3000\n",
    "params = range(-1, -19, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.25, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  18:08:10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 3250/3250 [10:37<00:00,  5.09it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fitting: 100%|██████████| 3248/3248 [11:37:46<00:00, 12.89s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonded weight =  -6.711187390772667\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = 5\n",
    "steps_between_points = 1\n",
    "configurations = 3250\n",
    "params = range(-1, -21, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.25, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  21:53:17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 1500/1500 [03:34<00:00,  6.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing input\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mIndexError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-2-940d4a7d5859>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     20\u001B[0m     \u001B[0mepoxy_tm\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcg_instructions\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mcg_ids_1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcg_ids_2\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     21\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 22\u001B[0;31m \u001B[0mepoxy_tm\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mprepare_training_data\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     23\u001B[0m \u001B[0mepoxy_tm\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mweights\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mload\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mPATH\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0;34mf\"epoxy_npt_{T}_{run}.npy\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     24\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/polymerisation-coarse-graining/tm_develop.py\u001B[0m in \u001B[0;36mprepare_training_data\u001B[0;34m(self, only_r)\u001B[0m\n\u001B[1;32m    225\u001B[0m                     \u001B[0mbonded_pairs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mpair\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpair\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    226\u001B[0m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbonded_pairs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mbonded_pairs\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 227\u001B[0;31m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcalculate_eq_bond_length\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    228\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    229\u001B[0m             \u001B[0mru\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;36m4\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/polymerisation-coarse-graining/tm_develop.py\u001B[0m in \u001B[0;36mcalculate_eq_bond_length\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    197\u001B[0m             \u001B[0mid1\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpair\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    198\u001B[0m             \u001B[0mid2\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpair\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 199\u001B[0;31m             \u001B[0mpos1\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mid1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    200\u001B[0m             \u001B[0mpos2\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mid2\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    201\u001B[0m             \u001B[0mlength\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msum\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpos1\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mpos2\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m**\u001B[0m \u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mIndexError\u001B[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "run = 5\n",
    "steps_between_points = 1\n",
    "configurations = 1500\n",
    "params = range(-1, -21, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "epoxy_tm.weights = np.load(PATH + f\"epoxy_npt_{T}_{run}.npy\")\n",
    "\n",
    "gamma = epoxy_tm.fit_gamma(T+3)\n",
    "print(f\"gamma = {gamma}\")"
   ],
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     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tm_develop import TrajectoryMatching\n",
    "from misc import plot_1component\n",
    "import time\n",
    "\n",
    "def basis_function(x, p):\n",
    "    return np.sign(x) * np.abs(x) ** p\n",
    "\n",
    "T = 350\n",
    "PATH = \"/home/markjenei/coarse-graining/epoxy/\"\n",
    "outfile = PATH + 'epoxy.out'\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 350\n",
      "Start at  18:03:21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading data: 100%|██████████| 3000/3000 [06:54<00:00,  7.31it/s]"
     ]
    }
   ],
   "source": [
    "run = 1\n",
    "steps_between_points = 1\n",
    "configurations = 3000\n",
    "params = range(-1, -21, -1)\n",
    "\n",
    "\n",
    "print(f\"T = {T}\")\n",
    "t = time.localtime()\n",
    "current_time = time.strftime(\"%H:%M:%S\", t)\n",
    "print(\"Start at \",current_time)\n",
    "\n",
    "\n",
    "epoxy_tm = TrajectoryMatching(outfile_path=outfile, basis=basis_function, basis_parameters=params,\n",
    "                                   simulation_timestep=0.5, cutoff=50, system_style='molecular',\n",
    "                                   every_n_from_output=steps_between_points, timesteps_in_fit=configurations)\n",
    "\n",
    "cg_ids_1 = [0,1,2,3,4,5,6,15,16,17,18,23,25,26,27,28,40,41,42,43,36,37,38,39,14]\n",
    "cg_ids_2 = [6,7,8,9,10,11,12,13,19,20,21,22,24,29,30,31,32,33,34,35,44,45,46,47,48]\n",
    "for i in range(int(epoxy_tm.data.shape[1] / 49 + 1)):\n",
    "    epoxy_tm.cg_instructions[i] = [cg_ids_1, cg_ids_2]\n",
    "\n",
    "epoxy_tm.prepare_training_data()\n",
    "\n",
    "epoxy_tm.fit()\n",
    "print(\"bonded weight = \", epoxy_tm.bonded_weight)\n",
    "\n",
    "x = np.linspace(10.0, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}a.png\")\n",
    "x = np.linspace(3.1, 60, 10000)\n",
    "y_fit_long = epoxy_tm.predict(x, correct=False)[0]\n",
    "plot_1component(x, y_fit_long, output_path=f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}b.png\")\n",
    "np.save(f\"/home/markjenei/polymerisation-coarse-graining/dgeba_hardener/epoxy/epoxy_npt_{T}_{run}.npy\", epoxy_tm.weights)\n",
    "\n",
    "\n"
   ],
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     "name": "#%%\n",
     "is_executing": true
    }
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