Ammonia_Imide_Cif_Creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94c2b01d",
   "metadata": {},
   "source": [
    "# Creating Imide-Ammonia Cif:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "515ec978",
   "metadata": {},
   "outputs": [],
   "source": [
    "import array as arr\n",
    "import numpy as np\n",
    "from mayavi import mlab\n",
    "from numpy import pi, cos, sin, arccos, arange\n",
    "\n",
    "def plot_axes(n, c=(0., 0., 0.)):    \n",
    "    # n is number of axes wanted, c describes colour of axes\n",
    "    o = np.zeros(2)\n",
    "    a = np.array([0., n])\n",
    "    mlab.plot3d(a, o, o, tube_radius=None, color=c)\n",
    "    mlab.plot3d(o, a, o, tube_radius=None, color=c)\n",
    "    mlab.plot3d(o, o, a, tube_radius=None, color=c)\n",
    "    return\n",
    "\n",
    "def spherical_grid(n=500):\n",
    "    # n is the number of points created on the spherical grid\n",
    "    indices = arange(0, n, dtype=float) + 0.5\n",
    "    phi = arccos(1 - 2*indices/n)\n",
    "    theta = pi * (1 + 5**0.5) * indices\n",
    "    x, y, z = cos(theta) * sin(phi), sin(theta) * sin(phi), cos(phi)\n",
    "    return x, y, z\n",
    "\n",
    "def get_incl(x, y, z):\n",
    "    # Gives inclination angle given cartesian coordinates x, y, z\n",
    "    xyd = np.sqrt((x**2) + (y**2))\n",
    "    return np.arctan2(xyd, z) \n",
    "\n",
    "def get_az(x, y):\n",
    "    # Gives azimuth angle given cartesian coordinates x, y \n",
    "    az = np.arctan2(y, x)\n",
    "    az[az < 0] += 2*np.pi\n",
    "    return az\n",
    "\n",
    "def polar_to_cart_elev(r, elev, az):\n",
    "    # converts polar coordinates r, elev, az into cartesian coordinates x, y, z\n",
    "    x = r * np.cos(elev) * np.cos(az)\n",
    "    y = r * np.cos(elev) * np.sin(az)\n",
    "    z = r * np.sin(elev)\n",
    "    return x, y, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a63ac8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt('force_map_data_basis.csv', skiprows=1, dtype='float', delimiter=',', usecols=(0, 1, 2, 3), unpack=True)\n",
    "data = data.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954db690",
   "metadata": {},
   "source": [
    "- Column 0 is x \n",
    "- Column 1 is y \n",
    "- Column 2 is z\n",
    "- Column 3 is force value "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437a6781",
   "metadata": {},
   "source": [
    "## Imide Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "998d2224",
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking lowest 1 % of 10,000 values\n",
    "n = int(10000 / 100)\n",
    "min_is = np.argpartition(data[:,3], n-1)[:n]\n",
    "x_min = data[:,0][min_is]\n",
    "y_min = data[:,1][min_is]\n",
    "z_min = data[:,2][min_is]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7cc9ccdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mayavi.modules.orientation_axes.OrientationAxes at 0x1905cef4ef0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualising the lowest 1% of force potential positions\n",
    "mlab.figure(bgcolor=(1., 1., 1.))\n",
    "mlab.points3d(data[:,0], data[:,1], data[:,2], data[:,3], scale_factor=0.1, scale_mode='none', colormap='viridis')\n",
    "mlab.points3d(x_min, y_min, z_min, color=(1., 0., 0.), scale_factor=0.1, opacity=1)\n",
    "plot_axes(1.5)\n",
    "mlab.orientation_axes()\n",
    "#mlab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1307a5f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_imide_index = np.argmin(data[:,3])\n",
    "x_m = data[:,0][min_imide_index]\n",
    "y_m = data[:,1][min_imide_index]\n",
    "z_m = data[:,2][min_imide_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7213161a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mayavi.modules.orientation_axes.OrientationAxes at 0x1905d078590>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualising force potential minimimum\n",
    "mlab.figure(bgcolor=(1., 1., 1.))\n",
    "mlab.points3d(data[:,0], data[:,1], data[:,2], data[:,3], scale_factor=0.1, scale_mode='none', colormap='viridis')\n",
    "mlab.points3d(x_m, y_m, z_m, color=(1., 0., 0.), scale_factor=0.1, opacity=1)\n",
    "plot_axes(1.5)\n",
    "mlab.orientation_axes()\n",
    "#mlab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110ac21d",
   "metadata": {},
   "source": [
    "## Ammonia Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e3f93da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ammonia_points(elev, az, n, psi, r=1):                   \n",
    "    #generate n random points at angle psi from the north pole (0,0,1)\n",
    "    e = np.pi/2 - elev\n",
    "    a = -(az + np.pi)\n",
    "    rdm = np.random.rand()\n",
    "    new_az = np.linspace(rdm, (rdm + 1), n) * 2 * np.pi\n",
    "    new_elev = (np.pi / 2. - psi * np.pi / 180.) * np.ones(n)\n",
    "    #find cartesian coordinates of these\n",
    "    x, y, z = polar_to_cart_elev(r, new_elev, new_az)\n",
    "    #now rotate points (by putting north pole back to point elev, az:\n",
    "    sin, cos = np.sin, np.cos\n",
    "    xp = cos(e)*cos(a)*x + sin(a)*y - sin(e)*cos(a)*z\n",
    "    yp = -sin(a)*cos(e)*x + cos(a)*y + sin(a)*sin(e)*z\n",
    "    zp = sin(e)*x + cos(e)*z\n",
    "    #generating random points at angle psi from point provided and the ones produced\n",
    "    az_3 = new_az + (psi * np.pi / 180.)\n",
    "    x3, y3, z3 = polar_to_cart_elev(r, new_elev, az_3)\n",
    "    sin, cos = np.sin, np.cos\n",
    "    xp3 = cos(e)*cos(a)*x3 + sin(a)*y3 - sin(e)*cos(a)*z3\n",
    "    yp3 = -sin(a)*cos(e)*x3 + cos(a)*y3 + sin(a)*sin(e)*z3\n",
    "    zp3 = sin(e)*x3 + cos(e)*z3\n",
    "    # returns ____\n",
    "    return xp, yp, zp, xp3, yp3, zp3\n",
    "\n",
    "x, y, z = spherical_grid(n=100)\n",
    "\n",
    "pt_elev = np.pi / 2. - get_incl(x, y, z)\n",
    "pt_az = get_az(x, y)\n",
    "pt_incl = get_incl(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "157f74d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_each_ammonia(n, a, ammonia_angle):                                           \n",
    "    # a = index of point on spherical grid\n",
    "    xp, yp, zp, xp3, yp3, zp3 = get_ammonia_points(pt_elev[a], pt_az[a], n, ammonia_angle)    # AMIDE ANGLE SPECIFIED HERE\n",
    "    x0, y0, z0 = polar_to_cart_elev(1, pt_elev[a], pt_az[a])\n",
    "\n",
    "    mlab.figure(bgcolor=(1., 1., 1.))\n",
    "    mlab.points3d(x, y, z, scale_factor=0.1, scale_mode='none')\n",
    "    mlab.points3d(x0, y0, z0, color=(1., 0., 0.), scale_factor=0.1)\n",
    "    mlab.points3d(xp, yp, zp, color=(0., 0., 1.), scale_factor=0.1)\n",
    "    mlab.points3d(xp3, yp3, zp3, color=(0., 1., 0.), scale_factor=0.1)\n",
    "    plot_axes(1.5)\n",
    "    mlab.orientation_axes()\n",
    "    mlab.show()\n",
    "    \n",
    "plot_each_ammonia(6, 1, 107.8)  # n = 6 is very tidy"
   ]
  },
  {
   "attachments": {
    "ammonia_1.png": {
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    }
   },
   "cell_type": "markdown",
   "id": "ed45ee3e",
   "metadata": {},
   "source": [
    "![ammonia_1.png](attachment:ammonia_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32ad834e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iterate(n_grid=10000, n_circ=6, ammonia_angle=107.8):\n",
    "    # returns arrays of the x,y,z coordinates of the three ammonium hydrogen positions at ammonia_angle from each other\n",
    "    x1 = arr.array('f', [])\n",
    "    y1 = arr.array('f', [])\n",
    "    z1 = arr.array('f', [])\n",
    "    x2 = arr.array('f', [])\n",
    "    y2 = arr.array('f', [])\n",
    "    z2 = arr.array('f', [])\n",
    "    x3 = arr.array('f', [])\n",
    "    y3 = arr.array('f', [])\n",
    "    z3 = arr.array('f', [])\n",
    "    \n",
    "    x, y, z = spherical_grid(n_grid)\n",
    "    pt_elev = np.pi / 2. - get_incl(x, y, z)\n",
    "    pt_az = get_az(x, y)\n",
    "    pt_incl = get_incl(x, y, z)\n",
    "    for i in range(len(x)):\n",
    "        xp, yp, zp, xp3, yp3, zp3 = get_ammonia_points(pt_elev[i], pt_az[i], n_circ, ammonia_angle, r=1.017)\n",
    "        x0, y0, z0 = polar_to_cart_elev(1.017, pt_elev[i], pt_az[i])                 # r in angstrom\n",
    "\n",
    "        x3.extend(xp3[0:5])\n",
    "        y3.extend(yp3[0:5])\n",
    "        z3.extend(zp3[0:5])\n",
    "        x2.extend(xp[0:5])\n",
    "        y2.extend(yp[0:5])\n",
    "        z2.extend(zp[0:5])\n",
    "        x1.extend([x0 for a in range(5)])\n",
    "        y1.extend([y0 for a in range(5)])\n",
    "        z1.extend([z0 for a in range(5)])\n",
    "        \n",
    "    return x1, y1, z1, x2, y2, z2, x3, y3, z3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6459c190",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1, y1, z1, x2, y2, z2, x3, y3, z3 = iterate(n_grid=100, n_circ=6, ammonia_angle=107.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "717c0fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mayavi.modules.orientation_axes.OrientationAxes at 0x1905f29ddb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visual check\n",
    "mlab.figure(bgcolor=(1., 1., 1.))\n",
    "mlab.points3d(x1[7], y1[7], z1[7], scale_factor=0.1, scale_mode='none')\n",
    "mlab.points3d(x2[5:10], y2[5:10], z2[5:10], color=(1., 0., 0.), scale_factor=0.1)\n",
    "mlab.points3d(x3[5:10], y3[5:10], z3[5:10], color=(0., 1., 0.), scale_factor=0.1)\n",
    "plot_axes(1.5)\n",
    "mlab.orientation_axes()\n",
    "#mlab.show()"
   ]
  },
  {
   "attachments": {
    "ammonia_2.png": {
     "image/png": 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ar9dbWVkRoAEA2CkCNAAA7EK/93u/973vfW844nnsMsPivLq6ury8PHzw4HAA9McB+nd/93dH7Q0AANdLgAYAgF3ogQceOHTo0JkzZzY3N4ehudVqtdvtYYAe2tjYWFlZGc6GvnTp0tbW1h/90R9NT09fY1sAALgh7VELAACA29Lc3Nx99923urq6tra2vr4+GAw2NzeHl4YZenNzc/gowuGB6K9+9asPPfTQNbcEAIAbI0ADAMCu9Yd/+IcPPvjg8vLy8vLyysrKMEYPBoPLu/PS0tLS0tLdd9/9la98ZdR+AABwY4zgAACA3ez3f//333333e9///sfffTR1NTU2NhYp9PZ2tpaW1tbXV29ePHiF7/4xS984QsPPvjgqJ0AAOCGCdAAALDL3Xfffd/85jcvXLjw2muv9Xq9999//4MPPjhw4MCDDz64b9++hx9+eNQGAABwkwRoAAC4I+zbt++3f/u3R60CAICdZAY0AAAAAAAlBGgAAAAAAEoI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAAAAAUEKABgAAAACghAANAAAAAEAJARoAAAAAgBICNAAA7Izz588nuffee0ctBACAO4UADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAAAAAUEKABgAAAACghAANAAAAAEAJARoAAAAAgBICNAAAAAAAJQRoAAAAAABKCNAAAAAAAJQQoAEAAAAAKCFAAwAAAABQQoAGAAAAAKCEAA0AAAAAQAkBGgAAAACAEgI0AAAAAAAlBGgAAAAAAEoI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAAAAAUEKABgAAAACghAANAAAAAEAJARoAAHbGe++9l+TAgQOjFgIAwJ1CgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAAAAAUEKABgAAAACghAANAAAAAEAJARoAAAAAgBICNAAAAAAAJQRoAAAAAABKCNAAAAAAAJQQoAEAAAAAKCFAAwAAAABQQoAGAAAAAKCEAA0AAAAAQAkBGgAAAACAEgI0AAAAAAAlBGgAAAAAAEoI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAwM546623kuzfv3/UQgAAuFMI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAAAAACghQAMAAAAAUEKABgAAAACghAANAAAAAEAJARoAAAAAgBICNAAAAAAAJQRoAAAAAABKCNAAAAAAAJQQoAEAAAAAKCFAAwAAAABQQoAGAAAAAKCEAA0AAAAAQAkBGgAAAACAEgI0AAAAAAAlBGgAAAAAAEoI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAGBnvPnmm0lmZmZGLQQAgDuFAA0AAAAAQAkBGgAAAACAEp1RCwAAYGd81DQfnjlz8fXX22+91XnzzbsOHdo7Ozv55S+PTU+PuhUAALgtCdAAAJRrmua1hYU35uYOJPckdyd7tj+JDpLlfn/18cfvOXJkxC4AAMDtxggOAABqNU3zJydO/Hhu7u8kM0k3SbKVtJKppJfsnZ+/Z25u+dSpERsBAAC3GwEaAIBabyws5NixfcldyZ5kavsz6MTPfxidnJvLuXNX3gIAALg9CdAAABT6P6+++j/n5rrJRNJNekk32ZPsTcY+ufq55z75MwAA4PYlQAMAUOVC0/z72dlhd+4k7aSV9LbPQY9/8ob5+RjEAQAAu4gADQBAlf/y8suDpJOMJa1kM9lKetuv1hXvmZtL01zxCgAAcNsRoAEAKHGxaU4ePTpIBkmSjWQj6Sad7SR9VSdOXOMiAABwGxGgAQAo8frCwp5kPFlL1pLVJEl7exDHtRw7tukQNAAA7AqdUQsAAOAm7UlayXqylLSTfckg2Uw2R904cFACAAB2BR/sAQAoMXPgQC+ZSNaTi9vnoNeS9WQ92bj6jYvXUagBAIDbggANAECVse3nDS5vv1aSlWQ1WdmeDf0LNpK3BWgAANgtBGgAAEp84fDhX+33hwF6MhkkS8lScumyNyvb46Evbf/87WT1+PHJ6elR2wMAALcBARoAgCq/9dRT7aSTTCaTyWbSJBeTj5KPkotJkywmf5N8mPw0eS95J7nn618ftTEAAHB7EKABAKjy64cPf+748U7SS8aTiaS9nZuHf36QnE/eT36avJO8myx++9sPHzw4amMAAOD20Bm1AACAq2ouNWnnzOKZ11defytvzU7PPrrn0YPj+unf+s0nn/zrY8fGkk7SSbpJO1lKlpNO0k62ksH2II7Nfn/uD/5g1JYAAMBtQ4AGALgZTdMsvLHw0k9fmu/OZ38ynexNtpK19Lf6jw8ef7T16MEpJTrT09P/+PTp/z47m6SdtJOxZCvZTFaTtWRleyT0Xf3+P3n22WnTnwEAYBcxggMA4IY1TfP0v3l67idz8535TCadZCwZnvLdm/mJ+bk9c49MPnLq/VOjdrojfP7w4XuPH19O1pJBspW0tj+GbiUbyUpyMfnC448/ZPgGAADsLgI0AMCNaZrmxJ+fmH94PvuSfcldSTcZJO1kKmltr2tl7lfmzi2eu9Zed4yvPfnkPzh5cqzf/ygZluj1ZHV7FsdUv//PTp/+zSNHRm0DAADcZozgAAC4AU3TvLzw8rHFY7k3mUgmk/Gkk+xJukn3F9f/8cwfP5NnrrTTnWXv9PRvHDnyG0eONN/61hsLC3cn51566a7HHpv50pcO/tqvGbsBAAC7lQANAHADzrx95ugPj+a+pLf9EL12sie5K5m4wvpjOTbXzB2ePnyFa3ek6enp4UnnX3feGQAA7gBGcAAAXK/mQvPC915IL+kmne1pG91kIhm/coBOMjs9e+ULAAAAu50ADQBwvc789Zn5s/PZSsaSbD9Qr3fl4RuX+06+c63LAAAAu5QADQBwvWb/9Wz2JBvJVrKebCSdZGz7dXVHc7RJc60VAAAAu5EADQBw3aaSPcl6spKsJJuXXdq66k1DZ9bOjFgBAACw63gIIQDAdesm48lmspS0k73JRrKRDJLNUd/srya9ay6AT6f5qDnz4ZnXL7x+tDmamfT39R/b99i9vXuPTHreIwAAvzROQAMAXK/+/f1MJN3kYrKSrCarydr26xqHoD9IVq9+FT6dpmmen39+5j/MzP7F7NF3j2YyGc98d/5o6+hca6612Xri3SdG7QEAACUEaACA63XonkPpJp2klSxvv1a2Y/RKMrhs9cfvP0reSVausCF8ek3TnPjuiWOrx/JwciCZSraSzWQz6SUTSTvz980/n+dH7QQAADtPgAYAuF5f/+rXM5aMb7+WkqXk0vafl7bfLycfJU3SJB8m76Tf9A8/eHjU9nAzFv73wrGlY9mfTCRTyVQykWwmnZ+b+nIsx07l1NW3AQCAEq2trVFPzAEAYNt3/sd3jv6vo0kykfSSTjKd7E+mk17S3f5+f2N7KMd68kFO33368N8XoHe/VquV5FZ+wG4uNjP/aia/mhxI9id7t/9m3pVMJfuS1s+tP5uzB3PwKpsBAMDOcwIaAOAGfOMffSPtpJv0kvFk4meJOR8mHybnk/PJ+8n7yU+Tt5N30v9BX32myIk/OZFOfjYZ5uOP9pPbR6F/vj4neS7P/eKPAACgkhPQAAA35tWzr87+59mfDTroJmNJks2kddn7jWQ92Uz/b/rP/tNnDz7kzOkd4RafgH719Vdn/+Vsfi25P9l/2annu5N9yeSV7zqd04fjGxEAAG4RJ6ABAG7M4UcOH3/geAbJVtJK2slY0kmSrCYXk/PJW8n/Tf98/6nfekp9pkJzsXnhP72QqWQ9GWx/7bGVdJKx7e9CrmQ2s1e9BgAAO02ABgC4YU9+9clcTJaStWSQJGklrWQrGSTryWqyksd/4/HDX3LUlBKLFxbnP5jPnmQ9WUtWktXtv4efmLzxC86tnRuxAgAAdkhn1AIAAH7R9J7pxaOLCz9eOH3+9LEPjmU8GUsGP+vO/cn+Y4889qX7vnT4i+ozZVrbQ2A2k9VkKRlPNrZPQ29e69aZ1sy1LgMAwM4xAxoA4FNpmubMW2fOj50//fbpB/LA7/zd3zn4sJkbd6hbOQP63NvnHvl3j2Q82UwGyd7k3uTe5MD2DOg9V/nnjk0WxxenJ6avdA0AAHaYE9AAAJ/K9PT04enDSY78+pFRa2HHzOybSTvpJYOklSxvv4azOFaTsWTiEw16M/33+nnwynsCAMCOMwMaAABuP9N7p4//vePpJL2kl+xJ1pKl5NLPv4ZJ+lLyYfJR8lYeW35sesrxZwAAbhEjOAAAYGfcyhEcSc795NwjLz+SdjJIJpJe0k2mkgPJdDKZ9JKxZCvZSFaTdvqL/Rf/4YujNgYAgB0jQAMAwM64xQE6yXdOfefo/zuaJJPbzyQc/hPH/clE0knayWaynqwkkzn7ubMH7zGjHACAW8cIDgAAuF1948g3jj98PGNJJ+kk3e2JHBeSnybvJu8kbyfvJO/k2/m2+gwAwC3mBDQAAOyMW38COklzoZn5jzOZSPYk40knaSVblx18vpRcSL/Xf/FJwzcAALjVBGgAANgZv5QAneTce+ce+d4juSuZSsaTsSTJ4G/rcxZz9ptnD97v+DMAALeaAA0AADvjlxWgkzSXmoW/XDh94fSxD49lLBlLVtPv9g/1Ds3eM/vlR788PT09ag8AANh5AjQAAOyMX2KA/ljTNItLix+ufri/tf/gQUeeAQD4JROgAQBgZ3wWAjQAAHymtEctAAAAAACAmyFAAwAAAABQQoAGAAAAAKCEAA0AAAAAQAkBGgAAAACAEgI0AAAAAAAlBGgAAAAAAEoI0AAAAAAAlBCgAQAAAAAoIUADAAAAAFBCgAYAAAAAoIQADQAAAABACQEaAAAAAIASAjQAAAAAACUEaAAAAAAASgjQAAAAAACUEKABAGDH9Pv9UUsAAOAOIkADAAAAAFBCgAYAAAAAoIQADQAAAABAidbW1taoNQAAwAhN08zMzCTxARsAAD7mBDQAAHxar7766tNPPz18/8QTT5w7d+7a6wEA4A7hBDQAANy8pmlOnDhx7NixJCdPnjx9+vTw/fHjx5955plRdwMAwC4nQAMAwM1ommZhYWFubi5Jv99/9tlnDx48mOTcuXPPPffc/Px8kpMnTx45cmTERgAAsHsJ0ADAZ92lpllZXGz+6q9WTp8ee/PNvYcO3TU7O/Hoo72DB0fdClVeffXVF154YX5+vt/vP/7445+szKdOnfpkmwYAgDuNAA0AfHY1TfPGwsJfzs3dk+xPppO9yWQynnST9Pvrjz22du+9v+KEKbfQxzM3rpaeL1/58ssvHz16NMnx48effPLJ6enpqy0GAIBdSYAGAD6jmqb5t08//eD8/N5kKrkr2ZfsSaaSPcn49rL15IOTJ+/ToKl3tZkbI+96+umnTeQAAODOJEADAJ9FTdP8+YkTOXZsJplKJpNuMp7sTfYnez6x/idnzz50HSkQbtrImRvXdvlEjqeeeurw4cOj7gAAgN2gPWoBAMCt1jTN90+c+BH1aQkAACAASURBVOjYsfFkIplKxpPxpLf9+qSHnnvuSj+GHdA0zfPPPz87O5vk5MmTL7744o3W5yRHjhxZXFw8efLk/Pz87OzsE0880TTNqJsAAOC25wQ0APCZ84NTp/7b3Ny9ya8k09sBejLZu92jx65019nTpx9xqpQd9QszN771rW99+iHOH4+QjokcAADcAQRoAOCz5ULT/IuZmc8lv5rck9yVTCST208gnBg+fvBqfLBh55w6deqll1666Zkb13b5RI4d3xwAAD47jOAAAD5b/uvLL28lnaSTtJLNZOuy4RvXqs/JxqlT17wO12U4c2MYiG965sa1XT6RY25u7oknnjh37tyomwAA4PbjBDQA8BlysWn++cxMO/l8cn+yN5lKppO7t09AX3H4xuU+Wlzc+6mHJHDHqpi5cW0mcgAAsLs5AQ0AfIZcWFycSqaSjWQjWU+2krGknbST1qjbk0yeOTNqCVxB0zSnTp2amZmZm5vr9/vDg8/V9TnJ9PT0M888c/bs2X6/PzwKfcpBfgAAdhEBGgD4DNk3MzOZTCYbyVKynAy2p3AMXyOtjloAnzQ8hnx5er7FJ5EPHjz44osvmsjB/2fv7mPjvvP7wL+HTyL1NLRkrx/WXnKzWXd3nTZHtVEv3d3EaUPikEbFXfuH9g/xr/EBRHX/pMRVRHpCA+iCjg7QFQXOha7Q9HCgi9qH9g5Xptd2uOklcdpcdIm4Sbp7ySZROF57/Sgvf5L1/MD7gx7tWBY5ehqJ5LxeGAgU5/v9EZZ+HIvv32fePwCAzUcFBwCwvvz3pVJfcjUZTsrJk8njya5mBcfAmnsvJB/V6595uNEhG1pr50a1Wp2amnoIU89raG3kqFarhw4dareDu3OhKC4vLZ15/fWdTz7Zm+z2cgEA0GECaABgffnlUmkouZwsJ48nTyZPJLubAfRg0rfKxhvJG8nF2dkvHziwyhL4oVvqng8ePDg2NtZu00PSaDSOHDlSq9WiGPoBOVsU3zl5cunUqfMzM7uT4WRHsi0ZTAYqlev79w/s3bvlkV57AADYrATQAMD68u9feeU7k5OXkhvJlpYAergZQA8kfUlPspxcS0rJysT0B8n3k+cWFz8zMtLui9DtiqKYnp6u1WqVSmX//v3rM+Gdn5+/mY8fPnx4xIl9r84WxX87PPzl5Nlka7KtGUCv3OZ0e5LkanJlcXGbP2QAgAdNAA0ArC8fFcXs8PDVpJRsSfqSJ5JdzcBoJYDuTUrJcnI1uZr0JxeSD5P3qtWfVVnAmloHn2dnZ/ft2/doOzfWtt4aQjaioij+6fR0qVZ7JiknW5svI0NJOdnd+qaKSiUnTqxxKAAA7oEAGgBYd/7jK698a3KytyVu3t6sgV4Zf+5NklxPriWXk57ko+R7yV9aXHzOACOrWM+dG2trLYbWyHG3fnN+/j9MTNy8jrUt2ZL0J1uSncnupNS6ulqNi1gAAA+UABoAWI/+51KpLxlqDkH3JD3JcDOP7kmSXE+uNtuizyaZnf0b2p9ZxYbo3FhbayPHBv1PePjOFsU/Gh7e3ryX6c5mAD2U7EiGkm23BNBJTp3KBrkyAQCwIQigAYD16D/Nz/+/ExNDyWDS3xx5vpGk+fFyywT0jeStSmXae+e5nY3VubG2W4a4jx07tnH/Wx6O/+Xo0T+cmfls8njyWDN0HmxW+mxd7aamfkQCAHhwetotAAB4BH5sfPyZavVaspyUkp6ktzkKfS25mCwl7yVvJH+SvF+p/NfHjrU7JF2nKIr5+fnh4eGJiYlKpbK4uHjgwIENndiWy+Xx8fGlpaVqtVqr1YaHh+fn59tt6l5vNRq/NTNzPVmJk1euWt1IBpL+pH+19DmJP1UAgAdHAA0ArFNfm5o6l5xPLic3k+iVN8vfaOnfGK5U/ubhwzs3cqpIJzQajenp6ZXouV6vnzhxYmSz9IOXy+VDhw6dOnUqycTExEsvvSSGvq1fffXVbcmN5GpyI7mWXEvSvKDVu8bOiYk1ngQA4K6o4AAA1q/zRfGnJ0/2JX8wMTGY9CXLyZXkUnIxGaxUnt+//8t7926XPtOiKIq5ubnJycls/M6Ntd3SyHH48OFNE7I/EH+3VBpIkmxNnkx2JDuSJ5p3NN2RDKyx2U9JAAAPiAAaANgYLhTF906eLCVn3n132wsvjPzIj2zWVJF71p2BbFEUx48fn5mZSTI7O3vArTib/rtSaShJci3ZlexIdidPJLubHdCDq2y8Vqn06ZQHAHhABNAAAGwGCwsLL7/8cq1Wq1Qq+/fvHx8fb7djU2k0GkeOHOna//xPO1cUx4aHtyWlZofPzuTJ5DPJ7uYNCbeuUsTxRqXyOQE0AMADIoAGAGBja+3cqNfre/fu7drp+Pn5+W4bAF/DP3nppfO1Wm9yNbmW9H0qgN6WbGnJoG8kPcmlZHF29ksGyQEAHhA3IQQAYKMqimJ+fn54eHhycrJSqSwuLo6Pj3dt+pxkfHx8aWlpdna2VquNjo4ePXq0KIp2mzatnzp4sCfpT/qTgWQouZicTy4kF5ofn08uJheTj5Kl5Gzy/Urlc/v2tTs2AAB3SgANAMCGtLCwMD09PTExUalU6vX6iRMnunzgd0W5XD5w4MDi4mKlUpmZmRkeHp6fn2+3aXP60tjY56vVlQx6KBlKtiaXk7PJueSj5KPkbLKU/CD5MPkgeS+59OKLW7v4GgYAwAMngAYAYIMpiuLo0aN79uxJshI9qzy+xcjIyIkTJ+r1epKJiYmXXnqp0Wi027QJ7fnGN5L0JAPJlmRLMpRcTz5shs5nkveT95J3kneSDyuVryjfAAB4oHRAAwCwYRRFcfLkSTXHd671T6xarU5NTXVbRcnJ+flvTUysRM9bkv7mDM6NpNT8+HpyNbmY/JVTpz43NrbW4QAAuEsmoAEA2Bh0btyDcrm8UgxdrVa7s5Fj7/j409XqtWRl7mYldO5N+pIkl5MieSc5nTw+Oyt9BgB44ATQAACsdzo37lO5XD506FBrI0dXxdD7Dh16qlq9kFxOriY3mmH0cnPw+aNkqFL5WeUbAAAdoIIDAID165bOjWPHjnVbg8SD1c0dJueLYvHkyR+cOvXuzEx/Ukr6KpXdL774+AsvbN2166mu+XMAAHjIBNAAAKxT8/Pzr732Wq1Wq1Qq+/fvN/X8oBRFcfz48ZmZmST1er3b/mCLoriwtDSQ7BY6AwB0ngAaAIB152ZCKnrunIWFhZVWE3/IAAB0jgAaAIB1ROfGQzY/P9+djRwAADwcAmgAANaFW6JnM7kPTWsjx+zs7AH34gMA4MERQAMA8Ojp3HjkGo3GkSNHarVaurIYGgCADhFAAwDwKLUOPler1ampKZ0bj5BGDgAAHiwBNAAAj4bOjfWpKIq5ubnJycm4JAAAwH0TQAMA8AgURTE9PV2r1UTP65NGDgAAHggBNAAAD1Xr4PPs7Oy+ffsM2K5brY0cBw8eHBsba7cDAAA+QQANAMBDckvnhkBzQ1DSDQDA/RBAAwDwMOjc2NCKojh+/PjMzEw0cgAAcDcE0AAAdJbOjU2jtZHDVQQAAO6EABoAgE65pXPj8OHDIyMj7Taxrvk7BQDgrgigAQDoiEajceTIEZ0bm5JGDgAA7pAAGgCAB0znRpdoNBqjo6PRyAEAwOoE0AAAPDD6GbpQazG0v3EAAG4hgAYA4MFYWFh4+eWXdW50odZGjmq1eujQoXY7AADoFgJoAADuV1EUc3Nzk5OT0bnRxW62fkcxNAAATQJoAADunc4NbqGRAwCAVgJoAADukc4Nbqt1Ir5arU5NTZmIBwDoWgJoAADuWmvnb71e37t3r4SRWxRFMT09rZEDAKDLCaABALgLOje4K62NHAcPHhwbG2u3AwCATUUADQDAndK5wT245aLFsWPHzMsDAHSPnnYLAAAgRVEcPXp0z549Ser1+okTJ6TP3KFyuTw+Pr60tFStVmu12vDw8Pz8fLtNAABsEiagAQBYi84NHqDWRg5D9AAA3UAADQDAqnRu8MC5pAEA0FVUcAAAcBs6N+iQWxo5RkdHNXIAAGxiJqABAPgEt4zjoWk0GkeOHDFiDwCwiQmgAQD42Er0/NprrwkEeZhai6E1cgAAbDIqOAAASJKiKI4fP76SA+rc4GG6pZHj6NGj7XYAALBhmIAGAOh2OjdYJ242ciSp1+sugQAAbAICaACA7nVL9Kxzg/VAIwcAwGYigAYA6FIrnRszMzOiZ9ab1ksj1Wp1amrKVD4AwAYlgAYA6DrSPTaEoiimp6c1cgAAbGgCaACALnJL58bBgwfHxsbabYJHqbWRw6g+AMCGI4AGAOgWN+dJBXlsLO6TCQCwcQmgAQA2v9b8bnZ2dt++ffI7NpybreXRyAEAsHEIoAEANjOdG2wyCwsLe/bsiUYOAIANQgANALBp6dxgU7rlssrhw4dHRkbabQIA4NEQQAMAbEI6N9j0Whs5ZmdnDxw40G4HAACPgAAaAGBTMRxKV2k0GkeOHDHmDwCwbgmgAQA2D2Ec3Wl+ft5FFwCA9UkADQCwGRRFMTc3Nzk5GZ0bdKXWb4FqtTo1NeVbAABgPRBAAwBsbDo34KabbwJIUq/XvQkAAOCRE0ADAGxgCwsLL7/8ss4NaKWRAwBg/RBAAwBsSK2FA/V6fe/evQoH4KbWdwZo5AAAeIQE0AAAG4zODbhDRVEcP358ZmYmGjkAAB4RATQA0KWKi0VKOX3u9Ac3PsgPMv6ljZFM6dyAu9XayOG7BgDgIRNAAwDdpThbnH7vdP379ZnzMyknO5NtyWAymMpgZf+N/c/3PD8ytB4Him/OcgrR4G553wAAwKMigAYAusj8/z0/UZ/IV5Ltya6knOxItiaDyUAykPQlF5MfZLFnceTp9ZJPyc7ggdDIAQDw8AmgAYBu0fheY/SfjObHku3JULIl2Z5sT7YmW5OdSemHiyvXKif6Tqx+sIdH5wY8WAsLC3v27IlGDgCAh6Kn3QIAgM2gOFuM/tPRPJ9saz62Jj3JjaQ3GfpE+pyk1ld75c9eWeVgD0lRFEePHl1Jyur1+okTJyRlcP/GxsaWl5fr9XqtVpuYmHjppZcajUa7TQAA3CMT0ADA5lecLY7/u+Mz78/k8eSxZEcymPQ3o+edyfCtAfSKxTcXR559BGUXt3RuHDt2rFwut9sE3J3WRo7Z2dkDBw602wEAwF0zAQ0AbH4nv3Ny5k9nsiUZSPqTvqQ36U22JduTgdunz0leffbV2z/RSfPz89PT0xMTE5VKZWXwWfoMnVAulw8dOrS4uFipVCYnJ0ul0vz8fLtNAADcHRPQAMAmV5wthn9xOD+SPJ7sTnY2C6C3tdyBsHfV7YsfLI48/pCGoG/OY6qmhYdsfn7+od3nsyiKpfNLr7/1+lsDb81cmansqux/fH96Mr7DtzwAsAkJoAGATe6VuVcm/+1kvpA8lQw370C4NSk3A+j+tbZXUz2UQ2uteBB0bsAjVxTF3Nzc5ORkkmq1OjU19cC/DRtvNF79jVdnvjeTzya7kseSnc0Xoi1JfypXKgdzcGx4rN2RAAA2DAE0ALCZFeeK4V8cTk/yXPLZZGeyNRlKHmsJoFfp37hpMYsj6dQ45C3Rs8FneLQajcaRI0dqtVqSer3+AL8fi7PF8KHhPJOMNF+LtjUD6G3JULL945WLVxZHBjr1mgMA8JDpgAYANrPTb57OUDKUXEtuJNeSa80C6N6kp336nOS7y99tt+QerXRutNY9P8C0C7gHIyMjJ06cqNfrSSYmJl566aWFhYV2m9orimL62HSeTp5Ndibbk23Jlubr0nIy+MPFowOjjeXG6gcDANhIBNAAwGa2q7wrQ8lgci25lFxqZj13493z77ZbcteKopifnx8eHp6ZmalWq8eOHRM9w/oxPj6+tLRUr9drtdqePXuOHj1aFEW7TWuZ+7/matdr2Z2P74a6pfnBQJLmJbEWR0pHbncYAICNRwANAGxmwzuG05dsSZaTC8mF5GpzDvp6cj250e4Q15OP2q25Gzej55uDz4cOHXrgVbPAfSqXyysxdLVanZmZGR4enp+fb7fp9oqimPydyQwlfUl/0tfyPoyBZHvSd+uFsVpqr+SVVY4HALCRCKABgM2svKOcnmQg6UvOJxeTK8mV5GpLEr229/PW+2+1W3SniqKYnp7WuQEbRblcPnToUGsjxz3E0HO/OpcrLc0/N8t/SsmWfFwT9KmfzCYz2YgiDgBgwxNAAwCbXPVL1Y8D6DRbOC43f115XPnkhtY5xKXkB5koT+S+3Rx8rtVqs7OzOjdgA2lt5FiJoRuNO42Gi3PF5Pzkx++6WMmdl5MbyY1mBUd/s4jjU17P67d/AgBg4xBAAwCb3De++o30Jv3Nx8XkQnKx+ViJpC82x6IvJxeSs8mF5MPknVTer4x9bqzdF1nLLZ0bp06dOnDggM4N2FhaGzlqtdro6OgdjkIf/9+Pf1z6vJJBLyfXk2tJKelN+pqXx25nMpNF7qt7GgDgkRNAAwCb3MhTI9UfqX4cAA0mA8n55KPkfHIhOd/87bnkbHI2KZIzyQfJu8n7Ofz5w+2+wlo+3bkxNnZfcTbwCK00ciwuLubOGzn6kq3J1uRq80LXleRas4ij1ByLXsXS5aW1ngYAWPcE0ADA5jf19amPh6BvxtCXkh8kZ5NzSfFx1UY+TM40H+8l76f6verIcyPtDn97OjdgsxoZGVleXr7DRo4//vCPM5gMJn3J5eZ7L1YqOJabdRxruLzmswAA654AGgDY/MrbyrM/PvvDd7uvJNE3kjPJD5rzzh8k7yfvNR9nUvmzytTPT7U79m3c0rmxuLiocwM2n1saOY4ePXrbZbX3ahlIBpMtyaXkfHKpeRPU681fV3Mhw/3Dqz8NALABCKABgK6w7y/sqwxXUkp6WnpX+5LzyVLyfvJu8k7z1++n8kHl8F87XN5516lxo9G4pXNjZOQeZ6iBde5mI0elUpmZmSmVSp9u5Kg8U/n4otdA0puca7kD6uXkavNxO5W3KuWhu34VAgBYV0rLy603egcA2LQa7zVG/9Votidbm0lQmu9/v5ZcSS4lF5KPkqupf70+/p/fXWNGURQnT56cmJhIMjs7u2/fPlPP0D3m5+dXvv0rlcrhw4dvXnla+O7Cnl/Zky3J9eRqspz0J59JHk92JcP5+EVpS9KXlJqvSL3JhZz66NTYM1rjAYCNzQQ0ANAtRj4zcupnTlV6KjmXXGwmQflkBn0xle2V+k/fXfqscwNYaeSYnZ292chRFEWSsefHKrsq6WmW/wwlW5uXu1bug3qxeTfUmzdEXUrOpPJ2RfoMAGwCJqABgK5TnC9ONk6mNxPfmUh/ci2VgcqLj7/4whMv7Nq6a+TJu2vMWFhYePnll2u1WqVS2b9/vzsNQpcrimJ6erpWqyWp1+vj4+ONNxujvzL68XTzyq1Q+5OeZCh5LNmZDDY/s3Ix7FKynFO7jT8DAJuBABoA6GrFxWLpwtLI7rsLnVcURTE3Nzc5ORmdG8AntTZyHDx4sP5H9ZliJqVkqBlArxRuLCfDLaVANz6uA6oMVk587cTaXwIAYEMQQAMA3LXWuudb+l4BVtzyQpGfTG259nHd80DS1yxEvJ4sNx/XkyvJhZx68dTYqPFnAGAzEEADANwdnRvAnSuK4vjx4zMzM0ny3yR/Kdna7Ny4eSvUlfsTXkw+SpZS/6n6+E94YQEANgkBNADAnWoNkur1+t69e3VuAHfiZiNHfjL5arK3pfd5ObmaXEjOpdJb2f/CfukzALCZCKABANrTuQHcp9aXkXwt1elqnsjMBzOVbZUXH3vxhd0v7Bq665ugAgCsfwJoAIA2dG4AD4o3UgAA3aan3QIAgO5VFMXRo0f37NmTpF6vnzhxQvoM3I9yuXzo0KHFxcVKpTIxMTE9PT0/P99uEwDABmYCGgDgNnRuAJ12sxjaiwwAsIkJoAEAbqVzA3g4Whs5qtXqoUOH2u0AANhgVHAAAPyQzg3gYWpt5JiZmSmVSho5AIBNxgQ0AEDyqc6NY8eOuTMY8DBp5AAANiUBNADQ7Vai59dee03nBvBotV4Jq1arU1NTroQBABudCg4AoKutFLCuxD06N4BHq1wuj4+PLy0trTRyDA8Pa+QAADY6E9AAQJfSuQGsZ62NHN6ZAQBsXAJoAKDr3BI9S3aA9cl1MgBgExBAAwDdZaVzY2ZmRvQMbAg3X7WS1Ot1r1oAwMYigAYAuoW7ewEb18LCwp49e+J9GwDARiOABgA2v1vexn7w4MGxsbF2mwDWl1teyg4fPjwyMtJuEwDAIyaABgA2uaIopqena7WasUFgE9DIAQBsLAJoAGDTap0WnJ2d3bdvn84NYHNoNBpHjhxxaQ0AWP8E0ADAJqRzA+gG8/PzGjkAgHVOAA0AbDY6N4DuURTF3Nzc5ORk3F4VAFiXBNAAwOahcwPoTjcbOaIYGgBYZwTQAMBmcEvnhreiA11IIwcAsA4JoAGADc/NuABWtF6N08gBAKwHAmgAYANrLT/VuQGwoiiK48ePz8zMRCMHAPCoCaABgA1J5wbA2lobObw7BAB4VATQAMDGs7Cw8PLLL+vcAFiba3UAwCMngAYANpLWzo16vb53716dGwBr08gBADxCAmgAYGMwxwdwPxYWFvbs2RONHADAwyWABgA2AJ0bAA9EazG0K3kAwEPQ024BAMCjVBTF0aNHV6b26vX6iRMnpM8A92x8fHxpaalardZqtdHR0VdeeaXdDgCA+2ICGgBYp3RuAHROo9E4cuSId5YAAJ0mgAYA1iOdGwAPgUYOAKDTVHAAAOuLzg2Ah2alkWN2dnalkePo0aNFUbTbBABwF0xAAwDrxS2dG8eOHSuXy+02AfAA3GzkSFKv1135AwAeFAE0ALAuzM/Pv/baazo3AB6h1kaOgwcPjo2NtdsBANCGABoAeMSKojh+/PjMzIzoGeCRa30zSrVanZqa8mYUAOB+CKABgEdG5wbA+nTz0mA0cgAA90cADQA8ArdEzwafAdah1kYOL9QAwL0RQAMAD5vODYCN4pbrhYcPHx4ZGWm3CQDghwTQAMDDo1oUYCPSyAEA3DMBNADwMOjcANjoGo3G6OhovIwDAHdDAA0AdFxRFNPT07VaTWYBsNG1FkNr5AAA2hJAAwAd1Dr4PDs7u2/fPp0bABtdayPH7OzsgQMH2u0AALqXABoA6IhbOjcOHjw4NjbWbhMAG0aj0Thy5EitVotiaABgdQJoAODB07kB0CU0cgAAaxNAAwAPks4NgG5TFMXc3Nzk5GSSarU6NTXllR8AuEkADQA8GLd0bpiD6xKXiuLd06dLu3Z9zl83m1FRFKVkpzj1Dtx870s0cgAALQTQAMADcLMJVOdGNyiK4s3Tpy99+9ult966MjOzPdmabK9Utu3ffzr58/722fiKonj79Omz3/720uRkORlKypXKY/v3v5f8qDN8Ta2NHNr/AYAIoAGA+6Rzo6u80WjMv/rqmZmZZ5PhpJzsTLYlQ8mWpD/pTa4mlyqVbYcPbzcTzQb0ewsLv1OvfzAz80TyWPJYsjPZkWxNBpOBpCe5WKkMHT68zRm+itb/L2jkAAAE0ADAfSmVStG50R2KovhH09P9tdpIsjXZ1gzmtiVbky3JUNLfXHyhUtl64sRah4P152xR/OLw8GPJ55LhZCjZ0RJAr2TQg0mS68m1xcUtXvRWVxTF8ePHZ2ZmkvipEwC6WU+7BQAAa6lUKvV6/cSJE9LnTe/X5+a21GqjzcHnrUlPstx8bGlJn5NsrdUuHj262qFgHSqKojo9vTN5Ntmd7Ei2JwPJjeR6ciPpaabPSXqTLUeOrHW4rlculw8dOlSv19stBAA2ORPQAAC09+2Fhf9zz56nm80bgy2dG0PJY8lwUvrUru8tLj7nygQbxD9/5ZXvTE4+kexKysmOlvO8J9mZ7Eq23rKnWs2hQ7c9GgAAK0xAAwDQxrmi+I2XX16ZBh1I+pup3MoHW5K+26XPSZ4zIsoGURTFb01ODjVP7P6kr/lrX7Ne5jYn+cxMGo1PfxoAgJsE0AAAtPHrc3Mf1moDzWCuNyklvUlfsi3ZlvSttrNWOzs/v9qTsH7Mz831Ns/q3qQn6UlKSU/Sn2xNhlb72Wl09LafBgBghQAaAIC1nCuKfzU5WWoJ5krNUdAtLY/V7JyYKIpi9efh0TtXFN+cnLye5JNn+IqVwf+1znNXWQAAVieABgBgVeeK4l8cP15q3oFt5d+ON5IbLU0F/e3+TVmem1vzeXjEfn1urie5nlxvfma5+dvW83xVExNxlQUAYBVuQggAwKreajT+h9HRnmQ4+WyyK9meDCbbk+FkR7I9GWp3kCRZWkq53G4RPBq/UCr1Jkl2Jk8mu5JtyVCyo3nXzbbn+fKpU6WxsTWXAAB0KRPQAACs6uyHHw4lg8m15FpyI7maXG9OQ/fc8b8mrywttVsCj8a5otiSDCVDyZXkanIluZJc+1QXxxou79rVbgkAQJe6wx8ZAADoRqVkSzKYlJKrycXkcnIPb6C71m4BPEL9zfN8ILmcXEwuJteTG8ly87G2K+0WAAB0rVXvWA4AADt37epP+pLryZXkYtLbnIa+3iyDbuuDtftz4VHrTQaSUnI9uZT0t5zn15pl0NeT3lW2f3hn3wgAAN3JBDQAAKvaMTzckwwk/cnF5HyzneBqcjW51vx1DdeSd9utgUdoR7n8TKXS1zzPe5OPksvNx5WWx2pz0G9XKo+PjKzyJABAtxNAAwCwqh3l8per1ZUh6P5mBUfrY6Uw9/rqR3g36alWd4vnWMe+fvBgT9LffKwUcVxqedw84a83GzmuJZeS68n7SfngwXZfAQCgewmgAQBYy8TUVE8zlRtqNkFf+uRjJZi+1pyJXpmVvpS8l5ypVEamptp9EXiUvjw2YqmQIQAAGcBJREFU9vlqtSfpa57nQ80y6AvNSuiVj88n55OPknPJUnImebdSeXZsrN1XAADoXgJoAADWsq1c/s9mZ1cmoAeToeR6cj658KnHSjB3NimSD5IzyTtJ3/79W8vldl8EHrGxb3wjSW/S2zzPVy63nE3ONXPnc8nZZCn5MDmTfJC8lzxz+HC7YwMAdLXS8nLbWzoDANDt/kmpdD0ZSLY0e3J7k8eScjLU/Eya1QRXkktJKVmuVP7iiRNtDg3rw2/Nz//BxESSwZbzvCdZToaToaRv5axunuQXk6FK5avOcACANZmABgCgvb9Sr/c0m6D7m7drO5f84JMDoe8n7yfvJe8n5yqVpw2HsnH85Pj4s9Vqb/P6SuvZ/lFz3vnd5J3k7eTt5EKl8uec4QAA7QigAQBo78fGx1dKcnua2Vxf815tZ5qh80o8t/J4O1l+8cVn3HuQDeXnDh1aqTJfTkpJ6wlfSq4lF5IfJO8kf5Y8vX//485wAIB2BNAAANyRr05NXUquJisNbq3ZXJKryYWkSN5L3kz6KpUXDxxY63CwLv2Xi4ufqVYvJCtn+40kSSlJcj25nJxPBiqVn67Xf2J8fK0DAQCQRAc0AAB37t1G49eOHLlaq21NBm9Xibtyo7ahSuVvHD5s/JmN63xRLJ482Zt8e2JiIOlNLid9lcro/v2fef75J53bAAB3TAANAMDdWcnmzpw6dWZmpj8pJZeT/krliRdffOKFF74wNtbuALCRXCyKD06ffs6JDQBwTwTQAADcu4tFceb06cFdu5ThAgAAnyaABgAAAACgI9yEEAAAAACAjhBAAwAAAADQEX3tFgAAABtSURSn3zv97QvffnLnk8/3Pz/yrJ5uAAAeNh3QAACwqRTnipPfOXnq3KmZKzPZmuxItiVDyWCqg9U9l/bs3bq3vKPc7jAAAPAACKABAGCTKIpi+uh07Uoto8njyXBSTnY2A+gtSX/Sm1xM9VL10NOH2h0PAADulwAaAAA2iVf+5SuT/3Eyo8lTyWAymOxsmYAeSrb98C4w9Sv18YHxtQ4HAAD3TQANAACbwcK3Fvb8r3vy+eSpZHszgB5IBpNtydZkONnyiS2LWRyJYmgAADqoJwAAwAZXFMXL/+HlPJvsSPqTLclgsiUZSHqS60nfbW5A/mpevd3BAADggRFAAwDAhnfy907WPqhlIBlI+pO+pL/lg6Gk5zb/9p/JzHzmb3c8AAB4MFRwAADAxlacLYb/x+H0J7uSXUk52Z4M5eM8enuyNdmSbL399qUslVO+/XMAAHB/TEADAMDGdvx/O553kuWkN+lJSkmSlTmTgebjk+3Pn9ie46s+BwAA98cENAAAbGCNNxujvzyaJE8nTyfDyc5kKBlMtic7mxPQA2sdxN0IAQDoEBPQAACwgb3++69nKNmaXEuuJcvJ9eR6cyD65mPtg+T1NisAAOCefOpO2AAAwMbx1g/eylCSZgB9Lbma9DW7OEotpRxrHOTqW+lvswYAAO6BCWgAANjIepItyWAykFxJLieXkuvJjeaCO6jc++y5z7ZbAgAA90IADQAAG1kp6U+2JFuSK8n55EJyrVnEsZJE30gbRbsFAABwTwTQAACwge35wp70JgNJf7KcnE+utDyuNh9rzEG/nRf6X1j9aQAAuHcCaAAA2MDGf2K88plK+pL+pD+5kVxKLiWXk8ufDKNbM+ibH19M5f3K2LNjtzk0AADct9Ly8h10wgEAAOtV493G6L8cTZqTzv3JYLI72ZWUk53J1mRLMpD0JqVkueVehe9m8bHFkV0jbb4GAADcExPQAACwsY08OVL9fDW9SV8ymAwl/cn5Zh/0heRiciE5n3yUnEuK5AfJB8n7qZ6vSp8BAOgcE9AAALAZlP5ZKeeSgWRL0p/0Jj3JYDKcbG9+ppTcSK4lV5KLqQxVDj93eGS3ABoAgE4xAQ0AAJvBqb2nUkr68nEf9EAykNxIPkx+kJz5eOQ57yXvJu8k7+fF0ovSZwAAOkoADQAAm8HYF8eqI9Uk6Ul68nEjR3/Sl5xPlpL3kneajzdTOVM58OMH2h0VAADuiwAaAAA2iamvT+VCcjm5niwnpaTUDKNXUulryfnkTCo3Kof/i8PtjgcAAPdLBzQAAGwexfni5OmTr/3pa7WrtQwm/UlPspxcTS4l51Ppq7z49Itf/9LXR55RvgEAQMcJoAEAYBMqLhQn3zj57qV3JxcnKzsqX+z/4sTIxK6hXSOfkTsDAPDwCKABAAAAAOgIHdAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYDm3hXFclGca7cKAAAAAOhSpeXl5XZrAAAAAADgrpmA5h599NHFy5dvXLp0o91CAAAAAKBLCaC5R3/8x28MDp4eGnprYeG77dYCAAAAAN1IAM09qtffSLYlg7//+4agAQAAAIDb0AHNvWg03hkd/Vay97HHbvzxH+/cvXug3Q4AAAAAoOuYgOZevPrqryWPJ31//+/v3L17YHFxqd0OAAAAAKDrCKC5a0VxbmbmfDL8pS/1Hzw4sLycb3zjVxqNN9rtAwAAAAC6iwCauzY395vJ40n+4T8c6OvL7OzZ3/7tHa+//u12+wAAAACA7qIDmrtTFOeGh48mf+2v//Wv/MqvPPnRR8vPP//222//UfKrS0uHyuUd7Q4AAAAAAHSLvnYL4BNOnvy95Klkx8GDw0mGhkrf/e5TyZPJ1/v7XcwAAAAAAH5IAM1dKIpzExP/R/KXk+39/aUkvb3Zvl2RCwAAAABwG6JD7sLp028kzyRbkoyPf1QqnS+VzpdK75dKf1gq/dtS6djCgiZoAAAAAOBjAmjuVFEU9frvJk8kTyRpGZ/fmiTZmTzx8sv/4vabAQAAAIDuI4DmTi0tnZuZ+W6SZCDpS3o/+fyuJLXa1YWFP/j0XgAAAACgCwmguVOvvvpvkqeSwWT7p55cmYYeTJ6q1/+fTz0LAAAAAHQjATR3pNF4Y2bmVLIz2ZnkU7ev3JJk5dmZmVONxpu37gcAAAAAuo8Amjvy6qv/Lnk2SfJ4kmbifIuVbPpHX3/9d2/3LAAAAADQXUrLy8vt1tDtiuLc8PDfTfYk5eQvJEl2N+89uOJCciZJcjJJMr+09I/L5R23HggAAAAA6CYmoGlvbm4+2ZpsaRl8vuUOhDeVkySfmZv796ssAAAAAAC6hQlo2iiKc8PDfzv5y8nO5Lnk6STJM5/KoL+XJPkw+ZPk/eRby8uztx4LAAAAAOgmJqBpY2mpSLY3+51XWjX6Vp+AHkiSPJFsX1j49iprAAAAAICuIICmjZGRZ5PdSZLBZPvqC/uSJNuTwSTJ7l27yqsvBgAAAAA2PwE07VWrX0/SUgDdd7tVN5/dmWR29udGRp693TIAAAAAoFvogOaOzM//fxMT55sT0OVmI0erD5PzSSqVK/v394+Pf/lTCwAAAACA7iKA5k4VxdWTJ8++9tpSrbYz2fqp5y9UKmf37x9+/vmtIyNDt9kPAAAAAHQZATR3rSiWl5ZuHDny27XaM8mVavXyN77xleHhG+Vyf7utAAAAAEAXuW2ZL6ylXC6Vy71f/OJis0P89MjIn096194FAAAAAHQbNyHkni1/6gMAAAAAgB8SQAMAAAAA0BECaAAAAAAAOkIAzT1TwQEAAAAArEUADQAAAABARwigAQAAAADoCAE0AAAAAAAdIYDmnumABgAAAADWIoAGAAAAAKAjBNAAAAAAAHSEABoAAAAAgI4QQHPPdEADAAAAAGsRQAMAAAAA0BECaAAAAAAAOkIADQAAAABARwiguWc6oAEAAACAtQigAQAAAADoCAE0AAAAAAAdIYDm/qngAAAAAABuQwDNPZM7AwAAAABrEUBzjz772SeaH5bWWgcAAAAAdCsBNPeiKIoP3mp8/Jv+82uuBQAAAAC6VGl5WZECd6Eoit89efI7ExN/LhnMvp/6189l4B9X3qrs/+z+8Z8db7cbAAAAAOgiAmjuQqPRqB858mSt9kzSn5xLvv4byc7kavJ2Km9WDv/c4ZGRkXaHAQAAAAC6ggCaO1IUxW/MzV2YnBxJhpKLyfeT05XK9Z/64szQTJ5LhpILSSPVi9WpvzlVLpfbHRIAAAAA2OQE0LRRFMW3Tp78k4mJH03KyfXk3eSNSuUL+/ePj48naTQaR+pHap+t5cmkNymSxdQ/Vx//GY0cAAAAANDVBNCsqiiK3zl58j9NTHw5eTwpJUvJ9yuVp/fv/6vjt4bL89+cn/jTiXwxeSxZTs6k8lZl/7OKoQEAAACgewmgub2iKP759PTTtdozyUDyUfK9ZMvs7F/dt2+1eo2iKE7+/smJxkQ+n2xPriRvp/K9yuGfVwwNAAAAAN1IAN0V3mw0zn/44Y0PPvjypyaXP60oil+fmzs/OTmabE0uJ28l369Ufv7wHeXIRVEc/9fHZ7bO5JlkS3I++bNUL1en/lb7YujiYnFy8WTOZO+P7y3v0CINAAAAABubAHrz+/bCwv+0Z88Lyc4kyVdnZ79w4MBtV67UPf/ZxMRoMpzcSD5I3qxUPrd//8/eQXLdan5+fuIPJvKV5DNJT7KUnE7986sWQxdXi5NnTk7UJ5LkbPLbWfwHiyPPts+7AQAAAIB1SwC9yZ0rir89PPyl5LkkyXPJ00l/pfKFEydal63UPTdee+3ZWm1381aC71Uqj+/f/zN3GT3fVBTFyYWTE9+dyPPJcHI9OZPKm5X9n7u1GLq4Wky/MV27WsuHyZ8kSb6X/GEWf3lx5HMyaAAAAADYqHp/6Zd+qd0aNqpzRfHq8eP55jd/JEkzfU5yY2Hh/Jtvln/6p0uDg0mKonhterr/7/ydLywslJOLSSN5t1r9+b/39770la+scfy1DQ4OfmH0C7/wo78w/FvD3yy+me3JY1nYvTD7weyb33zzx5/68eHh4STFcjH95nTtci1JhpKh5MOknHyUp4qnvvYXv9bmywAAAAAA65UJ6M3sN+fn/9nExE8maUmfkwwmTyS91erlqalfm5s7Pzk50rxr4DtJo1L56sGDY2Njqx737jUajSP/5kjtmVqebt7T8M9SvVT9xt/6xqtDr85cmcmHyaXm6ptz0L+X+n9VH//aPY5gAwAAAACPlgB603qj0fgHo6N7ki0t6XNfsq1ZBp3kj5IiGU6Wkw+TtyuV/7+9O4aN8jzAOP4AaSFAuItRrMYU5UqBRGKzujSqukR4ixRFqtLB2y3dGaIOUYcsYeoSVUjlKlUeytYBdYmjKKq6JK2KOlQq0BI7wUQYcO61Q1w7cdLBHDKcjSvU1/VZv990uu/9Pi83/fXp8chrr730uJsbm5p8d3Ls8lhOJoeTXUk3GUqO9y7PJ6X3ebVBLyV/NAYNAAAAAINKgN6ZFkr51ZkzBzqdQxvX51Uzyc3k4+TJiYmXXn650Wis87j/nVLKh3/7cOyjsRxLhpLWg5eXktne59UGPZ/2nfb5XzywWA0AAAAADITdmx1gIL138eJXffV5uK8+J9mT/LXd/tHU1Kvj47Xrc5JGo3H6x6e7r3Tf+uStPN13eW8ykuxLkgwlR5ND6RzunP312b6jAAAAAMB25w3oHejvly79bnT05Jr63FgvPd+3v9vdVT89P6R8U5pfNDe8fKs3CT2VzCZXMvUzQxwAAAAAMGC8Ab3TLJTy9pr6/MRm9TnJ8sWLj7xexcXlR/7RZ5LDSZJWMpycTOuXrenr04+6BQAAAADYZgToHWWhlN+fOze6pj4PPbI+ryRzyfX339/4SC0zszOZT1Y2PrE/GU7Sa9Av5MJ7FzY+DQAAAABsOyY4dpQPJif/PDZ2qlefh5M96x1bSZaSu72ViyQvbO3PoHxZmv/q7W+s+78R71tt5P9OppJ/ZmJkYvzV8Q2OAgAAAADbiwC9c1yfnv5tq/Vi8uzGsxsrSUnu9n3//W73W1s4Az29ON2abj38bSM5sEEyn0/KvQY99YoxaAAAAAAYDCY4doiFUibffPPF5Oh69Xkl+SK5ldx4sD4vJ3PJVDLf7WYLzZW5XE4+TZbXfFuSG8mnyXzfDYeS4aSVHE/rN62+ywAAAADAdvTEZgcYDH84d67V6Rztm93of+V5Ofk8mU/m10xw7G/2BjG2xLHGsZSkJJ8k+5JnkiTPJkm+yr1LD70QvTcZufeDPXvh7Os/fX29BwMAAAAA24gJjp3g8qVLN0ZHv9cLuelbeb4fnZeS8uC9S0mz3f7J+fPZWrve3rXOSshDMTp9C9Grk9D/yDvD75wePd13PwAAAACwjQjQA2+xlHebzR/0Iu1KcjcpyXKynCz09pNXLfX2LT5LFpPZ5Hi73X7jje8+t9WrytPXp1s/b+VgkuRw8mTSSPYme3snVmP0U8nBXoa+/0L0fPKnTP3QGDQAAAAAbGsC9GBbLOWDM2ee73QOJEvJZ8l8LzrPJuntbCwmi8mhdjvJyIkTw0eOrCTPnzr1dfL00NDW1+dVZaF0S/fKtSvZnZuzN2fuzFz9+GpnqpODvSS9L9mbHFoTo5u9F6KX0v5L+/zYVr+4DQAAAAD89wTowXZrcnLP2NjnyUfJ9STt9kLSPHGiceTIcnL81KkkjaGhRrP5VKOx2cO2kbJQup9158rc7XI7yc2FmzNl5uri1U63k+HkueQ7yVAm7kyMnxjf7GEAAAAAwP+HAD3Avill+dq1meTbA5iYH1spJbvTvdud+3Lu9p7bp0csQQMAAADANiVAAwAAAABQxe7NDgAAAAAAwOMQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqEKABgAAAACgCgEaAAAAAIAqBGgAAAAAAKoQoAEAAAAAqOI/W+o++XE7EnIAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "1be35735",
   "metadata": {},
   "source": [
    "![ammonia_2.png](attachment:ammonia_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798ff281",
   "metadata": {},
   "source": [
    "## Ammonia Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af4a1c69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum energy cif: 1\n",
      "Maximum energy cif: 700\n",
      "[1. 1. 1. ... 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "%run -i Force_Heat_Maps.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b647bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ammonia_forces(n_grid=10000, n_circ=6, ammonia_angle=107.8):\n",
    "    # returns 3 arrays of each ammonia hydrogen's coordinate set and that orientations total force potential\n",
    "    x1, y1, z1, x2, y2, z2, x3, y3, z3 = iterate(n_grid, n_circ, ammonia_angle)\n",
    "    pts1 = np.column_stack((x1, y1, z1))\n",
    "    pts2 = np.column_stack((x2, y2, z2))\n",
    "    pts3 = np.column_stack((x3, y3, z3))\n",
    "    f1 = find_approx_forces(known_pts, pts1, f_t)\n",
    "    f2 = find_approx_forces(known_pts, pts2, f_t)\n",
    "    f3 = find_approx_forces(known_pts, pts3, f_t)\n",
    "    \n",
    "    ftot = f1 + f2 + f3\n",
    "\n",
    "    return pts1, pts2, pts3, ftot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e64c2ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pts1, pts2, pts3, ftot = ammonia_forces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f556e4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking lowest 1% of 50,000 values\n",
    "n = int(50000 / 500)\n",
    "min_is_ammonia = np.argpartition(ftot, n-1)[:n]\n",
    "pts1_min = pts1[min_is_ammonia]\n",
    "pts2_min = pts2[min_is_ammonia]\n",
    "pts3_min = pts3[min_is_ammonia]\n",
    "\n",
    "pts1_minT = pts1_min.T\n",
    "pts2_minT = pts2_min.T\n",
    "pts3_minT = pts3_min.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c40209b",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_ammonia_index = np.argmin(ftot)\n",
    "h1_am = pts1[min_ammonia_index]\n",
    "h2_am = pts2[min_ammonia_index]\n",
    "h3_am = pts3[min_ammonia_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51861f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mayavi.modules.orientation_axes.OrientationAxes at 0x190092ee630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualising lowest 1% of force potentials of all orientations and minimum orientation\n",
    "mlab.figure(bgcolor=(1., 1., 1.))\n",
    "mlab.points3d(grid[0], grid[1], grid[2], fc, scale_factor=0.1, scale_mode='none', colormap='viridis')\n",
    "mlab.points3d(pts1_minT[0], pts1_minT[1], pts1_minT[2], color=(0.2, 0.2, 0.2), scale_factor=0.1, opacity=1)\n",
    "mlab.points3d(pts2_minT[0], pts2_minT[1], pts2_minT[2], color=(0.2, 0.2, 0.2), scale_factor=0.1, opacity=1)\n",
    "mlab.points3d(pts3_minT[0], pts3_minT[1], pts3_minT[2], color=(0.2, 0.2, 0.2), scale_factor=0.1, opacity=1)\n",
    "mlab.points3d(h1_am[0], h1_am[1], h1_am[2], color=(1., 0., 0.), scale_factor=0.1, opacity=1)\n",
    "mlab.points3d(h2_am[0], h2_am[1], h2_am[2], color=(1., 0., 0.), scale_factor=0.1, opacity=1)\n",
    "mlab.points3d(h3_am[0], h3_am[1], h3_am[2], color=(1., 0., 0.), scale_factor=0.1, opacity=1)\n",
    "plot_axes(1.5)\n",
    "mlab.orientation_axes()\n",
    "#mlab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e990a72f",
   "metadata": {},
   "source": [
    "## Cif Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdb63250",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymatgen.io.cif import CifParser, CifWriter\n",
    "fname = 'C:\\\\Users\\\\Path\\\\NaNH2.cif'\n",
    "cif = CifParser(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf8ff044",
   "metadata": {},
   "outputs": [],
   "source": [
    "structure = cif.get_structures()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5690b239",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n",
    "rstructure = SpacegroupAnalyzer(structure).get_refined_structure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "afff3c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rstruc_coords(number_Na=16, number_H=32, number_N=16):\n",
    "    Na_x = arr.array('f', [])\n",
    "    H_x = arr.array('f', [])\n",
    "    N_x = arr.array('f', [])\n",
    "    \n",
    "    Na_y = arr.array('f', [])\n",
    "    H_y = arr.array('f', [])\n",
    "    N_y = arr.array('f', [])\n",
    "    \n",
    "    Na_z = arr.array('f', [])\n",
    "    H_z = arr.array('f', [])\n",
    "    N_z = arr.array('f', [])\n",
    "    \n",
    "    for i in range(0, number_Na):\n",
    "        Na_x.append(rstructure.sites[i].coords[0])\n",
    "    for j in range(number_Na, (number_Na+number_H+1)):\n",
    "        H_x.append(rstructure.sites[j].coords[0])\n",
    "    for k in range((number_Na+number_H+1), (number_Na+number_H+number_N)):\n",
    "        N_x.append(rstructure.sites[k].coords[0])\n",
    "        \n",
    "    for i in range(0, number_Na):\n",
    "        Na_y.append(rstructure.sites[i].coords[1])\n",
    "    for j in range(number_Na, (number_Na+number_H+1)):\n",
    "        H_y.append(rstructure.sites[j].coords[1])\n",
    "    for k in range((number_Na+number_H+1), (number_Na+number_H+number_N)):\n",
    "        N_y.append(rstructure.sites[k].coords[1])\n",
    "        \n",
    "    for i in range(0, number_Na):\n",
    "        Na_z.append(rstructure.sites[i].coords[2])\n",
    "    for j in range(number_Na, (number_Na+number_H+1)):\n",
    "        H_z.append(rstructure.sites[j].coords[2])\n",
    "    for k in range((number_Na+number_H+1), (number_Na+number_H+number_N)):\n",
    "        N_z.append(rstructure.sites[k].coords[2])\n",
    "        \n",
    "    return Na_x, H_x, N_x, Na_y, H_y, N_y, Na_z, H_z, N_z\n",
    "        \n",
    "Na_x, H_x, N_x, Na_y, H_y, N_y, Na_z, H_z, N_z = rstruc_coords()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "451a1b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select amides you wish to convert \n",
    "# rotate the ammonia/imide configuration to correct orientation within cif\n",
    "# add to imide/ammonia centre (the coordinates of the nitrogen in the amides being converted)\n",
    "# move hydrogen to new site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "725caf0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "imide_centre = np.array([1.9336, 0.0000, 5.2348])\n",
    "ammonia_centre = np.array([0.1524, 2.2513, 7.8522])\n",
    "\n",
    "new_struc = rstructure.copy()\n",
    "new_struc.sites[0].coords = [(imide_centre[0] + x_m),(imide_centre[1] + y_m),(imide_centre[2] + z_m)]\n",
    "new_struc.sites[1].coords = [(ammonia_centre[0] + h1_am[0]),(ammonia_centre[1] + h1_am[1]),(ammonia_centre[2] + h1_am[2])]\n",
    "new_struc.sites[2].coords = [(ammonia_centre[0] + h2_am[0]),(ammonia_centre[1] + h2_am[1]),(ammonia_centre[2] + h2_am[2])]\n",
    "new_struc.sites[3].coords = [(ammonia_centre[0] + h3_am[0]),(ammonia_centre[1] + h3_am[1]),(ammonia_centre[2] + h3_am[2])]\n",
    "    \n",
    "w = CifWriter(new_struc)\n",
    "file = 'C:\\\\Users\\\\Path\\\\New.cif'\n",
    "w.write_file(file)"
   ]
  }
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