On-the-fly transformations blog post

otf-blog.ipynb

{
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   "metadata": {},
   "source": [
    "# OTF blog post "
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "scrolled": true
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   "outputs": [
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      },
      "text/plain": [
       "_ColormakerRegistry()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # some attributes are missing \n",
    "import MDAnalysis as mda\n",
    "from MDAnalysis import transformations\n",
    "import nglview as nv\n",
    "\n",
    "import MDAnalysisData\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "peptide = MDAnalysisData.datasets.fetch_membrane_peptide()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/oliver/MDAnalysis_data/membrane_peptide/memb_pept.tpr\n",
      "/Users/oliver/MDAnalysis_data/membrane_peptide/memb_pept.xtc\n"
     ]
    }
   ],
   "source": [
    "print(peptide.topology)\n",
    "print(peptide.trajectory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "view = nv.show_mdanalysis(u)\n",
    "view.add_unitcell()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
    "view.control.zoom(-0.3)\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nglview.contrib.movie import MovieMaker\n",
    "movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_raw.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unwrap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from MDAnalysis import transformations\n",
    "\n",
    "# a custom atom group can be passed as an argument. In this case we will use all the atoms\n",
    "# in the Universe u\n",
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "\n",
    "ag = u.atoms\n",
    "# we define the transformation\n",
    "workflow = transformations.unwrap(ag)\n",
    "\n",
    "u.trajectory.add_transformations(workflow)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
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      "text/plain": [
       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.show_mdanalysis(u)\n",
    "view.add_unitcell()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
    "#view.control.zoom(-0.3)\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "93486664ac9444cb8347bbb2e5cdef9b",
       "version_major": 2,
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      "text/plain": [
       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_wrapped.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Center "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "\n",
    "prot = u.select_atoms(\"protein\")\n",
    "ag = u.atoms\n",
    "# we will use mass as weights for the center calculation\n",
    "workflow = (transformations.unwrap(ag),\n",
    "                   transformations.center_in_box(prot, center='mass'),\n",
    "                   transformations.wrap(ag, compound='fragments'))\n",
    "u.trajectory.add_transformations(*workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.show_mdanalysis(u)\n",
    "view.add_unitcell()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/3, 'rzyz').tolist())\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "fbcf1a66f8e54b18a177aae631230e9c",
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      "text/plain": [
       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_centered.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "\n",
    "prot = u.select_atoms(\"protein\")\n",
    "# we load another universe to define the reference\n",
    "# it uses the same input files, but this doesn't have to be always the case\n",
    "ref_u = u.copy()\n",
    "reference = ref_u.select_atoms(\"protein\")\n",
    "ag = u.atoms\n",
    "workflow = (transformations.unwrap(ag),\n",
    "                   transformations.center_in_box(prot, center='mass'),\n",
    "                   transformations.wrap(ag, compound='fragments'),\n",
    "                   transformations.fit_rot_trans(prot, reference))\n",
    "u.trajectory.add_transformations(*workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
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       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.show_mdanalysis(u)\n",
    "view.add_unitcell()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/3, 'rzyz').tolist())\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_fitted.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1562e6dc1d7439abf5ce687052c5454",
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       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = nv.show_mdanalysis(prot)\n",
    "w.add_line()\n",
    "w.control.zoom(0.5)\n",
    "w\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "670ba0b0afe241bbbac9ac1c73044be3",
       "version_major": 2,
       "version_minor": 0
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       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(w, fps=24, in_memory=True, output='peptideonly_fitted.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### xy fit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "\n",
    "prot = u.select_atoms(\"protein\")\n",
    "ref_u = u.copy()\n",
    "reference = ref_u.select_atoms(\"protein\")\n",
    "ag = u.atoms\n",
    "workflow = (transformations.unwrap(ag),\n",
    "                   transformations.center_in_box(prot),\n",
    "                   transformations.wrap(ag, compound='fragments'),\n",
    "                   transformations.fit_rot_trans(prot, reference, plane='xy', weights=\"mass\"))\n",
    "u.trajectory.add_transformations(*workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c4dd8aa4e23403c829ee7d16af3fc49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "protein_P = u.select_atoms(\"protein or name P\")\n",
    "view = nv.show_mdanalysis(protein_P)\n",
    "view.add_line()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, 0, 'rzyz').tolist())\n",
    "view\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad471e554ad8429587f4dbacfca25ee8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(view, fps=24, output='peptide_P_fitted_xy.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## custom 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def protein_up_by_2(ag):\n",
    "    def wrapped(ts):\n",
    "        # here's where the magic happens \n",
    "        # we create a numpy float32 array to avoid reduce floating\n",
    "        # point errors\n",
    "        ag.positions += np.asarray([0,0,20])\n",
    "        return ts\n",
    "    return wrapped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = mda.Universe(peptide.topology, peptide.trajectory)\n",
    "u.transfer_to_memory(step=10)\n",
    "\n",
    "ag = u.atoms\n",
    "prot = u.select_atoms(\"protein\")\n",
    "workflow = (transformations.unwrap(ag),\n",
    "                   protein_up_by_2(prot),\n",
    "                   transformations.wrap(ag, compound='fragments'))\n",
    "u.trajectory.add_transformations(*workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
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       "version_major": 2,
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      },
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       "NGLWidget(max_frame=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.show_mdanalysis(u)\n",
    "view.add_unitcell()\n",
    "view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
    "view.control.zoom(-0.3)\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "IntProgress(value=0, description='Rendering ...')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie = MovieMaker(view, fps=24, output='peptide_up2.gif')\n",
    "movie.make()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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peptide_centered.gif

peptide_fitted.gif

peptide_P_fitted_xy.gif

peptide_raw.gif

peptide_up2.gif

peptide_wrapped.gif

peptideonly_fitted.gif