ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from api.rest import QueryEngine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Queries\n",
    "- **band_gap_range**  \n",
    "Range of bandgaps in *eV* as a two valued list eg. [1.2, 3.1]  \n",
    "- **Formation_energy_range**  \n",
    "Range of formation energies in *meV* as two values list eg. [50, 150]\n",
    "- **elements**  \n",
    "List of elements contained in entry eg. ['Na', 'Cl']  \n",
    "note: this will return all entries that contain Na and Cl as well as any other element \n",
    "- **space_group_number**  \n",
    "integer of space group number eg. 5\n",
    "- **dimention**  \n",
    "integer of dimension of material eg. 2\n",
    "- **crystal_system**  \n",
    "crystal system as a string eg. 'Tetragonal'  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query info     band_gap_range: [1, 1.5]  formation_energy_range: [124, 150] elements: [] space_group_number: None dimension: None crystal_system: None\n",
      "Pd2S4 \t\t BandGap = \t1.1746 eV\t Formation Eneergy = \t148.0 meV\n",
      "Ge2Se2 \t\t BandGap = \t1.2107 eV\t Formation Eneergy = \t133.0 meV\n",
      "Pt4I8 \t\t BandGap = \t1.4215 eV\t Formation Eneergy = \t135.0 meV\n",
      "Pd4I8 \t\t BandGap = \t1.1512 eV\t Formation Eneergy = \t133.0 meV\n",
      "MoO3 \t\t BandGap = \t1.1381 eV\t Formation Eneergy = \t136.0 meV\n"
     ]
    }
   ],
   "source": [
    "qe = QueryEngine() \n",
    "\n",
    "a = qe.get_calculation(band_gap_range=[1,1.5], formation_energy_range=[124,150])\n",
    "\n",
    "for e in a:\n",
    "    if len(str(e).split('@')[0])< 8:\n",
    "        print(str(e).split('@')[0] + '\\t\\t BandGap = \\t' + str(round(e.band_gap,4)) + \n",
    "              ' eV\\t Formation Eneergy = \\t' + str(e.formation_energy)+ ' meV')\n",
    "    else:\n",
    "        print(str(e).split('@')[0] + '\\t BandGap = \\t' + str(round(e.band_gap,4)) +\n",
    "              ' eV\\t Formation Eneergy = \\t' + str(e.formation_energy) + ' meV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[Errno 17] File exists: 'example'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e9bb2b69a360>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'example'\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      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'example'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'example'"
     ]
    }
   ],
   "source": [
    "import os \n",
    "os.mkdir('example')\n",
    "%cd example\n",
    "for e in a:\n",
    "    os.mkdir(e.entry.name)\n",
    "    os.chdir(e.entry.name)\n",
    "    e.write()\n",
    "    os.chdir('..')\n",
    "%ls\n",
    "%cd ..\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.rmdir('example')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = a.last()\n",
    "c.get_soap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd /home/jason/dev/materialsweb\n",
    "%rm -rf formation_energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.prep_ml_formation_energy()\n",
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.entry.structure.lat_param_string()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymatgen import MPRester\n",
    "from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter\n",
    "import os\n",
    "from pymatgen.io.vasp import inputs\n",
    "os.mkdir('mldir')\n",
    "os.chdir('mldir')\n",
    "a = MPRester('7JGUQgNZyOTTp8Tc')\n",
    "entries = a.query('Cd-Te',properties = ['structure','formation_energy_per_atom'])\n",
    "#entries = a.query('Cd-Te',properties = ['formation_energy_per_atom'])\n",
    "formation_e_actual=[]\n",
    "i= 0 \n",
    "for b in entries:\n",
    "    b['structure'].to(fmt='poscar',filename=str(i)+'.poscar')\n",
    "    formation_e_actual.append(b['formation_energy_per_atom'])\n",
    "    i = i +1 \n",
    "entries = a.query('Cd',properties = ['structure','formation_energy_per_atom'])\n",
    "for b in entries:\n",
    "    b['structure'].to(fmt='poscar',filename=str(i)+'.poscar')\n",
    "    formation_e_actual.append(b['formation_energy_per_atom'])\n",
    "    i = i +1 \n",
    "entries = a.query('Te',properties = ['structure','formation_energy_per_atom'])\n",
    "for b in entries:\n",
    "    b['structure'].to(fmt='poscar',filename=str(i)+'.poscar')\n",
    "    formation_e_actual.append(b['formation_energy_per_atom'])\n",
    "    i = i +1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.poscar   21.poscar  automatminer_2020-07-31_10-38-16.log  README.md\r\n",
      "10.poscar  22.poscar  automatminer.log                      requirments.txt\r\n",
      "11.poscar  2.poscar   automl.ipynb                          \u001b[0m\u001b[01;34mscripts\u001b[0m/\r\n",
      "12.poscar  2test.p    \u001b[01;34mdocs\u001b[0m/                                 \u001b[01;34msimulation\u001b[0m/\r\n",
      "13.poscar  3.poscar   \u001b[01;34melectronic_visualization\u001b[0m/             \u001b[01;34mstatic\u001b[0m/\r\n",
      "14.poscar  4.poscar   Energy_Prediction_Tutorial.ipynb      \u001b[01;34mtemplates\u001b[0m/\r\n",
      "15.poscar  5.poscar   \u001b[01;34mexample\u001b[0m/                              temp.py\r\n",
      "16.poscar  6.poscar   fill.py                               test.p\r\n",
      "17.poscar  7.poscar   \u001b[01;32mmanage.py\u001b[0m*                            Untitled1.ipynb\r\n",
      "18.poscar  8.poscar   \u001b[01;34mmaterialsweb2\u001b[0m/                        Untitled2.ipynb\r\n",
      "19.poscar  9.poscar   ml.ipynb                              Untitled.ipynb\r\n",
      "1.poscar   \u001b[01;34maccounts\u001b[0m/  \u001b[01;34mpages\u001b[0m/\r\n",
      "20.poscar  \u001b[01;34mapi\u001b[0m/       \u001b[01;34m__pycache__\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%pwd\n",
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://10.5.46.39/static/models/Cd-Te.sav\n",
      "././0.poscar\n",
      "././1.poscar\n",
      "././2.poscar\n",
      "././3.poscar\n",
      "././4.poscar\n",
      "././5.poscar\n",
      "././6.poscar\n",
      "././7.poscar\n",
      "././8.poscar\n",
      "././9.poscar\n",
      "././10.poscar\n",
      "././11.poscar\n",
      "././12.poscar\n",
      "././13.poscar\n",
      "././14.poscar\n",
      "././15.poscar\n",
      "././16.poscar\n",
      "././17.poscar\n",
      "././18.poscar\n",
      "././19.poscar\n",
      "././20.poscar\n",
      "././21.poscar\n",
      "././22.poscar\n",
      "[-0.2345306884765801, -0.23756596240215133, -0.2517413380170951, -0.24755761868019177, -0.21340579726371633, -0.174576704233126, -0.1015552396174737, 0.004477855414455778, -0.21861336949718124, -0.21254714925762286, -0.03439547798190107, -0.09146698577491291, -0.041978300920243905, -0.05403810819080779, 0.12163482960658245, 0.27791645581802105, 0.1489733938814622, 0.15136123233625237, 0.15027820143149928, 0.142179602194758, 0.1361387019914332, 0.15660528984760114, 0.14949782832236092]\n"
     ]
    }
   ],
   "source": [
    "formation_e_predict=[]\n",
    "model=qe.get_KRR('Cd-Te')\n",
    "for i in range(0,23):\n",
    "    #print(i)\n",
    "    formation_e_predict.append(model.predict(qe.get_soap('./'+str(i)+'.poscar'))[0])\n",
    "print(formation_e_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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as7/77rt5+eWXoy5HCpgCX2QQdT9Ae+WVV0ZdkhQwBb7IIEkP+//xt3/H9Ivn8/ruZg63xqMuTQpUKIFvZvPMbJuZNZrZygzvjzCzHyff/5WZTQ+jX5Fctn37djY1NPBX3/4BVy+8iXHlMeLtnQp9iUzWZ+mYWTHwEDAX2AtsMrM17r41rdmXgGZ3/5CZLQK+C9yUbd8iuaijo4Pi4mJmzZrFM+s3UzZqDOWxrn9qqa+7Dh5n9jRdbCVDK4wZ/kVAo7vvdPc48CRQ161NHfB48vFTwJWm68glDwVBwPz583nggQcAsBGjKCstPqVNWWkxLUEiivKkwIUR+GcBe9Ke702+lrFN8qbnR4AJ3T/IzJabWYOZNTQ1NYVQmsjQSa3Zr127lgkTuv56V5aVEiQ6Tm2X6KCyrDSKEqXA5dRBW3df5e617l5bXV0ddTkifZZ+gDb9huM1VRW0xttpjbfj7icf11RVRFuwFKQwAv8dYGra8ynJ1zK2MbMSYAxwKIS+RSLX2dnJwoULPxD2AGPLY8yeNo5YSRHNrXFiJUXMnjaOseVav5ehF8bWCpuAGWZWQ1ewLwI+363NGmAJsAH4HPDv7u4h9C0SuaKiIhYuXMjNN998StindIW+Al6il3Xgu3u7ma0AngeKgdXuvsXM7gMa3H0N8Bjwj2bWCLxH138KIsNaEARs3bqVCy+8kNtvvz3qckR6Fcrmae7+LPBst9fuSXvcBtwQRl8iuSB1D9oNGzawc+dOdMxJhgPtlinST+k3HF+9evUpYX+4Nc6ug8dpCRJUlpVSU1Wh9XrJGQp8kX7oHvbpa/aHW+P8x/YmjgQJ2tudkhJjb3Mrc2ZUK/QlJ+TUaZkiue7hhx/OGPYAb+49zN7mgGIrorKslGIrYm9zwJt7D0dTrEg3muGL9MNXv/pVamtrufzyyz/wXuOBo4wZWcrI5JW1I0uLGeOlNB44ymXnTRzqUkU+QDN8kV4EQcDy5cvZu3cvxcXFGcMewDHg1LONW+MJ9jQHrN92QJumSeQU+CKnkbqC9tFHH+WXv/zladvOmDiKlrYEbYmuq2rfO97GtnePMnnMSO2UKTlBgS/Sg+7bJdx4442nbT9rylimjCuno9M5EsQ50HKCM8eW87Ezx2JmlMdKKI+VsOvg8SEagciptIYvkkFPe+OcztjyGJ+aUX3ytMxO4MOTRjN65PsbpZWVFtOsGb5ERIEvkkFrayuHDh3qc9inpG+jUFlWSry985T3tVOmREmBL5ImCAKKioqYMGECv/71rykpGfg/kZqqCl7f3Qx0zeyDRAet8XbOP2NcWOWK9IvW8EWSUss4N910E+6eVdiDdsqU3KMZvginrtnX19cT1g3ZtFOm5BLN8KXgdQ/7JUuWRF2SyKBQ4EvBW7JkicJeCoKWdKTgff3rX2fBggUsXrw468/SbpmSyzTDl4IUBAFPPPEEABdeeGFoYf/67mbi7Z26slZyUlaBb2bjzewFM9ue/JrxfDMz+1czO2xm/y+b/kTCkFqzX7x4MW+99VZon7vr4PGTV9MO9Mra1H8a2ntHBkO2M/yVwDp3nwGsSz7P5PvAF7LsSyRr3Q/Qzpw5M7TPbgkSlCV3ykwpKy2mJUj06ffrJwQZbNkGfh3wePLx48DCTI3cfR1wNMu+RLIy2GfjVJaVEiQ6Tu2zH1fWhvETgsjpZBv4k9x9X/LxfmBSNh9mZsvNrMHMGpqamrIsTeRUL774Ii+++OKgnY1TU1VBa7yd1njXbpmpxzVVFX36/dn+hCDSm17P0jGztcAZGd66K/2Ju7uZeYZ2febuq4BVALW1tVl9lkh311xzDdu2bePcc88dlM9PXVm76+BxmlvjVJaVcv4Zfb+yNvUTQnns/X+W2ntHwtRr4Lv7VT29Z2bvmtlkd99nZpOBA6FWJ5KlIAhYtGgRX/7yl/nTP/3TQQv7lGyurNXeOzLYsl3SWQOkfjZeAvw8y88TCU3qhuP/8i//wv79+6Mup1fae0cGW7YXXt0P/MTMvgS8DdwIYGa1wG3uviz5/D+ADwOjzGwv8CV3fz7LvkV6lAr7tWvXUl9fz6233hp1SX2ivXdkMGUV+O5+CLgyw+sNwLK053Oy6UekP9ra2k4Je22XINJFV9pK3onFYtTU1CjsRbrRXjqSN4Ig4NChQ0yZMoVHHnkk6nJEco5m+JIXUmv2l19+OW1tbVGXI5KTNMOXYS/9AO3q1asZOXJk1CWJ5CTN8GVY6x72/bnhuEihUeDLsPbXf/3XCnuRPtKSjgxr99xzD5/+9Kepq6uLuhSRnKcZvgw7QRBw77330tbWxrhx4xT2In2kwJdhJbVm/61vfYv169dHXY7IsKIlHRk2um+XcPXVV/f59+pesyKa4csw0T3s+3MFre4kJdJFgS/Dwu7du3njjTcGtF2C7iQl0kVLOpLTEokEJSUlnH/++TQ2NlJZWdnvz2gJEozrtnxTVlpMs2b4UmA0w5ecFQQB11xzDd/85jcBBhT2kP29ZkXyhQJfclL6Dcdramqy+qxs7zUrki+yCnwzG29mL5jZ9uTXD9yLzcw+bmYbzGyLmb1pZjdl06fkv/SwD2OLY91JSqRLtmv4K4F17n6/ma1MPv96tzatwK3uvt3MzgQ2m9nz7n44y74lD7k7n/3sZ0ML+xTdSUok+yWdOuDx5OPHgYXdG7j77919e/LxH+i60Xl1lv1KnjIzFi9erJuXiAyCbGf4k9x9X/LxfmDS6Rqb2UVADNjRw/vLgeUA06ZNy7I0GU6CIOC1117jj//4j1m8eHHU5YjkpV4D38zWAmdkeOuu9Cfu7mbmp/mcycA/AkvcvTNTG3dfBawCqK2t7fGz8tHh1jhv7j1M44GjOMaMiaOYNWVsQawzp9bsX3nlFXbu3MnkyZOjLkkkL/Ua+O5+VU/vmdm7ZjbZ3fclA/1AD+0qgV8Ad7n7xgFXm6fePnScNW+8w573AiZUxJgwKsbWPxyhJUjwqRnVeR366QdoV69erbAXGUTZruGvAVILrUuAn3dvYGYx4Gngh+7+VJb95Z3DrXFe2PouR1rjVI8eQXFREe+2nKC0uJjDrYm8vhq0e9hrP3uRwZVt4N8PzDWz7cBVyeeYWa2ZPZpscyNwGbDUzN5I/vp4lv3mjV0Hj9PR2UmRFRErLiJWUsSIkmJaggSJjk5agkTUJQ6a1atXK+xFhlBWB23d/RBwZYbXG4Blycf/BPxTNv3ks5YgwfjyGEdaEyQ6nFiJUVpiNLcmqB49Iq+vBr399tuZPXs2l156adSliBQEXWkbscqyUsZXjGDUiBKOnohzor2DY0Gcjs5OxpaX5uzVoKkdKNdvO9CvnSeDIGDp0qXs2LGDoqIihb3IEFLgR6ymqoKiIjj/jErOqargaFuCo/EOLj23alAO2A40qDN9Rn+3G06t2f/whz9k06ZNAx2CiAyQdsuMWOqy/10Hj1NSbMyaOm7Qbs6RCuryWAnjymMEiQ5e393c720G0rcbBk5+3XXweI9Xs3Y/QLto0aLsByQi/aLAzwFDddn/QII6XequUa9ub2JS5UjOGlfO6JFdxxhOt92wzsYRyQ0K/AKSzb7w6T8dTKocSdOxE/xu/1EmjBrBxNEjGFceY/yozP9pJBIJWltbFfYiEVPgF5DUvvCpmT30fV/49J8OxpTHeH13M7HiItri7RxrK+Kd5lYWfmLKKb8nCALcncrKSl5++WWKi4tDH5OI9J0O2haQbPaFbwkSlJUWn3x87sTRjCkv5b3WOKNGlvKxs8by3vH3f1JILePU1dXR2dmpsBfJAZrhF5D0A8TNrXEqy0o5/4y+HbBN/+ng2Il2xpXHKI+VMHV8BeefUYm7n1wa6r5mX1SkeYVILlDgF5iBHiCuqarg9d3NAFTEimkJ4jjG2RNGA+8vDekArUju0tRL+iT9rlHlI0pod2fa+HJGjSg5ZWlo2bJlCnuRHGXuubkLcW1trTc0NERdhvQgdYpmS5Cgsqz05LUDW7Zs4Y033uCWW26JukSRgmRmm929NtN7WtKRAUlfGgqCgB/96J/44he/yEc/+lE++tGPRlydiGSiJR3JSmrNftmyZWzevDnqckTkNBT4MmDdD9DW1mb8KVJEcoQCXwZEZ+OIDD8KfBmQDRs2sH79eoW9yDCS1UFbMxsP/BiYDvwXcKO7N3drczZdtzgsAkqB/+3uf59NvxIdd8fMuOKKK2hsbGTatGlRlyQifZTtDH8lsM7dZwDrks+72wdc4u4fBz4JrDSzM7PsVyIQBAF/9md/xjPPPAOgsBcZZrIN/Drg8eTjx4GF3Ru4e9zdTySfjgihT4lAas3+ueee48iRI1GXIyIDkG34TnL3fcnH+4FJmRqZ2VQzexPYA3zX3f/QQ7vlZtZgZg1NTU1ZliZhST9AW19fz5IlS6IuSUQGoNc1fDNbC5yR4a270p+4u5tZxst23X0PMCu5lPOMmT3l7u9maLcKWAVdV9r2oX4ZZCdOnFDYi+SJXgPf3a/q6T0ze9fMJrv7PjObDBzo5bP+YGa/AeYAT/W7WhlysViMWbNmsXjxYoW9yDCX7dYKa4AlwP3Jrz/v3sDMpgCH3D0ws3HAp4D/mWW/MsiCIGD//v3U1NTwwAMPRF2OiIQg2zX8+4G5ZrYduCr5HDOrNbNHk20+AvzKzP4TWA/8rbu/lWW/MoiCIKCuro45c+Zw/PjxqMsRkZBkNcN390PAlRlebwCWJR+/AMzKph8ZOqmwX7t2LfX19VRU9H43LBEZHnSKpJzUPey1Zi+SXxT4ctI3v/lNhb1IHtN++HLS3XffzZw5c7j22mujLkVEBoFm+AUuCAJWrlzJsWPHGDVqlMJeJI8p8AtYas3+e9/7HuvXr4+6HBEZZAr8AtX9AK1m9iL5T4FfgHQ2jkhhUuAXoP3797N161aFvUiB0Vk6BeTEiRPEYjFqamrYtm2bLqoSKTCa4ReIIAj4zGc+w5133gmgsBcpQAr8ApC+Zj9rlna5EClUCvw8pwO0IpKiwM9j7s4NN9ygsBcRQAdt85qZsWzZMm644QaFvYgo8PNREARs2LCBK664goULP3BfeREpUFrSyTOpNft58+axe/fuqMsRkRySVeCb2Xgze8HMtie/jjtN20oz22tmf5dNn9Kz9AO0//AP/8C0adOiLklEcki2M/yVwDp3nwGsSz7vybeAl7PsT3qgs3FEpDfZBn4d8Hjy8eNAxgVjM7sQmAT8W5b9SQ9+9KMfKexF5LSyPWg7yd33JR/vpyvUT2FmRcADwGK6bnTeIzNbDiwHtBzRT1/84hf5oz/6Iy688MKoSxGRHNVr4JvZWuCMDG/dlf7E3d3MPEO7O4Bn3X2vmZ22L3dfBawCqK2tzfRZ0gMzU9iLyGn1Gvju3uOs3MzeNbPJ7r7PzCYDBzI0uwSYY2Z3AKOAmJkdc/fTrfeLiEjIsl3SWQMsAe5Pfv159wbufkvqsZktBWoV9iIiQy/bg7b3A3PNbDtd6/P3A5hZrZk9mm1xIiISHnPPzaXy2tpab2hoiLoMEZFhxcw2u3ttpvd0pa2ISIFQ4IuIFAgFvohIgVDgi4gUiJw9aGtmTcDbQ9RdFXBwiPoaTPkwjnwYA+THOPJhDJAf4+jPGM529+pMb+Rs4A8lM2vo6aj2cJIP48iHMUB+jCMfxgD5MY6wxqAlHRGRAqHAFxEpEAr8LquiLiAk+TCOfBgD5Mc48mEMkB/jCGUMWsMXESkQmuGLiBQIBb6ISIEoyMDv683XzWyamf2bmf3WzLaa2fShrfT08uEm8n0Zg5l93Mw2mNkWM3vTzG6KotbuzGyemW0zs0Yz+8CW32Y2wsx+nHz/V7n29yelD+P4WvLv/5tmts7Mzo6iztPpbQxp7a43MzeznDxNsy/jMLMbk9+PLWb2RL86cPeC+wV8D1iZfLwS+G4P7V4C5iYfjwLKo659IONIvv+/gCeAv4u67v6OATgPmJF8fCawDxgbcd3FwA7gHCAG/CdwQbc2dwB/n3y8CPhx1H/eAxzHn6T+7gO359o4+jKGZLvRwMvARrruyxF57QP4XswAXgfGJZ9P7E8fBTnDpw83XzezC4ASd38BwN2PuXvr0JXYJ/lwE+y7fCYAAALcSURBVPlex+Duv3f37cnHf6DrzmoZryQcQhcBje6+093jwJN0jSVd+tieAq603u7zOfR6HYe7v5j2d38jMGWIa+xNX74XAN8Cvgu0DWVx/dCXcfx34CF3bwZw90x3GexRoQZ+rzdfp2tWedjMfmZmr5vZ982seOhK7JP+3ET+zqEsrB/68r04ycwuomv2s2OwC+vFWcCetOd7k69lbOPu7cARYMKQVNd3fRlHui8Bzw1qRf3X6xjM7BPAVHf/xVAW1k99+V6cB5xnZq+a2UYzm9efDrK9xWHOCuHm6yXAHGA2sBv4MbAUeCzcSk9vKG8iP1hCGEPqcyYD/wgscffOcKuU3pjZYqAWuDzqWvojOel5kK5/v8NdCV3LOp+m6yetl81sprsf7utvzkue/c3X9wJvuPvO5O95BriYIQ78EMYR+U3kQxgDZlYJ/AK4y903DlKp/fEOMDXt+ZTka5na7DWzEmAMcGhoyuuzvowDM7uKrv+gL3f3E0NUW1/1NobRwMeAl5KTnjOANWa2wN1z6bZ6ffle7AV+5e4JYJeZ/Z6u/wA29aWDQl3SSd18HXq4+Tpdf4BjzSy1VnwFsHUIauuPXsfh7re4+zR3n07Xss4PhzLs+6DXMZhZDHiartqfGsLaTmcTMMPMapL1LaJrLOnSx/Y54N89eaQth/Q6DjObDTwCLOjvmvEQOe0Y3P2Iu1e5+/Tkv4ONdI0ll8Ie+vZ36hm6ZveYWRVdSzw7+9xD1Eemo/hF1zrqOmA7sBYYn3y9Fng0rd1c4E3gLeD/ArGoax/IONLaLyX3ztLpdQzAYiABvJH26+M5UPs1wO/pOp5wV/K1++gKE4CRwE+BRuDXwDlR1zzAcawF3k37s18Tdc39HUO3ti+Rg2fp9PF7YXQtT21N5tKi/ny+tlYQESkQhbqkIyJScBT4IiIFQoEvIlIgFPgiIgVCgS8iUiAU+CIiBUKBLyJSIP4/a3u8FPoJroIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(formation_e_actual, formation_e_predict, alpha=0.2)\n",
    "plt.plot([-0.4, 0.4], [-0.4, 0.4], 'k--')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12654181694674277\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.sqrt(np.mean(np.subtract(formation_e_predict, formation_e_actual)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.2826777850000002   -0.2345306884765801\n",
      "-0.35400042333333354   -0.23756596240215133\n",
      "-0.28274510500000005   -0.2517413380170951\n",
      "-0.2933791050000001   -0.24755761868019177\n",
      "-0.4606972275000003   -0.21340579726371633\n",
      "-0.10257885499999997   -0.174576704233126\n",
      "0.06771613999999992   -0.1015552396174737\n",
      "0.019554669999999774   0.004477855414455778\n",
      "-0.4637142999999999   -0.21861336949718124\n",
      "-0.45802311750000024   -0.21254714925762286\n",
      "0.001414409999999977   -0.03439547798190107\n",
      "0.0   -0.09146698577491291\n",
      "0.0014720200000000183   -0.041978300920243905\n",
      "0.017789549999999932   -0.05403810819080779\n",
      "0.12278132750000026   0.12163482960658245\n",
      "0.12740174000000026   0.27791645581802105\n",
      "0.046555940000000184   0.1489733938814622\n",
      "0.0   0.15136123233625237\n",
      "2.4029999999915692e-05   0.15027820143149928\n",
      "0.04365088666666672   0.142179602194758\n",
      "0.05410132000000001   0.1361387019914332\n",
      "0.04913071499999999   0.15660528984760114\n",
      "0.04706251750000012   0.14949782832236092\n"
     ]
    }
   ],
   "source": [
    "for ab, cd in zip(formation_e_actual,formation_e_predict):\n",
    "    print(str(ab)+'   '+ str(cd))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}