{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Chromosome Simulation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial should take between 20 to 30 minutes of reading and performing simulations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chromatin Dynamics Simulations on Chromosome 10 of GM12878 Cell Line"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to import the **OpenMiChroM** module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**note**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append('../../')\n",
    "import OpenMiChroM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from OpenMiChroM.ChromDynamics import MiChroM\n",
    "from OpenMiChroM.CndbTools import cndbTools"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`MiChroM` class sets the initial parameters of the simulation:\n",
    "\n",
    "- `time_step=0.01`: set the simulation time step to perfom the integration<br>\n",
    "- `temperature=1.0`: set the temperature of your simulation<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ***************************************************************************************     \n",
      "    **** **** *** *** *** *** *** *** OpenMiChroM-1.1.1rc *** *** *** *** *** *** **** ****     \n",
      "\n",
      "         OpenMiChroM is a Python library for performing chromatin dynamics simulations.         \n",
      "                            OpenMiChroM uses the OpenMM Python API,                             \n",
      "                employing the MiChroM (Minimal Chromatin Model) energy function.                \n",
      "      The chromatin dynamics simulations generate an ensemble of 3D chromosomal structures      \n",
      "      that are consistent with experimental Hi-C maps, also allows simulations of a single      \n",
      "                 or multiple chromosome chain using High-Performance Computing                  \n",
      "                            in different platforms (GPUs and CPUs).                             \n",
      "\n",
      "         OpenMiChroM documentation is available at https://open-michrom.readthedocs.io          \n",
      "\n",
      "         OpenMiChroM is described in: Oliveira Junior, A. B & Contessoto, V, G et. al.          \n",
      "      A Scalable Computational Approach for Simulating Complexes of Multiple Chromosomes.       \n",
      "                  Journal of Molecular Biology. doi:10.1016/j.jmb.2020.10.034.                  \n",
      "               We also thank the polychrom <https://github.com/open2c/polychrom>                \n",
      "                 where part of this code was inspired - 10.5281/zenodo.3579472.                 \n",
      "\n",
      "                    Copyright (c) 2024, The OpenMiChroM development team at                     \n",
      "                                        Rice University                                         \n",
      "    ***************************************************************************************     \n"
     ]
    }
   ],
   "source": [
    "sim = MiChroM(temperature=1.0, timeStep=0.01)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are four hardware platform options to run the simulations: \n",
    "```python\n",
    "platform=\"cuda\"\n",
    "platform=\"opencl\"\n",
    "platform=\"hip\"\n",
    "platform=\"cpu\"\n",
    "```\n",
    "\n",
    "Choose accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using platform: CUDA\n"
     ]
    }
   ],
   "source": [
    "#sim.setup(platform=\"opencl\")\n",
    "sim.setup(platform=\"cuda\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the directory name in which the output of the simulation is saved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.saveFolder('output_chr10')\n",
    "saveFileName = \"traj_chr10\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to load the chromatin compartment sequence for chromosome 10 and generate an initial 3D structure to start the simulation. We can use the [createSpringSpiral](https://open-michrom.readthedocs.io/en/latest/OpenMiChroM.html#OpenMiChroM.ChromDynamics.MiChroM.createSpringSpiral) function to set the initial configuration of the polymer based in the sequence file.\n",
    "\n",
    "The first column of the sequence file should contain the locus index. The second should have the locus type annotation. A template file of the chromatin sequence of types can be found [here](https://github.com/junioreif/OpenMiChroM/blob/main/Tutorials/inputs/chr10_beads.txt).<br>\n",
    "\n",
    "The loci positions are stored in the variable **chr10** as a NumPy array $[N:3]$, where $N$ is the number of beads. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr10 = sim.createSpringSpiral(ChromSeq='inputs/chr10_beads.txt', isRing=False)\n",
    "# or write as：\n",
    "# chr10 = sim.initStructure(mode='auto', CoordFiles=None, ChromSeq='inputs/chr10_beads.txt', isRing=False, chromosome=None)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the position of the first five beads:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[410.61654383 221.72955137 221.17249751]\n",
      " [410.62605506 222.10093022 222.10093022]\n",
      " [410.61654383 222.47230906 223.02936292]\n",
      " [410.5880139  222.84357838 223.95743532]\n",
      " [410.54047655 223.21462872 224.88478724]]\n"
     ]
    }
   ],
   "source": [
    "print(chr10[:5])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial structure should then be loaded into the `sim` object.\n",
    "\n",
    "The option `center=True` moves your system to the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.loadStructure(chr10, center=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial 3D chromosome structure can be saved in [.ndb file format](https://ndb.rice.edu/ndb-format). The file is stored in the path given in `saveFolder`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim.saveStructure(mode='ndb') # need sim.createSimulation first, but before that forces should be set"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to add the force field in the simulation object `sim`.\n",
    "\n",
    "In this tutorial, the forces can be divided into two sets:\n",
    "\n",
    "**MiChroM Homopolymer (Bonded) Potentials**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addFENEBonds(kFb=30.0)\n",
    "sim.addAngles(kA=2.0)\n",
    "sim.addRepulsiveSoftCore(eCut=4.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MiChroM Non-Bonded Potentials**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addTypetoType(mu=3.22, rc=1.78)\n",
    "sim.addIdealChromosome(mu=3.22, rc=1.78, dinit=3, dend=500)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last potential adds a spherical constrain to collapse the initial structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addFlatBottomHarmonic(kR=5*10**-3, nRad=15.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run a short simulation to generate a collapsed structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "block = 3*10**2\n",
    "n_blocks = 2*10**3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two variables control the chromatin dynamics simulation steps:\n",
    "\n",
    "`block`: The number of time-steps performed in each cycle (or block)</br>\n",
    "`n_blocks`: The number of cycles (or blocks) simulated. \n",
    "\n",
    "The initial collapse simulation will run for  $3\\times10^2 \\times  2\\times10^3 = 6\\times10^5$ time-steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FENEBond was added\n",
      "AngleForce was added\n",
      "RepulsiveSoftCore was added\n",
      "TypetoType was added\n",
      "IdealChromosome was added\n",
      "FlatBottomHarmonic was added\n",
      "Setting positions... loaded!\n",
      "Setting velocities... loaded!\n",
      "Context created!\n",
      "\n",
      "Simulation name: OpenMiChroM\n",
      "Number of beads: 2712, Number of chains: 1\n",
      "Potential energy: 64.29285, Kinetic Energy: 1.46175 at temperature: 1.0\n",
      "\n",
      "Potential energy per forceGroup:\n",
      "                                  Values\n",
      "FENEBond                   55318.664162\n",
      "AngleForce                     0.989383\n",
      "RepulsiveSoftCore              0.000000\n",
      "TypetoType                  -222.474259\n",
      "IdealChromosome               -1.086155\n",
      "FlatBottomHarmonic        119266.123097\n",
      "Potential Energy (total)  174362.216227\n"
     ]
    }
   ],
   "source": [
    "sim.createSimulation()\n",
    "sim.saveStructure(mode='ndb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\"Progress (%)\"\t\"Step\"\t\"Speed (ns/day)\"\t\"Time Remaining\"\n",
      "1.7%\t10000\t--\t--\n",
      "3.3%\t20000\t4.94e+03\t1:41\n",
      "5.0%\t30000\t4.7e+03\t1:44\n",
      "6.7%\t40000\t4.51e+03\t1:47\n",
      "8.3%\t50000\t4.37e+03\t1:48\n",
      "10.0%\t60000\t4.28e+03\t1:49\n",
      "11.7%\t70000\t4.18e+03\t1:49\n",
      "13.3%\t80000\t4.1e+03\t1:49\n",
      "15.0%\t90000\t4.04e+03\t1:48\n",
      "16.7%\t100000\t4e+03\t1:47\n",
      "18.3%\t110000\t3.96e+03\t1:46\n",
      "20.0%\t120000\t3.94e+03\t1:45\n",
      "21.7%\t130000\t3.9e+03\t1:44\n",
      "23.3%\t140000\t3.86e+03\t1:42\n",
      "25.0%\t150000\t3.84e+03\t1:41\n",
      "26.7%\t160000\t3.83e+03\t1:39\n",
      "28.3%\t170000\t3.82e+03\t1:37\n",
      "30.0%\t180000\t3.81e+03\t1:35\n",
      "31.7%\t190000\t3.8e+03\t1:33\n",
      "33.3%\t200000\t3.79e+03\t1:31\n",
      "35.0%\t210000\t3.79e+03\t1:29\n",
      "36.7%\t220000\t3.78e+03\t1:26\n",
      "38.3%\t230000\t3.77e+03\t1:24\n",
      "40.0%\t240000\t3.76e+03\t1:22\n",
      "41.7%\t250000\t3.76e+03\t1:20\n",
      "43.3%\t260000\t3.75e+03\t1:18\n",
      "45.0%\t270000\t3.75e+03\t1:16\n",
      "46.7%\t280000\t3.75e+03\t1:13\n",
      "48.3%\t290000\t3.74e+03\t1:11\n",
      "50.0%\t300000\t3.74e+03\t1:09\n",
      "51.7%\t310000\t3.73e+03\t1:07\n",
      "53.3%\t320000\t3.73e+03\t1:04\n",
      "55.0%\t330000\t3.73e+03\t1:02\n",
      "56.7%\t340000\t3.73e+03\t1:00\n",
      "58.3%\t350000\t3.72e+03\t0:58\n",
      "60.0%\t360000\t3.72e+03\t0:55\n",
      "61.7%\t370000\t3.72e+03\t0:53\n",
      "63.3%\t380000\t3.72e+03\t0:51\n",
      "65.0%\t390000\t3.72e+03\t0:48\n",
      "66.7%\t400000\t3.72e+03\t0:46\n",
      "68.3%\t410000\t3.72e+03\t0:44\n",
      "70.0%\t420000\t3.72e+03\t0:41\n",
      "71.7%\t430000\t3.72e+03\t0:39\n",
      "73.3%\t440000\t3.72e+03\t0:37\n",
      "75.0%\t450000\t3.72e+03\t0:34\n",
      "76.7%\t460000\t3.71e+03\t0:32\n",
      "78.3%\t470000\t3.71e+03\t0:30\n",
      "80.0%\t480000\t3.71e+03\t0:27\n",
      "81.7%\t490000\t3.71e+03\t0:25\n",
      "83.3%\t500000\t3.71e+03\t0:23\n",
      "85.0%\t510000\t3.71e+03\t0:20\n",
      "86.7%\t520000\t3.71e+03\t0:18\n",
      "88.3%\t530000\t3.71e+03\t0:16\n",
      "90.0%\t540000\t3.71e+03\t0:13\n",
      "91.7%\t550000\t3.71e+03\t0:11\n",
      "93.3%\t560000\t3.7e+03\t0:09\n",
      "95.0%\t570000\t3.7e+03\t0:07\n",
      "96.7%\t580000\t3.7e+03\t0:04\n",
      "98.3%\t590000\t3.7e+03\t0:02\n",
      "100.0%\t600000\t3.7e+03\t0:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'for _ in range(n_blocks):\\n    sim.run(nsteps = block, checkSystem=True, blockSize=block) '"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since there's no need to record xyz position, just run that is ok\n",
    "sim.run(nsteps = n_blocks*block, checkSystem = True, report = True,blockSize = block)\n",
    "\n",
    "'''for _ in range(n_blocks):\n",
    "    sim.run(nsteps = block, checkSystem=True, blockSize=block) '''"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Details about the output of each simulation block:\n",
    "\n",
    "- `bl=0`: index number of the simulated block. The parameter `increment=False` is used to ignore the steps counting.<br>\n",
    "- `pos[1]=[X,Y,Z]`: spatial position for the locus 1. <br>\n",
    "- `dr=1.26`: average of the loci displacements in each block (in units of sigma). <br>\n",
    "- `t=0`: current simulation time. <br>\n",
    "- `kin=1.5`: kinetic energy of the system (reduced units). <br>\n",
    "- `pot=19.90`: total potential energy of the system (reduced units). <br>\n",
    "- `RG=7.654`: radius of gyration at the end of the simulated block. <br>\n",
    "- `SPS=12312`: steps per second of each block."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The radius of gyration is a good parameter to check the performance of the collapse.(But the function to calculate that is deleted)\n",
    "If the chromosome polymer is not collapsed, it is necessary to rerun the initial collapse steps. We can also save the structure for inspection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(sim.chromRG()) # chromRG function is already removed\n",
    "sim.saveStructure(mode='ndb', fileName = saveFileName)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The structure can also be saved using stardard file formats used for macromolecules, as the `pdb` and `gro` formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.saveStructure(mode='gro', fileName=saveFileName)\n",
    "sim.saveStructure(mode='pdb', fileName=saveFileName)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to remove the spherical constrain force to run the production simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed FlatBottomHarmonic from the system!\n"
     ]
    }
   ],
   "source": [
    "sim.removeFlatBottomHarmonic()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If necessary, one could remove any of the forces applied in the system. To see the forces in the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FENEBond': <openmm.openmm.CustomBondForce; proxy of <Swig Object of type 'OpenMM::CustomBondForce *' at 0x000002492838F6F0> >,\n",
       " 'AngleForce': <openmm.openmm.CustomAngleForce; proxy of <Swig Object of type 'OpenMM::CustomAngleForce *' at 0x000002491CC8CF30> >,\n",
       " 'RepulsiveSoftCore': <openmm.openmm.CustomNonbondedForce; proxy of <Swig Object of type 'OpenMM::CustomNonbondedForce *' at 0x000002492838F840> >,\n",
       " 'TypetoType': <openmm.openmm.CustomNonbondedForce; proxy of <Swig Object of type 'OpenMM::CustomNonbondedForce *' at 0x000002492838F990> >,\n",
       " 'IdealChromosome': <openmm.openmm.CustomNonbondedForce; proxy of <Swig Object of type 'OpenMM::CustomNonbondedForce *' at 0x000002491CC8C900> >}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.forceDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim.removeForce(forceName=\"TypetoType\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the production simulation, it is necessary to initialize the .cndb file to save the chromatin dynamics trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.createReporters(statistics=True, traj=True, outputName = saveFileName, trajFormat=\"cndb\", energyComponents=True, interval=5*10**2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the parameters of the production simulation:\n",
    "\n",
    "$block = 5\\times10^2$ </br> \n",
    "$n\\_blocks = 2\\times10^3$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "block = 5*10**2\n",
    "n_blocks = 2*10**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101.7%\t610000\t3.35e+03\t23:59:58\n",
      "103.3%\t620000\t3.36e+03\t23:59:55\n",
      "105.0%\t630000\t3.36e+03\t23:59:53\n",
      "106.7%\t640000\t3.36e+03\t23:59:50\n",
      "108.3%\t650000\t3.37e+03\t23:59:48\n",
      "110.0%\t660000\t3.37e+03\t23:59:45\n",
      "111.7%\t670000\t3.37e+03\t23:59:43\n",
      "113.3%\t680000\t3.37e+03\t23:59:40\n",
      "115.0%\t690000\t3.38e+03\t23:59:37\n",
      "116.7%\t700000\t3.38e+03\t23:59:35\n",
      "118.3%\t710000\t3.38e+03\t23:59:32\n",
      "120.0%\t720000\t3.38e+03\t23:59:30\n",
      "121.7%\t730000\t3.39e+03\t23:59:27\n",
      "123.3%\t740000\t3.39e+03\t23:59:25\n",
      "125.0%\t750000\t3.39e+03\t23:59:22\n",
      "126.7%\t760000\t3.39e+03\t23:59:20\n",
      "128.3%\t770000\t3.4e+03\t23:59:17\n",
      "130.0%\t780000\t3.4e+03\t23:59:15\n",
      "131.7%\t790000\t3.4e+03\t23:59:12\n",
      "133.3%\t800000\t3.4e+03\t23:59:10\n",
      "135.0%\t810000\t3.41e+03\t23:59:07\n",
      "136.7%\t820000\t3.41e+03\t23:59:05\n",
      "138.3%\t830000\t3.41e+03\t23:59:02\n",
      "140.0%\t840000\t3.41e+03\t23:59:00\n",
      "141.7%\t850000\t3.41e+03\t23:58:57\n",
      "143.3%\t860000\t3.42e+03\t23:58:55\n",
      "145.0%\t870000\t3.42e+03\t23:58:52\n",
      "146.7%\t880000\t3.42e+03\t23:58:50\n",
      "148.3%\t890000\t3.42e+03\t23:58:47\n",
      "150.0%\t900000\t3.43e+03\t23:58:45\n",
      "151.7%\t910000\t3.43e+03\t23:58:42\n",
      "153.3%\t920000\t3.43e+03\t23:58:40\n",
      "155.0%\t930000\t3.43e+03\t23:58:37\n",
      "156.7%\t940000\t3.43e+03\t23:58:35\n",
      "158.3%\t950000\t3.43e+03\t23:58:32\n",
      "160.0%\t960000\t3.43e+03\t23:58:30\n",
      "161.7%\t970000\t3.44e+03\t23:58:27\n",
      "163.3%\t980000\t3.44e+03\t23:58:25\n",
      "165.0%\t990000\t3.44e+03\t23:58:23\n",
      "166.7%\t1000000\t3.44e+03\t23:58:20\n",
      "168.3%\t1010000\t3.44e+03\t23:58:18\n",
      "170.0%\t1020000\t3.44e+03\t23:58:15\n",
      "171.7%\t1030000\t3.44e+03\t23:58:13\n",
      "173.3%\t1040000\t3.45e+03\t23:58:10\n",
      "175.0%\t1050000\t3.45e+03\t23:58:08\n",
      "176.7%\t1060000\t3.45e+03\t23:58:05\n",
      "178.3%\t1070000\t3.45e+03\t23:58:03\n",
      "180.0%\t1080000\t3.45e+03\t23:58:00\n",
      "181.7%\t1090000\t3.45e+03\t23:57:58\n",
      "183.3%\t1100000\t3.45e+03\t23:57:55\n",
      "185.0%\t1110000\t3.45e+03\t23:57:53\n",
      "186.7%\t1120000\t3.45e+03\t23:57:50\n",
      "188.3%\t1130000\t3.45e+03\t23:57:48\n",
      "190.0%\t1140000\t3.46e+03\t23:57:45\n",
      "191.7%\t1150000\t3.46e+03\t23:57:43\n",
      "193.3%\t1160000\t3.46e+03\t23:57:41\n",
      "195.0%\t1170000\t3.46e+03\t23:57:38\n",
      "196.7%\t1180000\t3.46e+03\t23:57:36\n",
      "198.3%\t1190000\t3.46e+03\t23:57:33\n",
      "200.0%\t1200000\t3.46e+03\t23:57:31\n",
      "201.7%\t1210000\t3.46e+03\t23:57:28\n",
      "203.3%\t1220000\t3.46e+03\t23:57:26\n",
      "205.0%\t1230000\t3.47e+03\t23:57:23\n",
      "206.7%\t1240000\t3.47e+03\t23:57:21\n",
      "208.3%\t1250000\t3.47e+03\t23:57:19\n",
      "210.0%\t1260000\t3.47e+03\t23:57:16\n",
      "211.7%\t1270000\t3.47e+03\t23:57:14\n",
      "213.3%\t1280000\t3.47e+03\t23:57:11\n",
      "215.0%\t1290000\t3.47e+03\t23:57:09\n",
      "216.7%\t1300000\t3.47e+03\t23:57:06\n",
      "218.3%\t1310000\t3.48e+03\t23:57:04\n",
      "220.0%\t1320000\t3.48e+03\t23:57:02\n",
      "221.7%\t1330000\t3.48e+03\t23:56:59\n",
      "223.3%\t1340000\t3.48e+03\t23:56:57\n",
      "225.0%\t1350000\t3.48e+03\t23:56:54\n",
      "226.7%\t1360000\t3.48e+03\t23:56:52\n",
      "228.3%\t1370000\t3.48e+03\t23:56:49\n",
      "230.0%\t1380000\t3.48e+03\t23:56:47\n",
      "231.7%\t1390000\t3.48e+03\t23:56:45\n",
      "233.3%\t1400000\t3.48e+03\t23:56:42\n",
      "235.0%\t1410000\t3.48e+03\t23:56:40\n",
      "236.7%\t1420000\t3.49e+03\t23:56:37\n",
      "238.3%\t1430000\t3.49e+03\t23:56:35\n",
      "240.0%\t1440000\t3.49e+03\t23:56:32\n",
      "241.7%\t1450000\t3.49e+03\t23:56:30\n",
      "243.3%\t1460000\t3.49e+03\t23:56:28\n",
      "245.0%\t1470000\t3.49e+03\t23:56:25\n",
      "246.7%\t1480000\t3.49e+03\t23:56:23\n",
      "248.3%\t1490000\t3.49e+03\t23:56:20\n",
      "250.0%\t1500000\t3.49e+03\t23:56:18\n",
      "251.7%\t1510000\t3.49e+03\t23:56:15\n",
      "253.3%\t1520000\t3.5e+03\t23:56:13\n",
      "255.0%\t1530000\t3.5e+03\t23:56:11\n",
      "256.7%\t1540000\t3.5e+03\t23:56:08\n",
      "258.3%\t1550000\t3.5e+03\t23:56:06\n",
      "260.0%\t1560000\t3.5e+03\t23:56:03\n",
      "261.7%\t1570000\t3.5e+03\t23:56:01\n",
      "263.3%\t1580000\t3.5e+03\t23:55:59\n",
      "265.0%\t1590000\t3.5e+03\t23:55:56\n",
      "266.7%\t1600000\t3.5e+03\t23:55:54\n"
     ]
    }
   ],
   "source": [
    "for _ in range(n_blocks):\n",
    "    sim.run(block) \n",
    "    # sim.saveStructure() # comment that this the structure will be automatically saved in the reporter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the simulation is completed, it is necessary to close the .cndb file to avoid losing the trajectory data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim.storage[0].close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation should generate the `traj_chr10_0.cndb` trajectory file in the output_chr10 folder. This file contains 2000 frames (one snapshot per block). In the new version, there's no code to set the name of the cndb file!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trajectory analysis using cndbTools"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`cndbTools` is a class that allows analyses in the chromatin dynamics trajectories using the binary format [.cndb](https://ndb.rice.edu/ndb-format) (compact ndb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cndbTools = cndbTools()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the cndb file in the variable `chr10_traj`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr10_traj = cndbTools.load('output_chr10/traj_chr10_0.cndb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<OpenMiChroM.CndbTools.cndbTools object at 0x24938dc2880>\n",
      "Cndb file has 2000 frames, with 2712 beads and {b'NA', b'A1', b'A2', b'B3', b'B2', b'B1'} types \n"
     ]
    }
   ],
   "source": [
    "print(chr10_traj) # Print the information of the cndb trajectory."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the loci XYZ position over the simulated 2000 frames and save in the variable `chr10_xyz`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr10_xyz = cndbTools.xyz(frames=range(0,2000,1), XYZ=[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max([int(key) for key in chr10_traj.cndb.keys() if key != 'types'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable `chr10_xyz` allows the cndbTools to perform several analyses.\n",
    "In this example, the radius of gyration can be obtained as a function of the simulated frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Simulation Frames')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "\n",
    "chr10_RG = cndbTools.compute_RG(chr10_xyz)\n",
    "plt.plot(chr10_RG)\n",
    "plt.ylabel(r'Radius of Gyration ($\\sigma$)',fontsize=11)\n",
    "plt.xlabel(r'Simulation Frames',fontsize=11)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`cndbTools` allows the selection of beads to compute the analyses. An example is the Radial Distribution Probability (RDP) for each chromatin subcompartments A1 and B1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr10_A1 = cndbTools.xyz(frames=range(0,2000,1), beadSelection=chr10_traj.dictChromSeq[b'A1'], XYZ=[0,1,2]) # revision:change frame list to numpy array\n",
    "chr10_B1 = cndbTools.xyz(frames=range(0,2000,1), beadSelection=chr10_traj.dictChromSeq[b'B1'], XYZ=[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing RDP...\n"
     ]
    }
   ],
   "source": [
    "print(\"Computing RDP...\")\n",
    "r_A1, RDP_chr10_A1 = cndbTools.compute_RDP(chr10_A1, radius=15.0, bins=200)\n",
    "r_B1, RDP_chr10_B1 = cndbTools.compute_RDP(chr10_B1, radius=15.0, bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005, 15.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r_A1, RDP_chr10_A1, color='red', label='A')\n",
    "plt.plot(r_B1, RDP_chr10_B1, color='blue', label='B')\n",
    "plt.xlabel(r'r ($\\sigma$)', fontsize=11,fontweight='normal', color='k')\n",
    "plt.ylabel(r'$\\rho(r)/N_{type}$', fontsize=11,fontweight='normal', color='k')\n",
    "plt.legend()\n",
    "plt.gca().set_xlim([1/200,15.0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `cndbTools` to generate the *in silico* Hi-C map (contact probability matrix).\n",
    "\n",
    "In this tutorial, the trajectory contains 2,000 snapshots of chromosome 10 of the GM12878 cell line. For this set of structures, we expect the *in silico* Hi-C to not be fully converged due to inadequate sampling. \n",
    "To produce a converged map, it is recommended to simulate around 20 replicas with 10,000 frames on each, which generates an ensemble of 200,000 chromosome structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating the contact probability matrix...\n",
      "Reading frame 0 of 2000\n",
      "Reading frame 500 of 2000\n",
      "Reading frame 1000 of 2000\n",
      "Reading frame 1500 of 2000\n"
     ]
    }
   ],
   "source": [
    "print(\"Generating the contact probability matrix...\")\n",
    "chr10_sim_HiC = cndbTools.traj2HiC(chr10_xyz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13566bf73a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(chr10_sim_HiC, norm=mpl.colors.LogNorm(vmin=0.001, vmax=chr10_sim_HiC.max()),cmap=\"Reds\")  \n",
    "plt.colorbar()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the chromosome's 3D structures in the standard visualization softwares for macromolecules, there are available scripts for converting the `ndb`/`cndb` file format to `.pdb` and `.gro`. For details, please check the [Nucleome Data Bank](https://ndb.rice.edu/ndb-format).\n",
    "\n",
    "The `ndb` plugin for visualizing the chromatin dynamics trajectories in VMD/Chimera/Pymol is under development."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "openmm_env",
   "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": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}