{
 "cells": [
  {
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   "cell_type": "markdown",
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   "source": [
    "### Temporally Correlated Active Noise Simulation Tutorial\n",
    "\n",
    "#### Introduction\n",
    "\n",
    "This tutorial is aimed at introducing nonequilibrium activity to OpenMiChroM simulations. First, we will go through a very brief introduction of the theoretical idea, then, the simulation procedures are discussed.\n",
    "\n",
    "##### Theory\n",
    "\n",
    "Activity, such as originating from molecular motors applying forces on the polymer, is modeled as \n",
    "an athermal drive experienced by each coarse-grained monomer. Since motors typically exhibit on/off dynamics, the athermal force is expected to be correlated in time, as opposed to the thermal noise\n",
    "that is temporally uncorrelated. To model activity, we add a novel noise-like term to the forces associated with a monomer, such that the equation of motion of the $n$-th monomer reads as follows:\n",
    "\n",
    "$$\n",
    "\\gamma \\frac{dr_n}{dt} = -\\nabla_n U + \\xi^{\\rm therm}_n + f^{\\rm act}_n\n",
    "$$\n",
    "\n",
    "where $\\gamma$ is the damping coefficient and $U$ is the passive potential, like the harmonic bonds joining neighboring monomers in a polymer. \n",
    "$\\xi^{\\rm therm}_n$ is the the thermal noise that is uncorrelated over time as well as across the chain:\n",
    "\n",
    "$$\n",
    "\\langle \\xi^{\\rm therm}_n(t)\\rangle=0\\quad \\text{and} \\quad \\langle \\xi^{\\rm therm}_n(t)\\xi^{\\rm therm}_m(t^\\prime)\\rangle = 2\\gamma T\\delta_{mn}\\delta(t-t^\\prime).\n",
    "$$\n",
    "\n",
    "The active noise kicks are given by $f^{\\rm act}(t)$, which are correlated over time and their correlation decays over a characteristic time scale $\\tau$:\n",
    "\n",
    "$$\n",
    "\\langle f^{\\rm act}_n  \\rangle=0 \\quad \\text{and} \\quad \\langle f^{\\rm act}_n(t)f^{\\rm act}_m(t^\\prime)  \\rangle=F^2e^{-|t-t^\\prime|/\\tau}\\delta_{mn}\n",
    "$$\n",
    "\n",
    "The dynamics of the active force of the $n$-th monomer is governed by the following equation:\n",
    "\n",
    "$$\n",
    "\\tau \\frac{df^{\\rm act}_n}{dt} = -f^{\\rm act}_n + \\sqrt{2\\gamma \\theta_a}\\eta(t)\n",
    "$$\n",
    "\n",
    "where $\\theta_a\\equiv F^2\\tau/\\gamma$ is the active temperature-like quantity, and $\\eta(t)$ is a delta-correlated stationary Gaussian process.\n",
    "\n",
    "\n",
    "##### Brownian Dynamics\n",
    "Brownian dynamics only has forces and displacements, and no velocities. The positions after time $\\Delta t$, given by $x(t+\\Delta t)$ are computed based on the current forces $h(t)$ and positions $x(t)$.\n",
    "\n",
    "$$\n",
    "x_n(t+\\Delta t)=x_n(t)+\\frac{h_n(t)}{\\gamma}\\Delta t+\\sqrt{\\frac{2T}{\\gamma}\\Delta t}\\mathcal{N}(0,1)\n",
    "$$\n",
    "\n",
    "where the last term is the thermal kick at temperature $T$ with $\\mathcal{N}(0,1)$ representing a random Normal variable with zero mean and unit standard deviation. The net force experienced by the $n$-th monomer at time step $t$ is given by $h_n(t)$.\n",
    "\n",
    "$$\n",
    "h_n(t)= -\\frac{\\partial}{\\partial x}\\sum_{m}U(|x_n(t)-x_m(t)|)+f^{\\rm act}_n(t)\n",
    "$$\n",
    "\n",
    "The net force for a monomer $h(t)$, depends on the active force of the monomer $f^{\\rm act}(t)$, which is related to the active force in the previous time step $f^{\\rm act}(t-\\Delta t)$.\n",
    "The active force on the $n$-th monomer at time $t$ is obtained as follows:\n",
    "\n",
    "$$\n",
    "f^{\\rm act}_n(t)=f^{\\rm act}_n(t-\\Delta t)e^{-\\Delta t/\\tau}+{F}\\sqrt{1-e^{-2\\Delta t/\\tau}}\\mathcal{\\tilde{N}}(0,1)\n",
    "$$\n",
    "\n",
    "\n",
    "where $\\mathcal{\\tilde{N}}(0,1)$ is  a random Normal variable that is different from the thermal one. \n",
    "\n",
    "Next is a step-by-step guidance on how to setup and run OpenMiChroM simulations with correlated active noise. Users not familiar with running OpenMiChroM simulations, would greatly benefit from first going through the practice of running equilibrium OpenMiChroM simulations (see https://open-michrom.readthedocs.io/en/latest/Tutorials/Tutorial_Single_Chromosome.html). For users already familiar with OpenMiChroM, it is still the same simulation object that is created and basic steps of running the simulations are the same. The main differences are two fold: \n",
    "1. Adding activity using a function **addCorrelatedNoise()**. One should make sure to add activity as the first force field. It is extremely important that the active force is assigned a force-group \"0\".\n",
    "2. Using a custom integrator **ActiveBrownianIntegrator** (see Integrator.py) that takes care of the active force which is hard-coded to be the force-group \"0\". Hence the catutionary statement above.\n",
    "\n",
    "Hope you follow along!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulations\n",
    "##### Import modules\n",
    "\n",
    "Active functions are \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# revised to be compatible with OpenMiChroM 1.1.0 version\n",
    "import sys \n",
    "sys.path.append('../../')\n",
    "import OpenMiChroM\n",
    "from OpenMiChroM.ChromDynamics import MiChroM\n",
    "from OpenMiChroM.Integrators import ActiveBrownianIntegrator\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Initialize simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "Using platform: CUDA\n"
     ]
    }
   ],
   "source": [
    "# Instantiate MiChroM object\n",
    "sim = MiChroM(name='active_Rouse_sim', temperature=0.2, timeStep=0.001, collisionRate=1.0)\n",
    "\n",
    "# specify platform and output destination\n",
    "sim.setup(platform=\"cuda\")\n",
    "sim.saveFolder('./out')\n",
    "active_correaltion_time = 5.0\n",
    "#specify the integrator for correlated noise\n",
    "#note that the force-group \"0\" is associated with the correlated active noise\n",
    "sim.integrator = ActiveBrownianIntegrator(timestep=sim.timeStep, temperature=sim.temperature, \n",
    "                                        collision_rate=sim.collisionRate, corr_time=active_correaltion_time)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load initial structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate a test sequence file of size n\n",
    "n=10\n",
    "with open('test_seq.txt','w') as seq_file:\n",
    "    for ii in range(n):\n",
    "        seq_file.write(f'{ii+1} A1\\n')\n",
    "\n",
    "# create a random configuration with the sequence file\n",
    "init_struct = sim.createRandomWalk(ChromSeq='test_seq.txt')\n",
    "sim.loadStructure(init_struct, center=True)\n",
    "\n",
    "#uncomment below to save the loaded structure\n",
    "#  sim.saveStructure(mode = 'auto')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add forces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            ==================================\n",
      "            Active Monomers (correlated noise) added.\n",
      "            Active correlation time: 5.0\n",
      "            Total number of active monomers: 10\n",
      "            Total number of monomers: 10\n",
      "            ==================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# For the purpose of the simualtion we will consider a simple simulation of an active Rouse chain - harmonically bonded monomers with zero rest length\n",
    "# the good things about active Rouse chain is that the dynamics may be calculated analytically and compared for verification.\n",
    "# Any force-field available for OpenMiChroM, like self-avoidance, may be used. \n",
    "# However, since Brownian Dynamics does not have momentum, it is prudent to make sure the time step is small enough for steep potentials. \n",
    "# Using too large a time step with steep (or worse, diverging!) potentials will generate huge forces, possibly leading to explosion! wear your safety goggles!\n",
    "\n",
    "# Now the force-fields(must be determined before sim.CreateSimulation()):\n",
    "# first, add correlated noise. \n",
    "# IMPORTANT: correlated noise should always be added as the *first* force-field.\n",
    "# This is because force-group \"0\" is hard-coded as the active force within ActiveBrownianIntegrator\n",
    "# act_seq contains the active force values F for each monomer. It needs to be the same size as the total number of monomers\n",
    "# the values F_i in the list need not be the same, they can be heterogenous depicting varied motor activity\n",
    "# below we consider a homogenously active polymer\n",
    "F = 0.5\n",
    "sim.addCorrelatedNoise(act_seq=F*np.ones(sim.N))\n",
    "\n",
    "# add Harmonic bonds between monomers of the chain. Use sim.chains to get the number of chains.\n",
    "# setting the rest length to zero since the analytical form is easier to calculate that way.\n",
    "kb=5.0\n",
    "sim.addHarmonicBonds(bondStiffness=kb, equilibriumDistance=0.0)\n",
    "\n",
    "# adding soft-core self-avoidance \n",
    "# repulsion is modeled using a sigmoid function with a finite overlap energy Ecut\n",
    "# sim.addSelfAvoidance(Ecut=4.0, k_rep=20.0, r0=1.0)\n",
    "\n",
    "# half-harmonic restraint as confinement\n",
    "# sim.addFlatBottomHarmonic(kr=10.0, n_rad=20.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Run intial collapse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ActiveForce was added\n",
      "HarmonicBond was added\n",
      "Setting positions... loaded!\n",
      "Setting velocities... loaded!\n",
      "Context created!\n",
      "\n",
      "Simulation name: active_Rouse_sim\n",
      "Number of beads: 10, Number of chains: 1\n",
      "Potential energy: 2.25000, Kinetic Energy: 0.56679 at temperature: 0.2\n",
      "\n",
      "Potential energy per forceGroup:\n",
      "                                 Values\n",
      "ActiveForce              -2.980232e-08\n",
      "HarmonicBond              2.250000e+01\n",
      "Potential Energy (total)  2.250000e+01\n",
      "#\"Progress (%)\"\t\"Step\"\t\"Speed (ns/day)\"\t\"Time Remaining\"\n",
      "10.0%\t3000\t--\t--\n",
      "20.0%\t6000\t545\t0:03\n",
      "30.0%\t9000\t548\t0:03\n",
      "40.0%\t12000\t545\t0:02\n",
      "50.0%\t15000\t545\t0:02\n",
      "60.0%\t18000\t545\t0:01\n",
      "70.0%\t21000\t549\t0:01\n",
      "80.0%\t24000\t545\t0:00\n",
      "90.0%\t27000\t537\t0:00\n",
      "100.0%\t30000\t539\t0:00\n"
     ]
    }
   ],
   "source": [
    "#run collapse simulation\n",
    "sim.createSimulation()\n",
    "\n",
    "sim.run(nsteps = 3000*10, report=True, totalSteps = 3000*10, checkSystem = False, interval=3000, blockSize = 3000)\n",
    "# if repeate the code block, the simulation will extend, I'm not sure how to end it.\n",
    "# save the collapsed structure -- uncomment below\n",
    "# sim.saveStructure(filename='collapse',mode='ndb')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110.0%\t33000\t537\t0:00\n",
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      "1490.0%\t447000\t524\t23:58:52\n",
      "1500.0%\t450000\t523\t23:58:51\n",
      "1510.0%\t453000\t523\t23:58:51\n",
      "1520.0%\t456000\t524\t23:58:50\n",
      "1530.0%\t459000\t524\t23:58:50\n",
      "1540.0%\t462000\t523\t23:58:49\n",
      "1550.0%\t465000\t522\t23:58:48\n",
      "1560.0%\t468000\t519\t23:58:48\n",
      "1570.0%\t471000\t517\t23:58:47\n",
      "1580.0%\t474000\t513\t23:58:46\n",
      "1590.0%\t477000\t512\t23:58:45\n",
      "1600.0%\t480000\t511\t23:58:44\n",
      "1610.0%\t483000\t511\t23:58:44\n",
      "1620.0%\t486000\t510\t23:58:43\n",
      "1630.0%\t489000\t509\t23:58:43\n",
      "1640.0%\t492000\t508\t23:58:42\n",
      "1650.0%\t495000\t507\t23:58:41\n",
      "1660.0%\t498000\t507\t23:58:41\n",
      "1670.0%\t501000\t507\t23:58:40\n",
      "1680.0%\t504000\t507\t23:58:40\n",
      "1690.0%\t507000\t507\t23:58:39\n",
      "1700.0%\t510000\t507\t23:58:39\n",
      "1710.0%\t513000\t507\t23:58:38\n",
      "1720.0%\t516000\t507\t23:58:38\n",
      "1730.0%\t519000\t507\t23:58:37\n",
      "1740.0%\t522000\t507\t23:58:37\n",
      "1750.0%\t525000\t507\t23:58:36\n",
      "1760.0%\t528000\t508\t23:58:36\n",
      "(501, 10, 3)\n"
     ]
    }
   ],
   "source": [
    "# For the purpose of the tutorial we will just store the data in the xyz array for plotting\n",
    "# you may instead save the data into cndb/ndb/pdb files and load into numpy arrays using cndbtools\n",
    "# if you would like to save the data uncomment the lines below\n",
    "\n",
    "# sim.initStorage(filename=\"active_Rouse_sim\"\") # initiate storage, however, this function has been deleted\n",
    "\n",
    "xyz = [sim.getPositions()]\n",
    "\n",
    "n_blocks = 500 # number of simulation blocks\n",
    "block_size = 1000 # size of each block\n",
    "\n",
    "for _ in range(int(n_blocks)):\n",
    "    sim.run(nsteps = int(block_size),blockSize = int(block_size))\n",
    "    # sim.saveStructure()\n",
    "    xyz.append(sim.getPositions())\n",
    "    \n",
    "# sim.saveStructure(filename='lastframe',mode='ndb')\n",
    "# sim.storage[0].close()\n",
    "\n",
    "xyz = np.array(xyz)\n",
    "print(xyz.shape) # the shape should be: (number of blocks, number of monos, deg. of freedom)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import the libraries for plotting\n",
    "from matplotlib.animation import FuncAnimation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ipympl is necessary for dynamic plotting\n",
    "# dynamic plotting is not supported inline\n",
    "#%matplotlib ipympl\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = fig.add_axes([0,0,1,1],projection='3d')\n",
    "\n",
    "R0=sim.N**(0.5)\n",
    "\n",
    "ax.scatter(xyz[0,:,0],xyz[0,:,1],xyz[0,:,2],'o',color='C0')\n",
    "ax.plot(xyz[0,:,0],xyz[0,:,1],xyz[0,:,2],'-',color='C0')\n",
    "ax.set_zlim(-R0, R0)\n",
    "ax.set_xlim(-R0, R0)\n",
    "ax.set_ylim(-R0, R0)\n",
    "\n",
    "def update(ii):\n",
    "    ax.cla()\n",
    "    p=xyz[ii]\n",
    "    ax.scatter(p[:,0],p[:,1],p[:,2],'o',color='C0')\n",
    "    ax.plot(p[:,0],p[:,1],p[:,2],'-',color='C0')\n",
    "    ax.set_zlim(-R0, R0)\n",
    "    ax.set_xlim(-R0, R0)\n",
    "    ax.set_ylim(-R0, R0)\n",
    "    \n",
    "anim = FuncAnimation(fig, update, frames=np.arange(1,400,1), interval=200, blit=False,repeat=False)\n",
    "\n",
    "# uncomment below to save the animation\n",
    "# anim.save('animation_active_sim.gif', writer='pillow')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Verify dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from OpenMiChroM.CndbTools import cndbTools\n",
    "cndbT = cndbTools()\n",
    "\n",
    "def active_gas_MSD(t_lag, temp, F, tau, g, d=3):\n",
    "    return d*(2*temp*t_lag/g + (2*F**2*tau/g**2)*(t_lag + tau*np.exp(-t_lag/tau)) - 2*F**2*tau**2/g**2)\n",
    "\n",
    "def tau_rouse(p,kb,N,g):\n",
    "    return g*N**2/(np.pi**2*kb*p**2)\n",
    "\n",
    "def active_Rouse_MSD(t_lag, temp, F, tau, kb, N, g, d=3):\n",
    "\n",
    "    rouse_modes=np.arange(1, N+1, 1)\n",
    "    tau_mode_i=tau_rouse(rouse_modes, kb, N, g)\n",
    "    val=(tau_mode_i*(temp+F**2*tau*tau_mode_i**2/(g*(tau_mode_i**2-tau**2)))*(1-np.exp(-t_lag/tau_mode_i))/(2*N*g)\n",
    "        -(F*tau*tau_mode_i)**2*(1-np.exp(-t_lag/tau))/(2*N*g**2*(tau_mode_i**2-tau**2)))*0.5\n",
    "    # print(val.shape)\n",
    "    \n",
    "    return (2*d*(temp+F**2*tau/g)*t_lag/(N*g) - 2*d*F**2*tau**2*(1-np.exp(-t_lag/tau))/(N*g**2) + 8*d*np.sum(val))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute_MSD returns an (N * T) array with mononers in the first dimension and lag time in the second\n",
    "msd_rouse = np.mean(cndbT.compute_MSD(xyz), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "\n",
    "#lag times\n",
    "ts = np.arange(0,xyz.shape[0], block_size*0.001)\n",
    "\n",
    "plt.loglog(ts, msd_rouse,'o', label='simulation')\n",
    "plt.loglog(ts, [active_Rouse_MSD(xx, sim.temperature*0.008314, F, sim.integrator.corrTime, kb, sim.N, 1.0) for xx in ts],'k--', label='analytical')\n",
    "plt.legend()\n",
    "plt.ylabel('Mean square displacement ($\\\\sigma^2$)')\n",
    "plt.xlabel('Lag time $t/\\\\tau_{sim}$')\n",
    "plt.title('Temperature={0:.2f}  $F={1:.1f}\\\\epsilon/\\\\sigma$  $\\\\tau={2:.1f} \\\\tau_{{sim}}$'.format(sim.temperature,F, sim.integrator.corrTime))\n",
    "plt.ylim(msd_rouse[1]*0.5,max(msd_rouse)*1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}