2DLattice_MJ_SimulatedAnnealing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aux import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The sequence of protein to fold\n",
    "# prot_seq = \"YYDPETGTWY\"\n",
    "prot_seq = \"YYDPET\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sched = np.power(2, np.linspace(5,0, 30*10))\n",
    "max(sched), min(sched)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(sched)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.exp(-10/sched))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "import multiprocessing\n",
    "\n",
    "num_cores = 6\n",
    "\n",
    "res = Parallel(n_jobs=num_cores)(delayed(simulate_trajectory)(prot_seq, sched, jobid) for jobid in range(360))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tra, H, E, seeds, results = zip(*res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tra = np.array(tra)\n",
    "history = np.array(H)\n",
    "energy = np.array(E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acp = history.cumsum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(acp.mean(axis=0))\n",
    "plt.fill_between(\n",
    "    range(acp.shape[1]),\n",
    "    acp.mean(axis=0) - acp.std(axis=0),\n",
    "    acp.mean(axis=0) + acp.std(axis=0), alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enes = [total_energy(pos, prot_seq) for pos in results]\n",
    "best = np.argmin(enes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_viz(tra[best], prot_seq, schedule=sched, movie=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_best = np.array(enes) == min(enes)\n",
    "(all_best).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.sort(enes))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_best = np.random.choice(np.argwhere(all_best).reshape((-1,)))\n",
    "print(rnd_best)\n",
    "plt.plot(energy[rnd_best])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_video()"
   ]
  }
 ],
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