molsgdemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "sys.path.append('/Users/iwatobipen/develop/chemoenv/molsg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook initialized with x3d backend.\n"
     ]
    }
   ],
   "source": [
    "from mayavi import mlab\n",
    "mlab.init_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from molsg import laplacemesh as lp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define show mol\n",
    "def plot_mol(mesh, scalars=np.zeros(1)):\n",
    "    \"\"\"Use mayavi to plot a molecule mesh.\"\"\"\n",
    "    vertices, faces = mesh\n",
    "    x, y, z = vertices[:, 0], vertices[:, 1], vertices[:, 2]\n",
    "    fig = mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))\n",
    "    mesh = mlab.triangular_mesh(x, y, z, faces, scalars=scalars)\n",
    "    mlab.show()\n",
    "    return fig, mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load molecule and plot\n",
    "mol = np.load('data/ampc/npy/actives18.pqr0.50.41.2.npy')\n",
    "eigs = lp.compute_lb_fem(vertices=mol[0], faces=mol[1], k=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "              7.07756 2.77892 5.48749,\n",
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       "            <Normal DEF=\"VTKnormals0000\" vector=\"\n",
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f,m = plot_mol(mol, scalars=eigs.phi[:, 1])\n",
    "f\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "            <Normal DEF=\"VTKnormals0000\" vector=\"\n",
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       "              \"/>\n",
       "          </IndexedFaceSet>\n",
       "        </Shape>\n",
       "      </Transform>\n",
       "    </Transform>\n",
       "  </Scene>\n",
       "</X3D>\n",
       "\n",
       "    <script type=\"text/javascript\">\n",
       "    require([\"nbextensions/mayavi/x3d/x3dom\"], function(x3dom) {\n",
       "        var x3dom_css = document.getElementById(\"x3dom-css\");\n",
       "        if (x3dom_css === null) {\n",
       "            var l = document.createElement(\"link\");\n",
       "            l.setAttribute(\"rel\", \"stylesheet\");\n",
       "            l.setAttribute(\"type\", \"text/css\");\n",
       "            l.setAttribute(\"href\", require.toUrl(\"nbextensions/mayavi/x3d/x3dom.css\"));\n",
       "            l.setAttribute(\"id\", \"x3dom-css\");\n",
       "            $(\"head\").append(l);\n",
       "        }\n",
       "        if (typeof x3dom != 'undefined') {\n",
       "            x3dom.reload();\n",
       "        }\n",
       "        else if (typeof window.x3dom != 'undefined') {\n",
       "            window.x3dom.reload();\n",
       "        }\n",
       "    })\n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<mayavi.modules.surface.Surface at 0xa46c36468>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f,m = plot_mol(mol, scalars=eigs.phi[:, 5])\n",
    "f\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate descriptor\n",
    "from scipy.spatial import distance\n",
    "\n",
    "from molsg.laplacemesh import compute_lb_fem\n",
    "from molsg.localgeometry import WKS_descriptor\n",
    "import molsg.covariance as cov\n",
    "evals = 32\n",
    "\n",
    "mol_names = sorted(os.listdir('data/ampc/npy/'))\n",
    "mols = (np.load('data/ampc/npy/{}'.format(m)) for m in mol_names)\n",
    "eigs = (compute_lb_fem(vertices=m[0], faces=m[1], k=100) for m in mols)\n",
    "wks = (WKS_descriptor(e, evals=evals) for e in eigs)\n",
    "covs = np.asarray([cov.covariance_descriptor(w).flatten() for w in wks])\n",
    "\n",
    "# use first molecule as reference\n",
    "similarity = distance.cdist(covs[:10], covs,\n",
    "                            metric='braycurtis')\n",
    "ranking = np.argsort(similarity)\n",
    "labels = np.asarray([1 if m[0] == 'a' else 0 for m in mol_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 1024)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.09230117e-05, 2.71652856e-05, 2.37224905e-05, ...,\n",
       "        1.46243371e-04, 1.51304765e-04, 1.56195547e-04],\n",
       "       [4.23007865e-05, 3.51307176e-05, 2.88876041e-05, ...,\n",
       "        8.37931399e-05, 8.68547261e-05, 8.98288537e-05],\n",
       "       [4.89053334e-05, 4.14805807e-05, 3.47848596e-05, ...,\n",
       "        8.90841519e-05, 9.20655185e-05, 9.49510127e-05],\n",
       "       ...,\n",
       "       [6.53880098e-05, 5.46076646e-05, 4.49903276e-05, ...,\n",
       "        8.51860637e-05, 8.78986016e-05, 9.05161684e-05],\n",
       "       [4.03589638e-05, 3.60930158e-05, 3.19959977e-05, ...,\n",
       "        8.99413803e-05, 9.29632995e-05, 9.59047178e-05],\n",
       "       [8.57205191e-05, 7.30392856e-05, 6.15872617e-05, ...,\n",
       "        4.91097210e-05, 5.07646684e-05, 5.23721508e-05]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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