SPCE_analysis.ipynb

{
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  {
   "cell_type": "code",
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   "id": "922cc733",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import signac\n",
    "import gsd\n",
    "import freud\n",
    "from scipy.stats import uniform\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.special import erf\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "096c6b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "done\n"
     ]
    }
   ],
   "source": [
    "#Extracting information\n",
    "\n",
    "poses = []\n",
    "boxes = []\n",
    "        \n",
    "f = open(\"spce_278.dump\")\n",
    "text = f.readlines()\n",
    "\n",
    "num_atoms = int(np.fromstring(text[3][:-1], sep=' ')[0])\n",
    "num_frames = int(len(text) / (9 + num_atoms))\n",
    "\n",
    "\n",
    "for i in range(0, num_frames, 1):\n",
    "        it = 9 + (9+num_atoms)*i\n",
    "        et = it + num_atoms\n",
    "        box0 = np.fromstring(text[it-4][:13], sep=' ')*-20\n",
    "        box1 = np.fromstring(text[it-3][:13], sep=' ')*-20\n",
    "\n",
    "        boxes.append([box0, box1, box1])\n",
    "        #print(box0)\n",
    "        print(i,it, et)\n",
    "        pos = []\n",
    "\n",
    "        for j in range(it, et):\n",
    "            pos.append(np.fromstring(text[j][:-1], sep=' '))\n",
    "\n",
    "        poses.append(np.asarray(pos))                \n",
    "\n",
    "\n",
    "        #break\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "f.close()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45117fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
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     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Computing fields ignoring the fact that they are Gaussian Charges\n",
    "\n",
    "projE = []\n",
    "\n",
    "for i in range(50, len(poses), 20): #Ignoring the first 50 frames, only analyzing every 20 frames\n",
    "\n",
    "\n",
    "    fbox = freud.box.Box.from_box(boxes[i])\n",
    "\n",
    "\n",
    "    pos = poses[i]\n",
    "    Opos = pos[:,2:][pos[:,0] == 1]\n",
    "    Hpos = pos[:,2:][pos[:,0] == 2]\n",
    "    \n",
    "    # Getting O to H vectors    \n",
    "    vHpos = np.reshape(Hpos, [int(len(Hpos)/2), 2, 3])\n",
    "    omat = np.copy(vHpos)\n",
    "    fbox = freud.box.Box.from_box(boxes[i])\n",
    "    omat[:,0] = fbox.wrap(vHpos[:,0,:] - Opos)\n",
    "    omat[:,1] = fbox.wrap(vHpos[:,1] - Opos)\n",
    "    omat = np.reshape(omat, [len(Hpos), 3])\n",
    "\n",
    "\n",
    "    fields = []\n",
    "\n",
    "    Hdel_list = (np.arange(0, len(Hpos))/2).astype('int')\n",
    "    Odel_list = (np.arange(0, len(Opos))).astype('int')\n",
    "\n",
    "    for j in range(0,len(Hpos)): # Need these to ignore intramolecular E-fields\n",
    "\n",
    "        Hpoint = Hpos[j]\n",
    "        tHpos = Hpos[Hdel_list != int(j/2)]\n",
    "        tOpos = Opos[Odel_list != int(j/2)]\n",
    "        dpos = fbox.wrap(Hpoint - tHpos)\n",
    "        dis = np.linalg.norm(dpos, axis=1)\n",
    "        dpos = dpos[dis > 1e-2]\n",
    "        dis = dis[dis > 1e-2]\n",
    "        direct = (dpos.T / dis).T\n",
    "\n",
    "        Hfields = (direct.T / dis**2).T\n",
    "\n",
    "        dpos = fbox.wrap(Hpoint - tOpos)\n",
    "        dis = np.linalg.norm(dpos, axis=1)\n",
    "        dpos = dpos[dis > 1e-2]\n",
    "        dis = dis[dis >1e-2]\n",
    "        direct = (dpos.T / dis).T\n",
    "\n",
    "        Ofields = (direct.T / dis**2).T\n",
    "\n",
    "\n",
    "        field = 0.4238*np.sum(Hfields, axis=0) -0.8476*np.sum(Ofields, axis=0)\n",
    "\n",
    "        fields.append(field)\n",
    "\n",
    "\n",
    "        #print(j)\n",
    "\n",
    "\n",
    "    projE.append(np.sum(omat*fields, axis=1))\n",
    "    \n",
    "    print(i)\n",
    "\n",
    "\n",
    "            \n",
    "projE = np.array(projE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1aaad392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Field Strength (V/A)')"
      ]
     },
     "execution_count": 14,
     "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": [
    "\n",
    "\n",
    "heights, bins = np.histogram(-14.4*projE, 30) # -14.4 is for conversion\n",
    "\n",
    "binw = bins[1] - bins[0]\n",
    "bin_centers = bins[:-1] + binw/2\n",
    "\n",
    "\n",
    "plt.plot(bin_centers, heights)\n",
    "\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Field Strength (V/A)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "269660f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5744f675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n",
      "300\n",
      "400\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0421c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dbd7952",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee786226",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72219e8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30ca5711",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c48f1c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33da42f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba416719",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f83671b",
   "metadata": {},
   "outputs": [],
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   "cell_type": "code",
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   "id": "7b2d7f71",
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   "cell_type": "code",
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   "id": "dd07b910",
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  {
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  {
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   "id": "4311a71c",
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  {
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  {
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  {
   "cell_type": "code",
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   "id": "341710bc",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb9dd52",
   "metadata": {},
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   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}