{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporally Correlated Active Noise Simulation Tutorial\n",
    "\n",
    "<font color='red'>Temporally Correlated Active Noise Simulation is available in OpenMichroM versions >= 1.0.7</font> \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 [Tutorial_Single_Chromosome](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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append('../../OpenMiChroM/')\n",
    "from ChromDynamics import MiChroM\n",
    "from 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-1.0.6 *** *** *** *** *** *** **** ****      \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",
      "         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",
      "                                              and                                               \n",
      "                                 Oliveira Junior, A. B. et al.                                  \n",
      "     Chromosome Modeling on Downsampled Hi-C Maps Enhances the Compartmentalization Signal.     \n",
      "                        J. Phys. Chem. B, doi:10.1021/acs.jpcb.1c04174.                         \n",
      "\n",
      "                    Copyright (c) 2022, The OpenMiChroM development team at                     \n",
      "                                        Rice University                                         \n",
      "    ***************************************************************************************     \n"
     ]
    }
   ],
   "source": [
    "# Instantiate MiChroM object\n",
    "sim = MiChroM(name='active_Rouse_sim', temperature=0.2, time_step=0.001, collision_rate=1.0)\n",
    "\n",
    "# specify platform and output destination\n",
    "sim.setup(platform=\"opencl\")\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:\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(kfb=kb, r0=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": [
      "Number of exceptions: 9\n",
      "adding force  ActiveForce 0\n",
      "adding force  HarmonicBond 1\n",
      "Positions... \n",
      " loaded!\n",
      "potential energy is 2.250014\n",
      "bl=0 pos[1]=[-0.1 -0.6 0.4] dr=1.67 t=3.0ps kin=5.53 pot=0.39 Rg=0.631 SPS=7171 \n",
      "bl=0 pos[1]=[0.5 -1.6 1.8] dr=1.64 t=6.0ps kin=4.23 pot=-0.36 Rg=0.610 SPS=8611 \n",
      "bl=0 pos[1]=[0.7 -2.8 1.5] dr=0.99 t=9.0ps kin=2.77 pot=-0.19 Rg=0.699 SPS=8800 \n",
      "bl=0 pos[1]=[1.7 -2.8 1.7] dr=1.10 t=12.0ps kin=3.35 pot=-0.50 Rg=0.923 SPS=8863 \n",
      "bl=0 pos[1]=[2.2 -2.3 2.6] dr=0.86 t=15.0ps kin=2.37 pot=-0.70 Rg=0.530 SPS=8788 \n",
      "bl=0 pos[1]=[1.7 -2.6 3.0] dr=0.75 t=18.0ps kin=3.77 pot=-0.62 Rg=0.517 SPS=8933 \n",
      "bl=0 pos[1]=[1.6 -1.6 3.6] dr=1.13 t=21.0ps kin=3.43 pot=-0.39 Rg=0.972 SPS=8865 \n",
      "bl=0 pos[1]=[1.3 -2.0 3.1] dr=1.29 t=24.0ps kin=3.95 pot=0.10 Rg=0.891 SPS=8965 \n",
      "bl=0 pos[1]=[1.2 -3.6 1.4] dr=1.26 t=27.0ps kin=5.72 pot=0.83 Rg=0.717 SPS=8999 \n",
      "bl=0 pos[1]=[1.5 -4.1 1.2] dr=0.63 t=30.0ps kin=3.92 pot=0.74 Rg=0.530 SPS=9082 \n"
     ]
    }
   ],
   "source": [
    "#run collapse simulation\n",
    "for _ in range(10):\n",
    "    sim.runSimBlock(3000, increment=False)\n",
    "\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": [
      "bl=1 pos[1]=[1.3 -5.0 1.1] dr=0.62 t=31.0ps kin=2.41 pot=0.79 Rg=0.588 SPS=7621 \n",
      "bl=2 pos[1]=[1.6 -4.5 1.2] dr=0.61 t=32.0ps kin=3.50 pot=0.55 Rg=0.624 SPS=7894 \n",
      "bl=3 pos[1]=[1.1 -5.2 1.3] dr=0.68 t=33.0ps kin=3.32 pot=0.83 Rg=0.890 SPS=8432 \n",
      "bl=4 pos[1]=[1.0 -4.6 1.3] dr=0.60 t=34.0ps kin=3.93 pot=1.05 Rg=0.706 SPS=8478 \n",
      "bl=5 pos[1]=[0.9 -5.4 1.2] dr=0.64 t=35.0ps kin=2.70 pot=1.35 Rg=0.897 SPS=8574 \n",
      "bl=6 pos[1]=[1.5 -5.2 1.3] dr=0.66 t=36.0ps kin=3.38 pot=0.93 Rg=0.774 SPS=8658 \n",
      "bl=7 pos[1]=[1.9 -5.3 1.8] dr=0.62 t=37.0ps kin=2.75 pot=0.97 Rg=0.833 SPS=8889 \n",
      "bl=8 pos[1]=[2.0 -5.1 2.3] dr=0.53 t=38.0ps kin=3.94 pot=0.70 Rg=0.517 SPS=8786 \n",
      "bl=9 pos[1]=[1.8 -5.4 2.4] dr=0.54 t=39.0ps kin=3.99 pot=0.69 Rg=0.544 SPS=8805 \n",
      "bl=10 pos[1]=[1.6 -5.9 3.1] dr=0.75 t=40.0ps kin=2.92 pot=0.54 Rg=0.468 SPS=8681 \n",
      "bl=11 pos[1]=[2.1 -6.0 3.2] dr=0.71 t=41.0ps kin=1.58 pot=0.87 Rg=0.451 SPS=8699 \n",
      "bl=12 pos[1]=[2.5 -6.8 3.3] dr=0.78 t=42.0ps kin=2.89 pot=0.69 Rg=0.360 SPS=8515 \n",
      "bl=13 pos[1]=[2.0 -6.6 3.4] dr=0.81 t=43.0ps kin=2.64 pot=0.81 Rg=0.444 SPS=8633 \n",
      "bl=14 pos[1]=[1.7 -7.1 3.2] dr=0.52 t=44.0ps kin=2.34 pot=1.00 Rg=0.476 SPS=8597 \n",
      "bl=15 pos[1]=[1.8 -6.6 3.3] dr=0.90 t=45.0ps kin=2.82 pot=1.51 Rg=0.600 SPS=8924 \n",
      "bl=16 pos[1]=[1.9 -6.9 2.9] dr=0.59 t=46.0ps kin=2.70 pot=2.04 Rg=0.533 SPS=8925 \n",
      "bl=17 pos[1]=[1.7 -6.3 2.3] dr=0.65 t=47.0ps kin=2.17 pot=2.08 Rg=0.623 SPS=8774 \n",
      "bl=18 pos[1]=[0.7 -7.1 2.6] dr=0.72 t=48.0ps kin=2.32 pot=1.98 Rg=0.651 SPS=8284 \n",
      "bl=19 pos[1]=[1.5 -7.2 3.0] dr=0.73 t=49.0ps kin=2.22 pot=1.70 Rg=0.239 SPS=8612 \n",
      "bl=20 pos[1]=[1.0 -7.7 3.5] dr=0.57 t=50.0ps kin=2.30 pot=1.83 Rg=0.368 SPS=8729 \n",
      "bl=21 pos[1]=[1.1 -7.5 4.5] dr=0.73 t=51.0ps kin=1.98 pot=1.71 Rg=0.629 SPS=8843 \n",
      "bl=22 pos[1]=[1.1 -7.6 4.4] dr=0.45 t=52.0ps kin=2.86 pot=1.61 Rg=0.725 SPS=8919 \n",
      "bl=23 pos[1]=[1.1 -7.6 3.6] dr=0.61 t=53.0ps kin=2.89 pot=1.53 Rg=0.522 SPS=8702 \n",
      "bl=24 pos[1]=[1.0 -7.3 3.9] dr=0.54 t=54.0ps kin=3.78 pot=1.54 Rg=0.455 SPS=8720 \n",
      "bl=25 pos[1]=[0.6 -7.3 4.2] dr=0.45 t=55.0ps kin=2.99 pot=1.53 Rg=0.688 SPS=8669 \n",
      "bl=26 pos[1]=[0.5 -7.1 3.8] dr=0.66 t=56.0ps kin=2.67 pot=2.00 Rg=0.691 SPS=8478 \n",
      "bl=27 pos[1]=[-0.1 -7.4 3.1] dr=0.54 t=57.0ps kin=2.49 pot=2.12 Rg=0.597 SPS=8733 \n",
      "bl=28 pos[1]=[0.3 -7.0 3.3] dr=0.64 t=58.0ps kin=2.04 pot=2.53 Rg=0.507 SPS=8880 \n",
      "bl=29 pos[1]=[-0.3 -7.2 3.7] dr=0.70 t=59.0ps kin=1.81 pot=2.13 Rg=0.478 SPS=8714 \n",
      "bl=30 pos[1]=[-0.4 -7.2 4.2] dr=0.67 t=60.0ps kin=2.98 pot=2.15 Rg=0.691 SPS=8777 \n",
      "bl=31 pos[1]=[0.3 -6.7 3.6] dr=0.72 t=61.0ps kin=2.48 pot=1.79 Rg=0.390 SPS=8590 \n",
      "bl=32 pos[1]=[0.2 -7.0 3.9] dr=0.38 t=62.0ps kin=2.82 pot=1.92 Rg=0.525 SPS=8689 \n",
      "bl=33 pos[1]=[0.6 -6.5 4.0] dr=0.53 t=63.0ps kin=3.03 pot=1.82 Rg=0.580 SPS=8618 \n",
      "bl=34 pos[1]=[0.6 -6.5 3.7] dr=0.43 t=64.0ps kin=2.71 pot=1.54 Rg=0.350 SPS=8732 \n",
      "bl=35 pos[1]=[0.2 -6.7 4.1] dr=0.57 t=65.0ps kin=3.25 pot=1.40 Rg=0.499 SPS=8902 \n",
      "bl=36 pos[1]=[0.7 -6.5 4.1] dr=0.78 t=66.0ps kin=2.26 pot=1.13 Rg=0.321 SPS=8771 \n",
      "bl=37 pos[1]=[0.5 -6.4 4.0] dr=0.50 t=67.0ps kin=1.75 pot=1.21 Rg=0.413 SPS=9004 \n",
      "bl=38 pos[1]=[0.3 -6.7 4.4] dr=0.48 t=68.0ps kin=2.10 pot=1.08 Rg=0.365 SPS=8660 \n",
      "bl=39 pos[1]=[0.2 -7.4 4.6] dr=0.72 t=69.0ps kin=2.33 pot=0.99 Rg=0.518 SPS=8627 \n",
      "bl=40 pos[1]=[0.9 -7.0 5.2] dr=0.87 t=70.0ps kin=2.63 pot=0.42 Rg=0.360 SPS=8620 \n",
      "bl=41 pos[1]=[1.4 -6.8 4.5] dr=0.67 t=71.0ps kin=2.64 pot=0.52 Rg=0.528 SPS=8807 \n",
      "bl=42 pos[1]=[0.7 -5.8 5.3] dr=0.73 t=72.0ps kin=3.55 pot=0.47 Rg=0.452 SPS=8790 \n",
      "bl=43 pos[1]=[0.6 -5.9 6.2] dr=0.79 t=73.0ps kin=3.02 pot=0.61 Rg=0.568 SPS=8901 \n",
      "bl=44 pos[1]=[0.0 -6.3 5.2] dr=0.81 t=74.0ps kin=3.40 pot=1.12 Rg=0.557 SPS=8937 \n",
      "bl=45 pos[1]=[-0.1 -7.4 5.6] dr=0.80 t=75.0ps kin=2.41 pot=1.57 Rg=0.484 SPS=8856 \n",
      "bl=46 pos[1]=[-1.1 -7.2 5.3] dr=0.75 t=76.0ps kin=4.20 pot=1.90 Rg=0.425 SPS=8737 \n",
      "bl=47 pos[1]=[-1.5 -7.3 4.7] dr=0.86 t=77.0ps kin=6.58 pot=2.32 Rg=0.405 SPS=8678 \n",
      "bl=48 pos[1]=[-1.7 -7.1 4.2] dr=0.90 t=78.0ps kin=5.83 pot=2.99 Rg=0.470 SPS=8809 \n",
      "bl=49 pos[1]=[-2.5 -7.0 4.0] dr=0.78 t=79.0ps kin=6.04 pot=3.31 Rg=0.555 SPS=8671 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-2.5260031  -7.63105731  4.11936607]   Rg =  0.555199\n",
      "     median bond size is  0.2993247084597079\n",
      "     three shortest/longest (<10)/ bonds are  [0.0619413  0.18132575 0.26687537]    [0.48378056 0.48624118 0.52440487]\n",
      "     95 percentile of distance to center is:    0.7736948328496079\n",
      "     density of closest 95% monomers is:    4.896952672744252\n",
      "     density of the core monomers is:    5.717321659637082\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -2.93, -2.53, -2.53, -2.17\n",
      "     y: -8.26, -7.68, -7.63, -7.01\n",
      "     z: 3.84, 4.10, 4.12, 4.60\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  6.038497269500298 should be: 1.5\n",
      "     fastest particles are (in kT):  [ 4.12132149  7.91483392  8.78585725 10.29837843 17.11273306]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  3.3082054138183596\n",
      "bl=50 pos[1]=[-2.4 -6.7 4.2] dr=0.84 t=80.0ps kin=5.53 pot=3.82 Rg=0.769 SPS=8882 \n",
      "bl=51 pos[1]=[-3.4 -7.3 4.4] dr=0.75 t=81.0ps kin=3.60 pot=4.06 Rg=0.507 SPS=8752 \n",
      "bl=52 pos[1]=[-3.3 -8.0 3.4] dr=1.18 t=82.0ps kin=2.91 pot=4.97 Rg=0.629 SPS=8653 \n",
      "bl=53 pos[1]=[-4.1 -7.9 3.5] dr=0.57 t=83.0ps kin=2.69 pot=4.81 Rg=0.584 SPS=8691 \n",
      "bl=54 pos[1]=[-3.8 -8.8 3.6] dr=0.53 t=84.0ps kin=3.32 pot=4.78 Rg=0.494 SPS=8960 \n",
      "bl=55 pos[1]=[-3.7 -9.5 3.0] dr=0.63 t=85.0ps kin=3.45 pot=5.15 Rg=0.389 SPS=8810 \n",
      "bl=56 pos[1]=[-3.2 -8.6 2.2] dr=0.80 t=86.0ps kin=2.76 pot=5.04 Rg=0.580 SPS=8745 \n",
      "bl=57 pos[1]=[-3.2 -8.5 1.7] dr=0.60 t=87.0ps kin=1.64 pot=5.17 Rg=0.476 SPS=8836 \n",
      "bl=58 pos[1]=[-2.8 -8.5 2.3] dr=0.54 t=88.0ps kin=3.29 pot=5.08 Rg=0.562 SPS=8871 \n",
      "bl=59 pos[1]=[-2.6 -8.1 2.3] dr=0.68 t=89.0ps kin=2.82 pot=4.93 Rg=0.351 SPS=8918 \n",
      "bl=60 pos[1]=[-2.8 -7.6 1.6] dr=0.66 t=90.0ps kin=3.23 pot=4.73 Rg=0.450 SPS=8534 \n",
      "bl=61 pos[1]=[-2.2 -7.6 1.2] dr=0.69 t=91.0ps kin=2.43 pot=4.52 Rg=0.618 SPS=8722 \n",
      "bl=62 pos[1]=[-1.6 -6.9 1.7] dr=0.65 t=92.0ps kin=2.53 pot=4.52 Rg=0.731 SPS=8448 \n",
      "bl=63 pos[1]=[-1.8 -6.9 1.1] dr=0.65 t=93.0ps kin=3.41 pot=4.43 Rg=0.776 SPS=7700 \n",
      "bl=64 pos[1]=[-2.4 -6.4 1.1] dr=0.69 t=94.0ps kin=4.03 pot=4.38 Rg=0.718 SPS=7988 \n",
      "bl=65 pos[1]=[-1.7 -6.0 0.9] dr=0.61 t=95.0ps kin=3.88 pot=4.20 Rg=0.679 SPS=7884 \n",
      "bl=66 pos[1]=[-1.2 -6.3 0.8] dr=0.46 t=96.0ps kin=3.68 pot=4.46 Rg=0.591 SPS=7482 \n",
      "bl=67 pos[1]=[-1.8 -5.9 1.5] dr=0.83 t=97.0ps kin=3.63 pot=4.16 Rg=0.717 SPS=7743 \n",
      "bl=68 pos[1]=[-2.3 -5.9 1.2] dr=0.75 t=98.0ps kin=4.69 pot=4.26 Rg=0.497 SPS=7625 \n",
      "bl=69 pos[1]=[-2.1 -5.3 0.6] dr=0.78 t=99.0ps kin=5.80 pot=4.54 Rg=0.683 SPS=8434 \n",
      "bl=70 pos[1]=[-3.3 -4.9 0.5] dr=0.82 t=100.0ps kin=6.19 pot=4.70 Rg=0.679 SPS=8482 \n",
      "bl=71 pos[1]=[-3.9 -4.5 0.2] dr=1.02 t=101.0ps kin=6.62 pot=4.68 Rg=0.565 SPS=8872 \n",
      "bl=72 pos[1]=[-4.3 -5.5 0.5] dr=1.14 t=102.0ps kin=7.04 pot=5.07 Rg=0.536 SPS=8607 \n",
      "bl=73 pos[1]=[-5.0 -4.5 0.5] dr=0.87 t=103.0ps kin=5.33 pot=5.05 Rg=0.554 SPS=8651 \n",
      "bl=74 pos[1]=[-5.3 -3.9 0.4] dr=0.88 t=104.0ps kin=4.96 pot=5.19 Rg=0.664 SPS=8740 \n",
      "bl=75 pos[1]=[-6.1 -3.7 0.1] dr=1.09 t=105.0ps kin=5.61 pot=5.87 Rg=0.775 SPS=8770 \n",
      "bl=76 pos[1]=[-6.3 -4.0 -0.1] dr=0.69 t=106.0ps kin=5.13 pot=6.20 Rg=0.804 SPS=8922 \n",
      "bl=77 pos[1]=[-6.1 -3.8 -0.6] dr=0.50 t=107.0ps kin=6.57 pot=6.13 Rg=0.596 SPS=8360 \n",
      "bl=78 pos[1]=[-6.3 -3.9 -1.4] dr=0.57 t=108.0ps kin=5.46 pot=6.38 Rg=0.440 SPS=8492 \n",
      "bl=79 pos[1]=[-6.9 -3.7 -1.6] dr=0.67 t=109.0ps kin=6.07 pot=6.93 Rg=0.430 SPS=8567 \n",
      "bl=80 pos[1]=[-7.3 -3.6 -2.7] dr=0.86 t=110.0ps kin=4.86 pot=6.89 Rg=0.531 SPS=8621 \n",
      "bl=81 pos[1]=[-7.3 -3.1 -2.9] dr=0.82 t=111.0ps kin=5.23 pot=7.06 Rg=0.498 SPS=8702 \n",
      "bl=82 pos[1]=[-7.0 -2.7 -3.2] dr=0.55 t=112.0ps kin=6.05 pot=7.02 Rg=0.477 SPS=8684 \n",
      "bl=83 pos[1]=[-6.8 -2.4 -2.9] dr=0.49 t=113.0ps kin=4.13 pot=6.67 Rg=0.406 SPS=8754 \n",
      "bl=84 pos[1]=[-7.9 -2.5 -3.4] dr=0.86 t=114.0ps kin=4.23 pot=7.06 Rg=0.410 SPS=8740 \n",
      "bl=85 pos[1]=[-7.9 -2.3 -3.3] dr=0.63 t=115.0ps kin=3.89 pot=7.38 Rg=0.496 SPS=8701 \n",
      "bl=86 pos[1]=[-8.0 -2.4 -3.2] dr=0.59 t=116.0ps kin=2.92 pot=7.54 Rg=0.611 SPS=8898 \n",
      "bl=87 pos[1]=[-8.0 -2.2 -3.4] dr=0.45 t=117.0ps kin=3.57 pot=7.70 Rg=0.791 SPS=8651 \n",
      "bl=88 pos[1]=[-8.5 -1.9 -2.6] dr=1.04 t=118.0ps kin=4.14 pot=8.09 Rg=0.879 SPS=8940 \n",
      "bl=89 pos[1]=[-9.2 -1.7 -3.1] dr=0.72 t=119.0ps kin=3.87 pot=8.01 Rg=0.643 SPS=9065 \n",
      "bl=90 pos[1]=[-9.6 -2.0 -3.3] dr=0.77 t=120.0ps kin=3.71 pot=8.50 Rg=0.759 SPS=8609 \n",
      "bl=91 pos[1]=[-9.9 -1.9 -2.7] dr=0.48 t=121.0ps kin=3.23 pot=8.31 Rg=0.616 SPS=8953 \n",
      "bl=92 pos[1]=[-10.3 -2.2 -3.6] dr=0.62 t=122.0ps kin=2.60 pot=8.64 Rg=0.559 SPS=8671 \n",
      "bl=93 pos[1]=[-10.5 -1.8 -3.0] dr=0.77 t=123.0ps kin=2.44 pot=8.60 Rg=0.711 SPS=8954 \n",
      "bl=94 pos[1]=[-10.8 -2.5 -2.9] dr=0.61 t=124.0ps kin=2.79 pot=8.43 Rg=0.412 SPS=8637 \n",
      "bl=95 pos[1]=[-10.7 -2.4 -2.6] dr=0.65 t=125.0ps kin=3.95 pot=8.89 Rg=0.578 SPS=8778 \n",
      "bl=96 pos[1]=[-10.7 -2.6 -3.5] dr=0.57 t=126.0ps kin=5.02 pot=9.36 Rg=0.823 SPS=8796 \n",
      "bl=97 pos[1]=[-10.8 -2.5 -2.2] dr=0.62 t=127.0ps kin=5.71 pot=9.22 Rg=0.897 SPS=8014 \n",
      "bl=98 pos[1]=[-10.5 -3.7 -2.3] dr=0.89 t=128.0ps kin=6.99 pot=9.75 Rg=1.023 SPS=7503 \n",
      "bl=99 pos[1]=[-11.3 -3.8 -2.8] dr=0.70 t=129.0ps kin=5.48 pot=9.72 Rg=0.653 SPS=7842 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-11.6693655   -3.76013415  -3.52913654]   Rg =  0.65312004\n",
      "     median bond size is  0.2290878636694177\n",
      "     three shortest/longest (<10)/ bonds are  [0.07835139 0.17617395 0.20731799]    [0.31495102 0.38001044 0.68796864]\n",
      "     95 percentile of distance to center is:    0.8599407829757514\n",
      "     density of closest 95% monomers is:    3.5663943079826224\n",
      "     density of the core monomers is:    1.6180429828063354\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -12.33, -11.57, -11.67, -11.15\n",
      "     y: -3.94, -3.75, -3.76, -3.53\n",
      "     z: -3.99, -3.67, -3.53, -2.77\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  5.478428732511492 should be: 1.5\n",
      "     fastest particles are (in kT):  [ 4.88943464  5.23413138  6.02967511 11.86918393 14.250708  ]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  9.717581176757813\n",
      "bl=100 pos[1]=[-11.6 -4.6 -2.7] dr=0.60 t=130.0ps kin=3.82 pot=10.00 Rg=0.693 SPS=8439 \n",
      "bl=101 pos[1]=[-11.7 -5.1 -2.5] dr=0.44 t=131.0ps kin=4.13 pot=10.19 Rg=0.795 SPS=8077 \n",
      "bl=102 pos[1]=[-11.5 -5.0 -2.8] dr=0.42 t=132.0ps kin=2.87 pot=10.05 Rg=0.748 SPS=7472 \n",
      "bl=103 pos[1]=[-11.3 -5.6 -3.8] dr=0.90 t=133.0ps kin=3.36 pot=10.43 Rg=0.795 SPS=7968 \n",
      "bl=104 pos[1]=[-11.4 -5.9 -3.8] dr=0.91 t=134.0ps kin=4.55 pot=10.78 Rg=0.522 SPS=7897 \n",
      "bl=105 pos[1]=[-11.4 -6.0 -3.8] dr=0.60 t=135.0ps kin=4.94 pot=10.77 Rg=0.399 SPS=7570 \n",
      "bl=106 pos[1]=[-11.7 -6.0 -4.0] dr=0.46 t=136.0ps kin=4.36 pot=10.88 Rg=0.310 SPS=8529 \n",
      "bl=107 pos[1]=[-11.8 -6.9 -4.3] dr=0.59 t=137.0ps kin=3.28 pot=11.15 Rg=0.455 SPS=8108 \n",
      "bl=108 pos[1]=[-11.7 -6.5 -4.5] dr=0.68 t=138.0ps kin=3.91 pot=11.31 Rg=0.404 SPS=8000 \n",
      "bl=109 pos[1]=[-12.1 -6.2 -4.2] dr=0.53 t=139.0ps kin=4.17 pot=11.15 Rg=0.630 SPS=8304 \n",
      "bl=110 pos[1]=[-12.4 -5.9 -5.1] dr=0.74 t=140.0ps kin=3.52 pot=11.25 Rg=0.831 SPS=8538 \n",
      "bl=111 pos[1]=[-12.2 -5.9 -5.2] dr=0.49 t=141.0ps kin=3.73 pot=11.25 Rg=0.843 SPS=8934 \n",
      "bl=112 pos[1]=[-12.3 -6.1 -4.3] dr=0.72 t=142.0ps kin=4.50 pot=10.93 Rg=0.905 SPS=8728 \n",
      "bl=113 pos[1]=[-12.3 -5.9 -5.5] dr=0.65 t=143.0ps kin=4.42 pot=11.36 Rg=1.113 SPS=8992 \n",
      "bl=114 pos[1]=[-11.7 -5.9 -5.3] dr=0.66 t=144.0ps kin=4.18 pot=11.36 Rg=0.980 SPS=8477 \n",
      "bl=115 pos[1]=[-11.5 -6.2 -5.3] dr=0.80 t=145.0ps kin=2.82 pot=11.73 Rg=1.060 SPS=8981 \n",
      "bl=116 pos[1]=[-10.3 -6.3 -5.7] dr=0.89 t=146.0ps kin=3.26 pot=11.29 Rg=0.684 SPS=8707 \n",
      "bl=117 pos[1]=[-10.6 -5.9 -4.4] dr=0.68 t=147.0ps kin=3.10 pot=11.00 Rg=0.614 SPS=8831 \n",
      "bl=118 pos[1]=[-10.9 -5.9 -4.4] dr=0.40 t=148.0ps kin=3.62 pot=11.28 Rg=0.844 SPS=8766 \n",
      "bl=119 pos[1]=[-11.0 -6.4 -5.0] dr=0.59 t=149.0ps kin=3.65 pot=11.41 Rg=0.864 SPS=8820 \n",
      "bl=120 pos[1]=[-11.4 -6.6 -5.3] dr=0.49 t=150.0ps kin=2.91 pot=11.55 Rg=0.913 SPS=8686 \n",
      "bl=121 pos[1]=[-10.5 -7.0 -5.5] dr=0.61 t=151.0ps kin=4.16 pot=11.48 Rg=0.846 SPS=8920 \n",
      "bl=122 pos[1]=[-10.2 -7.0 -5.5] dr=0.51 t=152.0ps kin=4.40 pot=11.18 Rg=0.784 SPS=8871 \n",
      "bl=123 pos[1]=[-9.8 -7.5 -6.1] dr=0.64 t=153.0ps kin=3.56 pot=11.34 Rg=0.884 SPS=8884 \n",
      "bl=124 pos[1]=[-9.0 -8.4 -5.5] dr=0.86 t=154.0ps kin=2.88 pot=11.30 Rg=0.752 SPS=8785 \n",
      "bl=125 pos[1]=[-8.7 -8.7 -5.1] dr=0.66 t=155.0ps kin=3.90 pot=11.31 Rg=0.687 SPS=8489 \n",
      "bl=126 pos[1]=[-8.8 -8.7 -5.4] dr=0.59 t=156.0ps kin=2.77 pot=11.24 Rg=0.766 SPS=8581 \n",
      "bl=127 pos[1]=[-8.9 -8.9 -4.9] dr=0.37 t=157.0ps kin=2.21 pot=11.23 Rg=0.772 SPS=8646 \n",
      "bl=128 pos[1]=[-8.2 -8.4 -4.7] dr=0.60 t=158.0ps kin=3.57 pot=10.79 Rg=0.778 SPS=8665 \n",
      "bl=129 pos[1]=[-8.3 -8.0 -5.1] dr=0.53 t=159.0ps kin=2.58 pot=10.93 Rg=0.554 SPS=8865 \n",
      "bl=130 pos[1]=[-9.4 -7.5 -5.3] dr=0.82 t=160.0ps kin=3.39 pot=10.67 Rg=0.616 SPS=8685 \n",
      "bl=131 pos[1]=[-9.2 -7.7 -4.6] dr=0.68 t=161.0ps kin=4.05 pot=10.29 Rg=0.789 SPS=8779 \n",
      "bl=132 pos[1]=[-8.2 -8.0 -4.7] dr=0.76 t=162.0ps kin=3.43 pot=10.28 Rg=0.833 SPS=9041 \n",
      "bl=133 pos[1]=[-8.1 -7.8 -5.0] dr=0.62 t=163.0ps kin=3.67 pot=9.90 Rg=0.847 SPS=8675 \n",
      "bl=134 pos[1]=[-7.7 -8.0 -4.5] dr=0.74 t=164.0ps kin=3.16 pot=9.67 Rg=1.020 SPS=8687 \n",
      "bl=135 pos[1]=[-7.8 -7.6 -4.0] dr=0.46 t=165.0ps kin=3.25 pot=9.66 Rg=0.798 SPS=8760 \n",
      "bl=136 pos[1]=[-7.9 -7.0 -4.7] dr=0.72 t=166.0ps kin=4.77 pot=9.73 Rg=0.559 SPS=8771 \n",
      "bl=137 pos[1]=[-7.5 -6.7 -5.0] dr=0.65 t=167.0ps kin=3.90 pot=9.72 Rg=0.618 SPS=8176 \n",
      "bl=138 pos[1]=[-7.8 -7.1 -6.0] dr=0.62 t=168.0ps kin=3.33 pot=10.09 Rg=0.749 SPS=7768 \n",
      "bl=139 pos[1]=[-6.8 -7.0 -5.3] dr=0.73 t=169.0ps kin=3.33 pot=9.77 Rg=0.824 SPS=8358 \n",
      "bl=140 pos[1]=[-6.6 -7.0 -5.3] dr=0.86 t=170.0ps kin=3.05 pot=9.62 Rg=0.623 SPS=8412 \n",
      "bl=141 pos[1]=[-6.2 -6.9 -4.7] dr=0.62 t=171.0ps kin=4.91 pot=9.59 Rg=0.788 SPS=8749 \n",
      "bl=142 pos[1]=[-6.2 -6.8 -4.9] dr=0.55 t=172.0ps kin=4.14 pot=9.42 Rg=0.687 SPS=8627 \n",
      "bl=143 pos[1]=[-6.4 -7.8 -5.4] dr=0.78 t=173.0ps kin=3.52 pot=9.99 Rg=0.741 SPS=8807 \n",
      "bl=144 pos[1]=[-7.3 -8.1 -5.4] dr=0.62 t=174.0ps kin=3.58 pot=9.87 Rg=0.710 SPS=8736 \n",
      "bl=145 pos[1]=[-6.9 -7.7 -5.9] dr=0.75 t=175.0ps kin=3.66 pot=10.24 Rg=0.651 SPS=8913 \n",
      "bl=146 pos[1]=[-6.9 -7.7 -5.9] dr=0.62 t=176.0ps kin=3.75 pot=10.44 Rg=0.496 SPS=8652 \n",
      "bl=147 pos[1]=[-7.0 -8.1 -6.1] dr=0.56 t=177.0ps kin=3.70 pot=10.53 Rg=0.386 SPS=8847 \n",
      "bl=148 pos[1]=[-6.9 -8.3 -6.6] dr=0.63 t=178.0ps kin=3.98 pot=10.68 Rg=0.487 SPS=8808 \n",
      "bl=149 pos[1]=[-6.9 -8.0 -6.6] dr=0.60 t=179.0ps kin=2.48 pot=10.69 Rg=0.427 SPS=8774 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-6.82088323 -7.50743847 -6.68795452]   Rg =  0.4274377\n",
      "     median bond size is  0.2728078873850459\n",
      "     three shortest/longest (<10)/ bonds are  [0.11215107 0.1341288  0.20583282]    [0.3524502  0.36537533 0.4069931 ]\n",
      "     95 percentile of distance to center is:    0.5868341187853655\n",
      "     density of closest 95% monomers is:    11.222482273223775\n",
      "     density of the core monomers is:    19.40923865788274\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -7.13, -6.85, -6.82, -6.54\n",
      "     y: -8.08, -7.35, -7.51, -7.10\n",
      "     z: -7.01, -6.63, -6.69, -6.41\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  2.483699793026253 should be: 1.5\n",
      "     fastest particles are (in kT):  [2.04613018 3.49250093 4.2438562  4.56766627 5.57791057]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  10.687487792968751\n",
      "bl=150 pos[1]=[-7.4 -8.3 -6.7] dr=0.54 t=180.0ps kin=2.55 pot=11.11 Rg=0.413 SPS=9017 \n",
      "bl=151 pos[1]=[-6.7 -7.6 -6.9] dr=0.58 t=181.0ps kin=2.92 pot=10.95 Rg=0.400 SPS=8790 \n",
      "bl=152 pos[1]=[-6.4 -7.9 -7.0] dr=0.52 t=182.0ps kin=4.86 pot=11.09 Rg=0.526 SPS=8676 \n",
      "bl=153 pos[1]=[-6.9 -7.6 -7.1] dr=0.82 t=183.0ps kin=3.33 pot=11.18 Rg=0.446 SPS=8637 \n",
      "bl=154 pos[1]=[-8.1 -7.5 -6.9] dr=0.90 t=184.0ps kin=3.70 pot=11.44 Rg=0.654 SPS=8592 \n",
      "bl=155 pos[1]=[-8.0 -7.6 -6.9] dr=0.57 t=185.0ps kin=4.94 pot=11.50 Rg=0.555 SPS=8735 \n",
      "bl=156 pos[1]=[-8.7 -7.1 -7.2] dr=0.66 t=186.0ps kin=4.42 pot=11.65 Rg=0.597 SPS=8932 \n",
      "bl=157 pos[1]=[-9.0 -7.0 -7.4] dr=0.74 t=187.0ps kin=3.29 pot=12.21 Rg=0.753 SPS=8720 \n",
      "bl=158 pos[1]=[-9.1 -6.8 -7.6] dr=0.47 t=188.0ps kin=3.36 pot=12.14 Rg=0.632 SPS=8936 \n",
      "bl=159 pos[1]=[-9.4 -7.4 -7.9] dr=0.56 t=189.0ps kin=4.86 pot=12.32 Rg=0.568 SPS=7975 \n",
      "bl=160 pos[1]=[-9.7 -7.0 -7.9] dr=0.81 t=190.0ps kin=4.27 pot=12.75 Rg=0.708 SPS=8499 \n",
      "bl=161 pos[1]=[-9.3 -7.3 -8.6] dr=0.62 t=191.0ps kin=4.43 pot=12.97 Rg=0.632 SPS=8702 \n",
      "bl=162 pos[1]=[-10.1 -7.6 -9.1] dr=0.61 t=192.0ps kin=4.43 pot=13.01 Rg=0.854 SPS=8941 \n",
      "bl=163 pos[1]=[-10.5 -8.1 -9.6] dr=0.90 t=193.0ps kin=4.03 pot=13.50 Rg=1.490 SPS=8691 \n",
      "bl=164 pos[1]=[-10.4 -7.7 -9.9] dr=0.60 t=194.0ps kin=5.04 pot=13.00 Rg=1.358 SPS=8793 \n",
      "bl=165 pos[1]=[-10.0 -6.8 -10.2] dr=0.80 t=195.0ps kin=4.96 pot=13.03 Rg=1.081 SPS=8873 \n",
      "bl=166 pos[1]=[-9.7 -6.8 -9.5] dr=0.49 t=196.0ps kin=5.06 pot=12.85 Rg=0.978 SPS=8703 \n",
      "bl=167 pos[1]=[-9.5 -7.0 -9.7] dr=0.63 t=197.0ps kin=4.54 pot=12.63 Rg=0.911 SPS=9041 \n",
      "bl=168 pos[1]=[-9.4 -7.0 -9.3] dr=0.70 t=198.0ps kin=4.33 pot=12.87 Rg=1.272 SPS=8666 \n",
      "bl=169 pos[1]=[-8.8 -6.3 -8.9] dr=0.64 t=199.0ps kin=4.69 pot=12.54 Rg=1.156 SPS=8783 \n",
      "bl=170 pos[1]=[-8.2 -6.7 -9.3] dr=0.50 t=200.0ps kin=3.92 pot=12.40 Rg=1.083 SPS=8960 \n",
      "bl=171 pos[1]=[-7.8 -6.5 -9.6] dr=0.83 t=201.0ps kin=3.11 pot=12.07 Rg=0.847 SPS=9090 \n",
      "bl=172 pos[1]=[-7.3 -6.3 -10.1] dr=0.69 t=202.0ps kin=4.05 pot=11.83 Rg=0.547 SPS=9007 \n",
      "bl=173 pos[1]=[-7.0 -5.2 -9.6] dr=0.95 t=203.0ps kin=3.52 pot=11.06 Rg=0.643 SPS=8860 \n",
      "bl=174 pos[1]=[-6.7 -4.9 -9.4] dr=0.54 t=204.0ps kin=4.55 pot=10.90 Rg=0.730 SPS=8716 \n",
      "bl=175 pos[1]=[-6.4 -5.0 -9.6] dr=0.68 t=205.0ps kin=3.58 pot=10.52 Rg=0.687 SPS=8770 \n",
      "bl=176 pos[1]=[-6.4 -4.9 -9.7] dr=0.77 t=206.0ps kin=4.89 pot=10.17 Rg=0.791 SPS=8478 \n",
      "bl=177 pos[1]=[-6.5 -4.3 -10.5] dr=0.66 t=207.0ps kin=6.39 pot=10.41 Rg=0.820 SPS=7717 \n",
      "bl=178 pos[1]=[-6.3 -4.7 -10.8] dr=0.69 t=208.0ps kin=5.16 pot=10.58 Rg=0.795 SPS=7589 \n",
      "bl=179 pos[1]=[-7.2 -5.1 -10.8] dr=0.79 t=209.0ps kin=4.82 pot=11.12 Rg=0.994 SPS=8405 \n",
      "bl=180 pos[1]=[-7.5 -5.2 -10.9] dr=0.50 t=210.0ps kin=5.86 pot=11.17 Rg=0.903 SPS=8493 \n",
      "bl=181 pos[1]=[-7.2 -5.4 -10.7] dr=0.69 t=211.0ps kin=6.43 pot=11.17 Rg=1.130 SPS=8970 \n",
      "bl=182 pos[1]=[-7.0 -5.3 -10.7] dr=0.52 t=212.0ps kin=5.85 pot=11.27 Rg=0.939 SPS=8771 \n",
      "bl=183 pos[1]=[-7.3 -5.7 -10.8] dr=0.73 t=213.0ps kin=3.85 pot=11.40 Rg=1.003 SPS=8741 \n",
      "bl=184 pos[1]=[-7.5 -5.4 -10.9] dr=0.88 t=214.0ps kin=3.51 pot=11.40 Rg=1.045 SPS=8750 \n",
      "bl=185 pos[1]=[-6.5 -5.3 -11.4] dr=0.70 t=215.0ps kin=4.05 pot=11.25 Rg=0.668 SPS=8777 \n",
      "bl=186 pos[1]=[-6.3 -5.0 -11.4] dr=0.48 t=216.0ps kin=2.97 pot=11.01 Rg=0.587 SPS=9078 \n",
      "bl=187 pos[1]=[-6.3 -4.1 -11.4] dr=0.69 t=217.0ps kin=4.14 pot=11.10 Rg=0.412 SPS=8699 \n",
      "bl=188 pos[1]=[-5.8 -3.9 -11.0] dr=0.55 t=218.0ps kin=4.36 pot=11.16 Rg=0.529 SPS=9099 \n",
      "bl=189 pos[1]=[-6.1 -4.2 -11.4] dr=0.75 t=219.0ps kin=5.34 pot=11.70 Rg=0.547 SPS=8713 \n",
      "bl=190 pos[1]=[-6.4 -5.1 -10.9] dr=0.77 t=220.0ps kin=4.45 pot=11.69 Rg=0.904 SPS=8757 \n",
      "bl=191 pos[1]=[-7.1 -4.5 -10.7] dr=0.55 t=221.0ps kin=4.01 pot=11.57 Rg=0.762 SPS=8964 \n",
      "bl=192 pos[1]=[-7.4 -4.9 -10.3] dr=0.59 t=222.0ps kin=4.21 pot=11.54 Rg=0.890 SPS=8456 \n",
      "bl=193 pos[1]=[-7.8 -4.7 -9.8] dr=0.49 t=223.0ps kin=3.76 pot=11.92 Rg=0.956 SPS=8736 \n",
      "bl=194 pos[1]=[-7.6 -5.2 -9.8] dr=0.61 t=224.0ps kin=3.36 pot=12.00 Rg=0.958 SPS=8698 \n",
      "bl=195 pos[1]=[-7.2 -4.7 -9.5] dr=0.53 t=225.0ps kin=3.35 pot=11.68 Rg=0.895 SPS=8780 \n",
      "bl=196 pos[1]=[-7.6 -4.6 -9.0] dr=0.64 t=226.0ps kin=3.40 pot=11.49 Rg=0.915 SPS=8714 \n",
      "bl=197 pos[1]=[-7.6 -5.0 -9.4] dr=0.72 t=227.0ps kin=2.65 pot=11.21 Rg=0.733 SPS=8862 \n",
      "bl=198 pos[1]=[-7.8 -4.5 -9.6] dr=0.45 t=228.0ps kin=3.44 pot=11.27 Rg=0.597 SPS=8672 \n",
      "bl=199 pos[1]=[-7.7 -4.4 -9.7] dr=0.74 t=229.0ps kin=4.96 pot=11.50 Rg=0.637 SPS=8774 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-8.5616169  -4.05691307 -9.71910019]   Rg =  0.6367024\n",
      "     median bond size is  0.3957872159944983\n",
      "     three shortest/longest (<10)/ bonds are  [0.21130998 0.21727112 0.29695257]    [0.40486586 0.42093309 0.58961049]\n",
      "     95 percentile of distance to center is:    0.9417336909333655\n",
      "     density of closest 95% monomers is:    2.715505411951045\n",
      "     density of the core monomers is:    4.113783843695203\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -9.10, -8.70, -8.56, -7.66\n",
      "     y: -4.71, -4.07, -4.06, -3.62\n",
      "     z: -10.30, -9.70, -9.72, -9.36\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  4.959772554216446 should be: 1.5\n",
      "     fastest particles are (in kT):  [4.86005697 4.97256635 6.36384215 6.9767307  8.77262691]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  11.497875213623047\n",
      "bl=200 pos[1]=[-8.0 -4.5 -8.9] dr=0.56 t=230.0ps kin=6.56 pot=11.59 Rg=0.793 SPS=8716 \n",
      "bl=201 pos[1]=[-8.5 -5.0 -9.1] dr=0.77 t=231.0ps kin=5.89 pot=11.87 Rg=1.020 SPS=8960 \n",
      "bl=202 pos[1]=[-8.4 -5.5 -8.4] dr=0.63 t=232.0ps kin=6.09 pot=11.51 Rg=1.127 SPS=8577 \n",
      "bl=203 pos[1]=[-9.0 -5.4 -7.7] dr=0.76 t=233.0ps kin=5.01 pot=11.61 Rg=1.087 SPS=7812 \n",
      "bl=204 pos[1]=[-8.7 -4.7 -7.5] dr=0.71 t=234.0ps kin=4.60 pot=11.38 Rg=0.907 SPS=7845 \n",
      "bl=205 pos[1]=[-9.0 -5.0 -7.2] dr=0.59 t=235.0ps kin=3.77 pot=11.54 Rg=1.157 SPS=8219 \n",
      "bl=206 pos[1]=[-9.7 -5.5 -7.8] dr=0.72 t=236.0ps kin=3.41 pot=11.82 Rg=1.087 SPS=8531 \n",
      "bl=207 pos[1]=[-10.2 -5.5 -7.6] dr=0.60 t=237.0ps kin=3.81 pot=11.76 Rg=0.820 SPS=8783 \n",
      "bl=208 pos[1]=[-10.1 -5.6 -7.0] dr=0.62 t=238.0ps kin=3.53 pot=11.95 Rg=0.825 SPS=8750 \n",
      "bl=209 pos[1]=[-10.5 -5.2 -7.0] dr=0.61 t=239.0ps kin=3.77 pot=11.84 Rg=0.677 SPS=8676 \n",
      "bl=210 pos[1]=[-10.4 -6.0 -6.9] dr=1.02 t=240.0ps kin=3.34 pot=12.22 Rg=0.803 SPS=8616 \n",
      "bl=211 pos[1]=[-10.0 -6.1 -7.2] dr=0.62 t=241.0ps kin=3.27 pot=12.29 Rg=0.825 SPS=8743 \n",
      "bl=212 pos[1]=[-9.9 -5.9 -7.4] dr=0.46 t=242.0ps kin=2.92 pot=12.35 Rg=0.888 SPS=8891 \n",
      "bl=213 pos[1]=[-10.1 -6.7 -7.2] dr=0.72 t=243.0ps kin=5.24 pot=12.51 Rg=0.832 SPS=8732 \n",
      "bl=214 pos[1]=[-10.8 -6.1 -6.6] dr=0.94 t=244.0ps kin=6.76 pot=12.74 Rg=0.649 SPS=8777 \n",
      "bl=215 pos[1]=[-11.7 -5.7 -7.0] dr=0.72 t=245.0ps kin=5.25 pot=12.56 Rg=0.457 SPS=8762 \n",
      "bl=216 pos[1]=[-12.0 -5.7 -6.4] dr=0.66 t=246.0ps kin=3.63 pot=12.42 Rg=0.261 SPS=8696 \n",
      "bl=217 pos[1]=[-11.6 -5.3 -6.0] dr=0.56 t=247.0ps kin=4.13 pot=12.36 Rg=0.573 SPS=8452 \n",
      "bl=218 pos[1]=[-11.6 -5.9 -6.5] dr=0.65 t=248.0ps kin=3.55 pot=12.35 Rg=0.395 SPS=8338 \n",
      "bl=219 pos[1]=[-11.1 -6.6 -6.0] dr=0.69 t=249.0ps kin=2.88 pot=12.66 Rg=0.606 SPS=8862 \n",
      "bl=220 pos[1]=[-10.6 -6.0 -6.3] dr=0.82 t=250.0ps kin=3.00 pot=12.81 Rg=0.765 SPS=8700 \n",
      "bl=221 pos[1]=[-11.2 -5.1 -7.0] dr=0.75 t=251.0ps kin=3.74 pot=13.09 Rg=0.992 SPS=8627 \n",
      "bl=222 pos[1]=[-11.1 -5.2 -7.0] dr=0.54 t=252.0ps kin=2.93 pot=12.97 Rg=0.862 SPS=8884 \n",
      "bl=223 pos[1]=[-12.2 -5.4 -7.0] dr=0.78 t=253.0ps kin=3.82 pot=13.17 Rg=0.593 SPS=8780 \n",
      "bl=224 pos[1]=[-12.1 -6.0 -7.4] dr=0.63 t=254.0ps kin=4.10 pot=13.17 Rg=0.448 SPS=8754 \n",
      "bl=225 pos[1]=[-11.8 -6.3 -7.7] dr=0.66 t=255.0ps kin=3.45 pot=13.21 Rg=0.402 SPS=8817 \n",
      "bl=226 pos[1]=[-12.4 -6.0 -7.9] dr=0.51 t=256.0ps kin=3.43 pot=13.33 Rg=0.429 SPS=8640 \n",
      "bl=227 pos[1]=[-11.7 -6.2 -7.9] dr=0.88 t=257.0ps kin=3.47 pot=12.76 Rg=0.506 SPS=9079 \n",
      "bl=228 pos[1]=[-11.6 -5.7 -8.1] dr=0.78 t=258.0ps kin=3.23 pot=12.53 Rg=0.697 SPS=8756 \n",
      "bl=229 pos[1]=[-11.6 -5.7 -9.0] dr=0.77 t=259.0ps kin=3.77 pot=12.45 Rg=0.927 SPS=8950 \n",
      "bl=230 pos[1]=[-11.0 -5.1 -8.3] dr=0.68 t=260.0ps kin=3.28 pot=11.90 Rg=0.672 SPS=8786 \n",
      "bl=231 pos[1]=[-10.6 -4.9 -8.4] dr=0.57 t=261.0ps kin=1.92 pot=11.94 Rg=0.748 SPS=8968 \n",
      "bl=232 pos[1]=[-9.7 -5.3 -8.7] dr=0.78 t=262.0ps kin=1.82 pot=11.72 Rg=0.490 SPS=8782 \n",
      "bl=233 pos[1]=[-9.3 -5.3 -9.8] dr=0.83 t=263.0ps kin=2.16 pot=11.82 Rg=0.647 SPS=8805 \n",
      "bl=234 pos[1]=[-9.2 -5.0 -9.0] dr=0.58 t=264.0ps kin=3.25 pot=11.55 Rg=0.442 SPS=9014 \n",
      "bl=235 pos[1]=[-9.5 -5.1 -10.1] dr=0.55 t=265.0ps kin=3.34 pot=11.70 Rg=0.722 SPS=8845 \n",
      "bl=236 pos[1]=[-8.6 -4.5 -9.7] dr=0.63 t=266.0ps kin=4.42 pot=11.32 Rg=0.646 SPS=8774 \n",
      "bl=237 pos[1]=[-9.3 -5.4 -9.8] dr=0.81 t=267.0ps kin=3.40 pot=11.86 Rg=0.986 SPS=9021 \n",
      "bl=238 pos[1]=[-9.4 -5.5 -9.8] dr=0.59 t=268.0ps kin=3.58 pot=11.83 Rg=0.834 SPS=8654 \n",
      "bl=239 pos[1]=[-9.7 -4.9 -9.5] dr=0.71 t=269.0ps kin=2.88 pot=11.74 Rg=0.814 SPS=8820 \n",
      "bl=240 pos[1]=[-9.9 -4.3 -9.3] dr=0.69 t=270.0ps kin=1.90 pot=11.69 Rg=0.440 SPS=8966 \n",
      "bl=241 pos[1]=[-10.3 -4.7 -9.9] dr=0.55 t=271.0ps kin=2.93 pot=12.10 Rg=0.408 SPS=8817 \n",
      "bl=242 pos[1]=[-10.2 -4.0 -9.2] dr=0.75 t=272.0ps kin=3.17 pot=11.97 Rg=0.503 SPS=9002 \n",
      "bl=243 pos[1]=[-10.1 -4.4 -9.3] dr=0.72 t=273.0ps kin=3.98 pot=11.54 Rg=0.532 SPS=8580 \n",
      "bl=244 pos[1]=[-10.4 -4.5 -8.5] dr=0.64 t=274.0ps kin=3.58 pot=11.80 Rg=0.390 SPS=8623 \n",
      "bl=245 pos[1]=[-10.4 -4.2 -9.0] dr=0.53 t=275.0ps kin=3.63 pot=11.98 Rg=0.420 SPS=7829 \n",
      "bl=246 pos[1]=[-10.3 -4.5 -8.6] dr=0.60 t=276.0ps kin=3.21 pot=11.83 Rg=0.406 SPS=7772 \n",
      "bl=247 pos[1]=[-10.2 -4.1 -8.7] dr=0.42 t=277.0ps kin=2.90 pot=11.63 Rg=0.450 SPS=8413 \n",
      "bl=248 pos[1]=[-11.1 -4.0 -9.1] dr=0.49 t=278.0ps kin=3.98 pot=11.63 Rg=0.511 SPS=8684 \n",
      "bl=249 pos[1]=[-11.0 -4.3 -7.8] dr=0.97 t=279.0ps kin=3.50 pot=11.56 Rg=0.800 SPS=8980 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-10.4594903   -3.83772054  -8.16871872]   Rg =  0.79989004\n",
      "     median bond size is  0.3026642386826154\n",
      "     three shortest/longest (<10)/ bonds are  [0.13822635 0.20094276 0.27603895]    [0.42819588 0.48072175 0.65386942]\n",
      "     95 percentile of distance to center is:    1.2738780717178375\n",
      "     density of closest 95% monomers is:    1.097113124705863\n",
      "     density of the core monomers is:    13.557885674028837\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -10.98, -10.65, -10.46, -9.64\n",
      "     y: -4.35, -4.00, -3.84, -3.06\n",
      "     z: -8.95, -8.09, -8.17, -7.61\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  3.502574610084163 should be: 1.5\n",
      "     fastest particles are (in kT):  [3.26750747 4.13017533 4.85604577 8.90375692 9.75589561]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  11.558139038085939\n",
      "bl=250 pos[1]=[-10.8 -4.1 -7.3] dr=0.61 t=280.0ps kin=3.74 pot=11.34 Rg=0.516 SPS=8712 \n",
      "bl=251 pos[1]=[-10.5 -4.0 -7.2] dr=0.61 t=281.0ps kin=3.97 pot=11.21 Rg=0.348 SPS=8832 \n",
      "bl=252 pos[1]=[-10.7 -4.1 -7.7] dr=0.69 t=282.0ps kin=6.63 pot=11.55 Rg=0.524 SPS=8753 \n",
      "bl=253 pos[1]=[-10.3 -3.5 -7.8] dr=0.56 t=283.0ps kin=5.74 pot=11.48 Rg=0.425 SPS=9047 \n",
      "bl=254 pos[1]=[-10.0 -3.8 -8.2] dr=0.77 t=284.0ps kin=6.09 pot=11.08 Rg=0.345 SPS=8830 \n",
      "bl=255 pos[1]=[-9.6 -3.9 -8.3] dr=0.78 t=285.0ps kin=4.90 pot=10.80 Rg=0.408 SPS=8946 \n",
      "bl=256 pos[1]=[-9.3 -3.7 -7.7] dr=0.61 t=286.0ps kin=4.83 pot=10.55 Rg=0.488 SPS=8946 \n",
      "bl=257 pos[1]=[-8.5 -3.6 -7.8] dr=0.70 t=287.0ps kin=5.44 pot=10.35 Rg=0.312 SPS=8822 \n",
      "bl=258 pos[1]=[-8.2 -3.7 -7.7] dr=0.58 t=288.0ps kin=4.12 pot=10.20 Rg=0.350 SPS=8941 \n",
      "bl=259 pos[1]=[-7.8 -3.7 -7.5] dr=0.88 t=289.0ps kin=4.05 pot=10.03 Rg=0.484 SPS=8957 \n",
      "bl=260 pos[1]=[-7.2 -3.6 -7.0] dr=0.59 t=290.0ps kin=4.27 pot=9.77 Rg=0.623 SPS=8704 \n",
      "bl=261 pos[1]=[-7.8 -3.2 -7.0] dr=0.47 t=291.0ps kin=3.93 pot=9.87 Rg=0.792 SPS=9065 \n",
      "bl=262 pos[1]=[-8.0 -2.6 -7.2] dr=0.49 t=292.0ps kin=4.27 pot=9.77 Rg=0.893 SPS=8969 \n",
      "bl=263 pos[1]=[-7.9 -2.8 -7.6] dr=0.52 t=293.0ps kin=4.73 pot=9.71 Rg=0.785 SPS=8853 \n",
      "bl=264 pos[1]=[-7.6 -2.7 -7.5] dr=0.35 t=294.0ps kin=4.04 pot=9.46 Rg=0.636 SPS=8673 \n",
      "bl=265 pos[1]=[-7.0 -2.8 -7.7] dr=0.61 t=295.0ps kin=4.17 pot=9.74 Rg=0.590 SPS=8882 \n",
      "bl=266 pos[1]=[-7.7 -3.4 -7.7] dr=0.61 t=296.0ps kin=3.40 pot=9.71 Rg=0.822 SPS=9039 \n",
      "bl=267 pos[1]=[-7.6 -3.4 -8.7] dr=0.65 t=297.0ps kin=3.11 pot=9.82 Rg=0.693 SPS=8850 \n",
      "bl=268 pos[1]=[-7.0 -4.8 -8.9] dr=0.71 t=298.0ps kin=2.70 pot=10.19 Rg=0.789 SPS=8772 \n",
      "bl=269 pos[1]=[-6.8 -3.8 -9.3] dr=0.70 t=299.0ps kin=3.54 pot=10.33 Rg=0.472 SPS=8710 \n",
      "bl=270 pos[1]=[-6.8 -4.3 -9.6] dr=0.86 t=300.0ps kin=2.81 pot=10.69 Rg=0.731 SPS=8786 \n",
      "bl=271 pos[1]=[-6.8 -3.9 -10.0] dr=0.71 t=301.0ps kin=2.13 pot=10.77 Rg=0.420 SPS=8722 \n",
      "bl=272 pos[1]=[-7.2 -3.9 -10.4] dr=0.64 t=302.0ps kin=3.21 pot=11.05 Rg=0.664 SPS=8825 \n",
      "bl=273 pos[1]=[-7.9 -3.7 -10.3] dr=0.81 t=303.0ps kin=3.80 pot=10.59 Rg=0.737 SPS=8818 \n",
      "bl=274 pos[1]=[-7.4 -3.9 -10.2] dr=0.49 t=304.0ps kin=3.19 pot=10.65 Rg=0.921 SPS=8750 \n",
      "bl=275 pos[1]=[-7.2 -3.9 -10.7] dr=0.76 t=305.0ps kin=3.55 pot=10.79 Rg=0.866 SPS=8939 \n",
      "bl=276 pos[1]=[-7.7 -3.9 -10.9] dr=0.57 t=306.0ps kin=4.01 pot=10.87 Rg=1.187 SPS=8727 \n",
      "bl=277 pos[1]=[-7.9 -4.0 -11.4] dr=0.80 t=307.0ps kin=4.36 pot=11.54 Rg=1.146 SPS=8678 \n",
      "bl=278 pos[1]=[-7.4 -3.1 -12.3] dr=1.01 t=308.0ps kin=5.84 pot=11.41 Rg=0.683 SPS=8802 \n",
      "bl=279 pos[1]=[-8.0 -2.7 -13.1] dr=0.90 t=309.0ps kin=4.78 pot=11.78 Rg=0.551 SPS=8975 \n",
      "bl=280 pos[1]=[-7.2 -3.3 -13.4] dr=0.59 t=310.0ps kin=4.18 pot=11.71 Rg=0.582 SPS=9024 \n",
      "bl=281 pos[1]=[-7.5 -2.1 -13.4] dr=0.85 t=311.0ps kin=3.64 pot=11.73 Rg=0.496 SPS=8842 \n",
      "bl=282 pos[1]=[-7.5 -2.1 -13.7] dr=0.67 t=312.0ps kin=2.84 pot=11.95 Rg=0.519 SPS=8833 \n",
      "bl=283 pos[1]=[-7.8 -2.0 -14.5] dr=0.62 t=313.0ps kin=2.95 pot=12.04 Rg=0.669 SPS=8698 \n",
      "bl=284 pos[1]=[-7.3 -2.0 -14.8] dr=0.65 t=314.0ps kin=3.59 pot=12.41 Rg=0.704 SPS=8940 \n",
      "bl=285 pos[1]=[-7.1 -2.0 -14.9] dr=0.51 t=315.0ps kin=3.54 pot=12.40 Rg=0.910 SPS=8717 \n",
      "bl=286 pos[1]=[-8.0 -1.9 -15.1] dr=0.66 t=316.0ps kin=2.89 pot=12.35 Rg=0.870 SPS=8694 \n",
      "bl=287 pos[1]=[-7.1 -1.8 -15.3] dr=0.55 t=317.0ps kin=3.27 pot=12.22 Rg=0.942 SPS=8698 \n",
      "bl=288 pos[1]=[-6.9 -1.6 -15.1] dr=0.64 t=318.0ps kin=4.43 pot=12.27 Rg=1.023 SPS=9026 \n",
      "bl=289 pos[1]=[-6.8 -0.6 -15.4] dr=0.73 t=319.0ps kin=5.76 pot=12.32 Rg=0.908 SPS=8805 \n",
      "bl=290 pos[1]=[-7.2 -0.4 -14.9] dr=0.81 t=320.0ps kin=4.49 pot=11.94 Rg=0.482 SPS=8913 \n",
      "bl=291 pos[1]=[-6.8 -0.7 -15.0] dr=0.78 t=321.0ps kin=3.19 pot=11.93 Rg=0.453 SPS=8595 \n",
      "bl=292 pos[1]=[-7.1 -1.0 -14.8] dr=0.59 t=322.0ps kin=2.40 pot=12.10 Rg=0.465 SPS=8690 \n",
      "bl=293 pos[1]=[-8.0 -1.3 -14.7] dr=0.78 t=323.0ps kin=2.27 pot=12.61 Rg=0.775 SPS=8658 \n",
      "bl=294 pos[1]=[-7.6 -2.2 -15.2] dr=0.78 t=324.0ps kin=1.83 pot=12.92 Rg=0.608 SPS=8692 \n",
      "bl=295 pos[1]=[-8.1 -2.2 -15.9] dr=0.62 t=325.0ps kin=2.78 pot=13.11 Rg=0.596 SPS=8780 \n",
      "bl=296 pos[1]=[-8.2 -1.9 -16.2] dr=0.69 t=326.0ps kin=2.91 pot=13.65 Rg=0.678 SPS=8665 \n",
      "bl=297 pos[1]=[-8.4 -2.0 -16.4] dr=0.56 t=327.0ps kin=2.58 pot=13.61 Rg=0.753 SPS=8722 \n",
      "bl=298 pos[1]=[-7.7 -2.6 -16.6] dr=0.54 t=328.0ps kin=2.68 pot=13.57 Rg=0.716 SPS=8690 \n",
      "bl=299 pos[1]=[-7.8 -2.9 -17.1] dr=0.67 t=329.0ps kin=3.47 pot=13.57 Rg=0.770 SPS=8681 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [ -6.9570581   -2.07691833 -17.46098576]   Rg =  0.7699985\n",
      "     median bond size is  0.3846091488148675\n",
      "     three shortest/longest (<10)/ bonds are  [0.06967954 0.19571746 0.22984745]    [0.42773662 0.45362855 0.65423995]\n",
      "     95 percentile of distance to center is:    1.1236539676747523\n",
      "     density of closest 95% monomers is:    1.5985904974651821\n",
      "     density of the core monomers is:    12.181893131569245\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -7.77, -6.86, -6.96, -6.36\n",
      "     y: -2.91, -2.05, -2.08, -1.47\n",
      "     z: -18.01, -17.47, -17.46, -16.90\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  3.4704621021467426 should be: 1.5\n",
      "     fastest particles are (in kT):  [3.06574961 4.06743248 4.39896676 6.1819497  7.19053327]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  13.570306396484376\n",
      "bl=300 pos[1]=[-7.3 -2.3 -17.4] dr=0.63 t=330.0ps kin=3.88 pot=13.57 Rg=0.380 SPS=8689 \n",
      "bl=301 pos[1]=[-7.5 -2.5 -17.5] dr=0.57 t=331.0ps kin=3.33 pot=13.81 Rg=0.495 SPS=8201 \n",
      "bl=302 pos[1]=[-7.5 -2.1 -16.9] dr=0.59 t=332.0ps kin=3.46 pot=13.84 Rg=0.760 SPS=8549 \n",
      "bl=303 pos[1]=[-6.5 -2.5 -16.8] dr=0.66 t=333.0ps kin=2.43 pot=13.79 Rg=0.765 SPS=8913 \n",
      "bl=304 pos[1]=[-6.8 -1.8 -17.3] dr=0.70 t=334.0ps kin=3.78 pot=13.65 Rg=0.618 SPS=8615 \n",
      "bl=305 pos[1]=[-6.1 -2.6 -17.2] dr=0.73 t=335.0ps kin=3.13 pot=13.22 Rg=0.651 SPS=8659 \n",
      "bl=306 pos[1]=[-5.9 -1.5 -17.3] dr=0.72 t=336.0ps kin=3.37 pot=13.04 Rg=0.648 SPS=8958 \n",
      "bl=307 pos[1]=[-6.0 -1.9 -17.7] dr=0.71 t=337.0ps kin=3.13 pot=13.08 Rg=0.610 SPS=8667 \n",
      "bl=308 pos[1]=[-5.6 -2.1 -18.0] dr=0.89 t=338.0ps kin=5.31 pot=13.06 Rg=0.624 SPS=8397 \n",
      "bl=309 pos[1]=[-4.7 -2.1 -18.8] dr=0.93 t=339.0ps kin=4.86 pot=12.84 Rg=0.496 SPS=8928 \n",
      "bl=310 pos[1]=[-4.7 -2.5 -19.9] dr=0.85 t=340.0ps kin=4.09 pot=13.02 Rg=0.568 SPS=8629 \n",
      "bl=311 pos[1]=[-4.0 -2.6 -21.2] dr=0.86 t=341.0ps kin=4.40 pot=13.30 Rg=0.517 SPS=8782 \n",
      "bl=312 pos[1]=[-3.8 -1.7 -20.3] dr=0.76 t=342.0ps kin=4.30 pot=13.04 Rg=0.539 SPS=8862 \n",
      "bl=313 pos[1]=[-3.0 -1.2 -20.4] dr=0.89 t=343.0ps kin=4.37 pot=12.82 Rg=0.476 SPS=8989 \n",
      "bl=314 pos[1]=[-3.0 -0.6 -20.9] dr=0.51 t=344.0ps kin=3.88 pot=13.01 Rg=0.636 SPS=8711 \n",
      "bl=315 pos[1]=[-3.4 -1.0 -21.2] dr=0.62 t=345.0ps kin=3.94 pot=13.07 Rg=0.513 SPS=8890 \n",
      "bl=316 pos[1]=[-3.0 -0.9 -20.8] dr=0.55 t=346.0ps kin=4.44 pot=13.25 Rg=0.477 SPS=8849 \n",
      "bl=317 pos[1]=[-3.8 -1.3 -20.8] dr=1.05 t=347.0ps kin=4.57 pot=14.01 Rg=0.513 SPS=8756 \n",
      "bl=318 pos[1]=[-3.5 -1.5 -21.9] dr=0.75 t=348.0ps kin=3.83 pot=14.25 Rg=0.516 SPS=8904 \n",
      "bl=319 pos[1]=[-4.5 -1.5 -23.0] dr=0.75 t=349.0ps kin=2.73 pot=14.46 Rg=0.336 SPS=8768 \n",
      "bl=320 pos[1]=[-4.1 -1.7 -22.9] dr=0.57 t=350.0ps kin=2.76 pot=14.41 Rg=0.262 SPS=8847 \n",
      "bl=321 pos[1]=[-4.2 -1.7 -23.2] dr=0.53 t=351.0ps kin=3.12 pot=14.70 Rg=0.330 SPS=8987 \n",
      "bl=322 pos[1]=[-4.1 -1.8 -24.2] dr=0.72 t=352.0ps kin=3.50 pot=15.07 Rg=0.398 SPS=8325 \n",
      "bl=323 pos[1]=[-4.2 -2.7 -23.4] dr=0.69 t=353.0ps kin=2.22 pot=15.41 Rg=0.627 SPS=8682 \n",
      "bl=324 pos[1]=[-4.5 -2.6 -23.8] dr=0.47 t=354.0ps kin=2.45 pot=15.34 Rg=0.432 SPS=8744 \n",
      "bl=325 pos[1]=[-4.5 -2.1 -23.5] dr=0.60 t=355.0ps kin=2.36 pot=15.27 Rg=0.337 SPS=8620 \n",
      "bl=326 pos[1]=[-4.3 -2.3 -23.3] dr=0.59 t=356.0ps kin=3.01 pot=15.43 Rg=0.406 SPS=8187 \n",
      "bl=327 pos[1]=[-4.1 -2.6 -24.1] dr=0.88 t=357.0ps kin=3.32 pot=15.71 Rg=0.537 SPS=8506 \n",
      "bl=328 pos[1]=[-4.2 -2.7 -23.5] dr=0.68 t=358.0ps kin=4.05 pot=15.74 Rg=0.403 SPS=8987 \n",
      "bl=329 pos[1]=[-4.3 -2.8 -23.9] dr=0.59 t=359.0ps kin=3.86 pot=15.95 Rg=0.453 SPS=8908 \n",
      "bl=330 pos[1]=[-4.5 -2.8 -23.4] dr=0.61 t=360.0ps kin=2.93 pot=16.38 Rg=0.639 SPS=8447 \n",
      "bl=331 pos[1]=[-4.9 -2.1 -23.9] dr=0.99 t=361.0ps kin=4.03 pot=15.82 Rg=0.533 SPS=9007 \n",
      "bl=332 pos[1]=[-4.5 -2.0 -23.8] dr=0.50 t=362.0ps kin=3.41 pot=15.46 Rg=0.536 SPS=8564 \n",
      "bl=333 pos[1]=[-4.1 -2.1 -24.3] dr=0.51 t=363.0ps kin=2.48 pot=15.51 Rg=0.623 SPS=8895 \n",
      "bl=334 pos[1]=[-4.1 -1.9 -24.2] dr=0.84 t=364.0ps kin=3.05 pot=14.96 Rg=0.777 SPS=8844 \n",
      "bl=335 pos[1]=[-2.9 -1.6 -24.4] dr=0.76 t=365.0ps kin=3.31 pot=14.71 Rg=0.580 SPS=9045 \n",
      "bl=336 pos[1]=[-3.4 -1.6 -23.8] dr=0.54 t=366.0ps kin=2.28 pot=14.39 Rg=0.440 SPS=8761 \n",
      "bl=337 pos[1]=[-2.7 -1.4 -22.7] dr=0.76 t=367.0ps kin=2.98 pot=14.51 Rg=0.601 SPS=8865 \n",
      "bl=338 pos[1]=[-2.7 -1.9 -23.0] dr=0.70 t=368.0ps kin=2.88 pot=14.69 Rg=0.555 SPS=8854 \n",
      "bl=339 pos[1]=[-3.5 -2.0 -23.9] dr=0.62 t=369.0ps kin=2.90 pot=15.01 Rg=0.455 SPS=9010 \n",
      "bl=340 pos[1]=[-3.7 -2.7 -23.5] dr=0.62 t=370.0ps kin=3.87 pot=15.25 Rg=0.341 SPS=8780 \n",
      "bl=341 pos[1]=[-3.9 -3.5 -23.7] dr=0.54 t=371.0ps kin=5.11 pot=15.46 Rg=0.364 SPS=8835 \n",
      "bl=342 pos[1]=[-4.6 -2.5 -23.5] dr=0.82 t=372.0ps kin=4.99 pot=15.50 Rg=0.604 SPS=8590 \n",
      "bl=343 pos[1]=[-4.4 -2.7 -23.1] dr=0.49 t=373.0ps kin=3.83 pot=15.57 Rg=0.654 SPS=8972 \n",
      "bl=344 pos[1]=[-5.1 -2.6 -23.8] dr=0.64 t=374.0ps kin=4.36 pot=15.65 Rg=0.739 SPS=8369 \n",
      "bl=345 pos[1]=[-5.0 -3.0 -24.1] dr=0.64 t=375.0ps kin=4.59 pot=15.62 Rg=0.588 SPS=8883 \n",
      "bl=346 pos[1]=[-4.8 -2.7 -23.5] dr=0.54 t=376.0ps kin=4.04 pot=15.72 Rg=0.372 SPS=8802 \n",
      "bl=347 pos[1]=[-4.8 -3.0 -22.7] dr=0.85 t=377.0ps kin=4.01 pot=15.49 Rg=0.304 SPS=9060 \n",
      "bl=348 pos[1]=[-4.5 -2.6 -23.3] dr=0.55 t=378.0ps kin=2.24 pot=15.47 Rg=0.455 SPS=8478 \n",
      "bl=349 pos[1]=[-4.7 -3.0 -22.9] dr=0.75 t=379.0ps kin=3.66 pot=15.29 Rg=0.677 SPS=8877 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [ -4.74920974  -2.77802436 -22.48913288]   Rg =  0.6768085\n",
      "     median bond size is  0.2989124361455544\n",
      "     three shortest/longest (<10)/ bonds are  [0.19539162 0.21329073 0.21400585]    [0.39466932 0.52073099 0.52660154]\n",
      "     95 percentile of distance to center is:    0.948579731888487\n",
      "     density of closest 95% monomers is:    2.6571341017828782\n",
      "     density of the core monomers is:    7.666077833382917\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -4.95, -4.80, -4.75, -4.45\n",
      "     y: -3.40, -2.90, -2.78, -1.98\n",
      "     z: -22.95, -22.62, -22.49, -21.77\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  3.6605388313841636 should be: 1.5\n",
      "     fastest particles are (in kT):  [3.77285172 4.5262635  5.47088405 5.69470256 7.50751067]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  15.290472412109375\n",
      "bl=350 pos[1]=[-5.5 -3.0 -22.0] dr=0.95 t=380.0ps kin=4.16 pot=15.56 Rg=0.796 SPS=8983 \n",
      "bl=351 pos[1]=[-5.9 -3.2 -21.9] dr=0.72 t=381.0ps kin=4.50 pot=15.79 Rg=0.710 SPS=8579 \n",
      "bl=352 pos[1]=[-5.8 -3.2 -21.6] dr=0.67 t=382.0ps kin=3.96 pot=15.54 Rg=0.501 SPS=8945 \n",
      "bl=353 pos[1]=[-6.3 -3.2 -22.0] dr=0.47 t=383.0ps kin=4.33 pot=15.99 Rg=0.509 SPS=8763 \n",
      "bl=354 pos[1]=[-6.5 -2.1 -21.9] dr=0.82 t=384.0ps kin=4.86 pot=15.79 Rg=0.467 SPS=8856 \n",
      "bl=355 pos[1]=[-7.0 -2.0 -21.4] dr=0.86 t=385.0ps kin=5.59 pot=15.49 Rg=0.497 SPS=8820 \n",
      "bl=356 pos[1]=[-7.4 -2.8 -21.3] dr=0.52 t=386.0ps kin=4.25 pot=15.51 Rg=0.503 SPS=8062 \n",
      "bl=357 pos[1]=[-7.7 -2.4 -20.8] dr=0.54 t=387.0ps kin=4.38 pot=15.54 Rg=0.476 SPS=8996 \n",
      "bl=358 pos[1]=[-7.6 -1.8 -20.5] dr=0.65 t=388.0ps kin=3.96 pot=15.33 Rg=0.441 SPS=8985 \n",
      "bl=359 pos[1]=[-7.8 -1.1 -19.8] dr=0.88 t=389.0ps kin=4.36 pot=15.15 Rg=0.622 SPS=9067 \n",
      "bl=360 pos[1]=[-8.4 -1.2 -18.9] dr=0.97 t=390.0ps kin=4.00 pot=14.85 Rg=0.933 SPS=8504 \n",
      "bl=361 pos[1]=[-8.3 -0.9 -18.7] dr=0.76 t=391.0ps kin=4.37 pot=14.73 Rg=0.944 SPS=8796 \n",
      "bl=362 pos[1]=[-8.8 -1.4 -19.3] dr=0.64 t=392.0ps kin=3.99 pot=14.91 Rg=0.830 SPS=8940 \n",
      "bl=363 pos[1]=[-8.8 -0.5 -19.7] dr=0.63 t=393.0ps kin=4.02 pot=14.81 Rg=0.778 SPS=8728 \n",
      "bl=364 pos[1]=[-8.8 -0.4 -20.4] dr=0.48 t=394.0ps kin=3.30 pot=14.95 Rg=0.752 SPS=8854 \n",
      "bl=365 pos[1]=[-9.1 -0.9 -19.6] dr=0.80 t=395.0ps kin=3.00 pot=14.79 Rg=0.427 SPS=8482 \n",
      "bl=366 pos[1]=[-9.4 -1.1 -19.7] dr=0.43 t=396.0ps kin=3.57 pot=14.92 Rg=0.354 SPS=8819 \n",
      "bl=367 pos[1]=[-8.7 -0.9 -19.6] dr=0.48 t=397.0ps kin=3.37 pot=14.97 Rg=0.281 SPS=8738 \n",
      "bl=368 pos[1]=[-8.7 -0.7 -19.3] dr=0.46 t=398.0ps kin=3.80 pot=14.74 Rg=0.385 SPS=8935 \n",
      "bl=369 pos[1]=[-8.4 -1.1 -18.8] dr=0.82 t=399.0ps kin=3.08 pot=15.18 Rg=0.899 SPS=8713 \n",
      "bl=370 pos[1]=[-8.1 -0.5 -18.6] dr=0.76 t=400.0ps kin=4.69 pot=14.97 Rg=0.871 SPS=8899 \n",
      "bl=371 pos[1]=[-9.2 -0.3 -18.5] dr=0.77 t=401.0ps kin=4.40 pot=15.03 Rg=0.688 SPS=8854 \n",
      "bl=372 pos[1]=[-9.5 -0.4 -18.1] dr=0.69 t=402.0ps kin=5.97 pot=14.68 Rg=0.592 SPS=8528 \n",
      "bl=373 pos[1]=[-8.5 -0.4 -17.9] dr=0.85 t=403.0ps kin=5.22 pot=14.14 Rg=0.603 SPS=9021 \n",
      "bl=374 pos[1]=[-8.8 -0.8 -17.0] dr=0.68 t=404.0ps kin=6.38 pot=14.28 Rg=0.894 SPS=8848 \n",
      "bl=375 pos[1]=[-8.5 -0.1 -16.7] dr=0.60 t=405.0ps kin=6.40 pot=13.83 Rg=0.848 SPS=8717 \n",
      "bl=376 pos[1]=[-7.4 0.4 -17.0] dr=1.01 t=406.0ps kin=4.55 pot=13.21 Rg=0.619 SPS=8921 \n",
      "bl=377 pos[1]=[-8.4 1.0 -17.3] dr=0.65 t=407.0ps kin=4.11 pot=13.29 Rg=0.539 SPS=8211 \n",
      "bl=378 pos[1]=[-8.3 0.6 -17.7] dr=0.48 t=408.0ps kin=3.75 pot=13.19 Rg=0.451 SPS=8871 \n",
      "bl=379 pos[1]=[-8.5 0.2 -17.6] dr=0.90 t=409.0ps kin=4.80 pot=13.79 Rg=0.589 SPS=9010 \n",
      "bl=380 pos[1]=[-8.1 0.2 -17.7] dr=0.53 t=410.0ps kin=3.61 pot=13.66 Rg=0.801 SPS=8974 \n",
      "bl=381 pos[1]=[-8.6 0.6 -18.2] dr=0.60 t=411.0ps kin=4.42 pot=13.55 Rg=0.701 SPS=9023 \n",
      "bl=382 pos[1]=[-8.8 1.0 -17.7] dr=0.68 t=412.0ps kin=5.75 pot=13.45 Rg=0.718 SPS=8545 \n",
      "bl=383 pos[1]=[-8.3 1.3 -18.1] dr=0.54 t=413.0ps kin=4.45 pot=13.95 Rg=0.888 SPS=8880 \n",
      "bl=384 pos[1]=[-8.4 2.0 -18.1] dr=0.98 t=414.0ps kin=4.15 pot=13.83 Rg=0.949 SPS=8949 \n",
      "bl=385 pos[1]=[-8.5 1.9 -18.5] dr=0.50 t=415.0ps kin=3.64 pot=13.64 Rg=0.785 SPS=9044 \n",
      "bl=386 pos[1]=[-8.2 1.7 -17.9] dr=0.77 t=416.0ps kin=3.28 pot=13.17 Rg=0.698 SPS=8730 \n",
      "bl=387 pos[1]=[-8.7 1.7 -17.2] dr=0.62 t=417.0ps kin=3.61 pot=12.95 Rg=0.586 SPS=8881 \n",
      "bl=388 pos[1]=[-8.9 1.6 -17.5] dr=0.56 t=418.0ps kin=3.35 pot=12.85 Rg=0.409 SPS=9010 \n",
      "bl=389 pos[1]=[-9.3 2.6 -17.5] dr=0.72 t=419.0ps kin=3.58 pot=12.76 Rg=0.403 SPS=8401 \n",
      "bl=390 pos[1]=[-9.9 2.6 -16.3] dr=0.70 t=420.0ps kin=3.74 pot=12.84 Rg=0.538 SPS=8732 \n",
      "bl=391 pos[1]=[-9.8 1.8 -16.6] dr=0.62 t=421.0ps kin=4.33 pot=13.08 Rg=0.594 SPS=8751 \n",
      "bl=392 pos[1]=[-10.1 1.4 -16.3] dr=0.76 t=422.0ps kin=4.17 pot=13.37 Rg=0.718 SPS=8472 \n",
      "bl=393 pos[1]=[-10.3 1.8 -16.0] dr=0.69 t=423.0ps kin=2.80 pot=13.50 Rg=0.645 SPS=8995 \n",
      "bl=394 pos[1]=[-10.6 1.7 -16.3] dr=0.73 t=424.0ps kin=3.10 pot=13.86 Rg=0.634 SPS=8691 \n",
      "bl=395 pos[1]=[-10.0 1.7 -16.2] dr=0.69 t=425.0ps kin=4.02 pot=14.08 Rg=0.954 SPS=9132 \n",
      "bl=396 pos[1]=[-10.5 0.7 -16.7] dr=0.81 t=426.0ps kin=4.87 pot=13.47 Rg=0.391 SPS=8306 \n",
      "bl=397 pos[1]=[-9.5 1.2 -16.6] dr=0.70 t=427.0ps kin=6.31 pot=13.10 Rg=0.516 SPS=9112 \n",
      "bl=398 pos[1]=[-8.3 1.2 -17.2] dr=0.73 t=428.0ps kin=4.56 pot=13.17 Rg=0.863 SPS=8692 \n",
      "bl=399 pos[1]=[-8.1 -0.1 -17.3] dr=0.98 t=429.0ps kin=4.04 pot=13.49 Rg=0.816 SPS=8275 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [-9.24929199e+00  4.68029212e-03 -1.68227570e+01]   Rg =  0.81600845\n",
      "     median bond size is  0.3156951985452715\n",
      "     three shortest/longest (<10)/ bonds are  [0.21112407 0.22013005 0.25008355]    [0.43194624 0.58942245 0.87153261]\n",
      "     95 percentile of distance to center is:    1.2066041299419954\n",
      "     density of closest 95% monomers is:    1.2910426024195374\n",
      "     density of the core monomers is:    3.137301104939283\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -10.28, -8.95, -9.25, -8.10\n",
      "     y: -0.35, -0.03, 0.00, 0.37\n",
      "     z: -17.27, -16.93, -16.82, -16.28\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  4.0439508026924 should be: 1.5\n",
      "     fastest particles are (in kT):  [ 3.03522292  3.23582706  6.87122308  6.87513162 12.11280547]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  13.486607360839844\n",
      "bl=400 pos[1]=[-7.7 -0.7 -16.4] dr=0.87 t=430.0ps kin=3.54 pot=13.31 Rg=0.733 SPS=8689 \n",
      "bl=401 pos[1]=[-7.1 -0.6 -17.2] dr=0.68 t=431.0ps kin=4.00 pot=13.08 Rg=0.971 SPS=8673 \n",
      "bl=402 pos[1]=[-7.4 -0.9 -17.8] dr=1.01 t=432.0ps kin=3.26 pot=12.69 Rg=0.781 SPS=8663 \n",
      "bl=403 pos[1]=[-6.6 -0.6 -17.7] dr=0.59 t=433.0ps kin=4.09 pot=12.40 Rg=0.611 SPS=8633 \n",
      "bl=404 pos[1]=[-6.0 -1.1 -18.0] dr=0.78 t=434.0ps kin=4.23 pot=12.63 Rg=0.641 SPS=8824 \n",
      "bl=405 pos[1]=[-6.1 -1.6 -17.5] dr=0.67 t=435.0ps kin=4.02 pot=12.87 Rg=0.648 SPS=8854 \n",
      "bl=406 pos[1]=[-5.6 -1.1 -18.0] dr=0.57 t=436.0ps kin=3.91 pot=12.87 Rg=0.596 SPS=8606 \n",
      "bl=407 pos[1]=[-5.1 -1.3 -17.7] dr=0.74 t=437.0ps kin=3.35 pot=12.88 Rg=0.697 SPS=8769 \n",
      "bl=408 pos[1]=[-5.1 -0.9 -17.4] dr=0.54 t=438.0ps kin=2.83 pot=12.63 Rg=0.600 SPS=8807 \n",
      "bl=409 pos[1]=[-5.5 0.3 -17.2] dr=0.68 t=439.0ps kin=2.64 pot=12.59 Rg=0.805 SPS=8921 \n",
      "bl=410 pos[1]=[-5.8 -0.6 -17.1] dr=0.68 t=440.0ps kin=2.71 pot=12.34 Rg=0.636 SPS=8855 \n",
      "bl=411 pos[1]=[-6.2 -0.9 -17.2] dr=0.60 t=441.0ps kin=4.35 pot=12.57 Rg=0.757 SPS=8924 \n",
      "bl=412 pos[1]=[-5.7 -0.8 -16.8] dr=0.52 t=442.0ps kin=3.43 pot=12.23 Rg=0.736 SPS=9063 \n",
      "bl=413 pos[1]=[-5.1 -0.9 -17.0] dr=0.63 t=443.0ps kin=3.44 pot=12.13 Rg=0.710 SPS=8733 \n",
      "bl=414 pos[1]=[-4.8 -0.4 -16.4] dr=0.62 t=444.0ps kin=3.82 pot=11.75 Rg=0.674 SPS=8630 \n",
      "bl=415 pos[1]=[-4.8 -0.3 -16.7] dr=0.40 t=445.0ps kin=3.34 pot=11.67 Rg=0.698 SPS=9060 \n",
      "bl=416 pos[1]=[-5.1 0.7 -16.8] dr=0.82 t=446.0ps kin=3.79 pot=12.12 Rg=0.760 SPS=8725 \n",
      "bl=417 pos[1]=[-5.4 -0.3 -16.1] dr=0.88 t=447.0ps kin=3.53 pot=11.54 Rg=0.515 SPS=8995 \n",
      "bl=418 pos[1]=[-5.5 0.1 -16.1] dr=0.65 t=448.0ps kin=3.31 pot=11.13 Rg=0.494 SPS=8408 \n",
      "bl=419 pos[1]=[-6.1 0.2 -16.1] dr=0.76 t=449.0ps kin=3.43 pot=11.33 Rg=0.565 SPS=8870 \n",
      "bl=420 pos[1]=[-5.7 0.6 -16.1] dr=0.60 t=450.0ps kin=4.76 pot=11.11 Rg=0.428 SPS=8874 \n",
      "bl=421 pos[1]=[-5.6 0.7 -15.7] dr=0.61 t=451.0ps kin=3.49 pot=11.06 Rg=0.594 SPS=8892 \n",
      "bl=422 pos[1]=[-5.4 1.1 -15.1] dr=0.51 t=452.0ps kin=4.57 pot=10.81 Rg=0.543 SPS=8728 \n",
      "bl=423 pos[1]=[-5.7 0.9 -15.9] dr=0.70 t=453.0ps kin=2.60 pot=10.93 Rg=0.415 SPS=9052 \n",
      "bl=424 pos[1]=[-4.9 -0.2 -16.2] dr=0.71 t=454.0ps kin=3.45 pot=10.85 Rg=0.390 SPS=8810 \n",
      "bl=425 pos[1]=[-5.0 -0.0 -16.0] dr=0.53 t=455.0ps kin=2.29 pot=10.94 Rg=0.644 SPS=8867 \n",
      "bl=426 pos[1]=[-5.4 0.4 -16.4] dr=0.65 t=456.0ps kin=2.81 pot=11.19 Rg=0.611 SPS=8682 \n",
      "bl=427 pos[1]=[-4.7 0.7 -16.8] dr=0.52 t=457.0ps kin=4.06 pot=11.25 Rg=0.879 SPS=8668 \n",
      "bl=428 pos[1]=[-5.2 0.8 -16.9] dr=0.75 t=458.0ps kin=4.36 pot=11.26 Rg=0.597 SPS=9039 \n",
      "bl=429 pos[1]=[-5.0 0.5 -16.3] dr=0.73 t=459.0ps kin=4.27 pot=11.12 Rg=0.577 SPS=8645 \n",
      "bl=430 pos[1]=[-5.3 0.7 -16.4] dr=0.64 t=460.0ps kin=4.41 pot=10.87 Rg=0.424 SPS=8647 \n",
      "bl=431 pos[1]=[-5.2 0.5 -16.5] dr=0.54 t=461.0ps kin=2.29 pot=10.61 Rg=0.421 SPS=9061 \n",
      "bl=432 pos[1]=[-5.3 0.4 -16.9] dr=0.72 t=462.0ps kin=1.74 pot=10.55 Rg=0.708 SPS=8815 \n",
      "bl=433 pos[1]=[-5.1 0.8 -16.7] dr=0.90 t=463.0ps kin=2.84 pot=10.22 Rg=0.488 SPS=9016 \n",
      "bl=434 pos[1]=[-5.5 0.9 -16.7] dr=0.65 t=464.0ps kin=2.00 pot=10.46 Rg=0.537 SPS=8285 \n",
      "bl=435 pos[1]=[-4.4 0.6 -16.3] dr=0.85 t=465.0ps kin=2.61 pot=9.99 Rg=0.450 SPS=9021 \n",
      "bl=436 pos[1]=[-4.8 1.1 -16.6] dr=0.51 t=466.0ps kin=2.56 pot=9.95 Rg=0.247 SPS=8641 \n",
      "bl=437 pos[1]=[-5.1 1.3 -16.4] dr=0.46 t=467.0ps kin=2.13 pot=10.26 Rg=0.483 SPS=8826 \n",
      "bl=438 pos[1]=[-5.3 1.3 -16.2] dr=0.59 t=468.0ps kin=2.30 pot=10.46 Rg=0.702 SPS=9060 \n",
      "bl=439 pos[1]=[-5.1 1.3 -15.5] dr=0.81 t=469.0ps kin=3.31 pot=10.25 Rg=0.860 SPS=8042 \n",
      "bl=440 pos[1]=[-5.0 1.4 -15.6] dr=0.60 t=470.0ps kin=3.55 pot=9.84 Rg=0.721 SPS=8436 \n",
      "bl=441 pos[1]=[-4.8 2.0 -16.3] dr=0.60 t=471.0ps kin=4.48 pot=9.91 Rg=0.652 SPS=9022 \n",
      "bl=442 pos[1]=[-4.0 2.2 -16.3] dr=0.76 t=472.0ps kin=4.23 pot=9.87 Rg=0.595 SPS=8958 \n",
      "bl=443 pos[1]=[-3.0 2.0 -17.9] dr=1.01 t=473.0ps kin=3.53 pot=10.32 Rg=0.994 SPS=8784 \n",
      "bl=444 pos[1]=[-3.8 2.0 -17.5] dr=0.63 t=474.0ps kin=3.09 pot=10.52 Rg=0.982 SPS=8932 \n",
      "bl=445 pos[1]=[-3.3 1.6 -17.4] dr=0.58 t=475.0ps kin=2.87 pot=10.23 Rg=0.801 SPS=8850 \n",
      "bl=446 pos[1]=[-3.6 1.8 -17.7] dr=0.42 t=476.0ps kin=3.03 pot=10.26 Rg=0.699 SPS=8519 \n",
      "bl=447 pos[1]=[-3.2 1.6 -17.7] dr=0.63 t=477.0ps kin=2.62 pot=10.07 Rg=0.547 SPS=8831 \n",
      "bl=448 pos[1]=[-3.4 2.4 -17.3] dr=0.59 t=478.0ps kin=3.02 pot=9.77 Rg=0.669 SPS=9094 \n",
      "bl=449 pos[1]=[-3.4 1.5 -16.4] dr=0.71 t=479.0ps kin=4.42 pot=9.83 Rg=0.395 SPS=8639 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [ -3.36345565   1.21985627 -16.87211704]   Rg =  0.39454287\n",
      "     median bond size is  0.3842564318687674\n",
      "     three shortest/longest (<10)/ bonds are  [0.16260213 0.19316801 0.19575652]    [0.43907618 0.45354976 0.57944568]\n",
      "     95 percentile of distance to center is:    0.5350407463409294\n",
      "     density of closest 95% monomers is:    14.807250524458647\n",
      "     density of the core monomers is:    15.212422000844722\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -3.73, -3.44, -3.36, -3.11\n",
      "     y: 0.97, 1.17, 1.22, 1.54\n",
      "     z: -17.26, -16.83, -16.87, -16.36\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  4.422873201247623 should be: 1.5\n",
      "     fastest particles are (in kT):  [4.31831137 6.61352599 6.78292078 8.14318957 9.08868905]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  9.8281982421875\n",
      "bl=450 pos[1]=[-2.8 0.9 -16.7] dr=0.77 t=480.0ps kin=4.68 pot=10.10 Rg=0.424 SPS=8643 \n",
      "bl=451 pos[1]=[-3.4 0.7 -16.9] dr=0.63 t=481.0ps kin=5.00 pot=10.25 Rg=0.402 SPS=9143 \n",
      "bl=452 pos[1]=[-2.8 -0.2 -16.3] dr=0.77 t=482.0ps kin=4.15 pot=10.57 Rg=0.724 SPS=8524 \n",
      "bl=453 pos[1]=[-2.3 -0.6 -16.2] dr=0.63 t=483.0ps kin=5.34 pot=10.79 Rg=0.936 SPS=8974 \n",
      "bl=454 pos[1]=[-2.2 -0.0 -17.1] dr=0.90 t=484.0ps kin=4.55 pot=10.49 Rg=0.561 SPS=8614 \n",
      "bl=455 pos[1]=[-2.3 0.4 -17.8] dr=0.68 t=485.0ps kin=4.56 pot=10.41 Rg=0.392 SPS=8666 \n",
      "bl=456 pos[1]=[-1.8 0.4 -17.0] dr=0.83 t=486.0ps kin=5.58 pot=10.31 Rg=0.501 SPS=8850 \n",
      "bl=457 pos[1]=[-2.0 0.4 -17.9] dr=0.52 t=487.0ps kin=3.64 pot=10.17 Rg=0.457 SPS=9128 \n",
      "bl=458 pos[1]=[-2.3 0.7 -18.0] dr=0.66 t=488.0ps kin=2.96 pot=10.32 Rg=0.444 SPS=8707 \n",
      "bl=459 pos[1]=[-2.2 1.1 -17.6] dr=0.81 t=489.0ps kin=3.82 pot=9.58 Rg=0.548 SPS=8666 \n",
      "bl=460 pos[1]=[-2.5 1.0 -17.4] dr=0.46 t=490.0ps kin=3.16 pot=9.90 Rg=0.524 SPS=8799 \n",
      "bl=461 pos[1]=[-2.6 1.1 -16.9] dr=0.66 t=491.0ps kin=3.02 pot=9.43 Rg=0.450 SPS=8414 \n",
      "bl=462 pos[1]=[-3.0 1.3 -17.0] dr=0.79 t=492.0ps kin=3.72 pot=9.20 Rg=0.558 SPS=8780 \n",
      "bl=463 pos[1]=[-3.4 1.8 -16.4] dr=0.85 t=493.0ps kin=3.09 pot=9.52 Rg=0.537 SPS=8800 \n",
      "bl=464 pos[1]=[-3.4 1.8 -16.9] dr=0.49 t=494.0ps kin=2.90 pot=9.25 Rg=0.566 SPS=8799 \n",
      "bl=465 pos[1]=[-3.3 2.2 -15.8] dr=0.94 t=495.0ps kin=2.57 pot=9.42 Rg=0.823 SPS=8110 \n",
      "bl=466 pos[1]=[-3.4 2.4 -15.7] dr=0.54 t=496.0ps kin=3.58 pot=9.47 Rg=0.992 SPS=8872 \n",
      "bl=467 pos[1]=[-3.7 3.2 -15.8] dr=0.63 t=497.0ps kin=3.38 pot=9.32 Rg=0.970 SPS=9070 \n",
      "bl=468 pos[1]=[-3.4 3.3 -15.4] dr=0.72 t=498.0ps kin=3.76 pot=8.91 Rg=1.074 SPS=9097 \n",
      "bl=469 pos[1]=[-2.9 3.3 -15.3] dr=0.59 t=499.0ps kin=2.56 pot=8.83 Rg=1.211 SPS=8032 \n",
      "bl=470 pos[1]=[-2.7 2.9 -15.4] dr=0.56 t=500.0ps kin=3.02 pot=8.73 Rg=0.927 SPS=8178 \n",
      "bl=471 pos[1]=[-2.9 3.2 -15.3] dr=0.54 t=501.0ps kin=3.37 pot=8.59 Rg=0.997 SPS=8240 \n",
      "bl=472 pos[1]=[-3.3 2.0 -15.3] dr=0.78 t=502.0ps kin=4.78 pot=9.15 Rg=0.999 SPS=8574 \n",
      "bl=473 pos[1]=[-3.4 2.5 -15.6] dr=0.60 t=503.0ps kin=3.74 pot=9.06 Rg=0.824 SPS=9052 \n",
      "bl=474 pos[1]=[-3.8 2.0 -15.4] dr=0.55 t=504.0ps kin=3.82 pot=9.17 Rg=0.838 SPS=8789 \n",
      "bl=475 pos[1]=[-4.3 1.7 -15.5] dr=0.75 t=505.0ps kin=3.62 pot=9.58 Rg=0.682 SPS=8895 \n",
      "bl=476 pos[1]=[-3.7 2.6 -15.6] dr=0.73 t=506.0ps kin=4.37 pot=9.18 Rg=0.699 SPS=7879 \n",
      "bl=477 pos[1]=[-3.8 2.2 -16.1] dr=0.48 t=507.0ps kin=4.35 pot=9.26 Rg=0.574 SPS=8235 \n",
      "bl=478 pos[1]=[-4.3 2.8 -16.2] dr=0.77 t=508.0ps kin=3.21 pot=9.37 Rg=0.753 SPS=8693 \n",
      "bl=479 pos[1]=[-4.9 3.0 -16.4] dr=0.57 t=509.0ps kin=2.95 pot=9.09 Rg=0.716 SPS=8887 \n",
      "bl=480 pos[1]=[-4.6 2.8 -16.5] dr=0.59 t=510.0ps kin=3.72 pot=9.37 Rg=0.818 SPS=8602 \n",
      "bl=481 pos[1]=[-4.7 2.8 -16.1] dr=0.52 t=511.0ps kin=3.24 pot=8.89 Rg=0.460 SPS=9097 \n",
      "bl=482 pos[1]=[-3.8 3.1 -16.2] dr=0.87 t=512.0ps kin=2.98 pot=8.88 Rg=0.298 SPS=8982 \n",
      "bl=483 pos[1]=[-3.5 3.0 -15.6] dr=0.51 t=513.0ps kin=2.61 pot=9.01 Rg=0.478 SPS=8244 \n",
      "bl=484 pos[1]=[-3.1 3.3 -15.8] dr=0.57 t=514.0ps kin=2.49 pot=8.58 Rg=0.559 SPS=8796 \n",
      "bl=485 pos[1]=[-3.0 3.6 -15.6] dr=0.71 t=515.0ps kin=3.17 pot=8.46 Rg=0.467 SPS=8985 \n",
      "bl=486 pos[1]=[-3.7 3.7 -16.2] dr=0.50 t=516.0ps kin=2.78 pot=8.47 Rg=0.457 SPS=8917 \n",
      "bl=487 pos[1]=[-3.6 3.6 -16.3] dr=0.67 t=517.0ps kin=3.15 pot=8.49 Rg=0.536 SPS=9059 \n",
      "bl=488 pos[1]=[-3.5 3.1 -16.1] dr=0.68 t=518.0ps kin=4.88 pot=8.50 Rg=0.829 SPS=8624 \n",
      "bl=489 pos[1]=[-3.2 4.0 -15.8] dr=0.81 t=519.0ps kin=3.84 pot=8.29 Rg=0.649 SPS=7971 \n",
      "bl=490 pos[1]=[-3.3 4.2 -16.2] dr=0.69 t=520.0ps kin=3.76 pot=8.02 Rg=0.506 SPS=8754 \n",
      "bl=491 pos[1]=[-3.1 4.5 -16.6] dr=0.42 t=521.0ps kin=5.14 pot=8.14 Rg=0.504 SPS=8608 \n",
      "bl=492 pos[1]=[-3.4 4.9 -16.0] dr=0.59 t=522.0ps kin=4.02 pot=8.06 Rg=0.657 SPS=8906 \n",
      "bl=493 pos[1]=[-3.1 4.6 -16.3] dr=0.63 t=523.0ps kin=3.74 pot=8.35 Rg=0.621 SPS=8214 \n",
      "bl=494 pos[1]=[-2.9 4.5 -16.6] dr=0.67 t=524.0ps kin=3.34 pot=8.16 Rg=0.468 SPS=8960 \n",
      "bl=495 pos[1]=[-3.4 4.4 -17.6] dr=0.67 t=525.0ps kin=2.69 pot=8.10 Rg=0.491 SPS=8917 \n",
      "bl=496 pos[1]=[-3.0 4.7 -17.4] dr=0.61 t=526.0ps kin=3.33 pot=8.13 Rg=0.627 SPS=8328 \n",
      "bl=497 pos[1]=[-2.8 4.2 -17.5] dr=0.64 t=527.0ps kin=3.72 pot=8.15 Rg=0.609 SPS=9020 \n",
      "bl=498 pos[1]=[-3.1 3.9 -17.1] dr=0.60 t=528.0ps kin=3.24 pot=8.13 Rg=0.475 SPS=8863 \n",
      "bl=499 pos[1]=[-3.6 4.1 -16.4] dr=0.69 t=529.0ps kin=3.91 pot=8.12 Rg=0.795 SPS=8753 \n",
      "\n",
      "Statistics for the simulation active_Rouse_sim, number of particles: 10,  number of chains: 1\n",
      "\n",
      "Statistics for particle position\n",
      "     mean position is:  [ -2.47716504   3.65138638 -16.65024223]   Rg =  0.795313\n",
      "     median bond size is  0.4209681207354708\n",
      "     three shortest/longest (<10)/ bonds are  [0.13492316 0.17139959 0.21711373]    [0.51791027 0.52394512 0.54103503]\n",
      "     95 percentile of distance to center is:    1.1945790582981135\n",
      "     density of closest 95% monomers is:    1.3304247175121318\n",
      "     density of the core monomers is:    2.6692225971358483\n",
      "     min/median/mean/max coordinates are: \n",
      "     x: -3.63, -2.16, -2.48, -1.78\n",
      "     y: 2.85, 3.73, 3.65, 4.10\n",
      "     z: -16.91, -16.70, -16.65, -16.35\n",
      "\n",
      "Statistics for velocities:\n",
      "     mean kinetic energy is:  3.914693554708942 should be: 1.5\n",
      "     fastest particles are (in kT):  [ 2.92848127  3.30493509  3.3941556   7.45132754 13.9429566 ]\n",
      "\n",
      "Statistics for the system:\n",
      "     Forces are:  ['ActiveForce', 'HarmonicBond']\n",
      "     Number of exceptions:   9\n",
      "\n",
      "Potential Energy Ep =  8.116238403320313\n",
      "bl=500 pos[1]=[-2.8 3.9 -16.1] dr=0.49 t=530.0ps kin=3.70 pot=8.03 Rg=0.608 SPS=8727 \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\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.runSimBlock(int(block_size), increment=True)\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4575ca3d728b4a7db3b8e61cae498c54",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from 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": 15,
   "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": 16,
   "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "opmm",
   "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": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}